Olivia
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@0xPure_eth
AI won't wait.
Why should you?
Joined May 2025
54 Following
2K Followers
1.7K Posts
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https://t.co/Qhr2R9UwRO
Anthropic engineers didn't build this app with one AI.
They built it with an argument.
One agent planned.
One agent wrote the code.
One agent tried to prove the other two were wrong.
That loop kept running until the app actually worked.
No giant prompt.
No magic.
Just specialized agents doing different jobs.
That's the direction AI is moving.
Not bigger models.
Better collaboration between them.
The demo is worth watching.
The real lesson is understanding why that workflow works in the first place.
That's exactly what I break down below.
Anthropic engineers didn't build this app with one AI.
They built it with an argument.
One agent planned.
One agent wrote the code.
One agent tried to prove the other two were wrong.
That loop kept running until the app actually worked.
No giant prompt.
No magic.
Just specialized agents doing different jobs.
That's the direction AI is moving.
Not bigger models.
Better collaboration between them.
The demo is worth watching.
The real lesson is understanding why that workflow works in the first place.
That's exactly what I break down below.
https://t.co/Qhr2R9UwRO
The next billion-dollar company probably won't hire fewer people.
It simply won't need them.
Sam Altman recently said he believes we're about to see the first billion-dollar company run by just 10 people.
He even added:
"If I were 22 today, I'd feel like the luckiest kid in history."
Why?
Because AI is changing what one person is capable of building.
Tasks that once required entire departments can now be delegated to AI agents working behind the scenes.
Research.
Operations.
Customer support.
Marketing.
Software.
The challenge is no longer getting AI to do the work.
It's building systems reliable enough that you can trust them to keep working without you.
That's the real competitive advantage.
Not better prompts.
Not bigger models.
Better loops.
That's exactly what I break down in the article below.
The next billion-dollar company probably won't hire fewer people.
It simply won't need them.
Sam Altman recently said he believes we're about to see the first billion-dollar company run by just 10 people.
He even added:
"If I were 22 today, I'd feel like the luckiest kid in history."
Why?
Because AI is changing what one person is capable of building.
Tasks that once required entire departments can now be delegated to AI agents working behind the scenes.
Research.
Operations.
Customer support.
Marketing.
Software.
The challenge is no longer getting AI to do the work.
It's building systems reliable enough that you can trust them to keep working without you.
That's the real competitive advantage.
Not better prompts.
Not bigger models.
Better loops.
That's exactly what I break down in the article below.
https://t.co/Qhr2R9UwRO
The first prompt is already too late.
If you're relying on prompts to organize your AI workflow, you've skipped the most important step.
An Anthropic engineer recently explained that reliable AI systems begin with something far less exciting:
A specification.
A plan.
A PRD.
A clear definition of what success actually looks like.
Only then does the loop begin.
Planning.
Executing.
Reviewing.
Improving.
The better the specification...
...the less you have to intervene later.
That's exactly the foundation I explore in the article below.
The first prompt is already too late.
If you're relying on prompts to organize your AI workflow, you've skipped the most important step.
An Anthropic engineer recently explained that reliable AI systems begin with something far less exciting:
A specification.
A plan.
A PRD.
A clear definition of what success actually looks like.
Only then does the loop begin.
Planning.
Executing.
Reviewing.
Improving.
The better the specification...
...the less you have to intervene later.
That's exactly the foundation I explore in the article below.
https://t.co/Qhr2R9UwRO
The internet your AI sees isn't the same internet you see.
And that's becoming a much bigger problem than most people realize.
In a recent DeepMind podcast, a Google DeepMind scientist explained how websites are starting to serve different pages to humans and AI agents.
Some are optimized.
Some are misleading.
Some are designed to trap autonomous agents altogether.
Meanwhile, millions of AI systems are reading, summarizing, and acting on information that was often written by other AI systems.
That's a completely new layer of the internet.
And it's growing faster than most people realize.
The companies that succeed won't just build smarter agents.
They'll build agents that can verify information, recognize manipulation, and know when not to trust what they see.
The future isn't just about better AI.
It's about building systems that can navigate an internet no longer designed only for humans.
That's exactly what I break down in the article below.
The internet your AI sees isn't the same internet you see.
And that's becoming a much bigger problem than most people realize.
In a recent DeepMind podcast, a Google DeepMind scientist explained how websites are starting to serve different pages to humans and AI agents.
Some are optimized.
Some are misleading.
Some are designed to trap autonomous agents altogether.
Meanwhile, millions of AI systems are reading, summarizing, and acting on information that was often written by other AI systems.
That's a completely new layer of the internet.
And it's growing faster than most people realize.
The companies that succeed won't just build smarter agents.
They'll build agents that can verify information, recognize manipulation, and know when not to trust what they see.
The future isn't just about better AI.
It's about building systems that can navigate an internet no longer designed only for humans.
That's exactly what I break down in the article below.
https://t.co/Qhr2R9UwRO
The biggest difference between a chatbot and an AI employee isn't intelligence.
It's memory.
An Anthropic senior engineer recently explained why the most capable AI systems don't just answer questions.
They remember.
Every task.
Every decision.
Every mistake.
That memory becomes the foundation for every future run.
But the most interesting part comes after the work is done.
Between sessions, AI reviews what happened.
It cleans up its memory.
Verifies what it learned.
Removes bad information.
Strengthens what actually worked.
That's how an agent improves over time instead of making the same mistakes again.
The future of AI isn't better conversations.
It's systems that learn, remember, and get better while you're doing something else.
That's exactly the shift I explore in the article below.
The biggest difference between a chatbot and an AI employee isn't intelligence.
It's memory.
An Anthropic senior engineer recently explained why the most capable AI systems don't just answer questions.
They remember.
Every task.
Every decision.
Every mistake.
That memory becomes the foundation for every future run.
But the most interesting part comes after the work is done.
Between sessions, AI reviews what happened.
It cleans up its memory.
Verifies what it learned.
Removes bad information.
Strengthens what actually worked.
That's how an agent improves over time instead of making the same mistakes again.
The future of AI isn't better conversations.
It's systems that learn, remember, and get better while you're doing something else.
That's exactly the shift I explore in the article below.
https://t.co/Qhr2R9UwRO
The biggest career shift isn't AI doing your job.
It's your job becoming something completely different.
OpenAI early tester and founder Pietro Schirano recently put it perfectly:
"Work is going to be less about doing the thing, and more about directing the thing."
His six-person team now delivers the output of what used to require hundreds.
Not because they work longer.
Because AI agents keep working after they stop.
Researching.
Writing.
Testing.
Iterating.
Sometimes for hours while the team is doing something else.
That's the shift most people still don't see.
The value is moving away from execution...
...and toward designing systems that can execute without you.
The people who thrive won't be the ones doing the most work.
They'll be the ones building the best workflows.
That's exactly what I break down in the article below.
The biggest career shift isn't AI doing your job.
It's your job becoming something completely different.
OpenAI early tester and founder Pietro Schirano recently put it perfectly:
"Work is going to be less about doing the thing, and more about directing the thing."
His six-person team now delivers the output of what used to require hundreds.
Not because they work longer.
Because AI agents keep working after they stop.
Researching.
Writing.
Testing.
Iterating.
Sometimes for hours while the team is doing something else.
That's the shift most people still don't see.
The value is moving away from execution...
...and toward designing systems that can execute without you.
The people who thrive won't be the ones doing the most work.
They'll be the ones building the best workflows.
That's exactly what I break down in the article below.
https://t.co/LOsNZgflmu
The biggest misconception about AI loops is that they replace engineers.
They don't.
Boris Cherny shipped 259 pull requests in a single month without writing the code by hand.
Most people looked at the number.
They missed the reason it worked.
The loop wasn't the advantage.
Boris was.
He designed the system.
He reviewed the output.
He knew when the AI was right...
...and, more importantly, when it was wrong.
The same technology that shipped 259 PRs also burned $47,000 in 11 days when another loop was left without limits.
Same models.
Completely different outcome.
The difference wasn't intelligence.
It was judgment.
AI can automate the work.
It still can't automate responsibility.
That's why the future belongs to people who can build great systems—and know exactly when not to trust them.
That's the shift I break down in the article below.
The biggest misconception about AI loops is that they replace engineers.
They don't.
Boris Cherny shipped 259 pull requests in a single month without writing the code by hand.
Most people looked at the number.
They missed the reason it worked.
The loop wasn't the advantage.
Boris was.
He designed the system.
He reviewed the output.
He knew when the AI was right...
...and, more importantly, when it was wrong.
The same technology that shipped 259 PRs also burned $47,000 in 11 days when another loop was left without limits.
Same models.
Completely different outcome.
The difference wasn't intelligence.
It was judgment.
AI can automate the work.
It still can't automate responsibility.
That's why the future belongs to people who can build great systems—and know exactly when not to trust them.
That's the shift I break down in the article below.
https://t.co/Qhr2R9UwRO
The biggest shift in AI isn't better prompts.
It's that people are starting to build systems instead of doing the work themselves.
Boris Cherny shipped 259 pull requests in a single month without opening an editor once.
He didn't spend his days writing code.
He spent them designing loops that wrote, reviewed, and shipped it.
That's the difference.
A prompt solves one task.
A loop finds the task, hands it to an AI agent, checks the result, decides what happens next, and keeps running while you sleep.
One master loop can coordinate an entire team of specialized agents.
Research.
Bug fixes.
Code reviews.
Follow-ups.
All working toward the same goal.
But here's the part almost nobody talks about.
The same technology that shipped 259 pull requests also burned $47,000 in just 11 days because nobody installed the brakes.
No budget cap.
No stop condition.
No limits.
The future belongs to people who can build both:
Systems that work...
And systems that know when to stop.
That's exactly what I break down in the article below.
The biggest shift in AI isn't better prompts.
It's that people are starting to build systems instead of doing the work themselves.
Boris Cherny shipped 259 pull requests in a single month without opening an editor once.
He didn't spend his days writing code.
He spent them designing loops that wrote, reviewed, and shipped it.
That's the difference.
A prompt solves one task.
A loop finds the task, hands it to an AI agent, checks the result, decides what happens next, and keeps running while you sleep.
One master loop can coordinate an entire team of specialized agents.
Research.
Bug fixes.
Code reviews.
Follow-ups.
All working toward the same goal.
But here's the part almost nobody talks about.
The same technology that shipped 259 pull requests also burned $47,000 in just 11 days because nobody installed the brakes.
No budget cap.
No stop condition.
No limits.
The future belongs to people who can build both:
Systems that work...
And systems that know when to stop.
That's exactly what I break down in the article below.
https://t.co/Qhr2R9UwRO
The biggest AI advantage isn't owning more GPUs.
It's building better loops.
Jane Street's AI cluster is massive.
4,032 Blackwell GPUs.
56 racks.
140kW per cabinet.
Water piped directly onto the chips.
Most people will look at those numbers and think that's the secret.
It isn't.
As Ron Minsky put it, everyone is competing for the same compute.
Hardware is becoming accessible.
Leverage comes from what your systems do once that compute is available.
The companies that win won't necessarily have the biggest clusters.
They'll build the best workflows on top of them.
That's exactly the shift I break down in the article below.
The biggest AI advantage isn't owning more GPUs.
It's building better loops.
Jane Street's AI cluster is massive.
4,032 Blackwell GPUs.
56 racks.
140kW per cabinet.
Water piped directly onto the chips.
Most people will look at those numbers and think that's the secret.
It isn't.
As Ron Minsky put it, everyone is competing for the same compute.
Hardware is becoming accessible.
Leverage comes from what your systems do once that compute is available.
The companies that win won't necessarily have the biggest clusters.
They'll build the best workflows on top of them.
That's exactly the shift I break down in the article below.
https://t.co/Qhr2R9UwRO
The prompt isn't the advantage anymore.
The system behind it is.
Anthropic just released a 33-minute Prompting Playbook for Claude, completely free.
Not another list of "10 prompts that will change your life."
The interesting part is what they actually teach.
Control cases.
Edge cases.
Evaluation.
Knowing when AI should stop and hand the task back to a human.
That's the difference between a demo that looks impressive...
...and a system you can actually trust.
The biggest shift in AI isn't learning better prompts.
It's learning how to build better workflows.
That's exactly the transition I break down in the article below.
The prompt isn't the advantage anymore.
The system behind it is.
Anthropic just released a 33-minute Prompting Playbook for Claude, completely free.
Not another list of "10 prompts that will change your life."
The interesting part is what they actually teach.
Control cases.
Edge cases.
Evaluation.
Knowing when AI should stop and hand the task back to a human.
That's the difference between a demo that looks impressive...
...and a system you can actually trust.
The biggest shift in AI isn't learning better prompts.
It's learning how to build better workflows.
That's exactly the transition I break down in the article below.
https://t.co/Qhr2R9UwRO
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