My PR #37928 to JAX library got approved.
- It fixes a sparse autodiff bug involving BCOO sparse arrays, vmap, and reverse-mode gradients.
- In short: a batched sparse matvec gradient could fail because the cotangent shape didn’t match the original sparse data shape.
- The fix uses JAX’s existing _unbroadcast helper to return the correct shape.
My PR #8220 was merged into @huggingface Datasets library.
- It adds support for composed splits in streaming datasets
- making split composition work more consistently between streaming and non-streaming dataset loading.
A small but practical fix for ML data pipelines.
Each time we release a model, we run the same test: give it code that trains a small AI model, ask the new model to speed it up. It takes a skilled human 4-8 hours to reach 4x faster.
In May 2024, Claude Opus 4 averaged a ~3x speedup. This April, Mythos Preview achieved ~52x.
Anthropic has confidentially submitted a draft S-1 registration statement to the Securities and Exchange Commission.
Pending completion of SEC review, this gives us the option to pursue an initial public offering.
Read more: anthropic.com/news/confident…
New training speed record for @karpathy’s 124M-parameter NanoGPT setup: 3.28 Fineweb validation loss in 3.7B training tokens
Previous record: 5B tokens
Changelog: new optimizer
1/8
RESTful APIs may be dead soon. Instead, web services may expose a single POST entry point for a prompt. Internally, an AI agent may decide how to interpret it and what to do with the data and the database.
🚨 We recently discovered that an unauthorized party obtained a token with access to the Grafana Labs GitHub environment, enabling the threat actor to download our codebase. (1/6)
-Ultimate cheatsheet for choosing the right Vector DB for your RAG applications.
-If you're building RAG systems, AI search, copilots, semantic retrieval, or agentic workflows, this will save you hours.
One person can't pay a $8,000 surgery bill. but 8,000 people can pay $1.
the app: someone posts their bill. you give $1. the app prompts: "share this chain." your friend sees it, gives $1, shares. THEIR friend gives $1, shares.
the chain grows. the bill shrinks. everyone gave $1. nobody felt it. the chain is the virality.