The only problem has been you don’t know how to build it because you’re not an engineer?

If yes, today’s issue is just for you!

Hi! Ankur here, and this is Edition #30 of Lazy AI. Today, I talk about how to actually use Claude Code to bring any idea of yours to life. A proper, step-by-step guide with a live example

I’ll talk about how I built my own website with Claude Code. The website is basic (and I’ve intentionally kept it that way), so it tells you, in simple words, everything you need to know about how to use Claude Code to build any app or software — and literally bring your ideas to life!

Disclaimer: This is not another “AI built my site in 5 minutes” post

You’ve seen those LinkedIn posts. One prompt, one perfect website, everyone clapping in the comments. That’s not what happened to me, and it’s not what’s going to happen to you either. That’s not how Claude Code (or Vibe coding) works.

What actually happened with me: a long, (occasionally messy) conversation with Claude Code — where it gave me some output, I got frustrated, it got things wrong, we fixed them together, and I eventually ended up with a website I’m genuinely happy to send people.

I’m not a developer. I don’t know how to code. And I was able to built it and host it online in a matter of hours. So I’m going to walk you through exactly what I did, in order, so that you can follow the same path.

Claude Code is Anthropic’s tool for building real software by just describing what you want, in plain English. No coding required — you talk, it builds.

Step 1: Create your workspace and start with one prompt

First, you need a Claude Pro subscription. Now, I know what you’re thinking: it costs $20 (or ₹1,999) per month. Is it even worth it?

Well, it has a free trial, but I’ll still tell you why paying for it is worth it: Because it closes the gap between “I have an idea” and “I’ve built this app” in a matter of hours.

And it’s not just to build your website or a business idea, but to also make something that’ll help you in your daily life. I know people who’ve built fitness trackers, money management apps and a lot more, just for themselves, with Claude Code.

There’s also a free trial, like I said. You can reply to this email or comment on the post, and I’ll send you the referral link. It gives you one week of free Claude Code usage.

Now let’s begin.

Open Claude Code and create a new folder first — mine was called ankur-jhaveri-website.

Think of the folder as a dedicated drawer for this one project. Everything Claude builds — the text, the design, every file — goes into that drawer, and nowhere else. It keeps this project contained and separate from anything else on your laptop, so you (and Claude) always know exactly where everything lives.

This folder is on your laptop, at your preferred location.

With that set, here’s the exact first message I typed:

“Given everything you know about me, build me a simple personal website. This will be used to showcase my work and my projects to any potential recruiter, potential client, or any of my subscribers/followers who want to know more about who I am and what I do. Keep the design clean, minimalist, and modern.”

That’s it. That’s the whole brief. Notice what I didn’t say — no mention of code, tools, or technical anything. Just what I wanted, and who it was for.

Claude didn’t just start building. It asked me questions first. What layout, which buttons and links I actually needed. Answer honestly and specifically here; the more real detail you give it, the less you’ll be fixing later.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day. You’ll understand AI well enough to be the smartest person in the room at work.

Step 2: Expect it to get something wrong. Just communicate..

Here’s the part every tutorial conveniently skips: it won’t be perfect on the first try. Mine wasn’t.

A few rounds in, unhappy with the design, I pointed Claude to my other website, The Decodr, and said: take inspiration from this. It came back with something that looked suspiciously familiar. Same fonts. Same colours. Same everything.

Claude had copied the design from my other website. Not “inspired by,” literally copied. To its credit, it didn’t argue. It owned the mistake instantly, and rebuilt from scratch — same structural idea, entirely fresh fonts and colours.

Here’s what I want you to take from this, because it’ll happen to you too: “inspired by” and “copy this” sound close in English. They are not close at all. So say exactly what you mean. And when Claude gets something wrong (and it will), just tell it, plainly, the way you’d correct anyone. It won’t get defensive. It’ll just fix it.

Somewhere in the smaller fights that followed — a crooked letter in one font, a logo that refused to resolve — I gave up on precision for one line and said: “just give me the website without the logo, you’re taking a lot of time. put a placeholder in there for now” Done, instantly.

That’s a real skill too — knowing when to stop perfecting and just move.

Step 3: Get it out of your laptop and onto the internet

A finished website sitting on your laptop is a diary that nobody’s reading. Here’s how you actually put it online, and yes, this part is free. You use GitHub.

A GitHub repository is basically a project folder that lives on the internet instead of your laptop — a place to store your files that anyone, or any tool, can reach.

You’ve got two or three genuinely free ways to host a site — GitHub Pages, Netlify, Vercel. I used GitHub Pages, since it ties in cleanly with the repository itself. Whichever you pick, the shape of the process is the same: create an account, turn hosting on, then point your domain at it.

If you’re confused at any point, just type this prompt:

I’m not an engineer. I need you to explain everything in simple language, step by step,

Claude will then tell you what to do, and how.

I already owned my domain, ankurjhaveri.com, and Claude walked me through connecting it with my newly built website — step by step, in plain language. There’s a bit where you configure a few technical settings so your domain knows exactly where your website lives. You don’t need to understand the mechanics behind it. Just tell Claude what you’re trying to do and let it tell you precisely what to click and where.

Step 4: Stop uploading it yourself

Every single change I made meant manually re-uploading files to GitHub. Tedious, and after the tenth time, genuinely annoying. So I asked — can you just do this yourself?

It could. I gave it access to my repository, and from that point on, it pushed every update directly — no dragging, no dropping, no me involved beyond typing what I wanted changed. Every push still shows up in GitHub’s history under my name, tagged with Claude as a co-author. Nothing hidden, nothing happening behind my back.

That’s the moment this stopped feeling like “using a tool” and started feeling like having help.

So what does this mean for you?

Claude Code isn’t a vending machine — put in a prompt, get out a perfect website. It’s closer to working with a fast, capable collaborator: it needs a clear brief, it’ll occasionally misread you, and once you let it, it’ll handle the boring infrastructure work without complaint.

The whole thing — folder to first draft, the Decodr saga, connecting the domain, automating the uploads — happened over one late night (and a can of Red Bull). Not five minutes. Not a magic prompt. Just a conversation, the same one I’ve walked you through here, that ended in a website I actually own: ankurjhaveri.com. Yours is a folder and a few hours away.

Just FYI: If you don’t have a domain, that’s okay too. You can create the website and upload it for free on Vercel, Netlify or Github pages. Just try it out :)

What else can you do with Claude Code?

Like I said, this website is pretty basic. But that was intentional: I didn’t want to complicate it for readers. But I’ve made a bunch of complex projects with Claude Code too.

Since I’m a marketer, I constantly need market insights, for which I’ve built projetcs like:

1. A brand audit tool that captures a company’s social media content, founder LinkedIn content, performance marketing ads, website content, and other surfaces to do an end-to-end audit of the company’s marketing strategy. Use it to pull your competitors’ data and see how they’re doing

2. A market insights tool that pulls Reddit conversations and YouTube comments, runs intelligence on them and surfaces user sentiments about a particular brand. This is useful to understand what your users actually think about you (if you want to do this too, just share this newsletter with 3 people, and I’ll share the details with you)

I cannot stress this enough — please try out Claude Code. It can be the difference between having an idea and building an app. It’s empowering, and just takes a coupe of hours (and a Red Bull, if you’re like me)

I’m an email away if you need any help with building.

That’s all for today folks. If this post was helpful and you’d want me to do more of them, please like the post; I’ll get the hint. The like button is below.

I’ll see you next time..

Cheers,

Ankur

I’m sorry, been away for a week. I have been caught up with some stuff on both the personal and professional front.

But I’m back, and so is your daily AI roundup. I’ve automated this now (of course with AI), so I should be more consistent :)

Let’s begin!

AI cracked a code in 60 hours that humans failed to break for two years!

Anthropic just published something that should make every cybersecurity team sit up. Their unreleased “Claude Mythos” model was pointed at HAWK — a post-quantum encryption scheme that had survived two full years of expert human cryptographers trying to break it.

Mythos halved its key strength in 60 hours.

Then it went after AES, the encryption behind almost every “secure” thing you touch — your banking app, WhatsApp messages, the padlock in your browser. It found an attack 200 to 800x faster than the best method humans had.

(Take a deep breath. Your bank is fine. For now.)

Anthropic says these are research advances that don’t hit production systems yet. But the direction of travel matters. Cryptography is the invisible glue holding the internet together, and it’s traditionally been humans-only turf because you need years of intuition. Turns out, maybe you don’t.

Every “unbreakable” system on the internet was designed assuming attackers are humans. What happens when they aren’t?

🔗 Anthropic

Amazon India’s freedom sale now lets you search better

The Great Freedom Sale drops August 7, and Amazon is using it to push Rufus — its AI shopping assistant — hard into Indian households. The pitch is dead simple. Type “budget wireless earbuds under Rs 2,000” instead of scrolling through 40 filters. Snap a photo of anything and Lens AI finds it for you. Get an alert the moment a product hits your target price.

There’s also an AI Review Highlights feature that reads through hundreds of reviews and gives you a two-line summary — which is genuinely useful if you’ve ever spiralled through 3-star reviews at 1am trying to figure out if the mixer will actually work.

Here’s the interesting bit. Amazon is not pitching this as “AI”. They’re pitching it as “shopping made easier”. That’s a real shift — most Indian consumers still don’t call ChatGPT “AI”, they just call it “the app”. When the tech disappears into the workflow, adoption stops being a decision.

If your product isn’t invisible yet, it’s still too early to be selling AI.

🔗 BusinessToday

Microsoft’s CEO just said something nobody in tech wants to hear

Satya Nadella — the guy running the second-most-valuable company on earth, mostly on the back of AI — went on record yesterday saying public trust in AI has “collapsed” (his word, not mine). And he blamed his own industry.

His pitch to every CEO listening: stop framing AI as a way to shrink headcount. Start using it to create jobs and set fair wages. He wants “community-first” deployment.

Here’s why this is a big deal. Roughly 117,000 tech jobs have already been cut in 2026 — almost matching all of 2025 — with Meta, Snap and Block openly citing AI as the reason. When the CEO of the company that sold everyone on Copilot is now walking back the “efficiency” narrative, something has shifted. Either the backlash got loud enough to spook them, or the numbers are worse than the press releases suggest. Probably both.

Would you trust an AI rollout at your workplace more if the pitch was “this hires people” instead of “this replaces them”?

🔗 BusinessToday

59% of Indian factories now bet their next 5 years on AI

A fresh PwC India survey (out today) says 59% of Indian manufacturers expect AI to drive their strategic growth over the next five years. And here’s the twist — Indian manufacturers are more ambitious about AI adoption than their global peers.

Read that again. The country that everyone keeps calling “behind” on AI is actually ahead when you ask the people who make the stuff we buy. Factories from Coimbatore to Pune are wiring AI into everything from shop-floor quality checks to customer service.

Why does this matter to you, sitting at your desk? Because “AI is the future” has been a slide in every founder’s deck for three years. But when 59% of a survey of manufacturers with revenues from $50M to $25B say it’s actually driving their P&L strategy — that’s not slideware anymore. That’s capex.

Your competitors aren’t waiting for the perfect use case. They’re already deploying imperfect ones.

🔗 BusinessToday

TCS and Infosys are becoming the thing they used to serve

For 30 years, Indian IT giants sold one product: people. Cheap, English-speaking, deployed by the thousands. That model is cracking. So TCS, Infosys, Wipro and Tech Mahindra are pivoting to become…data centre operators.

They’re building physical AI infrastructure. Selling “AI GCC-as-a-service”. Upskilling nearly 500,000 developers across the four firms to build AI agents instead of writing Java. The asset-light, people-heavy model that made them $200B+ companies is being replaced by servers, chips, and consulting.

But here’s the big concern: If your future is data centres and AI infrastructure, you don’t need 600,000 employees. TCS is already cutting 12,000 roles. This isn’t a rounding error — it’s the beginning of a decade-long unwinding of the outsourcing model that built middle-class India.

If you’re a corporate slave employee at an IT services firm, what does your five-year plan look like now?

🔗 BusinessToday

That’s all for today! I’ll see you at the next roundup. Share this with someone who should stay updated with AI!

Cheers,

Ankur

Yes — Meta rolled back their Muse Image feature — which received a lot of backlash because it allowed photos of public accounts to be used for AI-based image creation. It was automatically applied to all public profiles, with the opt-out burden on the user.

And that story is part of today’s daily dose of AI.

Let’s begin..

TCS is hiring 8,900 people just to babysit AI

Wait, what? India’s biggest IT services firm just told Reuters it’s building an army of “forward-deployed engineers” — up to 8,900 people, or 1–1.5% of TCS’s entire headcount — whose only job is to sit inside client offices and make sure the client’s AI actually works.

The bigger news: CEO K Krithivasan also said TCS is finally hunting for AI, data-security and cybersecurity acquisitions. This is a company that famously avoided M&A for two decades. The pivot is telling.

Here’s the read: AI models are becoming a commodity. The differentiator isn’t which model you use — it’s the sweaty, unglamorous work of plugging it into a bank’s messy legacy systems and making it not break. TCS is betting that “the last mile of AI” is where the money is.

If you work in a services role — consulting, ops, project management — pay attention. “Forward-deployed” is the new hot title. It’s the one AI can’t easily replace, because it’s mostly about the human sitting in the room understanding a client’s actual problem.

🔗 Business Standard

India’s going to pour more GPUs into its AI mission — and try a “data trust” experiment

On Friday, IT Minister Ashwini Vaishnaw told industry leaders that India will augment national AI compute capacity (read: more GPUs, more data centres) to keep up with demand, while urging the industry to build with Indian universities.

The interesting bit: the government is considering a pilot project at IIT Hyderabad to build sector-specific data trusts — legally-managed pools of high-quality Indian datasets that AI companies can access under controlled conditions. Think of it as public data lending libraries for AI, with guardrails.

If you’re building a startup or product in India — health tech, agri tech, fintech — the data you can’t buy or scrape is often what’s stopping you. A working data trust could change that.

The question everyone’s dancing around: whose data ends up in there, and who gets to say yes? That’s the interesting fight — not the GPU count.

🔗 ANI

Apple sued OpenAI for poaching 400+ employees. Musk called him “Scam Altman.”

Apple filed a lawsuit against OpenAI last Friday — and the 41-page complaint is brutal. It accuses OpenAI’s Chief Hardware Officer (a former Apple VP of iPhone design) of orchestrating a systematic poaching campaign, telling incoming hires to bring actual Apple hardware samples for “show and tell” sessions.

Over 400 former Apple employees now work at OpenAI. One is alleged to have exploited a network flaw to download 1,000+ pages of engineering docs on his way out.

Within hours of the filing, Elon Musk was on X calling Sam Altman “Scam Altman.” Altman fired back that Musk was jealous of a new OpenAI model. Two of the most powerful men in tech, sniping like reply guys.

Here’s why it matters beyond the popcorn: OpenAI is very clearly gunning for its own AI-first hardware. When you buy your next “AI phone” or wearable in 2027, it may not come from Cupertino. And the winner of that race is the winner of whose AI stack your daily life runs on.

🔗 Business Today

Meta pulled its Instagram AI photo grab after backlash. Public pushback works.

And this is the story of the day!

Remember Muse Image — Meta’s feature that let anyone @-mention a public Instagram account inside an AI prompt to generate images using that person’s face?

Last week, it launched. This week, Meta yanked it.

Complaints came fast. Privacy advocates. Users. Journalists. Even Meta admitted the feature “missed the mark,” despite technically offering an opt-out.

Two lessons hidden in this. First: an “opt-out” is not the same as consent — and people are getting good at spotting the difference. Second, and more interesting: when enough users complained, a $1.5 trillion company backed down within days.

Muse Image was never coming to India first (new AI rules and all). But this is the second time in two years that mass complaint has forced Meta into an AI retreat. Save the receipts. That leverage is real.

🔗 AP News

Alright, that’s all for today! I’ll see you tomorrow!

Cheers,

Ankur

Today’s piece is less “how AI works” and more “how to stop AI from working against your schedule.” Small trick (and slightly ridiculous). But it works.

Let’s begin.

Claude users’ biggest token problem: Session limits

Imagine your day. You open Claude at 11 AM, ready to get into deep work. Two hours in, right when you’re mid-flow, you get a message: “You have crossed 90% of your session limit.”

So now you’re locked out till 4 PM.

Now, here’s what most people miss about Claude’s tokan usage: the lockout is not about how much you used. It’s about when you started.

Rolling usage window: Claude doesn’t give you a fixed daily quota that resets at midnight. Instead, the moment you send your first message, a 5-hour clock starts. Whatever you use inside that window counts towards token usage. Once the window closes, it resets. But the next window only starts when you message Claude again.

So the constraint is not “how much AI can I use today.” It’s “when did my clock start ticking”

Why this matters more than it sounds like it should

Most of us don’t use Claude evenly through the day. You’re probably like me: Quiet mornings, then a 2-3 hour Claude sprint somewhere in the afternoon or evening where you actually need the tool hard. The problem: if your first message of the day happens to land right before your Claude work sprint, your 5-hour window burns out during the crunch. Mid way. It’s just bad timing, not bad usage.

The fix is simple: Start the clock before you need it, not when you need it.

So if your real work with Claude begins at 11 AM, send Claude a simple “hey” at 8 or 9 AM. The 5-hour window starts then — but you’re not actually using tokens yet. By the time you sit down at 11, you’ve already burned 2-3 hours of the window doing nothing, and you get a fresh, almost full window layered on top of your actual working hours. You’ve effectively pushed your capacity to land exactly when you need it, instead of wherever it randomly started.

The lazier (and more reliable) way to do this is to automate the “hey.” I run a scheduled task every day at 2 PM — before my research and writing block starts around 5 PM. The task gives me the day’s update about my industry (fintech). So it’s not even wasted; I get a small daily brief about my industry, and my window opens 3 hours ahead of when I need it. Set it once, forget it, and my usage clock is always warmed up before my actual sprint.

How to setup the scheduled task

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Subscribe now

First things first: You need a Claude Pro plan for this. If you have one, here’s how to set it up in under 2 minutes:

1. In the left sidebar, click on “Scheduled”

2. Once this opens, click on “New task” on the top right → Click on Setup manually

3. You’ll see the below screen:

   

   Add a name, a one-liner description and a one-line prompt like “Fetch the news and give me updates about <my industry>

4. Important point here: Where you see Default model, choose Haiku 4.5 or Sonnet 4.6. Don’t choose an expensive model, else you’re burning more tokens (unless this is something that you need accuracy and better results for; in that case, choose a higher model)

5. Under Frequency, choose Daily and pick a time slot based on your working hours (2-3 hours prior to your sprint window)

That’s it. Click on Save and your task will run every day at the scheduled time, opening up your session window earlier.

If you have more working blocks on Claude throughout the day (for eg: if you work 2-3 hours in the morning and 2-3 hours in the evening), you can schedule more tasks in the same way.

And if your work hours are not fixed, then you can just start the session manually with a “hey” 2-3 hours before starting work. Again, crude fix, but it works 🤷🏻

So what does this mean for you?

Figure out when your real Claude crunch happens. Most people have one. Then start your session 2-3 hours before that (either manually, or via a scheduled task), so your window is already warmed up and ready to absorb your heaviest usage.

But here’s a caveat: It’s not going to save you if you’re a genuinely heavy daily user. There’s a separate weekly cap sitting above the 5-hour window, and this trick doesn’t touch that. For those hacks, you can read the details here. But for the “I got locked out right when it mattered” problem, this fix works.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Subscribe now

Well folks, that’s it for today. If you liked this piece, please share it with your network.

I’ll see you next time..

Cheers,

Ankur

1. ChatGPT can now interrupt you mid-sentence

OpenAI just made talking to ChatGPT feel less like walkie-talkie ping-pong. Yesterday it launched GPT-Live — a voice model that listens and speaks at the same time. It goes “mhmm” while you’re thinking. It shuts up when you need a beat. It jumps in when you trail off. Basically, it stopped acting like a customer service bot.

Here’s the sneaky bit. If you ask something hard, GPT-Live silently kicks the question to a bigger model (GPT-5.5) in the background, then comes back with the answer mid-conversation. You never see the seam.

Why does this matter to you? Voice AI was always the idea everyone loved and the experience everyone closed. If you’re a founder doing customer calls, a manager rehearsing a hard conversation, or just someone who thinks better talking than typing — this is the version worth trying. Free users get GPT-Live-1 mini. Plus/Pro get the full model. Rolling out across iOS, Android, web.

The awkward pause era is going.

🔗 Introducing GPT-Live — OpenAI

2. India’s top 5 IT giants just lost ₹15 lakh crore in market value

TCS, Infosys, Wipro, HCL Tech and Tech Mahindra — the five companies that basically built modern corporate India — have watched their combined market cap crash from ₹33.71 lakh crore in August 2024 to ₹18.15 lakh crore by July 2026. That’s a 46% wipeout in under two years.

Let that sink in.

The reason isn’t a scandal. It isn’t a scam. It’s just AI eating their business model faster than they can retrain their people. These companies made their money selling people-hours — armies of engineers doing repeatable code work.

AI does that work now, in seconds, for pennies.

TCS shed ~13,000 employees last fiscal. Infosys, ~15,000. Fresher hiring is down from 1,20,000 in 2025 to a projected 75,000–85,000 this year. Entry-level roles have shrunk 20–25%.

If your career plan was “get an IT job, ride the wave” — that wave broke. If you’re already in one of these companies, the question isn’t if AI touches your role, it’s when. Make sure you’re preparing for the next skill.

🔗 Top 5 IT stocks’ m-cap slips — Business Today

To get updated on all the latest happenings in AI, subscribe to this daily newsletter. 5 minutes of reading to stay updated with everything AI in India.

Subscribe now

3. AI hiring in India is up 16%. Entry-level jobs are down 25%.

Read that sentence twice. It’s the whole story of Indian tech in 2026.

AI-specific roles — MLOps engineers, prompt architects, AI product managers — grew 16% year-on-year in June, even as overall IT hiring fell 3%. Meanwhile, junior developer and support roles got hollowed out. NASSCOM says India already has 4.2 lakh AI professionals against a demand for over 6 lakh. By the end of this year, the shortfall could hit 14 lakh people.

Translation: If you’re a mid-career professional who learns AI, you’re suddenly the most expensive person in the room. If you’re a fresher waiting for the traditional IT ladder to reappear, it isn’t coming back.

The ladder isn’t broken. The bottom rungs are gone. The remaining rungs pay more than they ever did — but you have to jump onto them.

The good news? You don’t need a CS degree to make the jump. You need six months of serious effort and a portfolio. What are you actually building?

🔗 AI hiring outpaces IT recruitment — TechChannel

4. India might soon get a legal “off switch” for dangerous AI

The Indian government is drafting an AI law that would classify every AI system by risk level. Chatbots, productivity tools, recommendation engines — low-risk band, barely any rules. AI used in banking, healthcare, critical infrastructure — high-risk band, tight leash. And — here’s the wild part — the government wants the power to shut down dangerous AI systems in an emergency.

This is a genuine shift. Just eight months ago, in November 2025, MeitY was saying India didn’t need a separate AI law. Now officials are telling ET the time to act is “getting closer.”

Why should you care? Because “graded, risk-based” is actually the sensible approach. It doesn’t strangle the chatbot your company built last week. But it does mean if you’re deploying AI in fintech, insurance, health apps, or anything customer-facing in a regulated sector — get your compliance house in order now, not when the rules land.

The EU took five years to argue over its AI Act. India might get there in eighteen months. Is that scary or smart?

🔗 India considers separate AI framework — CySecurity

5. Google picked 20 Indian AI startups from 2,500 applicants

The 2026 Google for Startups Accelerator just announced its India cohort. 20 startups — healthcare, agri, climate, finance, cybersecurity — filtered from ~2,500 applications. That’s a 0.8% acceptance rate, tighter than most Ivy League MBAs.

Why does this matter for a non-tech reader? Because it’s a signal about where the next wave of jobs is being built. These aren’t Bengaluru-only companies chasing SaaS clones anymore — they’re solving problems in Indian farms, Indian hospitals, Indian small businesses. The winners get three months of access to Google’s AI stack, mentors, and go-to-market help.

If you’re a professional wondering “which Indian companies should I be watching to work at, invest in, or sell to?” — this list is a shortcut. Google has already done the vetting.

And if you’re a founder still sitting on an idea? The bar just went up. Building an AI startup in India in 2026 means competing with 2,499 other founders for the same slot. The good news: the ceiling went up too.

🔗 Google picks 20 Indian AI startups — Business Standard

To get updated on all the latest happenings in AI, subscribe to this daily newsletter. 5 minutes of reading to stay updated with everything AI in India.

Subscribe now

Alright, that’s all for today! I’ll see you tomorrow with the latest goss in the AI world :D

Cheers,

Ankur

Hey! Ankur here. Hope you’re having a fun week. Wednesdays are the best days if you ask me. They just tell you that you’re through the half-way point! :D

Anyway, here’s your daily dose of AI news.

1. Your Naukri resume will be screened by AI

If you applied to a job on Naukri anytime in the last few months, a human recruiter probably didn’t look at your resume first. An AI did.

Naukri just rolled out AI-Rex — an agentic system that reads job descriptions, sources candidates, personalises outreach, and screens applicants. Almost the whole recruiter’s job, on autopilot. It’s live across 4,000 enterprise customers, and hiring mandates on the platform grew 3.7x between February and June 2026 (that’s not adoption — that’s a stampede).

Which means the resume game just changed.

Because the keyword-stuffed CV that worked for the last 15 years now has to please a machine that actually reads context, not just matches strings.

So think — If you had to rewrite your resume tomorrow for an AI reader instead of a human one — what would you actually change?

🔗 Business Standard

2. India is now #2 in the world for AI-driven layoffs

Here’s a stat nobody wants on a slide. In the first half of 2026, India accounted for 7.16% of all global AI-driven tech layoffs — second only to the US at 71.33%.

The sectors bleeding the most? EdTech (21.67%) and FinTech (14.73%). Both were the “AI-first” darlings a year ago. Turns out being AI-first also means being AI-vulnerable-first.

But — and this is the twist — AI hiring in India jumped 25% year-on-year in June. Naukri says AI-specific roles are now 16% of all vacancies, up from 2.9% in early 2023. India is losing jobs and adding jobs. They’re just not the same jobs. And they’re not going to the same people.

The takeaway isn’t “AI is killing jobs” or “AI is creating jobs.” Both are true. The real story is that “AI role” vs “non-AI role” is now the biggest divide in Indian white-collar work. And it’s widening every quarter.

🔗 ETV Bharat

3. India is Anthropic’s second largest market

The company that makes Claude officially opened its Bengaluru office in India a few months back — the second Anthropic office in Asia after Tokyo. Led by ex-Microsoft India leader Irina Ghose.

The line buried in the press release is the interesting one: India is now Anthropic’s second-largest market globally, behind only the US. Their India revenue has doubled since October 2025. That’s not a “we care about emerging markets” cameo. It’s a business decision.

But the part that actually changes something for you: Claude is rolling out native support for Hindi, Bengali, Marathi, Telugu, Tamil, Punjabi, Gujarati, Kannada, Malayalam, and Urdu.

Which quietly kills one of the biggest excuses Indian professionals have used to avoid AI: “But it doesn’t really get how I write.” Well, now it does. In your mother tongue. Meanwhile OpenAI still doesn’t have an India office (the queue at Anthropic is going to be interesting).

Question worth sitting with: what’s the first task you’d trust an AI with in your mother tongue, that you didn’t trust it with in English?

🔗 Anthropic

4. WhatsApp can now Photoshop your neighbour's Instagram photo

Meta just launched Muse Image inside Meta AI, Instagram Stories, and WhatsApp. Free. Built by their new Superintelligence Labs. Announced July 7.

The features are legitimately impressive — it edits photos iteratively (fix one thing at a time, not regenerate the whole image), blends multiple photos, handles readable text inside images, and even generates working QR codes. The stuff that has embarrassed every image AI for years.

But here’s the part that’s going to end up in court somewhere. Muse lets you take any public Instagram user’s photo and manipulate it with AI. Meta’s own promo showed a person teleporting a secondhand Facebook Marketplace couch into their garage. Cute example. Less cute: doing the same thing with a stranger’s face.

For an Indian small business owner though? This is free product photography, banner design, and ad creative — all inside the apps you already use every day. No Canva subscription. No designer. Just type.

The tool is genuinely useful and genuinely uncomfortable. Both things at once.

What’s the first thing you’d ask WhatsApp AI to make for your business — knowing your customers can do the same thing to your photos?

🔗 TechCrunch

5. Indian AI startups raised $676M. OpenAI raised 165x that in one round.

Proportion problem of the year. In H1 2026, Indian AI startups raised $676 million across 57 deals — a beefy 4x jump from H1 2025.

Feels like a lot? OpenAI raised $112 billion in a single round. That’s more capital in one deal than every Indian AI startup has raised, combined, in history (let that sit for a second).

But this doesn’t have to be depressing. Indian AI money is quietly going to the right places — sovereign compute, Indian-language foundation models, and vertical AI in finance, healthcare, defence, and agriculture. Not another me-too chatbot with an Indian flag on it.

The bet is simple: India can’t win on scale, but it can win on specificity. GPT-5.6 won’t understand a Marathi loan application or a Tamil farmer’s WhatsApp voice note the way a purpose-built Indian model can. That has to be built here — nobody in San Francisco is doing it.

Small cheques. Big niches. That’s the actual play. And it’s arguably the smarter one.

🔗 Inc42

That’s it for today!

Please tell me if you’re liking the daily news bytes, so that I know whether I should continue or not :)

See you tomorrow!

Cheers,

Ankur

As promised yesterday, here’s what’s happening in the world of AI today!

Tell me if you’re liking this daily roundup. If not, you can unsubscribe by clicking on the button below, and toggling off “AI Pulse”.

Unsubscribe here

1. India has the world’s best AI talent — and the worst brain drain

India produces 16% of the world’s AI talent. And then promptly exports most of it.

A new Stanford report reveals India had the biggest net outflow of AI research talent globally in 2025 — at -16.9. More AI professionals left India than returned. Meanwhile, 39% of Indian companies say they’re confident in finding skilled AI talent locally, far above the global average of 24%.

So which is it?

Actually, both.

India has the raw supply. What it doesn’t have is the capital, infrastructure, and exit opportunities to keep the best people here. Top AI researchers and startup founders leave for the US because that’s where the serious money and serious problems are.

India is a talent factory. The question is whether we’re building products or just training exports.

🔗 BusinessToday

2. India’s AI honeymoon is officially over

Indian CEOs have a new favourite phrase: “Show me the P&L impact.”

After two-plus years of pilots, demos, and proof-of-concept projects, India Inc is done being impressed. Boardrooms are shifting from “which AI to try” to “what did AI actually deliver.” Proof-of-concept fatigue is real — and it’s hitting hard.

Here’s the number that stings: 47% of Indian enterprises now have multiple AI use cases live in production. But most can’t clearly link those deployments to revenue or cost savings.

The honeymoon phase was inevitable. So is this reckoning. (Companies bought shiny toys before asking what they were actually for.) The good news? The ones who survive this phase — who actually figure out where AI moves their numbers — will have a real competitive edge.

The rest will still be running pilots in 2027.

🔗 BusinessToday

3. AWS will send AI engineers to live at your company for 45 days

AWS just dropped $1 billion on a new team of “Forward Deployed Engineers” — experts who will show up at your company for 45-day sprints and build AI systems with you, not for you.

The idea is borrowed from Palantir, which basically invented this model a decade ago. OpenAI and Anthropic have already launched similar units. Now Amazon wants a piece of it. The pitch: instead of waiting 18 months for a consultant to hand you a PDF, AWS engineers sit inside your business and get AI into production in days.

(This is what “implementation support” looks like when you charge billions for compute.)

The detail that stood out: clients leave with the actual capability — the workflows, the skills, the architecture — not just the finished solution. No dependency, just enablement. That’s a genuinely different promise from how consulting usually works.

If you’re an Indian company wrestling with how to actually use AI beyond the pilot stage, this model is worth watching.

🔗 TechCrunch

4. The US banned the world’s most powerful AI models — then reversed it

Last month, the US government ordered Anthropic to cut off global access to Claude Fable 5 and Mythos 5 — its two most powerful AI models. No explanation. Just “national security concerns.” Employees outside the US lost access. So did millions of users worldwide.

This week, the ban was lifted. All export controls on both models are gone. (You’re welcome, rest of world.)

The reason it matters: this happened with almost no public debate. A government order took world-class AI tools away from users globally overnight — no notice, no appeal, no transparency. Anthropic was as blindsided as its users.

This is the new reality of AI as infrastructure. The most useful tools can disappear (or be restricted to specific countries), based on decisions made by Trump, not product roadmaps. For anyone building workflows that depend on a specific AI model, that’s a supply-chain risk most people haven’t thought about.

How resilient is your AI stack if the model you rely on gets switched off tomorrow?

🔗 CNBC

Alright, that’s it for today! See you in the roundup tomorrow!

(And yes, I’ll be back with an interesting AI use case on Saturday!)

Cheers,

Ankur

From today, I’ll be starting a new version of LazyAI called “AI Pulse”.

While I send out one article on how to best use AI every Saturday (and that will continue), some readers reached out saying they want to know what is happening in the AI world.

So here’s what I’m doing: Every evening, I’ll be sending out the latest developments that have happened in the recent days in the world of AI. Just some interesting pieces that will help you get more ingrained in the world of AI.

This is an experiment I’m running, so let me know if you’d like this or not. If not too many people need it, I’ll stop and just continue with the weekly newsletter :)

If you’re already sure you don’t want a daily email and just want to stick to a weekly newsletter, you can unsubscribe to AI Pulse too, don’t worry :)

Just toggle the “AI Pulse” switch to off, as shown below. You’ll then only get the Saturday emails.

Unsubscribe here

Let’s start today’s stories!

1. You typed one sentence and got a full presentation back

Google just changed how presentations get made. Gemini inside Google Slides can now build a full, multi-slide, fully editable deck from a single prompt — grounded in files you already have in Drive, styled to match a deck you already like.

Describe the deck, point it at your Drive, pick a style reference, and it builds. No blank slide. No hunting for the brand template your colleague saved in some forgotten folder (we’ve all been there).

For anyone who makes decks regularly — founders pitching investors, marketers presenting campaign results, managers running quarterly reviews — this is a genuine “I just saved two hours” moment. The feature is live in Google Workspace plans through at least August 2026. Open Slides, look for the Gemini side panel, start there.

🔗 Google Workspace Blog

2. Claude Fable 5 is back, but…

Anthropic’s Fable 5 — the best available AI for research, writing, and complex analysis — was pulled from global access on June 12. One jailbreak, found by Amazon researchers, was enough for the US government to invoke national security authorities and cut off access for everyone outside the US for 18 days.

It’s back as of today. Anthropic trained a new safety classifier, agreed to proactively flag risks to the government, and committed to reporting malicious activity. If you use Claude for your work — strategy, writing, research, analysis — you now have the best version back.

But here’s what should actually stick with you: a government can now cut global access to an AI tool overnight. If your AI workflows run on one AI provider with no fallback, that’s a fragile setup. It’s worth knowing what you’d switch to and how fast.

🔗 CNBC

3. Meta’s AI moderates India..but can’t read Hinglish

Meta has replaced most of its human content moderators with AI. 50% of review requests already go to LLMs, and that number could hit 90% for some content types by end of 2026. For Indian social media managers, brand community admins, and marketers running India campaigns: your moderation reality just changed.

The problem is Hinglish. India doesn’t post in clean Hindi or English — it posts in code-mixed Roman-script combinations that no tidy multilingual training dataset was built for. Researchers confirm AI models perform significantly worse in low-resource, high-code-mix contexts like “kya bakwaas hai yaar” typed in Roman script.

What this means practically: your harmless Hinglish campaign copy might get flagged. Actual abusive Hinglish comments might slip through. If you’re seeing unusual moderation behaviour on Indian pages you manage, this is a likely reason — and it’s not a setting you can change. The problem is baked into the model’s training data.

🔗 MediaNama

4. RBI says every bank AI needs a kill switch

The RBI released a draft last week requiring every regulated entity — all commercial banks, NBFCs, payment banks, co-ops — to build an instant kill switch into every AI model they deploy. If the AI misbehaves, someone must be able to shut it down immediately. And the board has to own the governance framework, not just the tech team.

If you’re in fintech, lending tech, banking product, or building anything that plugs into banking APIs: this draft changes your compliance roadmap. Third-party AI models you integrate are also in scope, not just what you build in-house.

Comments are open until July 24. If you work in this space, this is one of the few windows where you can actually shape a regulation before it becomes law.

🔗 MediaNama

5. OpenAI’s next model just started rolling out

On June 26, OpenAI began a limited preview of GPT-5.6 — three models called Sol, Terra, and Luna. Right now it’s only available to ~20 government-vetted partner organizations, not in ChatGPT, not publicly accessible. But broader rollout is weeks away.

Early reports say the jump from GPT-5.5 to Sol is meaningful — particularly for complex reasoning: financial analysis, research synthesis, multi-step problem solving, detailed content strategy. The kind of tasks where you’ve learned to break things into smaller prompts because the current model loses the thread.

If you use ChatGPT Plus or the OpenAI API in your work, what you’re running today will feel noticeably slower once Sol hits general availability. Worth identifying now which of your workflows would benefit most — so you’re not scrambling to re-prompt everything when it drops.

🔗 OpenAI | Thurrott

That’s all for today! Let me know if you found this useful by answering the poll below.

I’ll see you tomorrow!

Cheers,

Ankur

Digest compiled by AI Pulse | July 1, 2026

Today, I’m going to be talking about one AI tool I love the most — Claude Projects.

Trust me, it’s a game-changer for any work that you do. The next 3 minutes will tell you why.

First, what is “Projects” in Claude?

It’s basically a dedicated workspace inside Claude that lets you work on a particular task, over a long time. This workspace has all the relevant files and the context needed for Claude to understand and help you do the task.

Think of it this way: Claude is a school. Claude Projects is a laboratory inside that school (let’s assume it’s the chemistry lab). There, students will study and create things related to chemistry. It has all the equipment needed (burners, chemicals, flasks etc) for the students to perform various tasks.

You can find it in the left sidebar in Claude.

What kind of work should you do in Claude Projects?

Any task that is repetitive or runs for a long time, or needs prior knowledge and context to be built upon, should be a Claude project. In the project, you gotta add all the relevant files needed to perform that task. There’s a “files” section in each project where you can add the relevant files/documents.

If you like what you’re reading, let’s stay connected! You don’t need to know tech to learn AI, subscribe to this newsletter — every week, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes (less than the time you spend doomscrolling before bed). Except, this actually moves your career forward.

Subscribe now

Below, I’ve given a list of tasks/activities that should actually be Claude Projects:

The main reason to create a Claude Project

This is the most important part: The major reason to create a Claude Project is Context. So that whenever you work on your website, your LinkedIn post, your analysis (or anything else), you don’t need to start your prompt with “Here’s the context..”

No. With Claude Projects, you just need give it context once — when you create the project. In a section called as “Instructions” in Claude. Here, you be as detailed as possible.

It’s a one-time activity, so don’t be lazy? 🤷🏻

See this is what I’ve done for my website “The Decodr” in Claude Projects:

If you want to know the detailed instruction set, just comment on this post or reply to this email, and I’ll send it to you.

The last (and magical) part

On a daily basis, based on your interaction with Claude for your project, it automatically keeps updating its “Project Memory” on its own. This means, it gives itself more context to perform the tasks you need it to perform.

This is what it made for my project (without me telling it to do it):

If you like what you’re reading, let’s stay connected! You don’t need to know tech to learn AI, subscribe to this newsletter — every week, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes (less than the time you spend doomscrolling before bed). Except, this actually moves your career forward.

Subscribe now

In a nutshell..

Claude projects is like onboardng a new employee in your team. You sit with it for an hour, give it all the context it needs, share templates, and he/she just gets going!

As simple as that.

Here’s how my project (The Decodr) looks in Claude projects:

(For context, The Decodr is the platform I’m building for all non-tech professionals to come and understand + use AI. Newsletters, how-to guides, readymade Claude skills and more. Check it out here)

So what does this mean for you?

Well, you can now start using Claude Projects to do your best work. Since you don’t need to give it context everytime, you can spend lesser time talking and more time reviewing — which is exactly how we’re supposed to use AI :)

Well folks, that’s it for today! If you liked it, please share it with others. And if you want to talk about this or anything else, please reply to this email or drop it in the comments. I would love to read what you guys have to say!

See you next Saturday!

Cheers,

Ankur

Okay, I don’t know about you, but I get confused AF about which Claude model to use when:

• How are all models different

• Which mode to use within each model

• When to use extended thinking/deep reasoning, and

• How to optimise tokens, because Claude is a bi**h when it comes to token consumption

If you’re in the same boat as me, today’s issue is for you.

I spent the last 2 days prompting each model with the exact same prompt (a simple writing task) to test how tokens are consumed. I tested the token consumption for each model with and without the extended thinking mode on, and the results were very interesting.

We’ll talk about that today.

If you like what you’re reading, subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

Subscribe now

First, how does Claude’s usage work?

(If you know about tokens, skip this section)

For example

• The word “chat” is one token

• “Chatting” would break into two tokens: “chat” + “ting”

• A punctuation mark like a full stop (.) is also a token

Every interaction with the AI consumes tokens. And you get a finite number of tokens to use on your plan (free/pro/max or whatever new plan they have)

Now, Claude doesn’t tell you how many tokens you get to use as an absolute number. It just gives you a percentage of the tokens you’ve consumed. And that too is in two parts:

1. Session tokens: The minute you send the first chat to Claude, a 5-hour session begins. You have a specific number of tokens that you can use in the next 5 hours only. Once that limit is hit, your usage stops, and then you can only resume after the 5-hour window is completed. And no, Claude doesn’t tell you how many tokens you can use. It just tells you what percentage of tokens you’ve consumed. Yeah, it’s weird.

2. Weekly tokens: Claude gives you a limit for the full week. The minute you exhaust the tokens for the week, your usage stops. It gets reset only after the week is over. Again, you don’t know the absolute number of tokens you can use.

To check your usage, go to Claude.ai → Click on your name in the bottom left → Click on “Settings” → Click on “Usage”

You’ll see something like this:

How many tokens are consumed for each prompt?

Like I said, you won’t know how many tokens are consumed for each prompt/task. But this newsletter will tell you how to determine an approximate. Read on to know how..

Tokens are essentially consumed for 3 activities:

1. Input tokens: consumed when you type words and hit send (to process the input)

2. Output tokens: consumed when the AI gives you the output

3. Reasoning tokens: if you use the “reasoning” or “thinking” mode (more on that later) → uses tokens

But there are 6 models of Claude (Haiku, Sonnet 4.6, Opus 4.6, Opus 4.7, Opus 4.8 and Fable 5). Within each of them, there are different effort levels (low, medium, high, extra, max), and each of them use a different amount of tokens. And each of these can be used with or without extended thinking (deeper research).

Phew! Just typing all of this is overwhelming 😰

So there are 60 different modes that will give you different ouput for the same prompt (6 models x 5 effort levels within each x 2 (extended research)).

I basically tested each of those modes and noted the output vs consumption (reward vs cost) for them.

Now, to understand token usage between models, here’s what I did:

I gave Claude one task, to write a LinkedIn post. Gave the exact same prompt to all models and noted the usage. Here’s the prompt:

The token consumption results were really interesting. But I can’t give you a table of 60 rows, or you’ll go crazy. So I’ll talk about Claude’s default mode for each model.

Now, before you go any further, I’ll ask you to make one assumption: Instead of the token usage as a percentage that Claude gives, we’ll assume that we get 100 tokens in a 5-hour session. So just for simplicity, we’ll assume that the usage is not in terms of percentage but in terms of number of tokens. It’ll be easier to compare.

Here’s the token consumption for each model:

Reiterating: The token consumption is actually a percentage. We’ve assumed an absolute value of 100 tokens per session for simplicity, so that you can compare models easily.

There were some interesting data points and insights that popped up. Let’s talk about token consumption first.

Base mode for all models uses similar number of tokens

If you like what you’re reading, subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

Whether you use Sonnet 4.6 or Opus 4.8, if you’re not using the “extended thinking” mode, it consumed anywhere between 1 and 3 tokens for a writing task.

So if you need better output (and don’t want deep thinking/reasoning), use the higher model. The cost won’t matter a lot.

Extended thinking costs 3-7x more

Well, nothing in life comes free. If you want extended thinking/reasoning, it uses 3 to 7 times more tokens than the normal mode. So use it only when needed. And make sure you check whether extended thinking is on or off before you hit enter. You wouldn’t want to waste 7x the tokens for just writing an email 🤷🏻

I’ve mentioned about where to use extended thinking and where not to use it at the end of the newsletter. Read on..

Opus 4.6 is a token guzzler

I rechecked this twice — Opus 4.6 used more or less the same tokens as Opus 4.8. In fact, it used more tokens than Opus 4.7 (the better model).

So I’m going to stop using Opus 4.6 from now on. Why pay the same (or even higher) for a worse result? 🤷🏻

Which model to use when?

Now, the interesting part.

Like I said, I gave the same prompt to all models (and sub-modes within them), and here’s what I saw:

1. Haiku and Sonnet were pretty sad for writing content. Even with extended thinking. They had bad hooks, lots of em dashes, and AI language written all over it (even after I specifically told it to avoid AI patterns, with examples). I’d never use either of those models for content generation.

   Use it for: Basic fact-finding, drafting a message, correcting grammar, converting text to table, etc.

2. Opus 4.6 onwards, the output got better. Still not usable, but at least it got the basics right. I might use it for low-effort, low-impact day-to-day stuff.

   Use it for: Writing emails, basic analysis of data

3. Now, Extended Thinking: This is the most important. The output was significantly better with extended thinking mode on. And incrementally better for each model. It did research and used data to substantiate the claims it made. Much more convincing.

   But the output was still not usable as-is. I’d need to double-check the sources, rewrite the hook and make edits to the flow.

   Use it for: Getting first drafts for LinkedIn posts or marketing-related writing that needs to convince someone. But just the first draft.

Frankly, none of the models gave me a direct, deployable output. Which is why I don’t use AI to write my posts. Sure, I might use it to brainstorm and get ideas on the writing, or at max generate a first draft. But that’s about it. It’s still not there yet. Not for something that’s a primary task for me.

Another observation I had was that without extended thinking, the ouputs for all Opus models were more or less the same. Just different language, not a different structure. Basically substantiates my earlier point about all models consuming more or less the same tokens when used without extended thinking — the ouput is also more or less similar.

So how do you use this in your daily tasks?

Well, now that you know the token consumption and the output each model gives, you can take better decisions about which model (and mode) to use in your tasks.

I’d say for any of your important tasks, you would need extended thinking. It’s the bare minimum. Yes, it’s expensive, but it eventually saves you time if you do it right.

(I’ve written about how to conserve tokens in a separate post. Check that one out too)

Do note that while I’ve used WRITING as a task for Claude in this example, the foundation still stands — Haiku and Sonnet are still very basic; and Extended Thinking is still something you must use for any important task — whether you’re writing a post, creating a presentation or doing research.

What next?

I haven’t covered the research mode yet in this newsletter. I was literally out of tokens, and wondering if Claude will think I’m an abusive user 😂

So if you want to read about the “Research” mode, comment on this post or reply to this email, and I’ll write about it next week 😊

Well folks, that’s all for today! If you enjoyed reading this one, share it in your network. Every share helps get more subscribers.

I’ll see you next week!

Cheers,

Ankur

The thing is, I’ve seen that most AI content is either clickbait, or superficial, or just too technical — meant for engineers. So I’m building The Decodr — a platform where non-tech professionals come to understand and use AI. Deeply researched stuff, free of the noise.

It hosts a newsletter with deep researched content on AI — explainers, how-to guides, platform comparisons and more — all meant for non tech folks. But that’s not it.

On top of that, we’re also building repositories of ready Claude skills that you can use to build stuff. We’ll compare tools, tell you what’s best for your use case, and also build a community slowly.

So before we go all-in, I’d like to know if you’d even use a platform like that. Check out the website and (PLEASE) tell me if you’d use it. It’ll help me understand what users want :)

Now, here’s what today’s issue is about:

And the proof is actually in the pudding this time: I

- purchased a domain
- built TheDecodr with Claude
- hosted it on Github (no, I’m not a coder; did it for the first time), and…
- got everything up and running, from zero, in under 6 hours!

And it’s the first time I did something like this — was genuinely proud of myself (and Claude, obviously), so I thought “Damn! THIS is what the next LazyAI edition should be about!”

So here we are.

Today’s read might be a bit long, but if you read it fully, I promise you can build what I built in less than half a day. If you don’t have time today, star mark this email and come back to it later.

But please do it. Just experience the joy of building from scratch once. It just hits different!

Let’s begin.

First, get the domain

The domain is the name of the website (www.yourbusiness.com). Whatever project you’re working on, think of a name you’d want to call it, go to a website like GoDaddy, BigRock or any other, and purchase the domain. The exact same name may or may not be available, so you may need to look at combinations.

Below is how you search for a domain.

Buy the domain you like. It’ll cost you about INR 1,000 a year.

If you don’t want to invest in the domain right now, that’s cool too. You can just use a free hosting service and still have your app up and running (more on that later). Although I’d advise you buy to it; because making a monetary investment is one of the best ways to get consistent at something :)

Moving on..

Now, start building with Claude

This is the meat of the entire operation. Open Claude, and just talk to it like you would talk to a product manager who is helping you make an app/website. In English language.

Step 1: Brainstorm with it and create a PRD

Start with the prompt:

I want to build a website/web app about [My project]. Can you help me run through how it should be built. I have some ideas, but I want to brainstorm with you and generate a PRD at the end of it.

If you’re making a basic app, make sure you use “Haiku 4.5” as the model. If you don’t like the output you get after a few iterations, then use Sonnet 4.6 — Medium.

Do not use Opus as the model. It will burn your usage within minutes!

Now, continue talking with Claude until all your specifications are clear. It’ll tell you how your website will be made. Then you ask it to create a PRD (Product Requirements Document) for it and download the .md (markdown) file of the PRD.

Step 2: Build it

Start a new chat. In the new chat, upload the PRD and ask Claude to build the app/website. Tell it that you’re not a technical person, so in any chat going forward, it should not use jargon.

The reason I’m asking you to create a PRD and start a new chat is because it’ll save tokens. Continuing a chat with 50 messages will hit your usage limit in minutes.

Claude will give you the actual website/app with the UI.

Step 3: Iterate

Once you get the output, iterate on it. Tell Claude things like “The button colour is not appealing. Change it. Use design pricniples as a designer would”

With a few iterations, you’ll get a nice-looking app/website.

Now, let’s make it live!

There are two ways to make your website live (in technical terms, deploy it):

1. Directly on the hosting platform (more on that later), or

2. Via Github (more on that later too)

You can use either, but I’d say use Github — it’s a cleaner way to do it, and also keeps track of all the changes you’ve made if and when you decide to update the app.

(And it’ll also give you developer feels when you understand and use words like Git, repo, commit etc. 😉)

Step 1: Sign up on Github and create your repository

Go to Github.com and register for a free account. Create a new folder (called as repository/repo). This is where you’ll be storing all the files of the project. The files work in tandem with each other (Claude would have already ensured that).

Use the below settings while creating the repo:

If you’re stuck somewhere, take a screenshot and ask Perplexity how to proceed.

I’m asking you to use Perplexity because it’s more accurate for things like this, and because Claude burns a lot of tokens for tasks. So don’t waste it on troubleshooting.

Step 2: Export the files from Claude

Ask Claude to give you all the files of your website/app. Download all of them in your computer. It might give you a zip file or not, depending on the project (and its mood :p).

If it’s a zip file, unzip it on your computer.

Step 3: Upload files on Github

Go to your repo, click on “Add file” and click on “Upload files” and drag-and-drop the files from your local system into Github.

Again, if you’re stuck somewhere, take a screenshot and ask Perplexity how to fix it.

Now here’s some nuance, just for your understanding:

Typically, the index.html file is the default home page or main entry point to your website. When a user types a website address (www.yourwebsite.com) in the browser, the web server automatically looks for this index file and displays it as the default landing page. Then this file internally navigates the user to different pages depending on how the user scrolls through the website and where he clicks.

Anyway, now the files are uploaded to Github. But your website isn’t live yet.

Step 4: Hosting your website

Now, you need a place on the server where you will store your files. That’s the hosting provider.

Think of it this way:

• Domain: It’s just the address of your website (like an address to your house)

• Hosting provider: This is actually your house where your website files live and are available to see

• Github: This is like a house construction notebook (or a blueprint) where you write down every change to your house design, and once approved, copy the changes on the actual house where your files are hosted

You can use any hosting provider that gives a free plan. I used Netlify.

So you upload the files on Github and connect it to Netlify. Just go to netlify.com and click on “Add New Project”. It will then give you the option to connect with your Github repository. Fairly straightforward.

Once you do that, choose the free hosting and deploy the website.

That’s it. It’s live!

Now, whenever you make subsequent changes to your project on Github and COMMIT the changes (which basically means approve the changes), it will automatically sync with Netlify and update your website within minutes.

But the story doesn’t end here. If you’ve purchased a domain, you need to connect your domain to Netlify’s hosting address.

I won’t get into the details of that, or this article just won’t end. You can do a quick Google search. It’s pretty straightforward.

But to begin with, just try the free hosting without the domain. Netlify will give you a free sub-domain (something like your-app-name.netlify.com). Use it and see how it feels. If you want to pursue it seriously, get the domain, If it’s just a fun project, use the free hosting and sub-domain with Netlify.

Now, what if you want to make changes to the website in future?

There are 2 ways of doing it:

1. The crude way: Interact with Claude in a new chat and give it context everytime.

2. The better way: Create a project on Claude with all the relevant instructions fed to it one-time, and just make changes within that project. whenever needed I’ll quickly touch up on how to do that.

For future updates, create a Claude project

The same chat window where you made your project by talking to Claude and exported your project files? You need to tell Claude to do one last thing. Give it this prompt.

“I want to create a Claude project for this website. Give me a detailed instruction file that will have all relevant information and context about this project: Objective, branding, messaging, and anything else that you feel is important for Claude to understand the complete context. I will upload this as the instructions in the Claude project, so I want you to be detailed such that you don’t lose any context. But don’t be verbose. Try to keep the length of the instruction file optimum.”

That’s it. Claude will generate an instruction file for you. Copy the content.

Now go to “Projects in Claude → Create a new project → Give it a name and description, and you’ll see the below screen:

Leave the Memory section alone. Claude will auto-update it every evening based on what you do with the project.

Open the Instruction section and paste the instruction file that Claude gave you in the earlier step. This will give Claude all the context it needs for updating your app/website in future.

In the files section, upload the index.html file and any other files that you feel are important. In fact, you can also sync your file with Github — just click on the “+” sign and click on Github. It’ll ask you for your Github credentials and connect it automatically.

Make sure you don’t upload all files in this section, since it has limited memory. Only the important ones.

Now, how do you make future updates?

Whenever you want to change anything, just go to the Project and start a new chat there. Talk to it like you’d talk to a product manager/engineer building this.

It’ll have all previous context and files, so it can make all the required changes.

Once you’re happy with the changes, ask it for the updated file, and upload it to Github the same way you uploaded the files earlier.

That’s it. Your website will now reflect the updates.

And that’s how you make your passion project come to life. With Claude.

If you read it till here, please check The Decodr and tell me what you think. It’s going to have a lot more content like this, along with tools, Claude skills, and more — for non-technical people who want to understand and use AI in their work.

If you want to talk about this or anything else, please reply to this email or drop it in the comments. I would love to read what you guys have to say!

I’ll see you next week!

Cheers,

Ankur

- “AI just killed copywriters”
- “Analysts are dead”
- “Claude is my content team”

But here’s a twist: Anthropic, the company that buit Claude, is hiring for

- a HUMAN copywriter
- a HUMAN accounting manager
- a HUMAN content head

For salaries in the range of $190K - $400K.

Read that again: Anthropic is paying up to $400K. To a human. For doing a job that people are saying it’s own tool has replaced.

What in the world is happening?

No, it’s not hypocrisy. Anthropic just knows what Claude is capable of, and where it needs humans.

And that’s what I’m going to be talking about in today’s newsletter. Tell me if you’d like more pieces on such topics, I’ll incorporate them in future editions :)

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.

Subscribe now

There are a few problems with AI today that are preventing it from replacing humans.

Problem 1: AI doesn’t have taste

I had written about it a few weeks ago. AI is based on probability. So it will generate an output based on patterns, not taste.

Basically, if you ask anything to an AI, it will give you the most “probable” answer. The one that has the highest probability.

This means that the output it gives you, will be more or less similar in depth to the ouput it gives your competitor. Or your colleague who’s 5 years junior to you. Or a second year college student. Which in turn means that you either accept that your work is equal to that of a college student’s, or you give it deeper instructions, a better prompt.

And that better prompt? That comes with taste. From knowing what doesn’t work after years of experience. From knowing that your user might not like what is produced. From knowing things that are not documented on the internet. Things that you just..know.

So that AI needs taste — which means it needs a human with expertise to guide it. Which eventually means that it cannot replace one.

Problem 2: AI is not accurate

You can never be 100% sure of AI output. And the AA-Omniscience benchmark shows why.

AA-Omniscience is a benchmark that tests how well AI models know facts, and whether they admit uncertainty instead of guessing.

Now, this study involved asking each AI model 6,000 questions across 6 domains to see if the AI

• answered them correctly

• said that it doesn’t know the answer, or

• just hallucinated to give some garbage

(The caveat was that these were difficult questions which only experts in their field could answer)

Below were the results. They’re surprising.

The highest accuracy was 57% for GPT 5.5 max.

Claude Opus, which we know (or rather believe to be) as the smartest model out there, answered only 46% of questions correctly.

Point being, AI can’t be trusted yet. Not for difficult questions/tasks at least. You still need a human for that.

Problem 3: The people maze

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.

Subscribe now

At the end of the day, an AI is still a computer. Even if it might have good hard skills in future, it has zero soft skills. And that’s where humans have the edge.

When you’re planning a project with multiple stakeholders, the technicalities are the easier problem to deal with. The more difficult problem to deal with, is people.

Stakeholder management, team management, upward reporting — navigating the people maze, is what AI sucks at. And that’s THE most important thing in a corporate job.

In fact, Daniela Amodei, the co-founder of Anthropic, explicitly mentioned that they “look for people who are great communicators, who have excellent EQ and people skills, who are kind and compassionate and curious and want to help other people”

So even if AI builds taste and does tasks with 100% accuracy (sometime in the future), soft skills and stakeholder management are not something it will be able to do. Not today. Not 10 years later.

And without that, a company cannot function.

So what does it mean for you?

If you’re an Individual Contributor, there are 3 things I’d want to leave you with:

1. Build expertise and taste. Get deep enough in your function that you can question AI’s output. Marketers should be able to challenge a weak hook or a story with no tension. Finance folks should understand when a model’s assumptions are off before checking the formula. Product folks must know when a feature solves the wrong problem. Build that depth so that you’re able to question AI

2. Polish your communication and people skills. AI handles the output. You still have to win the room. Irrespective of your function, know your stakeholders well — clients, users, cross functional teams — anyone. That’s a gap AI will never be able to fill.

3. Verify every claim AI makes. Verify all claims that AI makes. Please. I cannot stress this enough. You saw the accuracy of AI. So just make sure that you’re verifying every single claim that your AI tool makes when you interact with it.
   Because what’s worse than getting replaced by AI, is getting replaced by a human — because you goofed up with your data.

If you’re a manager/leader:

1. That expertise we spoke about? You need to build it too. Not because you need to use AI on a daily basis. But because you should be able to tell your team when to use it and when not to use it.

2. Communication is even more important for you. Your team will move faster with AI. Your job becomes less about output management and more about direction-setting and stakeholder alignment. That's a harder skill, not an easier one.

3. Lastly, and most importantly, do not expect your team to give you 10 hours of worth of work in 1 hour, just becaue “AI can do it with one prompt”. That’s the 2024 narrative. Unless you want AI slop, that won’t happen.

AI is not there yet. Stop expecting it to be.

Yes, the world is getting tougher. Entry-level jobs are going. But if you’re a mid-career professional, you’ll see the hype calm down in the next one year.

Till then, just hold on tight.

That’s it for today folks! If you found some solace after readng this, please share it with a friend. Tell them to breathe. It’s okay :)

Cheers,

Ankur

If you’ve ever stared at a “you’ve reached your usage limit” screen on Claude and wondered what you did wrong, this article is for you.

It’s not the questions. It’s the conversation.

Picture this. You open Claude on a Monday morning. You ask it to help draft an email. Then you tweak it. Then you ask a follow-up. Then another. By noon, you’ve had a 25-message back-and-forth — and suddenly, Claude tells you you’re out of credits for the day.

What happened?

Well, here’s what you should know: Claude doesn’t just read your latest message. It re-reads the entire conversation every single time you hit send. Right from message #1.

For every conversation, it uses tokens.

A token is simply a chunk of text. AI doesn’t read sentences the way we humans do. It breaks down sentences into tokens, each of which could be a whole word, part of a word, or even a punctuation

(I’ve written more about tokens in an earlier newsletter. You can read about them below:

Now, Chatbots consume tokens for every single chat message in the window. And that’s why a 25-message conversation feels like it eats up your credits. Because it’s not 25 separate questions. It’s 25 questions where each one carries the full weight of everything that came before it.

One creator tracked his Claude usage in detail and found that 98.5% of his tokens were going towards re-reading conversation history. Not the actual work he was asking Claude to do.

Read that again.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.

So how do you ensure you don’t hit Claude’s limits?

Once you understand the token problem, the fixes are simple. Every habit below is answering one question: How do I reduce unnecessary token consumption?

1. Start a new chat for a new topic

This is the one people resist most, because starting a fresh chat feels like starting over. It’s not.

When you switch topics inside the same conversation, you’re forcing Claude to carry the full history of the previous topic into every reply about the new one. Imagine asking your accountant a tax question in the middle of a meeting about your marketing strategy, and they insist on re-reading the entire marketing discussion before answering. That’s actually what’s happening.

New topic. New chat. The context you actually need takes 10 seconds to re-paste. Everything else is just weight.

2. Edit the message, don’t send a correction.

When Claude doesn’t get it right, most people just type “make it shorter” or “no that's not what I want. I want XYZ” and hit send.

But what that does is, it adds one more message to the stack that Claude now keeps re-reading forever.

(Before you scroll ahead, I’m going to use Claude in this article, because this article IS about Claude. But the structure and process is pretty much the same across all Chatbots. ChatGPT, Gemini and the rest — all follow the same token usage process)

In Claude’s chat interface, you can click Edit on any message you’ve already sent, change what you wrote, and regenerate the response. The new version replaces the old one. It doesn’t stack on top of it.

This single habit probably saves more tokens than anything else on this list. Please use it. Aggressively.

If you like what you’re reading, subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

3. Ask for everything at once.

“Summarise this article.” (reads response) “Now pull out the three key points.” (reads response) “Now suggest a headline.”

That’s three separate messages. Three full context reloads. Claude re-reads the entire conversation each time.

Instead, keep it one message — “Summarise this, pull out three key points, and suggest a headline”. It’s one reload. Same output. The answers are usually sharper too, because Claude sees what you’re building toward from the start.

4. Don’t redo the whole thing when one part is wrong.

“Rewrite this email” asks Claude to regenerate every word. “Only fix the opening line — keep the rest exactly as is” asks Claude to touch one sentence.

If your email is 300 words and you ask for a full rewrite, you’ve just spent that many output tokens fixing something a 20-token surgical edit would have handled.

Be specific about what’s broken. Point to the exact paragraph. And add “no explanation needed, just the updated version”. Otherwise Claude will spend a paragraph telling you what it changed. And that paragraph costs tokens too.

5. Summarize and restart when a conversation gets long.

Every conversation has a point where it becomes more expensive to continue than to start fresh.

When you feel a session getting bloated — say, past 20-25 messages, ask Claude to write a summary of everything important so far. Copy it. Open a new chat. Paste the summary as your first message. And then continue from there.

You lose nothing. You carry forward the context that matters, without the token cost of re-reading every exchange that got you there.

If you like what you’re reading, subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

6. Use Projects for files you keep going back to.

Every time you upload a document to a new chat, Claude processes it fresh. Upload the same 10-page brief to five different chats and you’ve paid to process it five times.

Claude’s Projects feature lets you upload a file once and reference it across multiple conversations without that repeated cost. It also uses smarter retrieval — pulling only the sections relevant to your question instead of loading the whole document every time.

If there’s a document you return to regularly, like a brand guide, a brief, a research report or something else — just move it into a Project. And then just use that project’s environment for new tasks.

Okay, quick poll. I want to understand if you guys have ever used Claude projects. If not, I’ll write a detailed article on it next. It’s one of the most underused features of Claude!

7. Choose your model carefully

Claude has three main models: Haiku (fast, light, inexpensive), Sonnet (balanced), and Opus (the heavy one, best for complex reasoning).

Most people leave it on Sonnet or Opus by default. And then wonder why their credits disappear quickly. Haiku handles a surprising amount: summarising text, fixing grammar, reformatting a document, brainstorming ideas. It’s not a downgrade for those tasks. It’s the right tool.

Save Opus for the work that genuinely needs it, like nuanced analysis, complex writing, and situations where the quality difference is actually visible. For everything else, Haiku is fast, capable, and uses a fraction of the cost. Or at least stick to Sonnet.

8. Stop asking Claude to do things it’s not built for.

This is the most important. We’re all lazy, and we want one tool that does everything.

Now, Claude is exceptional at reasoning, writing, and analysis. It cannot generate images. It’s not the fastest at real-time search.

If you spend six messages trying to get Claude to produce a visual — describing the colours, the layout, the style — you’re burning tokens on a task it was never designed to solve. Use Gemini. Same logic for live news or real-time data: there are better tools for that job.

I’m not saying Claude is limited. I’m saying every tool has a lane. Keeping Claude in its lane isn’t a workaround — it’s just using it well.

So what does this mean for you?

The usage limit is not arbitrary. It’s just math — and most of us are doing the math wrong without realising it.

Pick just three habits from this list. Start with: new chat per topic, edit instead of correcting, and ask for everything at once. Those three alone will feel like a noticeable difference by the end of the week.

The limit stops being a wall once you stop carrying the whole conversation on your back.

Let me know if this helped!

See you next time..

Cheers,

Ankur

AI slop is the flood of low-quality AI-generated content, like text, images, audio, and videos, often created for clickbait, ad revenue, or mass production rather than real value.

53.7% of posts written on LinkedIn in 2025 were AI. Yes, more than half of the stuff we’re reading is AI slop. No wonder that the term was added to the Cambridge dictionary as more and more internet users kept complaining about it.

It took me some time to understand what was happening. And once I did, it was clear.

What I was noticing was AI’s fingerprint. And once you understand why that fingerprint exists, you’ll spot it everywhere — LinkedIn posts, comments, Instagram Reels. In about 10 seconds.

First, why does AI have a fingerprint at all?

When an AI model like ChatGPT or Claude is trained, it learns by consuming billions of pieces of human-written text — articles, books, social media posts, forum threads, everything. It then learns to predict: "given what came before, what word or sentence is most likely to come next?"

It doesn’t actually think. It looks at what you asked, cross-references everything it was trained on, and produces the statistically most likely response. It's just completing a pattern — very, very convincingly.

Think of it like your phone's autocomplete, but on steroids. When you type "Happy" on your keyboard, your phone suggests "Birthday". Because that's what billions of people type after "Happy." It's not thinking. It's just repeating the most common pattern it's seen. AI writing works the same way, just at a scale that can produce 500 words instead of two. Ask it for a "motivational LinkedIn post" and it reaches for the most common patterns it's seen for that phrase — because statistically, that's what works. Every time.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.

Subscribe now

Now that you know that there are tells, let me tell you how to identify them.

On LinkedIn posts

You’ll see one of more patterns when someone has written a post with AI.

1. Lot of high-impact words: Posts will have words like “boasts a, exemplified, terrible, quietly’ etc.

2. Rule of three: This is a powerful concept in storytelling, where sentences are structured using groups of 3 to have higher psychological impact. For example, “He lost his job. His money. And the respect that came with it”.

3. Phrases that show exclusivity to the writer: Things like “Here’s what nobody’s telling you”, or “Here’s why nobody talks about it”. These phrases are used because it makes the reader feel like they’re reading exclusive information. The problem is, every second writer uses them :D

4. Antithesis in structure: Sentences like “It’s not A. It’s B” or some variant of them

5. Overuse of em dashes (—) in the post

I could go on and on. The list is endless. But these are the most common AI patterns you’ll see in LinkedIn posts.

One thing to keep in mind though: Not everyone who writes using these patterns is using AI. Some people genuinely write well. A lot of people use em dashes naturally (I’m one of those). The key is to see if it’s overdone. If it is, it’s mostly AI.

On LinkedIn comments

This is where it gets sad.

1. AI comments don’t engage. They validate. “Such a valuable perspective!” “This really resonates.” “Thanks for sharing this insight!”

   These aren’t thoughts. They’re the text equivalent of a “I agree”.

2. A real comment reacts to something specific in the post. An AI comment could be copy-pasted onto any post on any topic and still make sense. If it fits everywhere, it came from nowhere.

Interesting fact: There are “engagement pods” on LinkedIn. These are tools like Crosslike.club, where you sign up, login with your LinkedIn credentials, and become part of a community of thousands of people. Every time anyone from that community posts on LinkedIn, randomly selected profiles of real humans from that community write comments on the post All automated in the background using AI, they don’t even know it’s being done. Here’s an example I saw yesterday (the guy shouldn’t have tagged me :))

3. If an engagement pod is being used, you’ll see another pattern: Timing. AI- generated comments come in consistently, especially in the first one hour of the post going live. Because that’s what the algorithm rewards.

Essentially, if it’s all praises in the comments section of the post, it’s most likely an AI.

On Instagram reels

I had written an entire newsletter on this. There are fake AI profiles on Instagram with more than 200,000 followers. They even get “paid subscribers” and make money off of it.

The images you see above, are all AI profiles. And here’s how you can identify them.

1. Skin looks too perfect. Real skin has pores, uneven texture, asymmetry. AI skin often feels polished or “designed”

2. Lighting and backgrounds don’t fully match. Faces lit differently from the scene, or subtly changing locations across posts

3. Hands, ears, teeth still glitch. Finger counts, mismatched earrings, unnatural teeth are common tells

4. Very few videos. Most AI influencers rely on photos because motion and expressions are harder to fake convincingly

5. Digital footprint feels empty. No old tagged photos, interviews, random friend uploads, or history outside the account itself

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.

Subscribe now

So where does it leave you?

The AI fingerprint isn't going away. If anything, it's going to get harder to spot as models get better. But AI can’t replace a real human experience. Nor can I replace real human writing.

So after reading this, you’ll know if something you read/watch is AI or not.

If you found this useful, please share it with a friend so that they don’t get fooled (or worse, scammed) by an AI.

Share

I’ll see you next time..

Cheers,

Ankur

Today, I want to talk about how I improve accuracy with my research, and why everyone MUST use Perplexity.

Your AI “remembers” more than it “thinks”

How many times have you used ChatGPT to research something — got a confident, well-structured answer — and then found out the statistic it cited doesn’t exist?

Happens to everyone.

But here’s the thing: it’s not a bug. It’s a feature of how most AI tools are built. And once you understand it, you’ll immediately know which tool to reach for — and when.

So What’s Actually Going On?

Most AI tools — ChatGPT, Claude, Gemini — are built on something called a Large Language Models (LLM).

An LLM is an AI that has been trained on an enormous amount of text from the internet — articles, books, forums, everything. It learns patterns from all of that, and when you ask it something, it generates a response based on those patterns. It’s completing — very, very sophisticatedly.

Think of it like a very well-read friend who read everything on the internet up to a certain date, and then went completely off-grid. Ask them something recent? They’ll answer confidently — based on what they last read. Whether that information is still accurate? That’s a different question.

And when they’re asked something they don’t know, they’ll just make something up.

That’s Claude, ChatGPT and Gemini for you

But Perplexity is built differently

Perplexity isn’t really a chatbot. It’s closer to a search engine with a brain.

Every time you ask it something, it goes to the web first — before generating a single word of response. It pulls relevant pages, reads them, and then builds its answer from what it just found. The language model’s job is just to translate those search results into something readable.

The internal design principle Perplexity built this around: “You are not supposed to say anything that you didn’t retrieve.”

That one sentence explains why using Perplexity feels different. It can’t invent a source. It can only cite what it actually found. And it shows you those sources, inline, next to every claim.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — once a week, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.

Subscribe now

But it’s not magic

I’ll be honest, because I don’t want to hype things just for engagement.

Perplexity still hallucinates. In one independent test, models were asked about a research paper that didn’t exist. ChatGPT, Gemini, and Claude all said they couldn’t find it. Perplexity fabricated an answer — and cited sources that didn’t support the claim.

How? When a search returns nothing useful, the model still tries to fill the gap. And when it does that, you’re back to the same hallucination risk as any other LLM.

That risk is lower with Perplexity than with other AI chatbots, but it’s still there.

Here’s the practical way to know: If a paragraph in Perplexity’s answer has no citation number next to it, the AI is speaking from training, not retrieval. That’s your cue to verify.

So what does this mean for you?

Here’s the thing: Perplexity reads the web for you. ChatGPT and Claude think for you. Both are useful, and neither replaces the other.

Use Perplexity when you need facts you can trace back to a real source — research, current events, checking if something is still true. Reach for ChatGPT or Claude when you need the AI to write, reason, or create something from scratch.

Here’s how I use it

I’ve started using Perplexity as the final step before I publish anything — articles, newsletters, social posts. I paste in whatever I’ve written and ask it to verify every claim I’ve made.

In all the time I’ve been doing this, I’d say more than 90% of the times, the sources are correct. With ChatGPT and Claude, that’s lower.

No, you will not find 100% accuracy anywhere, and manual checks will still be needed. But with Perplexity, the errors are lower, which just saves your time

Try it before your next piece of writing goes out. It takes 2 minutes, and it might be exactly what you need.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.

Subscribe now

Before you go, I have one request

I’ve been writing for over 3 months now, and I would love to know if I’m on the right track. So I need you to help me with two things:

1. Tell me if the weekly articles are adding value

2. Tell me what you would like to read on, so that it’s just more relevant for you

It’ll take a minute to reply, but will improve the quality of my work by 10X, so if you think my work has added ANY value to your AI learning journey, I’d love to see a reply to this email with your thoughts 😊

I’ll see you next week!

Cheers,

Ankur

Yes, the email subject seemed counter-intuitive. An AI newsletter saying DON’T keep up with AI?

But hear me out..

Today’s piece is an attempt to calm the panic that hits when you open LinkedIn and every third post is about an AI tool you’ve never used.

Open any feed for 30 seconds and you’ll see it — someone teaching prompt engineering, someone evangelising agentic workflows, someone showing off a Veo 3 clip, someone selling a Claude skills course. Every one of them is good. Every one of them is making you feel like you should be doing what they do.

So you start dabbling. A bit of this, a bit of that. Two weeks later you’ve forgotten most of it, a new tool drops, and the cycle repeats. I’ve done this. In fact most days, I’m still doing it.

But the panic is built on two assumptions that fall apart the moment you examine them.

The two assumptions

Assumption one: the things you’re missing today will still matter tomorrow.

They won’t. Most of them. Because AI tools have a brutally short half-life — most of what’s trending this month is forgotten in month twelve.

Half-life is the time it takes for something to lose half its relevance

Think about it. 18 months ago, people were paying for ChatGPT prompt packs on Gumroad. Entire newsletters were dedicated to AutoGPT. “Prompt engineer” was a real job title at high salaries.

Today? Prompt packs are obsolete. AutoGPT is a footnote. Prompt engineering as a standalone role is mostly gone. And Sora — the AI video tool everyone was obsessed with six months ago — already got shut down.

Look at what’s trending right now. Some of it will matter in 2027. Most of it won’t. You just don’t know which is which — and neither does anyone teaching it on LinkedIn (this is why everyone sounds so confident — confidence in this space is mostly performance).

Spread your 5–7 hours a week across six trending things, and you’re memorising train timetables that get rewritten every quarter.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.

Subscribe now

Assumption two: Each new AI skill is a separate thing you should learn from scratch.

Also wrong. And this one’s the good news.

The skills underneath AI tools are supremely transferable. Spend 100 hours building Claude skills — really learning how to scope a model’s behaviour, structure instructions, handle edge cases — and you’ve also picked up most of what you need for agentic workflows, custom GPTs, and even Claude Code. Because at the core, they’re all the same skill: Telling a model precisely what you want, in a way it can actually act on.

Same with image and video generation. Get genuinely good at one model — Midjourney, Veo 3, Nano Banana, pick your weapon — and the iteration loop you’ve internalised (prompt, judge the output, adjust, repeat) is exactly the loop you’ll use for every visual model that ships next year. The interface changes. The instinct doesn’t.

The generalist stays a beginner everywhere. The deep person becomes a fast learner everywhere. Those are very different futures.

Here’s what actually bothers me

We’ve been sold the idea that staying current with AI means staying broad. It doesn’t. Broad familiarity is what everybody already has. It’s worth nothing.

And here’s the part that should make you exhale — the depth you need is probably already sitting in front of you.

According to a study conducted last year, 66% of ChatGPT users only use it for basic stuff. They type a question, copy the answer, close the tab. They’ve never set up a project. Never written a custom instruction. Never used artifacts properly. Never chained two prompts. Never built a reusable system prompt for the work they do every single day.

The gap between a casual user and a deep user of the same tool is enormous. Way bigger than the gap between a casual user of one tool and a casual user of five.

So the highest-leverage move isn’t a new tool. It’s going embarrassingly deep into the one you already open every day.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now.

Subscribe now

So what should you take from this?

Tonight, don’t bookmark another tutorial. Just open the AI tool you already use. Ask yourself how many of its features you’ve actually pushed against. Projects? Custom instructions? Artifacts? Memory? Whatever your tool’s version of those is.

That’s where your depth is hiding. Not in the next thing on your feed.

You’re not behind on AI. You’re behind on the AI you already have. That’s a much smaller, much kinder problem to solve.

Breathe. It’s okay.

If this got you thinking, please share it with someone who keeps bookmarking AI tutorials they’ll never finish.

It takes a lot of time to research and write this. Sharing means a lot 🙂

See you next time..

Cheers,

Ankur

Today’s piece is about the AI job panic. The one in every WhatsApp group, every coffee chat, every uncle at every wedding. “Beta, will AI take your job?”

Most people are worried about the wrong thing. And I’ll tell you why.

What everyone’s reading

Last week, Goldman Sachs put out a number. AI is wiping out about 16,000 US jobs every month. Most of them entry-level GenZs.

Then a separate poll came up. 1 in 5 American workers said AI is already doing parts of their job.

Yes, sounds scary. But there are two studies that came out last year, which are not published in the media. Because they’re..well..not dystopian.

Let’s go through them

Study 1: AI vs doctors

A research team pulled together 83 different studies that compared AI diagnoses against actual doctors.

The finding: It performed almost as well as physicians overall and non-expert physicians, but it was 15.8% worse than expert physicians

Read that again.

Not “AI is amazing.” Not “AI is useless.” Just, “AI matches the average, but loses to the experts”

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

Study 2: AI vs senior developers

This one’s crazier.

A research group called METR took 16 senior developers — people with 5+ years on the job, working on their own code. Real bugs. Real features. Nothing fake about the setup.

And they split the work randomly: Half the tasks with AI, and half without. Then they measured the results.

Before starting, the developers guessed AI would make them 24% faster. After finishing, they felt they’d been 20% faster.

The reality? AI made them 19% slower.

And the worst part? They couldn’t even tell. They walked out of the study sure AI had helped them. But the numbers told a different story.

So does AI actually do better than you?

Well, here’s where it gets interesting.

Both studies were in different fields, but showed the same pattern: AI does great work at average. But it gets worse the more expert you are.

Because AI has pattern. Not taste.

You see, Pattern is what AI does. It predicts the most likely answer based on everything it has read. It “predicts”. The “most likely” answer.

And that’s the problem.

Because taste is what you do. You know the answer is wrong even when you can’t explain why. Built from being wrong enough times — in exactly the right ways.

Think about the finance guy on your team. Not the junior, the senior one — the one who’s seen 10 budget cycles. He looks at a number in a model and says, “this can’t be right.” He can’t always tell you why in that moment. He just knows the number is off. Two hours later, he finds the broken formula three sheets deep.

That’s taste. AI doesn’t have it.

Or your best marketer. She reads the AI-written brief and changes three things. Not because the brief is wrong on paper. Because she knows that for this audience, a short subject line will work better. She knows this product sells better with a story than a stat. She knows the room. She can’t always explain it. She just…knows.

That’s taste too.

And AI can’t see any of it.

Here’s where this happens with me

I write this newsletter every week. I use AI for it. A lot.

But you know what AI keeps getting wrong for me? The numbers. It will put in a stat that doesn’t exist, or a broken link, or a study that just doesn’t have the number we’re basing our research on. The writing looks fine and the flow is clean. But the numbers are wrong.

So I check every single number and every single fact before I finalise the draft.

The other thing it messes up is subject lines. Claude writes subject lines that sound like every other AI newsletter on the internet. Sometimes cringe, other times forgettable.

So that’s the part I just do myself. Because I know what you guys will end up opening.

Claude doesn’t have that taste yet.

Look, I’m not saying that AI is sloppy. I use it every day. In fact I even burnt through Claude’s tokens last month, in spite of being on the paid plan 🥲

But the part of my work that actually matters — knowing which number is wrong, which subject line you’ll actually click on, which sentence to cut — none of that comes from AI.

It comes from writing this 21 times in a row. And being wrong enough times to know the difference.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

So what should you take from this?

The panic is not wrong. Average work IS in trouble. Entry-level jobs ARE getting cut.

But every tech wave before this — calculator, excel, internet, mobile — went the same way. The people who survived were the ones who spotted where it fell short and got really good at that.

So the question is not really “will AI replace me?”

It’s “have I built enough taste to know when AI is wrong?”

Because the day AI develops taste, it’s not AI anymore. It’s AGI. And that’s when the problem really begins.

But that’s a story for another day.

Until then, this gap between pattern and taste is what you need to identify. Because that’s exactly where you can excel.

If this got you thinking, share it with someone who’s quietly worried about their job and doesn’t know what to do about it.

See you next time..

Cheers,

Ankur

Today’s piece is about something you’ve almost certainly been scrolling past without noticing. Or may be you have.

But once you see the pattern, you won’t be able to unsee it.

Let’s play a small game

Check out these 3 Instagram profiles, and tell me what’s common between all of them.

Here’s what’s thing: None of these people are real. They’re all AI Influencers, created by humans.

Take Naina, for example. She’s a 22-year-old from Jhansi who moved to Mumbai to become an actor. She has over 350,000 Instagram followers and brand deals with Nykaa, Puma, and Pepsi. Her Instagram handle is naina_avtr — go look, I’ll wait.

She’s not real. She’s a digital avatar built by a Mumbai company called Avtr Meta Labs, run by co-founder and CEO Abhishek Razdan. Razdan speaks at conferences about Naina. Naina “speaks” through the team he built.

Another one — Kyra (@kyraonig) has 260,000 followers. Created by George Tharian and Himanshu Goel at FUTR Studios. She’s walked a virtual MET Gala and been featured on the cover of Travel+Leisure India. Appeared on Shark Tank.

These aren’t rare experiments. According to Market.us, 58% of internet users now follow at least one virtual influencer (I’m part of this 58% btw), and Ogilvy projected that CMOs would allocate 30% of their influencer budgets to virtual creators by 2026 — a roughly $10 billion reallocation from human creators.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

Here’s where it gets interesting.

There isn’t one type of AI influencer. There are 3, and they work very differently.

1. The brand-backed avatar. Naina, Kyra, Miquela. These are (mostly) publicly disclosed — as “AI accounts'“, “virtual influencers” or something similar. They’re essentially honest marketing vehicles built by agencies that sell them to brands as “influencers who never cancel, never age, never go off-brand.” A Gartner survey found replacing human influencers with virtual ones can cut campaign costs by roughly 30% — no travel, no talent fees, no contract renegotiation every six months. For a CMO, that’s a very easy line on a budget sheet.

2. The niche persona. Fitness coaches, travel bloggers, tech reviewers, “study with me” accounts. They’re often undisclosed. Run by small operators who found that generating a consistent face and voice is cheaper than hiring one. You’ll see these a lot in affiliate-heavy niches where trust is built through repetition rather than personality.

3. The synthetic creator. This is where it gets cynical. Operators running AI personas primarily for subscription revenue — often on platforms like Fanvue, which now makes 15% of its $100M annual revenue from AI creators. Almost always undisclosed. Almost always a man behind the account. Almost always a “woman” in the front.

   In other cases, they’re just Instagram accounts that run paid subscriptions. Some of them have thousands of subscribers paying them 399/month. You do the math.

All three types share one workflow:

• Generate the character

• write a backstory

• post 3-5 times a week

• drive traffic and monetise

What differs is the monetisation — brand deals, affiliate links, or subscriptions.

What’s common is the deception gradient: from “disclosed novelty” at one end to “outright fraud” at the other.

Should you be worried about AI influencers?

Well, people have two extreme opinions about AI influencers — that they’re either futuristic, or they’re dystopian. But I don’t think that opinion should live at two ends of the spectrum.

See, I’m not saying AI influencers are bad. Kyra doing a boAt campaign isn’t the problem. The problem is everything downstream — the undisclosed fake profiles. The synthetic creators selling fake intimacy.

Most people think AI will replace human creators. I think it won’t. I think it’s building a parallel economy where the operator makes the money — not the creator, not the AI. Same thing that happened with dropshipping in 2017 and content farms in 2020.

So how do you identify a fake AI profile?

There are signs. They’re not perfect, but you’ll be fairly sure.

• Skin that’s too even. Real skin has pores, shine, asymmetry. AI skin is too perfect by default — even in “candid” shots. Even if it has pores, they seem “designed”

• Static or mismatched backgrounds. Outdoor shots where the light on her face doesn’t match the light in the background. Same “café” that subtly changes between posts

• Hands, ears, teeth. Generative models still struggle with these. Count fingers. Check if earrings match. Look at how teeth sit.

• Video is rare or cut short. This is the biggest tell — Most AI influencers generally post only photos. Very few videos. And the motion and micro-expressions in the video can be identified if you look closely.

• Comments feel off. Either no replies from the account at all, or generic one-liners. Real creators have idiosyncratic voices.

• Reverse image search returns nothing. A real person with 300k followers has a trail — a tagged photo, a YouTube guest spot, a wedding album from 2019. AI influencers are born on Instagram.

The more of these you see, the more convinced you should be that it’s AI.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

So what should you take from this?

Two things.

One — the question is no longer if your feed is part-synthetic. It is.

The question is what percentage, and whether you can tell which accounts are which. That’s a skill now, the same way spotting Photoshopped magazine covers became a skill in the 2000s.

Two, and this is the bigger lesson: Every time a new tech makes content cheap to produce, the winners aren’t the creators or the engineers. They’re the operators — the ones who understood the distribution funnel first. Naina is not winning because AI is clever. She’s winning because Abhishek Razdan built a company around the funnel before most brands knew the category existed.

Next time you see a shiny new “AI is going to replace ABC” headline, ask a better question:

Who’s already running the new version of ABC, and what’s their funnel?

That’s usually where the game is.

If you found this useful, please share it with a friend so that they don’t get fooled (or worse, scammed) by an AI.

Share

I’ll see you next time..

Cheers,

Ankur

This one’s about a mistake I see smart people make all the time — including me, until recently.

You’re probably using AI where you don’t need to

A few weeks ago, I was setting up a workflow for my newsletter. Every time someone new subscribes, I wanted to send them a welcome email, tag them in my CRM, and add them to a specific list.

And I was considering building an AI agent for it.

(I know, I know.)

Then I realised, nothing about this task requires judgment. The steps don’t change. The inputs are predictable. There’s no ambiguity to navigate.

Why use Artificial Intelligence for something that doesn’t need intelligence?

That’s when I realised — most people have no mental model for when to use AI vs when to use automation. And without that model, you either overuse AI (expensive, slow, unpredictable) or underuse automation (doing repetitive things manually when a simple tool could do it while you sleep).

So what’s the actual difference?

Let me define both:

1. Automation is rule-based. If X happens, do Y.
   It doesn’t think. Not does it adapt. It just executes the same steps, every time, without fail.

2. AI is pattern-based. It handles ambiguity, reads context, and makes judgment calls. It can deal with situations it hasn’t seen before.

The easiest way to tell them apart: Can you write the task as an if-then flowchart today, without any gaps?

If yes — automate it. If the “then” requires reading a situation, understanding language, or making a call — that’s AI’s job.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

The one question that separates AI vs automation

Does this task require judgment?

That’s it. That’s the whole framework.

Here’s what “judgment” looks like in practice:

• Reading an email and figuring out if it’s a complaint, a sales query, or just noise → judgment → AI

• Sending that email to the right folder once it’s been categorised → no judgment → automation

• Writing a first draft of a reply to that complaint → judgment → AI

• Sending the approved reply at 9am → no judgment → automation

• Reading a standard pdf file whose format is prescribed → no judgment → automation

• But reading a set of files that have different formats → judgment → AI

See the pattern? AI handles the thinking. Automation handles the doing.

Why this distinction actually matters

Most AI tools are billed as magic. “Just let AI handle it.” But AI is slow, probabilistic, and costs money per use. Automation is fast, deterministic, and runs almost for free once it’s set up.

If you’re using ChatGPT to do something that a simple Zapier workflow could do — you’re paying for a chef to make instant noodles.

Zapier is a no-code automation tool that can help you build workflows for if-else statements. It also has provisions for embedding ChatGPT within the workflow if needed. But like I said, not all workflows need AI

The bigger thing to understand though, is that the best workflows combine both. Automation handles the trigger and the routing. AI handles the judgment step in the middle. Then automation picks it back up and executes.

A real example:

A new support ticket comes in → automation detects it and pulls it into your system → AI reads the ticket, categorises it, and drafts a reply → automation sends the reply and logs it.

Three steps. Two tools. Neither doing the other’s job.

So what does this mean for you?

Next time you’re about to “use AI” for something — pause for five seconds and ask: does this actually require judgment, or am I just pattern-matching to a tool I’ve heard of?

If it’s the latter, use Zapier (or any other automation tool). Save AI for the stuff that actually needs a brain.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

If this got you thinking, please share it with someone who is using AI for everything — including things a simple rule could handle in two seconds.

It takes a lot of time to research and write this. Sharing would mean a lot 🙂

See you next time..

Cheers,

Ankur

I tried something last week that made me feel like I was living in 2030. And it took exactly 3 minutes, one browser install, and a one-line prompt.

You’ve seen the phrase “agentic AI” everywhere by now. But it seems too technical to even start.

Here’s the thing — it’s not supposed to be that hard. And now, it genuinely isn’t. Let me tell you why today..

First, what even is Agentic AI?

Agentic AI is AI that doesn’t just answer your questions — it does things. It clicks buttons, fills forms, navigates websites, completes multi-step tasks. Think of it as the difference between asking someone for directions versus having someone actually drive you there.

Every chatbot you’ve used — ChatGPT, Gemini, Claude — is great at talking. But the moment you need something done, you’re back to clicking, copying, tabbing, and pasting. Agentic AI closes that gap. And the simplest way to try it right now? Install a browser. For free.

Now how do you run an AI agent without coding?

Well, you install a browser.

Perplexity has built a browser called Comet. It’s just like any other browser that you use (not Microsoft edge though, that’s a different league legacy 😇)

And here’s where it gets cool — Comet has an AI agent built in. No plugin. No setup. You tell it what to do in plain English, and it does it.

“Find the cheapest one-way flight from Mumbai to Lisbon in August, only Emirates, max one stop.” Comet opens tabs, searches, filters, compiles results. You don’t have to touch a single dropdown.

“Summarise this 14-paragraph email in my Gmail.” It reads your open Gmail tab — no backend connection, no dark magic API needed — and gives you a 3-line summary.

Honestly, when I first read about this, I thought it was one of those things that sounds amazing in a demo and falls apart in real life.

But it actually works.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

The best agentic AI use case with Comet

Here’s where it gets interesting — and where I have a strong opinion.

LinkedIn is the most important professional platform in the world. But..it’s also one of the most locked-down.

If you’re a marketer, recruiter, or founder trying to automate anything on LinkedIn, you’;; most likely be stuck. Because LinkedIn’s API gives approved developers barely anything: A name, a photo, a headline.

Want connections, comments, engagement data? Good luck with that.

API stands for Application Programming Interface — it’s basically a door that lets external software talk to a platform’s data. LinkedIn’s API door has been mostly shut since 2015.

So what can people actually do to automate tasks on LinkedIn? Browser-based automation. And this is where Comet becomes genuinely useful.

People have used Comet to open LinkedIn profiles, ask it to extract job titles, companies, years of experience — and dump that data straight into a Google Sheet open in another tab. No code, no scraping tool, no API call that LinkedIn can block.

(Fair warning: It doesn’t handle hundreds of profiles. We’re still early for that kind of magic. But for small-batch research? THAT magic works!)

Basically, for platforms that deliberately lock you out of programmatic access, a browser-based agent is often the only accessible way to automate anything.

Other agentic AI use cases

While LinkedIn is the best use case for the Comet browser, there are hundreds of other agentic AI use cases you can run with Comet. I’m explaining two below:

1. Job hunting. Open any job board. Tell Comet: “Find marketing roles in Mumbai, 8+ years experience.” It navigates, filters, compiles. You review and decide. The grunt work searching is automated; the judgement is still yours.

2. Competitor research. “Go to these 5 company websites and pull their pricing into a comparison table.” It opens tabs, reads pages, structures data. What takes you 30 minutes, Comet does in 5. Without you having to spend more than 60 seconds.

You don’t need to be an engineer to talk AI. Subscribe to this newsletter — every other day, you’ll understand AI well enough to be the smartest person in the room at work.

Each issue is just 5 minutes — less than the time you spend doomscrolling before bed. Except, this actually moves your career forward. Join 8,000+ subscribers now!

So what does this mean for you?

Here’s what I keep coming back to. Conversations about agentic AI are full of technical jargon and developer talk, which makes many smart and curious people feel like it’s not meant for them.

Until now.

Because now Comet is that saviour who comes to our rescue.

But again, is Comet the most powerful agentic AI system out there? No.
Is it the most accessible one? Absolutely. And honestly, that matters more right now.

Before you go, I need your input..

I’ve been writing about AI since a few months now, but writing what I think will help, without knowing what you want, is like shooting arrows in the dark.

So I would love it if you could tell me what more you’d want to understand in AI. I’ll make sure I cover it in the upcoming editions.

It’ll help me and you, and take just 60 seconds for you to tell me. Here’s where you can do it.

Share my input

So go ahead — install Comet, stalk connect with some industry peers on LinkedIn, or just plan your next travel by finding the cheapest tickets! I’d love to know what you used it for!

If this got you thinking, please share it with someone who keeps hearing about agentic AI but has no idea where to actually start.

See you next time..

Cheers,

Ankur