Software engineering
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Writing Is Closer to Thought Than Code
opinion
11 min read
Daniel Miessler argues that written articulation of a project’s intended outcome should be the primary reference for AI-assisted development, rather than source code. He disagrees with developer Matt Pocock’s view that code is the central project artifact, contending that code records what was built but cannot fully capture why it was built or what people originally wanted. Miessler says many AI failures in 2026 stem from incomplete communication of intent rather than poor execution by AI systems. Miessler says he adopted an “Ideal State Artifact” (ISA) at the end of 2025 to document projects. An ISA includes a prose description of the desired state, constraints, dated decisions made during development, and Ideal State Criteria (ISCs): atomic, testable requirements paired with evaluations. Evaluations can range from basic assertions to multi-stage processes involving code execution, question sets, rubrics, tournaments, or an LLM judge. The ISA is intended to preserve requirements separately from a particular implementation, allowing teams to discard and rebuild inadequate code against the same criteria. Miessler describes two iteration loops: refining the written description as intent becomes clearer, and testing implementations against that description. He argues that increasingly capable AI shifts the main risk from implementation errors toward gaps between a person’s actual intent and the intent captured in writing.
Personal software factories are a waste of time
opinion
3 min read
Peter Piekarczyk argues that individual software engineers should avoid spending personal time building “software factories” intended to automate the creation of side projects. He says AI tools already let users begin building by prompting a chatbot directly, while a personal factory can become another form of procrastination that keeps developers in familiar technical work rather than testing product ideas. Piekarczyk says AI-generated applications still involve difficult product decisions, including miscommunication, choosing the wrong problem, and refining an unclear desired outcome. He recommends learning tools such as Grok Bot and Claude, but argues that frontier AI labs are better positioned to reduce software-building friction because they develop both models and user-facing products and can improve models from product-use feedback. He identifies workplace automation as the stronger case for building an internal software factory. Employees may understand their organization’s legacy systems, edge cases, workflows, and teams well enough to create trusted platforms that help product managers and designers ship features with less friction. For personal projects, he recommends developing an idea-generation habit by recording problems and writing down three ideas daily. He cites examples of someone using Claude to simplify a parent’s Android TV interface and someone bringing a fruit-fly brain into a real-world flying system as examples of problem-driven experimentation.