I ve been hunting and playing around with AI agents for a while now, and while the progress is impressive, I keep running into things that make me think: Why can t it just do this already?
What s one thing you wish agents could do today that they just can t (yet)?
been thinking about this a lot lately. most marketing advice i read online asumes you can just post on twitter and get 1000 followers overnight. that's not how it works here in Bangladesh.
so i'm curious. if you're a maker from a smaller country or non-english market how did you actually find your first users? did you launh on product hunt, post in local commnities, cold email, something else?
When users cancel right after their trial ends, it's easy to panic and think "We're charging too much." So the instinct is to offer discounts or lower the price barrier.
But whenever we dug into exit surveys, price wasn't even the top complainti t was usually that they didn't reach the "aha!" moment fast enough to justify any price tag. Lowering the price wouldn't have saved them if they weren't getting value.
For founders who went through early monetization: how do you diagnose whether churn stems from pricing structure or a failure to communicate core value quickly?
There is something strange about how we build things. Whenever I spend weeks sketching out features, setting up fancy task boards, and thinking about growth strategies, the project usually dies within a few months. But when I just spend a Saturday night messing around with a fun code idea without worrying about the final outcome, it somehow finds a real audience.
I think when we stop overthinking and just create things for the pure fun of it, we end up solving real problems more naturally. How do you decide which random side ideas are worth shipping versus the ones you carefully plan on your roadmap?
Building an AI agent is one challenge, but knowing it's ready for production is a completely different one.
Traditional software can often be verified with unit tests and integration tests, but AI agents introduce additional complexity. They rely on reasoning, external tools, changing context, and non-deterministic model outputs, which makes testing much less straightforward.
I'm curious how other teams approach this before deploying AI agents to real users.
For the longest time my weekly dopamine hit was watching the signups counter tick up. I thought traction meant volume.
Then we started tracking 7-day and 30-day active usage and the reality check was brutal. We were pouring water into a leaky bucket, celebrating every new registration while users slipped away silently within 48 hours.
For founders who survived this transition: what specific metrics or milestones forced you to stop celebrating signups and actually fix your activation loop?
A while back, I threw together a tiny script in a couple of hours just to fix a small personal annoyance. No long-term plan, no roadmap. Years later, it s still running, and I still get occasional emails from strangers using it. Meanwhile, the bigger startup ideas I spent months planning with actual business models ended up going nowhere. It taught me that you really can't guess what will resonate until you just put stuff out there. Have you ever had a tiny weekend build outlive your actual planned projects?
A while back I noticed a steady stream of bug reports coming in every week. My first instinct was to blame tech debt and start rewriting logic. But when I took a step back and walked through the exact screens users were complaining about I realized they were just guessing how the feature worked because our error messages were useless. We spent a few days fixing the empty states and making the onboarding clearer and the complaints instantly dropped. It made me realize how easy it is to blame the code when the real issue is just poor design communication.
Do you track why users get stuck or do you jump straight into code fixes?
Early on, I thought customer support requests were basically my product roadmap. Whenever a few users asked for a custom toggle or a specific export option, I d rush to build it thinking it would boost retention.
Fast forward a few months, and the UI felt bloated, new users were confused, and nobody was even using those extra features we spent weeks coding. It turned out the initial request was just a surface-level fix for a deeper problem they couldn't clearly explain. Now, before adding anything new, I force myself to ask "why" three times until we find the actual gap.
For those managing an active user base, what's your personal rule of thumb for deciding if a request is a real product gap or just a one-off request from a noisy user?
