LocalLLaMA users seek small local models for reverse engineering

Stanford adjunct professor Andrew Ng compares AI governance to a hammer: if you dent a wall, the fault lies with the person wielding the tool, not the manufacturer. He argues responsibility should fall on the user rather than the AI developer.

An r/artificial post argues that AI models, built from every discovery and invention contributed to human knowledge, should be legally treated as humanity's collective inheritance. The author says the piece was written by a human, except for a section of "4 commitments."

A GitHub project, GVS5H, claims five Qwen3.8-27B models reach parity with Fable 5 when run through its harness. No benchmark numbers, methodology, or independent verification accompany the claim.

A LocalLLaMA user asks whether ISTA's GSQ-RCO GGUFs for DeepSeek-V4.1-Flash really match the base model, noting the published benchmarks are saturated (90%+, AIME25 at 100 for every version) and no KLD data was found.

ScreenPlink turns a monitor into a laser target: a webcam beside the screen detects hits from a laser trainer pistol or a laser cartridge in the user's own gun. Built with Claude Code over roughly two weeks and released open source and free.

Saluki is a 7.9 GB quantization of Qwen3.8-27B that its makers say keeps tool calling intact at ~2 bits per weight and beats the full 54 GB model on their own 120-task benchmark. The tutorial compares it against ISTA-DASLab's GSQ-RCO quantization in stock llama.cpp, scoring both on seeded BFCL tasks.

Cerebras splits Kimi K3's ~3 trillion parameters across multiple WSE-3T wafers, each with 44 GB of on-chip memory, to hit 1000-2000+ tokens per second. The 93-layer model uses 4-bit weights and an 8-bit KV cache, with one dense layer per wafer to leave room for KV cache.