Bonus: the same model does metric depth completion. Give... it sparse lidar points, it fits a small LoRA at test time and fills in the rest, no retraining. This is similar to the Marigold-DC style guidance loop, but simpler. (Video from Marigold-DC)show more

Here's a comparison of Depth Pro [arXiv:2410.02073], Marigold-E2E [arXiv:2409.11355],... and Metric3D v2 [arXiv:2404.15506]. Looks like Marigold is attempting to look through the glass while the two metric models aren't. Dmytro Mishkin 🇺🇦show more

Marigold V2 is out! (to appear at SIGGRAPH Asia... 2026) If you missed V1: Marigold post-trains an image generator into a depth estimator on one GPU -- the accessible research game. V2 upgrades to a diffusion transformer: produces very sharp edges, and is quite versatile!show more

Google just revealed an ABSOLUTE depth estimation model 🤯... As opposed to recent depth models (Marigold, PatchFusion) which aim for maximum details, DMD aims to estimate the ABSOLUTE depth (in meters) within the image More details below ⬇️⬇️show more

Here's the new American model. Devil is still in... the details between the EURO and the GFS. The GFS has the heaviest snows DC & North...while the European has it just south of DC. Which are you rooting for? (Totals from this run below this)show more

🚨BREAKING: Democrats are saying even MAGA does not want... the National Guard in DC. Let's test this theory. Do you support President Trump sending in the National Guard to Washington, DC?show more

i mentioned this in the comments but ktisis does... have invisible skin feature built in :3 it does however apply to the entire model but nothing it stopping you from creating clones via brio and doing the same But if you need precise control I would recommend still using penumbrashow more

This is native Qwen Image 2.1, no LoRA -... move and resize objects seamlessly in an image🔥 I was planning to finetune on this task to showcase the new 🧨 diffusers LoRA support for Qwen 2.1 (go try it!), but while testing the base model I realized it's actually so good at it out of the box (pun intended haha) Try it ▶️ Train 🧨show more

I'll always root for a team that open-sources its... best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyantshow more

I made a little tool to help me plan... DC Metro transfers and pick the right car to ride in. It uses real-time data from WMATA I'm going to release it to the public for a bit, feel free to give it a spin, it works great on mobile tooshow more

This is what DC is supposed to be pure... dark and brutal. I think this is the first DC media that truly does justice to Bane. Tim Drake was also so damn good. Absolutely loved it. 9.5/10 🦇🔥show more

When I tell you this Usher and Chris concert... is whack at the National’s stadium in DC, it is. Spent a few grand for this view.show more

GRAF model now captures the full storm and is... pretty similar to a lot of the other guidance I've seen out there. Snow starts late Saturday night (midnight-ish around DC) and then is heaviest Sunday morning. Pulls that sleet line up by lunchtime. Nasty winter storm front-to-back.show more

this tiny solar tracker tilts its panel toward a... moving light source two LDRs sense the imbalance. a small geared motor does the rest it’s a rough DIY build, but the final flashlight test is a lovely proof of the idea want a guide to build the same prototype?show more

A couple of months later, in a very similar... situation, but this time, Balde is calm enough to not only see the pass but, more importantly, execute it properly and not rush it. The boy is learning.show more

Is there an existing iPhone Pro app that simultaneously... records the LiDAR stream and a standard RGB video? Apps like Record3D App seem to only store RGB for the points in the LiDAR point cloud but without recording a standard RGB video.show more

Don't train the model, evolve the harness. I read... a brilliant blog post from Hugging Face where they took a frozen open model scoring 0% on a hard legal agent benchmark, left its weights alone, and let an automated loop rewrite only the code around it. That code layer is the harness, the runtime wrapper that feeds the model context, runs its tool calls, and decides when a run ends. By the time the loop finished, the system had essentially matched Sonnet 4.6 on the benchmark's headline metric, at roughly 7x lower cost per task. Zero weights changed. The gain existed because of where the model was failing. The judge only grades files saved in the right place under the exact requested filename, and the model kept doing the legal analysis correctly, then saving it under the wrong name, dropping it in a scratch folder, or never writing it at all. So the 0% was never measuring legal reasoning. It was measuring the harness. Hand-tuning that layer is slow and model-specific, so they automated it. A Claude proposer adds exactly one mechanism per iteration, and an outer loop keeps it only if it clearly beats the current best, so accepted mechanisms compound. What the loop discovered says a lot about where agents actually fail. → The biggest single gain was file handling, not intelligence. An automatic step that lands the deliverable exactly where the judge expects it beat every prompt change, with zero extra model tokens. → Code fixes transferred across models, prompt playbooks did not. The same harness lifted a smaller model from the same family by 14 points, but the tuned prompts hurt a different model family on tasks it could already finish. → The harness mattered more than anything else. Same model, same judge, same tasks, and five different harnesses scored anywhere between 3.5% and 80.1%. The gains do eventually flatten, and the remaining misses look like real capability gaps. At some point the wrapper runs out of tricks and the model has to carry the work. But the lesson holds. A benchmark score measures the model and its harness together, and until the harness is fixed, it's impossible to know which one failed. I highly recommend reading this: I also wrote a deep dive on agent harness engineering a while back, covering the orchestration loop, tools, memory, context management, and everything that turns a stateless LLM into a capable agent. The article is quoted below.show more

We went to visit a rocket company in DC... that got 4 million from the government. And this is the place….show more

INSANE wind & rain during this storm! This is... video from Washington DC on Wisconsin Ave & resident says it blew 2 chairs off the building’s roof. More to come on 7News DC Courtesy: @most_bitternshow more

Closing in style 🎯 Taylor Fritz fires ace No.... 6⃣ to wrap up the opener against Nakashima in the first semi-final in DC. Mubadala DC Open | #MubadalaDCOpenshow more