HSRD-100: 100 High-Quality 3D Human Scans for AI, Graphics & Research
IvoDRL
• • 1NEW Articles from Team or Enterprise organizations will get promoted to the main section.
Extract Text and Knowledge from Images with Open Vision Language Models
dvilasuero
• • 5🎚️ Batch Normalization — Quand ton réseau a besoin de chill pills ! 😤➡️😌
RDTvlokip
• • 2🎚️ Batch Normalization — When your neural network needs anger management! 😤➡️😌
RDTvlokip
• • 2Australian-made LLM beats OpenAI and Google at legal retrieval
isaacus
• • 28Nemotron’s Open Secret: Accelerating AI Development with Open Models, Data, and Recipes
nvidia
• • 11Promoter-GPT: Writing DNA Instructions with Language Models
hugging-science
• • 26TIL: How a Harmless Refactor Exposed a Hidden CUDA Bug in Vision-Language Models
albertvillanova
• Vision Tokens vs Text Tokens: Understanding the 10× Compression
onekq
• • 8Agentic CLIs Can Do So Much More Than Code-Gen
danielrosehill
• The Missing Semester of AI for Organizations #2: Risk of Pickle
huseyingulsin
• • 1Model Card of Xylaria 2 Exempted
Reality123b
• • 2Llama‑Embed‑Nemotron‑8B Text Embedding Model Ranks First on Multilingual MTEB Leaderboard
nvidia
• • 14🔄 Transfer Learning — Quand l'IA apprend de l'expérience comme toi ! 🎓🚀
RDTvlokip
• • 2🔄 Transfer Learning — When AI learns from experience like you! 🎓🚀
RDTvlokip
• • 2Companion AI After the Panic: It’s Okay to Treat Adults Like Adults
Clock070303
• • 5Scaling Test-Time Compute to Achieve Gold Medal at IOI 2025 with Open-Weight Models
nvidia
• • 19Art of Focus: Page-Aware Sparse Attention and Ling 2.0’s Quest for Efficient Context Length Scaling
RichardBian
• • 14Introducing MTEB v2: Evaluation of embedding and retrieval systems for more than just text
isaacchung
• • 38How I Built Lightning-Fast Vector Search for Legal Documents
adlumal
• • 15