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Introducing Superwhisper Meetings The first fully on device meeting recorder and note taker.

What can you build with Jev? Practical use cases for fast, cheap decision models. rubriclabs.com/blog/what-can…

we merged 4x more commits last week than we did a month ago (thanks opus 5.5). our dev setup and CI couldn't keep up, so we reworked both: • lint: 4x faster • memory per agent session: half • CI setup step: 50x faster • CI type check: 10x faster we often run 10+ agents at once locally, and each one runs its own lint, type checks and tests. from time to time, several agents would run these at the same time and block each other. a 64 GB MBP would run out of memory and crash. so most of the work was making things faster and lighter, or getting rid of them we revamped our local setup to make checks faster and use less memory: • oxlint and oxfmt instead of eslint and prettier, and type-aware lint only runs in CI. linting a changed file went from over 10s to a few seconds • a hook formats every file an agent edits, so agents don't have to take a turn to format it • typescript 7's native LSP instead of tsserver. each agent session uses about half the memory • agents run one check script after each change. it installs missing packages and env files first, then lints, type-checks and runs the tests for what changed • only 3 type checks or big test runs can run at once across all worktrees, so they don't slow each other down we revamped our CI to make it faster: • every CI job installs pnpm before it can install our packages. the standard github action for this (pnpm/action-setup) installs pnpm through npm, and that hung for 3-7 min on most of our test jobs. we now download the pnpm binary from github releases, which takes about 4s • our type check needs about 9.5 GB of memory. github's standard runners have 8 GB, so it ran out of memory and took 206s. on a 16 GB machine it takes 20s • vercel builds type-check with typescript 7 (85s to 45s), and previews only build for PRs that change the database if you're running lots of agents, look at what they're waiting on. for us it was mostly lint, type checks and CI setup

Today, we are launching a preview of our new model, Mistral Large 4 (ML4), aka le Chonk 🐈. ML4 is a 1T-parameter model with 49B active parameters, trained natively with multimodal capabilities. It is at the frontier of open models, and by far the strongest open-weight model from the US or Europe. The RL run behind this preview is still in flight and shows no sign of saturation -- we will release a final version before the end of the month along with the weights of the model. 🧵 1/n