Kristof Slechten

Dad of Bo & Mila & Husband. MSc Computer Science, Data scientist and Machine learning engineer

[Kristof Slechten @KristofSl]

https://t.co/ncCzayRe0i

[Kristof Slechten @KristofSl]

https://t.co/pskZ96lTKX

[Kristof Slechten @KristofSl]

Read “Building an Agentic Knowledge Graph for Billions of Events“ by Fareed Khan on Medium: https://t.co/BFS5gqxqIc

[reine @reinexbt]

This is f*cking insane. Demis Hassabis spent 16 years building DeepMind and won a Nobel Prize

Last week, he packed what he knows about the next 5 years into one free 1-hour talk

He explains who survives what's coming and who doesn't

Chatbots → Agents → Workflows → Automated Departments → One-Person Companies

One person who really understands AI will outperform an entire startup team

People pay $15K for bootcamps that teach less than this

You probably don't have 1 hour right now

Don't let this disappear from your feed

Bookmark it and watch it tonight

Then read how to build agents that actually finish the job in the harness engineering guide below ↓

[Kristof Slechten @KristofSl]

@pmddomingos @ylecun An opinion from @ylecun vs a random computer science professor who only has a world audience via the X sedert..

[Kristof Slechten @KristofSl]

https://t.co/PYh2DRZXC1

[Residual @res1dualedge]

Andrew Ng just dropped the best 2-hour course on Graph Engineering: from single agent to full automation

9:14 - your first agent
33:11 - loop engineering
1:02:46 - graph engineering
1:30:15 - agents that rewrite themselves
1:49:05 - full graph system

two hours, and it replaces every agent tutorial you bookmarked this year

Prompts → Agents → Loops → Graphs

most people will stop after the first agent and call it automation

he saves the last forty minutes for the graph that runs it without him

same model, same tokens, completely different week

watch it today

the step-by-step guide is below, save it while it is still early ↓

[Kristof Slechten @KristofSl]

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[Kristof Slechten @KristofSl]

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[Kristof Slechten @KristofSl]

https://t.co/7EnJQZMvZG

[Kristof Slechten @KristofSl]

Read “The Complete Guide to Agent Harnesses (With Code)“ by Paolo Perrone on Medium: https://t.co/U7oXPGOlNI

[Kristof Slechten @KristofSl]

https://t.co/vsGH5kfa7z

[Kristof Slechten @KristofSl]

Integrating Agentic AI with Existing Machine Learning Pipelines https://t.co/5by9HLNdZS

[Lunar @LunarResearcher]

Andrej Karpathy just explained the 5 shifts turning LLMs into agentic systems.

00:00 - Memory turns chat into personal AI
06:41 - Multimodal AI reads the world
16:58 - Thinking models solve harder tasks
24:51 - Search makes LLMs live
30:58 - Tools turn LLMs into workers

Most people are still treating LLMs like chatbots.

Karpathy is showing the full stack:

Memory → Vision → Reasoning → Search → Tools

Prompting is the old workflow.

Agentic systems are the new one.

This 40-minute talk is worth more than most paid AI agent courses.

Bookmark and watch it before everyone catches up.

Then read how to turn LLMs into self-improving agent loops below

[Kristof Slechten @KristofSl]

Dreamy sunrise tech house set

https://t.co/6gpYBdUi4C

[John A De Goes @jdegoes]

https://t.co/Nld7BciQoW

[How To Prompt @HowToPrompt__]

Google Deepmind argues that LLMs can never make real scientific discoveries.

They published a paper breaking down Albert Einstein’s private view of scientific discovery.

In a famous letter to his friend Maurice Solovine, Einstein drew a diagram of how science actually happens.

It is a cyclical loop.

First, you experience raw sensory data. Then, through a mysterious, non-logical act of intuition, you make an intuitive "jump" to abstract axioms. Finally, you use strict logical deduction to derive consequences from those axioms.

Generative AI has completely mastered two-thirds of this loop.

• Induction: Statistical pattern matching across billions of tokens.
• Deduction: Formal proof generation, like AlphaProof solving complex math Olympiads.

AI can crunch data and it can prove theorems.

But it cannot make the jump.

The paper argues that AI completely lacks Abduction, the generation of novel explanatory hypotheses when observational data is scarce.

The prevailing tech myth says that "creativity is just data compression." That if you feed an LLM enough text, scientific breakthroughs will naturally pop out.

Einstein’s formulation of General Relativity proves that is a delusion.

When Einstein formulated relativity, the observational data didn't demand a new physics framework; classical mechanics was still massively successful. The breakthrough required a conceptual rupture. An intuitive leap from physical reality to a brand-new set of foundational axioms.

An LLM can execute the math once the axioms are given. But it is structurally incapable of formulating those premises on its own.

It can interpolate inside existing human thought, but it cannot transcend it.

The translation of physical reality into formal axioms remains the absolute, hard bottleneck of artificial scientific invention.

We can build models with trillions of parameters. We can scale compute into the stratosphere.

We can make the calculator infinitely fast.

But until we solve grounding, the machine can process all the data in the universe.

It still can't make the jump.

[Movez @0xMovez]

Andrew Ng:

“AI agents are doing almost 100% of my tasks now - the hype has exceeded my expectations.

in 4-6 months, we’ll all be building graphs to orchestrate self-improving agents. No more prompting.”

In a 20-minute talk, Andrew Ng explains how to build self-improving agentic systems from scratch.

Worth more than a $500 agentic course.

Watch this video, then read the article below on how to become a graph architect.

[Nagma parween @TechByNagma]

Don't just prompt Claude. Build a system that can prompt itself.
In this 45-minute session, she explains how Anthropic builds AI agents that can remember past work, learn from mistakes, and improve with every run.Ione of the best free resources for learning how AI agents work.

[Rishabh @Rixhabh__]

Andrej Karpathy just challenged one of the biggest assumptions in AI.

Most people think better agents come from bigger models.

He argues the opposite.

In 16 minutes, he explains why small models, the right tools, and closed feedback loops are where real capability comes from.

Watch it.

Bookmark it.