Open source just caught the frontier. GLM-5.2 sits 1% below Claude Opus 4.8 on real-world coding, beats GPT-5.5, and costs 1/6 the API price. Here's the link to get started with GLM 5.2 👍 https://docs.google.com/document/d/1q8WAqnyl-tE5r0qX8gvm3wfI6xXg0KbkOZCeEbwWfV4/edit?usp=sharing

Meet GPT-5.6 Sol, Terra, and Luna, OpenAI's new flagship lineup, and they launched under direct coordination with the US government before anyone outside ~20 partner orgs could touch them. - Sol is the flagship. Built for deep research, complex coding, and agentic work that runs for hours unsupervised. - Sol also has "Ultra Mode," which spins up multiple sub-agents working in parallel on the same task. - On cybersecurity benchmarks, Sol matches Anthropic's Mythos Preview using roughly a third of the output tokens. That's a real efficiency jump. - Terra is the mid tier. Performance close to GPT-5.5, at about half the price. - Luna is the speed and volume layer. Fast, cheap, built for high frequency lightweight tasks. - Here's the catch. None of this is publicly available yet. No waitlist, invite only, roughly 20 partner organizations. - This is reportedly the first US frontier model to launch under a government managed access list, which is a new thing in AI releases. - Broad access is "coming weeks" with no confirmed date. If you're building with AI right now, this is worth watching closely. Not just because of what these models can do, but because it might be a preview of how frontier AI gets released going forward, government checkpoint first, public access second. So I'm curious, if you had to pick your tier today, are you a Sol person running long agent workflows, a Terra person who just wants solid performance without burning budget, or a Luna person doing high volume lightweight stuff? As for me, I'll be with Terra. Link: https://openai.com/index/previewing-gpt-5-6-sol/

Community member @Iain Munro walks through Magical Nodes, a node-based AI workspace platform he built from scratch using Emergent, a natural-language app builder. The platform lets users connect different data sources and AI chat nodes on a visual canvas, then publish outputs as live web pages or embeddable chatbots, all without writing code. Really useful tool. I highly recommend checking this one out! 👉 View in classroom: https://www.skool.com/automate-what-academy/classroom/2d14ec0a?md=a25f9499604244b0bc7d90b038374cd3

This one feels like a big hint at where local AI is heading next. Google introduced DiffusionGemma, an experimental open model that generates text in parallel instead of one token at a time, making it up to 4x faster on dedicated GPUs. - Up to 4x faster text generation on GPUs - 1000+ tokens per second on a single NVIDIA H100 - 700+ tokens per second on an RTX 5090 - Built for low-latency local AI workflows - Generates 256-token blocks in parallel - Better fit for in-line editing, code infilling, and rapid iteration - Uses bi-directional attention, so tokens can see the whole block - Iterative self-correction while generating output - 26B MoE model, but only 3.8B active parameters during inference - Can fit in 18GB VRAM when quantized - Great signal for faster desktop AI agents and local automation tools - Not meant to beat Gemma 4 on quality yet - Speed vs quality trade-off is the big theme here Where do you guys think faster local text generation matters most: coding, agents, editing, support bots, or something else? I would say coding and support bots for me. Read the full article here: https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation

Tested a simple logic prompt across ChatGPT, Gemini, Claude, and Grok. Some passed. Some confidently failed. The prompt: "I want to wash my car. The car wash is 50 meters away. Should I walk or drive?"