If you look at that code it’s possibly the worst rust code I’ve seen in my life. There are several files with 5000 to 10000 lines of code in a single file.
It looks 100% vibe coded by someone who’s a complete neophyte.
This looked interesting because I prefer rust over npm.
The first issue I had was to figure out the schema of the models.json, as someone who hadn't used the original pi before. Then I noticed the documented `/skill:` command doesn't exist. That's also hard to see because the slash menu is rendered off screen if the prompt is at the bottom of the terminal. And when I see it, the selected menu items always jumps back to the first line, but looks like he fixed that yesterday.
The tool output appears to mangle the transcript, and I can't even see the exact command it ran, only the output of the command. The README is overwhelmingly long and I don't understand what's important for me as a first time user and what isn't. Benchmarks and code internals aren't too terribly relevant to me at this point.
I looked at the original pi next and realized the config schema is subtly different (snake_case instead of camelCase). Since it was advertised as a port, I expected it to be a drop-in replacement, which is clearly not the case.
All in all it doesn't inspire confidence. Unfortunate.
Fwiw @dicklesworthstone / jeff Emanuel is definitely my favorite dragon rider right now, doing the most with AI, to the most effect.
Their agent mail was great & very early in agent orchestration. Code agent search is amazing & will tell you what's happening in every harness. Their Franktui is a ridiculously good rust tui. They have project after project after project after project and they are all so good.
This matters less and less in the new world. that fact that a fully compatible 10x faster clone came up, and is continuously working and adapting/improving, tells you that this is hugely valuable. It has users and it's thriving.
Caring about taste in coding is past now. It's sad :( but also something to accept.
Yeah, I tried to use this clone of pi for a while and its very, very broken.
First of all it wouldn't build, I have to mess around with git sub-modules to get it building.
Then trying to use it. First of all the scrolling behavior is broken. You cannot scroll properly when there are lots of tool outputs, the window freezes. I also ended up with lots of weird UI bugs when trying to use slash commands. Sometimes they stop the window scrolling, sometimes the slash commands don't even show at all.
The general text output is flaky, how it shows results of tools, the formatting, the colors, whether it auto-scrolls or gets stuck is all very weird and broken.
You can easily force it into a broken state by just running lots of tool calls, then the UI just freezes up.
Fair. The first doc you land on is in a read-only workspace (I used the app itself to write the onboarding docs, and display them in read only mode. dogfooding it). I can see how that's a bad first impression when you just want to start typing. I'll look at dropping new accounts into an editable doc instead. Thanks for checking it out.
Thanks for reporting! This is a packaging issue - need to create a proper .app bundle. On the roadmap for v0.3.0 (macOS signing & notarization). For now, running from terminal is the workaround.
I was wondering about this myself. My guess is no, since AFAIK the only way to do this sort manual memory management is to use unsafe code. But there's also things like the (bumpalo)[https://docs.rs/bumpalo/latest/bumpalo] crate in Rust, so maybe you wouldn't need to do this sort of thing by hand, in which case you're as leak-free as the bumpalo crate.
Maybe fight fire with fire and respond as default with a non-sensical question and see if the bug reporter responds genuinely confused or happily tries to engage in a non-sense convo…
I am working on a visual search & exploration engine: https://digger.lol
The goal is to create beautiful and useful maps of interesting data, empowering the user to explore more intuitively guided by semantic similarity. No user data needs to be tracked for this to work, the data speaks for itself.
This roughly works by translating semantic (visual or textual) similarity into spatial proximity. Diggers major features are: semantic mapping, text search and image search. The text and image search works bidirectionally, allowing to search for images (e.g. product images) using text and for text (e.g. books) using images.
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