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Escucha y lee

Jonathan Cutrell

Developer Tea exists to help driven developers connect to their ultimate purpose and excel at their work so that they can positively impact the people they influence.

With over 17 million downloads to date, Developer Tea is a short podcast hosted by Jonathan Cutrell, engineering leader with over 15 years of industry experience. We hope you'll take the topics from this podcast and continue the conversation, either online or in person with your peers. Email: developertea@gmail.com

Developer Tea

If you are a manager, a lead engineer, or anyone growing into more responsibility, this throwback episode is built for you. We keep hearing the same question, now louder than ever: "Why can't this go faster?" AI and agentic coding have made the literal coding step dramatically cheaper, so product leaders reasonably expect the whole pipeline to speed up. But it hasn't—and in today's short, focused episode I explore why. The answer isn't new at all. It's the theory of constraints, and it has everything to do with friction you may not be looking at.

 Speed Isn't the Story—Friction Is: • When a fast component gets introduced into the pipeline, the instinct is to celebrate the velocity. But pay attention to what comes 
after • . The real question is what keeps work from naturally flowing faster, and that lives in the friction, not the energy you're pouring in upfront.

 The Universal Bottleneck: • I rarely claim universal truths on this show, but here's one: anything that looks like a pipeline will have a bottleneck. If you're not paying attention to it, it doesn't matter how fast every other step gets. Faster coding just exposes where the constraint really sits.

 The Two Places Friction Shows Up: • For teams fully adopting agentic coding, the bottlenecks cluster in two spots—requirements gathering at the front, and verification, validation, and testing at the back. Rushed requirements upstream create even more painful rework downstream.

 Why Agents Punish Vague Specs: • Human engineers fill in gaps by being close to the work. Agents fill in gaps too, but sometimes incorrectly. If your requirements aren't detailed, the agent guesses, and you pay for it in review. Spend more time in the planning phase, not less.

 The Foundation You Build On: • Agents glob extra code onto a weak structure—unnecessary models, redundant endpoints, patterns that don't fit. A code base organized with clear conventions, good documentation in your CLAUDE.md or AGENTS.md, and dependable patterns lets the agent discover and extend rather than guess and hope.

 Specification and Validation Are Bookends: • Good requirements translate directly into good tests. Acceptance criteria on one end, changes in the middle, validation on the other end—directly connected. Poor specification sitting on a poor structure guarantees poor execution and poor validation.

 Reframing Your Objections: • Think scope creep is the problem? That's a requirements issue. Think you lack the talent? That's a foundation issue—because the engineer's job now is to cultivate the foundation so generated code enriches it instead of toppling it.

This is not a new problem. We asked it of the internet, of web frameworks, of CSS. Now it's time to apply the same principles to agentic velocity: look at your requirements, your foundation, and your validation. Somewhere in those three is your bottleneck. I guarantee it.

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Why Can't You Go Faster With AI? Focus on the Friction to Find Out

The skills problem isn't going anywhere — it's just wearing new clothes. In this episode, I unpack how the lessons we learned decades ago (limiting work in progress, the theory of constraints, test-driven development) are coming roaring back as the fundamentals that will carry you through the agentic shift. The bottleneck has moved, and knowing where it went changes how you should work.

A lot of what we're learning about building with agentic tooling isn't new at all — it's a re-emphasis on lessons software engineers learned twenty years ago, just arriving in a new form. In today's episode, I walk through why the fundamentals are becoming more important than ever, why so many of us feel scattered despite having the most powerful tooling we've ever had, and where the real bottleneck in software delivery has quietly moved. My goal isn't to convince you that your job is now babysitting AI — it's to show you which parts of the work are still squarely yours, and how older principles can make you faster and more confident right now.

 Limiting Work in Progress Is Back: • Just because you can spin up fifty agents doesn't mean you should split your focus across fifty things. Orchestrated fan-outs are powerful, but a human juggling agents across hiring, on-call, and a project all at once still pays the same old context-switching tax — and the quality drops while the speed never improves.

 Work Deeper, Not Wider: • Instead of spreading yourself shallowly across more tickets, run multiple sessions on the 
same • domain. Write a competing or adversarial version that critiques your assumptions, develop better documentation, or capture what you're learning as a reusable skill. Depth beats breadth.

 The Scattered-Engineer Epidemic: • Engineers are burning out faster, not slower. We have the capacity to push more through the pipeline, so we're getting handed (or choosing) more than we can carry. Reducing parallelism often holds your delivery speed steady while dropping your cycle time and raising quality.

 The Theory of Constraints, Revisited: • Treat your software development lifecycle as a pipeline with a bottleneck — and if you can't find one, you've optimized one part too far. Writing code used to be the choke point, so we spent enormous energy de-risking work before it ever reached an engineer.

 The Bottleneck Has Moved: • When production gets cheap, it's no longer worth heavily de-risking upstream — which is why engineers are picking up more experimental, proof-of-concept, discovery work, and product folks are prototyping with these tools too. The new constraint isn't writing the code; it's verifying the agent didn't ship something broken.

 Verification Scales With Your Effort: • The more an agent produces, the bigger the pile of PRs, MRs, and outputs waiting on human review. That backlog is the new bottleneck — and skepticism is creeping in because we're not even sure our tests are sufficient to verify what the agent built.

 Why TDD Fits This Moment: • The honest question isn't "Can I trust the agent?" — it's "What verification loop do I need to build so I can trust it more?" Clear requirements feed a clear testing loop: write the failing test, let the agent write the code to turn it green, and you bridge the gap between requirements gathered and requirements met. It's not as simple as "go write a test," but it's a strong fit for where we are right now.

 Episode Homework: • Go dig into the fundamentals — limiting WIP, the theory of constraints, test-driven development. Find the old lesson that still applies to your workflow today, bring it to your team's flow, and email me about what you discover.

🙏 Today's Episode is Brought To you by: Unblocked

Your coding agents probably have access to your codebase — and maybe your tools and MCPs too — but access doesn't mean context. Agents don't know your architectural decisions, your team's patterns, or why your API is shaped the way it is, so Claude ends up building a new model when it should have changed an existing one, and you're left clawing back bad outputs and burning tokens on correction loops. Unblocked is the smart context layer your agents are missing. Instead of dumping everything into a giant context window, it builds reasoning over shared context — turning code, docs, tickets, and conversations into actionable context so engineers move faster, agents make better plans, write higher quality code, use fewer tokens, and need fewer corrections. If you're running Claude Code, Cursor, or any agentic workflow, go check it out. Free three-week trial at getunblocked.com/developertea.

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If you enjoyed this episode and would like me to discuss a question that you have on the show, drop it over at: developertea.com.

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Software Engineering Principles That Still Hold Up in an Agentic World - Old Lessons Made New

The skills that survive every industry shakeup aren't the ones you can Google — they're softer, harder to name, and far more durable. In this episode, Jonathan explores principle-oriented thinking: the practice of stripping away the labels we attach to tools, roles, and even ourselves to see what something actually does at its core. It's the difference between handing your coding off to an agent and rethinking your entire workflow around what these new materials are truly capable of.

If you've been following along with our recent focus on durable skills, you know we've been hunting for the abilities that translate beyond this month, this year, or whatever AI does to our industry next. Today's skill doesn't have a tidy name you can search for — it's softer than that. Jonathan calls it "principle-oriented thinking": the habit of deconstructing the labels we put on things to understand their core components, properties, and capabilities. It's how NASA engineers turned a sock into a water filter on Apollo 13, and it's how forward-thinking engineers are reframing what AI can actually do rather than jamming it into a predetermined slot.

 Labels Are Useful Shortcuts — Until They Aren't: • Every label, from "software engineer" to "sock," carries baggage, heuristics, and presupposition. That's not a flaw — labels are how we move through the world quickly. But when a label is the only lens you have, it quietly caps how much value you can get out of the thing you're looking at.

 The Apollo 13 Sock: • When the crew needed to fix a life-threatening problem with mismatched parts, the engineers on the ground had to forget what a sock was 
for • and ask what it actually 
is • — a piece of cloth with tensile strength, flexibility, and filtering properties. Strip the assumption that it goes on a foot, and a whole new set of uses opens up.

 Stop Slotting AI Into Old Roles: • The common move is to take one responsibility — coding, debugging, refactoring — hand it to an agent, and keep everything else the same. That works, but it's low-leverage. The more powerful approach starts by asking what the agent is fundamentally capable of, then rebuilding the workflow around those raw materials.

 See Things as Materials, Not Fixed Functions: • When you deconstruct out from under a label, tools and concepts start to look like craftable raw materials. You can then combine them in new, valuable ways they haven't been combined before — alloying old methods with new capabilities to create properties neither had on its own.

 Reason From Properties, Not Personas: • Ask what the actual properties of an LLM are. Non-determinism isn't a bug to apologize for — it's a property you can exploit. The existence of many different models is a property too, which is exactly what makes adversarial review possible. That's principle-oriented thinking applied to agents.

 Extend the Latticework: • Charlie Munger talked about a latticework of mental models that weave together rather than sit in isolation. The durable skill isn't quarantining your concept of "AI" off to the side — it's grafting a new section onto the existing tapestry and letting it reshape everything you already understood.

 Episode Takeaway: • Look at how you spend your time and ask new questions of it. What is the material here? What kind of thinking does the agent actually do? What can a human do that an LLM can't — and the other way around? That's how you avoid believing a sock is only ever good for a foot.

🙏 Today's Episode is Brought To you by: Unblocked

Your coding agents have access to your code, your repos, and probably a pile of connected MCPs filling up their context — but access isn't the same as good context, and a bloated context window can actually degrade the very reasoning you're relying on. Agents don't know your architectural decisions, your team's patterns, or why an API was shaped the way it was, so they look in the wrong place and deliver bad outputs you then spend time and tokens correcting. ● Unblocked is the smart context layer your agents are missing. ● Instead of ingesting everything and getting lost, it builds reasoning over shared context. ● It turns code, docs, tickets, and conversations into actionable context — so engineers move faster, agents make better plans and write higher-quality code, and you burn fewer tokens and fewer correction loops. If you're running Claude Code, Cursor, or any agentic workflow, it's worth a look. A free three-week trial is available at getunblocked.com/developertea.

📮 Ask a Question

If you enjoyed this episode and would like me to discuss a question that you have on the show, drop it over at: developertea.com.

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Principles Oriented Thinking as a Durable Skill in an AI First World

A lot of what we've been talking about lately is durable skills — the abilities that last regardless of how our tools and tech environment change. In today's episode, I want to step back from the AI conversation and focus on one of the most durable skills of all: feedback. We've all been on both the giving and receiving side, and we can probably count on one hand the times someone gave us feedback that genuinely drove a good change — that left us wanting to do better without feeling torn down. So how do we accomplish that kind of feedback, on both sides of the table? That's what this episode is all about.

 Start With Your Goal, Not Your Frustration: • Before you give feedback, recognize that your gut impulse often comes from a negative emotion — frustration, feeling slighted, feeling disrespected. Those feelings are valid signals that something is off, but they aren't a sufficient reason to give feedback. Effective feedback is goal-oriented: ask yourself what you actually want to change before you say a word.

 Premature vs. Mature Feedback: • Premature feedback is really about making sure someone knows how you feel — which can quietly turn into an attack so they share your pain. Mature feedback is forward-looking and aimed at improvement. Venting may give you catharsis in the moment, but if the behavior worsens or the relationship is damaged, the net outcome is negative.

 Why Asking for Feedback Changes Everything: • Even hearing "can we meet for ten minutes, I have some feedback" measurably raises your heart rate and pushes you into a defensive state. But when you 
ask • for feedback, your mind and body register that you're in control — same information, completely different physiological response.

 Make It Behavior-Based and Specific: • Good feedback is about observable behavior — what a camera would have caught — not someone's core identity. If your feedback violates a person's self-concept (painting a competent engineer as incompetent), they have to change who they believe they are to accept it, and that gap rarely gets bridged in a 30-minute call.

 Use a Model — But Add the Intervention: • The popular SBI model (Situation, Behavior, Impact) is a strong backbone, but it stops short. Don't just describe the past — partner with the person on what comes next. Think of it as SBI + Intervention: what can you commit to trying differently so the impact changes? That's where feedback becomes coaching.

 The Netflix Four A's: • Aim to assist, make it actionable, show appreciation, and accept or discard. Lead with the intent to help, get specific about the behavior, appreciate the person's willingness and intent, and recognize that not every piece of feedback will be useful — both sides get to keep what's valuable and let the rest go.

 Receiving Feedback Well: • When someone hands you messy, un-modeled feedback, you can walk 
them • through the framework — "help me understand the situation, what behavior did you see, what was the impact?" People respect that you're engaging, shift into problem-solving mode, and give you more actionable feedback as a result.

 Episode Homework: • Pay attention to patterns over time. One piece of feedback shouldn't be attached to your identity — but three or four that point in the same direction are worth introspecting on. Career development and feedback are two sides of the same door; walk through it and you grow.

🙏 Today's Episode is Brought To you by: SerpApi

No matter what you're building, SerpApi is the web search API for your needs. If you're building an application that needs real-time search data—whether that's an AI agent, an SEO tool, or a price tracker—SerpApi handles it for you. ● Make an API call and get back clean JSON. ● They handle the proxies, CAPTCHAs, parsing, and all the scraping so you don't have to. ● They support dozens of search engines and platforms, and are trusted by companies like NVIDIA, Adobe, and Shopify. ● If you're building with AI, they even have an official MCP to make getting up and running a simple task. Get started with a free tier to build and test your application before you commit. Go to serpapi.com.

📮 Ask a Question

If you enjoyed this episode and would like me to discuss a question that you have on the show, drop it over at: developertea.com.

📮 Join the Discord

If you want to be a part of a supportive community of engineers (non-engineers welcome!) working to improve their lives and careers, join us on the Developer Tea Discord community today!

🗞️ Subscribe to The Tea Break

We are developing a brand new newsletter called The Tea Break! You can be the first in line to receive it by entering your email directly over at developertea.com.

🧡 Leave a Review

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What the Science Actually Says About Effective Feedback

Right now, the questions we have about our careers feel existential. We keep coming back to the same theme: how do you prepare for an industry that's changing this fast, and what mindset actually works in this new reality? One skill keeps surfacing as the answer — your ability to update your own mental models. In today's episode, I want to push on that further and put some of software engineering's most beloved thinking models under scrutiny. Some of these models served you well for years. Some of them now deserve to be challenged, replaced, or thrown out entirely — and learning how to tell the difference is itself the skill that will determine whether you hit a ceiling.

 Move Past "So What" Questions: • The typical engineering objection to agentic coding is that it produces quality issues. But the people deciding to adopt these tools already accept that. Our job is to stop arguing the surface-level point and start asking the real one: so what do we actually do about this new economic reality?

 The Economics of Acceptable Loss: • Abstraction always leaves something to be desired. An agent's code may not match what a staff engineer produces by hand over months — but that gap is usually an acceptable trade against shipping something two, three, or four times faster. Understand the cost-benefit picture instead of pretending the cost doesn't exist.

 Abstraction Has Always Done This: • This isn't new. The calculator dissolved the specialization once required for complex math. Spreadsheets commoditized ledgering and accounting. Agentic coding is the same pattern arriving for our work — making something that required deep specialization suddenly far more accessible.

 Roles Are Blurring: • As these generic tools raise everyone's ability to abstract, the boundaries soften. You're already seeing product managers open pull requests and engineers making product decisions. The neat lines around "what an engineer is" are not as fixed as they used to feel.

 Why Your Hard-Won Wisdom Is the Target: • If you've spent years in this industry, your models were bought with blood, sweat, and failed projects. That experience is real wisdom — and it's exactly what I'm asking you to be willing to challenge, because the thing that always worked for you is the thing most likely to become a ceiling.

 This Skill Survives Either Way: • Even if you think AI is mostly hype and I've been infected by it — fine. The ability to challenge your pre-existing models is a critical skill regardless. It's how you keep growing as you get more senior instead of repeating what used to work.

 Models Are Approximations: • The whole point of a model is to approximate the reality around us. That's their value and their limitation. When the underlying reality shifts this dramatically, holding tightly to an old approximation stops being wisdom and starts being a liability.

🙏 Today's Episode is Brought To you by: Unblocked

Your coding agents have access to your codebase and probably a lot more — tools connected through MCPs, skills, and more. But access isn't the same as context. Agents aren't great at reasoning across MCPs, and they don't know your architectural decisions, your team's patterns, or why your API is shaped the way it is. So they look in the wrong place and deliver bad outputs, and you burn time and tokens correcting them. ● Unblocked is the smart context layer your agents are missing. ● Instead of dumping tons of data into a giant context window and getting lost, it builds reasoning over shared context. ● It turns code, docs, tickets, and conversations into actionable context, so engineers move faster and agents make better plans, write higher quality code, use fewer tokens, and need fewer correction loops. ● If you're running Claude Code, Cursor, or any other agentic workflow, it's worth a look. Get a free three-week trial at getunblocked.com/developer-tea.

📮 Ask a Question

If you enjoyed this episode and would like me to discuss a question that you have on the show, drop it over at: developertea.com.

📮 Join the Discord

If you want to be a part of a supportive community of engineers (non-engineers welcome!) working to improve their lives and careers, join us on the Developer Tea Discord community today!

🗞️ Subscribe to The Tea Break

We are developing a brand new newsletter called The Tea Break! You can be the first in line to receive it by entering your email directly over at developertea.com.

🧡 Leave a Review
If you're enjoying the show and want to support the content head over to iTunes and leave a review!

Rebuilding Your Mental Models In the Midst Of an AI Tech Revolution

If you're a software engineer right now, you likely feel like your world is changing overnight. We are writing half or less the amount of code that we wrote even a year ago, which represents a seismic, groundbreaking shift in our industry. For many of us, this career has always been engaging for deeply creative and intellectual reasons—and that excitement is still here. But our mental models of what it means to be a good engineer, and what it means to keep improving, have gone a little stale. In today's episode, I want to talk about a distinction that I believe will become the cornerstone mistake for seasoned engineers: confusing _practice_ with _adaptation_, and leaning on the wrong one at the worst possible moment.

 Two Surfaces Coming Into Contact: • Picture your knowledge, skills, and toolset as one surface, and the actual state of the art as another. We've always known the surface area we 
could • learn far exceeds what we 
can • learn, which forces us to place bets on a learning strategy. What's changing is how fast that second surface is moving underneath us.

 Improvement by Practice vs. Improvement by Change: • Practice is wielding what you've already adopted—smoothing out errors, building muscle memory, refining what you already know. Adaptation is fundamentally folding something new into your repertoire. Both are real forms of improvement, but they are not interchangeable.

 The Cornerstone Mistake for Senior Engineers: • Later in your career, the time you spend adapting naturally goes down as you settle into practice. The biggest error I'm already watching engineers make is moving 
too quickly • toward practice when the industry is loudly calling for adaptation instead.

 Inspect and Adapt—at the Right Altitude: • Sprint retros were never really about getting marginally better at the thing you already do. The intent of "inspect and adapt" is to step up one level and examine the 
system • . The trap is treating adaptation like a minor refinement—getting a little better at prompting—when it should mean asking whether you're thinking about prompting in the wrong way entirely.

 Question the Ratio, Not Just the Output: • Real adaptation looks like asking whether you have the right mix of human and agent on a problem. Are you leaning on the agent for things you shouldn't, or failing to lean on it for the things you should? Have you genuinely thought about how sub-agents or an agent team are working the problem you're producing?

 A Spectrum, Not a Binary: • On one end, you make micro-adjustments to your refinement process. On the other end of experimentation, you ask whether refinement—or even having engineers plan the work—is the right thing at all. The point isn't that practice is dead; it's that the industry is changing fast enough that the adaptive end of that spectrum deserves far more of your attention than it used to.

 Episode Homework: • Take something you currently treat as a practice problem—"how do I refine tickets faster?"—and step up a level. Ask the adaptive version of the question instead: "Is refinement even the right thing anymore?"

🙏 Today's Episode is Brought To you by: SerpApi

No matter what you're building, SerpApi is the web search API for your needs. If you're building an application that needs real-time search data—whether that's an AI agent, an SEO tool, or a price tracker—SerpApi handles it for you. ● Make an API call and get back clean JSON. ● They handle the proxies, CAPTCHAs, parsing, and all the scraping so you don't have to. ● They support dozens of search engines and platforms, and are trusted by companies like NVIDIA, Adobe, and Shopify. ● If you're building with AI, they even have an official MCP to make getting up and running a simple task. Get started with a free tier to build and test your application before you commit. Go to serpapi.com.

📮 Ask a Question

If you enjoyed this episode and would like me to discuss a question that you have on the show, drop it over at: developertea.com.

📮 Join the Discord

If you want to be a part of a supportive community of engineers (non-engineers welcome!) working to improve their lives and careers, join us on the Developer Tea Discord community today!

🗞️ Subscribe to The Tea Break

We are developing a brand new newsletter called The Tea Break! You can be the first in line to receive it by entering your email directly over at developertea.com.

🧡 Leave a Review
If you're enjoying the show and want to support the content head over to iTunes and leave a review!

Practice Isn't Enough for Senior Engineers - Adaptation Is a Key Skill in an AI-First Industry

If you've heard that your job in the agentic coding era is to "become a manager of agents," you may have noticed something doesn't quite fit. Most of us never trained to be managers, and frankly, that's not the role most engineers want. In today's episode, I unpack what that shift _actually_ means — it's closer to a tech lead or architect mindset — and zoom in on a specific interviewing and on-the-job skill that will help you stay employable: how you think about, talk about, and take ownership of failure.

 Don't Just Bring Star Stories — Bring Failure Stories: • Interviewers don't only want to hear how you succeeded. They want to know what you do when the pressure's on and things fall apart. If every story you tell is a highlight reel, there's a built-in social signal that you're hiding something. Get comfortable telling the other kind of story.

 Identify the Real Problem, Not the Proximal One: • The most common failure story I hear in interviews is "the knowledge transfer was bad" or "the docs weren't good." That's not wrong — it's just incomplete. The senior mindset asks 
why • that happened. Why didn't we have docs? Why was context insufficient? Walk it back until you hit something actionable but not too abstract.

 The Systemic Diagnosis is the Leveled-Up Answer: • Fixing the proximal cause fixes 
this • instance. Fixing the root cause fixes the system that keeps producing instances like this. When you connect what you learned to a systemic adjustment, you stop sounding like someone who survived a bad project and start sounding like someone who improves the organization around them.

 Ownership Means Owning the Outcome, Not the Task: • Use the homeowner metaphor. A homeowner doesn't personally fix every leaking pipe — but the outcome of the home is theirs. As an engineer, your scope of ownership has expanded dramatically in the agentic era. You're now responsible for outcomes of code you may not have even read, and the deciding skill is how you carry that responsibility.

 The Word to Pair With Ownership is Relentlessness: • Not in an anxious, burn-yourself-out way. Relentlessness means following a thread to its natural end — through escalation, through asking the next question, through finding the right person if it's not you. It's the antidote to "I'll let someone else handle it" syndrome.

 You Don't Have to Do It All Yourself: • Relentless ownership is 
not • "carry every task across the finish line personally." If you're not qualified, the owner's job is to find who is, communicate risk to stakeholders, and keep the trail alive until the outcome is resolved. That's the differentiator between a senior thinking engineer and a junior one working through assigned tickets.

 Failure Is Usually a Lapse in Ownership: • If you make a list of five things you've failed at (and you should), you'll often find the through-line isn't lack of skill — it's that you stopped escalating, stopped following up, stopped staying with the thing until it was actually resolved.

 Episode Homework: • Write down five real failures. For each one, ask: where did I stop being relentless? What system produced this outcome — and what would I change upstream next time?

🙏 Today's Episode is Brought To you by: SerpApi

No matter what you're building, SerpApi is the web search API for your needs. If you're building an application that needs real-time search data—whether that's an AI agent, an SEO tool, or a price tracker—SerpApi handles it for you. ● Make an API call and get back clean JSON. ● They handle the proxies, CAPTCHAs, parsing, and all the scraping so you don't have to. ● They support dozens of search engines and platforms, and are trusted by companies like NVIDIA, Adobe, and Shopify. ● If you're building with AI, they even have an official MCP to make getting up and running a simple task. Get started with a free tier to build and test your application before you commit. Go to serpapi.com.

📮 Ask a Question

If you enjoyed this episode and would like me to discuss a question that you have on the show, drop it over at: developertea.com.

📮 Join the Discord

If you want to be a part of a supportive community of engineers (non-engineers welcome!) working to improve their lives and careers, join us on the Developer Tea Discord community today!

🗞️ Subscribe to The Tea Break

We are developing a brand new newsletter called The Tea Break! You can be the first in line to receive it by entering your email directly over at developertea.com.

🧡 Leave a Review
If you're enjoying the show and want to support the content head over to iTunes and leave a review!

Senior Skills to Maintain Employment Through the AI Wave

What happens when you discover that a book that fundamentally changed how you think is built on a shaky foundation? In today's episode, I share my own struggle with the replication crisis surrounding Daniel Kahneman's *Thinking Fast and Slow*, and I use it as a springboard to talk about a much bigger skill: knowing how to update your beliefs when reality shifts underneath you. This isn't about throwing out science or losing trust in your heroes. It's about developing the muscle to replace old explanations with better ones — a skill that has never been more important for software engineers.

 The Replication Crisis, Briefly Explained: • Understand the difference between 
reproducing • a study (re-running the analysis on the original data) and 
replicating • one (recreating the study from the ground up), and why a surprisingly large portion of well-respected psychology research, including studies cited in 
Thinking Fast and Slow • , doesn't hold up under scrutiny.

 Base Rates Matter: • Kahneman didn't pick uniquely bad studies. If you randomly sampled from the broader academic literature, you'd hit the same failure rate. The lesson isn't about one author — it's about how we evaluate any body of knowledge.

 The Beginning of Infinity Framework: • Drawing from David Deutsch's book, explore the idea that all progress is rooted in the assumption that we are fundamentally incorrect, and that improvement comes from continually building better explanations on top of incomplete ones.

 Beliefs as Calibration, Not Truth: • Your beliefs about what makes a good engineer, what makes good code, or what makes a good career move are not eternal truths. They are calibrations to your current reality, and that reality is changing fast.

 The Ego Trap of Old Beliefs: • Notice the very human, very subtle pull to defend things you previously argued for — not because they're still right, but because admitting otherwise creates a discontinuity with your former self. This is one of the biggest blockers to learning.

 Two Competing Explanations of AI Adoption: • Walk through a worked example of holding two predictions about AI in tension and asking honestly which one better explains the reality you're seeing — at both a macro industry level and the micro level of debugging a system.

 Moving Goalposts Aren't a Conspiracy: • A lot of what feels like shifting goalposts in our industry is just goalposts moving on their own. A big part of our job as engineers is figuring out where they are now and predicting where they're heading next.

 Episode Homework: • Pick one belief you hold strongly about your work — about what makes a good engineer, about a tool, about a process. Try to deconstruct it into its parts and ask whether a better explanation exists for what you're actually seeing.

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You're Wrong All the Time, But All You Need Are Better Explanations

Why I'm Not "Picking a Fight" on AI: • A listener asked if I'm intentionally stoking a flame war by treating agentic coding as a foregone conclusion. The honest answer is that I've used it, the data points one direction, and a show built around pretending otherwise would slowly drift away from reality — and away from being useful to you.

 Respecting the Misgivings, Without Getting Stuck in Them: • Ethical concerns, skill atrophy worries, and questions about long-term effects are all legitimate. But the goal of this show is practical applicability, so we focus on mental models you can use Monday morning rather than litigating every angle of the debate.

 The "Minecraft" Principle: • If I ask you to "build Minecraft," I've handed you several chapters of specification in a single word. That's meaning-rich abstraction — language that points at a huge amount of shared context with very little token cost.

 Meaning-Rich AND Specific: • "Human history" is meaning-rich but uselessly broad. "Block-building game" is specific but loses fidelity. The sweet spot is vocabulary that is both compact and unambiguous — sitting in the top right of the meaning-density / specificity graph.

 A Real Example — Strategy Pattern: • When working on authorization rules, I didn't want a pipeline. Instead of describing base classes, shared interfaces, and parallel execution to the LLM, I used the words "strategy pattern." Three words did the work of three paragraphs, and the output landed where I wanted it.

 Vocabulary as Leverage: • Named patterns, named algorithms (Monte Carlo, etc.), named architectural concepts — these act like compressed pointers. The more of them you genuinely understand, the higher the leverage of every prompt you write and every conversation you have with another engineer.

 How to Build This Vocabulary: • Have conversations with senior engineers. Ask an LLM what patterns are at play in a codebase, which ones you're using incorrectly, and which ones you're tricked into thinking you're using. Learn the abstraction layer that sits one step above your day-to-day implementation work.

 The Asterisk — Shared Context Required: • This only works when both sides know the term. Public, well-documented concepts (patterns, papers, algorithms) translate immediately to LLMs. Private or organization-specific concepts need to be loaded into context — via CLAUDE.md, AGENTS.md, or skills — before that compression kicks in.

 Episode Homework: • Pick one area of your current codebase. Ask an LLM to name the patterns in play, the patterns you're using incorrectly, and the ones you might be missing. Use that conversation to add at least one new piece of meaning-rich vocabulary to your working set.

🙏 Today's Episode is Brought To You by: Unblocked

Your coding agents have access to your code base — and probably more — but access isn't the same as context. Agents can't reason well across MCPs on their own, they don't know your architecture decisions, and they don't know which docs are reliable versus written by someone in their free time two years ago. ● Unblocked is the context layer your agents are missing. ● It synthesizes your PRs, docs, Slack messages, and Jira issues into organizational context that agents actually understand. ● That means better plans, higher quality code, fewer tokens, and fewer correction loops. ● Whether you're running Claude Code, Cursor, Codex, or any agentic workflow, it's worth a look. Get a free three-week trial at getunblocked.com/developertea.

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AI-Proofing Your Skillset - High-Meaning, High-Specifity Vocabulary is the Path to Growth

The Coding-Is-My-Value Trap: • For years, we've treated the ability to write code as the flagship skill of software engineering. It's concrete, it's teachable, it's the thing big box stores sell kits for. But conflating "what I enjoy about the job" with "what I'm actually valuable for" is dangerously reductive — and AI is now exposing that gap.

 The Skills You've Been Discounting: • Domain expertise, systems thinking, risk and bottleneck analysis, organizational design, tech-lead-level sequencing of work, relational skills that unblock hard moments in a company's life. These have always been where a lot of your real value lived. You probably just weren't writing them down.

 The Three-Part Framework — Valuable, Durable, Transferable: • A skill worth investing in hits as many of these as possible. 
Valuable • means it meets a clear business need. 
Durable • means it survives industry shifts. 
Transferable • means it applies across domains and scales up as you grow more senior.

 What "Durable" Actually Means: • Ask yourself: what would have to change for this skill to become obsolete? Coding, on its own, has a lower durability answer than it used to. Relationship building, architectural thinking, and the ability to reason about complexity require much bigger shifts before they stop mattering.

 Transferability Is Vertical, Not Just Lateral: • Don't just ask whether a skill moves across industries. Ask whether it keeps paying off as you move into more senior, higher-leverage roles. Soft skills, systems thinking, and mental models like compound interest compound themselves the further up you go.

 Episode Homework: • Make your own list. Which of your skills are valuable, durable, 
and • transferable? Every engineer's list looks different — and the ones you've been quietly discounting are often the ones that matter most going forward.

🙏 Today's Episode is Brought To you by: Unblocked

Your coding agents have access to your codebase — but access isn't context. They don't know your architectural decisions, your team's patterns, or why the API was shaped the way it was. So they look in the wrong places and deliver bad outputs, and you burn time and tokens correcting them.

 ● Unblocked is the context layer your agents are missing.

 ● It synthesizes your PRs, docs, Slack messages, Jira issues, and more into organizational context agents actually understand.

 ● Better plans, higher quality code, fewer correction loops, fewer tokens spent.

 ● Works with Claude Code, Cursor, and any agentic workflow. Get a free three-week trial at getunblocked.com/developertea.

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Building Real Skills During the AI Boom - No, Not That Kind of Skill

Our ability to make sense out of things is powerful. When we hear "90% of the way there", our brain fills in the gaps and often stretches that to mean "basically 100%." However, this is a cognitive error. In this episode, we talk about the importance of combining statistical thinking with sensemaking.
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When Sense-making and Statistics Collide

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