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Honest reports about the systems we build, the assumptions we break, and the lessons that only appear after software meets reality.
A practical way to turn business outcomes, workflows, risks, and unknowns into a software scope that can survive contact with reality.
AI projects rarely fail because the model is not impressive. They fail because ownership, data quality, evaluation, and operational feedback were never designed.
The difficult part of replacing spreadsheets is not rebuilding cells in a browser. It is preserving the exceptions, ownership, and decisions hidden inside them.
Internal tools succeed when they reduce decisions, respect real permissions, and make the common path faster than the workaround employees already know.
A framework for choosing between a rewrite, incremental modernization, and leaving a stable system alone-based on business risk rather than technical fashion.
A deep dive into the massive architectural debt caused by premature optimization, and why the majestic monolith is almost always the right answer before product-market fit.
Reviewing standard SaaS bloat, migrating away from managed relational endpoints, and fixing infinite un-cached read loops that scale linearly with user counts.
Don't fall for flashy pitch decks. Here are the 5 highly technical questions that will instantly expose an agency that outsources or copy-pastes boilerplate code.