Just out of curiosity, why would the LLM need network access for this? I.e. feeding the doc to an LLM and asking "is this sensitive information according to these criteria: [...]" should get you there most of the way, no? Probably need a handful of (carefully designed) tool calls and a human in the loop somewhere, but it seems achievable.
I had similar thoughts. The readme intro explicitly mentions hallucinations, that's why I thought I'd ask.
If you're dealing with uid in -> uid out, where you're hoping to get the same uid out, intuitively the entropy would be greatly reduced anyways. Then the question becomes, are words conducive to keeping input->output consistent, given the way LLMs work (e.g. attention mechanism)? I could see it go either way, that's why I'm supporting the idea of running your experiment.
Okay, but you can also validate uids. What I'm asking is whether the human readable uids cause fewer hallucinations, as that would be the real win imo.
That's nice, I've had the issue where LLMs would return non-existent uids. But does this package actually help with that? Token savings are nice, but not really my main concern. If this can measurably reduce hallucinations, it would be really useful.
> Where UUIDs cost ~23 tokens and get hallucinated by LLMs, id-agent produces memorable word-based IDs at ~14 tokens with equivalent collision resistance.
Didn't expect it to get hammered like that, just added caching for the sheets request. Thanks, my guy ;)
Backfilling it further is definitely in the cards, I just want to stabilize the methodology first.
If a comment just mentions Opus without being more specific and in the absence of relevant context clues, it gets mapped to Opus Latest. So it's saying more about the model family than a specific version. Tbh I'll probably remove all "-latest" data points going forward, as I mentioned in another comment.
Yes! Going forward I'm definitely doing that, once there is enough data. Might even backfill the data more into the past. I just want to stabilize the methodology before burning more tokens.
And it's probably a good idea to create a list of model release dates, so older comments can't accidentally map to models that weren't released yet.
From the comments that I've checked manually it's pretty good. You can go to the "User Ratings" tab in the Google Sheet and check some comments to get an idea.
That's fair, my immediate concern would be that there would be very few comments comparing any two models, so the data would be very anecdotal.
The context would be really nice to have, but reading the comments myself, it often just isn't very clear what exactly users are building or which programming language they are using.
I think analyzing more comments is promising. If you get enough data, you can generalize across use cases and get more meaningful ratings. The obvious lever is including more posts, although it might hit diminishing returns. I'll play around with it.
For the context, I want to try giving Gemini a "scratch pad", where it can note down strengths and weaknesses per model that it finds in the comments. Something like "some users say that model x is good for writing tests". Then on each run, I let it update the scratch pad and publish the results as more of a qualitative analysis.
For the wording, I'd like to keep a certain amount of click bait, sorry ;)
Calling it sota might be a bit provocative, but what actually is the "state of the art"? We have benchmarks, but those are getting increasingly gamed and don't necessarily reflect the actual performance of a model, see Opus 4.7. So I think it's useful to have real world data from actual users as an additional data point.
It's actually ChatGPT at the moment for the first filtering step, for no other reason than having a code snippet ready that I could point Cursor at (I know, so 2025). The Gemini call is using batch processing, so it's handled differently.
That's exactly what Cursor's "plan" mode does? It even creates md files, which seems to be the main "thing" the author discovered. Along with some cargo cult science?
How is this noteworthy other than to spark a discussion on hn? I mean I get it, but a little more substance would be nice.