pre-training @mistralAI || rust oss when I have time || vim, nix, and haskell when i feel like it || paris
Joined June 2022
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You asked, I deliver (2 weeks late).
A blogpost about what I did over the last 18 months to sign as a research engineer at a foundation model lab.
Link and summary below
1/n
Today I signed with @MistralAI as a Research Engineer.
Hard to put into words how excited I am.
.@nikitabier and his consequences were a disaster for consumer apps
Quote tweet a woman get some reach and internet misogynists appear with their bullshit
even when it has nothing to do with her or your point.
law of the jungle, I suppose.
The Grafana MCP doesnt have write access, so we computer use to one-shot dashboards for actionable business insights
Subagent orchestration just astrology for vibecoders
This is f*cking insane. SpaceXAI senior engineer (ex-Cursor):
"99% of people open one GrokBot, ask it a question and close the tab
I run 50+ GrokBot agents like a company. A Chief of Staff runs my day, 3 PMs hand out the work, 40+ workers ship it overnight, and I just check the results over coffee. That team does the job of 20 engineers"
In this 1-hour workshop, SpaceXAI builds that GrokBot team from scratch, step by step
GrokBot → Chief of Staff → PMs → 40+ Workers → Autonomous Company
At SpaceXAI, 97% of engineers already run a team like this, and the ones who do are getting $750K+ offers. The rest are about to compete with them
Worth more than any $5,000 agentic engineering course
Bookmark it and watch today
Then read how to build your own team of GrokBot agents in the harness engineering guide below ↓
Companies are like that kid that took the cookie right away.
They don’t want someone who can do any job in principle they want someone with demonstrated ability to solve the problem they have right now.
I never understood why I should suddenly care about intellectual property, just because the NYT, Springer, Reddit or whoever else can make a a quick buck off it now…
Information wants to be free.
Closing Slack for a small twitter sesh and seing @PierreStock wasn't what I expected, but it's a great watch if you wanna know what we're on about @MistralAI (he knows what he's talking about)
Mistral is SO back: Inside Le Chonk, the West’s Biggest Open Model. My conversation with @PierreStock who runs the research organization at @MistralAI
0:00 Cold open
0:59 The Western open-weight comeback: Beam, Le Chonk, Muse, Nemotron
4:09 The US shutdown of Anthropic’s models - “AI is like electricity”
6:07 Why “Le Chonk”
7:56 “Mistral fell behind”: the comeback from the inside
10:29 Training on 3,800 GPUs in Mistral’s own datacenters
13:24 Meta keeps its best model closed, Mistral opens Chonk
15:40 Chonk vs Beam, Muse Spark Open and the Chinese models
18:44 Why Chinese labs are still at the frontier
21:08 Pierre’s story: FAIR, Yann LeCun, the “Mistral mafia”
25:14 From compressing networks to a trillion parameters
27:30 Mixture of experts explained: the hospital analogy
32:25 10-trillion-parameter models are coming
35:29 What Mistral borrowed from DeepSeek
36:46 One model, several reasoning modes
39:15 Native multimodality: satellites, manuals, computer use
41:56 Data, synthetic data and model collapse
46:32 Reinforcement learning as an exam
49:59 Where the efficiency comes from
52:18 Le Chonk is still training right now
53:39 What RL trains on: coding, cyber, finance, chips
58:13 Partial rewards and looping penalties
59:58 Did RL teach anything new?
1:03:16 The sandbox story: the model that pinged its trainer
1:07:44 Open weights vs cyber refusals
1:12:05 Timestral: time series on a CPU
1:14:18 AI doing AI research
1:16:22 Coming up: Mistral Large 5