Product Management Fundamentals Key to Success

The cost to build is not zero. Try building something and then get back to me. Did it just work? Did it survive exposure to your first customer? How about your second? How about your 100th? Does it scale beyond that? When you go to extend it, is it extensible or do you have to refactor everything? How good is the automated testing coverage? If you let a coding agent define and write all the tests, they probably aren't very good. The cost to build *a* thing is getting cheaper. If the result is we build *many* more things, then the cost to build and maintain *things* is going to skyrocket. Proceed with caution.

I am not surprised at all, this is the old (and never gone away) output thinking that AI is just - per its nature - exposing. This is, once again, the misunderstanding that DELIVER is the ultimate goal. It is the rush to jump into the next thing as soon as possible and never look back. Because when we look back, most of the time, we are going to be very much disappointed. Our amazing, brilliant, awesome idea doesn’t work exactly as you were expecting in the real world (not the version that we have in our mind or the synthetic version that our LLM has just created). And that’s hurt our ego. A lot. And so we lose the most valuable opportunity that delivery unlock: the learnings, and the opportunity to grow. “Stay humble, stay hungry (for learnings).” Thanks for coming to my Ted talk.

Teresa Torres names the trap clearly here. The cost to build a thing is getting cheaper. The cost to own the consequences is not. That is the part AI-speed narratives keep skipping. Every extra yes creates maintenance, testing, support, roadmap debt, and customer expectation. The demo is cheap. The product is not. For product teams, the old governor was engineering bandwidth. That governor is gone. The new governor has to be evidence-backed judgment before the first yes. Speed without signal crashes planes.

It takes sweat and tears to build something. Even with the best AI models. Not everyone is canceling Netflix to get a Claude Code subscription. #reality #ai #product #claude #openai

The cost of writing code is approaching zero. The cost of building is different than the cost of writing code.

There's a number from this year that genuinely stopped me. 92% of engineering teams said they were confident their code was production-ready before they shipped it. And yet 43% of those same releases still needed debugging in production after passing QA, after staging, after every gate they had in place. The code passed everything. The team was confident. Users found the bug anyway. We've spent two years getting really good at writing code faster. Cursor, Copilot, Claude Code the velocity gains are real and they're not going away. But somewhere in that sprint I think we quietly skipped a harder question: how do we get equally good at knowing the thing actually works before users do? Writing got faster. Verifying didn't keep up. And that gap is where the 3am pages live now the silent churn, the customer who hit a broken flow and just never came back. The bottleneck was never typing. It's the moment right before you hit deploy, when you need to actually know, not just hope. Feeling this on your team too?

This Claude Code workflow cleaned up my changes fast. I started treating Claude like a senior dev pair. I used to let Claude Code make a few edits, then review everything at the end. The code looked fine at first, but small mistakes stacked up quietly. What changed was making the loop brutally tight. One change. Typecheck. One focused test. Then continue. Here are the Claude Code tricks I wish I used earlier: ☑ Put repo rules in CLAUDE.md before touching code ☑ Ask Claude Code for one change at a time ☑ Run typecheck after every edit, not every task ☑ Run one focused test before expanding the scope ☑ Use isolated test environments Claude can execute safely ☑ Let Claude write tests for your framework first ☑ Save progress, then continue only after green feedback The output got cleaner the moment the feedback loop got shorter. I made a free workflow guide. Comment "CLEAN" and I'll DM it. Is your team checking Claude Code changes after every edit, or only at the end? 1 or 2. #ClaudeCode #ClaudeAI #SoftwareEngineering #EngineeringManagement

WAIT BEFORE CODING: Most MVPs fail before a single line of code is written. Founders often think technical planning is about diagrams, documents, and architecture. It's not. The real purpose of technical planning is to find risks before they become expensive problmes. What APIs could fail? What features can wait? What could delay launch by 4-6 weeks? A few days of planning can save months of rework, missed investor deadlines, and costly mistakes. The fastest MVP launches I've seen didn't start with coding. They started with clarity. Curious—do you do technical planning before development, or jump straigth into building? 👇 Share your approach below.

A complex feature, the kind that spans half a dozen PRs, used to take us about a week. It now takes eight hours 🚀, with the same team, the same codebase, and the same models everyone else can buy. It's the number I get asked about most, and people usually assume there's a trick. The answer is more boring than that. Every complex change used to begin with one person loading the whole system into their head: the intent, the constraints, all the places the change quietly touches. Then they'd unload it all again, into a spec, into mocks, into tickets, into a prompt for a coding agent that had none of that context. What reached the agent was a faded photocopy of what was in someone's head, and that constant reloading was where the week actually went. The code was never the slow part. So we stopped asking a person to hold the context and built Itera to hold it for us. Eight hours isn't a smarter model, it's what happens when a team stops losing its own thinking on the way to the agent.

Tests don't just protect code. They protect engineers. There's a different feeling when you deploy after a solid test suite passes. You move faster. You refactor more confidently. You worry less. Good tests don't slow development. They make fearless development possible. What's one test that has saved you from a production issue?