Typed Models: How we stopped silently losing data
The old universal model stored data as shapeless strings, leading to silent information loss. We rewrote the core with strict types, replacing silent degradation with loud validation errors.
Build in public: architecture notes, engineering decisions, and progress updates.
The old universal model stored data as shapeless strings, leading to silent information loss. We rewrote the core with strict types, replacing silent degradation with loud validation errors.
Dead cross-references between handwritten guides and auto-generated API pages quickly explained why managing guides away from Flude, as plain Markdown, was a mistake.
Writing a fast documentation generator is fun. Legalizing it in a hardcore enterprise environment is another story. Here's how we proved Flude's reliability by putting it head-to-head with the old standard.
Corporate infrastructure, colleagues on vacation, and slow tools forced us to build an independent CI/CD pipeline. Here's how AI helped me master YAML in a few evenings.
How a single question from our Infrastructure Lead forced us to rewrite our documentation pipeline, abandon HTML, and accidentally create Flude.
Why it's easier to rewrite AI code from scratch, how tests became our contract, and how to force the neural network to write its own checks.
How we tried to parse XML, why AI suddenly pulled in tree-sitter, and why control is more important than genius code.
How we tried to outsmart Doxygen, adapt its markup to our standards, and why it led us to build our own engine.
Why stacks like Doxygen+DoxyBook2+Hugo fall short on large projects, and how Flude solves the problem.