Today we're launching @akonlabs backed by @ycombinator
We build GitNexus, an open-source knowledge graph for your repository. Coding agents query it to get exactly the context a task needs symbols, call paths, dependencies instead of grepping around and stuffing half your codebase into a context window.
Context is the bottleneck for coding agents. Bigger windows haven't fixed it. Buyers of agents are paying more per token to get worse code, and the agents themselves have no map of the repo they're editing.
We're fixing this. On DeepSWE, the same agent with GitNexus vs. without:
1. 37% → 68.4% of tasks passed
2. Half the cost per fix
3. On the hardest tasks, the gap more than triples
One stack: knowledge graph, coding agent, automated PR review. Managed, self-hosted, or air-gapped.
45k GitHub stars. 65k weekly npm downloads. 10+ enterprise deployments.
Sign up at: https://t.co/8SLwkzhe7P
Coding agents can now go beyond writing code on Render.
🔐 Connect via MCP OAuth
🚀 Trigger deploys & change service plans
🧠 Tap into 20+ Render Skills
⌨️ Manage services, Postgres & Key Value with the CLI
Build, deploy, and operate - read the announcement → https://t.co/UQomO7jROA
GitNexus, the open-source code intelligence platform by Akon Labs, now deploys to Render in one click.
Get a live instance in minutes with no config or infra setup → https://t.co/L9X8X7CAYP
RLMs and loop engineering are the same bet in different clothes.
RLMs: don't cram the context into one call, recurse over it.
Loop eng: don't cram the task into one prompt, loop around it.
The unit of intelligence isn't the prompt anymore. It's the loop.
OpenAI just dropped a training challenge:
Train a <16MB language model in 10 minutes on 8×H100s and minimize held-out loss on a fixed FineWeb dataset.
Basically NanoGPT Speedrun. They’re sponsoring $1M in compute.
I can summon my autoresearch army to win it… if I have time.