Working Notes

Most teams adopt an ORM for good reasons. In the early sprints, having the database feel like a natural extension of the application — models mapping to tables, relationships expressed in code — lets teams move fast without needing deep SQL fluency on every story. The ORM earns its place well before the first release. What I've observed is what happens after. The product ships, sprint priorities shift toward feature work and production issues, and the ORM quietly becomes tribal knowledge. The engineers who set it up carry a mental model of how it works and why certain decisions were made, but that understanding rarely survives team rotation — much like documentation, it gets treated as something someone will formalize eventually. New engineers learn just enough to be productive, the ORM fades into the background, and by the time something goes seriously wrong with the database, it's effectively a museum piece. This is the Day 2 problem with ORMs — not that they're the wr...

When people say “schema as interface,” they usually mean naming and semantics. That’s part of it. But in a production system, the real interface is the set of access paths the query planner can execute cheaply. Every `SELECT`, join, background job, and dashboard query depends not just on tables and columns, but on index shape, cardinality, constraints, and data distribution. The schema is not only a contract of meaning; it is a contract of performance. I’ve seen more systems struggle from schema drift than from application bugs. Not because the code was wrong, but because the data model made correct code slow. A table starts simple and well-indexed. Features accumulate. Nullable columns get added “just in case.” Enums become free text. Indexes are added reactively. At 50k rows, everything works. At 50 million, joins that used to be cheap become dominant costs. Nothing is technically broken—but the interface has changed. We would consider an API broken if latency jumped from 40ms to 4 s...

There’s a growing cottage industry on LinkedIn devoted to spotting AI-written content. People trade lists of tells—emojis, bullet points, excessive em dashes—as if the problem is detection. It isn’t. Those tells are just symptoms. The real issue is that a lot of what’s being published has no point of view, regardless of how it was produced. I came to this topic the same way many posts start: with an observation and a vague sense that something felt off. Using ChatGPT didn’t give me the idea. It helped me sharpen it. Through iteration, the problem became clearer—not that AI is writing content, but that it’s being used as a substitute for thinking. When that happens, the output is predictable: fluent, tidy, and empty. This failure mode isn’t new. Long before AI, we were already summarizing conference talks, management books, and other people’s blog posts. The same ideas get rephrased, lightly personalized, and pushed back into the feed. What’s usually missing isn’t information—it’s i...

When teams select a database, they're not just picking a storage layer—they're locking in product constraints for years. Database choices determine your application's latency characteristics, operational costs, and failure modes long before users ever see a feature. Yet these decisions often happen in technical silos, treated as implementation details rather than the product-shaping commitments they actually are. The most expensive databases aren't the ones with the highest licensing fees; they're the ones chosen without understanding how their fundamental tradeoffs will constrain what you can build and how quickly you can ship. The room where these decisions happen matters. When it's just backend engineers and DBAs, you get choices optimized for developer familiarity and operational comfort. That's not wrong, but it's incomplete. Product managers should be present because they understand user expectations around responsiveness and availability. Finance ...

Technical debt doesn’t accumulate by accident. In practice, it’s the predictable outcome of the choices we make about what gets done now versus what we consciously defer. When delivery pressure consistently outweighs investment in sustainability, teams respond exactly as designed. Technical debt as a deliberate trade-off: borrowing against the future to move faster today, with the expectation that the debt will be repaid later. The failure isn’t taking on debt; it’s pretending the loan doesn’t exist.  I didn’t fully appreciate this until I was accountable for backlog decisions myself. As a Product Owner, I regularly had to choose between delivering in the current sprint or taking the time to fully complete the design. Those decisions felt tactical in the moment — but they accumulated over time.  That’s why technical debt isn’t an engineering failure. In most organizations, engineers don’t own prioritization mechanisms, funding decisions, or success metrics. Those choices liv...

In the previous post, we argued that estimation is a smell—a signal that teams are compensating for unmanaged uncertainty. Velocity, as a direct descendant of estimation, inherits that smell. Scrum reminds us that the purpose of a Sprint is to produce an *Increment of value* , not to consume a predetermined number of points. Velocity measures effort expended inside the team. Customers, however, experience outcomes: features delivered, bugs fixed, and capabilities unlocked. No customer has ever benefited from a higher velocity. Why Velocity Persists Velocity survives because it is visible, numeric, and easy to chart. It gives leaders something to point at and teams something to optimize. Unfortunately, that optimization rarely aligns with value delivery. When a measure becomes a target, it ceases to be a good measure. When velocity becomes important, teams respond predictably. Estimates inflate. Stories are sliced to satisfy point targets rather than user needs. Lower-risk, lower-valu...