Comments URL: https://news.ycombinator.com/item?id=48940581

Points: 2

# Comments: 0

All of the models, including the Apache 2.0 ones, can be configured to go higher than 800x800. The difference between the ones with the PML license and the Apache 2.0 ones is the backbone, not the resolution.

I'd suggest you read the ICLR paper[1] which shows clearly the difference between the backbones at various latencies in Figure 1.

> For many domain-specific (often less common and odd dimensioned) objects, downscaling will severely reduce recall.

We released an entire paper[2] at Neurips about the long-tail transferability of models across a multitude of domains and benchmarked RF-DETR against that benchmark. The Apache 2.0 model is pareto optimal over the larger PML model at latencies less than the XL size.

(I'm one of the co-founders of Roboflow and worked on RF-DETR and RF100-VL.)

[1] https://arxiv.org/abs/2511.09554 [2] https://arxiv.org/abs/2505.20612

Regardless, you can do whatever resolution you want with the Apache 2.0 model. Just change the config at runtime; it was trained to be resolution agnostic.

You are correct that we also released larger models with a larger backbone under a different, non open-source license.

> In parallel, DETR [5] cast detection as end-to-end set prediction, and its real-time descendants (RT-DETR [98], D-FINE [55], DEIM [21], RF-DETR [62]) have narrowed the accuracy gap with CNN based detectors on standard benchmarks.

RF-DETR is both faster and more accurate and truly open source with an Apache 2.0 license: https://github.com/roboflow/rf-detr

Full disclosure: I’m one of the co-founders of Roboflow (we made RF-DETR, wrote this blog post, and are a sub-licensor of Ultralytics’ models.)

It makes a great target to distill SAM3 to.

Two years ago we released autodistill[1], an open source framework that uses large foundation models to create training data for training small realtime models. I'm convinced the idea was right, but too early; there wasn't a big model good enough to be worth distilling from back then. SAM3 is finally that model (and will be available in Autodistill today).

We are also taking a big bet on SAM3 and have built it into Roboflow as an integral part of the entire build and deploy pipeline[2], including a brand new product called Rapid[3], which reimagines the computer vision pipeline in a SAM3 world. It feels really magical to go from an unlabeled video to a fine-tuned realtime segmentation model with minimal human intervention in just a few minutes (and we rushed the release of our new SOTA realtime segmentation model[4] last week because it's the perfect lightweight complement to the large & powerful SAM3).

We also have a playground[5] up where you can play with the model and compare it to other VLMs.

[1] https://github.com/autodistill/autodistill

[2] https://blog.roboflow.com/sam3/

[3] https://rapid.roboflow.com

[4] https://github.com/roboflow/rf-detr

[5] https://playground.roboflow.com

The two areas I think this model is going to be transformative in the immediate term are for rapid prototyping and distillation.

Two years ago we released autodistill[1], an open source framework that uses large foundation models to create training data for training small realtime models. I'm convinced the idea was right, but too early; there wasn't a big model good enough to be worth distilling from back then. SAM3 is finally that model (and will be available in Autodistill today).

We are also taking a big bet on SAM3 and have built it into Roboflow as an integral part of the entire build and deploy pipeline[2], including a brand new product called Rapid[3], which reimagines the computer vision pipeline in a SAM3 world. It feels really magical to go from an unlabeled video to a fine-tuned realtime segmentation model with minimal human intervention in just a few minutes (and we rushed the release of our new SOTA realtime segmentation model[4] last week because it's the perfect lightweight complement to the large & powerful SAM3).

We also have a playground[5] up where you can play with the model and compare it to other VLMs.

[1] https://github.com/autodistill/autodistill

[2] https://blog.roboflow.com/sam3/

[3] https://rapid.roboflow.com

[4] https://github.com/roboflow/rf-detr

[5] https://playground.roboflow.com

Comments URL: https://news.ycombinator.com/item?id=44826439

Points: 10

# Comments: 1

I also wonder what other personality vectors exist.. would be cool to find an “intelligence” vector we could boost to get better outputs from the same model. Seems like this is likely to exist given how prompting it to cosplay as a really smart person can elicit better outputs.

Got really excited then realized I couldn’t figure out what “Google AI Edge” actually _is_.

Edit: I think it’s largely a rebrand of this from a couple years ago: https://developers.googleblog.com/en/introducing-mediapipe-s...

Comments URL: https://news.ycombinator.com/item?id=43257085

Points: 2

# Comments: 0

I can't imagine someone non-technical building something like this with prompting. The success of the project was highly dependent on my direction of the model to do what I wanted it to do (even though I gave it leeway in exactly how to do it). It did feel a bit like managing another engineer to do something vs doing it myself.

I don't use agents like this in my day to day work yet (I experimented with OpenHands a couple of months ago but it was frustrating, expensive, and took just as long as doing the task myself). But I'm thinking I probably will be a year from now.

A few times when the model got stuck I copy/pasted some stuff into o1 and pasted its response back into v0 (felt kind of like "escalating" to a more senior engineer) and that helped it get unstuck. Future models will be even more capable than o1. I imagine there will likely need to be a UI for "bringing in the big guns" of a smarter model in the future even if the grunt-work is done by a fast+cheap base model.

There's probably also something to letting the model "speak its native tongue". I don't know next.js but letting the model work with patterns it's been trained on probably helped it be more effective (compared to having OpenHands work in my own codebase using a structure it's unfamiliar with).

While the app is cool, the thing that blew my mind is that the entire app was coded by Vercel's v0 agent. In other words: I did not write a single line of code to create the app (though my teammate did write the backend scraper & DB filler by hand).

[1] Writeup: https://blog.roboflow.com/nycerebro/

[2] Repo (including the generated code + initial meaty prompts): https://github.com/yeldarby/nycerebro

[3] v0 session: https://v0.dev/chat/nyc-erebro-app-RwzRUEMGveH?b=b_6AuWalvG7...

Comments URL: https://news.ycombinator.com/item?id=42804278

Points: 4

# Comments: 2

Comments URL: https://news.ycombinator.com/item?id=42773967

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=42410353

Points: 4

# Comments: 0