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Built an open-source AI coding agent that was 7%–75% cheaper than a cold "claude -p" run on 6/6 well-localized tasks across repositories up to ~82k LOC. The biggest difference: Cold agent: $6.83, 207 turns AutoDev Studio: ~$1.70 for the same bug The full benchmark (including cases where it loses) is in the README. So what's different? Most AI coding agents re-explore a repository from scratch on every task just to figure out where the change belongs. AutoDev Studio pays that localization cost once. It ingests a repository and builds a persistent knowledge base using static analysis and a local embedding index. Every future task reuses that knowledge, turning localization into a lookup instead of another cold search. What it does: PM agent asks clarifying questions and drafts tickets Dev agent writes code on an isolated branch QA runs tests A different model family reviews the diff (author ≠ reviewer) If needed, it goes through a bounded revise loop Opens a real GitHub PR It also includes a live Kanban board and tracks token usage and cost per ticket/agent. Where it doesn't win: Tiny, easy-to-find edits can be cheaper with a single-shot agent because of the pipeline overhead. On one complex cross-cutting bug, it produced a cheaper but narrower fix than the baseline. Other features: Provider agnostic (Anthropic, Claude Code, OpenAI-compatible APIs, Groq, Gemini, xAI, OpenRouter, Ollama, etc.) Runs completely free/offline by default using Groq's free tier + local embeddings FastAPI + SQLite Hand-rolled UI Tests + CI MIT licensed Repo (screenshots + full benchmark): https://github.com/krishagarwal314/autodev-studio I'd love any feedback, criticism, or contributions. Happy to answer questions about the architecture or benchmarking. submitted by /u/NeighborhoodOwn8510 [link] [留言]

Hello, I'm Rijul. I'm building git-lrc, a micro AI code reviewer that runs on every commit. It's free...

In Pasadena, California, there's a cute red brick courtyard where one storefront isn't like the rest. The glass doors open onto a sparse industrial hallway, which leads to a sunlit foyer with a large spiral staircase. Go up, and you'll see a typical coworking space: open tables, healthy snacks, a collection of meeting rooms with […]

TikTok has attracted the ire of the European Union over its protection of children who use the video sharing platform. The European Commission announced preliminary findings today under the Digital Services Act (DSA) that offer specific issues to address. The Commission suggests that TikTok should adjust the default settings of minors' "public" accounts so the […]

A deep dive on Go 1.24’s map redesign, where the runtime moved from the classic bucket + overflow-chain model to a Swiss Table-inspired implementation: how the old map layout worked and where pointer-chasing hurt cache locality how control-byte metadata + h2 filtering changes lookup behavior why this can improve practical load factor and memory efficiency Go-specific constraints (iteration semantics, GC/runtime integration, incremental growth behavior) benchmark context: large microbench wins vs smaller full-application geomean gains trade-offs still being worked on (cold-cache and certain delete/clear-heavy paths) submitted by /u/OtherwisePush6424 [link] [留言]

These are the AC units we’ve trusted to cool our homes for months, if not years.

US robotics researchers flew to Venezuela with snakebots after getting a call.