Found 14 posts with tag #Claude Code
Putting inference in a chat window was the first interface revolution. The second is one assistant in front of all those conversations: it decides which session, agent, or machine does the work, keeps the context, and comes back with the outcome. OpenAI's dots and Instinct show two versions of it, and why every frontier lab will need one.
Goals break down as they grow: they outgrow the box, turn into pointers to files, and get judged by what the working agent chose to show. Desired-state development declares what must be true instead, and lets a governing agent, increasingly a personal assistant, reconcile toward it until the evidence and the owner's verdict say it holds.
Codex has become the center of my development workflow, and the model is only part of the reason. Sessions that read other sessions, voice steering, goal mode, side chats, subagents on request, MCP, and an app server other harnesses can drive: the environment around the model is where the work happens now. Coding assistant is no longer a complete description.
A magic prompt is not a clever wall of instructions. It is a small, portable entry point into an engineered, tested workflow that takes a human and an agent from intent to a verified outcome — pre-flight, prerequisites, installation, authentication, validation, and the next step included. Here is how it should behave, and how to build one in layers.
Software is no longer just its source code. Its harness — the tools, memory, skills, and rules guiding the agents that build and run it — is now first-class.
Every week there is a new must-have AI tool, and somewhere inside the buzz there is usually a grain of truth. Buzzcut is a small agent whose only job is to find that grain: map the shiny new thing against what you already run and return keep, skip, or watch, with one flip condition. Not anti-hype. Anti-hype-driven architecture.
The Unix philosophy — small programs, composed through pipes, doing one thing well — turns out to be an ideal operating model for AI agents. Skills leverage this by guiding agents to use battle-tested programs rather than reinventing them. Here's why that beats building custom MCP servers for most workflows.
Documentation drift is the silent killer of codebases. Your roadmap says one thing, your website another, and your README hasn't been updated in months. Here's how I built a pluggable Claude Code skill that detects drift, shows exactly what's out of sync, and fixes it—with your approval.
A deep dive into fixing broken zsh tab completions with Oh My Zsh, and how I turned the solution into a Claude Code skill. Learn why fpath order matters, how compinit timing breaks completions, and the pattern of encoding debugging knowledge for AI assistants.
There's a strange moral superiority creeping into how people talk about AI-assisted development. We mock AI for making mistakes we'd forgive in ourselves. We demand one-shot perfection from tools while accepting iteration from humans. Maybe it's time for a little more empathy—and a little more honesty about how software has always been built.
How I built an automated system that generates social images for blog posts using multiple AI providers, with Claude analyzing results and a PR comment interface for human-in-the-loop selection. A case study in agent-orchestrated workflows.
When you're cooking, there will be shells. When you're vibe coding, there will be markdown files scattered everywhere. AI-generated artifacts aren't bugs in the process—they're receipts of creation. Here's why fighting the mess is futile, and why hiring a cleanup crew (of agents) is the answer.
Progress doesn't ask for permission. As AI transforms how we build software, every engineer faces a choice: resist, observe from the sidelines, or learn to orchestrate the future. This is our Hamlet moment.
Revisiting my advice to "read more, write less code" in the age of AI. While research remains crucial, developer workflows now include a vital new step: co-creating with AI tools before coding.