We spent weeks perfecting our product's core engine, assuming users would stick around once they saw what it could do. But our dashboard analytics told a harsh story: most signups dropped off right at the empty-state screen before hitting the "Aha!" moment. we replaced the blank dashboard with pre-filled sample templates and a 2-step setup. Activation rates jumped overnight not because the product got better, but because time-to-value got shorter.

Is onboarding friction usually a product design issue, or a failure to set expectations before the user even signs up?

I recently started building a tool for content creators and startup founders who want to stay consistent on Threads. I ve realized how hard it is to promote something when you re just starting and don't have an audience yet. If you don t have a big following or a budget, what worked for you?

• Did you focus on one platform at a time?

• Did you document your process?

• Did you post daily even if no one was watching?

Curious to hear how others approached early stage promotion. Right now I m experimenting with scheduling, batching, and smart tagging to stay organized while posting on Threads consistently but I want to learn what s working for others too.

Put up a simple feature list for my app a while back and users loved seeing what was coming. But the second a feature got delayed, people started emailing to check up on dates.

Nobody was mean about it but having strict timelines out in the open created way more stress than I expected. switching to a simple now/next/later setup helped a lot.

How do you guys share future plans without making yourselves feel bound to specific deadlines?

Almost every agent memory system I see is built to remember more and find it faster. Nothing in any of them knows how to stop believing something. A note that says "we tried X, it did not work" is written once and then never expires. Nothing in the system changes on the day it stops being true. It just sits there, gets retrieved, and reads as more authoritative every time it survives another run. A system that only accumulates gets more confident and less correct at the same time, which is a genuinely nasty combination because the confidence is the part people can see. There is a smaller version of this that shows up constantly. When an agent runs out of context mid task, everyone reaches for a handoff note, and handoff notes work strangely well. Worth asking why, because the agent had far more information available than the note contains. They work because they are lossy in the right direction. A note keeps the decision and throws away the reasoning that produced it. The raw context does the opposite: it keeps everything, including the three wrong turns that got corrected later, and restoring it restores the confusion along with the conclusion. So decisions and context are two different objects and most memory work treats them as one. A decision is "we went with X over Z because Y". Context is the forty thousand tokens of finding that out. Only one of those is worth carrying forward, and it is not the big one. What I actually want is a memory entry that carries the conditions under which it was true, so it can be invalidated by the world changing rather than by someone remembering to go and delete it. Nobody is asking a knowledge base to forget. Curious whether anyone here has shipped expiry properly, and what triggered it. Time, contradiction, or someone noticing by hand?

Between Samsung's engineers leaking their own source code into ChatGPT, DeepSeek leaving a database of a million chat logs sitting open with no login required, and Grok's share links somehow getting indexed on Google with 370k private conversations searchable, I've basically stopped believing "trust us" is a real answer from any frontier lab.

None of these were sketchy no-name startups either. These are the companies everyone's building on top of right now.

Hey Makers!

Handing over repo access to autonomous coding agents is awesome, but honestly, it's also a great way to break things if you aren't careful.

My team recently set up a strict "Zero Trust" baseline to keep things under control. Here are the 5 rules we actually enforce right now:

1. Bound the blast radius before the first run

2. Assume it reads everything it can reach

3. Treat everything the agent reads as a potential instruction

4. Verify like it's a stranger's pull request

5. Be ready to do forensics

Hey Product Hunt community!

As a serial maker, my biggest challenge has never been "what to build" it s deciding "what NOT to build."

Right now, I have over 50+ software ideas sitting in my backlog (from AI tools like ManyGPT and HeyAxo to specialized funnel builders).

When you have a long list of ideas, the temptation to jump straight into coding is real. But last year, I wasted weeks building a product that no one actually needed because I skipped validation.

I keep coming back to the same realization when I talk to founders running more than one business or location. Nothing dramatic causes the miss. A lead sits three days longer than it should. A vendor renewal gets noticed the day it charges, not the month before. A client goes quiet and nobody flags it until the account cancels.

None of these look like a failure at the time. They just sit in the gap between two people, or between a system and a person who was too busy that week to check it.

Over the past few months, the concept of vibecoding building products almost purely by guiding AI agents, prompt-driven iterations, and high-level system architecture has picked up massive momentum.

For those of you actively shipping side projects or production tools using Cursor, Replit Agent, or Claude CLI, how has this shifted your actual workflow? Are you finding that you spend more time on system architecture, or are you hitting walls with code maintenance down the line?

Would love to hear real experiences, tech stacks, and takeaways from builders here!

When a team revises a recurring process, the new version usually makes the next run clearer. But it can make last month s records hard to read: a question has been renamed, split into two, or removed altogether.

I ve found that treating history as something to preserve, not merely migrate, makes later reviews more honest. It forces you to distinguish what was observed then from how you would ask it now.