Live data from Hacker News

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

kanbots.dev

111–120 of 191 posts

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

#111

> "Local-first, zero servers. Everything lives in .kanbots/ next to your repo: SQLite database, configs, worktrees. No cloud account, no telemetry, no HTTP server. This is the open-source desktop edition." This is table-stakes for me to consider adoption of a tool like this.

From their page, they say they require cloud account login for this to work, even locally which is why I decided not to try it out. Looks cool tbh. But I have quite a few tools that look cool.

I'm confused, I downloaded their repo and ran it and I had the option to continue locally without signing up/in.

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

#112

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.

Most of the narrative is about how AI is writing all/most code, but I’d wager that the fraction of human reviewed code is approaching zero far faster than anyone is realizing or willing to admit.

[deleted]

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

#113

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.

Most of the narrative is about how AI is writing all/most code, but I’d wager that the fraction of human reviewed code is approaching zero far faster than anyone is realizing or willing to admit.

i admit. agentic coders do not look at the code except by accident. not much point unless you're working on enterprise applications

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

#114
post #76

Earlier quoted context omitted.

People already barely reviewed code, most of it was imported libraries.

The assumption used to be that you respected the library enough and believed it was well reviewed and architected by the maintainer(s). But now even that's unreliable because libraries are being slopified at an unreviewable pace too.

It's weird that you think humans weren't slopifying code until LLM's came along. At least now they are implementing tests and CI and far more documentation, updating API versions, etc. OOMs above the amount they did before.

I'd also wager that far more % of code gets more coverage of review, via prompting AI to do it, than it did before.

Most PR's pass as long as they A. pass checks, B. dont introduce regressions, C. fix a bug or implement a feature. People talk about this era of humans reviewing code with nostalgia... but that never existed at scale.

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

#115

Earlier quoted context omitted.

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?

European or African Polo?

You're not supposed to post that you just like a comment, but this was best comment on HN in ages.

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

#116
post #10

This is basically what Windsurf is doing right [0]? Ultimately all this UI stuff is just window dressing on top of agents. [0] https://windsurf.com/blog/windsurf-2-0

I don’t see a problem here. Do you? Also, Linear themselves are also working on this.

I never said there was a problem.

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

#117

Earlier quoted context omitted.

Most of the narrative is about how AI is writing all/most code, but I’d wager that the fraction of human reviewed code is approaching zero far faster than anyone is realizing or willing to admit.

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.

Especially if you're having an LLM write non-interactive scripts to calculate complex things from large datasets, glancing at the output is not enough to know if the output is remotely accurate (unless the output is so trivial you could literally do it in your head).

Case in point: I recently asked an LLM to write a pile of code to compile historical baseball stats to test betting success against the results of my hand-written code that evolves genetic algorithms. I marveled for a little while at the unbelievable improvement in EV/ROI that this script was showing could have been achieved from certain small tweaks. I only noticed after pushing a total bet that the push registered on the output as a win - and only because I was carefully staying on top of it. A single stupid recursively operating >= instead of > had caused completely nonsensical results that looked plausible.

Imagine, like, trusting a 10k loc script to give you data for something you were going to build in the physical world, and hoping an LLM hadn't made a mistake like that.

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

#119

Earlier quoted context omitted.

European or African Polo?

You're not supposed to post that you just like a comment, but this was best comment on HN in ages.

You can't beat this though:

https://news.ycombinator.com/item?id=35015#35079

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

#120

Earlier quoted context omitted.

I don't think the same things that help humans help agents. Simplicity helps humans, for agents parsing complexity is a breeze. Not saying code quality isn't important - it is. But I think what is described as quality code will change.

Agents still pay a penalty for complexity even if it is a smaller one.

Parsing single file is easier than navigating a file system for an LLM. Until the models have context windows large enough to hold the entire codebase in one shot, single files will beat multiple files every time.
Post reply on HN