Or, you know, it's an easy excuse because they're already behind on many of them.

Curious why you use Gemini 3.5 Flash Lite instead of SAM/Moondream? Any specific workflows where it outperforms?

I used the model and it was good enough for email classification. I was wondering if I'm supposed to be doing multiple runs for topic classification with grouping related topics i.e. if its sport,economy or tech related news and then another run for soccer,basketball or something else or batching them like the News article topic classification example in a single run.

Swiss Style/Typographic Style.

too bad it's just LARPing, underneath the covers it's the typical bloated Framer site.

Well, they do need to try to compete with SubQ’s longer context and image processing that has been coming out ahead in benchmarks, plus the thousand self-hosted options that now exist.

I walked away with _mostly_ the opposite impression. They have a specific tone of voice! I think that's good.

I've yet to see any company match the copy writing skills of Valve though.

You can turn a lot of process and workflow systems into deep decision trees.

My team hasn't finished writing their blog posts yet, but the customer demos [2] went out the moment TypeSafe took our call and sorted all the paperwork out.

There's a tiny section at the bottom of the demo which explains how it actually works (or the github repo is the code).

We have a single decision API[1] over all similar models, so we run evals across all the "System One" api products & definitely don't trust any benchmark that you haven't run. All standardized benchmarks have been benchmaxxed already.

PS: just in case it wasn't clear, the demo is a game where we play to calibrate the human vs machine decision making (or just do it in auto-pilot mode).

[1] - https://github.com/prasanthj/duckdb-dual-cognition

[2] - https://demos.isotopes.ai/demo/apps/chain-command

I use it as a drop in replacement for LLMs I had previously doing filtering and classification in a pipeline. It's loads cheaper and faster with roughly equivalent performance on my own internal tests. The confidence scores (which are also reasonably well calibrated on my data) are a super nice bonus.

Automatic tagging of images is what I'm doing with it. I'm using a modified Gemma model, which ingests images (and audio, for that matter).

I found an example online, I cannot find the github again, but it was interesting: an instant dynamic generation of React component, you type what you want to build using natural language, and it directly add one to the page.

If I remember well, in the background, it does the multi-choice question: "What is the component requested ? --> Calendar, Slider, ..."

it's actually more usefull than it looks. it is not much useful on its own but it can play a big role in a complex workflow or custom harness.

i used luna 5.6 as the ranker/filter to remove useless items from upstream rag(mixing embedding, regex etc) and it did a good job but took 5-10 seconds each time. now less than 1 second and no observable precision loss. hope it helps

Judging from the tilte, I guess dreams.

They don't. Generally we already have trillions tokens worth of classifier datasets so there's no reason to create synthetic data that will be worse than the data it was trained on.

I don't think they can. But they probably monitor usage and test for patterns to then block/ban accounts

With the speed they're adding features shows you're correct.

Which others?