Worked examples and arguments about AI systems: how they work, implementation choices, evaluation, costs, failures and what they change about organizations.
Build a workflow around your coding agent. The dark software factory workshop connects the explanations to one runnable application, acceptance checks and a release decision.
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Coding agents are fluent at saying they are done. In the dark factory that builds this site, every claim of progress must name the check that would end it, and a gate refuses the ones that do not. What that control catches, and what it cannot.
Every station in a dark software factory is a make-or-buy decision. Coase and Williamson worked out the answer decades before agents: buy what is generic, build what is specific to you, and keep the right to decide when the specification runs out.
A Claude Code permission rule is a string pattern, not an intent. Write the commands it should stop, run them through a headless session in a throwaway VM, and count the unintended allows before you trust the rule.
An eval set merged from three sources has mixed label spellings, near-duplicate prompts and empty references. A coding agent will tidy it. Three counts, computed by your own script, show whether it dropped a row that would fake a gain.
As model calls get cheaper, what a dark software factory's output costs comes to be set by review time and acceptance rate, not tokens. The arithmetic, the error bar that decides a route, and the accounting that keeps it honest.
Every call in an agent loop resends the whole context, so input grows with the square of the calls. Derive the cost with caching, price the solved task with an interval, with Claude Code as the worked instance.
An agent can refactor an eval script and pass every test because it rewrote the expected values. A short guide to using Claude Code or any coding agent on research code and take-homes, built around that check.
Decide how much autonomy a coding agent gets from what the task can be checked against, how cheaply it undoes, and what METR's published studies do and do not show.
Keep a lab notebook for agent-driven research: the commit, config, data version, results with their error bars and unresolved jobs. Then test whether a fresh session can continue from it alone.
A dark software factory turns requests into tested software with no person on each change. The same pattern runs any work you can write as code. What caps it is review, not compute: the arithmetic, the control layers, and where it breaks.
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