
v0.7.0
LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
Stay updated with the latest developments in Artificial Intelligence

LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
Almost none of the pull requests Polylane opens were asked for by an engineer, and a longer description does not make them easier to review. We are experimenting with a narrated video for every fix. Why, what it looks like, and what happened to merge rates.
Agents that run software need a context graph of the app: every resource, what it connects to, the repository that deploys it and the team that owns it.
Our on-call agent makes thousands of small decisions a day. We moved them from LLMs to Jev, a model that doesn't write text, and cut the cost by 39%.
Nothing in your pipeline asks whether a change is safe for prod. This is the check we built that does, and how we got it to finish before your tests do.
Polylane's autofix used to be a workflow of multiple agents, each responsible for a single task, with an orchestrator managing state between them. We moved to a single agent which does the entire job, from evidence gathering, hypothesis testing to pull request.
Cloudflare Durable Objects run in V8 isolates with a hard 128 MB limit. Ours idled at ~140 MB and was reset ~300 times a day. 130 MB of zod schemas were built at module load, most of them by code that was never called.
Agents can do almost anything you ask. That's the problem, you still have to ask. The next evolution is agents that figure out the job to be done. I started with operations, because nobody should be on-call in 2026.
Showing 7 of 7 stories
You're all caught up.
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news from Polylane Blog
Join 15,000+ AI enthusiasts