AI21 pitches open models post-trained on customer data

Speculative decoding executes more total FLOPs per sequence, not fewer — a small draft model proposes tokens that the big model verifies in one parallel forward pass. The gain comes from moving the GPU off memory-bound single-token decoding, where all active parameters (e.g. 27B for Qwen3.5-27B) must be streamed per token.

Berlin-based Just 4 Noise trained Just 4 Kicks on fully owned, in-house audio recorded in studios worldwide over months. It costs €79 upfront with free future updates, and Just 4 Snares, Just 4 Hats and Just 4 Drums are planned next.

Scenario analysis uses evidence-based diagnostic performance as the driving force and clinician trust and adoption as the major uncertainty. The piece argues healthcare needs a workforce that understands AI well enough to use it appropriately.

A r/LocalLLaMA post describes Qwen 3.8 Flash Next as drastically faster and more precise at reaching results when generating Python-drawn paintings. The model reportedly self-checks its first draft and adds corrections quickly.

Podcast interview with Alex Rives, Head of Science at Biohub and former lead of the ESM protein language model project, on building a predictive world model of life. The effort is backed by an expanded $1.8 billion Virtual Biology Initiative with partners including the Department of Energy.

The $320M comes from General Catalyst's Customer Value Fund, which supplies capital without diluting equity and earns returns on marketing results. Otter has raised just $50M total before this, crossed $100M ARR last year, and claims 40M users to date, up from 25M.

Yelp's "Check for Me" feature lets users enter party size and phone number, then an AI agent calls the restaurant and texts back wait times. The app also gains an OpenTable booking integration, 30-day chat history, and custom reminders.