Early-stage founders often try to improve their product as much as possible and tend to take almost any feedback into account.
Sometimes they end up adding every feature users (even non-paying ones) ask for, even when those features are unnecessary. The product then becomes more complicated and harder to use.
And I m not even talking about the stage when the product is already established. At that point, there are more users, and their expectations start to differ.
Before AI, I always thought I would NEVER learn how to code. I genuinely admired technical people, watching them code felt like watching magic. I remember wishing that maybe one day, I could do something like that too.
I ve never had any formal education in programming, and I had zero experience building apps. But with AI, I was able to start from just an idea and slowly figure things out on my own experimenting, setting things up, and eventually creating my first interface that I could actually interact with.
It honestly felt magical. It made me realize how fast the world is changing. Coding is no longer something completely out of reach. AI is making it possible for people like me to turn ideas in our heads into real, tangible drafts for the first time.
There has always been a framework for pricing that considers: Costs Competitor pricing Typical price ranges in the country What the client or company can afford to pay (meaning their business size) Your personal brand and authority
The more people ask for my services and want to claim my time, the higher I need to set my price (not surprisingly, I then often get ghosted).
Today, I came across an article on TechCrunch: The great computer science exodus (and where students are going instead).
It shows that UC campuses saw a drop in computer science enrollment for the first time since the dot-com crash (6% in 2025, 3% in 2024), but students are shifting to AI-focused programs.
After our first launch on Product Hunt, our team spent a little over a month upgrading the product. There were major changes to the UI and several new features added, so the process took time from discussions and redesigning the interface to testing, fixing bugs, and updating AI prompts.
We re also a very small team, so everyone had to push themselves to give 200%. Time and resources are limited, and at the same time, we also had to work on securing funding for the next six months to keep the team running and continue developing the app.
We spent a year building Lovon with a PhD psychologist with 40+ years of clinical experience. What makes it different:
Therapeutic, not agreeable (like gpt). Evidence-based frameworks (CBT, Emotion-Focused Therapy) designed to gently challenge unhealthy thinking - not reinforce it.
It took longer than it should have, mostly because it kept getting deprioritized in quarterly planning. And the annoying part is: the longer you wait on dark mode, the bigger it gets. More components to adjust, more edge cases, more workflows to test.
So we stopped debating it. No justification, no comparisons. We just shipped it.
Today, I read in Techcrunch that India has an ambition to "compete" with the US and China in the startup scene:
India has updated its startup rules to better support deep tech companies in sectors like space, semiconductors, and biotech, which take longer to mature.
Today, the productivity domain in tech is very well developed - there are tools for almost any need!
But at the same time, there s always a feeling that there might be something else, something better. All the time.
What I like about this space is that once people start using tools like Miro, Notion, Trello, ClickUp, etc., they tend to keep testing new things and experimenting with different tools.
10 years ago, when I started in marketing, I had never heard of Product Hunt.
Today I m here and honestly, I feel like a newcomer trying to find my way among people who build and launch world-class products. What I genuinely love so far is how open and supportive this community feels. It s a beautiful ecosystem to witness.
AI is everywhere right now - from copilots and chat assistants to analytics, research, and planning tools. But beyond the hype, I m curious about what s truly useful in day-to-day product work.
From a PM or founder perspective:
Where has AI genuinely saved you time?
What tasks do you trust AI with - and what do you never delegate?
Has AI changed how you write specs, manage roadmaps, or talk to users?
What AI use cases sounded great in theory but failed in practice?
Personally, I see a lot of potential, but also a lot of noise. I believe that in the future, AI should help us much more. Create good roadmaps, convert product specs into concrete tasks, prioritise them, assign people, push for realisation, and much more.
