Coach, architect and developer at @bam_lab Fan of lean management. Always eager to learn and contribute back if possible to open source.

Paris, France
Joined May 2012
Tycho Tatitscheff retweeted
I built bbtree, the foundation of databases and filesystems, and the fastest concurrent btree. Code: github.com/XiangpengHao/bbtr… Blog post & demo: bbtree.xiangpeng.systems/
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Tycho Tatitscheff retweeted
Before limited-releasing Claude Mythos Preview, we investigated its internal mechanisms with interpretability techniques. We found it exhibited notably sophisticated (and often unspoken) strategic thinking and situational awareness, at times in service of unwanted actions. (1/14)
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Tla+ rocks.
HOLY FUK I JUST LEARNED ABOUT TLA+ AND IT'S SO GOOD FOR AGENTIC CODING ur telling ME that i can mathematically fact check every possible scenario of my design STATE to prevent bugs and crashes AND IF IT FINDS SOMETHING THE AGENTS GET INSTANT FEEDBACK AND LOOP FIXING IT TILL IT ALL POSSIBLE BUGS IN THE DESIGN ARE PATCHED LOL THIS IS OP
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Tycho Tatitscheff retweeted
I just published the most recent batch of optimizations we applied to Elixir's set theoretic types that is allowing us to push the envelope in Elixir v1.20: elixir-lang.org/blog/2026/02… (in-depth technical article)
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Tycho Tatitscheff retweeted
🚨BREAKING: Microsoft Research + Salesforce just dropped a paper that should scare every AI builder. They tested 15 top LLMs GPT-4.1, Gemini 2.5 Pro, Claude 3.7 Sonnet, o3, DeepSeek R1, Llama 4 across 200,000+ simulated conversations. Single-turn prompt: 90% performance. Multi-turn conversation: 65% performance. Same model. Same task. Just... talking normally. The culprit isn't intelligence. Aptitude only dropped 15%. Unreliability EXPLODED by 112%. → LLMs answer before you finish explaining (wrong assumptions get baked in permanently) → They fall in love with their first wrong answer and build on it → They forget the middle of your conversation entirely → Longer responses introduce more assumptions = more errors Even reasoning models failed. o3 and DeepSeek R1 performed just as badly. Extra thinking tokens did nothing. Setting temperature to 0? Still broken. The fix right now: give your AI everything upfront in one message instead of back-and-forth. Every benchmark you've seen was tested on single-turn prompts in perfect lab conditions. Real conversations break every model on the market and nobody's talking about it.
Readers added context they thought people might want to know
This is not breaking news, the paper citied was submitted on the 9th of May 2025 and is almost a year old. Link to the paper: arxiv.org/abs/2505.06120
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Tycho Tatitscheff retweeted
If you explain how to do it, Claude Code is able to use Codex when it is incapable of solving certain issues. This way you have the best of both worlds. Here is the skill file that lets Claude use Codex. gist.github.com/antirez/2e07…
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Tycho Tatitscheff retweeted
I let Claude Code turn @karpathy's post into agent skills. It first generated a bunch of skill files and around 800 lines of descriptions. Then I let it use these agent skills to review itself. Boom, it cut itself down to 70 lines of clean, solid instructions. github.com/forrestchang/andr…
A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.
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Tycho Tatitscheff retweeted
Listen to this foreigner’s recap of Trump‘s world economic forum speech in Davos. “To the Americans that have not lost their sanity, I am so sorry. I hope your democracy survives this mad man…”
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Tycho Tatitscheff retweeted
DeepMind just did the unthinkable. They built an AI that doesn't need RAG and it has perfect memory of everything it's ever read. It's called Recursive Language Models, and it might mark the death of traditional context windows forever. Here's how it works (and why it matters way more than it sounds) ↓
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Tycho Tatitscheff retweeted
Just announced: Hermes V1 🚨 Hermes V1 is 60% faster than current version!! 😱
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Tycho Tatitscheff retweeted
zod4 is 5x faster than zod3 for object validation. arktype is 20x faster than zod4.
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Tycho Tatitscheff retweeted
Replying to @TheFl0orIsLaVa
this is the answer you’re looking for
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Bip bip bip #DirectAN
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Tycho Tatitscheff retweeted
Spent the afternoon comparing Cline in Cursor vs Claude Code to build an A3 problem-solving editor. The idea: a text-only editor that transforms into a decent A3 using markdown, mermaid.js and some css tricks. Outcome: I prefer Cline to Claude Code, seeing diffs in the IDE is much better than in the terminal.
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Tycho Tatitscheff retweeted
Excited to share that I’ve teamed up with @antoinethibierg, @antoine_co_tech, and @TychoTa on a new RFC to bring OkLab and P3 color support to #ReactNative! 🎨🚀 We’d love your feedback! github.com/react-native-comm…
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Tycho Tatitscheff retweeted
Ever wondered what happens during an Expo prebuild? 🤔 I broke it down in my first technical article! Huge thanks to the @expo team for their incredible documentation. Hope this helps other devs that are starting out ! Let me know what you think :) medium.com/@gabrieIa/expos-c…
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