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NVIDIA Fetch: Rethinking How Large Language Models Retrieve Information
boxiangwanglm
• Ling 3.1 Flash 1019: same setup, new weight shards
Open data from a one-founder AI company: RTX 5090 serving runs, fine-tuning results and an evidence ledger
pavle-scalably
• Day of the Dead Pet AI: a one-photo memorial workflow, prompts and checks
gdsgafdsafdxg
• Seeing ViT Registers: Model Size, LayerNorm, and Where the High-Norm Tokens Go
turhancan97
• • 1
Tiny Desk AI Video: Line Drawings, Wan 3.0 and Seedance 2.5 (Notes From 25 Test Renders)
Smooth Levels from Sparse, Gappy Panels
Draw a MOSS layout once, then run it in ComfyUI
Persistent Agent Memory Should Make the Next Task Easier
MemTensor
• • 1
MeshyRig: Agent-Driven Rigging Beyond Standard Templates
meshy-ai-team
• How Distributed Training Works Across Multiple GPUs
prismberry-technologies
• Accuracy plateaued. The model kept changing.
LLM SEO for Developers: How AI Search Retrieves and Cites Pages
How One Python Dictionary Replaces 2,000 Lines of Distributed Communication Code
rishiraj
• • 1
ClawBench Lite: inspect the task schema before running an agent
DecisionFormer
FrontiersMind
• • 8
protoAgent: an open-source agent you run on your own machine, with your own models
artificial-citizen
• Tiny Desk AI Video with Seedance 2.5: How to Make the Outstanding Meme
52 full-attention layers versus 16: mapping Nanbeige and Qwen