AI for developmentInfoQ
AI for development
In this episode, Thomas Betts and Adi Polak talk about the need for context engineering when interacting with LLMs and designing agentic systems. Prompt engineering techniques work with a stateless approach, while context engineering…
A 600-run benchmark by Ruby committer Yusuke Endoh tested Claude Code across 13 languages, implementing a simplified Git. Ruby, Python, and JavaScript were the fastest and cheapest, at $0.36- $0.39 per run. Statistically typed languages…
Anthropic introduces a three-agent harness separating planning, generation, and evaluation to improve long-running autonomous AI workflows for frontend and full-stack development. Industry commentary highlights structured approaches,…
This is the second post in the Ranking Engineer Agent blog series exploring the autonomous AI capabilities accelerating Meta’s Ads Ranking innovation. The previous post introduced Ranking Engineer Agent’s ML exploration capability, which…