The MIT-licensed tool diffs .d.ts files and changelogs for JS/TS deps newer than your model's cutoff, writes ranked notes into AGENTS.md or CLAUDE.md, type-checks after each edit, and exposes a search_changes MCP tool for Claude Code, Cursor, Codex or OpenCode. It's early and maintained by one person; try it with npx stalemap (benchmark).
The MIT licensed MCP server takes your agent's SQL and a short layout, runs each query read only against Postgres, SQLite, Excel or CSV, and draws charts, tables and headline numbers in a browser or a pane beside your terminal, with every number showing its query. Version 0.4.1 (Oct 22) adds a Claude desktop extension; install in Claude Code with /plugin install showboard marketplace rohanbeingsocial/showboard , and treat the author's 2.7x cost saving as a single dataset test (benchmark breakdown)…
The PUBG maker's AI team trained the 30B total, 4B active mixture of experts model from scratch for Korean and English chat and tool calling agents, and ships NVFP4 (vLLM, single GPU), MLX and GGUF builds alongside the main weights. It's a gated preview with automatic approval, so specs and results may change before the final release, and the hosted API and playground fall under separate terms…
Released Oct 23, a new Priority class lets you register an assign function in langgraph.json that gives each thread a priority and a fairness group (say, premium users first, grouped by org); the server calls it on a thread's first run and keeps the result for later runs. It's a beta API and needs LangGraph CLI 0.4.38+ (reference)…
Now GA (announced Oct 23), the new option runs Claude on Google Cloud infrastructure with Anthropic as data processor, so platform features like Claude Managed Agents (beta), skills and code execution land on day zero while you keep Google Cloud IAM, audit logs and commitment drawdown at the same pricing. The existing Google managed option stays for VPC Service Controls, CMEK and data residency, but gets new platform features only once Google integrates them…
Open sourced Oct 24 under MIT, Agensh has each agent claim its own sub task, share findings and merge into a shared Gitea repo, with Mattermost for messages; on the five hardest ProgramBench tasks, going from 1 to 128 agents lifted the mean test pass rate from 19.31% to 28.78%. The release ships a single node Docker Compose setup (eight workers by default) on Copilot ACP sessions, and token costs for big swarms aren't disclosed (paper)…
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