Developer Tea

You're tired of hearing that you need to pick up AI, so today I'm not going to add to that pile. Instead, we'll look back at two moments in history that look a lot like this one: the arrival of Fortran in the 1950s, and the spread of autopilot in commercial aviation. Both show how new abstractions change the shape of our work without being purely good or purely bad.

You've heard plenty of engineering leaders tell you to use more AI and more agents, often in a way that seems out of touch with your day-to-day work. I'm not going to add to that today. Instead, I want to share some history. If it feels like our industry is in a completely unique moment, it helps to look at earlier times that had a similar shape. In this episode, I look at the early skepticism around Fortran and the effects of autopilot on pilot skill. Then I explain why a dualistic "good or bad" view of AI leaves out most of what is actually happening.

Fortran and the Skeptics: In the 1950s, Fortran became widely known as the first high-level language, generating machine code so programmers didn't have to write it by hand. Skeptics doubted it. John von Neumann reportedly questioned why a serious scientific machine should do "clerical work," and many engineers didn't trust the generated code.

The Critics Were Partly Right: Early generated code wasn't very good and needed a lot of massaging. The Fortran team didn't go back to the old way. They put their effort into improving the new abstraction. The criticisms of the new tool became the fuel for making it better.

Autopilot and Cognitive Saturation: Commercial aviation is one of the safest forms of travel, and automation is a big reason why. Taking the manual work of flying off the pilot frees up mental capacity for radio calls, navigation math, and diagnosing problems. That adds a safety margin to every flight.

Children of the Magenta Line: Automation also brought a new risk. When pilots rely on GPS and autopilot, their manual flying skills can atrophy, and that matters in the rare cases where the automation fails. Something similar can happen to engineers who lean heavily on tools like Claude Code. Some might choose to work in their codebase without AI now and then, or practice LeetCode-style problems, just to keep those skills sharp.

Beware Dualistic Thinking: New technology rarely pushes things in only one direction. It brings new easier things and new harder things, more quality and new risks to quality, skill atrophy and room to focus on completely new problems. Whenever you catch yourself framing something as a simple good-or-bad duality, ask whether your brain is just trying to make it easier to understand.

A Warning for Team Leaders: It's a mistake to push agents into your existing workflow as if they were a simple accelerant. These tools change who is attracted to the work. They may draw in new people who were never interested in engineering before, and they may push away engineers who loved the job as it was five years ago.

Where This Is Probably Headed: If this follows the pattern of past technological revolutions, today's rapid pace will eventually level off. The unreliability we see now is likely to drive future reliability, and wider adoption tends to follow once tools stabilize. In the meantime, the fatigue is real, and it's worth paying attention to what it's doing to your mindset.

Episode Homework: Pick one opinion you hold about AI in your work. Ask yourself whether you've framed it as all good or all bad, then list the new risks and new opportunities it creates together.

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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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In 1967, a study on shock avoidance gave us "learned helplessness," and for nearly fifty years that was the prevailing explanation for why people give up. Then the original authors published a follow-up with brain scan data showing they had it backwards. We don't learn to be helpless — passivity is the default. What we learn is control. In today's episode, I explore what that reversal means for anyone facing a hard problem right now, and why a single small action can shift your entire orientation toward a challenge you haven't solved yet.
The Study That Reversed Itself: Understand how the 1967 learned helplessness research was revised roughly a decade ago by its own authors. The brain scan data showed the neurological change wasn't happening in the animals who gave up — it was happening in the ones who discovered their actions relieved the negative experience.
Passivity Is the Default, Not the Result: This is a fundamental shift. If helplessness were learned, we'd have a lot to reverse. But if passivity is simply where we start, then exerting control isn't undoing damage — it's activating something that was always available.
Your Action Doesn't Have to Match Your Problem: If you're out of work, you don't need to land a job to get the neurological realignment. The brain doesn't measure whether your control input is equal in magnitude to the thing making you feel helpless. A much smaller input does the work.
The Effect Persists: The research showed the behavioral change held up a week later, even against a new stressor that didn't pattern match the original. This isn't a momentary mood shift — it changes how you operate in the world.
Why "Action Orientation" Shows Up on Every Job Description: These words look like corporate filler until you see the neuroscience underneath them. Being proactive isn't a personality trait some people happen to have; it's a mode you can enter by noticing that your control has an impact.
Agency, Not Control: A careful distinction — this isn't about controlling outcomes. It's about your immediate environment. Set the temperature in your office. Clear your desk before you start work. Take back your calendar. Learn one thing you didn't know yesterday. Simple inputs where you can actually see the feedback.
Episode Homework: Find the area of your life or work that feels most chaotic right now. Don't try to fix it. Find one 5% improvement — a single step in the right direction — and take it. My bet is you'll feel the realignment before the problem is anywhere close to solved.
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As you grow as an engineer, something counterintuitive happens: the systems you build get simpler, not more complex. In this episode, I explore why senior engineers tend to collapse abstractions, accept certain risks, and reduce the surface area they're responsible for — and what drives that shift underneath the surface. Understanding the reasons behind the trend is what lets you get there on purpose instead of waiting a decade to arrive there accidentally.

Early in your career, output goes up fast. You're building more than ever, especially with AI in the mix, and a lot of what you build carries real complexity. But watch engineers who have been doing this a long time and you'll notice the opposite of what you'd expect: as their craft improves, their code, their architectures, and their systems get simpler. In today's episode, I dig into why that happens — and why chasing simplicity directly is less useful than understanding the underlying forces that produce it.

Simplicity Isn't the Goal — It's the Byproduct: Trying to "make things simpler" as a directive doesn't get you very far. If you can understand the core reasons complexity tends to fall as engineers mature, you can aim at those reasons instead and arrive at simplicity organically.

Why Simpler Code Pays Off: Simpler things are easier to understand, which means junior engineers can pick up your code, and future-you can return to a project you've long since left and still make sense of it. Adapting a simple thing is almost always easier than adapting a complex one.

The Pain of the Refactor Teaches You: A big part of this shift is scar tissue. Once you've lived through a massive refactor of a complex system, you start making different trade-offs — not from theory, but because you don't want to do that again.

Collapsing Vertical Abstractions: One of the most common refactors senior engineers reach for is collapsing long chains of abstraction that only ever get used in one place. It's abstraction without reuse, and it forces you to re-load enormous context just to make a small change.

Refining Your Risk Tolerance: Early on, we hedge against every possible risk. Later, we learn some risks are acceptable. Hedging is insurance, and sometimes it's very expensive insurance — paid in velocity, in onboarding difficulty, and in only being able to hire people who can hold all that complexity in their heads.

Knowing When to Break Best Practices: Maybe the best practice says abstract this. But if it isn't that hard to understand, and you can get most of the benefit through better naming or tighter scoping, the "correct" move might be the wrong one.

The Library Trap (In Both Directions): Seniors often take a trip through "let's not use external packages, we don't know what's in them" — and end up maintaining a shadow version of the thing they avoided. The more experienced call is often to accept the trade-off, adopt the well-documented dependency, and shrink what you are responsible for.

Reducing Surface Area Creates Focus: The through-line in all of these trade-offs is a shrinking surface area. Fewer things to maintain means more focus, and more focus means the things you are responsible for get done very well. It's an open question whether those behaviors follow seniority or cause it.

Drive to the Fundamentals: Think about a machine built from a ramp, a screw, a rubber band, and a motor. A more senior craftsperson recognizes the problem is fundamentally about conservation of energy, and reconfigures it down to two parts instead of eight. Slightly less efficient, maybe — but far less to teach, maintain, and break.

Episode Homework: Ask yourself: what am I responsible for right now that I could simplify? Where can I get away from the tactics and the surface-level stuff, get down to the core of the thing, and focus on doing that core really well?

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Accountability is a word you'll hear in your performance reviews, from your manager, and increasingly as you step into leadership. It's also one of the most abused and misunderstood concepts in our industry. Many of us have a visceral, negative reaction to it—not because the concept is broken, but because the companies and people we've worked with have bastardized its meaning. In today's episode, I want to strip away the scary parts and offer you a pragmatic, working definition of accountability: one that a good engineer would actually invite rather than dread.

What We Cover

The Bad Version of Accountability: Recognize the weaponized version of this word—being audited for things you didn't know you were responsible for, having unrealistic goals coerced onto you, and getting surprised at performance review time. This is how leaders shirk their real responsibility to grow people and create clarity, hiding hard performance conversations behind a single loaded term.

Accountability as Debugging: Shift the frame to how we think about software. When payroll software overpays your team by $100, you file a bug report—something deviated from the agreement about what should happen. The engineer who investigates is performing an accounting function, tracing a root cause. That's what being "held to account" really means: identifying what happened against a known standard.

The Churning Customer Problem: Understand what happens when the bug report never gets filed. A customer who hits something unexpected and just leaves never gives the company a chance to fix it. The same thing happens between you and your manager when the communication lines aren't established—you find out at review time instead of when it could have mattered.

Accountability at Every Step: In a healthy team, accountability isn't a once-a-year audit. It's continuous, giving you the ongoing opportunity to notice when something has deviated from the agreement—so you have something concrete to hold to account before it becomes a surprise.

Estimates vs. Commitments: Learn to separate the two things you say to a stakeholder. One is a forecast, a guess, a belief. The other is a commitment. Bad accountability coerces the estimate into a commitment it never was; too-soft accountability treats everything as up in the air. The healthiest organizations hold both: a high-confidence forecast, plus a commitment to do whatever is necessary if it looks like it won't get done.

Why Good Engineers Invite It: If you want to grow in your career and confront reality, you invite accountability and commitment inspection. Done well, it creates clarity of expectation for exactly how you're doing—which is a gift, not a threat.

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