Towards Self-Driving Codebases

kept by hugows sep 13

The article argues that to achieve self‑driving codebases, teams must invest in agent‑friendly dev environments, global memory, and rot‑prevention primitives, allowing engineers to focus on high‑value ideas.

How to Eval AI Agents — The 2026 Guide
Evaluating AI agents requires floor-raising error analysis, code-aware offline tests, production monitoring, and a tight feedback loop rather than benchmark-maxxing.
also on Braintrust, #ai-agents, #software-engineering, #developer-tools
Thoughts on Evals – Raindrop Blog
Production monitoring and A/B testing offer more reliable evaluation of AI agents than offline evals, which fail to capture real-world performance.
also on Braintrust, #ai-agents, #software-engineering, #developer-tools
The Pulse: a new trend, smart model routing - The Pragmatic Engineer
Several vendors now offer intelligent model routers that automatically select the cheapest sufficient LLM per task, promising 20-30% cost savings, a trend expected to become table stakes.
also on OpenRouter, GitHub Copilot, Cursor, #ai-coding, #developer-tools
Stripe wants to turn your AI costs into a profit center
Stripe previewed a billing feature that lets AI companies track token usage, pass through costs, and automatically apply a profit markup on underlying model fees.
also on OpenRouter, GitHub Copilot, Cursor, #ai-coding, #developer-tools
Coding After Coders: The End of Computer Programming as We Know It - The New York Times
A.I. coding agents shift programmers from writing code to orchestrating agents, debugging, and prompt engineering, fundamentally changing the role.
also on GitHub Copilot, Cursor, #ai-agents, #ai-coding, #software-engineering, #developer-tools