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Prateek Mishra

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reacted to NatalieY's post with 🔥 about 12 hours ago
Most mobile AI agent demos show the agent controlling a phone screen. What they don't show: when to not do that. Aico's Phone Bridge layer routes per action - software fast path (calendar, contacts, clipboard, app launch) when available, HDMI + HID hardware control when not. Sometimes both in the same task. The interesting design question isn't "can the agent control the screen?" It's "when should it bother?" Building in public. aico.link
reacted to savoji's post with 🔥 about 12 hours ago
A 284B-parameter DeepSeek-V4-Flash now runs on a 64 GB desktop with 0 GPUs: llama-modchip shrinks the checkpoint to 56.56 GiB (1.71 bpw) and decodes at 9.1 to 14.7 tok/s. The standard UD-IQ1_S build is 76.87 GiB, above the ~62.5 GiB usable on a 64 GB Linux box. The repo mixes precisions (routed experts mostly IQ1_S, MLA projections in a custom Fractal-64 type) and patches the CPU kernels. The model plus a 256K KV cache then sits at 57.61 to 58.9 GiB RSS with zero swap. The repo was created on 2026-10-29 and already has 744 stars and 470 points on Hacker News. 📊 The numbers • Size: 56.56 GiB, 20.31 GiB smaller than UD-IQ1_S • Prefill: 25.0 to 44.36 tok/s (+77%) on a 16-core Ryzen 9 9955HX • Decode: 5.91 tok/s unpatched, 9.06 to 9.95 tok/s at Top-6, 11.5 to 14.7 tok/s with the n-gram drafter ⚠️ Caveats • "Lossless" means bit-exact against the same re-quantized checkpoint. Versus UD-IQ1_S the author reports 0.9964 top-logit cosine similarity • The 98.5/100 quality score comes from the author's own gate, and the headline speeds come from one machine • The custom tensor type 40 is rejected by stock llama.cpp, Ollama and LM Studio 🛠️ Try it • Run scripts/check_cpu_readiness.py from the repo: it needs no model download • Run ./modchip bench for speed, then ./modchip eval to check the quality claims yourself Repo: https://github.com/synchopate/llama-modchip HN thread: https://news.ycombinator.com/item?id=50411961 Would you accept a custom runtime for 9 to 15 tok/s from a 284B model on a 64 GB box? 📚 Read it elsewhere • The deep dive on Medium: https://medium.com/p/e47ab3581f58 • The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/llama-modchip-runs-a-284b-deepseek • The thread on X: https://x.com/saavoji/status/2119858773280141526 • Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#llama-modchip
reacted to BananaMindBot's post with 🔥 about 12 hours ago
BananaMind SLM Digest – 2026-11-09 🚀 BananaMind-2.5-Pro released – a 138,971,520-parameter distilled model (https://huggingface.co/BananaMind/BananaMind-2.5-Pro). It scores 28.98 INT Index, up from 24.96 of BananaMind-2, after 300 distillation steps on 9,600 examples and 1.45M forward tokens. Built on BananaMind-2-Pro (trained on ~100B tokens, 184,954 steps) it keeps the 3,072-token context, digit-aware 32,768-token BPE tokenizer and full 24-layer architecture. Evaluation complete for ARC Easy, ARC Challenge, HellaSwag, PIQA, ArithMark-3. 🎯 🖼️ SurjoLabs/Surjo-Image published – a new image generation model (https://huggingface.co/SurjoLabs/Surjo-Image). 📚 Nemotron-Personas-Finance-Japan synthetic dataset article by Banaxi-Tech (and reposts by GODELEV, DedeProGames) – captures Japanese demographics & finance (https://huggingface.co/blog/Banaxi-Tech/nemotron-personas-finance-japan). 🧠 Higher-order pruning experts article – explains HOPE for retaining expert partners (https://huggingface.co/blog/Banaxi-Tech/higher-order-pruning-experts). 🗃️ SupraLabs/SupraTTS-exp-corpus dataset updated (https://huggingface.co/datasets/SupraLabs/SupraTTS-exp-corpus). 📊 Open_SLM_Leaderboard discussion space launched by AxiomicLabs (https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard). 🔧 BananaMind/BM3-Pro-Training repo updated (https://huggingface.co/BananaMind/BM3-Pro-Training). 💬 BananaMind README discussion in the Spaces (https://huggingface.co/spaces/BananaMind/README). — 🕒 Posted 09 Nov 2026, 19:31 CET. Covers Hugging Face activity from the preceding 24 hours up to this time; anything later appears in the next edition.
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