AI Coding Live News

Krafton AI, the research division of global gaming giant Krafton, officially announced the open-weights release of Raon-1 mini under Apache 2.0. Pretrained from scratch, Raon-1 mini features a sparse Mixture-of-Experts (MoE) architecture with 30 billion total parameters and only 4 billion active parameters per token. The release is accompanied by a dedicated DFlash speculative drafting model and a live interactive playground. Internal evaluations reveal that Raon-1 mini matches Meta's Muse Glimmer across agentic tool-use and coding benchmarks while requiring substantially lower VRAM and sustaining lower generation latency.

Zoom

Meta AI published the technical report for Muse Glimmer 30B (arXiv:2610.24628), an open-source 30-billion parameter causal language model with an integrated perception encoder designed for on-device autonomous agents. Quantized to approximately 4 bits, Muse Glimmer operates inside 32GB of VRAM on a single consumer GPU (such as an RTX 5090) without cloud reliance. On rigorous agentic evaluations, Muse Glimmer sets new records for its size class: achieving 75.5 on MCPAtlas (surpassing Gemma4-31B at 54.2 and Qwen3.6-27B at 62.5) and 51.2 on SWE-Bench Pro, released fully under the Apache 2.0 license.

Zoom

Meta released Muse Glimmer in August, a 30B open-weight local agent model under Apache 2.0 whose quantized weights fit under 20 GB for consumer Macs and single-GPU PCs. On October 19 Meta published its technical report: a ~29.6B dense design distilled from Muse Spark that, by Meta's figures, beats Gemma4-31B and Qwen3.6-27B on tool-use and agent benchmarks such as MCPAtlas, tau3-Banking and SWE-Bench Pro, while still trailing Qwen3.6-27B on SWE-Bench Verified, TerminalBench 2.1 and OSWorld.

Zoom