Anyway, here's mine, still wip:
Why? Because LLMs are always going to be dumb when they're trained at scale. Their ability to speak software diverges from their friendly user input layer. A harness won't overcome that, but an LLM saddle ontop of a larger model would provide the type of feedback loops you'd want to look into.
I don't think you'll find two deterministic systems will produce much.
I'm working towards both in my homelab to see which works better with little qwen
Your thoughts on the matter seem clearer than mine.
I’m kind of relieved to see so many stages; that’s something I’ve grappled with conceptually. The volume of (sometimes optional) stages has made it hard to resist the temptation to hack something together with a GHA workflow.
I salute your choice to support distributed; that’s a requirement in my book.
I look forward to trying it out.
- a workflow is a series of steps
- a step is one or more tasks that can be done in parallel
- a step is complete when all of its tasks are complete
- a task can be a unit of work, or a pointer to another workflow