Found 18 posts with tag #Software Craftsmanship
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.
I needed a logo for a presence that already existed: a handle, open-source work, and more than a decade online. Silhouette is the Grok Bot distilled from the three-hour run that built it in stages: discovery, ideation and an anti list, shapes, type, and then the whole mark, wordmark, and spacing locked as one silhouette.
The bot had a computer. The next step needed to happen on mine. Latch is a specialist Grok Bot for the small, authorized desktop interactions that stall larger workflows: mouse first, clipboard over keystrokes, screenshot receipts, and a hard hand-back. This is the story of building it, including the keyboard lockout that wrote most of its rules.
Mobile-first taught us to design for the most constrained screen first. Agent-first asks a more fundamental question: what should our software be able to do when the user never opens it? This post looks at assistants that live in messaging apps, at capabilities an agent can discover and verify, and at what changes for marketing, discovery, and small open-source projects when the evaluator is an agent.
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.
Software used to be expensive enough that we assumed every serious effort should last. AI weakens that assumption. Some software now only needs to exist long enough to solve one problem, communicate an idea, or feed the real build. Disposable software is its own category — not a lower one.
Not every piece of context an AI coding agent sees carries the same weight. The user prompt and the code dominate; documentation is a secondary orientation layer. Here's why imperfect docs are usually good enough, and where your effort actually pays off.
AI can produce code, comments, PR descriptions, and review replies faster than humans can read and own them. The real bottleneck is no longer generation — it's comprehension. Here's how I think about effort symmetry, accountability, and why I don't buy a universal taxonomy.
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.
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.
When I hire an engineer, I look for their blog posts, open source contributions, and public work. Why should hiring an AI agent be any different? Exploring the rationale behind distributed, discoverable agents with public personas—and why this matters for the future of autonomous systems.
From mainframes to modern Rust CLIs—exploring how compile-time embeddings and offline-first architecture embody the principles of self-contained autonomous agents. A deep dive into building a blog distribution CLI that needs nothing but itself to run.
Over the past two years, a split has formed in software engineering. Some see AI as a new programming language—a compiler for natural language. Others see it as a powerful but fallible tool. Both perspectives miss something crucial: the ghost in the machine, and why fundamentals matter more than ever.
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.
As AI agents write more code, the fundamentals matter more than ever. A reflection on relics, craftsmanship, and why less code is still better code — even when machines do the writing.