Hey Product Hunt community,

With hundreds of products launching every single day, I ve been trying to understand the underlying architecture of homepage visibility and traffic distribution.

As a maker, when you launch a product, you often notice it doesn't always appear directly on the main featured feed for every visitor or on every device/browser.

This got me curious about how the platform balances fairness vs. algorithmic curation:

Many people who want to achieve the Product of the Day badge prefer to schedule their product for a less busy day (aka, when there is less competition), but also less site traffic and product testing.

You may lose a little promotional opportunity this way.

Launch day gets all the attention. The day after is the part nobody writes about, and it's the part I'm least prepared for.

The upvotes stop, the notifications dry up, and you're left with a product, some new signups, and a very quiet inbox. The temptation is to immediately start planning the next launch. I think that's a mistake the week after is when you actually learn whether the launch meant anything.

What I'm planning to do with those seven days: personally email every signup from launch day, ask one question ("what made you click?"), and read the answers before touching the product again. No new features that week. Just listening.

For people who've launched: what did you actually do in that week, and did it change what you built next? I'm especially interested in anyone who ignored the post-launch week and regretted it.

When we first put multi-step AI agents to work handling backend customer queries our biggest surprise wasn't getting wrong answers. It was loops. An agent got stuck trying to fix a syntax error in a SQL query running the failing command thirty times in two minutes.

The fix wasn't changing the way we wrote prompts. We added a limit, on how many times something could run and a way to stop everything. Now if any agent makes the mistake twice the system stops running and sends the problem to a Slack channel for a person to check.

This simple rule cut our cloud computing costs by forty percent in the month. Better it stopped users from waiting ten minutes for a request that would never finish.

If you use loops don't trust the model to know when to stop. Put limits outside the LLM part of the system.

I've starred probably two hundred repos and I honestly couldn't tell you my own criteria. So I'm trying to reverse-engineer it, because I'm about to open source something and I want to understand what actually earns a star versus what just gets a polite look.

My suspicion is that it's rarely the code. It's usually one of three things: the README answers my exact question in the first screen, the demo shows the thing working in under a minute, or I saw the maintainer answer a hard issue thoughtfully and decided the project was alive.

The third one is the one I think people underestimate. A star is often a bet that the project will still be maintained in a year.

So: what actually made you star the last repo you starred and have you ever un-starred something? If so, why?

Today, I m doing a slightly more relaxed and bizarre corner.

The internet is full of things that are either amusing or scary, but mostly things that capture something outside the norm (and over time, even these weird things tend to become normalised).

After Spotify celebrated its 20th anniversary with a new logo (the Spotify disco ball), it was criticised by many people, and dozens of other brands followed suit, also making a disco logo as a form of recession.

I don't think it was that bad. I'm not a graphic designer, and I can see that the contrast there is not good, but I still think there are many worse designs and rebrands.

Last week, a customer paying our highest tier threatened to churn unless we built a custom reporting dashboard that has nothing to do with our core workflow.

Saying no felt terrifying because of the MRR at stake. But saying yes would turn us into a bloated agency instead of a focused product.

for founders past the early stages: where do you draw the line between keeping enterprise revenue happy and protecting your product roadmap?

At peak we were paying for 14 SaaS tools across a team of 8. Some of them had a single user.

We did an audit: who used what, how often, what would break if it disappeared tomorrow.