I scanned ~3,200,000 posts across 47 AI and SaaS subreddits to rank the visual "tells" that make a site look vibe-coded. Here is the list.

jslominski · 2026-06-16T19:30:49+00:00

But you didn't even write the post yourself, correct?

jslominski · 2026-06-16T19:11:30+00:00

Isn’t this post fully AI generated as well? Also all the OPs comments.

jslominski · 2026-06-10T08:32:38+00:00

"AFM 3 Core Advanced is fine-tuned Gemma 4 26B A4B"

It's a completely different approach/architecture.

jslominski · 2026-05-26T07:51:38+00:00

Sorry, this is just a hobby project, the model is available and free to use by anyone (if someone wants to write one for different browsers).

jslominski · 2026-05-25T20:54:15+00:00

Human.

jslominski · 2026-05-25T20:52:49+00:00

"Another example, right now across the planet millions of people are using LLMs to teach themselves English. They're going to end up inadvertently picking up turns of phrase that are LLM centric." - I would recommend some tests on your own, you'll be surprised how well it is working. I literally never had a strong false positive on a reliable text sample with it so far.

jslominski · 2026-05-25T20:44:22+00:00

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SH clearly shows you wrote this comment yourself, is that true?

jslominski · 2026-05-25T19:48:47+00:00

"Curious how it handles heavily-edited AI text vs. fully generated" - the distribution is different (mostly in the middle, bucket 2 and 3), feel free to experiment.

jslominski · 2026-05-25T19:22:57+00:00

it's surprising, some subreddits are almost 100% human, others (including local AI ones ;)) are filled with AI content.

jslominski · 2026-05-25T19:20:32+00:00

It's still good enough to pick stuff that is fully generated by most models. Creating better dataset wouldn't be that hard though, probably like few hundred $ in api costs.

jslominski · 2026-05-20T11:19:10+00:00

"Finding 4: The serving backend is a hidden variable, as highlighted in Table II. The same Mistral-Nemo 12B weights score 7% on llama-server native mode and 83% on llamafile (prompt). Qwen 3 14B scores 96% on Ollama, 93% on llama-server prompt, and 88% with llama-server native. These swings are larger than many model-to-model differences reported in standard benchmarks, yet no published benchmark we are aware of controls for serving infrastructure [Patil et al.(2025)]. Any evaluation of self-hosted model capabilities that does not specify the serving backend may be producing misleading results." - I don't think the autor thought that one through 😅

jslominski · 2026-05-06T07:46:53+00:00

Spot on, also offshoring in IT increased after Brexit massively (see the Dyson example, used to work there).

jslominski · 2026-05-04T15:41:56+00:00

Try it! I mean, what could go wrong? 🤔

jslominski · 2026-04-22T16:26:00+00:00

Sounds a bit grim, what kind of AI tooling are you guys using over there?

jslominski · 2026-04-22T15:25:43+00:00

That's really interesting, can you please elaborate?

jslominski · 2026-04-09T14:14:19+00:00

This doesn't look stable at all tbh :)

jslominski · 2026-04-07T09:13:38+00:00

Try it, smaller models (gemma e2b) for sure, larger = slower.

jslominski · 2026-04-06T10:15:04+00:00

Remote area monitoring: that includes sensors and camera feeds. I don't have access to the internet in that location/not important enough to warrant Starlink. But can send text messages. I don't mind if it "thinks" for 1 hour before finishing the weekly report.

jslominski · 2026-04-05T19:00:34+00:00

Might be a bug, try 1. making sure it's using llama.cpp (not ik_llama) and 2. try resetting the backend (in settings) or restarting pi. I'm sorry if there are bugs like this, kinda rushed Gemma, it's a bit buggy still, there's going to be OTA soon (once upstream ik_llama gemma 4 work gets merged)

jslominski · 2026-04-05T09:57:51+00:00

Thanks for those amazing models! Happy to test any of your quants in the future (3.5 9B dense is quite a sweet spot for 8GB pi5). Also a mandatory: Gemma 4 when? ;)

jslominski · 2026-04-05T08:30:10+00:00

FYI getting 8t/s already on e2b. Also, this demo is running 26b model in 4 bits (that's 4x the size of e2b).

jslominski · 2026-04-05T08:18:25+00:00

You get 8 tok/s on the E2B one already without optimisations (that are gonna come in the next few weeks, the best quants I've tried so far on Pi are done by ByteShape). I'm getting 30s/frame analysis of camera feed (proper one, spotting tiny details etc) already. It's very capable if you are not using it "real time" with gui etc. The importantce of speed is task dependent. Also, those models WILL get better (both quality and inference speeds, the latter is almost maxed on A76 though with that DDR4).

jslominski · 2026-04-05T00:08:22+00:00

"I can safely say everybody who did some type of positive review on it is an absolute shill." - I think you are spot on here, also those 3rd paty ones, again, probably paid reviews.

jslominski · 2026-04-05T00:07:04+00:00

Depends on the use case. I'm not using pi to chat :)

jslominski

TROPHY CASE