How to choose, evaluate and use skills for your work.
Start with the work you want finished and the computer you already own. This guide explains how models, runtimes and tools fit together, then gives you a small workflow you can use to judge a setup.
Read story →Liquid’s new LFM3 text model targets specialized local agents. Small Qwen3.5 checkpoints add several sizes and built-in visual input. The useful comparison starts with the work you need either model to finish.
Read story →An existing codebase changes what you need from a coding agent. Compare five options by how you work, what you can hand over, and the evidence they return.
Read story →Most familiar language models generate the next token, then repeat. NCM changes what gets predicted and in what order. Here is the mechanism, from tokenizers and KV caches to parallel code levels.
Read story →Turn a Raspberry Pi or supported ESP32 board into a Muse gadget. Understand what runs locally, pair the SDK, and choose community skills for the devices you already own.
Read story →Laya accepts Jev-shaped requests, but it can choose a different route. Compare the open models and the checks that matter before switching.
Read story →More agent skills can make selection harder even when SKILL.md files use progressive disclosure. Learn where context bloat starts, what research found, and how to evaluate skills.
Read story →The small decisions inside an agent can add up to a lot of waiting and expense. Here is where Jev could help, what the reported numbers mean, and how to find the fit.
Read story →Choose AI agent skills that fit your work. Use this checklist to compare instructions, compatibility, permissions and maintainers, then test a skill on a real task.
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