I can see a lot more unnecessary code, multiple supabase calls that can be avoided, direct table access that shouldn't happen. Single lengthy edge functions. This is just the starting if you actually work on an app for a month. Imagine what would happen after a year ? and if you've multiple projects... God help.
How are you managing it ? are you not facing it ? or just not accepting/realizing it ?
I also want to gather feedback from senior level managers who are leading teams in big tech, are you able to focus on low hanging fruits, small refactorings or code improvements that SDEs never want to work on or you don't prioritize due to business needs ?
For those of you who ve been following along first off, thank you. I ve been building Probado, a platform that helps early-stage founders get structured, paid feedback on their MVPs from vetted testers. It s affordable, customizable, and enhanced with AI that helps summarize insights and recommend improvements.
We ve got now 100+ vetted testers onboarded, and the MVP is just about done.
But now I m facing the part that honestly feels the hardest so far: marketing.
On Product Hunt, I can see many people launching their products using "vibe-coding tools" like @Lovable , @bolt.new , or@Replit
I reckon many people who created something with them are usually developers who didn't have enough time for building a side idea before, but with AI, they could make it happen.
These days, almost every product that launches comes with some form of AI. It's become the default AI for this, AI for that. And honestly, most of them don t really need it. The result? Everything starts to feel the same. The only real selling point becomes we use AI.
That s exactly why I started building @HumanEye because not every problem should be solved by AI. Some things, like resume reviews and career guidance, still deserve the human touch. Real feedback, from real people.
Would love to hear your thoughts:
Are we overusing AI just for the sake of hype?
Have you come across products that felt forced because of their AI features?
What are some areas where human input still matters most?
Coming soon to PH https://www.producthunt.com/prod...
Were you able to easily vibe code a backed which require integrations with vendors, ML models (for NLP) and LLMs all in one product ? - I found it really hard, prove me wrong !
Traders - stay away. Specifically for folks who are into value investing.
Fair value calculation using different methods, tracking fair value with current value and financial statement analysis is too much of grunt work. Simplify everything with Valiwise.
Just a month ago, we were nervously prepping for Lifetoon s MVP launch here on Product Hunt. Now, over 700 users have jumped in and turned their ideas into comics, and we re incredibly grateful for all the support and love from this community. Thank you!
For me, keeping track of the wins is a priority in general, but now as Lifetoon grows, I prioritize this even more - I don't want to let important moments slide. For a startup (and especially one that's at an MVP level), every step is worth celebrating.
What s that one task you always end up doing but really wish you didn t have to?
For me, it s the scrappy stuff like cold outreach or chasing feedback (and getting no reply). It's essential, but always pulls me away from deeper work.
After using a lot of AI-generated code lately, I've found myself spending a lot of hours on checking and repairing a lot of easy-to-spot security flaws. That being said, AI generally sucks at actually implementing secure code (or architectures), as well as recommending what to do to make your app more secure (sometimes even decently secure).
Have you had this problem as well? If yes, how do you tackle it?
