Project Name: Podcode

Repo/Website Link: https://podcode.io

Description:

Quick background, since this matters for the rest. I run a small private AI stack for my own work. Coding agents like Claude Code and Codex are part of my daily flow. They are great when they work. The thing that drove me nuts is what happens when they don't.

An agent would be 40 minutes into something, then die. Context window blown. Rate limit hit. The agent loop glitched. tmux session got reaped. Whatever. Then I'd come back, see half-finished work, and have to figure out if I should try to continue or just start over. Starting over meant the agent redid the same investigation it had already finished two minutes ago. Continuing meant hoping the working state was actually intact, which it usually wasn't. Either way I lost an hour.

So I built the thing I wanted around the agent. Podcode is a single binary and a single dashboard. The point of it is to stop the babysitting:

  • keeps the agent alive across failures. Auto-resume from the last persisted state. No half-finished work, no redoing the same investigation. This is the original reason it exists.
  • swap providers without rewriting anything. Local Ollama, vLLM, llama.cpp, RunPod serverless, hosted OpenAI or Anthropic, anything OpenAI-compatible. Same interface, agent doesn't care.
  • RunPod integration. Bring a RunPod API key and it spins up a private LLM endpoint for you. No manual GPU setup.
  • layered memory. Session, project, cross-session. Vector + BM25 retrieval, confidence decay, git-backed sync across machines. This is what made a small local model actually usable for real work instead of toy demos. Before this layer I was forced to a bigger model any time a task spanned more than one session.
  • embedded browser the agent can drive. Read URLs, fill forms, check state. No more copy-pasting between a browser tab and a terminal.
  • works with Claude Code and Codex as the agent, not instead of them. I still use them. Podcode just stops the part where I am sitting there refreshing a session to see if it finished.

It is self-hosted. Homelab box, RunPod, your own VPS, whatever you already run. Nothing leaves your infra unless you point it at a hosted provider.

Deployment:

Single electron app builds for all my devices, windows, mac, linux whatever. Docs cover the local providers (Ollama, vLLM, llama.cpp), the RunPod deploy, the memory layer, and the embedded browser: https://podcode.io/docs

AI Involvement:

I'm the founder. Built by myself in just 90 days or so. Its been a blur of waking up at 4 am, or going to bed at 4am while grinding this out. Boilerplate, refactors, doc drafts, test scaffolding, second pair of eyes. Every change gets reviewed and tested by a human before merge. The product architecture, the memory model, the provider abstraction, the security posture, and the failure-recovery logic is all mine. The codebase has real test suites. Happy to be specific about which parts were AI-assisted if anyone wants to check.

What it doesn't do yet:

  • no image gen. The harness is text and tool use.
  • no hosted offering. Self-hosted only for now. It did deploy my website to firebase though from the cli, but any agent harness can do that.
  • RunPod integration is both serverless endpoints and pod based, with managed GPU deployments as my preferred option. Seems to be cheaper at scale hosting a 3$ an hour pod than paying for serverless go figure.
  • the memory layer is tuned for code and agent work.

Questions I actually want feedback on:

  1. anyone here already wired a memory layer into a local stack? Real numbers at 8B, 14B, 32B would help us sanity check what we have.
  2. RunPod as a private deployment target. Useful, or would you rather have a bring-your-own-GPU path only?
  3. the embedded browser. Overkill, or did you end up building something similar yourself?
submitted by /u/ElledyneLabs
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