Nikita Pavlichenko

@gianmalio I don’t think I was even aware of their existence at that time

@nv_pavlichenko Have you looked at Citadel / BAM?

I interviewed at Jane Street for a Quant Intern in 2019. This was the only time in my life when I had an interview on the **phone**. A guy called me from NY and ask some 9-th grade math questions, I think about how many steps you take going down an escalator and the probs of winning in paper rock scissors with some restrictions. I was so shocked that the phone interview is actually by phone so I fucked it up. I also found it hard to write something with a phone in hand but probably ngmi the moment I took a pen.

@iamgrigorev @poolsideai Finally! Congrats!

We just released our first models at @poolsideai – including Laguna XS.2 (open weights) that competes with Qwen3.6-35B. I worked across pretraining — happy to answer questions! We now have great understanding of exactly all components that went into training through principled ablations, and now we're confident to scale. This year would be 🔥 for us! More coming very soon

@_avichawla Literally interview is over after this answer. DeepSeek dsa requires you to have mla, enabling swa in mid training is sketchy, no mentioning of yarn on rope theta scaling as well as long context data job. FlashAttention irrelevant. Nice rage bait

You're in a Research Scientist interview at OpenAI. The interviewer asks: "How would you expand the context length of an LLM from 2K to 128K tokens?" You: "I will fine-tune the model on longer docs with 128K context." Interview over. Here's what you missed:

@HungryMinded @deedydas First model to discover gambling

@deedydas First model to become a part of the grind culture?

Claude Mythos just obliterated every single benchmark in AI. I can't believe what I'm reading.

nvidia/Nemotron-Instruction-Following-Chat-v1 is my favourite hf dataset from now on. THIS is how we build AGI

They don’t rely on hype rather they get you addicted on claude code so you then go beg your enterprise to buy you credits. The moment everyone got addicted and can’t work without it anymore, they rug pull the consumer plans and force everyone to enterprise. Was clear as day from the start

Redditor claims Claude Code is nerfed for Pro/Max users vs Enterprise customers and the strategy is to use the paid plan users to generate hype on X and LinkedIn so companies would reach out to them.

We're currently wrapping up our first big pre-training run at JetBrains: a coding-focused LLM, which can apparently be the second best fully european open pre-train after Mistral (they have larger models). Though gotta do a research on that, please drop the models we need to compare with. Probably need to shitpost here a little bit since we're really lacking publicity Here's the loss plot for the start (periodic loss spikes are interesting, probably gonna have a section in the report dedicated to what they are). Benchmarks are honestly insane for the first team's run

@teortaxesTex So indeed “wrong climate?”

The funny thing about living in the tropics is that you can tell these people are less competent than Russians in every day-to-day manner, have like infinity time preference, goof around, but they end up with a society that's *more* livable than Russia + no fascism or cortisol

@icanvardar By other harnesses being overoptimized for a benchmark

how is claude code the worst harness in opus 4.6 benchmarks 💀

Was waiting for this since 2019

@lvwerra The old architecture will be slow and expensive but definitely better if you count in RL envs. Practically all the architecture changes in the last three years were dedicated to making a dense llama-like model faster (moe, mla, hybrid models, etc), not better

Which LLM would be better: - today's best architecture trained on 2023's best data - 2023's best architecture trained on today's best data

@OjasSharma276 Tasteless rlhf data annotators

I don’t get it. ChatGPT and other AI models use so many emojis. On what data were they trained? Because I’ve never seen this much emoji usage anywhere else.

@0xkyle__ It’s literally r/wallstreetbets type of portfolio with long on ai infra and hedge on nvidia

Bringing this back to remind you that a 25 year old German woke up, got tired of building AI, decided he would outperform all of finance, raised 1.5 billion, and put 20% of his fund into INTC His name is Leopold Aschenbrenner, and INTC is up 10% today.

One of the main unsolved problems in coding AI is a cold start of new libraries, languages, and frameworks. The models perform best on what is represented in the training data the most. This is usually old frameworks and JS/Python code. If you ship the next best thing, all the models will struggle with using it. This is why Claude still wants to use pip instead of uv sometimes even when you explicitly tell it not to. There isn’t a proper solution to it really. You can practically go with two general directions: 1. Fine-tune on the frameworks code or hope for continuous pre-training. Helps a little bit but not dramatic since the relative (and absolute) size of it in the datasets will be tiny. Targeted RL (so the data is potentially unlimited) post-training is the closest thing to make it work. 2. Drop the docs into context window inference time either directly or with RAG. Bloats the context window, requires manual work and overall can be done only with a limited amount of specifically targeted technologies. This is a real problem that can prevent adoption of nice things in the industry and when most of the code is generated by LLMs it only becomes worse. We can end up in the 2025 state of tech for longer than we’d want. I do think this highlights some big flaws in the current approach to LLMs that will accelerate the progress when resolved.

@rieszspieces @fluxtheorist @DeepDishEnjoyer 75 is enough for measure theory. literally the most intuitive thing from this thread

@fluxtheorist @DeepDishEnjoyer to be fair you need to have a 175 iq to understand measure theory, the nuances of borel sigma-algebras and convergence in measure do not come easy to mere mortals

>gets asked for evidence >makes it totally up i can't think of a single graduate level math class that 130 IQ kids can't handle but 140 IQ suddenly can

@GenAI_is_real hope this post was written by deepseek then and not 5.2

86 pages of pure technical dominance. People keep asking how DeepSeek mastered the synergy between Infra and Algo. It’s not magic; it’s a radical rejection of corporate rot. While xxxx is drowning in "safety" bureaucracy, yyyy is paralyzed by middle management bloat, and zzzz is bogged down by legacy silos, DeepSeek has built the ultimate spec-ops squad. Their blueprint is lethal: They only hire "Full-stack" hybrids who master both Infra & Algo. They pay at the absolute top of the market to keep the elite together. They grant total autonomy with maximum GPU-per-capita. They operate in a flat, high-density environment—no "alignment" meetings, just execution. DeepSeek is the definition of an AI-era elite organization. Everything else is just legacy overhead. The empires are crumbling.

@GergelyOrosz There was only like a two years period around covid when you didn’t need a degree anyway

One trend that will likely return in tech, thanks to AI: Increasingly only possible to be hired as a new grad with a CS or similar degree. AI agents render writing code less relevant: but eveything else about building software (aka software engineering fundamentals) more!

i feel so ashamed that i didn’t end up digging into JEPA papers saga largely because in russian this names kinda sounds like “ass” and it prevents me from taking it seriously enough the only thing that’s helping me feel better is that it was also the reason for multiple respectable ai folks that I personally know