This is one of the most crucial lessons in First Break AI. It teaches you how to navigate @huggingface like a pro. Not just: download model → run notebook → move on In this lesson, we go deeper. We look at how open model repos are structured, how to read model files, how config.json connects to the actual model class, and how to trace from a Hugging Face model page into the Transformers code that runs the model. We use Qwen3-0.6B as the learning model. We also look at why Markdown matters so much in AI workflows: model cards, GitHub issues, README files, Discord, Cursor, Claude Code, planning docs, and AI-assisted work. Then comes the biggest win: datasets. Working with datasets is a core AI engineering skill. I show 3 ways to analyze datasets on Hugging Face: Croissant endpoint Data Studio / browser viewer load_dataset with Python, pandas, and plots We inspect dataset structure, categories, response lengths, distribution, short examples, long examples, and how to think about dataset quality before using it for training or fine-tuning. And this sets up the next part: running Qwen3 directly in C, without treating Transformers as magic. Lesson 01: Hugging Face Beyond Upload Watch: youtu.be/MjZio-A9oUY Free cohort: cohort.bubblnet.com/lessons/…

Today, with the US banning some frontier models from foreign access, it is a wake up call for India. By 2030 India needs to run on own frontier AI models, OS & Apps, and digital currency. Or 2030s may become the new 1730s - if you know what I mean.

Spine always grows in the back that is against the wall. America denied us crucial LOX/LH₂ cryogenic engine technology. We developed it indigenously and using it we now send American satellites into space. India can remain sovereign only if it has a sovereign AI. @narendramodi

Where is AI in India?

Sir, they have blocked access to the very AI models you said we should share instead of build

Is there a prompt guide for Fable?. Fable uses most of the quota in just few prompts and still feels nerfed. I tried to use fable for a serious task like product analysis. It gave sharp analysis however looks like model is shy about tool calls. It doesn't want to collect a lot of information and I had to push it hard to do real analysis. Analysis overall is sharper than Opus , however this feels like a nerfed model . Is there prompt guide or direction how to use this model effectively?

This is not a day for celebrating, Andrej. It's a very dark and very sad day, and the damage may be impossible to undo.

Labs starting to pull up the ladders on the ability to diffuse AI was inevitable. Doing it without telling the user is misaligned.

I built a small visualization layer on top of a local Qwen3 in Pure C to understand LLM output Image shows why sampling is not greedy decoding: a lower-probability token can still get selected when temperature/top-p keep it inside the candidate pool. I would also love feedback on what would make a visualization like this more useful for learning: - KV cache view? - attention heatmaps? - speculative decoding comparison? - greedy vs top-p side-by-side?

have been recently thinking about why pretrain research matters among the seemingly more crucial data/compute/rl bottlenecks and sharing my take here on what makes pretrain research (still!) vital: 1. better computational efficiency: scalinglaw shifts, 2x less FLOPS needed to achieve the same loss, etc. plus e.g. long context settings where switching to hybrid or sparse attn can save you >90% FLOPS. many model arch / optimizer improvements can save you >20% flops needed for the same loss - those are research innovations on every axis from training iter dimension to inter-layer and intra-layer. the effect of compounded architecture advantage is very distinctive given that ur always improving against your sota baseline. good pretrain research might very well have already delivered you a 10x more efficient (and likewise, better under the same compute) model arch compared to three years ago, and there's still obv many inefficiencies left to be optimized. over half of the compute is still spent on pretraining when you do new from-scratch model trainings rn, and having weeks & months saved there could really allow much more rapid iterations across the entire stack, compounded. 2. to train models one couldn't have been able to previously: residuals, optimizers, etc. this one's less common since most of the arch innovations don't offer more beyond the expressivity gain. but there are significant ones which can e.g. provide more stable learning dynamics (both theoretically and in practice) at all scales so one could scale up. new model configs or forms of training also come back to better efficiency data/compute/FLOPS bottlenecks certainly exist but are relatively more orthogonal to pretrain research and imo it is unclear whether one will be a clear intelligence bottleneck a year from now than the other. in hindsight ive been using "pretrain research" tho this itself is an inefficiency (with further inefficiencies under its scaling law) and "deep learning research" is a better phrasing.

My response to the claim that Muon is a renamed version of Shampoo x.com/kellerjordan0/status/2…

one of my favorite projects is Marin from the stanford folks, they have a scientific approach to training, are ready to take risks and are fully open (even open development where you can follow everything on github!) github.com/marin-community/m…

Claude Cowork with blender is so much fun, still work in progress will post the final scene soon. Trying out if it can build basic geometry nodes scene like waves hitting a beach 🌊🏖️

We have another 65 page frontier model report from Nvidia to read @eliebakouch @stochasticchasm and gang

52% of MCP servers are dead within 90 days. But the median server has 6 commits — lifetime. The protocol works. The logic layer doesn't exist. Content goes stale. Tools stay isolated. Nobody monitors what fails. Full research: fetchlens.ai/research/mcp-se…

Extremely Rare Red Sprites Spotted Flashing Over Tibet. They are caused by high levels of electrical activity and form in the upper atmosphere during powerful thunderstorms.

KV Cache re-use is the most important thing for agentic rollouts. We've integrated Mooncake Store into prime-rl with vLLM, you can now use it as a drop-in replacement for native CPU/Disk offloading, giving you cross-node prefix cache reuse to make your agents go brrr🚀

WOW microsoft new "MAI Thinking 1" model comes with a 109 page tech report that looks REALLY detailed, this is amazing

🎯 Andrej Karpathy on how to learn.

Fantastic. Mostly 𝐬𝐢𝐧(𝐱). Name it... Fireeel? Remix in #Wolfram Mathematica. Full code below. x = Range[0., 9999]; k = 4 Cos[x/21]; e = x/1880 - 20; d = Sqrt[k^2 e^2]; m = UnitStep[k^2 - 15]; Manipulate[With[{ q = 3 Sin[2 k] .3/k k*Sin[x/4465](9 2*Sin[14*e-3*d 2*t])}, Graphics[{ Blend[{White, Red}, Sin[t]^2], Opacity[.5], PointSize[.01], Point@Pick[#, m, 1], White, Opacity[.75], PointSize[.0025], Point@Pick[#, m, 0]}&@ Transpose@{q 50*Cos[d-t] 200, 875-q*Sin[d-t]-39*d}, PlotRange -> {{100, 300}, {75, 320}}, Background -> Black]], {t, 0, 2 Pi}]