I have been using OpenCode for most work related stuff now. It works well with my ChatGpt Pro subscription as well as with our locally hosted Qwen3.8.
Is it worthwhile to spend time and effort into setting up and switching to Pi? I always see people praising Pi online but I am curious to know from people who switched over from OpenCode why they did so and what I am missing.
Yes, I switched from OpenCode to Pi and never looked back.
What's great with Pi is that it's very easy to extend, because it knows its own doc. So you tell it "implement a plugin that does this" and it does it directly in its own folder.
That means I have a plan mode that works exactly the way I want, I have a hook that cleans up added comments after each change, I can ask "pull this github PR and assess the comments", etc.
How does this work in practice? You type /plan and it runs something that got linked into pi, or an interpreter that can run stuff and also print back out to the terminal?
I've been avoiding investing in these kinds of tools and workflows partially because I don't understand the UX (and partially because I worry UX and tooling will change so fast that I won't get a positive ROI). Your hook sounds like a script, but your plan mode sounds like an interactive TUI, and "pull this PR and assess" sounds like a reverse proxy tool or something?
I can definitely see why the custom plan mode is useful vs just calling a script or asking the model to do something, but what's the draw to building comment cleanup and github API access into the harness vs running the comment cleanup as part of CI, or just asking the model to pull a PR and review it using gh/the web UI/the github api (with no special harness handling)? Does the model get updated with changes you make to files it already read or recently wrote?
The way I normally use coding agents is by giving them a very large task specification in a fresh session with some kind of verifiable exit criteria, and sending them off + staying out of their way. Normally I would just use other sessions or run scripts to do these things because the coding agents I've used queue any /command I give them (very frustrating when you have to wait 30+m just to check usage!) while the model is working, and in my sessions the model is working >90% of the time, so I usually have other terminal windows or applications open anyway.
sid_talks · · focus · HN ↗
Is it worthwhile to spend time and effort into setting up and switching to Pi? I always see people praising Pi online but I am curious to know from people who switched over from OpenCode why they did so and what I am missing.
eloisant · · focus · HN ↗
What's great with Pi is that it's very easy to extend, because it knows its own doc. So you tell it "implement a plugin that does this" and it does it directly in its own folder.
That means I have a plan mode that works exactly the way I want, I have a hook that cleans up added comments after each change, I can ask "pull this github PR and assess the comments", etc.
Mashimo · · focus · HN ↗
Same with opencode, no? It has a default skill just for that.
weitendorf · · focus · HN ↗
I've been avoiding investing in these kinds of tools and workflows partially because I don't understand the UX (and partially because I worry UX and tooling will change so fast that I won't get a positive ROI). Your hook sounds like a script, but your plan mode sounds like an interactive TUI, and "pull this PR and assess" sounds like a reverse proxy tool or something?
I can definitely see why the custom plan mode is useful vs just calling a script or asking the model to do something, but what's the draw to building comment cleanup and github API access into the harness vs running the comment cleanup as part of CI, or just asking the model to pull a PR and review it using gh/the web UI/the github api (with no special harness handling)? Does the model get updated with changes you make to files it already read or recently wrote?
The way I normally use coding agents is by giving them a very large task specification in a fresh session with some kind of verifiable exit criteria, and sending them off + staying out of their way. Normally I would just use other sessions or run scripts to do these things because the coding agents I've used queue any /command I give them (very frustrating when you have to wait 30+m just to check usage!) while the model is working, and in my sessions the model is working >90% of the time, so I usually have other terminal windows or applications open anyway.