GLM-5.1 vs RuView

GLM-5.1 is Zhipu AI's latest open-weights language model — a 744B parameter mixture-of-experts (MoE) architecture that activates 40B parameters per forward pass. Released under the MIT license with a 200,000-token context window, it has quietly topped the SWE-Bench Pro leaderboard, surpassing both Claude Opus 4.6 and GPT-5.4 on expert-level software engineering tasks. The MoE architecture means GLM-5.1 is significantly cheaper to run per token than a dense 744B model, with inference costs approaching dense 40B models for most workloads. Zhipu AI (a Tsinghua University spin-out) has steadily iterated on the GLM family to produce a text-focused reasoning model that holds its own against proprietary frontier models — now, for the first time, reportedly exceeding them on coding benchmarks. The MIT license is the headline for enterprise and research users: full commercial use, no usage restrictions, no API dependency. This puts GLM-5.1 in direct competition with Qwen3.5 for the "best open-weights model you can actually use for anything" crown, with a differentiating edge in software engineering tasks specifically.

RuView is an open-source platform that performs real-time 3D human pose estimation, vital sign monitoring, and presence detection using nothing but cheap WiFi signals from $9 ESP32 microcontrollers. No cameras, no video, no cloud subscription required. The system tracks 17 COCO body keypoints and measures heart rate and breathing by analyzing how bodies disrupt WiFi Channel State Information (CSI) — the same physics used in research labs, now running on a microcontroller you can buy in bulk for single-digit dollars. The architecture fuses WiFi CSI with optional depth and mmWave radar data into a real-time 3D spatial model. On-device spiking neural networks adapt to a new room's RF geometry in under 30 seconds. Total hardware cost for a full room setup: around $140. The software stack is written in Rust with pre-trained models on Hugging Face and an active Python binding layer for downstream ML pipelines. The privacy implications are significant — and cut both ways. RuView can monitor a care home resident's breathing without a camera in their bedroom, or let a smart home detect when all occupants have left. The open-source release makes the technology accessible to indie builders for the first time, but also means the underlying sensing capability is now commodity.