When AI systems break, it s rarely with a crash or error log. It s a slow drift, outputs that seem fine, context that fades, retries that quietly multiply. Everything still runs, until one day it doesn t.
A few days ago, I listened to a Czech video cast where the idea was that in a few years, the teaching position will lose its relevance.
This seems like a quite realistic prognosis to me, because:
The teaching position is not particularly valued,
AI knows more information than a teacher,
AI does not sharply confront the user, which encourages people to ask questions and think critically (this can sometimes not be said about the school system)
More and more young people prefer to communicate with Chatgpt than with an "educational authority"
Nobody said running a startup was easy but some issues whether it's increasing DAU, perfecting ad copy, increasing MRR, hiring, or whatever can be pull your hair out levels of frustration.
What's one pain point you are struggling with at the moment? Drop it in the replies and then help someone else based on your experience!
I'm seeing a growing trend of B2C products actively advertising their AI features as a USP, claiming AI being the prime solution.
However, being back in my hometown for a weekend, I've heard a lot of apprehension around data privacy and a general lack of understanding "what happens in that blackbox". Nothing I hear very often back in Berlin, so demographic differences are clearly playing a big role in user receptiveness.
Transparency is crucial, no doubt. Advertising AI on platforms like producthunt or in decks for investors makes a lot of sense - that's the right audience. But are we far enough along the AI adoption curve for "AI-powered" to be a major selling point on the customer-facing side? Or are we scaring off potential users with concerns about data usage and complexity? Let's discuss!
Have you seen AI transparency hurt or help your user acquisition efforts?
To be honest, I find Cursor s VS Code experience quite uncomfortable. However, the fact that it runs seamlessly within my codebase is what keeps me from abandoning it.
That said, if Copilot were to offer the same level of integration, I don t think I d continue using Cursor. Copilot is gradually catching up with the features Cursor provides, and it works within IntelliJ, which is a big advantage for me.
Lately I realise that I use AI to automate stuff etc, but when it comes to growing products that I built or my social presence, it does not help me much.
Like, there are probably a BILLION tools for AI-powered content creation and blah blah blah, but they haven't really helped me generate any meaningful content. So, I always end up writing the stuff I want myself, cause they are just better than the AI tools create.
One of the biggest pain points in AI chatbots has been their forgetfulness having to repeat the same context over and over again. AI memory aims to solve this by allowing models like ChatGPT and the newly launched Gemini to retain past interactions.
But how well do these memory features work? Which AI ChatGPT or Gemini handles memory better? And more importantly, does AI memory provide more value in personal use or enterprise settings?
We are launching Predictor Games by Guul! Predictor Games by Guul is live now: https://www.producthunt.com/post... You can play Football, Formula 1, Oscars and Eurovision Prediction Games with your team on Slack and Microsoft Teams (coming in April) Let me know what you think! Your feedbacks are appreciated!
