But companies are still opening internships, which suggests something deeper than just skill-building still matters (like understanding systems, workflows, and how companies actually operate the management part).
A story and an experiment have been spreading on X: Scientists uploaded the brain of a fruit fly into a computer, and now it lives freely in its own simulation.
We managed to clone the physical form of animals more than 30 years ago (for example, the cloning of a goat using SCNT in 1999). There was even a controversial case in China where a scientist was sued after attempting to create gene-edited babies in 2018.
Let me start from the creator s perspective: I personally don t have a product (apart from hiring people for creative work or offering personal consultations).
But as a creator, I constantly share content, insights, and information, value that helps me build trust (for free). Based on that perceived expertise, people eventually decide to work with me (a paid service).
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.
