Fundamentals get talked about constantly, but rarely defined. In this episode I dig into what a fundamental actually is — the building blocks, heuristics, and core characteristics that don't change — and why they matter more than ever in a profession where abstraction moves faster than almost anywhere else. If you feel like you can't keep up with every new framework, model, or tool release, the answer isn't to read faster. It's to step up a level.

Fundamentals get talked about constantly — in sports, in hobbies, in software — but we rarely stop to define what one actually is. In today's episode, I unpack what makes something fundamental, why software engineering in particular buries its fundamentals under layer after layer of abstraction, and how principles-first thinking gives you a way to keep up with an industry that never stops moving without needing to learn every single new thing that ships.

• What Actually Counts as a Fundamental: Fundamentals aren't rules — they're building blocks, behaviors, habit patterns, and heuristics. In basketball it's dribbling. In football it's blocking. In software it's readability, reliability, the setup-execute-teardown shape of a test, basic logic structures. In communication it's sender, receiver, feedback, and noise.
• Why Software Abstracts Faster Than Almost Anything Else: Compare the practice of law, where the fundamentals look largely the same as they did fifty years ago, to software, where the work is symbolic at its core. We build symbols that mean something to humans but map down to a hard physical reality of flipped bits and moving electrons — and that symbolic nature is exactly what lets abstraction pile up so quickly.
• The Rise of Linguistic Abstraction: Our abstractions have become increasingly language-shaped. Object, function, flow, durable object, mesh, graph — these are primitive ideas expressed as words, and understanding that shift explains a lot about how the industry actually progresses.
• Abstractions Have Fundamentals Too: This is the key move. If you understand what makes Postgres a good fit — structured, predictable, relational data — you can evaluate any other relational database without learning it from scratch. Understand the primitives of NoSQL, or of an index lookup, and you no longer need to chase every implementation.
• Decompose, Then Recompose: Principles-first thinking means breaking things down into their underlying characteristics so you can recombine them into solutions you care about. It works on databases, on business offerings, on competitive positioning — anywhere you'd otherwise be tempted to evaluate things as wholly separate.
• Applying It to LLMs: When a new model drops from one provider or another, you don't need to start over. If you know which characteristics matter, you can see what changed here versus there — and treat them as variations on shared fundamentals rather than two entirely different things.
• Practice vs. Analysis: There are two flavors here worth separating. Fundamentals in practice are the things you do repeatedly — giving feedback regularly, for example. Fundamentals as analysis is a way of looking at new technology and asking what core characteristics make it up. You want both: composable actions and composable tools.
• Risk as a Fundamental: A live example from my current role — determining the right level of risk in a given scenario can't be a one-size-fits-all rule. It requires understanding the fundamental characteristics of the risk and reward you're accepting.
• Episode Homework: Look at your work and ask where you're too deep in the weeds. Where are you thinking too granularly? Where are you missing the abstraction class — the consistent things shared across multiple iterations that you could be watching from a step away? That distance is what gives you leverage to notice when something fundamental actually changes.

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