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Open source Kanban desktop app that runs parallel agents on every card

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Re: Open source Kanban desktop app that runs parallel agents on every card

#51
post #38

Personally, this is somewhat close to what I want. I want to have a fullblown cursor instance/window for each task I have, and a central Hub that manages spawning those instances, setting up the worktrees, etc. Cursor seems to pretty much have all the available tools there already (it can already spawn agents to their own worktrees with proper setup scripts, for example). I don't get why they don't do it and instead…

> I want to have a fullblown cursor instance/window for each task I have, and a central Hub that manages spawning those instances, setting up the worktrees, etc.

I am working on exactly this interface for my new tool called Kotkit. You start with kanban board management of workspaces. Each workspace (worktree on one/multiple repos) is a feature-rich IDE interface in a remote-capable in-browser desktop. You can spawn multiple agents with a good UI wrapper and full auditable logs, solve worktree rebase/merge with 1-click AI features, and there is also an embedded VSCode to solve edge cases. It also supports very deep plugin integration like IntelliJ.

Currently dogfooding it on my own projects and will be released sometime soon.

Re: Open source Kanban desktop app that runs parallel agents on every card

#52

I keep wondering how people accept a nights worth of agent activity. I feel 30 minutes of planning and 30 minutes of implementation in my solo side project's repo is too big to review. At minute 5, I may ask the AI to redo stuff even as its spitting out code.

That depends. When I'm working on a 1 in a million race condition in some multi-threaded code, the agent needs hours to figure out what is going on. (I would probably need weeks - I don't know as I've given up on some of these before I could point an agent at it)

Re: Open source Kanban desktop app that runs parallel agents on every card

#54

I keep wondering how people accept a nights worth of agent activity. I feel 30 minutes of planning and 30 minutes of implementation in my solo side project's repo is too big to review. At minute 5, I may ask the AI to redo stuff even as its spitting out code.

Yes it is too big to review for you - the human - so you simply don't review code anymore. Isn't that difficult to comprehend, is it?

Re: Open source Kanban desktop app that runs parallel agents on every card

#55
post #49

Earlier quoted context omitted.

I wonder the same. The answer I usually get from people who do manage is that they don't look at the code – or at least not in detail. Personally, I always end up tweaking something the agent produced. I wonder if I should let go of that control...

I never look at code. It used to be that it quickly became unmaintainable spaghetti where the agent struggled to make any change at all, but in the past year (and with a three step plan/develop/review workflow), the quality is so good that I basically just don't look at the code any more. It definitely has fewer bugs than a senior developer, but it really hinges on getting the plan right. 20 minutes of planning and 2…

This brings to mind two thoughts:

First, that this is challenging to scale across large orgs. Even if your plans produce high quality code, that isn’t true for everyone. I’m definitely struggling with slop code being collectively mailed to me for review my our 1,000 engineers that were told to use their AI subscription all at once.

I feel like we should be taking “prompt engineering” more seriously. And when people mail me code to review, it should also include the agentic workflow and plan. So that when code isn’t up to quality, and can have a discussion about the prompts used to generate it.

My second thought is related to your senior engineer comment. This isn’t surprising, because in most engineering orgs, seniority is completely unrelated to code quality. In fact, many orgs incentive the opposite: “senior” devs that push out buggy code quickly and push accountability downhill to the junior devs.

Re: Open source Kanban desktop app that runs parallel agents on every card

#56
post #49

Earlier quoted context omitted.

I never look at code. It used to be that it quickly became unmaintainable spaghetti where the agent struggled to make any change at all, but in the past year (and with a three step plan/develop/review workflow), the quality is so good that I basically just don't look at the code any more. It definitely has fewer bugs than a senior developer, but it really hinges on getting the plan right. 20 minutes of planning and 2…

This brings to mind two thoughts: First, that this is challenging to scale across large orgs. Even if your plans produce high quality code, that isn’t true for everyone. I’m definitely struggling with slop code being collectively mailed to me for review my our 1,000 engineers that were told to use their AI subscription all at once. I feel like we should be taking “prompt engineering” more seriously. And when people m…

Eh, everything is challenging to scale across large orgs. Even before LLMs, the code was a huge ball of spaghetti that barely held together. Now we just get there faster.

About senior engineers, I guess that depends on the org you have experience with. My experience doesn't match yours.

Re: Open source Kanban desktop app that runs parallel agents on every card

#58
post #50

Earlier quoted context omitted.

Very true. Last year I at least glanced at every line of AI generated code. Now if some AI makes a 10k line program for some one-off tasks, I run the program, glance only over the output, and move on.

Which one-off tasks need 10k lines of code?

Calculate the engine power of a 2015 VW polo when travelling 70 mph on a flat road behind a box truck. Draw a chart of drag Vs follow distance. How significant is humidity on the result?

Re: Open source Kanban desktop app that runs parallel agents on every card

#60
post #45

I keep wondering how people accept a nights worth of agent activity. I feel 30 minutes of planning and 30 minutes of implementation in my solo side project's repo is too big to review. At minute 5, I may ask the AI to redo stuff even as its spitting out code.

So I've been in a hobby project for a few weeks -- transforming an old software modem binary to c code. I gave it the existing modem, and had it build rigging to build test vectors. I had it specify the work in the modem. And to confirm that legacy legacy produced the same streams as the new code. I've also recorded test vectors vs. other modems. I've since launched it on targeted refactoring and code reduction proje…

I don't like that there are any good sounding stories, but this sounds pretty good.
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