Agentic-ai

Coding agents get better every month, and the way to stay in control is to give them clear instructions and context rather than fight the shift. This covers agent instruction file conventions worth adding to a repo (AGENTS.md, SKILL.md, CLAUDE.md, Cursor skills) and two open-source tools, Ponytail and Caveman, that cut token spend by trimming what agents write and compressing what you send.

Model choice is an engineering decision that shows up in both output quality and your bill. This walks through models.dev and artificialanalysis.ai as the two resources for shortlisting on specs/price and then checking capability, cost per task, and latency, with worked comparisons across GPT-5.6 Sol, Claude Fable 5, Gemini, LongCat-2.0, and Seedance 2.0.

Google quietly published OKF, a plain markdown-plus-frontmatter format for agent knowledge, built on a pattern Karpathy sketched in a gist months earlier. Working through both taught me to stop thinking of an LLM as one brain and start thinking of it as two.

I followed the Mastra tutorial to build and publish a simple MCP server, then connected it to a self-built agent and to Claude. Here is what the protocol actually does under the hood and what surprised me.