{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"AI News Today | Julian Goldie Podcast","title":"NEW GLM 5.2 DESTROYS Claude?","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/fa9085c2\"></iframe>","width":"100%","height":180,"duration":577,"description":"\nGLM 5.2 vs Kimi K 2.7 vs Opus 4.8: Which AI Model Builds the Best Apps?\nThe script compares GLM 5.2, Kimi K 2.7, and Opus 4.8 by giving them identical build prompts inside an agent operating system and judging results across five tests: a Temple Run–style voxel runner (GLM 5.2 best), an inner solar system/orbit HUD simulation (Kimi K 2.7 best for zoom, speed, and customization), a liquid-in-a-bowl particle/metaball interaction (GLM 5.2 best), an Apple-style AI model landing page (GLM 5.2 best), and a neon arcade game (GLM 5.2 most fun). The narrator notes GLM 5.2 is very new and not yet on OpenRouter, contrasts origins and context windows, highlights that Kimi and GLM can be used inside AI agents unlike Claude/Opus, and concludes GLM 5.2 wins four of five tests while promoting the AI Profit Boarding community and agent OS download.\n00:00 Model Showdown Setup\n00:57 Test 1 Temple Run Runner\n02:01 Test 2 Solar Orbit Map\n03:27 Test 3 Liquid Metaballs\n04:39 Test 4 Apple Style Landing Page\n05:40 Test 5 Neon Arcade Game\n06:24 Benchmarks And Model Specs\n07:51 How To Use Them Together\n08:15 Get The Agent OS\n09:30 Community Wrap UpFusion (OpenRouter) Lets You Combine Multiple Models to Reach Fable-Level Intelligence for Less\nThe script covers a new OpenRouter Fusion API update that runs a prompt across a parallel panel of up to eight models (with web search and bash tools), then uses a judge model to extract consensus, contradictions, unique insights, and missing coverage before returning one fused answer. Fusion is presented as a way to boost benchmark performance and reduce token costs versus relying on a single frontier model, with tests on 100 hard deep-research tasks showing much of the lift coming from synthesis rather than diversity. Examples compare solo models versus panels, including a “budget panel” of cheaper models landing within 1% of Claude Fable 5 on intelligence tests, and demonstrations of using Fusion in chat and via API to generate outputs like SEO...","thumbnail_url":"https://img.transistorcdn.com/Tp0JSUlyiAe2ran44b13cjk4ImQYS1QEMKCAa9Q7go0/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9lNTYz/NGQxMTU2ZTM1MzU5/MDNiYjcwZjJmYTY5/ODJmOS5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}