I built a debugging MCP server that saves me ~2 programming hours a day
Hi! Deebo is an agentic debugging system wrapped in an MCP server, so it acts as a copilot for your coding agent. Think of your main coding agent as a single threaded process. Deebo introduces multi threadedness to AI-assisted coding. You can have your agent delegate tricky bugs, context heavy tasks, validate theories, run simulations, etc. The cool thing is the agents inside the deebo mcp server USE mcp themselves! They use git and file system MCP tools in order to actually read and edit code. They also do their work in separate git branches which provides natural process isolation. Deebo…
What it does
In the maker’s words, at launch
Hi! Deebo is an agentic debugging system wrapped in an MCP server, so it acts as a copilot for your coding agent. Think of your main coding agent as a single threaded process. Deebo introduces multi threadedness to AI-assisted coding. You can have your agent delegate tricky bugs, context heavy tasks, validate theories, run simulations, etc. The cool thing is the agents inside the deebo mcp server USE mcp themselves! They use git and file system MCP tools in order to actually read and edit code. They also do their work in separate git branches which provides natural process isolation. Deebo scales to production codebases, too. I took on a tinygrad bug bounty with me + Cline + Deebo with no previous experience with the tinygrad codebase. Deebo spawned 17 scenario agents over multiple OODA loops, and synthesized 2 valid fixes! You can read the session logs here and see the final fix here. If you’ve ever gotten frustrated with your coding agent for looping endlessly on a seemingly simple task, you can install Deebo with a one line npx deebo-setup@latest. The code is fully open source! Take a look at the code! https://github.com/snagasuri/deebo-prototype I came up with all the system design, implementation, etc. myself so if anyone wants to chat about how Deebo works/has any questions I'd love to talk! Would highly appreciate your guys feedback! Thanks!
Does the same job
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Hey HN! I’m Gui from deco (decocms.com). We’ve been using this tool internally as the foundation for a few customer AI platforms, and today we’re open-sourcing it as MCP Mesh. MCP is quickly becoming the standard for agentic systems, but… once you go past a couple servers it turns into the same problems for every team: - M×N config sprawl (every client wired to every server, each with its own JSON + ports + retries) - Token + tool bloat (dumping tool definitions into every prompt doesn’t scale) - Credentials + blast radius (tokens scattered across clients, hard to audit, hard to revoke) - No…
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Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
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