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ArjinFerman  Tinfoil Gigachad  2d ago (text post)   264 thread views

Tinker Tuesday for June 9th, 2026

This thread is for anyone working on personal projects to share their progress, and hold themselves somewhat accountable to a group of peers.

Post your project, your progress from last week, and what you hope to accomplish this week.

If you want to be pinged with a reminder asking about your project, let me know, and I'll harass you each week until you cancel the service.

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Do we have many local LLM users here? I'm curious what people are doing: what models are people using? For what jobs? For what reason? On what hardware? With what runner?

As I mentioned to @WhiningCoil a few days ago, I mostly run a Qwen3.6-A3B-Q4_K_XL on llama.cpp's llama-server and connect to it from https://pi.dev/, using a Radeon 780M in my laptop. It's been decent for grinding through smaller coding jobs under close observation, though like any Chinese model it'll just give you the party line if you start asking it about Taiwan or Tiannemen Square. I've also been using a gemma4-26B-A4B for general questions about the world when I'm at session quotas. The other big reason I'm getting into this stuff is that I never want to be locked out by a subscription. Haven't looked at image or video generation at all.

Well, I switched to gemma4:12-it-qat and it worked a lot better. Made an opencode command that takes a specific file and asks for unit tests on the class in it, and it worked ok. I need to explore setting up a good agents.md file to jump start them on understanding a project. I may check out some random open source project and start fucking around with custom commands. I know the workflow we want is to have some automated review and test creation, so if I can cobble together the right opencode commands, agents, skills, etc to make that easy for anyone to just go /review_and_write_tests and things start happening, great.

I still have no fucking clue what skills are supposed to be. They just look like extra prompts that hypnotize an LLM into believing it can do shit.

Skills are essentially a pointer to a loadable chunk of context, a fuzzy program written as a .md file. "If you want to know how to do X, read this file first" sort of thing. The models get more confused as their context (message history) grows, and especially when the context window fills and the initial stuff gets cut off. Then they have no idea what they're doing. So a "skill" is detected by the harness (e.g. opencode) which injects the short summary into the prompt that goes to the model, which can then ask to read the full file if necessary.

I still have no fucking clue what skills are supposed to be. They just look like extra prompts that hypnotize an LLM into believing it can do shit.

The key with skills is that they are conditional, so you can inject those additional prompts into the context window only when the skill is used rather than having them always around. This lets you write smaller generalized prompts for agents, which in turn improves the output of those agents by keeping them focused on the task at hand and not distracted by instructions unnecessary for the current task.

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