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Maker
📌
Hey everyone 👋
I’m Yutish, cofounder of Comie.
While using AI coding tools like Claude Code and Cursor, we realized something strange:
AI can write production code… but it has no idea what’s actually happening in production.
It can’t see:
* logs
* database scale
* Redis state
* analytics
* production errors
So developers still end up deploying code just to see if something breaks.
We thought that made no sense.
So we built Comie.dev a production context layer for AI coding agents.
You connect your production stack (Postgres, Redis, Sentry, PostHog, etc.) and now your AI can:
* inspect logs
* analyze production queries
* debug backend issues
* trace production errors
directly inside Claude Code, Cursor, Codex, and more.
The craziest part is how simple it is:
1. Select your tools
2. Generate tokens
3. Paste one command
Setup takes under 60 seconds.
We’re still very early and would genuinely love feedback from developers here.
Happy to answer everything in the comments ❤️
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Bridging the gap between logs and AI context is a huge pain point. Does Comie support real-time streaming of logs into an LLM context window, or is it batch-based?
@rivra_dev Hey CTO here, it streams data into your LLM context directly inside your agent, so it acts like an actual input for your LLM to think with actual data
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This hits a real pain point — I’ve had Claude Code write what looked like solid error handling, then realized it had zero context on our actual Sentry patterns. How do you handle schema drift when the DB changes? Does the agent need explicit migration context or does it infer from query errors?
Bridging the gap between logs and AI context is a huge pain point. Does Comie support real-time streaming of logs into an LLM context window, or is it batch-based?
Comie.dev
@rivra_dev Hey CTO here, it streams data into your LLM context directly inside your agent, so it acts like an actual input for your LLM to think with actual data
This hits a real pain point — I’ve had Claude Code write what looked like solid error handling, then realized it had zero context on our actual Sentry patterns. How do you handle schema drift when the DB changes? Does the agent need explicit migration context or does it infer from query errors?