The most productive tension in product teams isn’t between engineering and design. It’s between maximizers and focusers. Maximizers bias toward speed, optionality, parallel bets. Focusers bias toward depth, coherence, doing less for more.

Neither is right. Both create value and risk.

This explains why some conflicts feel unsolvable. A maximizer sees a focuser killing momentum. A focuser sees a maximizer torching trust for short term wins. Different languages about what “good” looks like.

I’m a maximizer. My instinct is “why not both?” It’s served me well. Parallel bets surface opportunities you’d miss with pure focus. But maximizing without constraint creates shallow progress everywhere and deep progress nowhere.

The skill is knowing when to maximize and when to focus. Early in a market? Maximize to find signal. Product-market fit achieved? Focus to compound advantages. Most teams get stuck in one mode.

What I’m working on: recognizing when my maximizer instincts help versus when they sabotage depth. The best maximizers I know build systems that force the choice. Focused sprints, public commits, accountability structures that override instinct.

Can you switch between modes deliberately instead of defaulting unconsciously?

The smartest person in the room isn’t always the most valuable. The person who actually does something is.

High agency is the belief that you can act, paired with the will to follow through. Seeing a problem and saying “I’ll figure it out” instead of waiting for instructions. Intelligence without action is potential energy that never converts to motion.

Agency is rare because we’ve spent decades optimizing for compliance. Schools reward right answers, not interesting questions. Companies bought obedience by the bushel. We trained people to wait for permission.

In a world where AI can handle intelligence on demand, what separates people is curiosity and courage to act. The scarcity isn’t brains. It’s people willing to bend the universe through their actions.

Bias toward action beats analysis paralysis every time. Ship it, then make it better.

The gap between prompt-driven hacking and production-grade AI-assisted work is engineering fundamentals. Tests, documentation, version control, code review. These matter more now. Running multiple agents in parallel works when you know what you’re orchestrating. Speed exposes who knows their craft.

“Vibe engineering” works because it draws a line. It signals accountability. You ship what you’re responsible for, not what AI generates. AI tools don’t level the field. They widen it. Engineers with fundamentals get leverage. The term acknowledges we’re figuring this out while establishing there’s real craft here, not just vibes.

Sometimes the best answer is the simplest one. This short video captures something essential about bias toward action and clarity in decision-making.

Willingness to commit without overthinking. Not recklessness. Confidence in making decisions and adjusting course as needed. Yes as a default stance, not a permanent commitment.

AI coding tools promise 10x speed. What they deliver is 10x more code to understand and fix. The trap isn’t the AI. It’s thinking faster code generation equals better software.

Treat AI agents like lightning-fast junior engineers who need structure, not carte blanche. The solution isn’t less AI, it’s more process. Specifications, documentation, design reviews, testing frameworks. Raw speed without context creates expensive technical debt.

The parallel to tech leadership is clear. Keeping complex work to yourself creates team brittleness. Relying on AI without building systems around it creates codebase brittleness. Both need intentional investment in structure.

40,000 vibrations per second in a chef’s knife sounds like engineering overkill, but the promise is compelling: cutting with half the effort while reducing food sticking.

I’m curious about durability and whether the vibration mechanism can handle daily kitchen abuse. Cool tech, but I’d want to see real chef reviews before committing to a pre-order.

The distinction between managing and leading becomes clear when you examine how people handle pressure. Managers retreat into control mode. Leaders step into trust mode.

The striking insight: information hoarding vs. transparency. Managers treat details like classified intelligence, thinking secrecy equals power. Leaders do the opposite. They call everyone together, explain the challenge, and ask for ideas.

Managers dodge discomfort. Leaders lean into it with care. “This is tough, but I care about you, so let’s talk.” That’s leadership distilled.

Claude’s memory system works differently from ChatGPT’s approach. Instead of building persistent user profiles, Claude starts fresh every conversation and only retrieves past context when explicitly asked.

The design choice prioritizes user control and transparency over convenience. Most AI companies assume users want seamless personalization. Anthropic assumes users want predictable behavior.

Both approaches have merit, but Claude’s feels more aligned with professional workflows where context matters more than continuity. When I’m working on a client project, I want to control what the AI remembers about previous projects, not hope it automatically surfaces the right context.

We’re still figuring out how AI memory should work. This isn’t the final answer. It’s one experiment in a much larger design space.

Go deep on multiple things early. Don’t choose between specialist vs generalist. Do both. Early expertise compounds into options you can’t build later.

Status lags behind reality. Today’s “safe” career paths become tomorrow’s dead ends. Banking, then consulting, then big tech. Each generation thinks their safe bet will last forever.

Build global networks online. Best opportunities come through people you meet digitally. Remote work changed the game. Don’t limit yourself to local coffee meetings.

Avoid train track thinking. Following predetermined paths (college → grad school → corporate → promotion) feels safe but is risky. You’re competing with everyone else on the same track.

When you follow the crowd, you compete with the crowd. When you bet on yourself, you compete with almost nobody.

Performance is something that we will give more time; behavior we won’t. And that’s because behavior is a choice, not a skill set.

We constantly have conversations around prioritization. We can’t do everything, so we have to choose. Not choosing is the worst thing you can do because now you’re compromising everything.

Larger companies … tend to become their own worst enemies. It’s not what the world does to them; it’s what they do to themselves.

Where does ambition come from? It is a lack of adjustment because if you were a perfectly balanced person, you wouldn’t have any ambition. You’re so happy with where things are, you barely have a reason to get up in the morning. But maladjusted people, they just have this disparity between where they are and what they want to do and what they want to prove.

I like people that have attitude, that have a chip on their shoulder, that have a burning need and desire to prove something.

The thing about sales is that great salespeople can’t sell a bad product, but lousy salespeople can sell a great product.

I’d rather hire more slowly but better instead of faster.

“Legacy” is not a word that I use, okay? I find … a lot of self-absorption around that stuff, like I need to live beyond the grave. I really don’t need to, and I don’t want to feel so important that that’s even a question.

Plenty of people speculate about AI replacing coding. Fewer talk about how it works today. Here’s what we’ve seen using Cursor and Claude Code inside a 12-year-old SaaS with about 40 developers.

Cursor works best if you’re fine switching editors. Claude fits better in the terminal and tends to take on bigger, feature-level work. I stick with Claude because I won’t leave Sublime Text.

Productivity is up—maybe 20 percent—but uneven. Agents are great for refactors, chores, and unblocking ideas. They’re weaker at subtle bug fixes and don’t write beautiful code. Over-commenting is common, and you still need human judgment.

The biggest gain is ambition. Solo developers now ship projects that used to take a team. People who once pushed a handful of PRs are shipping hundreds with an agent beside them. Costs are low—our heaviest Claude users barely spend fifty dollars a month.

The hard part of programming is still deciding what the software should do. Turning ideas into syntax just keeps getting faster.

A very nice this documentary about Fatboy Slim talking about UK DJ culture

The messy, fascinating world of turning lat/lon coordinates into human-friendly addresses. What sounds simple quickly unravels into questions about user experience, data trust, and the real purpose behind OpenBenches.

Horowitz’s distinction between good and bad product managers applies directly to CRM architecture. The best CRM architects take ownership like “CEOs of their solution.” They anticipate user needs rather than fighting fires. They focus on business value, not just technical specs.

A great CRM architect, like a good product manager, creates clear documentation, makes decisive choices, and measures success through business outcomes, not feature checklists.

A useful guide on building AI agents based on OpenAI’s recommended approach

The Frugal Architect’s laws are essential for sustainable CRM implementations. Cost consciousness must be woven into requirements from day one, not treated as an afterthought.

Measuring and observing system costs aligns with modern CRM best practices. Successful architectures keep expenses proportional to actual business value delivered.

Optimization is incremental. CRM maturity evolves over time, requiring continuous refinement based on genuine usage data, not untested assumptions.

The Director of one of my all time favorite movies (Dune) picks a few of his favorite movies