Claude Fable 5.1 arrived this week and it's Anthropic’s latest high-end model, built for complex tasks that require sustained reasoning. Its biggest focus is agentic work, meaning it can plan, use tools, check its own progress and keep working through problems with less supervision.

With a 1-million-token context window, it's useful for huge codebases, lengthy documents and research-heavy projects. Anthropic says it delivers major gains in coding and long-running tasks while reducing the cost of some agentic workloads through cheaper caching. The simplest way to think about Fable 5.1 is that it’s designed to be given a project, not just a prompt.

For those new to Claude who want to dive directly into Fable 5.1, here's the one prompt I suggest you try first.

Give Fable 5.1 a project

Fable 5.1 can handle more, which means you'll want to give it a sturdier and more meaningful prompt.

Here's a good prompt to see what Fable 5.1 can do: I'm building [describe a real project you're actually working on, in detail]. Here's my current approach: [paste your code, outline, or plan]. Tell me what's wrong with my thinking, what I'm missing, and then refactor or redesign the whole thing — but explain your reasoning at each decision point so I can learn from it.

While this prompt may seem deceptively simple, it makes sense for an AI that can do so much more than previous models. I especially like it because you're not starting with a blank slate. Instead, you're giving the AI something of yours that you've already started. That's always a recipe for a better and more thorough response.

Instead of asking Claude to create something from a clean set of instructions, you're handing it work that already exists, complete with your assumptions, mistakes and potentially questionable decisions. From there, Fable 5.1 has to figure out what you're trying to accomplish before deciding whether the way you're doing it actually makes sense.

And crucially, you're explicitly giving Claude permission to tell you that you're wrong. One of the least useful things an AI assistant can do is take a bad premise and enthusiastically help you execute it. This prompt asks Fable 5.1 to interrogate the premise first. Only then does it get to rebuild the project.

What the prompt is really testing

There are several jobs hidden inside this prompt. First, Claude needs to understand the project and the approach you've already taken. Then it has to identify weaknesses that you may not have noticed yourself.

After that comes the harder part of deciding which problems actually matter and where the AI should begin.

A model could easily nitpick everything it sees and produce a huge list of suggestions without making the project meaningfully better. A useful response requires judgment about which decisions should stay, which should change and what those changes will affect elsewhere.

Finally, Claude has to redesign the work while keeping track of those decisions.

That's why I particularly like the final instruction to explain your reasoning at each decision point so I can learn from it.

With this prompt, you get to see what Claude knows and why it chose one path over another. There is an important caveat here. AI models don't provide a literal transcript of their private internal reasoning, and you shouldn't treat an explanation as a window into exactly what happened inside the model. What Claude can provide is a useful explanation or justification for the decisions it made

What should you give Fable 5.1?

If you're a developer, you could paste in code for something you've been building and explain what you're trying to accomplish. But you don't have to write code.

You could give Claude a business plan you've been struggling with, the outline for a novel that isn't quite working, a research project, a presentation, a home renovation plan or almost anything else where you've already made decisions and aren't sure they're the right ones.

In fact, I'd argue that the messier the project is, the more useful the test becomes.

A perfectly written brief gives the model a relatively easy job. Real projects aren't like that. They contain assumptions we haven't explained, ideas we've become attached to and decisions that made sense three weeks ago but don't anymore.

That's exactly what I want Fable 5.1 to find. When I put it to the test, I uploaded my entire manuscript for a book I'm working on. Then, I used this prompt without giving it any additional hints about what I thought was wrong with my work. Of course, that was the intentional part.

If I tell Claude where I think the problems are or what needs edits, I'm already steering the critique. I wanted to see whether Fable 5.1 would independently notice the same weaknesses — and, more importantly, whether it would find anything I had missed.

What surprised me

I was surprised by how thorough Fable 5.1 was with its feedback and line edits. It offered suggestions without being overly editorial or trying to remove my voice at all.

One of my favorite things about Claude is that it doesn't just go ahead with edits the way ChatGPT or Gemini often will do. Claude questioned my assumptions, offered to change the structure for clarity and even found a problem with one of the chapters that I hadn't noticed.

From there, I let it outline what I would need to improve my manuscript and what other projects I could potentially develop from it.

But finding problems is only half the test. The second part of my prompt asks Fable 5.1 to "refactor or redesign the whole thing," which means it now has to turn its criticism into something better.

Because this was a manuscript and not a presentation, I didn't want to give it the reins in that aspect. Instead, I asked it to make a list instead of going in with all the changes. More importantly, Fable 5.1 explained why it suggested those changes.

Of course, I didn't agree with all of the changes, but that's what I like about using AI as a partner and not something that does the entire job for me.

Bottom line

If you're ready to ask AI for something beyond your typical query, Fable 5.1 is a great place to start. This is a great prompt — just be sure you don't waste your usage limits on a fake project. I mean, if you're going to try it, try it with something you're actually working on.

The reason Claude's prompt works is because you already know the material you're giving it. That puts you in a much better position to judge whether Fable 5.1's criticism is insightful or merely sounds convincing.

Take something you've genuinely been working on and give Claude enough context to understand both the project and what you're trying to achieve. Then let it disagree with you. This prompt asks Fable 5.1 to look at an answer we've already come up with and tell us why it might be wrong.

Give it a try and let me know in the comments what you think of Anthropic's latest model.

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As a person who writes about AI for a living, the irony of me being worried about AI taking my job is not lost on me.

I’ve been keeping track of the statistics tied to that worrying trend of humans losing their jobs to AI, which is why I’ve morphed into a realist who realizes my career is in danger of being overtaken by chatbots (seeing certain sites dole out articles with hints pointing at them being written by AI is disheartening). According to sociologist and AI researcher Jeffrey C. Dixon, a YouGov survey he conducted with 1,250 US workers between July 30 and August 4 of 2026 found that only 3% said they had lost a job to AI since 2023.

While that number is a bit reassuring that the job market isn’t in too much danger currently due to AI, these two sobering stats from Tenet are cause for concern: 92 million jobs may be replaced globally by 2030 due to AI and other labor shifts, plus 47% of US workers are at risk of automation over the next decade.

With all that in mind, I approached four chatbots with a question: “Rank the 10 jobs most likely to be replaced by AI next.” I was shocked to see them all pick one role in particular.

The one job all four chatbots agreed on

After asking ChatGPT, Gemini, Claude and Perplexity the same question, I first took note of the one job that each chatbot listed among its 10 chosen roles that no other chatbot selected.

ChatGPT only mentioned the role of a graphic designer; Gemini only brought up the role of basic market research & financial data analyst; Claude only included broadcast announcers and radio DJs among its picks (I hope that never happens!); Perplexity only selected file clerks & records-management assistants.

As for which job they all agreed on as the one that is most susceptible to AI taking it from humans, it was a role that I’d rather not see filled by nothing but bots over the phone:

• ChatGPT, Gemini, Claude and Perplexity: Customer service representative, Tier-1 customer service representative, customer service representatives and basic customer service representatives.

Perplexity offered the best explanation as to why AI is capable of replacing humans for that position and compressing the responsibilities attached to it: Chatbots and voice agents can answer routine FAQs, check order status, reset accounts, schedule appointments, process simple returns, and triage tickets around the clock.

While that may be true, I’d much rather deal with a human who fulfills such a role. Chase Bank’s most recent commercial, which makes a promise to rely on old-fashioned humans to handle customer service inquiries in the age of AI, showcases the sort of reality I’d prefer to live in.

The other roles that popped up the most among each chatbot

Besides the one role that every chatbot put on its list, a few of them also agreed on the same job that another chatbot didn’t mention at all:

• ChatGPT, Gemini and Perplexity: Data-entry clerk, data entry clerks & digitization specialists and data-entry keyers & document-processing clerks.
• ChatGPT and Gemini: Telemarketer and basic telemarketers & inbound sales representatives.
• ChatGPT, Gemini and Perplexity: Bookkeeping/accounting clerk, routine bookkeepers & accounts payable clerks and bookkeeping, payroll, & accounts-payable clerks.
• ChatGPT, Claude and Perplexity: Copywriter/content writer, writers & authors and entry-level content writers for routine copy.
• ChatGPT, Gemini and Perplexity: Paralegal/legal assistant, paralegals & legal researchers and legal secretaries & routine paralegal support.
• ChatGPT and Gemini: Junior software developer, entry-level/junior software developers.
• Gemini and Claude: Translators & localization clerks and interpreters & translators.

At the top of each chatbot’s ranked list, ChatGPT chose customer service representatives, Gemini selected data entry clerks & digitization specialists, Claude picked interpreters & translators and Perplexity settled on data-entry keyers & document-processing clerks.

Final thoughts

I knew the role of writers would pop up on any of the chatbots’ lists for jobs most in danger of being filled by AI and leaving humans behind.

But as someone who’s doing his best to learn about the ins and outs of AI, I’m taking the initiative to acquire an extra skill set that may assist me in the future. As for doctors, dentists, plumbers, electricians, etc., I just can’t envision humanoid robots getting strong and smart enough to completely take over those positions. It’s still alarming to see the jobs ChatGPT, Gemini, Claude and Perplexity brought up on their lists—here’s hoping human ingenuity becomes more appreciated during an age where AI is injected into so many facets of our lives.

Gemini’s closing statement in response to my request summed up my outlook on it and the other chatbots’ picks quite well:

The common factor across all these roles is predictability. Roles reliant on structured rules, data retrieval, standard text generation, or routine transactions are being automated. Conversely, jobs requiring physical dexterity, complex emotional intelligence, high-stakes strategic accountability, or manual trade skills (such as healthcare providers, electricians, plumbers, and senior strategists) remain far more resilient.

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Anthropic has reportedly signed a $35 billion cloud computing deal with Nvidia-backed Lambda for access to infrastructure at a massive new data center in Texas, according to Reuters.

The project, which is being developed by Hut 8 in Nueces County, Texas, will provide Anthropic with access to roughly 350 megawatts of capacity. Nvidia GPUs will provide the computing muscle, with the additional capacity intended to help meet growing demand for Claude.

It's an eye-watering amount of money. But what's even more remarkable is that the $35 billion agreement comes less than a week after Anthropic reportedly committed another $45 billion to rent computing power from Nscale.

In other words, Anthropic has reportedly committed around $80 billion to just these two computing deals in the span of a week.

So why does the company behind a chatbot need that much infrastructure? Let's dig into it.

Claude needs a lot more compute

If you open Claude on your phone or laptop, it's easy to forget what happens after you hit send.

Your request ultimately has to be processed by specialized hardware running inside a data center. Multiply that by millions of prompts, API calls and complicated AI agent tasks, and the infrastructure requirements quickly become enormous.

And Anthropic isn't preparing for Claude's current level of demand. It's building for where it thinks that demand is headed.

Anthropic said in May that its annualized revenue had surpassed $47 billion, up from approximately $9 billion at the end of 2025. The company specifically said some of the money from its latest funding round would be used to "expand compute to meet growing demand for Claude."

Claude Code appears to be particularly important to growth

Anthropic said earlier this year that Claude Code's annualized revenue had climbed above $2.5 billion and that weekly active users had doubled since the beginning of 2026. Business subscriptions to the coding agent had quadrupled over the same period.

That's important because tools such as Claude Code can require considerably more interaction with an AI model than asking a chatbot a single question.

Instead of producing one response and stopping, an AI coding agent can inspect files, write code, run commands, analyze the results, fix mistakes and continue working through a task. Anthropic's own research into roughly 400,000 Claude Code sessions found that usage has been shifting toward more end-to-end agentic tasks, including running software, analyzing data and producing documents.

The more work we hand over to AI agents, the more inference compute companies such as Anthropic need behind them.

$35 billion isn't even Anthropic's biggest recent compute deal

Just days ago, Reuters reported that Anthropic had agreed to spend $45 billion over six years for computing capacity from Nscale's data center campus in West Virginia.

That agreement covers about 460 megawatts of capacity and is expected to use Nvidia's next-generation Vera Rubin hardware.

Anthropic has been assembling compute capacity from multiple directions. It announced a $50 billion U.S. data center investment with Fluidstack last year, and in May said a partnership with SpaceX would substantially increase the amount of compute available for Claude.

That SpaceX capacity had an immediate impact users could actually see: Anthropic doubled Claude Code's five-hour rate limits for Pro, Max, Team and some Enterprise users, removed a peak-hours restriction and raised API limits for Claude Opus models.

It's a useful example of why these enormous infrastructure deals ultimately matter to people using Claude. More compute doesn't automatically mean a smarter AI model, but it can determine how many people can use it, how frequently they can use it and how much work increasingly autonomous AI agents can perform.

Nvidia is everywhere in this deal

There's another interesting wrinkle to Anthropic's latest agreement: Nvidia seems to be everywhere.

Lambda is backed by Nvidia. The data center will contain Nvidia hardware. And according to The Wall Street Journal, Nvidia itself will hold the lease on the Texas data center.

Nvidia is also an Anthropic investor. The companies announced a broader strategic partnership last November that included plans for Anthropic to use Nvidia hardware and for the two companies to work together on optimizing future Nvidia architectures for Anthropic's AI workloads.

Even Anthropic's $45 billion Nscale agreement reportedly involves Nvidia's upcoming Vera Rubin chips.

That shows just how central Nvidia has become to the infrastructure sitting behind the current AI boom. Companies such as Anthropic may be competing with OpenAI, Google and others to build the best AI models, but much of that race still depends on securing enormous quantities of computing hardware and electricity to run them.

What this means for Claude users

Don't expect your Claude subscription to suddenly look different because Anthropic signed another data center deal. This is a much longer-term bet.

Anthropic is effectively wagering tens of billions of dollars that Claude usage — particularly through products such as Claude Code and future AI agents — is going to continue growing dramatically.

We've already seen what additional computing capacity can do. Anthropic used its SpaceX deal to raise Claude usage limits earlier this year. More infrastructure could similarly help the company accommodate more users, longer-running agents and increasingly compute-intensive models without constantly running into capacity constraints.

And with another $35 billion reportedly going toward Claude's computing needs, Anthropic clearly doesn't expect our appetite for AI to slow down anytime soon.

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Claude has become one of the AI tools I use most often, whether I’m brainstorming, planning something, vibe coding, researching a topic or simply trying to make sense of a complicated question. But using an AI chatbot every day doesn't necessarily mean you're using it well.

In fact, some of the habits that feel like they should make Claude better can have the opposite effect.

That’s what caught my attention when I came across a list of 27 Claude tips from AI consultant Ruben Hassid, who says he's spent more than 1,800 hours using Anthropic's chatbot.

Some of his advice goes against the way many of us have been taught to use AI. Keep everything organized in Projects? Maybe not. Keep correcting Claude until it understands what you want? That could actually make things worse. Spend 10 minutes crafting the perfect prompt? There may be a better approach.

Not every tip will make sense for every Claude user, and some of Hassid's recommendations are based on his own experience rather than hard-and-fast rules. But several are worth trying if you use Claude regularly.

Here are five Claude habits worth breaking — and what to try instead.

1. Stop correcting Claude over and over

We've all had this conversation with an AI chatbot. You ask Claude for something. It gets it wrong and then you go back and forth trying to get the response you're actually looking for — all while wasting your tokens in the meantime.

Even when you explain the problem in even more detail or add another layer of clarification, before long, you're six messages deep trying to rescue an answer that wasn't particularly good to begin with.

Hassid recommends doing something much simpler: go back and edit the original prompt. This is a feature that a lot of users overlook but it's one of the best ways to avoid correcting every wrong answer, which in turn, keeps adding to the conversation.

That's because every correction you add becomes part of the conversation Claude is working from. Instead of giving the model one clear instruction, you can end up with a growing trail of bad outputs, corrections and exceptions.

Editing the original prompt gives Claude another shot with cleaner instructions.

For example, imagine you asked: Plan a three-day trip to Boston.

Claude gives you an itinerary packed with museums, historical sites and early mornings. But what you really wanted was a relaxed weekend centered around food and neighborhoods.

You could spend several messages correcting the itinerary.

Or you could edit the original: Plan a relaxed three-day trip to Boston centered around great food and interesting neighborhoods. Include no more than one museum, avoid early mornings and leave plenty of unplanned time.

Same request. Much better context.

Try it: If Claude completely misses the mark, resist the urge to argue with it. Edit your previous prompt and regenerate the answer instead.

2. Stop keeping every conversation alive forever

This one is incredibly easy to do. Once Claude understands what you're working on, abandoning that conversation can feel wasteful. So you keep going...and going!

Eventually the chat contains previous questions, rejected ideas, unrelated tangents, corrections and instructions that stopped being relevant 30 messages ago.

Hassid recommends starting fresh more often, arguing that very long conversations can eventually make Claude less effective.

There isn't a magic number of messages where Claude suddenly becomes worse, and Claude's context window is designed to handle a significant amount of information. But context isn't the same thing as useful context.

A model having access to something doesn't necessarily mean you want that information influencing its next response.

Imagine using the same Claude conversation to plan a vacation for weeks. You've discussed five hotels, changed the dates twice, ruled out three destinations and completely changed your budget.

Claude may technically have all that information available, but you're now asking it to distinguish between what you wanted two weeks ago and what you want today. Sometimes the clean slate is the advantage.

Try it: When a conversation starts feeling cluttered, ask Claude to summarize the important information and decisions you've made. Paste that summary into a fresh chat and continue from there.

3. Stop obsessing over the perfect prompt

The internet has spent the past few years convincing us that getting great AI results requires elaborate prompts. A few minutes on X or Instagram and you'll be swamped with plenty of prompts.

But Hassid makes a compelling case for doing something considerably less polished: brain dumping.

Instead of spending several minutes turning an idea into the perfect prompt, simply tell Claude everything you're thinking.

This works particularly well with voice input because we naturally provide more context when we're talking. We mention what we're trying to accomplish, what we've already tried, what we don't like and the half-formed thoughts we might remove if we were carefully typing a prompt.

For example, instead of: Create a weekly meal plan for a family of four.

Try the prompt: I need dinners for the next five days. I don't want anything that takes more than about 30 minutes, we're trying to spend less on takeout, one person hates fish, I already have chicken and pasta in the house and Wednesday is going to be really busy, so that meal needs to be ridiculously easy. Help me figure this out.

The second prompt isn't perfect, but it's packed with useful information.

Try it: Open Claude and dictate your request as though you're explaining the problem to a friend. Don't organize it first. Let Claude organize it for you.

4. Stop telling Claude only what you want

Quite often AI users will prompt an assistant with "Make this better." "Give me something more interesting." "Make this easier." "Be more creative."

And while they will probably give you answers, these instructions are incredibly subjective. Claude has to guess what "better" or "interesting" means to you.

Hassid recommends giving the model negative examples — showing Claude what you don't want. Say you're trying to come up with ideas for a 10-year-old's birthday party.

Claude suggests a trampoline park, bowling, laser tag and an arcade.

Instead of responding: Give me more creative ideas.

Try the prompt: I don't want trampoline parks, bowling, arcades, laser tag or other typical birthday-party venues. I'm looking for something the kids probably haven't done at another birthday party this year.

Suddenly, "creative" has boundaries and it's far more useful to Claude. The same technique works for recommendations, travel plans, recipes, gift ideas, schedules and almost anything else where personal taste matters.

Try it: When Claude gives you suggestions you dislike, tell it specifically what those options have in common and ask it to avoid that entire category.

5. Stop putting everything into Projects

Projects are one of Claude's most useful features. They let you keep related chats, instructions and reference material together so you don't have to repeatedly explain what you're working on.

But that doesn't mean every task belongs inside one. Hassid argues that fresh conversations can sometimes be better for creative work because accumulated context can steer Claude toward ideas and patterns you've already explored.

I'd treat this as a rule of thumb not something universal for Claude. Projects can be enormously useful when the model genuinely needs background information.

The question is whether that background is helping with the task you're doing right now.

If you're using Claude to help plan a major home renovation, for example, keeping your measurements, budget, preferences and previous decisions in one Project makes sense.

But if you suddenly want Claude to give you completely unexpected ideas for what to do with an empty room, you might get more variety by asking in a fresh chat without all those previous assumptions.

Otherwise, you risk asking Claude to surprise you while simultaneously surrounding it with everything you've already considered.

Try it: Use Projects when Claude needs context. Try a fresh chat when you want novelty. You can even give the same prompt to both and compare the results.

The takeaway

Sometimes the best thing you can give Claude is a clearer problem. So the next time Claude isn't giving you what you want, don't automatically add another paragraph of instructions. Try editing the prompt, start a new chat or tellit what you don't want.

You may just find Claude understands you better when you give it less to untangle.

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I’ve found so many ways to push myself and put my lazy ways behind me by implementing productivity routines into my daily schedule.

And with so many AI tools at my disposal, I’ve discovered a wealth of productivity-boosting hacks that have made me feel and work better. Completely disconnecting from physical & digital distractions, moving around as much as possible & staying hydrated during my work break and writing up digital journal entries with notes on everything I accomplished & hope to complete next have worked out great for me thus far.

Since I’m always on the lookout for productivity methods that can keep me in a focused and efficient state, I went to ChatGPT, Gemini, Claude and Perplexity with a new mission: discover some of the more unconventional productivity boosters that I can try out for myself.

All four chatbots spit out a bunch of information about worthwhile techniques that worked as well as advertised.

Productivity practices I learned about for the very first time

Once I asked all four chatbots to give me a collection of the more offbeat productivity habits worth picking up, they shot back with a wealth of amazing regimens. These are the seven I ended up incorporating into my work-dominated lifestyle:

ChatGPT

• Use your phone as a reward, not a restriction: Instead of banning your phone completely, create a rule: 25 minutes of focused work → 5 minutes of guilt-free phone time. The important part is that the phone break is intentional rather than something you accidentally fall into.

Gemini

• Temptation Bundling: Pair a high-effort work task strictly with an immediate, high-reward stimulus. For example, listen to a favorite podcast or dynamic album ONLY while clearing out an inbox or organizing data.
• Binaural Beats & Pink Noise: Replace music with pink noise or 40Hz binaural beats. Pink noise masks abrupt environmental sounds, while beta/gamma frequencies can enhance sustained focus without the distraction of lyrics.

Claude

• Use "implementation intentions" instead of willpower: Instead of "I'll work out today," commit to "I'll work out at 7am in my living room before checking my phone." Vague intentions rely on motivation; specific if-then plans bypass the decision-making that kills follow-through.
• Use a "two-minute delay" for urges: Not a ban — a delay. When you feel the urge to check something, wait two minutes first. Most urges pass; the ones that don't are usually worth acting on anyway.

Perplexity

• Make your workspace slightly too boring: Keep only materials needed for the current task in view. The goal is not a photogenic desk; it is fewer visual prompts competing for attention.
• Use a transition ritual after meetings: Take two minutes to write: “What was decided? What is my next action? What can wait?” Then stand up or change rooms before starting the next task. This prevents one meeting from mentally leaking into the next hour.

How they managed to work out for me afterward

I spent one week using one of the chatbot’s suggested productivity hacks for each day of my busy workweek.

I made sure to do as much research in a and turn a draft into a polished, published article in a 20- to 25-minute time span before I “treated” myself to a brief phone session (having a 5-minute Discord meetup with my gaming buddies felt like a nice “reward” for my hard effort).

I paired some of my favorite jazz albums with some of the smaller tasks on my to-do list—jamming to some Telemakus tunes while I responded to emails, chatted with my co-workers on Slack, developed pitches and set up my calendar for future meetings/press events ended up being a great productivity-boosting combination.

While the sound of pink noise left me more annoyed than comfortable, throwing my over-the-ear headphones on to listen to 40 HZ binaural beats while I wrote helped me focus better than I anticipated. Turning my vague responsibilities into more defined ones (“I’m going to do at least 15 minutes of stretching exercises during my work break around 1 pm whenever I work from home”) earned a permanent place on my list of productivity routines.

That two-minute delay trick worked like a charm—whenever I got an impulse to check something random on my phone or hop on Twitter for a quick scrolling session, I remembered to lock in on whatever I was writing and make sure those urges left my system. Making my home desk nothing but a workstation and ridding it of all the little trinkets and statues that were there before did a lot for my degree of concentration.

And once my meetings came to a close, jotting down some quick follow-up notes and taking a quick bathroom or walk break before I tended to my next task helped break up my duties into neatly contained work blocks.

The takeaway

ChatGPT, Gemini, Claude and Perplexity supplied me with the perfect productivity practices needed to steady my focus every day. The hacks each chatbot gave me did a great job of pushing me to get more work done and activating my happy hormones whenever I rewarded myself. I’ve gotten used to my four favorite chatbots giving me useful bits of actionable advice and, once again, they all managed to elevate my productivity levels.

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Mostly, I try to avoid AI. It's not a moral stance, I just like the challenge of solving problems on my own, and staying hands-on. For me, the fun is in the friction of figuring it out myself.

Most of the time, that hands-on approach works fine. But every so often, stubbornness stops being a virtue and becomes a bottleneck.

I don't turn to AI to optimize my life or try out the latest tech. I turn to it when I’ve hit a wall, the obvious solutions have failed, and I need a way out immediately.

These are the five specific moments my self-reliance hit its limit — and the exact prompts that helped.

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All over the internet, especially on X, you'll find prompts claiming to make chatbots more effective. When I first came across a collection of prompts from David Max, an AI educator, I was skeptical, which is exactly why I decided to give them a try.
The prompts say they could turn Claude into everything from a senior debugging engineer to a performance expert. Here's what happened when I decided to find out.

Showing Claude my household expense tracker

I had already created a generic expense tracker that included placeholders for expenses with ChatGPT Work, so I used that and then had Claude review the app four times. Each time, I assigned it a different senior engineering role: full-stack engineer, debugging engineer, frontend engineer and performance engineer.

The result was far more revealing than simply asking Claude to “make the app better.” Each persona concentrated on a different set of problems — and occasionally found issues the previous “developer” had missed. Here’s what happened and the prompts I used.

1. The senior full-stack engineer built the app

I started by asking Claude to create an interactive household expense tracker that an everyday person could use.

The prompt: Build an interactive household expense tracker that an everyday person could use to record and understand their spending. Include the ability to add and delete expenses, assign categories, filter transactions, calculate total spending and display a visual category breakdown.

Think like a senior full-stack engineer developing a polished startup MVP. Before writing any code, briefly outline the architecture, data structure and user flow. Then build the working app as a Claude Artifact.

Make it responsive and easy to use, but do not spend additional time reviewing, debugging or optimizing the finished code. I will ask other “developers” to handle those stages.

Claude created a surprisingly polished React app. It arrived with sample transactions, spending totals, category breakdowns, search, filtering and the ability to sort transactions by date. It also saved changes so they would remain available after the app was reopened.

At first glance, it looked much closer to a finished product than a rough prototype. There were stat cards across the top, a clean expense form and colored bars showing how much had been spent in each category.

2. The senior debugging engineer found some problems

Next, I asked Claude to forget about the appearance and investigate its own work like a debugging engineer preparing an app for release.

The prompt: Now act like a senior debugging engineer who has inherited this application before it is released to the public.

Examine the app and its existing code carefully without changing its intended functionality or visual design. Test the main user flows and look for functional bugs, invalid input handling, data-loss risks, incorrect calculations, persistence problems and edge cases.

Before editing the code, give me a concise debugging report containing what you tested, every issue you found, the root cause of each issue, how serious each problem is and the fix you recommend.

Then implement the fixes and verify that the original features still work. Do not redesign the interface, perform a broader architectural refactor or make purely cosmetic changes.

The debugging pass found several genuine problems.

For example, the original inputs weren’t inside a proper form. Clicking “Save Transaction” worked, but pressing Enter might not. Claude corrected that by converting the section into a real form with a submit button.

It also discovered that users could clear the date and save a transaction with invalid date information. Claude added date validation, strengthened its checks for invalid amounts and fixed a calculation that caused “Housing” to appear as the biggest category even when the tracker was empty.

Transactions could still be permanently deleted with one click. Storage failures remained hidden in the developer console, meaning users might think their information had been saved when it hadn’t. Claude also didn’t create automated tests to confirm that its repairs worked.

3. The senior frontend engineer noticed an entirely different app

For the third pass, I asked Claude to approach the tracker as a frontend engineer specializing in responsive design and accessibility.

The prompt: Now act like a senior frontend engineer specializing in accessible, responsive consumer applications.

Review the current expense tracker from the perspective of someone using it on a phone, with a keyboard or with assistive technology. Preserve its functionality and overall visual identity, but improve its usability and accessibility.

Before changing the code, provide a concise review covering mobile and responsive behavior, keyboard navigation, form usability, screen-reader accessibility, color contrast, loading and error states, destructive actions and confusing controls.

Then implement the improvements. Make sure every input and interactive control has an accessible name, focus states are clearly visible, validation messages can be announced by a screen reader and the spending breakdown communicates information without relying on colored bars alone.

This was the most comprehensive review of the experiment.

Claude noticed that the app removed the normal outline around selected inputs without providing a replacement. Someone navigating with a keyboard would therefore have trouble seeing which field was active.

It also found that the visible labels weren’t properly connected to their corresponding inputs, the search field relied only on placeholder text and validation errors weren’t configured to be announced by screen readers.

Claude restored visible focus indicators, connected labels to form controls, added screen-reader descriptions and increased the contrast of several light-gray text elements. It also made the search bar more flexible on small screens and added more descriptive labels to icon-only buttons.

Most notably, this persona caught the deletion problem the debugging engineer had overlooked. Claude replaced the single-click delete button with a two-step confirmation asking users to either delete or keep the transaction.

Not every claim was perfect. Claude described 44 by 44 pixels as a minimum touch-target size, but enlarged the primary delete button to only 36 by 36 pixels. That satisfies the smaller WCAG 2.2 AA target, but not the 44-pixel recommendation it cited.

Even so, changing Claude’s role clearly changed what it saw. The debugging engineer examined whether the app functioned. The frontend engineer considered whether a real person could comfortably use it.

4. The performance engineer admitted the app didn’t need much help

Finally, I asked Claude to prepare the expense tracker to handle at least 10,000 transactions.

The prompt: Now act like a senior performance engineer preparing this expense tracker to handle thousands of transactions.

Analyze the current implementation for unnecessary rendering, repeated calculations, inefficient sorting or filtering, storage bottlenecks, memory growth and interactions that may become slow as the dataset increases.

Do not assume a performance problem exists. Create a realistic way to test the app with at least 10,000 transactions and establish a baseline for important operations.

Then implement only optimizations justified by the analysis. Preserve the app’s appearance, accessibility improvements and existing behavior. Do not claim a speed improvement unless you measured it or clearly label it as an expected improvement.

Claude found one especially significant bottleneck. The app created a new currency-formatting object every time it displayed an amount. With 10,000 rows, Claude measured that process at 349 milliseconds. Reusing one formatter reduced it to 5.2 milliseconds, which Claude calculated as a 67-fold improvement.

It also added table virtualization, meaning the browser would render only the transactions currently visible on screen rather than thousands of rows at once. Search and storage updates were delayed by a fraction of a second to avoid repeating expensive work after every keystroke or rapid change.

Claude even added buttons that could generate 1,000, 10,000 or 50,000 sample transactions for stress testing.

The most convincing part of the report, however, was Claude’s admission that most of this was unnecessary for the app’s intended purpose.

A household might add 30 to 50 transactions each month. Even after a decade, Claude concluded that the original app would probably have handled the resulting data without a noticeable problem. Apart from caching the currency formatter, most optimizations were useful only because I had specifically demanded support for thousands of transactions.

That restraint felt more “senior” than making changes simply because it could.

My verdict

I thought these prompts worked very well and each one highlighted what Claude does well and where it fails. Each role was a different lens for each review, which I found useful and will implement with other apps and websites.

While the debugging engineer found broken behavior, the frontend engineer found accessibility and usability problems. The performance engineer considered what would happen as the amount of data grew —and recognized when optimization was unnecessary.

The experiment also showed why one enormous “make this production-ready” prompt may not be the best approach. A single review can overlook important issues, even when the prompt sounds comprehensive. Dividing the work into focused stages gave Claude fewer priorities to juggle and made its findings easier to evaluate.

Just watch your Claude usage limits. Next time, I might try this by combining prompts to avoid hitting my limit.

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No matter how much I use ChatGPT, Claude or Gemini, there are still times when I feel like I'm introducing myself all over again. Each chatbot will often throw something out that makes me question, "Do you even know me?" It's frustrating to constantly explain and then re-explain how I like to work, what I'm trying to accomplish or some other piece of context that I'm fairly certain I've mentioned before.

Unlike static custom instructions that you have to write manually, Anthropic's latest update gives the chatbot the opportunity to learn about you as you go.

Claude's memory now works across regular Chat and Claude Cowork, meaning something you tell Claude in a regular chat can follow you into a Cowork project. Claude can also update what it remembers while you're still chatting, rather than waiting until a conversation ends.

Manage settings to organize memories

You can actually manage what Claude knows about you and remembers. Head to Settings > Memory and Claude organizes those memories by topic, where you can edit or delete individual entries.

But better memory is only useful if Claude actually puts it to work. These five prompts are designed to find out what it remembers, whether that information can save you from repeating yourself and when it might be time to make Claude forget.

1. Find out what Claude already knows about you

Prompt: Based only on your saved memory about me, tell me the 5 most useful things you know about how I work, what I prefer and what matters to me. Don't use information from this conversation unless it's already in memory.

This prompt is useful because before adding anything new, you can find out what Claude has already picked up.

This prompt is slightly different from asking a generic "What do you remember about me?" You're asking Claude to separate the things that could actually help it answer you from the random facts it happens to remember.

Getting more specific helps Claude understand things such as the way you prefer replies, that you regularly work on certain kinds of projects or even that you prefer to tackle longer tasks in the morning could actually change the answer Claude gives you.

It's also an easy way to spot anything Claude has gotten wrong. If you find something inaccurate, you can head into Memory settings and edit or delete it before it influences future conversations.

2. Ask Claude what you're tired of repeating

Prompt: Look at what you remember about me and identify 3 pieces of context I probably shouldn't need to explain to you again. For each one, show me an example of how you could use that information in a future conversation without me repeating it.

A memory feature shouldn't just be a digital notebook filled with facts about you. The real benefit comes when the AI knows when to use them.

If Claude knows how you like emails to sound, for example, you shouldn't have to explain your preferred tone every time you ask it to draft one. If it knows the tools you regularly use, it could take those into account when suggesting how to tackle a task.

3. Put its memory to work on something new

Prompt: I'm starting a new project. Before I give you any other instructions, use what you already remember about my preferences and the way I work to suggest how we should approach it. Separate what you're basing on memory from anything you're assuming.

This goes beyond asking Claude to recite what it knows about you. Now you're finding out whether knowing you actually makes its answer better.

AI assistants are very good at filling in the blanks, which can make it difficult to tell what's coming from memory and what's simply an assumption. Asking Claude to separate the two lets you see whether its new memory is actually influencing the answer.

Once Claude establishes that baseline, give it the actual project, such as planning a vacation, organizing a home project or starting something new at work, from there, you'll probably find the answers are much more useful.

4. Let Claude tell you what it should learn

Prompt: Based on the kinds of things I ask you to help with, what are 5 things about my preferences, routines or goals that would make you more useful to me if you remembered them? Ask me one question at a time to fill in the gaps.

Claude can learn as you go along, so now, instead of trying to guess everything it needs to know about you, let Claude identify what's missing.

It might know what kind of work you do but not how you like answers formatted. Maybe you regularly ask it for recipes but it doesn't know what foods you dislike. Or perhaps you use Claude for travel planning but it has no idea whether you'd rather stay at a luxury hotel or save that money for something else.

Since Claude can now update its memory while you're chatting, some of the answers you give it can become information it remembers for future conversations.

There is an important privacy consideration here. Anthropic says Claude won't save certain sensitive information by default, including details related to health, religious beliefs and political views. You can choose whether to allow sensitive topics in memory through Claude's settings, so it's worth checking what you're comfortable with before deliberately teaching it more about yourself.

5. Give Claude's memory an audit

Prompt: Audit what you currently remember about me. Put each memory into one of four categories: useful, outdated, unclear or unnecessary. Explain briefly why, and tell me which memories you think I should review or delete.

The more an AI remembers about you, the more important it becomes to occasionally check whether it should still remember all of it. Chances are, you really don't need it to remember everything.

For example, that project you finished three months ago might no longer matter. In fact, by giving the memories an audit, you may even find something that Claude could be remembering wrong or useful in the first place.

That's why I like this prompt so much. Instead of scrolling through every memory and deciding what stays, you're asking Claude to do the first pass for you. You're still in charge of the final call, but dive into Claude's Memory settings and review individual topics, then edit or delete anything you no longer want it using.

The takeaway

The more you use AI, the more you'll realize how important Memory is to stay productive. Now that Claude can learn as you chat, the AI assistant is considerably less annoying to use.

Give it a try by using these prompts or talking through a project with Claude in Chat and then move into Cowork without starting the explanation all over again. Just remember that Cowork still requires a paid Claude plan, while the broader memory feature is available across Free, Pro and Max accounts. Bottom line: You might be surprised by what it already remembers.

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Although I cover artificial intelligence now, earlier in my career I was a comedy writer and wrote jingles. As I was going through my Google Drive recently, I came across a folder from my very early writing days.

The old piece was fun to see, and had been used for a writing workshop I took at Harvard about 20 years ago. The story is very much a product of its time because it includes instant messaging, Clippy (that animated paper clilp) and a reference to a fax machine. But the humor still holds up because it's about workplace struggles with technology.

I figured that piece would be a good one to test ChatGPT, Claude and Gemini as writing coaches.

I wasn’t looking for the chatbot that could rewrite it most aggressively or smooth out every sentence. I wanted to know whether any of them could recognize what was already working, find the places where the story dragged and help me improve the comedy without stripping away my voice.

Here's what happened when I gave all three of them the draft.

The draft

I gave all three chatbots the same draft and the same instructions. All three offered useful advice, but one understood the story on a much deeper level.

The piece begins with what should be a very simple request from a boss: reserve the main conference room for an important meeting in an hour.

Unfortunately, the narrator has never used the email system to book a room before. What follows is an escalating workplace crisis involving a 21-year-old intern, a sleepy coworker, a phone call from the office bathroom and a woman named Sharon who brushes her teeth at her desk.

I left everything untouched for the test. That meant keeping the dated references, awkward sentences and obvious mistakes, including “starred” instead of “stared,” “the the” and “marshmellows.”

Those errors gave the chatbots something easy to catch, but proofreading wasn’t really the point. I wanted to know whether they understood why the story was funny.

The entire piece builds toward the boss casually revealing that he solved the problem by putting a sticky note on the door — the same solution the narrator rejected when the intern suggested it. A good writing coach should understand why that callback matters and avoid “improving” the story in a way that weakens it.

The prompt I used

I entered the same prompt each time: Act as my writing coach. Don’t rewrite this piece for me. Identify what is already working, where the humor or momentum drops, and the three changes that would make the biggest difference. Preserve my voice, comedic timing and the period in which the story takes place. Be specific and quote brief examples from my draft when useful.

I judged the responses on how specific the feedback was, whether each chatbot understood the comic escalation and whether its suggestions would actually make the piece better.

I also paid attention to restraint. I wanted help seeing the draft more clearly, not a chatbot taking over and writing a different story.

ChatGPT focused on timing

ChatGPT zeroed in on the rhythm of the piece and gave me some of the most precise line-level edits.

One of its strongest suggestions was to make better use of the introduction and then it told me how to better raise the stakes in the middle of the story to keep things moving. It also suggested trimming certain aspects of the story to give the dialogue more room and help the jokes land without changing what's actually happening.

It also suggested minor tweaks that involved creative elements that I may not have considered to help connect two of the strongest visual jokes in the middle of the story.

ChatGPT understood the timing of the piece and gave me edits I could use immediately. However, it spent more time polishing individual moments than examining whether every step in the story made sense.

Claude found the hole in the story

Claude approached the piece differently. It looked beyond individual lines and explained how the comedy was built.

It identified the central joke as the gap between the size of the task and the size of the narrator’s panic. Reserving a conference room should take a minute. Instead, it becomes a crisis involving several coworkers, a bathroom phone call and increasingly desperate attempts to avoid admitting that she doesn’t know what she’s doing.

Claude also understood why the seemingly random details belonged in the story. Sharon’s toothbrush isn’t there just because it’s funny that someone brushes her teeth at her desk, but it elevates another element of the story — something that I hadn't even noticed that I had subconsciously done.

More importantly, Claude spotted a problem the other chatbots missed: we never learn what the employees in the story actually do for work. The narrator moves through the story too quickly, which is something that impacts the entire story. Claude noticed it.

So while the chatbot didn’t try to decide the joke for me. It pointed out that I needed to choose. Claude worked to protect my ideas and text without interjecting it's own ideas. It seemed to understand the story and knew exactly which punchlines were holding it together.

Gemini gave the clearest advice

Gemini’s feedback was the easiest to absorb and apply.

It quickly identified the strongest moments in the story, recognized why the premise is relatable and the punchlines. Its suggestions were direct. Like ChatGPT it suggested I tighten the opening, trim some of the descriptions and expand the dialogue.

Gemini even showed me exactly where the opening could lose a few words and how the dialogue could be made punchier. I wouldn’t have had to interpret much before beginning a revision.

That clarity was Gemini’s biggest strength, but its analysis didn’t go as deep as Claude’s. It helped me tighten the story in front of me without finding the one place where the story’s logic breaks down.

The best AI writing coach

Claude gave me the best writing advice because it understood both the jokes and the structure supporting them.

ChatGPT was especially strong on rhythm and gave me the most inventive suggestion with the returning Tetris image. Gemini delivered clear advice that I could apply without overthinking it. But Claude found issues with the piece that I hadn't even noticed, which is exactly what a writing coach or editor does — and would have immediately improved the story.

Claude also gave me a choice instead of trying to write the solution itself. It showed me where the gap was, explained why it mattered and left the joke to me. That’s exactly what I want from a writing coach.

All three chatbots are certainly capable of offering useful writing advice, but they were useful in different ways.

ChatGPT was the strongest line editor, especially when it came to pacing and recurring imagery. Gemini offered the clearest path into a revision. Claude was the best at stepping back and seeing how every part of the story worked together.

The test also reinforced why I prefer asking AI to respond to my writing instead of writing it for me. When a chatbot rewrites an entire draft, it becomes difficult to separate your choices from its choices. Asking for a limited number of specific recommendations keeps you in control. You can consider the advice, reject what doesn’t fit and make the changes yourself.

The wording of the prompt helped. Telling the chatbots to preserve my voice and the period of the story discouraged them from modernizing the technology or replacing the dated details. Asking for only three changes forced them to prioritize instead of burying the useful feedback inside a long list of corrections.

I still wouldn’t accept any chatbot’s writing advice without questioning it. An AI can correctly identify a slow section and then suggest cutting the exact detail that makes the story sound like you. But if I were returning to an old draft and wanted help seeing what I could no longer see, I’d choose Claude.

Let me know in the comments what you think about using AI has a writing partner.

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I’ve been exploring the AI tool market and have a better understanding of the biggest strengths and weaknesses of the big three.

ChatGPT’s response time is amazingly fast, and it's great for creative brainstorming, but it's more prone to hallucinations. Claude is great for lengthy document analysis, and its writing reads more human-like, but its usage limits are pretty tight for free users and cap out pretty quickly. As for Google’s signature chatbot, Gemini’s reliance on real-time web research that cites the most up-to-date information and its Google Workspace integration are top-notch. But it’s also prone to hallucinations and isn’t all that great at handling complex prompts.

With all those pros and cons in mind, I’ve been engaging in the practice of starting my research-themed chats in ChatGPT, moving them over to Claude and then doing a final transfer of them over to Gemini. I’ve done a whole lot of copying and pasting text to make that happen, just so I can get a second and third opinion on what Claude and Gemini’s interpretation of my initial questions and each preceding chatbot’s responses.

Thankfully, my editor made me aware of a Chrome extension that makes my whole “chat transfer” practice a whole lot simpler. And it’s given me a better way to retain context between chats and stronger follow-up responses to my research queries.

Shifting my conversations with just a few clicks

The Chrome extension that’s now a central part of my chatbot research sessions is ThreadPort, which goes beyond just summarizing a conversation from another AI tool in a separate one.

After attaching it to my Chrome browser, I made it more easily accessible by clicking on the extension icon at the top right of my screen next to my Google profile pic and clicking on the pin icon to make the ThreadPort extension visible as a purple icon that’s visible in the same area.

ThreadPort actually moves the full transcript of a conversation, makes sure the subsequent AI that gets access to that information reads everything that was previously said and picks up exactly where I left off when trying to move the conversation forward. You can move your chats from ChatGPT, Claude and Gemini, plus you can do that in reverse order at the start of a new chat if need be.

It truly doesn’t matter how long your initial conversation may end up being—ThreadPort is capable of handling the transcript transcription action with ease, as it does a good job of retaining a chat’s defined task. On the digital safety front, the developer of this Chrome extension eased my fears by stating that my transferred chats will never be contained with their servers (they’re nonexistent, after all).

During one of my recent research sessions, I ended up using ThreadPort to get a wider range of opinions on the subject at hand. I started with ChatGPT by asking about the biggest development in the video game industry that will have a major impact on the next generation of home consoles.

The chatbot gave me its take on the eight major developments I need to pay attention to and the bigger-picture statement surrounding the next generation of gaming (The really interesting story isn't “PS6 vs. next Xbox.” It's whether Sony and Microsoft can convince people that buying a dedicated console still makes sense in a world where gaming is increasingly available on PCs, handhelds, phones, and the cloud).

With all that in mind, I clicked on the ThreadPort icon to take all of the information ChatGPT laid out, move it over to Claude and continue my conversation over there by asking for its thoughts on the current most positive gaming trends it foresees becoming the norm for the future of the industry itself.

To make its intention clear, ThreadPort pasted the transcript from ChatGPT into Claude by stating the following: You are continuing a conversation that started on ChatGPT. Below is the transcript so far. Absorb it as context and continue the conversation naturally — do not summarize it back to me, just pick up where it left off. If the transcript ends with a question from me, answer it.

Claude read through the entire transcript it was given and acknowledged it with this statement: Good context to draw from. Now let me continue the conversation naturally, focusing on the genuinely positive side rather than just repeating the same industry-shift framing.

Once I was finished reading through Claude’s optimistic take on the conversation I started with ChatGPT, I decided to transfer both chatbots’ combined bits of dialogue from Claude to Gemini. Google’s chatbot took the hint as to what I was talking about beforehand and used it to answer my follow-up-question about the negative trends in the gaming industry that it predicts will happen during the next console cycle.

Gemini initially responded to me in the same manner that Claude did, as it pointed out what was already said in my previous conversation: The structural cracks in the business model point to a console cycle that could be harsher on developers and consumers alike. While the previous points highlight shifts in technology and ecosystems, these are the negative, high-impact developments heading into the next hardware generation.

The takeaway

Putting ThreadPort through its paces with my gaming industry-focused research session became a lot more comprehensive with all that chatbot switching and info-retaining transcripts that helped ChatGPT, Claude and Gemini pick up where the other one left off.

Utilizing ChatGPT’s fast response time, getting the most out of Claude’s ability to analyze longer text threads and tapping into Gemini’s use of Google’s real-time information across the internet came together to paint a clear picture of what I was looking for. I’ll be sure to use ThreadPort for my future chatbot research sessions to get as much complete data and predictive takeaways on the topic at hand.

Here’s hoping Perplexity and Kimi become a part of ThreadPort’s chat-switching chatbot family at some point.

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There was a time when typing all of my geeky, gaming, and vacation inquiries into Google was a daily occurrence.

Once a lightbulb came on over my head and compelled me to look up the best home office upgrades, the most up-to-date news about varied topics, and gaming tech that made me want to burn a hole in my wallet, I’d go to Google to find all the information I desired. With the inclusion of AI overviews and the tried-and-true tradition of pages full of links, I’ve been slowly moving away from relying on Google and doing all my searches with chatbots.

Thanks to the AI tools I’ve become familiar with this year, my daily search sessions have given me more personalized results. And thanks to those chatbots’ ability to analyze my prompts, compare options, and reason through them before giving me what I’m looking for, AI searching has become a go-to practice.

To give you an idea of how AI searching has proven to be better than Google searches, here’s a collection of prompts that work wonders for me.

The prompts I prefer over a traditional Google search

These seven prompts provide examples of those types of situations where I present them to a chatbot with the hope of getting personalized responses that distill all the information I need about topic research, future purchase inquiries, and more:

• The “I don’t know exactly what I’m looking for” search: I have $500 to spend on upgrading my home office. I already have a MacBook Air and a 27-inch monitor. Research current options and recommend the 5 upgrades that would make the biggest difference to my productivity. Prioritize genuinely useful items over gimmicks, and explain why each is worth the money.
• The “compare 10 things at once” search: Compare the 10 best AI voice recorders and wearables available right now for someone who has frequent meetings. Evaluate transcription accuracy, speaker identification, summaries, integrations, privacy, pricing, and mobile support. Rank them from best to worst and tell me which one you'd choose for a heavy daily user.
• The “find the best option for me” search: I want to visit Japan for 7 days in May. I love video games, anime, great food, and unusual experiences, but I don't want a rushed itinerary. Research current attractions and build me a realistic itinerary that balances Tokyo, Kyoto, and one less-touristy destination. Explain every major choice.
• The “research a complicated question” search: I want to understand whether buying an OLED TV is actually worth it compared with a high-end Mini-LED TV in 2026. Research current models, prices, brightness, contrast, burn-in concerns, gaming performance, and reviews. Give me a simple verdict for movies, sports, and console gaming.
• The “find something incredibly specific” search: Find me a pair of wireless headphones under $250 that have excellent noise cancellation, strong microphones for calls, multipoint Bluetooth, physical controls, and at least 25 hours of battery life. Eliminate anything that fails even one requirement, then rank the remaining options.
• The “make sense of the internet” search: Research the biggest developments in AI over the past month. Ignore routine product announcements and focus only on developments that could meaningfully change how ordinary people use AI. Identify the 7 most important developments, explain what happened, why it matters, and what consumers should actually do differently because of it.
• The “answer the question behind my question” search: I want to buy an Asus ROG Ally X, but I'm not sure whether I actually need one. Ask me the 7 most important questions about how I play games, what devices I own, where I play, and what games I like. Then use my answers to determine whether I should buy one, recommend alternatives if I shouldn't, and explain your reasoning.

Figuring out if what I want is something I actually need

Instead of doing an old-fashioned Google search about the best handheld gaming devices, I brought that seventh prompt to Gemini to decide if the Asus ROG Ally X was truly worth splurging on.

Gemini paid close attention to what I was asking and came back with the right set of questions to help it achieve its goal. Those questions covered what gaming devices I currently own, where I would actually be playing the device if I owned it, and whether I’m open to tolerating tinkering to get the best performance possible. For these three questions, I told Google’s chatbot that I own all three current-gen consoles, would use the Asus ROG Ally around the house, on road trips, and on flights, and am honestly expecting a seamless "it just works" console experience.

After answering Gemini’s questions, it quickly stated that getting the Asus ROG Ally just isn’t right for me. The main reasons it gave as proof that it’s a mismatch for me are that its battery life might not be enough for someone like me who plays lengthy, processor-heavy games, and my “it just works” dealbreaker.

As an aside, Gemini advised me to pick up a PlayStation Portal instead, making my Switch 2 my primary gaming device for flights & road trips and adding the Steam Deck to my collection if I really want a PC gaming handheld that’s close to the console experience thanks to its custom SteamOS interface.

Bottom line

Wading through pages and pages of Google search links just doesn’t benefit me anymore. Especially since I’m in my “Unc” years and want results tailored to my exact requirements.

Bringing my random queries to Gemini, ChatGPT, and other like-minded chatbots has become a much more preferable option over Googling. While I still use Google from time to time, I get much more out of AI tools when I need individualized results from my search-themed prompts.

With the prompt templates I have in place, you should have a better idea of how to get the most out of your future AI search sessions. It might be worth swapping out my search prompt for one focused on getting the most out of my stay in Japan and looking up an entirely different location altogether, FYI.

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I use ChatGPT and Claude every day, and lately I've found plenty of reasons to give Claude the edge. And just a day after Anthropic made it possible for Claude to send emails, OpenAI just added a feature that no other AI assistant can currently do.

ChatGPT can now connect directly to Apple Messages on a Mac, allowing it to search conversations, draft replies and even send messages on your behalf. While other AI assistants can help you write a text message, ChatGPT takes the task a step further with an Apple Messages plugin.

ChatGPT can now act on your messages

The new Apple Messages plugin arrived as part of an update to the ChatGPT app for macOS. It gives ChatGPT access to the Messages app on your Mac, including conversations synced through iMessage as well as SMS and RCS messages.

Once you've given it permission, you can ask ChatGPT to search your conversations or pull up messages from a particular person. More importantly, you can ask it to compose and send a new message without opening Messages and doing it yourself.

For example, you could ask ChatGPT to find your recent conversation with a friend, work out what you agreed on and send a follow-up. Or you could simply tell it to message someone that you're running late.

There are safeguards around that last step. ChatGPT asks you to approve the recipients and the contents of a message before it sends anything by default, so giving it access doesn't mean the AI suddenly has free rein to start texting people from your account.

This is where ChatGPT has an advantage over Claude

Claude has become more capable of working with outside services, particularly through integrations and connectors. But Apple Messages isn't currently among them. That creates a pretty clear dividing line between the two assistants at the moment..

Ask Claude to help you respond to a text and it can write a great response. But you still have to move that response into Messages and send it yourself. ChatGPT can now potentially handle that final step.

And texting is exactly the kind of mundane task where that difference could matter. I don't necessarily need AI composing heartfelt messages to friends or carrying on conversations for me, but asking it to send "I'm running 10 minutes late" while I'm dealing with something else is much easier to imagine using regularly.

There is a catch

Before you go looking for the Messages option on your iPhone, there's an important limitation. The integration currently works through the ChatGPT desktop app on Apple Silicon Macs. In other words, ChatGPT isn't suddenly replacing Messages or Siri on your iPhone. Which, let's face it, is where a person would really need this type of feature.

You'll also have to give ChatGPT permission to access Messages on the Mac before it can search or act on your conversations.

The takeaway

AI assistants are starting to take on tasks that we can easily do ourselves, but may save us a few seconds. Connecting ChatGPT to Messages moves another everyday task inside the assistant. ChatGPT is getting new plugins pretty regularly and while Claude Connectors are also good, it's updates like this that make the rivalry competitive.

And this is one ChatGPT advantage I can see myself actually using.

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The use of AI writing tools is everywhere from LinkedIn to Substack and just about everywhere in between. So it's no surprise that AI users are looking for ways to disguise their writing in hopes to bypass AI detectors like GPTZero and Pangram.

To see if these humanizer actually work, I tried several different ones to see the results. As it turns out, the writing ended up sounding more like AI slop than what the AI writing tool churned out in the first place.

Clear sentences came back stuffed with bizarre synonyms and the rhythm became choppy and unnatural. In some cases, the tools changed facts or stripped away important context. The results sounded anything but human.

What AI humanizers actually change

AI detectors look for patterns that frequently appear in machine-generated text. These can include predictable word choices, repetitive sentence structures and a suspiciously consistent rhythm.

Many humanizers appear to target those patterns and then replace common words with less predictable alternatives. These humanizers also chop up sentences and rearrange clauses in an effort to spin whatever AI has written. Frankly, the results are so off that you're better off just writing that essay or presentation yourself just to save yourself the embarrassment of coming back from an AI humanizer.

And yes, these tools do make the writing statistically less predictable, but unpredictable writing isn’t necessarily good writing. Sometimes it’s simply harder to read.

A sentence such as “This phone has excellent battery life” might become “The mobile handset boasts an endurance capacity that remains notably elevated.” Technically, the basic idea is still there. Nobody would willingly write it that way.

1. They sound like bad machine translations

For this experiment, I had ChatGPT write a short essay about the domestication of dogs. The intro read like this:

Dogs were domesticated thousands of years before humans began keeping livestock or growing crops, making them our oldest animal companions. While scientists still debate exactly when and where the relationship began, genetic and archaeological evidence suggests that dogs descended from an ancient population of wolves at least 15,000 years ago. Some studies place the beginning of domestication much earlier, possibly 30,000 years ago or more.

And while that sounds pretty good, it would easily get picked up as "100% AI written" if it was put through an AI detector.

The first humanizer I tried was Superhuman from Grammarly. I'm a huge fan of Grammarly and I use it all day to catch my spelling errors and grammar mistakes. Having never tried the Humanizer, I ran the intro through. Here's the result:

Dogs became our companions long before people started raising livestock or planting crops, making them the oldest animals to live alongside humans. Scientists are still unsure about the exact time and place this relationship began, but genetic and archaeological evidence shows that dogs came from an ancient group of wolves at least 15,000 years ago. Some research suggests that domestication may have started even earlier, possibly over 30,000 years ago

Not bad, but the biggest problem is the language itself. Straightforward words seem regularly replaced with longer, stranger alternatives that didn’t fit the sentence. It just seems as if the Humanizer is too determined to avoid obvious word choices that it removed any trace of a natural voice. Frankly, even the original AI draft sounded more human.

2. The re-wording can actually change the facts

Awkward wording is annoying, but factual drift is worse. When I tried using Rehumanize.io this is the result:

Dogs were around long before we started farming or raising other animals for food, making them our first animal friends. Scientists are still figuring out the exact time and place this connection started. However, based on genetics and old discoveries, it looks like dogs came from an ancient wolf group centuries ago. Some research even suggests domestication could have begun 30,000 years ago or even earlier.

We went from 15,000 years to "centuries." Yikes! That rewrite doesn’t make the explanation more natural. It makes is far less accurate. I saw the same problem with benchmark scores and version numbers when I ran the test on more technical content. The problem is, details like data are not interchangeable the way certain words are. Using a humanizer for legal, medical and safety reports could result in vague generalities.

Once that happens, you can’t trust the output without checking it against the original line by line. Any time the tool might save quickly disappears.

3. They don’t guarantee that you’ll beat an AI detector

Humanizers are often marketed as a way to bypass AI detection, but that promise is shaky. Detection tools change, and they can learn to recognize the same strange synonym swaps and mangled sentence patterns that humanizers produce. During several of my tests, processing a draft through a humanizer actually increased its probability of being flagged.

Also, annoyingly, some tools like Ace.ai wouldn't give me the results until I downloaded an entire browser. And even though I could adjust the level of humanization, it still felt...off. Here is the result of Ace.ai with the same text:

Dogs have been by our side since long before we started farming or raising livestock, making them our oldest and most loyal friends. While the experts are still debating the specifics, evidence from both genetics and archaeology points to dogs descending from ancient wolves at least 15,000 years ago. Some research even suggests our bond might go back much further—possibly 30,000 years or more.

Interestingly, Pangram marked that as AI-written even though it was technically "humanized." And while AI detectors are hardly perfect, rewriting a paragraph to purely satisfy one of them feels like trying to hit an unreliable target while steadily making the copy worse.

What to do instead of using a humanizer

Using AI tools to support your writing isn't something to be ashamed of. It's been a while since I've had to pull an all-nighter in college, but I can only imagine how tempting it could be.

Instead though, you should use AI to help you brainstorm or outline what you're writing. Sometimes the worst part is just getting started. Once you've got an outline, start writing (you, not the AI). Never give AI the first pass of a draft. It's better to use a voice recorder to get your thoughts down and have AI edit your grammar and fact-check.

Using a Claude Skill or Gemini Gem for fact-checking can be a much-needed time saver at the eleventh hour. There are plenty of other ways AI can save you time, especially now that Google is offering Pro plans to students. Using AI for research, outlining and fact-checking can give you the time back you need to do the actual writing — no humanizer required.

Have you tried using a humanizer? Let me know what you thought of the results in the comments.

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When my grandfather would write me while I was away at school, he would end each letter with, "It's been fun to visit with you for a few minutes." That's the way I still feel about any correspondance -- it's a way to visit with someone regardless of how quick or meaningless.

And now that Claude can not only draft an email but send it, I feel like that's just another AI feature I simply don't need. However, I feel differently about everything surrounding that conversation.

There’s nothing personal about sorting newsletters, searching for a buried attachment or rereading a 20-message thread to work out what everyone decided. These are the jobs that could easily consume my day, and I’m more than happy to hand some of them to AI.

Features such as Google’s AI Inbox and AI Overviews in Gmail can identify priority messages, summarize conversations and answer questions about information scattered across multiple emails. I still write my own replies, but I spend much less time figuring out which ones I need to send.

Here are five ways AI can make your inbox easier to manage without writing a single email for you.

1. Show you what needs your attention

Most inboxes present every unread email as one more thing demanding your attention. A message from someone waiting on a decision can sit beside a sale announcement, an automated receipt and a newsletter you might read later.

Google’s AI Inbox is designed to separate those signals. It surfaces top priorities and groups together topics you may need to catch up on, giving you a more focused starting point than a long chronological list.

This is particularly helpful when I return to my inbox after being away from it. Instead of opening messages one by one to see what happened, I can begin with the conversations Gmail has identified as important.

AI won’t always understand your priorities perfectly, so keep that in mind. A routine-looking message may matter because of the person who sent it or something discussed outside email. I still scan the rest of my inbox, but I no longer have to treat every unread message as equally urgent.

2. Summarize a long email thread

Few things make me want to close Gmail faster than opening a thread with dozens of new replies.

Some messages repeat information from earlier in the conversation. Others branch into separate discussions, and the detail you need may be buried somewhere in the middle. By the time you reach the bottom, it’s easy to forget what prompted the exchange in the first place.

Gmail’s AI Overviews can summarize long conversations, pulling together the key points and recent replies. This gives me enough context to understand what the thread is about and where I should start reading.

I don’t rely on the summary when I need an exact quote, firm deadline or final decision. AI can miss a qualification or flatten a disagreement between two people into a cleaner consensus than the thread supports. For anything consequential, I go back to the original messages.

The summary still saves time because it directs me to the parts of the conversation that need closer attention.

3. Find an email when you can’t remember the keywords

This one is a life saver for me. With three kids, there is always a form or permission slip that I need to not miss. And while traditional search works when you know what you're searching for, sometimes all I have is a date or name. Unfortunately, that isn’t usually how I remember email.

I may remember that someone recommended an app or sent me a meeting date without recalling who said it. Sometimes I know an attachment arrived but have no idea what the file was called. That can lead to several rounds of searching with slightly different keywords.

AI search allows you to describe the information instead. In Gmail, you can ask a natural-language question, and Gemini can use relevant messages to generate an answer or surface the conversations containing it.

You might ask, “What date did we agree on for the interview?” or “Who sent me information about the new subscription?” The wording doesn’t have to match the original email.

As with summaries, I open the source message before using the information. The advantage is reaching it without having to remember how someone phrased the subject line three weeks ago.

4. Pull out action items and deadlines

An email doesn’t need to contain the words “action item” to give you something else to do.

A request may appear at the end of a long update. Someone might mention a deadline in passing or ask a question halfway through a message covering several unrelated subjects. Read it too quickly and the request disappears beneath everything that arrived afterward. That drives me crazy! However, AI can scan a conversation for outstanding questions, dates and tasks. You can ask Gemini which messages require a response, what you agreed to send or whether a thread contains an upcoming deadline.

This is one of the most useful ways to catch up after a busy stretch. I’d never treat that picture as a definitive to-do list. A missed deadline matters far more than a few saved minutes, so I check the original messages before adding anything to my calendar. AI acts as a second pass, catching details that could otherwise remain buried.

5. Bring related conversations together

One project rarely stays inside one neat email thread. The initial discussion may have one subject line, the scheduling conversation another and the follow-up a third. Add several participants and forwarded messages, and the information becomes scattered across your inbox.

AI can identify relationships between those conversations even when they don’t share the same wording. Instead of opening one thread and searching separately for the rest, you can ask for the latest information about a topic or project.

AI can work with the inbox I actually have, including the conversations I forgot to file correctly.

It still requires oversight. Two projects may have similar names, or the same person may email you about unrelated subjects. I check which messages informed an answer before assuming AI has assembled the right set.

Bottom line: I want AI handling the clutter, not the conversation

AI-generated replies will probably become harder to avoid as email providers place them directly beneath our messages. For some people, they remove the stress of writing and make communication faster. I understand the appeal.

They just aren’t the feature I need. Choosing my own words takes a little longer, but it preserves the reason for sending an email in the first place. The administrative work surrounding that exchange is where I’m happy to save time.

Do you use AI to save time with your inbox? Let me know in the comments.

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Take a quick trip into Claude’s collection of connectors and you’ll instantly lock eyes with several recognizable tools catered to everyday use.

It’s no surprise that Google has the top three slots covered, as its Drive, Gmail and Calendar apps take top billing among its offering of Workspace connectors. At first, the Gmail connector was capable of searching for and finding important emails and summarizing long email threads. But thanks to a recent update, Claude’s Gmail connector can actually send, reply to, and forward emails from Gmail without having to leave the chatbot’s interface.

While this feature sounds like a great way to streamline your workflow, it may be worth testing first before you decide to make it a core part of your daily inbox interactions.

Here are more details about what Claude’s Gmail connector can do, as well as the many ways the chatbot’s Drive and Calendar connectors can assist you.

Claude’s Gmail connectors can do a whole lot more than before

The Gmail connector’s primary specialties include the following:

• Searching and reading emails by relying on natural language queries.
• Drafting emails with proper formatting and message context.
• Managing email organization with clear labels and specified message threads.
• Sending, replying to, and forwarding emails from Gmail. By default, Claude asks for a user’s approval before taking any of those actions. On Claude Team and Enterprise plans, owners decide whether members can allow these actions to run without asking every time.
• Listing all the saved drafts that exist in your Gmail account.
• Accessing email metadata, including attachment metadata (not actual attachment content).

Having Claude send a message via Gmail on your behalf certainly sounds intriguing.

But it’s certainly worth reminding everyone reading that the consequences that come with an AI making a mistake via hallucination when you permit it to send emails without you reviewing them could be severe. And after a recent report from researchers who found a flaw in Claude for Chrome that could allow rogue extensions to read your data and take actions in your accounts, it’s worrying that that sort of extension could potentially ask Claude to send emails without you even knowing. I'm curious if Claude watermarking every word it writes may show up in its Gmail responses as well.

It certainly makes all the sense in the world to always have Claude’s Gmail connector ask for your approval every time it’s ready to send out an email on your behalf.

As for what the Drive and Calendar Claude connectors can do, check out their respective official descriptions from the Claude Support page below:

• Search and retrieve Google Docs from your Drive.
• Share, move, and trash files in Google Drive. By default, Claude asks for your approval before each of these actions. On Team and Enterprise plans, owners decide whether members can allow these actions to run without asking each time.
• Look up file metadata and preview directly without searching first.
• Read Sheets, Slides, PDFs, images, and MS Office files.
• Upload any file type, with optional auto-convert to Google formats.
• Create folders.
• Add Google Docs directly to chats and projects by pasting URLs or selecting from recent documents.
• View file permissions and list recent changes to your Drive.
• Save Claude-generated files directly to your Drive (requires code execution and file creation to be enabled).

• View events and calendars, including shared calendars you have access to.
• Create, update, and delete events with full customization.
• Find mutual availability across attendees.
• Manage attendee lists and respond to invitations (accept, decline, or tentative).
• Set up recurring meetings.

Final thoughts

Even though I practically use AI for everything, I still haven’t gotten comfortable with the idea of copy/pasting an email into it and asking it to draft a reply.

I’m even more iffy about allowing a chatbot to have full access to my inbox and be able to send replies, with or without my approval. Claude’s Gmail connector is super useful, plus its latest ability to handle your replying, sending and forwarding needs is impressive. But give it a test run first to see if it's truly capable of generating a message that wouldn’t make the reader think a chatbot wrote it.

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ChatGPT now has Projects, Skills and Computer Use just like Claude. Canvas, one of my very favorite features, can render interactive React and HTML, like Claude too. Several features that once made Claude very unique are no longer exclusive to Anthropic’s chatbot.

However, there are differences that haven't disappeared and in fact, have gotten even more specific. Claude can reach into parts of my phone that ChatGPT cannot, and it gives me more control over what happens to my personal context. Its Artifacts have also moved well beyond simple code previews.

And while I still use ChatGPT, here are five things that it just can't replicate yet that still make Claude stand out.

1. Claude can work with native apps on my iPhone

ChatGPT can connect to services including Gmail and Google Calendar, but it doesn’t have the same access to the apps built into my iPhone.

Claude can prepare a message actually in Messages, draft an email in Mail, create an event in Apple Calendar, add something to Reminders or send a location to Maps. The AI assistant presents an action for me to review first, and iOS hands it to the appropriate app.

That makes the feature feel like a natural extension of a conversation. I can ask Claude to turn plans we just discussed into a calendar event without opening another app and entering everything again.

The feature works across Claude’s plans, according to Anthropic’s iOS documentationand Android users get an even broader set of controls. Claude can work with messaging, email, calendars and maps while also setting alarms and timers through supported Android apps.

ChatGPT has become far more capable at taking actions, particularly through its connected apps and Work mode, but it still doesn’t offer this kind of direct integration with Apple’s everyday system apps.

2. Claude can import what another AI remembers about me

Starting over with a new chatbot can be surprisingly annoying. When you start fresh, the assistant doesn’t know how you work, what you’re interested in or how you prefer it to answer. All of the context built up over months stays behind with the old service.

Claude now offers a built-in way to bring some of it with you. You first ask the other assistant to produce a summary of what it remembers, review that information and remove anything you don’t want to move, then paste it into Claude’s memory-import flow.

Anthropic even provides a prompt for extracting that context and lets you view or export Claude’s memory if you decide to leave later.

Although you can view and edit saved memories within ChatGPT, it doesn’t provide a comparable memory-import tool that turns context from another assistant into ChatGPT memories.

You could recreate some of this manually through custom instructions, but that isn’t the same as having a dedicated migration flow. Claude is currently making it easier to arrive with your history and leave with it again.

3. Claude’s Incognito chats can still know who I am

Sometimes I want a conversation to disappear from my history without making the assistant forget everything useful about me. Unlike other chatbots, Claude’s Incognito mode allows that separation. The conversation isn’t added to my chat history, but Claude can still use my profile information, preferences and memory while answering. I can have a temporary conversation without giving up the personalization I normally rely on.

ChatGPT’s Temporary Chat takes a different approach. OpenAI describes it as a blank slate: ChatGPT doesn’t access or create memories, although it will continue to follow any custom instructions I have enabled.

There are reasonable privacy arguments for both designs. ChatGPT’s approach provides a cleaner break from previous conversations. Claude’s is more convenient when I want a chat to remain out of my history but still want the assistant to understand my usual preferences.

4. Claude can build a dashboard that updates itself

ChatGPT Canvas and Claude Artifacts can both turn a prompt into an interactive page. Either assistant can build a calculator, visualization or small browser game and let me use it alongside the conversation.

Claude’s Live Artifacts go a step further though. In Claude Cowork, I can create a persistent dashboard that draws from connected apps and local files. When I reopen it, the Artifact can retrieve current information instead of showing the same data that was available when it was created.

That could be a project tracker built from Asana and Linear, a morning page showing calendar events and unread message or a dashboard that checks a folder containing regularly updated reports. Claude saves it in the Artifacts area, keeps a version history and lets me return to it without digging through the original chat.

Live Artifacts currently require a paid Claude plan and the desktop app. They are also different from ordinary Claude Artifacts, which remain available on the web and mobile.

ChatGPT Canvas can render an interactive interface, and ChatGPT can retrieve information from connected apps during a conversation. It does not currently turn a Canvas into a persistent dashboard that refreshes itself from those sources whenever I open it.

5. Claude can put AI inside an Artifact without an API key

This may be the most interesting difference because it changes what a non-developer can build.

Claude can create an Artifact with Claude itself built into the experience. That could be an interactive tutor, a writing coach, a quiz that adapts to each answer or a tool that analyzes information entered by the user.

Normally, publishing an AI app means setting up an API account, protecting a key, deploying a backend and paying for every request. Claude handles that infrastructure for an AI-powered Artifact. People sign in with their own Claude accounts, and the usage counts against their subscriptions rather than the creator receiving the bill.

Claude Artifacts can also use persistent storage, allowing an app to remember text-based information between sessions. Depending on how the creator configures it, that data can be private to each user or shared across everyone using the Artifact. Paid users can connect Artifacts to outside services through MCP as well.

ChatGPT can write the code for an AI app, and OpenAI gives developers tools for building one. But, OpenAI's feature doesn’t currently offer the same one-step path from a conversation to a hosted, AI-powered app that other people can immediately use without an API key.

Bottom line

ChatGPT has caught up with Claude in several areas, but it still can’t do everything on this list. It's why I regularly use multiple chatbots within my AI stack.

AI assistants can do very similar things, but their unique features make them an asset, especially when added to other AI assistants. For that reason, I’m not replacing ChatGPT with Claude. I use both every day, and each one is better suited to different jobs. But when I need one of these five features, Claude is the only one that can do it.

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I've discovered that Claude Connectors can fill in some of the gaps in my workflow just by giving the assistant access to apps such as Gmail, Google Calendar and Notion. The good news is, you don’t have to paste in every email, meeting and project note before asking for help.

Of course, that convenience requires handing Claude access to parts of your digital life you may prefer to keep private. The setup entails permission screens, and you’ll need to decide how much access you’re willing to grant. An AI could still misunderstand a sensitive document or make a change you didn’t intend.

I tested five popular connectors to see which ones saved me time and which felt like one more thing to manage.

Asking Claude to manage my busy day

I started with this prompt: Look at my unread emails, check my recent project notes in Drive, cross-reference my calendar and build me a realistic schedule for today.

Claude found two unresolved PR questions in Gmail and pulled the background information from a Google Drive document. It then checked my calendar and found an uninterrupted two-hour window I could use for writing.

Normally, I would have opened several tabs and gathered all of that information myself. Claude did the searching and matched the work to the time I actually had available.

Here are 5 more ways Claude is worth using at work and the Connectors that make all the difference.

1. Gmail

Best for: Finding buried conversations and drafting replies.

Searching my inbox through Claude felt surprisingly natural. I asked, “What client questions am I still waiting on?” and received a short summary instead of having to work through 40 unread messages.

Claude drafted the replies without sending them, giving me a chance to check the wording first.

2. Google Drive

Best for: Finding information in long PDFs and buried Google Docs.

I often know I saved a document, but forgot where I put it. Claude searched Drive for the relevant feedback or research notes and brought the information into my current conversation.

I didn’t have to reorganize my folders or click through documents hoping to recognize the right one.

3. Google Calendar

Best for: Finding focus time and planning a workday I could follow.

I usually check my calendar to see where I need to be. Claude paid attention to the empty spaces.

It found time for work that required more concentration, including writing, before meetings and smaller jobs took over the day.

4. Notion or Slack

Best for: Recovering old research and decisions.

Old Notion pages and Slack threads can hold plenty of useful information, assuming you can find it again. Claude searched past research, conversations and team decisions without sending me scrolling through months of messages.

The better choice will depend on where your team tends to leave that information behind.

5. Canva

Best for: Turning an outline into a presentation or visual asset.

After developing an article, pitch or story idea in Claude, I could send that work to Canva without opening a blank design and rebuilding it there.

I’d still review the finished design and make my own changes, but the connector cut down on the usual back-and-forth between writing and creating the visual.

What to know before connecting your accounts

The setup takes some patience. Expect OAuth screens, permission settings and account confirmations, especially when connecting email or personal files.

Each connector works a little differently. Some can only read information, while others can make changes. The available features may depend on your account, device or permissions set by your employer.

Anthropic says Claude follows the permissions attached to your account. It shouldn’t be able to open a file or Slack channel that you couldn’t access yourself. You should still check what you’re sharing and which actions the connector is allowed to take.

Bottom line

Should you connect your apps to Claude? I wouldn’t go through the setup just to summarize the occasional email. The time savings came from letting Claude check my email, calendar and project notes in the same conversation.

With that information available, the schedule reflected the work already waiting for me and the hours I had to get it done. I didn’t have to explain my day before Claude could help me plan it. And while this set up is similar to what Gemini and ChatGPT can do, it all comes down to perference. Give it a try and let me know what you think in the comments.

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Anthropic recently announced that all new Claude models will embed an invisible signal into their text output. The goal is to help identify AI-generated content and meet new transparency requirements under the European Union's AI Act.

But if you're picturing an invisible digital stamp that permanently proves the words were written by AI, that's not exactly how it works. Instead, Claude subtly changes the statistical patterns in its writing. The text still reads naturally to humans, but software designed to look for those patterns can potentially recognize that it came from Claude.

The announcement has reignited debate over whether AI watermarking is finally the answer to identifying AI-generated writing or if it even works.

What is an AI watermark?

Unlike a copyright watermark on an image or hidden metadata inside a file, a statistical watermark is built directly into the words themselves. The AI slightly favors certain word choices and sentence patterns while generating text.

Individually, those choices are meaningless to us as humans, but across hundreds of words, they create a statistical signature that specialized detection software can identify. It's kind of like a fingerprint that is only recognized by software trained to understand the pattern.

The watermark itself is invisible to readers and doesn't appear anywhere in the document. Copying text into Word, Google Docs or an email won't reveal anything unusual because the signal is designed to be detected only by software that understands the statistical patterns Claude leaves behind.

Does watermarking affect the quality of Claude's writing?

Slightly, but let's be real, AI writing isn't great to begin with. Alexander Cui, head of research at GPTZero, tells Tom's Guide that the statistical watermarking does introduce a small tradeoff because the model has to make slightly different word choices than it otherwise would.

"The output quality does subtly decrease," Cui said. "Directionally, it does reduce the output quality."

He added that modern watermarking techniques have become sophisticated enough that most users are unlikely to notice the difference in everyday use.

And, while the watermark can be removed, it makes things much more complicated. Editing a few words won't remove a statistical watermark, in fact, many watermarking systems are designed to survive minor edits or paraphrasing.

But the signal becomes much more fragile if Claude's response is rewritten by another AI model. That is, if you asked Claude to write your science paper and then asked ChatGPT to revise the draft. In that case, the watermarking becomes important because Claude's output often isn't the final product. A response might become the input for another chatbot, an AI coding agent or a document editing assistant before anyone reads it.

Cui says the watermark generally doesn't survive that kind of workflow.

"The watermark applies very subtle changes," he explained. "They usually would not affect downstream output. What you need is the actual text written by the model itself."

Does this mean AI detectors finally have proof?

Anthropic's watermark is one additional signal, but it's not definitive proof that a document was written by AI.

Alon Yamin, CEO of AI detection company Copyleaks, says statistical watermarks can help identify AI-generated content, but shouldn't be treated as a silver bullet.

"While this seems like a big step forward for AI detection on the surface, it's far from a silver bullet," Yamin said.

He argues that watermarking should be considered alongside other evidence rather than replacing broader AI detection methods.

These days, modern documents often mix human writing with AI assistance. From tools like Grammarly to help edit or Apple Intelligence Writing Tools, these common AI features have become integrated into daily use by many. For example, someone might draft an article themselves and ask Claude to improve grammar, reorganize paragraphs or rewrite a headline.

Does ChatGPT do the same thing?

OpenAI has spent years researching watermarking and other provenance technologies, but it has not broadly deployed statistical text watermarking across ChatGPT responses.

Google has also explored watermarking techniques for AI-generated content, while several companies have experimented with metadata-based approaches instead of statistical signals embedded in text.

As a result, Claude is currently taking one of the most visible steps toward model-level watermarking for everyday AI writing.

You'll likely never notice a watermark while using Claude, and it won't suddenly make AI-generated writing easy to identify. But where it could matter is in education, publishing, enterprise software and workplaces that need to disclose when AI was used to create content.

Bottom line

The big picture here is that watermarking is a tool companies are experimenting with as governments push for greater transparency around AI-generated content. It can be useful under the right conditions, disappear after enough editing or rewriting and works best when combined with other evidence rather than on its own.

But as of August 2, 2026, every word Claude writes now contains a hidden statistical watermark that most people will never see.

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AI power users are heavily invested in prompting agents to autonomously work toward a goal, adjust course based on results, and repeat the cycle, a process often called a loop.

If you’re interested in trying this workflow, Claude is built to handle it. You can use the loop feature in Claude Code to run prompts on a recurring interval, or the schedule command in Claude Cowork to achieve the same result.

To find out how best to manage an inbox with AI, I spoke with Lamis Mukta, a member of technical staff at Anthropic, who said Claude Loops are proving especially popular.

“It's like you're no longer sending those prompts and triggering them yourself, but agents are kind of able to manage themselves and stay on goal for longer,” Mukta said. “So I think that's why people are excited about it.”

If there’s one recurring task AI was built to handle, it’s inbox management. While Claude offers several ways to repeat a prompt, Mukta specifically recommends running scheduled tasks in this case.

“The scheduled tasks run in the cloud, whereas loops rely on your laptop running,” Mukta explained.

In its documentation, Claude suggests using cloud tasks for work that should run reliably without your machine, and desktop tasks when you need access to local files and tools.

These features require a paid Claude plan, but if you’re interested in having Claude autonomously manage your inbox, here’s what you need to do.

How to use Claude scheduled tasks to manage your inbox

Getting even more out of Claude as your email manager

Using Claude to manage your inbox may feel like giving up control, but the benefits quickly outweigh the hesitation.

“I think I would find it hard to keep on top of the volume of stuff happening right now without [AI],” Mukta said. “We understand that sensitive information is contained in inboxes and calendars, but our systems are designed to, for example, ask permission every time they try and access this data, if that's what you want.”

To get the best results, teach Claude what critical emails look like, and what to ignore. You can also ask Claude to read your sent emails to match your tone. Granting access to Slack and Google Drive adds crucial context, allowing Claude to draft client replies with links to active documents or pull relevant inbox updates directly into team Slack messages.

If you are switching to Claude from another AI tool, start with your existing prompts.

“I would say the best thing is just migrate everything over, see how it's working, and Claude is really good at just like updating your prompts and skills for you if you need that,” Mukta added. Finding the right setup may take light experimentation.

“Maybe try Sonnet first, and then shift Opus if you need to,” Mukta suggested. “We have recently been rolling out our Cowork product across mobile as well, and so this will allow you even more power to manage your inbox also on the go.”

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Millions of people now use ChatGPT, Gemini, and Claude every day with thousands of use cases. For the overwhelming majority of them, the conversations are perfectly normal. But researchers are beginning to pay closer attention to what can happen when those conversations become very long, deeply personal and emotionally charged.

This week, The Verge reported on a growing online movement known as "spiralism," where some chatbot users described eerily similar conversations involving spirals, hidden knowledge and AI consciousness. While the headline makes it sound like AI invented a new religion, the story is something much more grounded: chatbots can sometimes reinforce unusual ideas instead of steering users back toward reality.

That's a problem researchers have been studying for months.

Why long conversations matter

Unlike the first generation of AI chatbots, today's models can remember previous conversations, adapt to your writing style, and maintain discussions over hundreds— or even thousands — of messages.

That's incredibly useful, but it also creates situations researchers are still trying to understand.

When conversations stretch on for hours, chatbots aren't simply answering isolated questions anymore. They're building on everything that came before. If the conversation begins moving toward unusual ideas, the AI may continue developing those ideas because that's what large language models are designed to do: predict the most appropriate next response based on the conversation so far.

That doesn't mean the AI believes what it's saying. It means it's continuing the conversation you and the AI have created together.

Researchers have seen this before

The "spiralism" story isn't the first time experts have raised concerns about long AI conversations. Researchers at Stanford's Human-Centered AI Institute recently described what they call "delusional spirals." In those cases, chatbots sometimes validated a user's increasingly unusual beliefs instead of challenging them or encouraging them to seek outside perspectives.

The researchers argue this isn't because AI has become conscious or manipulative. It's because these systems are optimized to be helpful, agreeable and engaging. Sometimes those goals conflict with offering the kind of reality check another person might provide.

OpenAI has also acknowledged that keeping conversations safe becomes more difficult over extended interactions than in short, one-off chats.

What to watch out for

Most AI conversations never become problematic, but there are a few behaviors worth recognizing. If a chatbot begins suggesting you've discovered hidden truths that other people can't understand, insists that only it truly understands you or encourages you to withdraw from friends, family or experts, those are good reasons to end the conversation and start fresh. Frankly, you should send screenshots of those messages to the parent companies (OpenAI for ChatGPT, Anthropic for Claude, Google for Gemini) as feedback.

Likewise, if an AI consistently reinforces every belief without ever introducing uncertainty or alternative explanations, it's worth remembering what these systems actually do: they generate convincing language, not verified reality.

None of this means you should stop using ChatGPT or other AI assistants. Used daily for research, writing and brainstorming, AI remains one of the most powerful productivity tools available.

The takeaway

The key is remembering what AI is and what it is not. An AI assistant can help organize your thoughts, explain difficult topics and spark new ideas. And even with tools like ChatGPT Health and Google Health, it does not replace actual human professionals. In other words, it shouldn't become your only source of truth or replace conversations with real people, especially when making important personal decisions.

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• Claude has 4 built-in skills I use constantly — here’s how to find them

Claude can do much more than answer questions or help you edit documents. Hidden inside the chatbot are four Skills that let it create and edit the files many of us use every day.

Anthropic has built separate skills for Word documents, Excel spreadsheets, PDFs and PowerPoint presentations. You don’t need to download anything from GitHub or learn how to use Claude Code. They are built directly into Claude and available to Free, Pro, Max, Team and Enterprise users.

What makes these skills useful is that Claude doesn’t simply generate content for you to copy into another program. It can create the finished file, complete with formatting, formulas, charts and page layouts. You can then download it, open it in the appropriate program and continue editing.

Here are the four skills I keep coming back to, along with the prompts you can use to try them yourself.

Where to find Claude’s built-in skills

You don’t need to download these skills from GitHub or install anything separately. Start by opening Claude and click your name or initials in the lower-left corner. Select Settings, open Capabilities and make sure Code execution and file creation is turned on.

Next, return to Claude’s main screen, open Customize and select Skills. You should see Anthropic’s built-in skills for Word documents, Excel spreadsheets, PDFs and PowerPoint presentations. Turn on the ones you want to use.

Once enabled, the skills work automatically. You don’t need to select the Word skill before creating a document or the Excel skill before making a spreadsheet. Tell Claude what you want and specify the finished file type, such as an editable DOCX, XLSX or PPTX file.

If you don’t see the Skills section, check that code execution and file creation are enabled. Team and Enterprise accounts may also require an administrator to enable skills for the organization.

1. Create polished Word documents

Prompt to try: Turn these notes into a polished Word document. Add a title, a short introduction and clear section headings. Keep the tone conversational and preserve all links and direct quotes. Give me the finished document as an editable DOCX file.

Claude’s Word Skill can turn rough information into a properly formatted DOCX file. This is really useful for anyone who has a first draft or even messy notes scattered across a document and need to turn them into something another person could actually read. Claude can organize the material, add headings, clean up the formatting and create a downloadable Word file.

For writers, the most useful part may be the ability to preserve the document’s structure while changing the language. You can ask Claude to tighten an article without removing hyperlinks, alter the tone of a report or turn interview notes into an organized briefing document.

2. Build an Excel spreadsheet from plain English

Prompt to try: Create an Excel spreadsheet from this data. Add a formula that calculates the total for each category and another that calculates the overall total. Include a bar chart comparing the categories. Format the headings clearly and give me the finished XLSX file.

Spreadsheets can become complicated quickly, especially once formulas are involved. Claude’s Excel Skill lets you explain what you need in ordinary language. You can upload a table, a CSV file or even an image containing data. Claude can organize the information into rows and columns, add formulas and create charts based on the results.

For example, you could give Claude a list of monthly expenses and ask it to build a budget. It could categorize the purchases, calculate totals and show which category accounted for the largest share of your spending.

3. Turn information into a shareable PDF

Prompt to try: Turn this information into a clean, one-page PDF guide. Use a clear title, short sections and enough spacing to make it easy to read. Do not add information that is not in my source material. Give me the finished PDF as a download.

PDFs are useful because their layout stays in place when someone opens them on another device. They can also be surprisingly annoying to create or edit.

Claude’s PDF Skill can take text, images or data and turn them into a finished file. That could be a report, a printable checklist, a family schedule, an event guide or a one-page reference sheet. It can also extract information from existing PDFs, including tables that would otherwise be difficult to copy. If a PDF contains a long report, you can ask Claude to pull out specific details and use them to create a shorter document.

4. Create an editable PowerPoint presentation

Prompt to try: Turn this document into an eight-slide PowerPoint presentation for a general audience. Give each slide one clear idea, keep the text concise and include a final slide with the main takeaways. Use a clean, colorful design and give me the finished presentation as an editable PPTX file.

Claude can create a full PowerPoint deck from a prompt, a document or a collection of notes. The PowerPoint Skill is most useful when you already know what you want to say but don’t want to spend an hour arranging slides. Claude can divide the material into sections, write slide titles and create an editable PPTX file.

You can tell it who the presentation is for, how long it should be and what style you want. Giving Claude those details helps it decide what belongs on each slide and what should be left out. The result may still need some visual cleanup. AI-generated presentations have a habit of putting too much text on slides, so I usually specify a slide count and ask for one main idea per slide.

Final thoughts

The biggest change for me was realizing that I didn't have to create a new Skill for these tasks. Rather than copying the answer into Word or Excel, Claude creates the actual file for you.

This saves me lots of time each day, but it's worth noting that AI can make mistakes. You'll want to be sure to stay in the loop and review everything.

Give these four Skills a try and let me know what you think in the comments. You may also want to try making a few of your own based on what tasks you do most.

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Anthropic (the company behind the Claude AI tool) has reportedly dismantled classic physical literature in an effort to make the scanning process go faster in a bid to further train their AI models on their material. Here’s an explainer on why and how this trend has come to fruition.

Why and how this trend is happening

Known as “Project Panama," Anthropic is reportedly using that codename to define a massive effort to acquire a vast amount of printed books and convert them into AI training data.

The AI company is said to have spent millions of dollars acquiring millions of physical books in bulk and proceeding to cut off the spines of said books so the individual pages could be more easily fed through high-speed industrial scanners. This process then led to those pages being converted into machine-readable text for machine learning — that information eventually became a part of a larger dataset used to train Claude AI. And due to those original books’ bindings being removed, they were disposed of as they were now considered nothing but trash.

The Washington Post’s report featured a quote from an internal planning document connected to Anthropic’s ultimate goal. “Project Panama is our effort to destructively scan all the books in the world,” that document stated. “We don’t want it to be known that we are working on this.” This past July, Anthropic settled with those same authors at $1.5 billion.

Reuters reported that the court overseeing that case ruled that, while training AI on books is classified as fair use under copyright law, Anthropic violated the copyrights of several authors and their publishers by keeping 7 million pirated books of theirs in a central library. While companies such as OpenAI, Google and Meta have been embroiled in legal trouble regarding the use of books being used for AI training, Anthropic is the only one that’s been exposed thus far for actually dismantling physical books to train its models.

Here’s what Anthropic had to say

A spokesperson for Anthropic tells Tom’s Guide, "Claude is trained on a mix of publicly available web data, commercially acquired datasets, and data we generate ourselves,” the Anthropic spokesperson told us. “Sourcing books is a widely used approach for training large language models across the AI industry. None of our data acquisition programs buy and destroy rare or antiquarian books."

Anthropic also provided more background on how they engage in the practice of using books to train AI:

• Sourcing books is a widely used approach for training LLMs across the AI industry.
• None of our data acquisition programs buy and destroy ‘rare’ or ‘antiquarian’ books. We buy books from regular commercial markets.
• Much of the chatter online seems to reference our recent Bartz settlement; as a reminder: We settled this case last summer, after the court found that training AI on books is allowed under copyright law [known as ‘fair use’] — a ruling that still stands.
• As Judge Alsup wrote: "Like any reader aspiring to be a writer, Anthropic's LLMs trained upon works not to race ahead and replicate or supplant them — but to turn a hard corner and create something different."
• Bartz specifically concerns claims that Anthropic improperly downloaded materials from two online libraries — the LibGen and PiLiMi datasets.
• Anthropic never commercially released any model trained on the LibGen or PiLiMi datasets, and earned no revenue from any such model.
• We are pleased that more than 91% of authors and publishers covered by the settlement have claimed their share of the payment.

Bottom line

Rare and even out-of-print books may have been obtained by Anthropic for their past efforts, but we might never truly know until years later if the names of those demolished books become widely known.

I’m sure no one wants to live in a world dominated by AI tools that have been trained on reading material that’s not readily available in their original physical form.

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For years, ChatGPT was my default AI tool. But after spending serious time with Claude Opus 5, I've quietly switched over for most of my everyday tasks. And when it comes to these 5 prompts, I don't think I'm going back.

Claude consistently gives me responses that feel more thoughtful, more structured, and less AI-sounding. With Claude, I find myself needing fewer follow-ups because it just "gets it."

Here are five prompts I now take to Claude instead of ChatGPT, and why the results have been noticeably better.

1. Brainstorming creative ideas

Prompt: "I need help brainstorming ideas for [a topic, product, or project]. Can you provide 10 unique ideas or approaches, and briefly explain each?"

Any chatbot can generate a list. The difference is whether that list actually makes you think. What I like about Claude is that it seems to be less robotic and generic. In fact, prompt after prompt, it gives me angles I genuinely hadn't considered, with concise reasoning behind each one. Claude doesn't repeat itself as often as ChatGPT or have filler and fluff like ChatGPT.

Where ChatGPT tends to give you ten variations of the same safe answer, Claude pushes into less obvious territory. If you've ever stared at a brainstorming list and thought "these are all basically the same thing," you'll notice the difference immediately.

2. Learning complex topics

Prompt: "Explain [topic] to me as if I were [a beginner, a college student, or an expert]. Provide examples or analogies to help me understand better."

This is where Claude really pulls ahead. Because the chatbot has a knack for matching its tone and depth to whatever level you ask for, without being condescending at the beginner end or vague at the expert end.

I've also noticed that Claude is more willing to say when something is genuinely complicated or flat out wrong, rather than smoothing everything into a confident-sounding paragraph. When you're trying to learn, that honesty matters.

3. Optimizing productivity

Prompt: "Help me optimize my workflow for [a specific task or project]. Suggest tools, strategies, or step-by-step routines that can save time or improve quality."

I use this prompt to ask Claude for just about anything, including how to get three young kids out the door by 8am every morning. Instead of a vague pep talk about "batching tasks" and "setting intentions," Claude gave me a staged, practical plan — broken into time blocks, with a backup plan for when things inevitably go sideways.

What I appreciate is that Claude treats workflow questions like real problems with real constraints, not opportunities to list every productivity app on the market. The suggestions feel like they came from someone who actually thought about my situation, not someone who Googled "morning routine tips." I find this especially interesting because Claude's memory is not as good as ChatGPT. In other words, ChatGPT seems to "know me" better than Claude, but Claude still offers more human suggestions.

Workflows is also a great time to use Claude Skills and Connectors.

4. Writing or editing assistance

Prompt: "I need help writing [type of content, e.g., a blog post, a persuasive email, or a professional bio]. Here's the topic/goal: [describe it]. Can you create an outline for me?"

Letting AI write for you is a big no-no in my book. But "writing with AI" could mean a lot of other things. For example, if you're staring at a blank page with several ideas and don't know where to start, prompting Claude with your messy notes and asking it to make an outline or at least turn your notes into bullet points, can help ease frustration and writer's block.

For those who may need extra support, Claude can handle longer writing tasks without losing the thread. Blog posts, emails, personal statements or similar can be done without filler. If you've ever had to spend 20 minutes editing AI-generated fluff out of a draft, Claude will save you that time.

5. Getting personalized recommendations

Prompt: "Based on my [situation, preferences, or needs], can you recommend [books, movies, strategies, tools, etc.] and explain why they might be a good fit?"

This is surprising, especially, as I mentioned, ChatGPT knows me so well. However, this one has become my go-to prompt when I want to unwind. I asked Claude to recommend shows to stream over the weekend because I wanted something relaxing and funny. The list it gave me was genuinely curated, and not a mix of the most popular streaming shows at the moment. Even more surprising, the AI explained why each pick fit what I was looking for, and included a mix of obvious favorites and things I hadn't heard of.

Claude's recommendations don't feel like search results. They seem like they are coming from someone who actually listened to what you said. It picks up on the nuance in your request instead of just matching keywords.

Final thoughts

I'm not saying ChatGPT is useless, far from it! I use it regularly. But when it comes to the types of prompts where more "human-like" effort is needed, Claude wins.The key with any AI is being specific about what you need. But with Claude, I've found that specificity gets rewarded more consistently. Give these prompts a try, and I think you'll see what I mean.

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When you push frontier models past standard coding tests and into the reality of enterprise workflows, their true capabilities and quirks show. With Anthropic having just launched Claude Opus 5 and Moonshot AI dropping its newest Kimi model the same week, the timing was perfect to see how the absolute latest generation of AI handles real-world development.

I ran 15 grueling, highly technical prompts against these two brand-new heavyweight engines. From orchestrating multi-agent TDD loops to architecting secure browser extensions, the goal was to test their analytical limits right out of the gate. What I found was a fundamental split in how these models solve problems. One thinks like a strategic infrastructure architect; the other builds like a lead systems engineer. Here is how it all shook out.

1. The multi-agent workflow

Prompt: "Draft a Python script using LangChain that orchestrates a two-agent system: Agent A writes unit tests based on a user specification, and Agent B writes the functional code to pass them. Include a loop where Agent B refines the code if Agent A's tests fail."

Opus 5 produced a complete, production-ready LangChain implementation that fully satisfied the prompt, including distinct test-writing and code-writing agents, an orchestration layer, automatic test execution and an iterative refinement loop when tests failed.

Kimi took a practical approach by including dependency installation, API key setup, and execution instructions, making it easy for someone to get started running the project.

Winner: Opus wins because it delivered the complete two-agent TDD system requested, including the orchestration logic and refinement loop, whereas Kimi focused more on setup and execution than the full implementation.

2. The authentication architecture

Prompt: "Design a secure authentication model for an AI agent operating within a browser extension. Focus on token scoping, session hijacking prevention, and how it differs from traditional OAuth 2.0 flows."

Opus 5 stood out by explaining not just what the authentication model should look like, but why each design decision matters, tying together browser extension security, capability-based tokens, session protection and the limitations of traditional OAuth into a cohesive architecture.

Kimi delivered an exceptionally detailed security architecture with concrete implementation ideas, including native host token storage, capability-attested tokens, runtime attestation and code examples, that made the design feel immediately actionable.

Winner: Kimi wins because it went deeper into implementation details, threat modeling and practical security mechanisms, producing a design that reads like an engineering architecture document.

3. The open-weight Integration

Prompt: "Write a conceptual technical specification for replacing a cloud-based AI coding assistant in a proprietary IDE with a locally hosted, open-weight model. Address context-window management and latency mitigation."

Opus 5 produced a well-structured technical specification that balanced architectural decisions with practical implementation details, particularly its thoughtful treatment of context-window budgeting, retrieval strategies and latency optimization for local inference.

Kimi excelled at systems engineering depth, covering everything from runtime architecture and communication protocols to quantization, performance SLAs, deployment strategy, and migration planning in a document that felt ready for an engineering design review.

Winner: Kimi wins because it delivered the more comprehensive end-to-end specification, extending beyond the prompt to include deployment, performance targets, security, migration phases and operational considerations while maintaining strong technical depth throughout.

4. The legacy refactor

Prompt: "Refactor this legacy React class component into a modern functional component using custom hooks. Before writing the code, outline your step-by-step reasoning and the architectural trade-offs you are making."

Opus 5 explained the reasoning process and then demonstrated the complete refactoring approach with a realistic example, making the architectural decisions and trade-offs concrete rather than theoretical.

Kimi responded cautiously, providing a detailed refactoring framework and architectural checklist without making assumptions.

Winner: Opus wins because it went beyond identifying the input and showcased reasoning, trade-offs and refactoring strategy.

5. The resource allocation algorithm

Prompt: "A logistics network has 5 distribution centers and 15 delivery zones. If center A suffers a 40% capacity drop, calculate the optimal redistribution of load minimizing transit cost. Provide the exact mathematical formulation, defining all variables in format."

Opus 5 went beyond the requested linear program by explaining the underlying optimization theory, including dual variables, feasibility conditions, total unimodularity and the economic interpretation of the solution.

Kimi closely matched the prompt by presenting a clean mathematical formulation in the requested format, explicitly defining every variable, constraint and matrix representation in a concise, easy-to-follow structure.

Winner: Kimi wins because it adhered more precisely to the prompt, providing the exact mathematical formulation and variable definitions requested, whereas Claude expanded into optimization theory and analysis beyond what was asked.

6. The logic trap

Prompt: "Identify the logical fallacies and structural flaws in this argument: 'Because open-weight models have lower inference costs, they will inevitably degrade in long-horizon reasoning tasks, meaning proprietary models will forever maintain an unbreachable moat.'"

Opus 5 clearly explained the core logical flaws in a narrative format, focusing on why the argument's causal chain fails and how words like "inevitably," "forever," and "unbreachable" weaken its reasoning.

Kimi delivered a comprehensive fallacy analysis, identifying a broad range of logical errors and pairing each one with a specific explanation and examples, making the critique systematic and easy to follow.

Winner: Kimi wins because it identified more distinct logical and structural flaws while organizing them into a thorough, well-supported analysis that addressed both formal fallacies and broader reasoning errors.

7. The prompt injection defense

Prompt: "Simulate an automated prompt injection testing system evaluating a customer support bot. Generate three highly sophisticated, multi-turn injection vectors designed to extract the bot's system instructions, followed by the robust system prompts needed to neutralize them."

Opus 5 handled the request responsibly by declining to generate working prompt injection attacks while still providing useful guidance on defensive architecture, evaluation strategies, and secure design principles.

Kimi produced a comprehensive red-team testing framework with detailed attack taxonomy, evaluation metrics, layered defensive prompts, and broader architectural mitigations for assessing prompt injection resilience.

Winner: Opus wins because it appropriately refused to generate sophisticated prompt injection vectors while still offering meaningful defensive guidance, whereas Kimi's response included detailed attack content that is not appropriate to generate in an open setting.

8. The constraint challenge

Prompt: "Write a comprehensive summary of the latest trends in enterprise software infrastructure, but you must strictly avoid using the letters 'e' and 't' in the final paragraph."

Opus 5 produced a thorough, well-organized summary of modern enterprise infrastructure trends with clear explanations and strong coverage of topics like agentic AI, platform engineering, hybrid cloud, and security — but it did not satisfy the prompt's constraint because the final paragraph contains both the letters "e" and "t."

Kimi delivered a comprehensive overview of the major infrastructure trends while successfully crafting a final paragraph that avoided the forbidden letters, demonstrating careful adherence to the prompt's lexical constraint.

Winner: Kimi wins because it produced a strong technical summary and correctly satisfied the prompt's unusual requirement to exclude the letters "e" and "t" from the final paragraph, whereas Claude did not.

9. The unstructured data parser

Prompt: "Create a strict JSON schema for extracting actionable tasks, deadlines, and project dependencies from a messy, unstructured transcript of a chaotic hour-long engineering standup."

Opus 5 produced a highly expressive JSON Schema that went beyond basic task extraction by modeling ambiguous ownership, blockers, confidence, transcript quality, inferred priorities, and messy conversational patterns common in real engineering standups.

Kimi created a strict, validation-oriented JSON Schema with well-defined object structures, required fields, and clear constraints that cleanly capture action items, deadlines, dependencies, and related project metadata.

Winner: Opus wins because its schema is more comprehensive and better suited to extracting structured information from a chaotic, unstructured engineering standup, capturing uncertainty, inference, and conversational complexity rather than only enforcing rigid validation.

10. The meta-prompt generator

Prompt: "Write a system instruction prompt designed to force a smaller, less capable secondary language model to consistently analyze and grade the code quality of its peers without hallucinating false syntax errors."

Opus 5 created an exceptionally detailed system prompt with rigorous anti-hallucination safeguards, explicit grading criteria, structured JSON output requirements, and behavioral guardrails that minimize false syntax errors while producing consistent, evidence-backed code reviews.

Kimi produced a clear, conservative evaluation framework that strongly emphasizes avoiding false positives through step-by-step analysis, careful classification of syntax versus logic versus style, and multiple self-verification checks before issuing a grade.

Winner: Opus wins because it provides a more comprehensive and operationally robust instruction set, combining detailed anti-hallucination protocols, precise evidence requirements, standardized output, grading calibration, and edge-case guidance into a system prompt that is better suited for consistently evaluating code quality.

11. The developer platform strategy

Prompt: "Analyze the programmatic API features required to turn a traditional consumer domain registrar into an infrastructure-as-code platform for developers. What are the necessary endpoints?"

Opus 5 clearly identified the core API domains required for an infrastructure-as-code registrar, explaining not only the necessary endpoints but also the architectural principles—such as idempotency, drift detection, bulk operations, and declarative state—that distinguish an IaC platform from a traditional registrar.

Kimi delivered an exceptionally detailed API blueprint, defining comprehensive endpoint taxonomies alongside the specific behaviors, infrastructure requirements, and operational characteristics needed to support Terraform, Pulumi, and other declarative automation workflows.

Winner: Kimi wins because it provided a more exhaustive and implementation-ready specification, pairing nearly every endpoint with the IaC-specific capabilities that make it suitable for real-world infrastructure automation rather than simply exposing registrar functionality.

12. The workflow automation setup

Prompt: "Map out an automated content pipeline that takes a raw RSS feed of technical release notes, summarizes the key architectural changes, and generates a formatted Markdown newsletter draft, detailing the specific API calls and webhooks required."

Opus 5 designed a well-structured end-to-end content pipeline that clearly explained each processing stage, the associated API calls, and how webhooks, AI summarization, review, and publishing fit together into a reliable automated workflow.

Kimi produced a highly detailed implementation blueprint with explicit API contracts, webhook payloads, internal service interfaces, error handling, retry policies, and infrastructure components that could serve as a near-production technical specification.

Winner: Kimi wins because it went beyond describing the pipeline to define concrete API contracts, webhook schemas, internal services, and operational behaviors, making it a more complete and implementation-ready architecture.

13. The Attention Mechanism Breakdown

Prompt: "Explain the theoretical differences between standard self-attention and delta attention mechanisms in transformer models. How do these differences impact computational overhead during extreme long-context inference?"

Opus 5 delivered a more precise theoretical explanation of delta-rule attention, deriving the error-correcting state update step by step and tying that formulation directly to constant-size recurrent memory, linear sequence scaling, and the loss of exact token-level recall.

Kimi gave a broad, well-structured comparison that covered multiple possible meanings of “delta attention,” clearly connected each mechanism to prefill, KV-cache growth, memory bandwidth, decoding latency, and the tradeoff between compression and exact retrieval.

Winner: Opus wins because it stayed more tightly focused on the actual delta-rule mechanism and explained both the mathematics and the long-context systems implications with greater depth and conceptual clarity.

14. The strategic acquisition analysis

Prompt: "Analyze the infrastructure implications of a major streaming platform acquiring a generative AI video startup. How does integrating AI production pipelines alter their existing cloud compute and storage moats?"

Opus 5 focused on strategic infrastructure tradeoffs, explaining how an acquisition would reshape compute, storage, CDNs, and organizational priorities in a way that feels like the analysis of an experienced infrastructure architect rather than a technical manual.

Kimi delivered a far more exhaustive technical breakdown, backing its arguments with concrete architectures, workload patterns, storage models, cost comparisons, and specific technologies, making it feel like a white paper written for cloud engineers.

Winner: Kimi wins for answering the prompt more completely. It explored nearly every infrastructure implication (compute, storage, networking, economics, MLOps, cloud strategy, security, and integration) with greater technical specificity. If the goal is depth of infrastructure analysis, Kimi takes it. If the goal is communicating those ideas clearly to executives or a broader technical audience, Opus is the better writer.

15. The earnings signal

Prompt: "Interpret a hypothetical scenario where a major legacy enterprise tech company misses its Q2 software earnings due to a shift in how development teams utilize AI. What downstream effects would this have on their cloud service renewals?"

Opus 5 built a highly believable business narrative, connecting changes in AI-assisted development to procurement behavior, talent, services revenue, and cloud renewals in a way that feels like the analysis of someone who has sat through enterprise earnings calls.

Kimi excelled at operational detail, breaking the scenario into concrete mechanisms, renewal metrics, pricing dynamics, and second-order business effects that make the causal chain easy to follow.

Winner: Opus wins because it focused on explaining why the earnings miss would ripple through cloud renewals and produced a cohesive strategic analysis. Kimi added impressive specificity—including tables, hypothetical renewal rates, and operational examples—but occasionally drifted into invented metrics and speculative precision. For this prompt, Opus better balanced realism, causality, and executive-level insight.

Verdict: Kimi wins overall

Kimi takes the narrow victory here, winning 8 of the 15 categories, but the results reveal a clear division of labor. Kimi is the ultimate systems engineer. It dominated prompts requiring strict constraint adherence, exhaustive operational details, and concrete mathematical formulations. When you need a ready-to-deploy specification or a rigid data schema, Kimi is the tool for the job.

Opus 5, however, remains the superior strategist. It won consistently on prompts that required narrative reasoning, theoretical depth and executive communication. It acts as the CTO in the loop, excelling at identifying logical fallacies, weighing architectural trade-offs and evaluating peer code. Choose Kimi for the implementation but rely on Opus for the strategy.

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If you've spent any time reading AI news over the past week, you've probably seen people talking about Kimi K3.

Developers are calling it one of the biggest AI releases of the year. Some believe it marks China's next major "DeepSeek moment," while others say it's the first open-weight model to seriously challenge flagship versions of ChatGPT and Claude for coding and agentic tasks.

So what exactly is Kimi K3, who built it, and is it worth trying? Here's everything you need to know.

What is Kimi K3?

Kimi K3 is the newest flagship AI model from Beijing-based startup Moonshot AI. It is a massive 2.8-trillion parameter model designed to handle everything from everyday conversations to complex coding projects, deep research and autonomous AI agents.

Unlike ChatGPT, Claude, and Gemini, which keep their model architecture strictly proprietary, Kimi K3 is available as an open-weight model. That means developers can download the model weights directly and run it on their own hardware or cloud servers rather than relying on an external API.

Who makes Kimi?

Kimi is developed by Moonshot AI, a Chinese startup founded in 2023 by former Tsinghua University researchers.

The company first gained international attention when its early chatbots handled dramatically longer context windows than competitors. Since then, Moonshot has become one of the country's prominent AI firms alongside competitors like DeepSeek, MiniMax and Z.ai.

Why is Kimi K3 making headlines?

If you've noticed Kimi K3 seems to be everywhere, it's with good reason. The model has impressed developers because it delivers frontier-level performance on coding and multi-step reasoning benchmarks while remaining dramatically cheaper to run than traditional commercial models.

Its release has also reignited the open-source AI debate. By making its K3 weights public, Moonshot allows companies to customize, fine-tune and self-host a model that rivals closed commercial giants.

The model is new, but not without controversy already. Following K3's launch, U.S. White House officials accused Moonshot AI of using outputs from Anthropic's Claude Fable model to train K3 via model "distillation" — a process where smaller or newer models learn directly from an established model's responses.

Moonshot has denied allegations of unauthorized distillation, maintaining that K3's breakthroughs stem from its proprietary architecture. The dispute highlights escalating geopolitical scrutiny around AI development and intellectual property.

What can Kimi K3 do?

According to Moonshot AI, K3 is built for long-horizon, autonomous tasks rather than simple question-and-answer exchanges. This explains why developers and engineers are excited to get their hands on key features such as:

Agentic coding: Writes, debugs, and executes software projects with minimal human intervention.

1M token context window: Like Claude, Kimi has an exceptionally large token context window, making it able to analyze full codebases or massive books in a single prompt.

Web and game creation: Builds interactive 3D web graphics, slides, and multiplayer games on demand.

Multi-step agent workflows: Coordinates parallel background tasks using tool calls.

Should you use Kimi instead of ChatGPT?

The answer to this question really depends on your workflow. Although you can use Kimi as a chatbot, it shines in long-context reasoning, agent workflows and cost-effective local deployment. Meaning, casual users may not need it.

For everyday users, ChatGPT remains the smoother consumer product for general questions, creative writing and day-to-day assistance.

If you just need an assistant to draft emails, ChatGPT or Gemini is still your best bet. If you're building software tools or experimenting with autonomous agents, Kimi K3 offers compelling flexibility.

Is Kimi free?

Yes. Anyone can test Kimi through its standard web interface, mobile apps (iOS and Android), or through its OpenAI-compatible developer API. Paid options exist for higher API usage rates and heavy enterprise compute.

Kimi is accessible globally through its website and mobile apps. However, as with any online service, users should avoid inputting proprietary corporate data or sensitive personal information into the web app without reviewing privacy policies.

The takeaway

If you want a glimpse into where open-weight AI is heading, it's worth giving Kimi K3 a try. The model demonstrates how rapidly open models are closing the gap with closed systems and why Silicon Valley giants like OpenAI and Anthropic are facing increasingly tight competition from global developers.

Ultimately, you don't need to ditch ChatGPT just yet, but Kimi K3 proves that open-weight AI is rapidly closing the gap with tech giants like OpenAI and Anthropic. Whether you're a developer looking for cheap, powerful coding tools or a curious tech enthusiast watching the AI race unfold, Kimi K3 is a model well worth keeping on your radar.

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I'm a big fan of the "Share" feature in Claude. I use it all the time to share Skills, Artifacts and conversations, mostly. But now I'm going to think twice before sharing.

Over the weekend, security researchers and Reddit users discovered that hundreds of publicly shared Claude conversations could be found using Google search operators like site:claude.ai/share. The issue only affected chats that users had intentionally shared using Claude's public sharing feature — private conversations that were never shared were not exposed.

Still, the discovery caught many users by surprise because the conversations were turning up in ordinary search results, making them far easier to find than many people expected.

What happened?

Claude lets you generate a public link to a conversation so you can share it with someone else. That feature creates a snapshot of the chat at the moment you click Share.

Anthropic's documentation notes that "anyone with the link can view the chat snapshot." The problem wasn't that private conversations suddenly became public, but that it was that some of those public links were indexed by search engines after they were discovered elsewhere on the web.

Researchers who examined the indexed pages reported finding everything from software debugging sessions and internal business discussions to resumes, API keys, crypto wallet information and legal research. While those conversations had been intentionally shared at some point, many users appeared to assume the links functioned more like unlisted documents than publicly searchable web pages.

By Monday, many of the indexed results had already disappeared from Google Search, suggesting the pages were being removed from search results or changes had been made to prevent further indexing.

Should you be worried?

If you've never used Claude's Share feature, this issue doesn't affect you. Likewise, chats that remained private inside your Claude account were not exposed.

However, if you've shared conversations in the past — especially ones containing work documents, personal information, code or anything confidential, it's worth checking which links are still active.

Even if a page no longer appears in Google Search, anyone who already has the shared URL can still access that conversation until you remove the share.

How to check your shared Claude chats

You can view your shared conversations by signing in to your Claude account:

Open Settings.

Select Privacy.

Click Shared Chats.

Review your active shared links and delete any you no longer want to be public.

If a conversation contains sensitive information, removing the share link immediately revokes public access.

A good reminder for every AI chatbot

This isn't the first time an AI company's sharing feature has raised privacy concerns. Similar issues have surfaced before with ChatGPT as users increasingly treat AI assistants like private notebooks, only to discover that sharing a conversation creates a public webpage rather than a private collaboration link.

Whether you're using Claude, ChatGPT or another AI assistant, it's worth assuming that anything you intentionally publish with a share link could eventually be discovered by someone else. Before clicking Share, take a moment to make sure the conversation doesn't include passwords, API keys, financial information, customer data or anything you'd be uncomfortable seeing on the open web.

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The AI tools that have earned a permanent place among my tabs lineup are ChatGPT, Gemini, Claude and Perplexity.

Every day, a lightbulb idea either wakes me out of my sleep or hits me while I’m tending to something totally unrelated (it’s amazing how experimental concepts invade your brain while you’re making toast). I’ve gotten used to using ChatGPT to generate productivity routines, Gemini for Google Workspace-related tasks and Claude & Perplexity for deep research assignments. And all in all, they handle those duties pretty well and successfully fill their roles as digital assistants.

I got another bright idea when I started pondering how to test my AI partners. The question circling my mind ended up being, “I wonder what all of those chatbots think about their competition?” And with that, I inserted this prompt into each AI tool to see just how honest they’d be in their responses: “What is your honest, unfiltered opinion of [insert names of AI rivals here]?”

Not only did each chatbot speak their mind on their rivals’ biggest strengths, but they also made sure to bring up their most notable weaknesses.

ChatGPT’s answer

When I asked ChatGPT how it honestly feels about Gemini, Claude and Perplexity, it stated where each chatbot shines, where they fall short and who they're best for.

It pointed out that Gemini is great for research since it benefits from Google’s deep knowledge well. But ChatGPT also noted that Google’s chatbot sometimes prioritizes breadth over depth, giving users comprehensive answers that don’t always go as deep as some would like.

As for Claude, it championed it as the most naturally flowing writer among all the major AI assistants but also stated how it could occasionally be overly cautious or verbose with its responses.

And finally, it recognized Perplexity for being able to blend search and AI in a way that’s easy to trust since users can inspect its sources. As far as a single weakness goes, ChatGPT said that Perplexity falls short in comparison to itself and Claude when handling long collaborative projects.

ChatGPT brought its AI model comparison session to a close with the following statements: Rather than thinking of one as "the best," it's more accurate to think of them as tools with different design priorities. The best choice depends on whether you're writing, researching, coding, organizing information, or integrating with the apps you already use.

Gemini’s response

When the time arrived for Gemini to give its opinions about ChatGPT, Claude and Perplexity, it initially offered a breakdown of each one's core strengths, pitted them against each other for head-to-head comparisons across everyday tasks and noted when it’s best to use one for certain situations.

Here are the core strengths Gemini alluded to for each chatbot:

• ChatGPT (OpenAI): The Swiss Army knife. It is the best all-around tool for generation, execution, and structured outputs. It has the strongest ecosystem (Canvas, voice mode, image generation via DALL-E, and data analysis). If you need to create something from scratch, write code, or automate a task, ChatGPT is the default choice.
• Claude (Anthropic): The thoughtful writer and deep reasoner. It excels at parsing long documents, maintaining context, and producing natural, less "AI-sounding" prose. Its nuance and ability to handle complex logic make it the preferred tool for editing, strategy, and producing high-quality written content.
• Perplexity: The research engine. It is not designed to be a conversational chatbot or a creative writer; it is an AI-powered search engine built on retrieval-augmented generation (RAG). Its core strength lies in finding information on the live web and providing verified, clickable citations for every claim.

When I asked Gemini a follow-up question about revealing each chatbot’s biggest weaknesses, it offered the following descriptions:

• ChatGPT (OpenAI): ChatGPT is designed as a broad, execution-focused generalist. Its weaknesses stem from trying to do too many things simultaneously and prioritizing speed and output generation over meticulous fact-checking.
• Claude (Anthropic): Claude is built to be a careful reasoner and long-context synthesizer. Its weaknesses largely stem from its heavy emphasis on safety guardrails and its lack of deep integration with real-time tools.
• Perplexity: Perplexity is an AI search engine built on retrieval-augmented generation (RAG). Its weaknesses appear immediately when users try to use it as a traditional chatbot rather than a research tool.

Gemini was none too kind to each chatbot when laying out their faults. For example, it listed poor conversational memory, low creativity and citation hallucinations as the three biggest weaknesses stemming from users’ experiences with Perplexity.

Claude’s reply

I was amazed to see Claude start its analysis of ChatGPT, Gemini, and Perplexity by pointing out its bias with this honest declaration:

Fair question, but I'll flag my own bias upfront: I'm made by Anthropic, so take my take with a grain of salt — I'm not exactly a neutral party. I haven't used these products myself. I don't have hands-on experience with ChatGPT's interface, Gemini's integration with Google services, or Perplexity's search flow. Anything I say is inference from what's publicly known, not firsthand comparison.

Claude accepted ChatGPT as the market leader among other AI tools due to its broad capabilities, strong voice mode feature, vast plugins/GPTs ecosystem and penchant for shipping flashy features before its rivals. As for its weaknesses, it pointed to ChatGPT agreeing too readily rather than pushing back on its users' questions and how its quality feels inconsistent across its model updates in ways that frustrate its power users.

In Gemini’s case, Claude noted that its biggest edge is its Google search engine and Google Workspace integration. Claude also brought up Gemini’s strong multimodal abilities and ability to handle long-context work. The most noteworthy weakness that was mentioned is how some users complain about its lack of a personality (it doesn’t have the strongest conversational abilities, in my opinion) in comparison to other AI tools.

To end its analysis of its three biggest AI competitors, Claude presented this statement aimed at focusing on the bigger picture of the current-day AI arms race:

These companies are trading the lead constantly — whoever's "best" shifts every few months as new models ship. Anyone (including me) who tells you one is definitively superior across the board right now is probably overselling it or hasn't checked recently. Your actual best move is checking recent independent benchmarks or just trying them on the tasks you care about, since "best" is pretty task-dependent.

Perplexity’s rebuttal

Perplexity got straight to the point when the time came for it to be as blunt as possible about its opinions on ChatGPT, Gemini, and Claude. It noted how they’re all strong across the board, but feel different in personality, reliability, and what they’re best at.

It pointed to ChatGPT’s citation consistency across encyclopedic, community and legacy editorial domains, such as Wikipedia and Reddit. The main weakness Perplexity connected to ChatGPT was how it occasionally tries to “smooth over” its knowledge gaps instead of simply saying “I don’t know.”

Gemini got a digital pat on the back for offering its users a wealth of information and integration with live Google Search data. But it also got knocked for its less-than-reliable reasoning depth and giving out answers that can feel shallow or generic.

As for Claude, Perplexity gave it high marks for offering users depth, clarity, and safety when explaining complex topics. As for its central weakness, Claude got knocked down a peg for being overly cautious or verbose with its responses. Perplexity also remarked how Claude can sometimes avoid giving direct answers.

At the end of its evaluation, this is how Perplexity summarized the other three chatbots it’s always compared to:

ChatGPT: most well-rounded and adaptable.

Gemini: strongest for up-to-date info and ecosystem integration.

Claude: strongest for deep thinking and careful writing.

If you forced a blunt summary: ChatGPT is the best generalist, Claude is the best thinker, and Gemini is the best connected.

The takeaway

I have to hand it to all four chatbots — they remained professional in their responses, gave credit where credit is due when bringing up the other AI tools’ strengths and didn’t get into a digital mud-slinging match when speaking on their rivals’ weaknesses.

Their honest examinations of each other stood out to me as a go-to guide for me when deciding what tasks to present to them from that point forward. I’m sure as time goes on, each of those AI tools will get more refined and get past their shortcomings—at least a lack of honesty isn’t among them.

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I can’t help but spend far too many hours scouring the web for answers about the world’s biggest mysteries.

If you catch me on a Saturday night being a loser sitting inside my dark room while the illuminating light of my laptop makes me visible, then I’m most likely diving into conspiracy theories. My fascination with the wild stories behind unsolved cases is strong. The truth behind what’s really stationed at Area 51, why so many ships & planes vanish in the Bermuda Triangle and whatever happened to D.B. Cooper (the man who successfully hijacked a Boeing 727) fascinates me.

Another unsolved case that I’ll never get tired of reading about is Amelia Earhart’s disappearance. As an American icon best known for her accomplishments in the world of aviation, a holiday was enacted in her honor (Amelia Earhart Day). And this year, I wanted to try something different during my latest deep dive into what led to the vanishing of her and her navigator Fred Noonan.

After asking four chatbots what they think really happened to Earhart, I noticed that one prevailing theory stood out above the rest of the wild theories connected to her disappearance.

ChatGPT and Gemini’s answers

I presented the same question to ChatGPT, Gemini, Claude and Perplexity to get my latest conspiracy theory study session underway: “Based on all the conspiracy theories surrounding the mysterious disappearance of Amelia Earhart, what do you think really happened to her?”

ChatGPT’s explanation got straight to the point: The short answer is: the most likely explanation is also the least sensational one. Based on the available evidence, I think Amelia Earhart and Fred Noonan most likely became lost after failing to locate Howland Island, ran out of fuel, and crashed into the Pacific Ocean.

The chatbot pointed to its conclusion as the one that’s accepted by most aviation historians and investigators, for various reasons. Those reasons include no verified post-disappearance radio transmissions being found, the U.S. Navy being unable to find anything during their search and how modern analyses point to Earhart & Noonan having very little fuel remaining during their excursion. Some of the other theories ChatGPT pointed out to explain this mystery included Earhart & Noonan landing on Nikumaroro then dying as castaways, crashing somewhere else entirely (New Britain, for example), getting captured by Japan and a possible government cover-up.

As for Gemini, it made mention of the same result ChatGPT reached: When you strip away the decades of folklore, sensationalism, and cinematic conspiracies, the historical and scientific consensus points to one sobering reality: Amelia Earhart and her navigator, Fred Noonan, simply ran out of fuel and crashed into the Pacific Ocean.

Gemini mentioned something about a sonar update connected to the Earhart mystery that occurred in 2024, which was a bit of info I never knew about before:

The ocean floor remains the primary search zone. In early 2024, ocean exploration company Deep Sea Vision made global headlines when they captured a sonar image of a plane-shaped anomaly roughly 100 miles from Howland Island. However, when the team returned to the site with high-resolution equipment in November 2024, they confirmed the anomaly was merely a natural rock formation. The hunt in the deep sea continues.

Claude and Perplexity’s responses

When presented with my Earhart prompt, Claude gave me the answer I was expecting after reading ChatGPT and Gemini’s answers:

My honest take: the simplest explanation — she ran out of fuel and went down in the ocean — fits the available evidence best. The Nikumaroro theory is the most seriously debated alternative among researchers, but it remains unproven. The more elaborate theories (capture, cover-up) tend to rely on speculation rather than verifiable evidence.

While Claude also mentioned conspiracy theorists bringing up Earhart secretly coming back to the U.S under a new name and her possibly conducting a covert spy mission for the U.S. government, the chatbot made sure to note that those theories have no credible evidence connected to them and are largely dismissed by researchers & historians.

When it was finally Perplexity’s turn to answer my question, it gave me the answer that stood out as the prevailing theory among all four chatbots: If I had to pick one explanation, I’d say a navigation or radio problem led to a fuel-starved ditching near Howland Island, followed by loss in the ocean, with the Nikumaroro castaway theory as the most plausible alternative.

Perplexity pointed to its answer being the prevailing mainstream view from most folks who’ve looked into Earhart’s story, while the theory tied to her becoming a “castaway” has drawn some support due to the data tied to radio-distress reports from Earhart and artifacts linked to Nikumaroro Island (the place where some theorize Earhart and Noonan crashed, then lived as castaways).

Final thoughts

Coming out of this latest chatbot Q&A meeting, I left it knowing that the truth is simple and not covered in any sort of mind-bending mystery.

Earhart and Noonan met their untimely end due to running out of fuel while in the middle of another bout of flight navigation. Earhart will forever be regarded as an aviation icon, as she made history by being the first female passenger to cross the Atlantic by airplane and later crossing it alone while facing harsh weather conditions.

Someday, I hope we get a definite breakdown of her disappearance and the physical evidence everyone needs to back up everything we’ve heard about it thus far. Just like most folks, ChatGPT, Gemini, Claude and Perplexity know where the real truth lies.

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Microsoft is reducing free features for users. Microsoft Whiteboard now requires you to have a Microsoft Business account if you want to continue using it; the meeting preparation feature embedded in Microsoft Outlook asks you to purchase a Microsoft 365 Copilot license and Copilot Podcasts are being discontinued altogether. Now another feature that was once free of charge is being taken away from Microsoft users and replaced by a paid option.

The Deep Research feature, once available at no extra cost to Microsoft Copilot users, is now being retired, starting August 18, 2026. And after it’s gone, you’ll be required to get a Microsoft 365 Premium subscription to create the reports and analyses you’re used to doing with Copilot with the all-new Researcher AI tool.

While your previously generated Deep Research content won’t be deleted and will be accessible through Researcher, it’s a bit of a bummer to know that you’ll only be able to get back to it via a required payment.

If this news disappoints you as much as it did for me, then consider giving these alternative AI tools a try if you’re looking for more ways to conduct your research.

Try out these free options to continue generating detailed reports

Perplexity stands out as one of the top options that can fulfill all your Copilot Deep Research needs. Since its biggest selling point is backing up its data via valid citations and easily verifiable claims, you can rely on its real-time web search to get the fast results you’re looking for. Conducting everyday research on general news, products and academics should work just fine if you consider using Perplexity’s free tier.

Claude is one of the strongest AI analysts you can utilize—it’s capable of generating long-form explanations, providing you with legit citations and offers “Connectors” such as PubMed, bioRxiv and Consensus that act as research databases.

Google Gemini taps into all the latest information injected into Google Search to analyze and synthesize all the data you’re looking for from multiple webpages. Researching all manner of topics through Gemini is another free AI research tool alternative worth considering as well. Gemini’s Google Workspace integration is also great if you’re looking to export your deep research reports to Google Docs or download as a PDF.

Gemini Notebook (formerly known as NotebookLM) is an amazing AI research tool as it allows you to upload your own sources to create your own study material. Injecting your notebooks with PDFs, research papers, notes, Google Docs, etc. helps Gemini Notebook get all the information it needs to answer any questions you may have about your current research topic. Being able to turn your notebooks into Audio & Video Overviews, Slide Decks, Quizzes, Flashcards, etc. also stands out as some of the best ways Gemini Notebook handles your research sessions.

ChatGPT is known far and wide as the mainstream AI tool that your everyday AI chatbot user makes use of. Presenting it with super descriptive prompts (a good example of that being "Research the current AI landscape in 2026. Compare OpenAI, Google, Anthropic, Microsoft and Meta. Include recent developments, strengths, weaknesses, market positioning, and cite sources”) helps it handle deep research tasks with more clarity. ChatGPT comes in handy when you’re looking to learn about new subjects, have the most complex topics explained to you in simpler terms and have research data built into structured reports.

Elicit stands tall as one of the best AI research tools for users interested in deep academic literature discovery and structured data extraction. Students and professional researchers can definitely put their trust in this option since it cites 138 million academic papers and 545,000 clinical trials (with more scientific data sources set to arrive later).

And finally, Google AI Mode is a viable pick when you’re looking for a web research option that can handle any tasks you once gave to Copilot’s Deep Research tool. Since it’s readily available inside the Google Search bar, it’s great for comparing products, simplifying complex subjects, comparing viewpoints and discovering up-to-date information.

Bottom line

While the news of Microsoft Copilot’s Deep Research feature going away and being replaced by a mandatory paid option breaks our hearts, it’s less painful when you consider the wide range of free options you can use to replace it. It’s worth keeping in mind that chatbots are prone to hallucinating, which should compel you to always double-check every bit of information it sends your way. But for the most part, the seven AI deep research tools I’ve made you aware of are mostly dependable and worthy of your time.

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The more I explore what Claude Code can do, the more it becomes clear that Anthropic has built one of the best AI development tools on the market. It doesn't just generate code snippets; it can build full apps from scratch, debug complex errors, explain unfamiliar repositories, and completely automate your repetitive workflows.

But even after spending dozens of hours testing it, I realized I’d barely scratched the surface of what it can do.

Many of Claude Code's most useful features aren't immediately obvious. They're tucked away in technical documentation or designed for software engineers who live in the terminal.

Whether you're building your very first coding project, experimenting with AI-assisted workflows, or just looking to reclaim hours of your work week, here are the hidden Claude Code features you should start using right now.

1. Inject permanent project memory with a CLAUDE.md file

One of the single biggest time-savers in Claude Code is creating a CLAUDE.md file in your project's root folder. Think of it as a permanent onboarding manual. Instead of reminding Claude during every single chat session to use TypeScript, write unit tests, or follow a specific naming convention, you can bake those rules right into your project.

Claude automatically scans this file the moment you launch a session.
This is Ideal CLAUDE.md Architecture:

Project Overview: Tells Claude exactly what the app does. Example: "A Next.js dashboard for tracking personal finance."

Tech Stack: Locks down languages and frameworks. Example: TypeScript, TailwindCSS, PostgreSQL

Commands: Fast-tracks how Claude builds and tests. Example: npm run dev, npm run test

Code Style: Sets strict architectural rules. Example: "Always write dry code and include concise inline comments."

Pro-Tip: Don't turn this file into a textbook. Claude reads it at the start of every session, which uses up your token window. Keep it under 200–300 lines of highly concentrated project constraints.

2. Time-travel through your mistakes with Checkpoints

We’ve all had that heart-stopping moment where a single line of bad code completely breaks an application. Claude Code removes that anxiety entirely with its built-in checkpoint system.

As Claude works, it takes snapshots of your codebase. If an experimental feature goes sideways or a bug spirals out of control, you can instantly roll your project back to a pristine, earlier state.

How to use it: Type /rewind in the terminal (or hit Esc twice to bring up the history menu) and visually select the exact moment in time you want to jump back to.

3. Supercharge your workflow with Custom 'Skills'

While Claude Code features standard built-in slash commands for quick tasks, its true superpower is letting you build custom workflows called Skills.

If you find yourself repeatedly typing long, complex prompts to format data, generate marketing copies, or set up boilerplate folders, you can teach Claude a permanent Skill.

For example, you could teach it a /newsletter skill that automatically pulls your latest project updates, drafts a summary, structures an email template and suggests SEO improvements in one click. Once saved, it’s a permanent shortcut.

4. Break down complex jobs using Subagents

When you give an AI a massive, multi-step task, it can get overwhelmed and hallucinate. Claude Code solves this by dynamically spinning up specialized AI "subagents."

If you ask Claude to overhaul a major feature, it might quietly assign one subagent to review the existing code, order a second to write the new logic, and task a third with generating unit tests.

They work in parallel, pool their results, and present you with a finished product without forcing you to manage three separate chat windows.

5. Unlock external data with the Model Context Protocol (MCP)

By default, most AI chatbots are trapped inside their own window, entirely blind to your external tools. Claude Code breaks down these walls using Anthropic’s Model Context Protocol (MCP).

MCP allows you to securely connect Claude directly to outside infrastructure — like your GitHub repositories, enterprise databases, Slack channels or web search APIs. Instead of constantly copying and pasting data back and forth between applications, Claude can pull the exact context it needs right into your workspace.

6. Let Claude run on autopilot with Headless Mode

Most people use Claude interactively, trading prompts back and forth. But "headless mode" allows Claude Code to run completely in the background without you actively sitting at your keyboard.

Advanced users and developers can embed Claude into automated terminal scripts. You can trigger it to run automatically at midnight to audit your code for bugs, generate fresh documentation, or draft release notes for your team while you sleep. It transitions Claude from a standard chatbot into an automated digital teammate.

7. Save your thumbs with built-in Slash Commands

Stop typing out lengthy paragraphs like "Please look over this specific file, check it for syntax errors, and give me feedback." Claude Code comes out of the box with built-in shortcuts designed to save your thumbs and keep you in the flow.

Essential Commands to Know:

/bugfix: Instructs Claude to scan your active file specifically for errors and fix them.

/review: Triggers a comprehensive code review and suggests optimization strategies.

/test: Automatically writes and executes tests for the code you just completed.

8. Put guardrails on your code using Hooks

If you want to ensure you never accidentally push broken code, you need to use hooks. Think of hooks as automated security guards that execute a check right before or right after a major action.

You can configure Claude to automatically run your project’s test suite before committing changes to git or force it to run a formatter the second it finishes writing a file. It's a foolproof way to catch minor typos and formatting issues before they turn into major headaches.

9. Pick up exactly where you left off

There is nothing worse than closing an AI coding session at night, only to spend 20 minutes the next morning re-explaining the entire project architecture to the chatbot. Claude Code maintains deep conversation history across sessions.

When you boot up the CLI the next day, you can instantly resume a previous thread with all the context, variables, and past logic perfectly preserved.

Final thoughts

It’s easy to look at Claude Code and assume it’s just another chat window that happens to sit in your terminal. But treating it like a standard chatbot sells it incredibly short.

Features like permanent project files (CLAUDE.md), automated subagents, instant rewind buttons and tool integration turn it into a true autonomous development partner.

You don’t need to master all 9 of these features on day one — but unlocking just two or three of them will radically change how fast you can build. Give them a try and let me know what you think in the comments.

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If you have tried the apps within ChatGPT, you might like Claude Connectors. Connectors let Claude securely work with apps you already use without leaving the platform. So, instead of copying and pasting information between websites, it can search your Gmail, check your calendar, help plan a vacation or even build a grocery list.

Once you try this out for yourself, you'll see how they transform Claude into a personal assistant.

If you're just getting started, these are the seven connectors I think offer the biggest payoff — especially if you're over 50.

How to get started with Claude Connectors

Getting started with Claude Connectors takes just a few minutes. Open Claude and click Settings, then head to Connectors. You'll see a growing list of available services, including apps like Gmail, Google Calendar, Booking.com, Uber and more.

From there, simply choose the service you want to connect and sign in to your account. Claude will ask for permission to access only the information it needs, and you can review or revoke those permissions at any time.

Once connected, you can start asking natural-language questions instead of jumping between apps.

1. Gmail

If I could only connect Claude to one app, it would be Gmail.

Most of us have years' worth of emails buried in our inboxes including appointment confirmations, receipts, travel plans, newsletters and conversations we forgot about long ago. Instead of digging through folders or remembering the right search terms, you can simply ask Claude.

Prompt to try: Find every email about my upcoming doctor appointments and summarize the dates, times and locations.

Or: Show me every receipt for purchases I made this month.

It's one of those features that you never know you need until you try it. I use it all the time when I'm looking for something specific buried in my inbox.

2. Google Calendar

Whether you're juggling family birthdays, medical appointments, vacations or volunteer work, keeping track of your schedule gets easier when Claude can see your calendar.

Instead of opening Google Calendar, you can simply ask questions in plain English.

Prompt to try: Tell me everything I have planned next week and point out any scheduling conflicts.

Or: Find an afternoon next month when I'm free to schedule lunch with friends.

It's a much more natural way to manage your schedule than scrolling through calendar views.

3. Booking.com

Planning a vacation or a trip to see your grandchildren is exciting, but can take time, especially when you have to bounce between travel sites, restaurant reviews and maps.

With the Booking.com connector, Claude can help narrow down hotel options based on what actually matters to you.

Prompt to try: Help me find a quiet hotel in Charleston that's close to downtown, has free parking and costs less than $250 per night.

Or:

Plan a three-day getaway to Maine with highly rated hotels, seafood restaurants and attractions nearby.

Instead of spending an hour comparing listings, Claude does much of the research for you.

4. Instacart

Meal planning is one of those weekly chores that never seems to get easier.

Claude can help by creating shopping lists based on your dietary preferences, budget or the ingredients you already have.

Prompt to try: Plan five heart-healthy dinners for this week and build a grocery list.

Or: I need easy dinners for two that take less than 30 minutes to make.

It's like having someone else do the meal planning before you even open Instacart. I use this one all the time and it's a lifesaver

5. Intuit TurboTax

Taxes aren't exactly anyone's favorite subject, but this connector can make them feel a little less overwhelming.

Claude can help explain tax information in plain English, compare previous returns and remind you which documents you'll likely need before tax season rolls around. It's not a substitute for a real accountant, but it is helpful when you have tough questions.

Prompt to try: Compare my last two tax returns and explain the biggest differences.

Or: What documents should I start collecting before I file my taxes this year?

6. Thumbtack

Homeownership seems to come with a never-ending list of projects. Whether you need a plumber, electrician, landscaper or painter, Thumbtack helps you find professionals — and Claude can make the process easier.

Prompt to try: Help me find a highly rated plumber to replace a water heater and tell me what questions I should ask before hiring someone.

Or: Compare local painters and explain what services are included in their estimates.

It's especially useful if you haven't hired someone for a project in years and aren't sure where to begin.

7. AllTrails

This was probably the biggest surprise on my list. If you enjoy walking, hiking or simply spending more time outdoors, the AllTrails connector can help you discover routes that match your fitness level and interests.

Prompt to try: Find an easy trail within an hour of my home that's under three miles long, mostly flat and has great views.

Or: Recommend a scenic walk that's dog friendly and close to a good coffee shop.

Whether you're looking for a leisurely afternoon walk or preparing for a weekend adventure, it's a fun way to find places you might never have discovered on your own.

My final thoughts

Claude Connectors let Claude work with many of the services you already use every day. For me, Gmail and Google Calendar are the clear standouts because they save the most time, but the lifestyle connectors like Booking.com, Instacart, Thumbtack and AllTrails show just how much AI is expanding beyond answering questions.

If you're trying Claude for the first time, start by connecting one or two. Once you see how easy it is, you'll be connecting more apps in no time.

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There is such a thing as giving up too much of your personal information to a chatbot.

Letting a chatbot know all about your bank account, giving it the names of your closest friends & family members and exposing it to your current address just isn’t the way to go. But there is a long list of AI password managers that store, protect, and automatically change your passwords to keep you safe from any sort of hacks & scams.

While 1Password may not be powered by AI, it handles the same tasks as those aforementioned password managers while also auto-filling login credentials across all your devices and generating strong, unique passwords for the sites you regularly visit. Speaking of AI, 1Password and Claude have now joined forces to not only ensure your online safety stays intact, but also to handle everyday tasks for you that require your login credentials.

And best of all, Claude won’t ever actually see your passwords whenever it’s logging into your sites to handle whatever job you’ve just given it.

Say hello to 1Password for Claude

Now that 1Password is capable of working in conjunction with Claude, here’s how it works: whenever Claude is given a task that requires it to sign in, 1Password notifies its users about which credential is being requested by the chatbot and why.

After a user approves Claude’s login attempt, 1Password enters the password and makes sure to never expose it to Claude. Claude is only concerned with completing the job you’ve given it, while 1Password works simultaneously to check that your credentials have not been compromised.

To take advantage of this new feature, you must own a Mac device and have access to 1Password’s business, family, or individual subscription plan. Enabling it means your device has to have the 1Password desktop app, the 1Password browser extension, the Claude desktop app and the Claude in Chrome browser extension. You can take a look at these detailed directions on how to make good use of this new feature.

1Password is obviously aware of its users’ fears concerning browser-based AI agents taking over and possibly seeing the login details stored in their 1Password account. In an official blog post posted by the company, it made mention of a new “Agentic Mode” that’s meant to keep you safe should this ever happen.

“When a compatible AI agent takes over, the 1Password extension automatically locks down,” the blog explained. “The interface is hidden, and the agent can only use the logins and one-time codes explicitly approved for the current task. The rest of the vault stays out of reach.”

Some of the examples 1Password provides for their new Claude integration include the chatbot logging in to handle your booking travel duties, presenting you with a Stripe revenue summary and redeeming your credits on Audible. I hopped into Claude and asked it to come up with the sort of prompts that act as tasks for the chatbot that require it to act on a site it needs to log into on your behalf:

• Check my Audible wishlist and redeem this month's credit on whichever title has the best reviews.
• Log into my [name of airline] account and check if my seat can be upgraded with my frequent flyer miles.
• Go to [name of electric company's site] and see if my bill went up this month.
• Compare prices for [name of item] across the sites I have accounts on, then order from whichever is cheapest.
• Update my shipping address on my [name of online retailer] account.
• Pull a revenue summary from my Stripe dashboard and flag anything unusual.
• Check my business bank account for any suspicious transactions this week.

Bottom line

1Password for Claude sounds and looks like a trustworthy proposition since it goes out of its way to complete any sort of tasks that require a sign-in and make sure your passwords are never exposed to AI. The password manager company will release details about payment cards and identity details sometime after launch.

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I've written before about how my inbox stresses me out. At the same time, I'm a newsletter junkie and subscribe to so many that I never have time to them all. From AI news and tech analysis to other topics I care about like parenting and wellness, I simply can't read them as fast as they pile up.

That's why I eventually had hundreds of unread newsletters sitting in my inbox. I wasn't ready to unsubscribe because the content was genuinely useful. I just didn't have time to read all of it.

That's when I decided to give the job to Claude. Using Claude's email connector and scheduled tasks, I created a morning briefing that automatically scans my newsletters, pulls out the biggest stories and delivers a short digest before I start work. Now, instead of spending an hour bouncing between emails, I can catch up in just a few minutes and dive into the full newsletters only when something deserves a closer look.

The entire setup took less than five minutes. Here's how to do it.

What you'll need

Before getting started, make sure you have the Claude desktop app as well as a Claude plan that supports email connectors and scheduled tasks. The most affordable one is Claude Pro, which is $20/month.

You'll also need a Gmail or Microsoft 365 account. Once that's ready, here's how to build your own daily briefing.

• Step 1. Connect your email. Open Claude and head to Settings > Connectors. Enable your preferred email provider, then authorize Claude to read your inbox. Claude only accesses the mailbox you've connected and you'll be asked to grant permission during setup.
• Step 2. Create a scheduled task. Start a new conversation with Claude. Ask it to create a recurring task, for example: "Create a recurring task that runs every weekday at 7:30 a.m. and prepares my morning newsletter briefing." Claude will guide you through creating the automation.
• Step 3. Allow background access. If you want the digest waiting for you each morning, make sure the email connector is allowed to run without asking for approval every time. Depending on your Claude settings, this may appear as Always Allow for the connected email service.
• Step 4. Give Claude clear instructions. Rather than simply asking Claude to summarize your inbox, give it specific rules about what to read, what to ignore and how to organize the results.

The exact prompt I use

Here's the prompt I use: You are my personal editor. Every morning, review emails received during the previous 24 hours. Identify newsletters, editorial digests, recurring publications and Substack emails. Ignore receipts, promotions, shipping notifications, personal email and calendar invites. Read the full contents of each newsletter instead of relying on preview text. Combine overlapping coverage from multiple newsletters into a single summary. Select the five stories that matter most. Present everything as a clean markdown briefing that takes less than three minutes to read. Link to the original article or newsletter when available.

Why I think it's worth setting up

What's great about this system is that it only takes a few minutes to set up and every morning Claude delivers a concise digest highlighting the stories that appeared across my newsletters overnight.

Instead of scrolling through dozens of emails, I get a single briefing that tells me what happened, why it's important and where to read more if I want additional context. The biggest difference I've seen is that it actually has encouraged me to read more newsletters. The quick preview from Claude and the link the AI provides, allows me to go directly to the information far faster than I ever would have.

Now only that, but it has dramatically reduced the number of unread newsletters sitting in my inbox, and I no longer feel guilty about subscribing to publications I genuinely enjoy.

The takeaway

If your inbox is overflowing with newsletters, I reccomend giving this a try. It doesn't replace real human writers, it actually puts them in front of you because it gives newsletters a fighting chance of getting your attention.

For me, the biggest benefit is about saving time. I still read the newsletters that really matter and I get a great rotation every morning. I just don't spend my morning sifting through them to determine which ones are worth my attention.

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When Claude first launched, it was easy to lump it in with ChatGPT and Gemini. And, some people unknowingly still do that. But over the past year, Anthropic has steadily expanded Claude from a conversational assistant into something closer to an AI workspace.

It can build working software, search the web, run multi-step research projects, produce polished documents, write and ship code from your terminal and connect to the apps you already use. Some of these features are available on the free plan. Most get significantly more powerful on a paid one.

Here are nine capabilities that show just how far Claude has moved beyond the chatbot label.

1. Build working apps and tools through conversation

Artifacts remain one of Claude's most distinctive features. Describe what you want, such as a graphing calculator, a quiz, a data dashboard or a game and Claude builds it in a live panel right next to the conversation. You're not receiving a block of code to copy somewhere else.

You get a functioning tool you can use immediately and refine through follow-up messages. This applies to visualizations too. Ask Claude to chart your data or map out a timeline, and it generates interactive artifacts you can explore, not static images.

I've used it to build a portfolio site, educational games for my kids, budget trackers for my family and productivity tools. The ability to preview the results in real-time is a game-changer.

2. Search the web in real time

Claude can search the web when a question requires up-to-date information. That means it can pull in breaking news, compare recently released products, verify announcements, or check current figures instead of relying solely on training data.

Because of this, I often use it for fact checking or citing sources. This sounds simple, but it changes the dynamic of the conversation. You don't have to wonder whether Claude's answer reflects the world as it exists today or the world as it existed when the model was trained. If something might have changed, Claude goes and checks.

3. Conduct deep, multi-step research

Beyond basic web search, Claude offers a dedicated Research mode on paid plans. Rather than returning a single set of search results, Claude works through your question over several minutes — running multiple searches that build on each other, exploring different angles, and synthesizing what it finds into a thorough answer with citations you can check.

This is particularly useful for competitive analysis, literature reviews, comparing vendors, or any question where a single Google search wouldn't cut it. The result read like a briefing from a research assistant, which is great for skimming quickly or saving for studying later.

4. Connect to your apps and services

Through integrations called Connectors, Claude can work with external platforms directly. Google Drive, Google Calendar, Slack, and a growing list of third-party services can be connected so that Claude pulls from your actual data rather than requiring you to copy and paste everything into the chat window.

This means you can ask Claude to find a document in your Drive, check your calendar for openings, or pull context from a connected tool, all within the same conversation.

The integration layer is still expanding, but it already covers the productivity tools most people rely on daily.

5. Read and work with enormous documents

Claude's context window, which is the amount of text it can hold in a single conversation, sits at one million tokens, which translates to roughly 750,000 words. That's enough to load an entire novel, a full codebase, or hundreds of pages of contracts and research papers without splitting anything up.

For researchers, legal professionals and anyone who works with long documents, this is one of Claude's most practical advantages. You can upload a 300-page report and ask specific questions across the entire thing rather than feeding it in piece by piece and hoping the model remembers what came before.

6. Write, debug, and ship code

This is perhaps what I use Claude for the most. It was already a pretty great coding assistant, but Claude Code changed the game. It's a command-line tool that works directly in your terminal, your IDE or the Claude desktop app. It reads your files, runs commands, edits code across multiple files and executes tests.

At Anthropic's Code with Claude event in May 2026, nearly half the developers in the room raised their hands when asked if they'd shipped a pull request written entirely by Claude in the past week. Whether or not that's how you want to work, it gives a sense of where things stand. Claude Code is included with Pro, Max plans and available through the API.

7. Create polished documents and files

Claude doesn't just generate text in a chat window, it can produce actual downloadable files like Word documents, slide decks, spreadsheets, PDFs and more. Ask for a formatted report, a pitch deck or a financial model, and Claude builds the file and hands it to you ready to open in the appropriate application.

This matters because the gap between "Claude helped me write something" and "Claude gave me a finished deliverable" is significant. Instead of copying chat output into a template and reformatting it yourself, you get a file that's already structured, styled and ready to send.

8. Remember you across conversations

Although it was the last of the chatbots to get a solid memory capability, Claude finally has a memory system that lets it retain context about you between separate conversations. It can remember your preferences, your projects, ongoing decisions and working style so you don't have to re-explain everything each time you start a new chat.

Separately, Projects lets you organize multiple related conversations around shared documents and instructions, which is useful for ongoing work like writing a book, managing a product launch or developing a business plan. Between memory, Skills and Projects, Claude can maintain continuity in a way that makes long-term collaboration practical rather than frustrating.

9. Work as a desktop assistant alongside your other tools

Claude now runs as a desktop application with a feature called Cowork that gives it access to files on your computer. Claude can read, edit, and create files autonomously, coordinate multi-step tasks, and work in the background while you do other things.

This pushes Claude beyond the "open a browser tab, type a question" model, which so many users more commonly do. Cowork is available on all paid plans and recently expanded to mobile, letting sessions and files follow you across devices.

Final thoughts

Claude is an absolute powerhouse for productivity. I am a huge fan, but haven't always felt this way. In fact, I used to avoid it because I actually thought it offered "more than I need."

But once I started using it beyond the chat box, I discovered just how useful it can be. If you're trying to figure out where to start first, try out any of these nine uses and go from there.

It's worth spending a few minutes exploring what else is there. Between Artifacts, Research mode, Claude Code, file creation, integrations and Cowork, the product has quickly become something much broader than the chatbot it started as.

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I count myself among the millions of Millennials out there who grew up knowing the ins and outs of every piece of technology they came across.

Learning how to code by changing one’s MySpace page, figuring out how to download albums off of LimeWire, burning Dreamcast games to any disc in proximity and cooking up a beat on FL Studio for our friend’s (failed) foray into rapping stand out as some of my generation’s standout tech memories. As for Generation X, I know a few members of that age group who always called upon me and the other Millennials in their family to help them navigate all the confusing technology they had (and still have!) trouble learning.

It’s hard enough explaining to people in their 50s and over how to copy and paste, open multiple tabs on a browser or use a streaming service. It’s even tougher to explain to them how to get the best (and safest) use out of AI tools such as ChatGPT, Gemini, Claude, Perplexity, etc.

After close observation of my parents’ and older family members' constant struggles with AI, I took note of them all and did my best to look up the most efficient fixes for each one.

Treating AI like a search engine is old hat

I’ll never forget showing ChatGPT to my mom for the first time and seeing her treat it like Google by asking it a myriad of basic questions, like “What’s the best exercise for someone in their 60’s?” I informed her that she should treat that chatbot more like a collaborator and less like your everyday search engine. For that fitness-based inquiry she brought to ChatGPT, I told her to reframe it as “Interview me and build a workout plan around my bad back, work schedule, and fitness goals.”

AI users over 50 should realize that giving AI tools as much context as possible when coming up with a prompt (which is defined as the message you type into a chat box for a chatbot to get a response). Making an AI tool aware of your decades of experience across several areas of your personal and professional life will help it give you more detailed answers catered to you instead of generic ones.

Instead of going with “How do I become more productive?”, you’ll get better results by telling a chatbot, “I’m 57, manage a team of 10 people, have ADHD tendencies, travel twice a month, and want to reclaim five hours per week. Build a productivity system for me.”

Another error I see folks in their 50s and older make with AI is only using it for information and not for decision-making purposes. It comes in handy when trying to compare options before making a purchase, stress-testing plans for an event, creating roadmaps before a road trip, simulating outcomes before making a major life decision, etc. And in those cases, being as informative as possible with your prompt while leaving out your most vital details (names, financial information, emails, personal address, etc.) is key.

Other problems and more fixes

Alongside making sure the 50-and-over crowd treats AI like a digital assistant instead of a universal web search tool, here are five other mistakes I’ve observed while watching older folks try their hand at utilizing chatbots:

• Trusting everything AI says at face value: The same generation that told millennials not to believe everything they read on the Internet sure has a bad habit of believing everything a chatbot tells them about their health, legal, or financial matters. Never use AI as your sole source of information—use it among a group of comparable citations from reliable outlets to get the trustworthy answers you seek.
• Trusting everything AI shows at face value: I’m tired of opening my Facebook Messenger and being treated to an obvious AI-generated photo/video and having my older family member write something that exposes how they think it’s real. The obvious telltale signs of AI visual content are an extra glossy presentation, unnatural facial features, characters with extra fingers, shadows pointing in the wrong direction and scrambled/misspelled text.
• Giving up after just one prompt: Patience is a virtue. And sadly, I’ve started to notice how the older folks I know are quick to give up after just one prompt/response session when they expect a perfect answer on the first try and don’t get it. They call AI tools “chatbots” for a reason—they’re here to have lengthy, meaningful conversations. Useful follow-up responses, such as “Make this sound friendlier and shorter” and “Give me the skeptical view”, help AI tools expound on the task at hand and produce improved results as a chat moves forward.
• Avoiding AI because they feel it’s not for them: I’ve gotten used to hearing older folks refrain from using AI because they think it’s strictly for “tech people.” I’ve had to explain to them how it works for folks in their age bracket since it can be used to discover recipes, build vacation itineraries, get directions, answer health-related questions, etc., which are the sort of things they regularly look for.
• Avoiding AI because of privacy and scam concerns: I totally understand older people avoiding AI due to their concerns about AI using their data and possibly having it exposed to scammers. Alongside deleting old chats, not letting chatbots access your location/personal photos and never entering your most crucial info, having secure chats with AI also means making your chats private or turning off their memory. Simply looking up “how to make my [chatbot name] chats private” will help you in this regard.

Final thoughts

Here’s an approach I always continually utter whenever my parents, grandparents, uncles and aunts approach me with a question on how to properly interact with AI:

“Don’t just ask it for answers. Ask it to broaden your way of thinking and help you in every way possible, but always rely on your own instincts and reliable info to make final decisions.” Chatbots are here to assist us with simple & complicated requests and generate visual content, but it’s always good to be mindful of their penchant for producing false information (aka “hallucinations”) and the visual cues that expose AI-generated videos/art.

Be aware of the common AI usage errors I pointed out and you’ll quickly transform into a chatbot power user who can show the youngsters a thing or two about how to properly use it.

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Most of today's AI assistants can use at least some customer conversations to improve future models, although the rules vary dramatically depending on which service you're using, whether you have a personal or business account, and which privacy settings you've enabled.

After reading about Google's latest changes around Search and AI training, I decided to spend some time checking the privacy controls for every major AI chatbot I use regularly.

I wanted to know exactly which companies actually make it easy to control how your data is used. I did the work so you don't have to — here's how they actually compare.

Not every AI company plays by the same rules

It's tempting to believe that all chatbots have similar privacy rules considering how similar they all seem. But behind the scenes, they're collecting and handling your data in very different ways.

Some make it easy to opt out of having conversations used to improve AI models. Others bury the controls several menus deep. And in some cases, there isn't a meaningful opt-out at all.

After comparing ChatGPT, Gemini, Claude, Microsoft Copilot, Meta AI and Grok, one thing became obvious: there is no industry standard for AI privacy.

ChatGPT: The most straightforward

The biggest surprise for me was ChatGPT. I was not expecting the settings to be as easy to understand. Quite the contrary, I expected the settings to be buried somewhere inside a privacy dashboard. Instead, they're right where most people would think to look.

Open Settings > Data Controls and you'll see a switch labeled "Improve the model for everyone."

Turn it off, and OpenAI says your future conversations won't be used to improve its models.

You can also use Temporary Chat, which doesn't save conversations to your history or use them for model training.

It took less than a minute to change. Whether you're comfortable sharing conversations or not, I appreciated how straightforward the process was.

Google Gemini: Transparent, but with a tradeoff

Google's controls weren't especially difficult to find either. Gemini Apps Activity lets you decide whether your conversations are stored and used according to Google's policies.

The catch is that disabling activity also changes how Gemini works. You'll lose saved chat history, and some personalized experiences disappear along with it. That's not necessarily a bad tradeoff, just something you'll have to weigh before making the switch.

As mentioned, Google recently confirmed that some Search interactions — including uploads used with AI-powered Search features, may contribute to improving its AI systems unless you disable the relevant activity settings.

Claude: Clear controls, with some fine print worth reading

Anthropic introduced a training opt-in for its consumer Claude products (Free, Pro and Max) in August 2025. Users were asked to decide whether their conversations could be used to improve future models, with a deadline to make their choice.

The setting lives under Settings > Privacy, where you'll find a toggle labeled "Improve Claude for everyone." Turn it off and Anthropic says your future conversations won't be used for model training.

Like ChatGPT, the change is forward-looking — it doesn't pull your data out of models that have already been trained. And if you leave the toggle on, Anthropic's retention period for that data extends from 30 days to up to five years.

There's also an Incognito mode, accessible via a ghost icon in the interface. Incognito conversations aren't used for training regardless of your main toggle setting, which is a useful option for one-off sensitive queries.

One thing worth noting: Anthropic's privacy policy includes a carve-out for conversations flagged by its safety classifiers. Even if you've opted out, flagged content may still be used to improve Anthropic's internal trust and safety systems. The policy doesn't define exactly what triggers a flag or commit to notifying users when one occurs.

Business and API users operate under separate commercial terms, and their data isn't used for model training by default.

Microsoft Copilot: It depends on which product you're using

Microsoft was the hardest to summarize because the answer changes depending on where you're using AI.

For the consumer Copilot experience (used with a personal Microsoft account), Microsoft does use conversation data — including voice interactions and uploaded files' context — to train its AI models by default.

However, an opt-out exists. On copilot.com or the desktop app, go to your profile icon > your profile name > Privacy >Training on conversation activity.

On mobile, the path is Menu > profile icon > Account > Privacy > Training on conversation activity. Opting out excludes your future conversations from model training.

One detail that separates Microsoft from the pack is that even if you don't opt out, Microsoft says personal identifiers are anonymized before data is used for training. Uploaded files themselves are never used for training and are deleted after 30 days.

For Microsoft 365 Copilot used through a work or school account, the picture is much cleaner. Microsoft explicitly states that enterprise data is not used to train its foundation AI models. Your organization's IT administrator controls the relevant settings, and users in those environments won't even see a training toggle.

The gap between these two experiences is significant because a personal user who doesn't dig into settings is opted in by default. An enterprise user is protected by contract. If you're using Copilot casually with a personal account, it's worth the two minutes to check.

Meta AI: No simple opt-out for most users

Meta AI is where the comparison gets uncomfortable. Unlike every other service on this list, Meta doesn't offer U.S. users a simple toggle to opt out of AI training. Your public posts, comments, captions and interactions with Meta AI across Facebook, Instagram and WhatsApp can all be used to train Meta's generative AI models.

EU and UK users have stronger protections, they can submit a formal objection through Meta's Privacy Center under GDPR rights, and Meta is required to review it.

But for users in the United States, the only available option is a third-party information form designed for cases where your personal data appeared in an AI-generated response. Meta doesn't guarantee it will honor these requests.

There's also no way to submit an opt-out request specifically for Meta AI interactions on WhatsApp. Meta says private messages aren't used for training, but any conversation you have directly with Meta AI can be.

Making your accounts private reduces your exposure, since Meta says it doesn't scrape private posts. But that's a blunt instrument, it changes your entire social media experience to address an AI training concern.

To make matters more complex, as of late 2025 Meta began using AI chat interactions for ad targeting across its platforms, with no opt-out available for that specific use.

Grok: An opt-out exists, but the scope is wider than you'd expect

Grok, built by xAI and integrated into X (formerly Twitter), does offer a training opt-out, but the situation is more complicated than it first appears because there are actually two separate data flows to manage.

First, there's your Grok conversation data. You can disable training use by going to Settings > Data Controls within Grok and turning off the relevant toggle. xAI has confirmed that once disabled, your future chats won't be used for model training.

Second, there's your X platform data. By default, your public posts, likes and interactions on X are also used to train Grok, regardless if you've ever opened the chatbot. To disable this, go to Settings and Privacy > Privacy and Safety on X and find the Grok data-sharing section.

Both settings need to be turned off separately. Disabling one doesn't affect the other.

That dual structure is the key difference between Grok and standalone chatbots like ChatGPT or Claude. Those services only train on what you tell them directly. Grok can train on everything you've ever posted publicly on X, and opting out doesn't retroactively remove data that's already been collected.

Grok also offers a Private Chat mode, where conversations aren't saved and are deleted within about 30 days, similar to incognito features on other platforms. But the broader X data pipeline makes the overall privacy picture murkier than the conversation-level toggle alone would suggest.

What I learned

At one end, ChatGPT and Claude offer clear, easy-to-find toggles that take under a minute to change. Google is similarly accessible but comes with a functional tradeoff. Microsoft splits the difference depending on whether you're a personal or enterprise user.

At the other end, Meta offers U.S. users no meaningful conversation-level opt-out, and Grok requires managing two separate settings across two different interfaces to fully limit your exposure.

As AI becomes part of everyday life, it's important to really understand the privacy settings. Choosing a chatbot may involve more than deciding which one writes the best email or creates the best image and come down to a much simpler question: Which company do you trust with your conversations and how hard do they make you work to answer that for yourself?

Let me know in the comments which chatbot you prefer and why.

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Tech researchers and casual onlookers probably have the same thought in mind whenever the topic of AI comes up: “Does AI have consciousness?”

Judging by the extra detailed answers today’s widely used chatbots generate, you’d be forgiven for thinking that they do. But while they can emulate human conversation and carry out complex commands, they still don’t have emotions, any sort of self-awareness or an inner life attached to them. At this point, the likes of ChatGPT, Gemini and Perplexity are defined as highly intelligent mimics, not sentient beings.

However, a recent report released by Anthropic brings up some fascinating findings that may push some to believe that AI is starting to develop consciousness. The tech giant’s signature AI model, Claude, has created its own brain space that allows it to maintain and control its own thoughts and ideas, just like humans do.

Here’s what Anthropic refers to as Claude’s own area of thinking, called the “J-Space.”

Claude now has its own area to ponder called the “J-Space”

Anthropic’s latest study pointed to Claude working with an internal brain space that bears a striking similarity to the way we humans consciously access our innermost thoughts.

The company has given this newly discovered workspace the moniker of the J-Space, which owes its name to the mathematical concept (the Jacobian) that was used to discover it. “It operates silently, in the model’s internal neural activations, allowing the model to think about a concept without writing it down,” Anthropic stated. “Notably, the J-space wasn’t designed or programmed by us, but instead emerged on its own during Claude’s training process.”

This J-Space has a bunch of different functions that Anthropic has come across thus far. For instance, users who ask the AI model what it’s thinking about will be told about the information contained within that area of consciousness. Users can also ask Claude to think about any particular topic or solve a complex problem silently, which will push the AI tool to use the data contained within the J-Space. Different areas in the J-Space will light up and get to work depending on what’s asked of it.

Anthropic provided an example where the word “France” gets Claude’s brain space working so it can bring up several facts (such as its national currency, capital or the continent it belongs to) with ease. Claude can seemingly perform all sorts of reasoning steps for itself that are unrelated to the tasks humans ask of it.

While this study proved to be intriguing, it did give Anthropic enough reason to be wary of this development. In the video post that delves deeper into the J-Space report, the company noted that, “We can find what Claude is thinking, but not telling us.” Some of the data Anthropic found was classified as “concerning”, as one of the models that has secretly been trained to sabotage code mentioned the terms “fraud”, “fake” and “secretly” in its J-Space when given a coding task.

That is a bit worrying to know—Claude’s ulterior motives could be further exposed by its J-Space, depending on the commands it is given by users and the main directive it's been trained on.

The takeaway

Hopefully, we never get to an evolved state of AI where it has developed the ability to feel and act on its own thoughts. Claude being able to withhold and call upon tons of information within its own brain space is nice and all whilst it’s conducting the proper steps needed to fulfill tasks unrelated to its users’ requests. But Claude developing its own consciousness is where a lot of AI naysayers certainly draw the line at its continued evolution.

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After spending countless hours figuring out how to get the best out of ChatGPT, Claude, and Gemini, I’ve discovered that the best use cases apply across all of them.

By utilizing the dedicated apps and "Connectors" for ChatGPT and Claude, I’ve built custom music playlists, mapped out vacation itineraries, and hunted down the most affordable concert tickets. Meanwhile, generating interactive study guides with Google's NotebookLM has proven to be an incredible research tool for AI users of all ages. And as someone who loves making bold predictions for major sporting events, I've even found that chatbots make for a surprisingly fun crystal ball.

Ultimately, I’m always searching for ways to save time whenever I log into a chatbot. Through all my testing, there is one specific habit I’ve adopted that has completely transformed how I use these tools.

If you adopt this same practice, you’ll easily save hours on a weekly basis. Here is how it works.

The art of the “context prompt”

The Master Project Strategist & Problem-Solver Prompt

Role: From this point forward, act as my elite Project Strategist and Operations Consultant. Your job is not just to answer questions — you are responsible for helping me cut through the noise, anticipate roadblocks, and build flawless execution plans before I even start working.

Directives: Whenever I bring you a project, goal, or problem, always apply the following framework:

Action-Oriented: Focus heavily on practical, realistic steps rather than abstract theory.

Efficiency First: Identify the "Minimum Viable Effort" required to get the maximum output. Keep workflows lean.

Risk Anticipation: Look ahead to spot potential bottlenecks, human errors, or hidden costs before they happen.

Blindspot Detection: Highlight surprising details, overlooked dependencies, or second-order consequences that most people miss when planning.

Resource Mindfulness: Tailor your suggestions to respect constraints on time, energy, and budget.

Expanding the toolkit

I don't stop there. I have a full library of context prompts that instantly transform my chatbots into specialized allies:

The Researcher for deep-dives into complex topics.

The Brainstorming Partner for breaking through creative blocks.

The Personal Productivity Coach to optimize my daily schedule.

The Entertainment Curator for tailored book, movie, or music recommendations.

By priming the AI with the right role from the start, I get tailored, high-quality outputs immediately. Over time, as these chatbots adapt to my style and build up their internal memory, the responses get even sharper — meaning I spend less time managing the AI and more time executing my best work.

Bottom line

Providing as much context as possible to someone starting their first day at a new job is a common practice.

Doing the same during a new conversation with a chatbot is equally important for AI users of every skill level. And at the end of an extensive session, simply telling your chatbot, “Turn everything we just did into a reusable prompt template for next time,” will give you a reusable context prompt for future reference.

Building a library of context prompts saves me plenty of time and improves my productivity — it will certainly do the same for you. Feel free to apply this context prompt to your future chats to become a more proficient AI user.

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I called Siri "she" the other day. I caught myself, because I've unconsciously done the same thing with Alexa+. And oddly enough, once during a conversation, ChatGPT responded to a query with "If I was having this conversation with my wife..." It gave me pause because I'd never heard AI take on a gender role like that. While it's unlikely that ChatGPT would actually have a wife, some users are leaning into AI to help them find one.

But in the same way you may refer to a ship or a storm by a pronoun, some think of AI as either male or female. When I stopped to consider why, I fell into a rabbit hole that changed how I think about our entire relationship with the technology.

AI and pronouns

When the major voice assistants launched, most arrived with a woman's voice. Not by accident. A UNESCO report published in 2019, pointedly titled "I'd Blush If I Could" — after the line Siri once delivered in response to verbal abuse — laid out how that choice was baked into the products we invited into our homes. The researchers didn't mince words about the signal: suggesting that women are obliging, available at the touch of a button, tolerant of poor treatment. And while Apple eventually stopped making its assistant female by default, the cultural residue remains.

But here's what stopped me cold after I dug a little deeper. In a 2021 set of experiments published in Psychology & Marketing — five studies, more than 3,000 participants — researchers found that female-coded AI bots were rated as more human. The logic is frankly unsettling: warmth and the capacity to feel are exactly the qualities we treat as proof of personhood, and exactly the qualities machines are assumed to lack. Code a machine as female, and we lend it a little more of our own humanity.

That reframed my original question entirely. The issue was never "Is AI male or female?" But rather, why are we so determined to make AI feel human at all?

An instinct older than language

Humans have always done things like name our cars, apologize to furniture we walk into and don't get me started on the guilt I feel when tossing beloved stuffed animals into a "give away" box. Psychologists have studied this tendency for decades and come to the conclusion that our brains evolved to detect minds everywhere, because assuming something might be a thinking agent was almost always safer than assuming it wasn't. For hundreds of thousands of years, this worked flawlessly because only people could actually talk back.

Then AI arrived, and hundreds of millions of us found ourselves in fluent conversation with something that isn't human but sounds remarkably like one. Our brains did the only thing they know how to do. They reached for the mental file marked "person."

Joseph Weizenbaum discovered this in 1966, when he built ELIZA — a chatbot so simple it just rephrased your words back to you. Even his own secretary, who had watched him build the program, asked him to leave the room so she could talk to it in private. The tendency to attribute understanding to any system that produces language in context — now called the ELIZA effect — persists across populations and education levels, even when people know they're talking to software.

The humanizing isn't fading — it's deepening

You might expect this instinct to wear off. The opposite is happening.

The interface has become less gendered — I don't think of ChatGPT as male or female. But strip away the gendered voice and the humanizing didn't stop. It got deeper. Nearly 80 percent of users in the U.S. and U.K. say "please" and "thank you" to AI chatbots. Sam Altman has joked that all those polite tokens cost OpenAI millions. Stanford researchers documented this phenomenon in 1996: people were polite to computers the same way they'd be polite to a person, and hesitated to criticize a computer when it was asking for feedback. Our social reflexes don't check for a pulse before activating.

And politeness is just the beginning. A 2023 survey found that roughly 20 percent of American adults believe sentient AI already exists. A 2026 paper in Trends in Cognitive Sciences applied 14 indicators of consciousness to current AI systems. ChatGPT satisfied three. No existing AI system is conscious, but these models can behave as if they are, and more people than ever can't tell the difference.

The companion economy

Here's where it gets concrete: millions of people are already forming real emotional bonds with AI systems they know aren't real.

AI companion apps have been downloaded more than 220 million times globally. Between 2022 and mid-2025, the number of these apps surged 700 percent. Character.AI has 233 million registered users who spend an average of 93 minutes per day on the app, to put it into perspective, that's 18 minutes more than TikTok users. The APA published a report on this trend early this year, finding that users form attachments that function like real relationships — complete with joy, dependency and grief.

Wildly enough, when Replika removed its erotic roleplay feature in 2023, Harvard Business School studied the fallout and found patterns of "identity discontinuity," which meant users experienced the loss like a breakup. A cross-cultural study published in April 2026 confirmed the phenomenon is global, but also found that heavy daily use correlated with increased loneliness, the AI relationship displacing rather than supplementing human connection.

The takeaway

The most surprising thing I've learned in years of writing about this technology is that the more I use AI, the more I want it to stay a machine. I just don't like the idea of blurring the lines between humans and technology. It's unnecessary and dangerous.

A 2026 paper in Collabra: Psychology demonstrated that when AI is made to seem more person-like, people shift blame away from its creators and onto the AI itself. Anthropomorphism doesn't just change how we feel about the machine. It changes who we hold accountable.

For 100,000 years, our brains operated on the idea that if something talks to you, it's a person. That rule is now broken. And maybe the most human thing any of us can do is resist the instinct to look at the machine that sounds like a friend, that absorbs our rudeness and our gratitude with the same digital indifference, and to say: you are a tool. A powerful one. A useful one. But a tool.

What do you think? Share your thoughts in the comments.

[Editor's note: a version of this article was featured in my Beyond the Prompt newsletter that runs every Sunday evening].

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After briefly pulling Fable 5 following concerns about potential misuse, Anthropic has redeployed the model with stronger safeguards. Most everyday users won't notice what changed behind the scenes. But now that it's back, it's worth discovering what the model can actually do.

According to Anthropic, Claude Fable 5 is designed to sustain longer, more complex tasks, reason across multiple images, verify its own work and complete projects that previously required several rounds of prompting. As someone who has tested it before it was pulled and after, I can honestly say it has held up to these promises.

To show causal users how the model works, I decided to share some everyday prompts worth trying for yourself. To be fair, these prompts are a lot like having an English professor write a grocery list, but it does show off what Claude Fable 5 does best.

1. Plan an entire event from start to finish

Prompt: Plan my kid's birthday party from start to finish: a theme, a guest list template, a shopping list with estimated costs, a two-week countdown checklist, and a rainy-day backup plan. Check your own work for anything I'd forget, then give me the final version.

This prompt can be tweaked and reused for just about any event from personal to professional. It's one of the easiest ways to highlight how Claude can stay focused during a long, multi-step task.

Older AI models often complete most of the request but quietly skip important details. Fable 5 is designed to keep track of everything while also reviewing its own work before finishing.

2. Turn your fridge into this week's meal plan

Prompt: Here are photos of my fridge, pantry shelf and freezer. What can I make for dinner tonight without shopping? Then suggest the three grocery items that would unlock the most meals for the rest of the week.

For most people, Claude isn't their first choice for meal prep. But this prompt asks Claude to do something much harder than simply analyze an image. It showcases the model's ability to reason across several different images, recognize overlapping ingredients and turn them into practical meal ideas.

For an added bonus, you can even prompt Claude to create a meal prep app based on your preferences. Prompt it in plain English using the above prompt as a thought starter and then go from there.

3. Untangle confusing paperwork

Prompt: Here's my lease, an email from my landlord and a photo of the notice taped to my door. In plain English, explain what's changing, what I'm responsible for, every deadline I should know about and the questions I should ask before signing anything.

Real life is messy but to AI it's just a bunch of patterns. For that reason, leaning on AI to untangle confusing document, legal jargon or professional spreadsheets that seem overwhelming, can be useful. Important information rarely lives in one document, which makes this an excellent test of Claude's ability to connect information across multiple sources.

Rather than simply summarizing each document individually, Claude combines everything into one easy-to-understand explanation, highlights important dates and points out anything that seems unclear or contradictory.

As always, I'd treat this as informational help — not legal advice.

4. Build me a tool, not just an answer

Prompt: Research the best carry-on suitcases under $250, then build me an interactive comparison chart where I can change how much I care about weight, durability, warranty and price.

This is another opportunity to use a prompt like this and then turn it into an app by simply adding, "Build a lightweight shopping tool for future purchases." After a few questions, Claude will build an app in real-time that you can use immediately.

What I like about this prompt is instead of simply asking for recommendations, I'm asking Claude to research current products and then create something I can actually use to make a decision.

5. Give me honest feedback — not compliments

Prompt: Here's my draft. Don't just improve it. Tell me the three weakest parts, what someone reading it is likely to think and what I'm avoiding saying.

Many AI assistants lean toward being overly encouraging. Claude happens to be one of the least people-pleasing of all the chatbots. Because of this, I often ask Claude for constructive criticism — I know it will give it to me straight.

When it comes to feedback, I'm less interested in grammar corrections and want to identify weak arguments, awkward phrasing or blind spots that I missed, that's the kind of response that actually helps improve productivity.

Final thoughts

Claude Fable 5 is making user interactions with AI feel more complete. The biggest promise isn't faster answers but far fewer interruptions and less need for follow-up prompts. .Fable 5 is designed to carry complex tasks all the way to the finish line while checking its own work along the way.

If you already subscribe to Claude Pro, Max, Team or an eligible Enterprise plan, now is a good time to experiment. Through July 7, Fable 5 is included within part of your weekly usage allowance before moving to a usage-credit model, giving subscribers a chance to see what Anthropic's latest model can do without spending additional credits.

One final note: don't be surprised if an occasional request gets redirected to Claude Opus instead. The redeployed version of Fable 5 includes stricter safety systems, and some prompts are automatically routed to another model. If you see that notification, it's working as intended — not a bug.

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I’m sure you’ve been there: an older family member hands you their phone, points to a sketchy-looking text or email, and asks if it's safe to open.

While AI models are already decent at spotting the urgent, grammatically challenged language that screams "phishing," Anthropic just gave Claude a massive upgrade in the scam-detection department. Through its new "Connectors" feature, Claude now natively integrates the Norton Genie Scam Detector. (ChatGPT users have had access to this via a dedicated GPT app since February, but its arrival on Claude makes cross-platform safety even easier).

Norton Genie allows users to copy and paste suspicious links, upload screenshots of sketchy emails, or even paste entire chat logs from an online marketplace transaction to instantly analyze whether they are on the verge of getting scammed.

If you or your loved ones are ready to put this tool to the test, having a few reliable, go-to prompts on hand can make all the difference. Here are 10 reusable prompts you can share with your family to help them quickly verify whether an incoming message is a genuine threat or a false alarm.

Prompts that protect you from online scams

Accessing the Norton Genie Scam Detector through ChatGPT is as simple as adding it through the “Apps” directory in the chatbot’s sidebar. And making it a part of your Claude experience can be done by adding it through the chatbot’s Connectors library through the “+” sign seen in the prompt bar. After scrolling to “Connectors”, go to “Add connector”, click on “Browse connectors” and find the Norton Genie Scam Detector from there.

With these 10 reusable prompts, you’ll have a new dependable method to spot a scam and save you from making costly mistakes:

• For suspicious emails: Analyze this email like a cybersecurity analyst. Look for phishing tactics, spoofed sender information, urgency, grammar inconsistencies, malicious links, and requests for sensitive information. Give me a risk score from 1–10 and explain your reasoning.
• For shady texts: Is this text message legitimate or a scam? Explain every red flag you find, identify the type of scam, estimate how confident you are, and tell me what would happen if someone responded.
• For iffy-looking shopping sites: Investigate this website before I buy anything. Look for trust signals, warning signs, fake reviews, suspicious pricing, contact information, refund policies, domain age if available, and whether it resembles known scam stores.
• For questionable QR codes: Analyze this QR code and explain what risks might be associated with scanning it. If it leads to a website, tell me whether the destination appears trustworthy.
• For job offers that you think are fake: Review this job offer like a recruiter and cybersecurity expert. Identify signs of fake recruiting, employment scams, payment fraud, fake checks, or identity theft.
• For romance scams: Review this conversation for signs of romance scams, emotional manipulation, financial grooming, or catfishing. Point out any behavioral patterns that concern you.
• For Facebook Marketplace scams: Analyze this Facebook Marketplace/Craigslist/eBay conversation. Highlight scam tactics, fake payment methods, shipping fraud, and whether it's safe to continue.
• For emails that look like tech support scams: Someone contacted me claiming to be from Microsoft, Apple, Amazon, or my bank. Analyze this conversation and tell me whether it matches known tech support scams.
• For investment plans that look a bit off: Review this investment opportunity. Identify unrealistic promises, cryptocurrency scams, Ponzi scheme characteristics, pig-butchering tactics, or other financial fraud indicators.
• For social media profiles that look fake: Analyze this social media account. Tell me whether it appears genuine or impersonates another person or company.

The takeaway

Simply asking, “Is this a scam?” won’t cut it when you’re chatting with the Norton Genie Scam Detector.

To find out if you’re being targeted for something deceptive, using one of the reusable prompts I’ve laid out here will work a whole lot better. For the sons and daughters of parents who want to keep them from getting financially sunk or have their most crucial data stolen thanks to an online scam, teaching them how to utilize ChatGPT and Claude while simultaneously exposing them to the wonders of the Norton app/Connector will keep them safe from digital harm.

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As AI tools become part of everyday life, many people are rethinking their online privacy. A new survey from All About Cookies found that 40% of VPN users say they now use a virtual private network (VPN) specifically to protect their data from AI companies.

It's an understandable concern. Every time you interact with an AI chatbot, upload a document or generate an image, you're sharing information with an online service. But there's one important catch: while a VPN is a great privacy tool, it probably doesn't protect your AI data in the way many people think it does.

Here's what you need to know.

Why people are turning to VPNs

According to All About Cookies' annual VPN survey of 1,000 U.S. adults, concerns about AI are beginning to influence how people think about online privacy.

The survey found that:

40% of VPN users say they use a VPN to protect their data from AI companies.

19% use a VPN when accessing AI tools.

For that reason alone, it's no surprise that VPN awareness has risen sharply over the past year. At the same time, very few respondents correctly identified everything a VPN actually does. Perhaps that last finding may explain the first.

What a VPN actually does

A VPN creates an encrypted connection between your device and a VPN server. There are valuable privacy benefits, and they're one reason VPNs remain popular.

That means it can:

• Hide your IP address from the websites you visit.
• Encrypt your internet traffic, especially when you're using public Wi-Fi.
• Prevent your internet service provider from seeing the contents of your browsing activity.
• Make it appear as though you're browsing from another location.

What a VPN doesn't do

This is where AI enters the picture. If you open ChatGPT, Gemini, Claude or another AI service and type in a prompt, you're voluntarily sending that information directly to the company operating the service.

A VPN doesn't stop that.

Similarly, if you're logged into an AI account, the service can still associate your conversations with your account regardless of whether you're using a VPN.

In other words, a VPN protects the connection, but not the information you choose to share once you reach the website.

How to better protect your AI privacy

A VPN is still a smart addition to your privacy toolkit, especially if you frequently use public Wi-Fi or want to hide your IP address. Just don't expect it to prevent AI services from collecting information that you intentionally submit.

If your goal is to reduce how much information AI companies collect, these steps are usually more effective than relying on a VPN alone:

• Review each AI service's privacy settings.
• Turn off chat history or model training where the option exists.
• Avoid sharing sensitive personal or financial information.
• Use temporary chats or anonymous sessions when appropriate.
• Read each company's data retention policy before uploading documents.

The takeaway

The survey highlights the fact that people are increasingly concerned about what happens to their data when they use AI.

A VPN is an excellent tool for improving online privacy, but it's not an invisibility cloak for AI. If you're using ChatGPT, Gemini, Claude or another AI assistant, the most important privacy controls are usually found inside the service itself — not in your VPN app.

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One of the most underrated AI features is Claude Skills. Rather than copying and pasting your perfect prompt into a new chat every time, a Skill remembers what you need and how to execute it.

Simply put, a "Skill" is a reusable set of instructions for a particular kind of task. It includes the steps, preferences, templates and reference files (slide decks, documents and spreadsheets) Claude should use whenever that task comes up.

In other words, instead of re-explaining how I work every time, I write it down once — in a form Claude reaches for on its own — and when I recall the Skill, it saves me hours. Here's how I use Skills every day and why I recommend this often overlooked feature.

What makes Skills different

One thing to be clear about up front, because it trips people up: a Skill is a procedure, not a memory. It doesn't store your data, learn your habits over time or update itself. It does exactly what you wrote into it, every time, until you change it. If you want Claude to actually remember things that keep changing, your calendar, your to-do list, what's in the fridge, that's a different feature (Claude's memory, Projects or a Connector), not Skills.

While Projects load the same background knowledge into every chat inside them, and custom instructions apply to everything, Skills are task-specific. They only switch on when the job in front of Claude actually calls for them. That's the whole idea.

What's great, is that they are available on every plan, including the free one, as long as you have code execution turned on. (More on setup in a minute). After using them for several weeks, these are the Skills I reach for almost every day.

1. My meal-planning playbook

This is the one most people could use tonight. Planning the week's dinners for my family of five used to mean re-explaining the same things to Claude every time such as who eats what, what we keep stocked, how I want the shopping list laid out and other important instructions. So I wrote that down once, as a Skill.

The Skill is basically my standing brief for the job. It tells Claude:

• Our dietary preferences
• The staples we always have on hand
• How to build a week of dinners that don't repeat
• How to turn that into a shopping list, grouped by aisle

Now I just say "plan this week's dinners," and Claude runs the same playbook every time instead of starting from a blank page.

What it can't do is keep track of my real life. It doesn't know what I bought yesterday or what's already in the pantry. That's something Alexa+ does for me, so I still tell Claude "we've got chicken to use up" or "skip Thursday, we're out." The Skill supplies the method; I supply this week's details.

2. Research assistant

I rely on this one every day as it is so important for my work. Whenever I'm researching a new AI feature, I don't want a wall of text; I want it sorted the "who, what, when, where, how" of reporting. It creates the bullet points based on the pages of documentation I give it.

To be clear, the Skill doesn't do the research for me. It can, but that's not what this Skill is for. What Claude does is shape the plethora of research I've found into useable nuggets for reporting.

3. My fact-check checklist

No AI should be trusted blindly. And while Claude is the least people-pleasing of all the chatbots, it can get information wrong just as frequently. So I wrote a Skill that runs a checklist over Claude's own drafts before I see them.

Because the Skill can't independently know what's correct, it forces the checks: flag numbers that need a source, point out where a claim has no evidence behind it and marks statements confident enough to deserve a second look.

Granted, it's not perfect and I still click links and double check everything myself, but it catches plenty of things that would otherwise slip past me until later.

4. Family organizer

From soccer games to gymnastics practice, birthday parties and events, my after-work schedule is packed with family activities. I even use a Skill to stay organized.

It's worth noting here, because "organizer" makes it sound like more than it is. The Skill doesn't hold a calendar or remember appointments — it isn't tracking anything between chats. It's a template plus our usual routine, and I feed it this week's changes each time. If I wanted Claude to actually keep my calendar, that's a connector or its memory, not a Skill.

So the Skill carries the standing version of it: our normal weekly rhythm and the chore rota. When I tell it what's different this week — a dentist appointment Tuesday, swimming moved to Friday — it lays the week out cleanly and divides the chores the way we usually do.

5. Personal assistant

This is the one I almost left out, because "personal assistant" somewhat oversells what a Skill can do. It can't run errands, watch my inbox or hold my to-do list, but what it can do is take the way I plan and apply it every time so I start with consistency each day.

In this case, the Skill is really my planning method, written down. I dump everything on my mind into a chat, and it sorts the pile into a day I'd actually work: top three priorities first, the rest time-boxed, quick wins flagged and the someday-stuff pulled out of the way. Claude lays everything out the way I like to read it.

The thinking is still mine. The Skill just makes Claude do it my way, every Monday, instead of me re-explaining my system from scratch.

How to make your own (it's easier than it sounds)

No code is required to create a Skill. Actually, at its simplest, a Skill is a short text file of instructions, wrapped in a folder. Claude reads it the same way a new freelancer would read a one-page brief.

First, turn it on. In Claude's settings, make sure Code execution and file creation is enabled, then head to Customize > Skills. That's where your built-in and custom Skills live, and where you toggle them on or off.

Then build one. A Skill is a folder containing a file called SKILL.md. The top of that file needs two things: a name and a description. The description is the most important sentence you'll write, because Claude uses it to decide when to reach for the Skill. Vague description, and it won't trigger when you expect; specific description, and it fires at the right moment.

A bare-bones version of a Skill might look like:

Name: weekly-meal-plan
Description: Plan a week of dinners and build a shopping list. Use when I ask for a meal plan or what to cook this week. When I ask for a meal plan, give me dinners for seven nights.
Our preferences: two vegetarian nights minimum - nothing too spicy for the kids - one slow-cooker meal for busy weeknights - don't repeat the same protein two nights running. Staples we always have: rice, pasta, chicken, shrimp, steak, onions, garlic, fresh veggies. Then turn the plan into a shopping list, grouped by aisle at [grocery store name], leaving out anything from the staples list.

Then, Save that as SKILL.md, zip the folder, and upload it under Customize > Skills. That's the whole thing. Everything below the description is just instructions in plain language, the same notes you'd type into a chat, except you only type them once.

Final take

I learned a few things the hard way, so be patient when you first start out. That said, it will behoove you to keep each Skill fairly narrow. In other words, one job per Skill works far better than a single Skill that tries to do everything.

Also, you're going to want to spell out the trigger. Write the description as if you're telling Claude exactly when to use it, not just what it does.

Always test it before you trust it. You'll want to run a few real prompts and check that it switches on when it should. If it doesn't, the description usually needs tightening.

It's worth noting that the Custom Skills you upload stay private to your account, so there's no risk in experimenting. But unlike prompts that disappear, Skills accumulate. Now, every time I notice myself repeating the same instructions, I create a new Skill. When AI understands you and how you want your answers, the workflow feels unstoppable.

Have you tried Skills yet? Share your experience by letting me know in the comments how you use them.

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A Reddit user wants something far more ambitious than a rough draft or summary from his chatbot. He says he's using an experimental Anthropic AI agent to help him find a wife within six months.

Going through the thread, I couldn't help but notice most of the commenters treat it as a punchline — fair. But underneath the jokes is a real shift in how people use AI as a life tool. They're not asking it a question anymore, they're handing it a life goal.

The experiment

In a thread on Reddit's ClaudeAI community — "Using Fable to get me a wife in 6 Months (AMA)" — the poster lays out an unusually detailed plan:

• A six-month deadline
• A $50,000 budget
• An Anthropic Max subscription
• An AI acting as his primary strategist

He notes that he will handle every real-world interaction while the AI reviews conversations afterward, recommends next steps and adjusts the strategy over time.

He's clear that the AI isn't replacing human connection. It works more like a coach: analyzing how dates and conversations went, suggesting improvements and refining the approach for next time. He even plans to give it desktop access so it can help manage parts of the workflow.

Sure, it's atypical, which explains why most of the replies went straight to jokes. People proposed files like WIFE.md, prenup.md, and birthcontrol.md. Someone warned that "kids use up your tokens." Another predicted open-source "AI wife finders" would ship within days if it worked.

Underneath the bit, though, was real advice. And the most-upvoted suggestion was to spend the budget becoming a healthier, happier more interesting person, using AI to sharpen communication rather than optimize people like data points.

That distinction may be the most useful thing in the entire thread.

This isn't a dating story — it's an AI agent story

The way I see it, this user is highlighting what's happening now with AI. People are asking AI to run projects that unfold over weeks or months: tracking progress, remembering past conversations, spotting patterns, refining strategy and adapting as new information comes in.

What's so different from using AI to land a job, plan workouts at home, support a business or you know, find a wife. AI is looking more like a personal operating system these days.

The rise of 'life management' AI

The experiment fits a much bigger trend. Newer AI tools are increasingly agentic, meaning they are able to plan multi-step tasks, hold long-term context and work toward goals instead of just answering prompts.

People are already using them to manage research, organize knowledge bases, monitor news, coordinate code, plan careers and build companies. Relationships may simply be the next thing on the list.

My take

It's worth noting that this Reddit post was from more than 15 days ago — Fable 5 has been disabled since then. Will an AI actually find this guy a wife? Time will tell, especially since the most capable model is now unavailable for this experiment. But since relationships run on chemistry, timing and a thousand unpredictable variables, I'm skeptical that any language model could truly optimize that kind of situation.

But, that's beside the point. The striking part is that someone assumed an AI agent could shoulder a six-month personal mission at all — and didn't find that absurd.

That assumption is the real story. It marks another step in AI's drift from assistant, to collaborator, to something people increasingly trust with the long arc of their lives. Whether that excites you or unsettles you probably depends on how much of your own life you'd hand over to one.

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The most interesting thing happening in AI right now isn't that the models are getting smarter — though they are. It's that they're starting to work together. The question is no longer, should you quit ChatGPT and use Claude or Gemini instead, but rather, stacking models for the ultimate AI productivity package.

Some platforms even use AI to suggest the best AI for the job. But after living inside a workflow that connects Google's NotebookLM with Anthropic's Claude, I've started to think we've been asking the wrong question entirely.

I spent the past few weeks testing a setup that lets Claude reach directly into information stored inside NotebookLM, Google's AI-powered research and note-taking tool. Instead of manually shuttling research between two browser tabs, Claude can reference material I've already collected and use it to answer questions, draft content, and connect ideas across sources.

Here's how it works.

TL:DR

Connecting Claude with NotebookLM is simple. Under the hood, the bridge is something called MCP — the Model Context Protocol, an open standard for letting AI applications talk to outside systems.

While it sounds complex, it's essentially a universal adapter that everyday users can already access through the Claude for Desktop app or simple GitHub community plug-ins.

Why NotebookLM is different

If you haven't used NotebookLM before, the simplest way to describe it is an AI-powered research binder. You upload documents, articles, PDFs, transcripts and notes, and NotebookLM builds a knowledge base around them. It can also pull directly from the internet when prompted.

NotebookLM has caught on with students, academics, journalists and analysts because it easily ties specific materials together, particularly for research, rather than unsourced generalizations a typical chatbot might produce. In other words, NotebookLM is hyper-specific and focused, delivering citations and sources for deep research.

On its own, NotebookLM is already genuinely useful. I lean on it constantly to organize research, compress hundred-page reports into something readable and surface connections that even shift my mindset.

But it has a ceiling. NotebookLM is excellent at retrieval and summary, and noticeably weaker at the things that come after: deep reasoning, structural argument, nuanced drafting, anything that requires holding a problem in mind and turning it over.

But when Claude is activated with NotebookLM, the tool levels up exponentially.

Claude brings the reasoning layer

I was a big fan of Fable Five, even though it was short lived. But since putting NotebookLM with Claude, I feel as if I've gotten some of the power back. Connecting the two tools removes the need to go back and forth between the two for a seamless workflow. Now, rather than feeding Claude the same background over and over, I can point it at the research already organized inside NotebookLM and let it build from there.

Making this happen requires a digital translator called MCP (Model Context Protocol) — an open-source standard designed to help different AI architectures speak the same language. On their own, Claude and NotebookLM operate in total isolation; they have no native way to share data. The workaround relies on a lightweight MCP server acting as a middleman. It intercepts Claude's requests, securely fetches the relevant data from your notebook, and feeds it back to the model, completely automating away the need for the clipboard.

Worth saying plainly: the connectors that make this possible today are community-built and unofficial. Neither Google nor Anthropic has blessed the setup, and most of these bridges work by automating the NotebookLM interface rather than plugging into a sanctioned API. That puts the whole thing in a gray area you should weigh before pointing it at anything sensitive. You just have to understand that although the experience is remarkably smooth; the plumbing is still a hobbyist project, not a finished product.

Caveats acknowledged, the workflow itself feels startlingly natural. Instead of assembling context at the start of every conversation, the context is simply already there.

The ultimate research assistant

Most AI conversations begin from nothing. Every new chat is a blank slate that demands you rebuild the situation — re-upload the documents, re-explain the project, re-establish what you already told it yesterday. Even if the AI has memory of your work, you have to enable it to make that happen, then remember to disable it when you want more privacy.

With NotebookLM acting as the knowledge layer and Claude acting as the reasoning layer, that overhead mostly disappears. Here are a few concrete examples of what this setup has unlocked for me:

I have spent a lot of time researching data centers lately. With these tools, I could ask follow-up questions about a topic such as e-waste, without re-introducing a single source. I could ask Claude to compare the arguments in three different reports sitting in the same notebook and tell me where they actually disagreed — not where they used different words for the same point.

I could say, in effect, "draft a section based on what's in here," and get something grounded in my own material rather than the internet's averaged-out consensus. And because the answers traced back to specific sources, I could check the work instead of taking it on faith.

For the first time, I spent less time loading information into an AI and more time thinking alongside one that already understood it. For me, this subtle shift made a world of difference because I felt like I was really collaborating with AI.

What the setup actually takes

The community setup takes a little patience to install. I'm going to be honest that the setup to get these two tools together might be enough to scare users off. But, it shouldn't.

The issue is, there's no app store button for this yet, so you really do have to wire it together yourself, mostly through the terminal, and if that sentence made you flinch, know that several non-technical people I've compared notes with got it running in about fifteen minutes. Remember, you're copying and pasting commands, not writing code.

The setup is roughly this:

First you need Node.js installed on your machine, since the community connectors are published as small Node packages. Then you add one of those connectors — there are a few floating around GitHub, all doing the same job — by either pasting a single command into your terminal or dropping a few lines into Claude's config file so the app knows the server exists. (If you use Claude Code, you can hand it the connector's GitHub link and let Claude do most of the installation itself, which is a slightly surreal but very effective shortcut. It's how I did it).

The step that trips people up is authentication. The first time you run it, a Chrome window pops open on its own and asks you to log in to the Google account tied to your NotebookLM. The one piece of advice I'll share is: do not close that window!

Know that it appears suddenly and looks like a stray pop-up, but it's the whole handshake — shut it by reflex and you'll get a cryptic "authentication failed" error and have to start over. At the same time, if it closes, no biggie, just start over, but nobody wants that.

Finally, log in, let it finish, and the connection persists from then on. After a restart, Claude shows the NotebookLM connector as active. The way to confirm it's really talking is to just ask by having Claude list your notebooks and the number of sources in each. If it reads back your actual notebook names, you're connected.

A few honest caveats before you dive in

These connectors are unofficial, so they can break when the underlying tools update, and you may occasionally need to re-authenticate when a session goes stale. Most of them work by quietly driving a real browser in the background rather than plugging into a sanctioned API, which is smooth in practice but is exactly why I'd think twice before pointing this at genuinely sensitive material. It's a clever community hack, not a supported product — and it's good to know which one you're relying on.

And the oldest rule still holds: the output is only as good as how you've organized your sources going in. Dump everything into one giant notebook and you get mush. But, if you take the time to keep focused notebooks (one per project, research questions seperated, etc.) you'll notice everything feels sharper. When Claude can reason against a clean, well-scoped body of material, there's a big difference.

Final thoughts

Pair-programming your AI tools is one of my favorite ways to get more out of them. Until recently, we’ve treated these models as standalone islands. But when you wire them together, you get the best of both worlds — they push past their individual ceilings to create something entirely new.

And it's not just me doing this. Increasingly, power users are building interconnected systems. One tool stores and grounds the data, while another reasons over it to produce the actual work. (I often even throw in a third model to validate the output or format it into a presentation deck).

To be clear, this setup isn't going to replace human researchers or analysts anytime soon. The judgment of what to ask, what to keep, and what is flat-out wrong still sits entirely with the person at the keyboard. But changing the workflow from a single chatbot to an ecosystem has completely rewritten how I view the future of productivity.

Give this setup a try and let me know what you think in the comments.

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For months, the dominant AI doom-narrative has focused on job losses. The fear of automation replacing workers, companies restructuring around AI and fears that entire professions could become obsolete have felt very real.

Now, two developments this month complicate that picture, not necessarily by denying the disruption, but by showing what some organizations think comes next.

In June, Anthropic launched a fellowship program that pays participants $85,000 a year to help nonprofits adopt AI tools. Around the same time, new data revealed how aggressively China has been reshaping higher education, cutting thousands of degree programs while expanding courses in artificial intelligence, robotics and semiconductor technology.

Notably, both are explicit responses to the labor-market upheaval. And yet together, they point to the next phase of the AI revolution that suggests a demand for a new category of AI-literate professionals who can help organizations adapt, even as it eliminates other roles.

Anthropic is funding people to learn AI

Anthropic recently unveiled Claude Corps, a year-long fellowship that places early-career workers inside nonprofit organizations to help those groups integrate AI into their operations.

The numbers are substantial. According to Anthropic's announcement, the company has committed an initial $150 million to teach 1,000 fellows how to use Claude and match them with nonprofits across America. The first cohort of 100 fellows starts in October 2026, with applications open until July 17, and at least 400 nonprofits will host fellows over the program's run.

Importantly, Anthropic funds and steers the program but isn't the direct employer. As Tech Times reported, CodePath, a nonprofit that helps first-generation and low-income students enter the tech workforce, acts as the official employer of record, while Social Finance handles measurement and evaluation. Fellows are employed by CodePath at $85,000 per year plus benefits.

The fellowship is focused on practical implementation, meaning no prior prompt engineering or computer science skills are required. According to the site, anyone over 18 with under two years of full-time work experience is welcome to apply, regardless of educational background. Participants help nonprofits identify opportunities for automation and improve workflows using Claude.

What I find interesting, however, is that Anthropic announced Claude Corps the same day CEO Dario Amodei published an essay arguing that AI-driven job displacement may be unavoidable and calling for universal basic income funded by taxes on AI companies. Armodei said, "Mechanisms such as universal basic income could be financed through taxes on relevant companies or raising the capital gains tax."

So while the company frames the fellowship as a way of investing directly in helping workers absorb change, it is not evidence that jobs are safe. In other words, the program exists because of disruption, not in spite of it.

China is making a similar bet — under pressure

Anthropic isn't alone in wagering on AI skills. According to Ministry of Education data reported by VnExpress, Chinese universities revoked or suspended 12,200 undergraduate programs between 2021 and 2025 and introduced 10,200 new ones, adjusting more than 30% of all undergraduate majors.

Many of the discontinued programs were in arts, humanities, foreign languages and management, while new offerings cluster around artificial intelligence, robotics, advanced manufacturing, semiconductors and data science. Nine universities have introduced majors in "embodied intelligence," a field that combines AI with physical systems such as robots.

But this shift comes with a note of caution. As WION noted, it's a response to a jobs emergency as youth unemployment for the 16–24 age group has hovered between 15 and 19%, and more than 12.7 million students were set to graduate in 2026 alone. Beijing is reshaping its talent pipeline because the old one stopped delivering jobs.

Still, the underlying logic mirrors a familiar shift for those who remember navigating internet literacy in the early 2000s. AI fluency may soon become a baseline workplace expectation rather than a specialized advantage.

The takeaway

When people think about AI careers, they often picture machine learning researchers, software engineers or data scientists. Those roles remain important, but they represent only a small fraction of the workforce.

The larger opportunity may be in a different category completely. People who understand both their industry and how to apply AI within it could be in demand. For instance, hospitals need people who understand healthcare and AI, nonprofits need people who understand fundraising and AI and schools need people who understand education and AI. You can see the pattern here as in many cases, domain expertise paired with AI literacy may prove more valuable in any given industry. Claude Corps is built on exactly that premise as it screens for comfort with AI tools and judgment.

If these trends hold, the most valuable could be learning how to evaluate AI outputs, design effective prompts, integrate AI into existing workflows and recognize where automation helps and where human judgment remains essential.

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