Interesting HuggingFace post. What if the easiest way to make long-context Local AI faster is to simply STOP feeding it everything? A new HF forum experiment tested 600 distractor-heavy long-context prompts with Qwen2.5-7B 4-bit. Instead of sending the entire ~14K-token context, a MiniLM semantic selector kept only the most relevant ~60%. The result ... 📚 Input tokens 13,124 → 7,702 ➡️ -41% ⚡ Prefill 871 ms → 463 ms ➡️ -47% 🚀 End-to-end latency 944 ms → 519 ms ➡️ -45% 💾 Peak VRAM 11.71 GiB → 9.16 GiB And here's the weird part... 🎯 Token F1 actually IMPROVED: 0.534 → 0.581 So the model got: ✅ less context ✅ lower VRAM use ✅ almost 2× faster prefill ✅ lower total latency ✅ BETTER answers Why? Because more context isn't always better context. Coding agents and RAG systems often drag around enormous histories filled with irrelevant junk. ⚠️ This was a controlled distractor-heavy HotpotQA stress test, not proof that deleting 40% of every real-world context will improve accuracy. 🔗 Link in ALT.

is the HuggingFace attack something that should be taken seriously? yes. is it the first warning shot of a machine takeover? no. will issues of this type look like a tractable / mostly solved problem with proper precautions in 6 months? yes. will that happen automatically? no. as always, will require work from smart people and their agents.

OpenClaw landed on @huggingface local apps 🦞🤝🤗 1. Pick any GGUF/MLX model on hf 2. Copy the openclaw onboard setup 3. Volla you've got a tool-calling agent running fully local. no cloud, no keys, no one watching. Get your claw localmaxxing. resistance is futile 🦞

Does anyone know if @huggingface (owned by @NVIDIA) still has access to the MI455X that AMD gave them under embargo? Does anyone know if AMD continues to give exclusive access to next-gen hardware to Hugging Face (owned by NVIDIA)? Will Hugging Face be working on making Nemotron3 Ultra work on MI455X?