engineering

That’s why we’ve decided to approach the software factory category as infrastructure rather than as a factory product or AI teammate.

Accepting program modifications
demanded by changing external circumstances to be an essential part of programming, it is argued that the primary aim of
programming is to have the programmers
build a theory of the way the matters at
hand may be supported by the execution
of a program. Such a view leads to a
notion of program life that depends on
the continued support of the program by
programmers having its theory. Further,
on this view the notion of a programming method, understood as a set of rules of
procedure to be followed by the programmer, is based on invalid assumptions and so has to be rejected.

I no longer have a firm mental model of what they can do and how they work, which means each additional feature becomes harder to reason about, eventually leading me to lose the ability to make confident decisions about where to go next.

give Sol a grid with a "randomized" obstacle to navigate.
Tell it to use a hashmap solution for formulating pathing.
As soon as it runs into its first pathfinding problem (It will) it will start writing edge-case solutions, rather than fixing the core hashmap.

Comprehension debt is the growing gap between how much code exists in your system and how much of it any human being genuinely understands.

Dr. Margaret-Anne Storey, professor of computer science at the University of Victoria, first used the term ‘cognitive debt’ in October 2025 while teaching an entrepreneurship startup course. She had encouraged her students to use AI to move faster, and that worked well: the students were putting products into the hands of users and getting feedback. However, they were struggling with implementing that feedback back into their product.

Cognitive debt, a term gaining traction recently, instead communicates the notion that the debt compounded from going fast lives in the brains of the developers and affects their lived experiences and abilities to “go fast” or to make changes. Even if AI agents produce code that could be easy to understand, the humans involved may have simply lost the plot and may not understand what the program is supposed to do, how their intentions were implemented, or how to possibly change it.

a cloud software factory is just an automation around the core loop of development, from triage → spec → implement → review → verify → ship → monitor, where at every step a mix of agents and humans moves the process forward.

Building the infrastructure to make verification routine is now a national-scale engineering problem.

handwrite your user level AGENTS.md that holds your preferences, and treat your project level ones like a neural net. Give it a size budget and train it on the sessions that actually ran.

Reacting to an event, running a sequence of isolated subagents, and separating their reasoning from the actions they’re allowed to take — it’s all just a workflow. One that could run just as well from a Slack message, a cron job, or a webhook as from a GitHub issue. Generalizing that realization into a runtime that works the same way regardless of where it’s deployed, or which model it’s driving, is what became Flue: an open, platform-agnostic framework for building durable agents and workflows.

The scope of a change (the subject of the change) is the most important part of a commit.