We spent three weeks writing granular permissions allowing team admins to assign custom read, write, and edit access for every single module. When we analyzed usage two months later, over 95% of workspaces were just using standard default roles.
Early-stage software needs sensible defaults, not complex configuration settings that slow down onboarding.
It's so easy to get high on the dopamine of seeing signup numbers tick up every Monday morning, even when most of those users never log back in after day three. We hit a wall when we realized our massive growth spike was just filling a bucket with a hole in the bottom. Getting the team to stop caring about vanity metrics and actually fix the onboarding friction took a real mindset shift. If you've been through this, what metrics finally forced your team to wake up and focus on actual usage instead of just new signups?
We talk a lot about AI automating work, but setting up AI products still requires a surprising amount of manual effort.
Take an AI customer support agent. You need to connect integrations, configure instructions, define business rules, set permissions, test different scenarios, and keep everything updated.
I have started noticing a pattern with AI coding tools: the first version of a feature looks finished long before it actually is.
Month I asked an AI assistant to add CSV export to a small internal tool. The happy path worked perfectly the UI looked clean. The generated tests passed.
Then someone uploaded a CSV with a column and a few unusual characters. The export technically completed,. The resulting file was unusable.
What changed for me was stopping at "the feature works". Asking, "What inputs would make this fail?"
As a video editor, I ve been experimenting with AI tools quite a bit lately. They re becoming useful for generating ideas, creating rough visuals, and speeding up some repetitive tasks.
But I ve noticed something interesting: making a video faster doesn t necessarily make it better.
A generated shot might look impressive on its own, but getting it to fit the story, match the visual style, and feel natural alongside other footage still takes a lot of creative work.
For me, AI works best when it helps with the process rather than trying to replace the creative decisions behind it.
I've spent the last 1.5 years deploying AI voice and chat agents for banks, insurers, car dealers and online stores. I'll be here all day answering questions.
Ask me about anything: getting through enterprise security reviews, what breaks when an agent writes to a CRM, how we handle human handoffs, pricing, or what goes wrong in a first pilot.
No question is too basic. What would you like to know?
Hey! I'm Yas, solo founder of Semita an AI travel planner that generates a full day-by-day itinerary in under 60 seconds.
A few things I learned building and launching solo:
Distribution is harder than building the product took months, getting people to see it is a whole different challenge
User feedback surprises you I assumed collaboration would be the most popular feature but Hidden Gems mode got way more attention than I expected. Goes to show that what you think users want and what they actually care about can be completely different
Plan your launch day better than I did it is chaotic trying to make sure everything works while also getting posts out everywhere. I got so caught up checking the product that I missed the time and lost the first few hours of momentum. Lesson learned schedule your posts in advance
Getting people to respond is hard I personally emailed early signups hoping for feedback and heard nothing back. Apparently that is normal but it is still a humbling reminder that getting people to engage takes way more effort than you expect
Would love to connect with other founders what was your biggest lesson from your own launch?
