Alexis d'Oultremont

Tweeting about startups and tech news. Co-Founder @joynbelgium & Dog Chef

[Hugo Clément @hugoclement]

Franchement c’est fort.
Arriver à faire rire une salle aux éclats dans une langue que tu ne maîtrisais pas quelques années auparavant, et avec une telle fluidité, ça représente un travail monstrueux.
Bravo @kevadamsss 👏

[Alexis d'Oultremont @adoul]

@hamzawy998 Would love an invit thanks

[Alexis d'Oultremont @adoul]

@SimslearnAi Steal

[Alexis d'Oultremont @adoul]

@mikefutia IG

[Alexis d'Oultremont @adoul]

@spwfeijen Nano

[Alexis d'Oultremont @adoul]

@godofprompt Mega

[vittorio @IterIntellectus]

HOLY SHIT IT'S HAPPENING

AI can now write genomes from scratch.
Arc Institute an NVIDIA just published Evo-2, the largest AI model for biology, trained on 9.3 trillion DNA base pairs spanning the entire tree of life.

it doesn’t just analyze genomes. it creates them
1/

[Andrej Karpathy @karpathy]

"Move 37" is the word-of-day - it's when an AI, trained via the trial-and-error process of reinforcement learning, discovers actions that are new, surprising, and secretly brilliant even to expert humans. It is a magical, just slightly unnerving, emergent phenomenon only achievable by large-scale reinforcement learning. You can't get there by expert imitation. It's when AlphaGo played move 37 in Game 2 against Lee Sedol, a weird move that was estimated to only have 1 in 10,000 chance to be played by a human, but one that was creative and brilliant in retrospect, leading to a win in that game.

We've seen Move 37 in a closed, game-like environment like Go, but with the latest crop of "thinking" LLM models (e.g. OpenAI-o1, DeepSeek-R1, Gemini 2.0 Flash Thinking), we are seeing the first very early glimmers of things like it in open world domains. The models discover, in the process of trying to solve many diverse math/code/etc. problems, strategies that resemble the internal monologue of humans, which are very hard (/impossible) to directly program into the models. I call these "cognitive strategies" - things like approaching a problem from different angles, trying out different ideas, finding analogies, backtracking, re-examining, etc. Weird as it sounds, it's plausible that LLMs can discover better ways of thinking, of solving problems, of connecting ideas across disciplines, and do so in a way we will find surprising, puzzling, but creative and brilliant in retrospect. It could get plenty weirder too - it's plausible (even likely, if it's done well) that the optimization invents its own language that is inscrutable to us, but that is more efficient or effective at problem solving. The weirdness of reinforcement learning is in principle unbounded.

I don't think we've seen equivalents of Move 37 yet. I don't know what it will look like. I think we're still quite early and that there is a lot of work ahead, both engineering and research. But the technology feels on track to find them.

https://t.co/JCxTdKpuzv

[Andreas Klinger 🦾 @andreasklinger]

Patrick asks a great question:
Why is Europe's productivity behind the US?

Everyone says Europe is stuck in decline
But is that the full story?
What's really going on?

I sat down with @yoramdw of @dealroomco to understand the newest data

Here the ultimate Eurodata breakdown⤵️

[Alexis d'Oultremont @adoul]

@henrythe9ths Ai guide please

[Henry Shi @henrythe9ths]

There's a shocking fact about AI that nobody tells you: You can catch up to the public AI research frontier in just 2 weeks. Yes, really.

I've built a $150M annual revenue startup over the last 8 years and If I were to start a company today, I’d drop everything and go all-in on AI.

But like many busy software builders, I felt lost—overwhelmed by the noisy, crowded and fast-moving modern AI landscape. And I wasn’t alone.

So I spent my entire holiday diving deep into AI research—reading 30+ papers, watching hours of lectures, analyzing trends, and catching up to the research frontier.

✨ Here’s what I learned:
- You don’t need months (or years) to catch up.
- You don’t need a PhD or decades of ML experience.
- You need fewer than 20 papers and 2 weeks to understand the major breakthroughs shaping AI today.

It's because the technology is extremely nascent and most techniques that came before are no longer relevant:
- ChatGPT is barely 2 years old and Transformers are only 7 years old.
- Most game-changing discoveries happened within the last 4 years, driven by a few breakthrough ideas, scaling laws, and efficient matrix multiplication.

The biggest secret?
Many groundbreaking AI papers with thousands of citations are surprisingly simple and applied, like adding "let's think step by step" to the prompt, or simply asking the LLM over and over again to improve its answer (Self-Refine).

I realized there are tons of founders and builders in the same boat—wanting to dive deeper into AI but unsure where to start.

I've created an essential AI Guide that helped me catch up, in just 2 weeks, to the frontier of public AI research to figure out where the next opportunities and gaps were:
- Curated list of only the most important papers
- Simple explanations of key concepts
- Clear pathway to understanding the frontier of modern AI

It’s perfect for:
- Founders expanding into AI
- Builders wanting to innovate at the frontier of AI
- Investors looking to separate the signal from the noise

👇 Want the full guide?
- Like and Share this post
- Comment "AI Guide"
- I'll send you the complete guide

(ps, I’m also teaming up with @VishalVasishth, co-founder of @obviousvc with @ev (focused on large-scale societal impact companies like Twitter, Medium, Beyond Meat), to host a small meetup to discuss what's working and needs to be solved in the AI stack in SF. Message me if you're interested)

[Alexis d'Oultremont @adoul]

@mzrnsh @vponamariov Agee with you @mzrnsh I would pay 200 and would not care if the video is public

[Alexis d'Oultremont @adoul]

@vinchbat https://t.co/brzLwnxaw6 article intéressant sur le sujet

[Paul Lê @paulichon]

😂so true

[Julian Lehr @julianlehr]

~ New essay ~

𝗦𝗶𝗴𝗻𝗮𝗹𝗶𝗻𝗴 𝗮𝘀 𝗮 𝗦𝗲𝗿𝘃𝗶𝗰𝗲

• Why there is no luxury software
• The reason why social networks are free to use
• How Tinder and Fortnite monetize signaling amplification

↓

https://t.co/UTIgtuoaCm

[Suhail @Suhail]

Things I am hearing from founders' board mtgs:

(1) Re-plan operating plan in 30-45 days (the future is hard to predict)
(2) Assume 18 months of recovery
(3) Tighten the belt: cut pay, cut costs, or layoff.
(4) Plan for the severe to extreme downside scenario
(5) Raise now

[Nacho Bassino @nachobassino]

This is the best I've read in a long time. Practical and detailed example on a very complex topic: "How Superhuman Built an Engine to Find Product/Market Fit | First Round Review" https://t.co/dkkIIkO5sE

[Y Combinator @ycombinator]

In the last two years, YC companies have raised 190 Series A's and over $2B in capital. We collected information on every round and distilled everything we learned into a Series A Guide. Today, we're making it public: https://t.co/HOcWIZPSMV

[Ryan Orbuch @orbuch]

👋 After more than a hundred conversations with experts across disciplines, I wrote an introduction to Negative Emissions Technologies.

📓This is for absolute beginners: it's the primer I wish I'd had when I started learning about the field last year!

https://t.co/s4xmS2vn19