I like that they're being up front about this. Benchmarks are a metric that can be gamed. They give you an approximate estimate of where things land, but you really need to test for your use case.
tyushk 35 minutes ago [-]
Cool to see another model with support for bounding boxes on images. Really wish to see segmentation in a future release; Gemini 3.5 Flash-Lite is mandatory in my use case as it's the best model for semantic image segmentation in my experience.
moonu 24 minutes ago [-]
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.
kypro 46 seconds ago [-]
I'm aware this is an unproductive comment, but why are all their releases so cringe? It's like they're written by a Reddit mod or something...
> For the builders AND dreamers
> Enter the danger zone at your own risk.
> Size does matter, apparently
> We’re trying something new out here: we’re going to treat you guys like adults
> Lots of people love to ask what kind of sci-fi we’re releasing next
That said, their docs are great and don't seem to include all the Reddit cringe.
jofzar 25 minutes ago [-]
Interesting way to release this, I'm not sure how to say this, but this has a real "enterprise"/old school taste to it verses how all the other models/AI companies are releasing things.
Very refreshing.
yipinwong 40 minutes ago [-]
Sounds like an incremental change with context size change, new "box" type, and composable images.
As it's minor change did not expect much, but having a higher context helps for sure. I will own the errors for passing bigger context.
therealpygon 31 minutes ago [-]
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.
hexasquid 33 minutes ago [-]
I'm curious what people are building with these models
Rudybega 11 minutes ago [-]
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.
vanuatu 3 minutes ago [-]
^ Same here. so much faster and cheaper than using an LLM to return structured data
nazgul17 22 minutes ago [-]
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).
LewisOrton 21 minutes ago [-]
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
ASalazarMX 30 minutes ago [-]
Judging from the tilte, I guess dreams.
matt3210 35 minutes ago [-]
How does JEV prevent distillation?
himata4113 34 seconds ago [-]
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.
nico 33 minutes ago [-]
I don't think they can. But they probably monitor usage and test for patterns to then block/ban accounts
benaduggan 40 minutes ago [-]
So they held back a bunch of features because they knew they had basically no moat, and now they're trickling out a bit more functionality to try to convince people they are better than others and move fast? And using Apple advert slogans to show how cool they are?
aryabakh 38 minutes ago [-]
With the speed they're adding features shows you're correct.
I like that they're being up front about this. Benchmarks are a metric that can be gamed. They give you an approximate estimate of where things land, but you really need to test for your use case.
> For the builders AND dreamers
> Enter the danger zone at your own risk.
> Size does matter, apparently
> We’re trying something new out here: we’re going to treat you guys like adults
> Lots of people love to ask what kind of sci-fi we’re releasing next
That said, their docs are great and don't seem to include all the Reddit cringe.
Very refreshing.
As it's minor change did not expect much, but having a higher context helps for sure. I will own the errors for passing bigger context.
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
I guess their cheeky "Warning: AI Benchmarks, don't trust them" is their way out of that one.