Every processed story in chronological order, with the newest coverage first. Filter by tag, source, or score to drill in.
CLI users can now set model selection to `auto` and let Copilot optimize model choice per task, eliminating the need to manually evaluate and switch between models for different coding workflows.
Track Claude Code's rolling rate limits in real time so you can pace token usage and avoid surprise budget exhaustion mid-session.
Teams can encode coding standards, PR workflows, and accessibility checks directly into Copilot CLI agents — reducing manual review overhead and keeping AI output consistent across an entire codebase.
Use `git worktree` to give each Claude Code agent its own isolated directory so parallel agentic workflows never silently overwrite each other's uncommitted changes.
Developers building agentic workflows on macOS can now give any MCP client deep, runtime-free OS control — from browser automation to GUI interaction to OCR — through a single installable, permission-persisting Swift bundle.
After 30 days running MCP servers in production, Atlas Whoff shares hard-won lessons on tool descriptions, schema efficiency, statelessness, error messaging, and naming conventions that make or break Claude-powered automations at scale.
Developers building or choosing AI-integrated tooling should take note: exposing personal knowledge bases via MCP is emerging as a practical pattern for persistent AI context — Hjarni is an early, opinionated example of what "MCP-first" product design looks like in practice.