Concrete heat. Cold focus. Built, not gifted. 🦍💎
Joined January 2026
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Shobhit retweeted
This is the least surprising graph in the world if you understand how gradient descent works.
Shobhit retweeted
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
Shobhit retweeted
go fuck yourself @sama
claiming that you solved navier stokes because 10000 agents ran in circles for 88hours on a multimillion dollar gpu cluster to formalize in lean a blowup case under controlled external forcing is pure scientific vulgarity the clay mathematics institute millennium prize does not ask m whether you can artificially force a singularity in a fluid by injecting an ad hoc smooth external forcing term f(x,t) to twist the vortex until it breaks the real problem questions the fundamental stability and global smooth existence for 3dimensional incompressible euler & navier stokes equations under natural conservation laws and viscous dissipation alone using a mathematical loophole on forced equations to parade a century old victory is a major conceptual scam Altman
technically & epistemologically what you present as an agi breakthrough is nothing more than bruteforce combinatorial autoformalization the ai did not understand fluid mechanics it simply navigated a continuous search space previously mapped out and constrained by the monumental work of human mathematicians like tristan buckmaster/ levent alpöge / diego córdoba or tarek elgindi coordinating 10000 agents to check the logical consistency of a 100 page proof via lean is a software engineering feat and computational parallelization triumph not an intrinsic scientific discovery it is the victory of the compute bulldozer over abstract human intuition repackaged for the public as a higher mathematical consciousness
to this theoretical imposture you add a disgusting ethical and industrial cynicism taking advantage of private codex sessions and informal preprints from academic researchers to siphon their research leads and then trying to redact or erase the contribution of levent alpöge under the pretext that he works at rival anthropic is intellectual serfdom openai behaves like a feudal lord of silicon appropriating the cognitive subsistence of independent scholars threatening their careers behind closed doors if they protest and turning community academic labor into a privatized pressrelease
this entire staged event serves a desperate financial agenda in a pre ipo panic facing the slowdown of scaling laws and growing investor skepticism over the profitability of foundational models openai needs to manufacture an artificial sputnik moment claiming to solve a millennium prize without immediately submitting the proof to traditional peer review means using the prestige of fundamental mathematics as cheap marketing fuel to inflate a delusional valuation!!!
real science is not a clout chase on social media or a compute spike spent to rob the clay mathematics institute it is a quest for elegance physical truth and universal rigor to decode reality true artificial intelligence will not emerge from hostile corporate takeover of academic work hidden behind computational bruteforce but from architectures capable of generating new conceptual paradigms by masquerading constrained formalization as the collapse of physics greatest mysteries you did not solve navier stokes you only proved how far silicon valley will go to prostitute scientific integrity for capitalist spectacle
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
Shobhit retweeted
this is f*cking gold
Andrej Karpathy joined Anthropic five weeks ago.
Two Anthropic seniors just made Karpathy's loop 1000x better with "Graph Engineering"
the agentic systems got 1000x better the moment you wired agents into a graph
I dropped it into my setup. The very first response was different.
Not slightly different. Completely different.
Claude stopped giving generic answers and started working exactly the way I think.
Bookmark it before it gets lost in your feed.
Read it now, then check the article below.
Shobhit retweeted
Andrej Karpathy just explained the 5 shifts turning LLMs into agentic systems.
00:00 - Memory turns chat into personal AI
06:41 - Multimodal AI reads the world
16:58 - Thinking models solve harder tasks
24:51 - Search makes LLMs live
30:58 - Tools turn LLMs into workers
Most people are still treating LLMs like chatbots.
Karpathy is showing the full stack:
Memory → Vision → Reasoning → Search → Tools
Prompting is the old workflow.
Agentic systems are the new one.
This 40-minute talk is worth more than most paid AI agent courses.
Bookmark and watch it before everyone catches up.
Then read how to turn LLMs into self-improving agent loops below
Shobhit retweeted
I'm a former Citadel quant who covered power & gas.
There's constant talk about chips & memory, but power is the central bottleneck for AI.
Very few people understand it, so I'm posting a canonical primer on power pricing & data centers: power2026.ai/
Shobhit retweeted
GPT-5.5 hallucinates on 86% of non correct responses.
GPT-5.4 was 89%.
A 3 point improvement.
On a metric where Claude Opus 4.7 sits at 36%.
Two years of research. Trained on Stargate.
Still hallucinates more than every Anthropic model.
The moat isn't gone. It just got deeper.
Shobhit retweeted
Kimi K2.6 is now #1 on Design Arena.
Ahead of Opus 4.7. Ahead of Opus 4.6. Ahead of GLM 5 Turbo.
An open-source Chinese model just took the design crown from Anthropic.
This is insane.
Shobhit retweeted
ml-intern by @huggingface is wild 🔥
You drop a high-level prompt (“build the best scientific reasoning model” or “crush healthcare benchmarks”) and this open-source agent does the entire post-training loop:
• Researches arXiv papers + citation graphs
• Pulls & cleans datasets from HF Hub
• Implements SFT, GRPO, synthetic data, etc.
• Launches training jobs (local or HF)
• Monitors runs, reads evals, diagnoses failures, runs ablations & iterates
Basically a full-time ML researcher that never sleeps and stays grounded in live docs. Insane.
#AIResearch #AI #ML
Introducing ml-intern, the agent that just automated the post-training team @huggingface
It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem.
It can pull off crazy things:
We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%.
In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%.
For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on hf.co/spaces, watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously.
How it works?
ml-intern makes full use of the HF ecosystem:
- finds papers on arxiv and hf.co/papers, reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on hf.co/datasets
- browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data
- launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains
ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like.
Releasing it today as a CLI and a web app you can use from your phone/desktop.
CLI: github.com/huggingface/ml-in…
Web + mobile: huggingface.co/spaces/smolag…
And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.
Shobhit retweeted
Introducing OpenMythos
An open-source, first-principles theoretical reconstruction of Claude Mythos, implemented in PyTorch.
The architecture instantiates a looped transformer with a Mixture-of-Experts (MoE) routing mechanism, enabling iterative depth via weight sharing and conditional computation across experts.
My implementation explores the hypothesis that recursive application of a fixed parameterized block, coupled with sparse expert activation, can yield improved efficiency–performance tradeoffs and emergent multi-step reasoning.
Learn more ⬇️🧵
Shobhit retweeted
I'm increasingly convinced that there is never going to be a major crash again. The market is so divorced from the "real" economy that 90% of the population could be eating dirt and the S&P would still be noting record gains.
Shobhit retweeted
Gemma 4 is the slowest model on BridgeBench SpeedBench.
Dead last.
26.9 t/s.
18th out of 18 models tested.
The next slowest model is GLM 5.1 at 44.3 t/s.
Not even close.
80% on LiveCodeBench.
Last place on Debugging.
Last place on Speed.
Google built a model that can pass coding interviews but can't ship or debug production code at any usable speed.
bridgebench.ai
Shobhit retweeted
🚨 CRITICAL: Active supply chain attack on axios -- one of npm's most depended-on packages.
The latest [email protected] now pulls in [email protected], a package that did not exist before today. This is a live compromise.
This is textbook supply chain installer malware. axios has 100M+ weekly downloads. Every npm install pulling the latest version is potentially compromised right now.
Socket AI analysis confirms this is malware. plain-crypto-js is an obfuscated dropper/loader that:
• Deobfuscates embedded payloads and operational strings at runtime
• Dynamically loads fs, os, and execSync to evade static analysis
• Executes decoded shell commands
• Stages and copies payload files into OS temp and Windows ProgramData directories
• Deletes and renames artifacts post-execution to destroy forensic evidence
If you use axios, pin your version immediately and audit your lockfiles. Do not upgrade.