AI is growing faster than ever, and honestly, it feels like we re riding a giant wave of creativity and innovation!
From generative art to powerful language models, automation at work, and AI-powered healthcare breakthroughs it s amazing how much is happening.
But here s the cool part: different things spark excitement for different people. Maybe it s a tool that made a tough job easier. Maybe it s a crazy new concept that opened your mind to fresh possibilities. Maybe it s just that "OMG" feeling you get when you see AI pull off something wild (like my Auntie, who literally gasped the first time she saw ChatGPT could calculate calories of her plates! ).

I'd love to hear what s making you most excited about AI right now! Big or small moments, drop them below and let's discuss together.

When users cancel, they almost always check the box saying "too expensive" in the exit survey. But when we actually dug into their usage logs most of them hadn't even logged into the app twice.

Pricing wasn't the barrier; they just didn't have the actual problem our tool was built to solve. People use price as an easy excuse when they don't see the value.

When your churn goes up how do you figure out if people are actually leaving because of money or because they aren't getting the right use out of it?

We used to think the safest rule for AI permissions was letting agents execute any task that could be easily undone in the database.

But we learned the hard way that database rollbacks don't fix customer relationships like when an agent automatically sends an incorrect status update or marks a feature request as "planned" without human context. Once the customer sees it the damage is already done.

How does your team handle permission boundaries for agents handling live product decisions?

I've tried a few online venues but none of them worked. If you met with your co-founder(s) online, where was it?

On the whole, I'm interested in education and career advising. Thanks!

There s a false sense of security in seeing signups tick upward when your actual core product usage stays completely flat.

We used to celebrate traffic spikes from launch directories only to find out those users vanished within 48 hours without touching a single key feature.

Chasing volume instead of actual activation was burning our engineering time on the wrong problems.

If you've run into this trap how did you restructure your core metrics to measure real engagement instead of empty vanity numbers?

There s a small difference in how you ask an AI to save something, and it changes what gets saved.
Save this for next time. The AI writes a message to the next AI, and it comes out as instructions:
Next session: confirm the hotel before booking trains. Do not change the Paris dates. Remind the user about the deposit.
Save what we settled. The AI writes down facts:
Paris is three nights. Trains between cities; a rental car was dropped because of city parking. The hotel deposit is due May 1.
The second note stays true after the conversation ends. A month later, it still says where things stand and why.
The first is a set of instructions from a moment that has already passed. A new conversation reads them and follows them, even after the plans have changed.
I saw this in my own records.
Notes written as instructions carried an earlier stage into new conversations. Notes written as facts read like a record: what was decided, and why.
Now when I save, I ask for what settled.
Have you noticed how your AI writes the notes it saves? I m curious what others have seen.

There is a huge difference between a user explaining their pain point and a user telling you how to redesign your app.

We spent months chasing one-off requests from non-paying or low-tier users completely derailing our core vision and confusing our active user base.

Now we never build a solution until at least five separate clients report the exact same underlying friction point.

How does your team separate actionable feedback from isolated user noise?

We fell into the trap early on of treating every angry feature request like a top priority, only to watch those exact users churn right after we built what they asked for.

We realized we were wasting weeks of engineering time building custom workarounds for individual edge cases instead of solving core issues that actually scale. What quick filter or rule do you use before agreeing to build a user-requested feature?

There is a massive difference between listening to user feedback and letting paying customers steer your entire product roadmap.

We learned the hard way that most custom requests are just poorly explained band aids for a workflow problem they haven't figured out yet. If you reject their feature ideas how do you keep the relationship strong and show them value elsewhere?