Product Management

Since early August, I’d been toying with the idea of taking poketto.me “out of beta.” But with travel planned for late September through mid-October, timing became critical. I needed to launch before leaving, so I set “early September” as the latest possible date—giving myself at least two weeks to handle any post-launch chaos. The first question I asked: What does “launch” actually mean? What’s different afterwards? Here’s what came to mind: ...

Finding the right price point—for anything—is part science, part art, part alchemy… and maybe a sprinkle of luck. Charge too little, and you leave money on the table. Charge too much, and you don’t close the deal. This trade-off is as old as commerce itself, but it’s especially tricky for intangible products like software—particularly when selling subscriptions instead of one-offs and purely product-led (without the benefit of a human sales manager in the loop). ...

Since adopting Posthog for user analytics at poketto.me, I’ve grown pretty fond of the tool. Beyond the basics and advanced insights, I’m now also using it for feature flagging. Fine-grained control (and easy rollback) of new features or major changes is becoming increasingly important as the poketto.me user base grows. Why? 🤕 In a B2C app, you can’t count on users to complain when something breaks—they’ll just leave. Especially for early-stage products, every irritated user is a missed opportunity. ...

Let’s face it: It’s hard to get a freemium model right. While thinking through pricing and packaging for poketto.me, I looked at a lot of other B2C apps—and most of them had some flaw, inconsistency, or irritation in their approach. One striking example is Strava. The fitness app is wildly popular (150M+ users worldwide) and valued at $2.2B. But their free-to-paid conversion strategy seems to be struggling. Why? 👉 The core value (activity tracking) is fully commoditized, with little room to differentiate (Garmin Connect, Nike Run Club, Apple Health, etc. essentially all do the same thing). ...

Recently, I wrote about adopting Posthog for poketto.me. At first, I thought I’d use it for the basics: 📆 Daily & weekly active users (DAU/WAU) 📎 Core events (URLs saved, links shared, etc.) 🚨 Error tracking and alerting But then I realized: analytics can do much more. In fact, Posthog replaced one of my home-grown tools — my “podcast heuristic accuracy guestimator.” Let me explain. When a user adds content to their podcast feed, poketto.me has to gauge three things: ...

I didn’t coin this phrase (sadly), but it keeps proving itself true—especially now that I’m working on GTM details for some of the more advanced features in poketto.me. Most users don’t care how your app works. They care what it does for them—and whether that’s worth paying for. Since LLMs became easy to embed, companies started slapping “powered by AI” stickers on everything as if that alone justified a price tag. But unless the user clearly feels the value, it doesn’t matter what's under the hood. Case in point: Garmin’s hilariously underwhelming $7/month “AI subscription”. The so-called “insights” offered nothing users couldn’t deduce themselves—or the app couldn’t have generated with much simpler logic. ...

As I pointed out a few weeks ago: The web, as it's designed today, is not ready for “Agents” of any kind—AI-driven or just plain old automation scripts. Why? Because there’s no agreed-upon way for machines to interact with websites on behalf of a user. Case in point: Paywalls. Publishers are getting more creative in protecting their content from scraping, and rightly so: no one wants their work stolen by AI companies or repackaged by Google. But at the same time, they want to provide a good user experience for those who pay. ...

Just like optimism vs. pessimism, there's another spectrum that every builder, founder, or product person lives on: Maximizing vs. Satisficing. In behavioral economics, a maximizer tries to achieve the best possible outcome. For example: spending hours to find the absolute best hotel for your vacation. A satisficer, on the other hand, picks the first option that meets their basic requirements—and moves on with their day. When developing products, it's incredibly useful to know where you fall on that scale because: There’s not simple answer when to apply which strategy. ...

There was a time when the golden rule of consumer app development was as simple as ABCD: Always Be Collecting Data. The strategy? 1️⃣ Grow your user base as fast as possible. 2️⃣ Track every interaction, every event, every click. 3️⃣ Figure out how to monetize the data — usually through targeted advertising, if you couldn’t think of anything more creative. But that game is changing. Consumers are more privacy-aware than ever. Regulators — especially in the EU, California, Japan, and a few other regions — have stepped in. And both founders and investors are realizing that data-harvesting at scale is not a sustainable or ethical business model. ...

I just finished The Bright Side by Sumit Paul-Choudhury—a solid deep-dive into the history, psychology, and applicability of optimism. The social science is clear: overall, optimists tend to achieve better outcomes. Why? Because they act. Optimists move toward a positive vision of the future, and in doing so, often stumble upon unexpected opportunities. Pessimists, by contrast, lean toward fatalism and inaction — and the world rarely arranges itself in exactly the way they’d like, anyway. ...