AI LABS explores the rising demand for forward-deployed engineers who bridge the gap between complex business processes and practical AI implementation. The session outlines a five-step framework for auditing existing workflows, identifying automation opportunities, and building systems that integrate seamlessly into a company's current operations without disrupting established productivity.
In this video, I look at the long awaited Qwen3.8-27B model. Both what it can do and how to serve it at the maximum tokens per second
Skills v1.2.0 is out with major improvements: new documentation site, Claude Code marketplace integration, five brand new skills including Wait What for Opus verbosity, an updated Grill Me with multi-question rounds, and the powerful Wizard skill for infrastructure provisioning.
The biggest AI model release of the past few days, at least among the developers and AI power users on social media, wasn't a frontier cloud model from OpenAI, Anthropic or Google.
It was a 27-billion-parameter model from Alibaba: Qwen3.8-27B landed on Hugging Face on Friday under an enterprise-friendly, open source Apache 2.0 license, giving developers downloadable weights for a dense multimodal model.
AI LABS explores the mechanism behind the popular Gauntlet Loop, a technique for building complex applications in one shot. By integrating Wayfinder, an intensive planning skill, AI LABS demonstrates how to overcome the method's inherent limitations regarding quality verification and project drift, providing a more structured approach to AI-driven software development.
Better Stack explores how Turbo Fieldfare utilizes the unique architecture of Apple Silicon to run massive mixture-of-experts models. By streaming specific model components directly from SSD and leveraging unified memory, this approach significantly reduces RAM requirements, making advanced AI performance more accessible on local hardware.
The skills you have already written don't have to stay locked to one coding agent. A new open standard called Agent Plugins lets you bundle Agent Skills and MCP servers into a single folder that works across Codex, ChatGPT, Cursor, GitHub Copilot, Kiro and VS Code, with Google joining more recently.
A plugin under this standard is just a directory. Your skills folder, exactly as you already have it, a plugin.json manifest, and an mcp.json where your MCP servers live. That is the portable core, and it is a much simpler file structure than you would expect from a spec with this many big names on it.
