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AI • Crypto • Future

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https://t.co/JmFOCR51XR

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@markproduct My grandma 👵

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@brian_armstrong Shipping vulnerable code might become harder than finding vulnerable code 😅

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@pmitu From vibe coding to invoice coding

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@greg16676935420 So basically loot boxes for adults 😏

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Last week I posted a kid building with AI and a lot of people treated it like a cute internet video.

I think they missed the point…

Now watch this one carefully. A 7-year-old has an idea, opens a terminal, types a prompt and lets an AI agent help him turn that idea into a working game. No Stack Overflow, no documentation rabbit hole, no adult developer sitting next to him explaining every step.

Pause the video around the moment he starts talking to the agent and watch it again. Slowly.

Because that moment matters more than most people realize.

For him, this probably doesn’t feel like “using AI.” It feels like using a computer. The same way my generation opened Google without thinking about it, his generation will open agents and expect software to appear on the other side.

My generation learned how to use software.

This generation is learning how to create it.

People keep asking whether AI will replace programmers, but that might be the wrong question. The more interesting question is what happens when millions of children grow up believing that software is something you can describe, test, break, fix and ship before dinner.

The first generation of AI natives is already here.

Most people just haven’t noticed yet.

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A kid in China is learning Python with Claude while most adults are still debating whether AI is a bubble.

That should concern you more than any AI demo.

He doesn’t care which model wins benchmarks.

He doesn’t know what AGI means.

He doesn’t argue on X about prompt engineering.

He just opens a laptop and starts building.

Previous generations grew up with Google.

This generation is growing up with AI teammates.

To them, asking Claude for help feels as normal as using a calculator felt to us.

That’s the part most people still don’t understand.

The AI revolution won’t happen when companies adopt AI.

It will happen when children who grew up with AI enter the workforce.

Because they won’t work faster than us.

They’ll work differently.

The same way people who grew up with the internet worked differently from people who didn’t.

The same way smartphone natives replaced desktop natives.

The same way social media natives replaced everyone before them.

The gap isn’t coming.
The gap is already here.

One generation is learning how to use AI.

The next generation is learning how to think with it.

And by the time most people realize the difference, it’ll already be too late to catch up.

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@Polymarket Anthropic ships more announcements than updates these days🤔

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@Cryptotea Bitcoin is a tool, what people choose to do with it is a different question entirely ✌🏼

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@GoingParabolic “Trust me bro” has evolved into its final form 😏

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@adxtyahq We’ve gone

from “build something useful”

to “fit as many buzzwords as possible into one demo”
🤷🏻‍♂️

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Most people still think sports betting is a guy sitting on a couch trying to predict football games better than everyone else.

Then you see a room like this and realize you’re probably looking at an entirely different industry.

There are live games running everywhere, multiple sportsbooks open at the same time, dashboards updating in real time and screens reacting to information faster than most people can even open Twitter. The first time I saw setups like this, it became obvious that nobody here was really trying to “guess the winner” anymore.

Actually, pause the video at around 0:32 and look carefully.

Then watch it again and maybe one more time!

Look at the number of screens, the tablets, the terminals, the dashboards and all the separate systems feeding information into a single decision. The longer you look, the less it starts to resemble gambling and the more it starts to resemble an operations center.

Because that’s what it really is.

This isn’t someone placing a bet because he likes one team more than another. This is infrastructure, workflows and information moving through systems faster than humans can process it alone.

The more I looked into how professional betting operations actually work, the more obvious it became that the next step wasn’t going to be more screens or more analysts sitting in front of them.

It was going to be AI

Because AI agents don’t get tired at 3am. They don’t miss injury reports buried in local media, they don’t ignore line movement across twenty sportsbooks because they were busy watching another game, and they don’t stop scanning for signals when humans need sleep.

That’s when I realized something that sounded obvious only after I said it out loud:

sports betting was never really about predictions.

It was always about information.

Most people just haven’t realized that the rules changed.

Yesterday I published a much deeper breakdown on how I’m thinking about combining AI agents with sportsbook data and market signals.

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https://t.co/JmFOCR51XR

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@w1nq_ Bro, yesterday you called Canada 1 over 0 South Africa, today Brazil 2-1 Japan

Be honest, you’re using some AI betting prediction system you posted about recently, aren’t you? 🤔

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@AshCrypto Just a casual $860 USD billion Monday

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@greg16676935420 The Hilton legal team woke up hungry too 😂

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@ToolySOL The point isn’t to actually achieve a 10-year goal in six months
The point is that aiming for six months changes how you think, how you prioritize and how hard you move

You become a different person in the process

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@lynk0x yeah, people underestimate how expensive being broke can be

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For years I genuinely thought sports betting was mostly about understanding football better than everyone else.

Watch more matches, know more players, understand tactics better, follow every injury update and eventually you’d find an edge.

The strange thing was that the people who seemed to win consistently rarely looked like the biggest football experts in the room. More often than not, they were simply seeing information earlier than everyone else.

They noticed line movement before the headlines, injury reports before the market reacted, and money entering positions before the public even understood that something had changed.

At some point I went way too far down that rabbit hole and somehow ended up building a system around Claude, Codex and signals from 47 sportsbooks. Probably a perfectly normal and healthy hobby.

I ended up writing down the whole process, the ideas behind it and a few things that genuinely changed the way I look at betting markets.

If you’re interested in AI, markets, crypto or information asymmetry, you might enjoy this one.

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https://t.co/JmFOCR51XR

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@greg16676935420 Bro discovered trespassing with extra steps 😅😅😅

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@greg16676935420 KPI = Knowledge Preventing Income

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A Chinese AI account posted this animation comparing Claude, ChatGPT, Grok and Gemini using Newton’s cradle. Most people treated it like another AI meme and immediately started arguing in the comments. Claude fans thought it proved Claude’s superiority. GPT users disagreed. Grok users did what Grok users always do. The entire conversation turned into another benchmark war.

But the longer I watched it, the more I felt people were looking at the wrong thing.

Pause the video for a second and forget about which ball moves first. Forget about which model is supposedly winning. Look at what the animation is actually measuring. Not intelligence. Not benchmark scores. Not reasoning. Momentum.

That’s the strange thing about AI right now. Everyone talks about models. Everyone talks about benchmarks. Every release becomes another battle over who is number one. Meanwhile, the people actually building things with these systems care about something completely different.

Can the model maintain momentum? Can it recover from mistakes? Can it keep moving through long workflows without falling apart? Can it still be useful after the excitement of the first prompt disappears?

The companies are competing to build smarter models. Builders are quietly looking for something else entirely.

Momentum.

Because the model that keeps moving usually beats the model that peaks.

The creator probably thought he was making a meme.

He accidentally visualized the thing that matters most.