••• AI ••• 33M •••

… AI/AGI …

[Movez @0xMovez]

https://t.co/OMBzNQETKM

[darkzodchi @zodchiii]

NVIDIA CEO, Jensen Huang:

"Nobody writes prompts anymore. The new job is to write and handle loops."

He calls it the shift that defines the rest of 2026.

Interview was out just yesterday.

Watch the 23 minute talk, then save the full framework below👇

[Codez @0xCodez]

Anthropic research lead:

"99% of our engineers are running swarms of 300+ self-improving agents.

close the agent loop. Give the model a way to verify its own output"

in a 20-minute session, Anthropic team member explains how to build a model that improves itself.

Claude + loops + plan mode + dynamic workflows -that’s the secret.

Watch the talk, then save the playbook below.

[Rahul @sairahul1]

Andrej Karpathy: "10x engineers are normal. real agentic engineers are 100x"

this guy just shipped the playbook to become 100x

context engineering. tool design. orchestrator-subagent. evals. the harness mindset.

watch & bookmark it for this weekend

[Riley West @rileywestreel]

Boris Cherny (creator of Claude Code, Anthropic):

"Plan mode probably has a limited lifespan, maybe a month from now. You'll just describe it at the prompt level, and the model does it in one shot."

in a 5-minute interview, Boris breaks down how swarms of agents are quietly starting to ship real features on their own.

he mentions, almost in passing, that the entire plugins feature was built by a swarm over a weekend, basically no human in the loop, then explains the one idea that makes it work.

Watch the talk, then read the article below.

That’s worth more than a $500 course on agent engineering.

[rari @0xwhrrari]

Google CEO, Sundar Pichai:

"If you don't learn how to orchestrate agents now, you'll spend 2027 catching up to people who started today"

In 30 minutes, he explains why the best engineers are moving from writing code to running agents

One agent researches
One writes
One tests
One reviews
One fixes

The human becomes the operator, not the bottleneck

Bookmark and watch the interview

[Movez @0xMovez]

Anthropic Product Lead:

"At Anthropic, our engineers are running swarms of 300+ agents daily.

Give your agents 100+ tools - just don’t load them all into context."

In a 30-minute talk, the Anthropic team shows how to deploy agents to production.

Claude + loops + routines + dynamic workflows - that’s the secret.

Watch the talk, then save the playbook below.

[Rahul @sairahul1]

🚨 CEO of Nvidia: "I'd hire the graduate who's expert in AI over the one who isn't. Every time"

he's not talking about people who use AI

everyone uses AI.

he's talking about people who know the stack.

agents. frameworks. tools. workflows. skills. automations

Bookmark it.

[Jon Hernandez @JonhernandezIA]

Roman Yampolskiy, AI safety researcher and professor known for his work on AI control and alignment, says many AI safety problems may not be engineering failures, but impossibility results.

You can predict that a smarter system will outperform you. You cannot reliably predict what it will do next.

"Control breaks where intelligence surpasses understanding."

[MIKE @mikenevermiss]

Anthropic engineer:

"you're not supposed to prompt Claude. you're supposed to build a system that prompts itself [loops]."

this is one of the best workflows I've seen in a long time

in this video he breaks down exactly how most people are building loops wrong:

- the memory file you never set up, so every loop starts from zero

- the sub-agents that 95% of builders have never split apart

- the stop condition setup that keeps loops from running forever and billing you in your sleep

- why writing one prompt a day is the slowest way to use Claude

if you've been using Claude for more than a month and still typing every task by hand, you've been running one prompt when you could be running a system of loops

instead of another prompt tonight, watch this

make sure to bookmark it before it gets buried

full guide in the article below

[darkzodchi @zodchiii]

Google CEO, Sundar Pichai:

"If you don't teach your agents to debug themselves now, you will keep wasting hours every week."

In 30 minutes he explains why the engineers pulling ahead let agents fix their own failures instead of doing it themselves.

Watch the talk, then save the exact setup below👇

[Rahul @sairahul1]

Andrej Karpathy: "90% of Claude's mistakes come from missing context, not a weak model."

41% mistake rate without a CLAUDE.md. 11% with the 4-rule baseline. 3% with the 12-rule version below

here are the 12 rules senior engineers settled on:

1. think before coding: state assumptions, don't guess. the model can't read your mind, stop hoping it will

2. simplicity first: minimum code, no speculative abstractions. the moment you let Claude add "for future flexibility," you've added 200 lines you'll delete next quarter

3. surgical changes: touch only what you must. don't let it improve adjacent code, that's how PRs blow up

4. goal-driven execution: define success criteria upfront, loop until verified. without them Claude either loops forever or stops too early

5. use the model only for judgment calls: classification, drafting, summarization, extraction. NOT routing, retries, status-code handling, deterministic transforms. if code can answer, code answers

6. token budgets are not advisory: per-task 4000, per-session 30000. by message 40 of a long debug, Claude is re-suggesting fixes you rejected at message 5

7. surface conflicts, don't average them: two patterns in the codebase? pick one. Claude blending them is how errors get swallowed twice

8. read before you write: read exports, callers, shared utilities. Claude will happily add a duplicate function next to an identical one it never read

9. tests verify intent, not just behavior: a test that can't fail when business logic changes is wrong. all 12 of Claude's tests can pass while the function returns a constant

10. checkpoint every significant step: Claude finished steps 5 and 6 on top of a broken state from step 4. nobody noticed for an hour

11. match the codebase conventions: class components? don't fork to hooks silently. testing patterns assumed componentDidMount, hooks broke them without surfacing

12. fail loud: "completed successfully" with 14% of records silently skipped is the worst class of bug. surface uncertainty, don't hide it

what actually compounds instead of the next framework:

- the CLAUDE.md file as institutional memory across sessions
- eval-driven changes, not vibe-driven
- checkpoints over speed
- explicit conflicts over silent blending
- discipline over framework, every time
- one repo, one rules file, no exceptions

you don't need a better AI

you need better context engineering

complete playbook below ↓

[Riley West @rileywestreel]

Andrej Karpathy (OpenAI founding member):

"This isn't the year of agents - it's the decade of agents. Stop chasing full autonomy: the AI products that win keep the model on a tight leash and make human verification instant."

in a 10-minute talk, Karpathy breaks down why "partial autonomy" - not fully autonomous agents - is the design pattern that's actually winning right now, from Cursor to Perplexity.

he calls the core mechanic an "autonomy slider" - and there's one loop every ai product lives or dies by.

Watch the talk, then read the article below.

That’s worth more than a $800 course on agent engineering.

[Ihtesham Ali @ihtesham2005]

Sergey Brin said compute is dessert. The companies winning the AI race right now are not the ones with the most chips. They are the ones with the best algorithms.

Every headline you read about AI is about data centers, Megawatts, and Nvidia orders. Billions in infrastructure and more. The entire investment thesis of the last three years has been built on compute scaling as the primary moat.

Sergey thinks that framing is wrong.

He pulled out an example from physics. The N-body problem. Scientists have been running those simulations since the fifties. Over the decades, raw compute improved on Moore's law. But the algorithms to solve the problem improved faster. Not slightly faster. Far faster. The algorithmic gains made the compute gains look small.

He says the same thing has happened in AI over the last decade.

Compute is not the meal. It is the dessert. You still want it. Nobody is turning down frontier compute. But the companies that figured out the algorithms first are the ones actually ahead.

The market is pricing AI winners by who has the most chips.

Sergey Brin just said that is the wrong scorecard.

The ones who win this are not building the biggest data center. They are solving the harder math problem.

[Jaynit @jaynitx]

Elon Musk explains his 5-step algorithm for solving any problem:

"The most common mistake of smart engineers is to optimize a thing that should not exist."

"I have this very basic first principles algorithm that I run as a mantra."

Elon breaks it down:

Step 1: Question the requirements.

"Make the requirements less dumb. The requirements are always dumb to some degree, no matter how smart the person who gave you those requirements. You have to start there, because otherwise you could get the perfect answer to the wrong question."

Step 2: Try to delete it.

"Try to delete the part or the process step entirely. If you're not forced to put back at least 10% of what you delete, you're not deleting enough. Most people feel like they've succeeded if they haven't been forced to put things back in. But actually they haven't, they've been overly conservative and left things in that shouldn't be there."

Step 3: Optimize or simplify.

"The most common mistake of smart engineers is to optimize a thing that should not exist. So you don't optimize until after you've tried to delete."

Step 4: Speed it up.

"Any given thing can be done faster than you think. But you shouldn't speed things up until you've tried to delete it and optimize it otherwise, you're speeding up something that shouldn't exist."

Step 5: Automate.

"And then the fifth thing is to automate it."

Elon explains why the order matters:

"I've gone backwards so many times where I've automated something, sped it up, simplified it, and then deleted it. I got tired of doing that. So that's why I have this mantra."

[SciTech Era @SciTechera]

Andrej Karpathy believes AI will automate almost everything.

- "Vibe coding" raises the floor, allowing almost anyone to build software.
- The best engineers are already achieving far more than the traditional "10x engineer."
- Agentic Engineering preserves professional quality while moving dramatically faster.
- Developers remain responsible for security, reliability, and performance.
- AI agents are powerful but imperfect, requiring careful coordination.
- Karpathy believes the productivity gains may exceed anything the software industry has seen before.

The future belongs to engineers who can effectively direct armies of AI agents.

[Podcast Alpha @PodcastAlphaX]

If you hold $NVDA or a hyperscaler with AI exposure, this changes an input.

80-90% of enterprise coding tasks can use open-source models. That's Matan Grinberg of Factory on @twentyminutevc. He is building the routing layer that switches enterprises off frontier models where open-source is good enough.

The Jevons counter-thesis still holds: cheaper tokens drive more total consumption, so volume can win even as per-token pricing falls. But the mix shift from frontier to open-source changes the margin profile of who captures that consumption.

Token volume growth is not the same as frontier pricing power.

What the open-source substitution trade looks like: https://t.co/ASQvy0RxEX

Source: 20VC with Harry Stebbings - https://t.co/XNe5OtaYA1

[darkzodchi @zodchiii]

Google CEO, Sundar Pichai:

"If you don't learn to how to orchestrate agents now, you'll spend 2027 catching up to people who started today"

In 30 minutes he explains why the best engineers stopped writing code and started running agents.

Watch the interview, then save the exact setup below 👇

[Anatoli Kopadze @AnatoliKopadze]

Demis Hassabis: "In the near future, one person who knows AI will outperform an entire startup team"

I've watched hundreds of AI talks, this 60-minute Cambridge lecture is the one I wish I had seen a year ago

this is the Nobel Prize winner in Chemistry, CEO of Google DeepMind and the guy who made AI solve biology

here's the part I can't stop thinking about:

> the AI you're using today is the dumbest it will ever be
> in 5 years the gap between people using AI and people who aren't will be impossible to hide
> companies will run on 10 people doing what 200 used to do
> the ones who get there first won't be the smartest, they'll be the ones who started right now

right now the average person opens Claude, types something, gets an answer, closes the tab

they think they're using AI, but they're using maybe 10% of it

the 10 people doing the work of 200 won't be typing prompts, they'll be running agents

that's exactly why I put together a step-by-step guide on building your first AI agent

agents are the part of AI moving fastest right now, full walkthrough in the article below

[Dustin @r0ck3t23]

Larry Ellison just called AI the biggest thing in human history.

He was being conservative.

Ellison: “It is a much bigger deal than the industrial revolution, electricity, whatever. Everything that’s come before.”

“Bigger” is the wrong word.

“Bigger” assumes the same axis. A taller building on the same foundation. A faster car on the same road.

This isn’t a taller building.

This is a new dimension.

Every revolution in human history extended the body.

Steam replaced muscle. Electricity replaced fire. The combustion engine replaced the horse.

Each one extraordinary. Each one civilization-altering.

Each one built on an assumption nobody ever thought to question.

That human cognition was the ceiling.

A hammer builds nothing the carpenter can’t envision. A telescope reveals nothing the astronomer can’t interpret. A calculator solves nothing the mathematician can’t frame.

300,000 years of invention. Every tool a servant to the mind that forged it.

AI ended that arrangement.

Ellison: “We created neural networks that can answer questions that human brains would struggle with.”

Not slower. Not less efficiently.

Struggle with.

We built something that thinks past the point where human thought stops.

The tool is no longer bound by the toolmaker.

Every ceiling humanity ever hit wasn’t physics. Wasn’t the universe setting limits.

It was us.

We were the boundary.

And we just built something that doesn’t know it’s there.

That’s not a bigger industrial revolution. That’s not even a revolution.

That’s a species discovering it was the only thing standing between itself and everything it couldn’t yet imagine.

And Ellison isn’t waiting for any of it.

He’s already past AGI. Already framing superintelligence not as theory. As a scheduling problem.

People hear that and reach for fear.

Wrong instinct.

Every prior revolution displaced labor. AI displaces limits.

The industrial revolution didn’t make blacksmiths more creative. It made them irrelevant.

AI inverts that. It doesn’t replace human cognition. It uncaps it.

The kid with no teachers now has the most patient tutor ever built.

The founder with no legal team now has one.

The researcher at the edge of what one mind can hold now has a partner with no edge of its own.

This isn’t automation. This is cognitive liberation.

Every revolution before this answered a human question.

This is the first one capable of asking its own.

And we haven’t heard the first one yet.