Anakin Netwalker

I shall return...

[Rahul @sairahul1]

Anthropic engineers just dropped 40 minutes on how they actually build loops with Claude Code.

Not prompts. Not tools. Loops.

3 agents. 1 loop.

One plans. One builds. One judges.

Plan → Build → Judge → Repeat.

The agent doesn't stop until the app works.

Most people are building agents that run once and hope for the best.

The best teams are building agents that cycle, self-correct, and improve with every iteration.

That's the gap.

Watch it, then read this before you build your next loop.

[Neha Singhal Trader @nsinghal211]

China just took AI infrastructure underwater.

The country has reportedly launched a wind-powered underwater data center near Shanghai, designed to cool AI servers using seawater instead of traditional cooling systems 🌊🤖 Around 2,000 servers are said to be sealed inside submarine-grade modules, helping reduce land use, water consumption, and cooling energy.

The project is powered by offshore wind and built for AI workloads, big data, and large-scale computing. Why it matters?

AI is creating massive demand for electricity, cooling, and infrastructure. If underwater data centers prove reliable, they could become a new way to scale AI while using less energy.

But the challenge is huge: corrosion, maintenance, sea conditions, and long-term reliability still need to be proven.

[Avi Chawla @_avichawla]

Karpathy said something you'll regret ignoring:

"Remove yourself as the bottleneck. Maximize your leverage. Put in very few tokens, and a huge amount of stuff happens on your behalf."

Loop engineering is the exact thing that does that.

In a hand-run session, the operator handles two things:

- deciding what the agent runs next
- and checking its output before the next step

Both are manual, and both decide how far the agent gets on its own without the operator.

Loop engineering moves both steps into the system.

A core operating structure surrounds the loop, and the diagram below depicts it.

- A schedule decides what to run
- Loop is the maker that produces the work
- A separate checker agent grades the output
- A file on disk holds the state they both read.

The loop runs until either done, max iterations, or an exhausted budget.

Here are some practical engineering considerations:

1) A model grading its own output justifies what it already did instead of catching where it failed.

That's why a separate checker's findings return to the maker as the next instruction. And the cycle repeats until the checker finds nothing left to fix.

2) A loop with no stop condition burns tokens, and the cost climbs fast once sub-agents and long runs add up.

That's why the exit must be set before the loop runs, not while it is running.

A simple exit could be:
↳ fix only the major issues, run one final pass, and stop after two loops, with "all tests pass and lint clean" as the rule that ends it.

3) State has to live on disk, not in context.

The model forgets everything between runs, so an MD file or a knowledge graph holds what is done and what is still open.

Each run reads it and writes back to it, which lets a loop pick up again after days.

4) The lower the verification bar, the safer the loop.

Boring, repetitive checks like a stale version string or a missing test are trivial to verify, so a loop runs them with little risk while the operator is away.

Judgment-heavy work is loopable too, but only as far as the checker can confirm the result.

Let's look at how an unattended loop fails in two ways.

1) It reports done when nothing is actually verified.

The separate checker exists to prevent it, but it merges code faster than anyone reads it, so over weeks, the team stops understanding its own codebase while every check stays green.

Green tests say the code passed the tests, not that anyone knows what shipped. Someone still has to read what the loop merges.

2) The checker keeps a running loop honest, but it only catches failures inside a run.

The harness around the loop, like the prompts, tools, and checks wrapped around the model, still drifts and breaks in production as models change.

That repair loop is usually run by hand based on observability traces.

My co-founder wrote a detailed walkthrough (with code) on making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it cannot recur.

Read it below.

[MOWESTGoHard_🌠 @Moweyyyy184273]

The Evolution of Elon musk (1998-2026)

[Elon Musk @elonmusk]

Good interview

[BijanBowen @bijanbowen]

Had Claude Fable 5 log network packets and display them as cars on a highway, different car types = different packet types

[Claude @claudeai]

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Its capabilities exceed those of any model we’ve ever made generally available.

[AilaunchX @Ai_Tech_tool]

ANDREJ KARPATHY COULD HAVE CHARGED $2,000 FOR THIS COURSE.

He put it on YouTube.

The full training stack. Tokenization. Neural network internals. Hallucinations. Tool use. Reinforcement learning. RLHF. DeepSeek. AlphaGo.

3 hours of the most comprehensive LLM education that exists anywhere at any price.

Not how to use the tools.

How the entire system was built from the ground up and why it behaves the way it does.

The engineers who understand this build things the ones who only use the tools cannot even conceive of.

The gap between those two groups is not 3 hours.

It is everything those 3 hours quietly unlock for the rest of your career.

[KARDASHEV @KardashevX]

Alien technology.

[JAMES WEBB @jameswebb_nasa]

Você está mais próximo do tamanho de todo o universo observável do que da menor escala conhecida da natureza, o comprimento de Planck.

Apenas reflita.

[SpaceX @SpaceX]

Onboard views from Starship and Super Heavy V3, which are equipped with upgraded cameras capable of streaming 4K video through every phase of flight via @Starlink

[Rahul @sairahul1]

Anthropic pays $750,000+ a year for engineers who know how to build LLMs from scratch.

Stanford just released the exact lecture that teaches it - 1 hour 44 minutes, free, straight from CS229.

Bookmark and watch it this weekend.

It'll teach you more about how ChatGPT & Claude actually work than most people at top AI companies learn in their entire careers.

[La Derecha Diario @laderechadiario]

🚨🇫🇷☪️ | PAÍS TERMINADO: El streamer surcoreano KangJoel fue atacado y amenazado con un cuchillo en plena transmisión por un grupo de inmigrantes africanos durante su visita a Marsella, reflejando el nivel de inseguridad y violencia que atraviesa Francia como consecuencia de las fronteras abiertas.

[Rohit @ai_rohitt]

🚨Anthropic just showed a 24-minute workshop on how to actually do prompts for Claude.

Taught by the people who built it.

Free. No registration. No paywall.

I've seen $300 courses that don't cover what they teach in the first 8 minutes.

Watch it and bookmark it now.