You give your AI coding agent a task. It gets to work. Thirty tool calls later you have code — and it’s not what you needed. The agent understood the words but missed the intent. It made a dozen small decisions that individually seemed reasonable, and collectively built the wrong thing. This isn’t a capability […]
Read More →If you use both Cursor and Claude Dev/Code, you’ve probably noticed they both want their own configuration folders (.cursor and .claude). Keeping your custom commands, rules, and skills in sync between them usually means a lot of copying and pasting. Here is a simple way to centralize everything in one .agents folder and use symlinks to keep both tools […]
Read More →I’ve spent the last few months living in Claude Code. If you’re wondering if it’s worth the hype, the answer is yes—but probably not for the reasons you think. It isn’t just a better autocomplete. It’s more like a competent pair programmer that actually reads your whole codebase, runs your terminal commands, and never needs […]
Read More →If you’ve been building with LLMs over the last year, you’ve likely hit the “Agent Wall.” You build a cool agent, give it a massive system prompt, and it works… until it doesn’t. As you add more capabilities, the context window gets bloated, the agent gets confused, and porting that logic to another platform (like […]
Read More →Large Language Models (LLMs) all predict text, but they differ a lot in how they follow instructions, use context, handle tools, and optimize for safety, speed, or cost. If you treat them as interchangeable, you’ll ship brittle prompts. If you treat them as different runtimes with different affordances, you’ll get reliable results. This post explains the major differences across […]
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