🔮 Predictive power - hugonoss · Scour

Pure C implementation of the TurboQuant paper (ICLR 2026) for KV cache compression in LLM inference. 🦙Ollama

github.com·1d·r/LocalLLaMA·

Bloom's 2 Sigma Problem 💡New and interesting problems

rochan.bearblog.dev·3d·

Activating Two Trap Cards at Once, or: A Gentle Response to the Popularity of Vibecoding 🏛️NetHack

gist.github.com·6h·Lobsters·

Qwen 3.5 9B LLM GGUF quantized for local structured extraction 🦙Ollama

huggingface.co·1d·r/LocalLLaMA·

Boyer-Moore Majority Element ⚖optimizing for consensus

NULL BITMAP by Justin Jaffray via buttondown.com·3d·

The Results Are In! 🏆Monumental success

justemile.bearblog.dev·2d·

Chinese Named Entity Recognition Model Selection for Small Pure CPU Environments 🦙Ollama

lawtee.com·2d·

Demystifying Softmax Loss: A Step-by-Step Derivation for Linear Classifiers 🧮Vector Databases

blog.aeilot.top·5d·

Aurora 🦙Ollama

together.ai·2d·Hacker News·

Make Your Prompts Boring 🦙Ollama

jigarkdoshi.bearblog.dev·4d·

Scaling Beyond 4k 🧮Vector Databases

rishirajacharya.com·2d·

What if AI doesn’t need more RAM but better math? 🦙Ollama

adlrocha.substack.com·4d·Substack·

Learning by Building, Part 3: Caltrain Bot, Local LLMs, and Reality 🦙Ollama

dima.us.kg·3d·

Inference Engines — A visual deep dive into the journey of a token down the transformer layers 🧮Vector Databases

femiadeniran.com·4d·r/LocalLLaMA·

Bigoish: Test the empirical computational complexity of Rust algorithms 🧮Vector Databases

docs.rs·6d·Lobsters, Hacker News·

Standard LoRA is quietly losing 68% of quality on FP8 hardware and most people have no idea 🦙Ollama

koscak.ai·6d·r/LocalLLaMA·

Some Local Aspects of AI 🦙Ollama

blog.raymond.burkholder.net·4d·

yashkc2025/turboquant: Python implementation of TurboQuant (arXiv 2504.19874). Data-oblivious, near-optimal 1–4 bit vector quantization for streaming KV-caches and databases. 🧮Vector Databases

github.com·4d·r/LocalLLaMA·

Fused INT8 Weight-Only Quantization in Pallas 🧮Vector Databases

rishirajacharya.com·3d·

TurboQuant for weights: near‑optimal 4‑bit LLM quantization with lossless 8‑bit residual 🦙Ollama

github.com·6d·r/LocalLLaMA·

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