I've been thinking about this while building my own software company.

You can spend months developing a product, researching the problem, testing features, and determining what you believe the solution is worth.

But there's a difference between what you believe your product is worth and what someone is willing to pay before they've experienced it.

365 days is more than enough time to get burned, mess things up, and (most importantly) learn some hard lessons.

My biggest wake-up call this year was neglecting my health until it completely derailed my business I had to turn down collabs, miss conferences, meetups, and a ton of opportunities.

Everyone focuses differently. And for a lot of people, where and how they work makes a huge difference.

Some need silence. Others need background noise. Some can t stand the cold, others lose focus if it s too warm. Coworking spaces? Only if they have the right vibe. Offices? Only if colleagues aren t interrupting every five minutes.

I built something last quarter that no customer requested. I built it because it annoyed me, and I assumed if it annoyed me it probably annoyed everyone.

It flopped. Almost nobody uses it. The people who do love it, which is a sentence I've written before and probably will again.

I'm in the marketing phase, where I'm trying to scale my first tool without the help of paid advertising.

• I'm trying to talk about it on my own social networks (while I have the link pinned to my profile)

• I'm helping people in the comments answer questions about the problem

• I'm going to create content for social networks around the problem

• I want to appear in as many articles as possible (third-party websites, newsletters, my own blog, etc.)

• Launch in directories

Hi all, there's a distinction I keep thinking about: apps built with AI (Cursor, Claude, Copilot writing the code) vs apps that depend on AI APIs at runtime to function.

I'm in the first camp. I used AI-assisted coding heavily to build Briskly (a cleaning app that scans your room and generates a timed task list), but the app itself makes zero LLM API calls. The scanning runs on Apple's Vision framework, entirely on-device. No tokens, no per-user inference costs, no latency, works offline, and my privacy label is basically empty.

This is a huge trap with AI coding right now.

It's so easy to prompt a quick prototype together over a weekend but if it gains any traction you're suddenly stuck maintaining a codebase full of logic you barely understand.

I try to force myself to write clean architecture docs early if I think an app has any long-term potential. Otherwise, refactoring later takes twice as long as building it properly from scratch.