Anthropic’s Claude Code source leaked via npm sourcemap; roadmap-level agent features exposed
One sourcemap shipped an entire agent playbook—secure your publish pipeline and get ready to govern always-on coding agents.
One sourcemap shipped an entire agent playbook—secure your publish pipeline and get ready to govern always-on coding agents.
Upgrade to Claude Code v2.1.90 for faster streams, stabler long sessions, and safer Windows tooling, then validate resume and CI guardrails in your workflows.
Pin Cursor to a stable build and design quota-aware fallbacks; recent releases show slowdowns and breakages near plan limits.
Agentic Copilot is getting enterprise-ready: CI-safe auth, persistent configs, and multi-agent orchestration are here—start wiring agents to your data and delivery pipelines.
Use DevTools MCP 0.21 to put real memory and perf checks into your agents, and watch for small config and SDK quirks.
Codex is maturing with Hooks and better limits, but double-check GPT-5.4 stop behavior before you ship.
Treat LLM cost like latency: measure it per call, alert fast, and kill the circuit before your budget melts.
Ship faster and safer by turning AI ethics into CI tests and auditing your stack before any big model upgrade.
You can now try Gemini with your existing OpenAI SDK by changing three lines, making multi-provider LLM setups much easier.
Local and edge inference are ready for real workloads; start testing placement and standardizing artifacts now.
Treat the OpenClaw buzz as a prompt to trial local LLM control loops—measure them on your stack before committing.
Shift agent evals from single shots to CI-era maintainability, add structured patch checks, and cut costs with smaller, smarter test suites.
Secure the agent’s execution boundary—tools, memory, packaging, and egress—not just the model.
Agentic engineering works when you add orchestration, trace memory, and evaluators—not when you just prompt harder.
AI amplifies what your process measures—so measure quality and wire those checks into the path to prod.
Apply filters inside the vector index, not after—Manticore now does this by default and your results get better fast.
Turn your workflows into skills: versioned, testable, and portable instructions that agents can pick and run reliably.
Actor now flows into LLM enrichments in Datasette, making audit trails and per-user logic straightforward.
A small release that removes config duplication and clarifies the Python API, making Datasette LLM integrations a bit cleaner.