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My Homelab AI Dev Platform

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Re: My Homelab AI Dev Platform

#61

Post by chamoda »

david-giesberg wrote: post #19

I've been doing something pretty similar, except instead of having a persistent opencode server, I've been using this workflow that runs opencode inside of the Forgejo action runners: https://codeberg.org/dragonfyre13/forgejo-opencode Still tinkering with it, but the gist is that I can invoke Opencode with /oc inside of an Forgejo issue, then it will come back with a PR for me to review.

I did this with github actions + opencode. It has mainly two components called daydream and nightwatch. Daydream runs on a daily schedule looking at the VISION.MD file in the project and also look at the project and suggest new ideas, security , maintenance tasks (you can set a ratio of maintenance vs new ideas) and create a new issue. Then nightwatch runs daily at night and picks up oldest issue and suggest a PR. I'm in total control of merging, can ask for changes which agent again will execute immediately.

This has been helpful to revive few old projects and keep momentum on active projects. I've been closing more PRs than merging but I think that's fine. I quickly ran out of free github action hours so I had to self host actions. However basic self-host actions setup may not be secure enough because VM is not destroyed after each action like github own action jobs environment.

Anybody can try it out actions from https://github.com/chamoda/agent-foundry, minimal setup don't need any API keys, works with mimo 2.5 free model by default.

Re: My Homelab AI Dev Platform

#65

Post by templar_snow »

ohyoutravel wrote: post #32
templar_snow wrote: post #31

This is great. Homelab AI feels like it's going to fun as heck. I currently have Claude maintain my homelab across all devices; it made homelab setup and maintenance go from "This is a trap that will fascinate you for years but never fully work right and waste time that would have better been spent elsewhere" to "This is actually a great idea and really extends my capabilities."

I’ve found, even using the latest models, there are some time saving nuggets but mostly subtle config difficulties that just cause an enormous amount of debugging and a net negative on balance, unless you’re just asking for super targeted tasks like “set up a docker compose file” or “give me an NSD config.” But with both of those you need to already know you need the underlying tech and what to ask.

Of course, it still requires tremendous domain knowledge, although that domain knowledge is faster acquired working with AI. However, although the debugging trap is of course real with ALL sub-Fable models it seems, it takes the tedium out of running a homelab and turns it into a net positive to even have in your home!

Re: My Homelab AI Dev Platform

#68

Post by sosodev »

nyrikki wrote: post #30
sosodev wrote: post #10

Earlier quoted context omitted.

I think it heavily depends on what you're asking the model to do. Qwen3.6, both 27B and 35B-A3B, do agentic tool use very well. Their decision making is sus, but the dense model is decent in that way. A 4-bit quant for either of those can run on many home systems with a bit of configuration. The biggest issue I've noticed is that the chat templates for open models are really hit or miss. The default Qwen3.6 chat temp…

My workflow is too different right now (gradually constrained to network less builds for reasons) but I am really enjoying how zeds agents have worked out in the past few weeks. I have 27b, 35B-A3B and a cpu backed gpt-oss configured and use them in parallel, checking if one is getting ratholed and adding context or manual fixes. I had various other systems setup and commercial models but really don’t use them. It ma…

Any artifacts or blogs I can check out? I'm curious how you manage to make them all useful in parallel. I have a hard enough time getting one instance of Qwen3.6-27B being useful full time haha.

Re: My Homelab AI Dev Platform

#69

Post by zjgkknk »

I’ve been building something similar for quite a while, with a stronger focus on minimizing dependencies on external services: https://github.com/kvladsrc/gym/tree/main/production

I find it convenient to interact with frontier LLMs through a Kanban board powered by Kanboard. For example, the LLM creates backlog tasks based on alerts from the cluster. I manually move selected tasks from the backlog to Ready, the model picks them up, works on them, and submits a patch for review in the comments. From there, the usual pipeline takes over: CI, LGTM, merge, and CD.

Another surprisingly useful part is desktop configuration management, covering everything from the compositor and system tray to text editor configuration with the same pipeline. For once, I genuinely enjoy using Linux on the desktop.

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