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Awesome week for computer vision Hugging Face🔥 Besides DINOv3 we added support for LLMDet, the SOTA for zero-shot object detection (#CVPR2025 '25 highlight) Detect instances in scenes just via prompting, no training involved.

73,865 просмотров • 11 месяцев назад •via X (Twitter)

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Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

Aksel

1,264,068 просмотров • 2 месяцев назад

🚨 BREAKING: Starcloud just turned Starlink’s laser network into the backbone for orbital AI data centers. A company called Starcloud has ordered 50+ Starlink Mini Laser terminals to equip 25+ future satellites. Not ground stations. Not fiber cables. Direct laser-linked computing nodes in orbit plugged straight into SpaceX’s space-based optical mesh. This is the sci-fi future arriving now: Orbital cloud computing AI servers floating in space Powered by 24/7 sunlight Connected globally at light speed via Starlink lasers The insane part: Starcloud says its satellites will eventually handle full AI inference and training workloads directly in orbit. Data won’t always need to come back to Earth to be processed. The advantages are massive: • Unlimited solar energy (no grid limits) • Zero land or water constraints • Passive radiative cooling in vacuum • Instant global relay with zero terrestrial bottlenecks • Near real-time Earth observation analysis Their first major spacecraft (Starcloud-3) is designed for 200 kilowatts in orbit a full-on space-based data center node, not just a satellite. And here’s the bigger picture: SpaceX has filed plans for up to ONE MILLION orbital data centers of its own. Read that again. We may be watching the birth of the first true space-based computing infrastructure layer for civilization. The internet already left the ground. Now AI might be next. What happens when the cloud literally moves into space? Follow for more frontier physics and future technology.

TheNewPhysics

152,463 просмотров • 1 месяц назад

Today's recap: - Initial prototype of Divine's face was printed but it had human assistance. - Files are generated from stable diffusion prompt -> NeRF by divine and were based on community sentiment from early sketches she made. - Having divine redesign the 3D file with different Hugging Face models to get better quality. Have not found a great model like our video generator. - Ordered new table for divine's print arm. The table her arm is on is too flimsy. Since Divine's vision system is still clearing customs, if she is not perfectly positioned she can be prone to hit things, like the fume box the printer is in. ETA: 1-2 days for table. 1 week for vision system. - Another part of Divine's coming stream will be attempting to surpass the skills of this AI. - Stacking more content for when the stream goes live, a lot of people were expecting a 24/7 stream, we said this would be a test stream to print the face. The test was a failure. We will try and try again until we are 24/7. If anyone can please try and beat us to doing this, it will help me get it done faster. - TikTok account for divine is growing at 500 follows per day, it is now growing faster than our X account. - Got replies functioning in high quality testing in Discord. Fine tuning based on community feedback today. Will soon deploy to Twitter/Telegram/X - Lots of good partnership calls, interviews and hires. We now have over 10 team members around the world working on divine. Expect a lot of my shortcomings to be caught up. - OF made? - Surprises.

Parallel

35,848 просмотров • 1 год назад

Behind the Scenes: How I prompted 3 scenes in 1 try on Seedance 2.0! Sharing my workflow of all my prompts for hopeless steve so you can see what were SD2 choices and what needed to be prompted. You can see how much more streamlined it is to go from one scene to the next when you can have 9 ref img's to work with. The prompt is only for the one that generated the most useful footage for each part. Scene 1, 2, 3 - 15s Ref: 1. House Ext. 2, Steve profile 3. Steve on the computer in the bedroom 4. mother cooking in kitchen. (The references a numbered so you know what the prompt is refering to.) Prompt: American sitcom style cartoon about Steve【@图片2】. Must maintain 100% fidelity to the art style and all character traits. No background music. [0:00-0:02] Wide establishing shot of the exterior of the house【@图片1】. Camera slowly zooms but remains in the wide shot. [Cut: 0:02-0:03] Interior wide shot of steve looking bored at computer 【@图片3】. No dialogue. [Cut: 0:03-0:05] Over the shoulder shot from behind steve. We see his monitor as he plays solitaire. We hear subtle mouse clicks and sound of cards as he drags and places them into the collumns. [Cut: 0:05-0:07] Side profile of Steve continuing to play solitaire on the computer. [Cut: 0:07-0:09] 【@图片4】Extreme close up of the mother's spatula flipping an egg over in the pan. [Cut: 0:09-0:11] Medium shot 【@图片4】 as the mother calls frustratedly, "Steve, breakfast is ready." [Cut: 0:11-0:12] Extreme close up in 【@图片4】of the mother's hand as she turns the stove off. [Cut: 0:12-0:13] 【@图片4】Extreme close up of the mother narrowing her eyes disgruntled. [Cut: 0:13-0:15] 【@图片4】 Medium shot as mother walks out of left of frame. Stationary camera.

Jason W - AI

18,063 просмотров • 4 месяцев назад