The primary use case for this is actually to push work to LLMs and handle its lifecycle (including failures). Each card on the board is a different task, and pressing start on the task triggers the LLM loop. So "task push from the board" is its raison d'etre.

In working with local models, the main issues I've seen are failed tool calls, getting stuck in loops, or some other upstream issue with the inference server. Auto-retry logic helps keep things going, and tasks will sit in review for your action if they get stuck. In those situations, you can either adjust the instructions or change to a more... obedient model and put it back in the work queue.

I've experimented with a few inference services too, which tend to have other issues like how they handle rate limits. I added configurable client side limits (concurrency and reqs per minute) to make this run more smoothly.

For programmatic access: this features a RESTful Web API using a Bearer token for authorization. After generating a token (via Settings -> About), you could them have a cronjob run basic curl with a payload to start tasks. I haven't spent much time on the documentation for this yet; the current docs were generated by agents so they're a bit painful to read. I only reviewed them for accuracy.

Thanks for checking it out!