Attribution problem I haven't solved, for anyone running a try-before-signup flow.

Last week I took the account requirement off the front of my product. You can now use the main thing without signing up; the account is only needed to export what you made. It was clearly right for the product nobody understood it from a description, and they understood it immediately once they'd used it on something of their own.

My measurement fell apart the same week.

• Top of funnel widened, conversion rate collapsed, and neither number is comparable to the month before. I don't have a trend line any more, I have two unrelated datasets.

• The compute is real, and it's spent on people I can't identify, can't email and can't count. Costs moved before any of the good numbers did.

• Worst one: somebody tries it anonymously, leaves, comes back four days later and signs up. In analytics that's a cold direct signup. Whatever actually did the work gets credited with nothing.

Hey everyone,
I m currently building a small SaaS product, and I started thinking seriously about monitoring.
Most tools (Datadog, New Relic, etc.) feel built for larger teams.
Powerful, yes but also complex and expensive.
So I m curious:
What do you actually monitor in your small or solo SaaS?
Do you track uptime only?
Do you track latency?
Do you rely on logs?
At what point does monitoring start feeling like overkill?
I m trying to understand what is truly essential vs. what is just enterprise noise .
Not selling anything just genuinely curious how other indie builders approach this.
Would love to hear your setups.

I decided to spend an entire Sunday finding free or cheaper alternatives to my current app subscriptions, mostly by digging through Product Hunt and old bookmarks. I made a list, set aside the day, and went through my tools one by one.
The task manager was the easiest swap. I found a free one in about ten minutes that does everything I need, plus a few extra features I did not expect. The password manager was much harder. I tried two free options, but both felt too clunky to stick with, so I kept my paid plan. The cloud storage swap was an absolute disaster. I spent two hours migrating files before realizing the free tier had a file size limit that made it useless for my work.
By the end of the day, I had only canceled two out of seven subscriptions, and I was completely exhausted. The main takeaway was learning which tools are actually worth paying for versus which ones I kept simply out of laziness.
Hunting for free alternatives often ends up trading time and frustration just to save a few bucks. Have you ever tried a subscription purge like this? Did you stick with the free tools, or did you end up going right back to what you were paying for?

I keep going back and forth on this, so I m curious how others think about it.

At what point do you start taking non-English markets seriously?

• only after you feel solid PMF in English?

• when inbound users from certain regions show up?

• by picking one market early (Japan, LatAm, etc.) and committing?

• or do you just keep pushing it off to stay focused?

I don't understand why I need a tutorial to use a website.

I wanted to change one thing.

One.

Clicked around for a while, opened 3 menus, got a popup, accidentally went to another page, came back...

I've been using Claude Code heavily for the past few months while building my startup (12k+ line Python codebase), and it's genuinely changed the way I develop. It navigates large codebases with context that I haven't seen from any other tool: understanding file relationships, catching upstream/downstream effects of changes, and reasoning about architecture-level decisions, not just autocompleting lines.

But what really caught my attention is the business side. Anthropic is forecasting cash-flow positive by 2027, while OpenAI is projected to burn through $115B+ through 2029. Claude Code alone reportedly crossed $1B in annualized revenue. Their API revenue is estimated at double OpenAI's. And they're doing all of this with significantly lower burn.

Autonomous AI agents can already browse websites, call APIs, use external tools, and complete tasks with limited human involvement.

Payments are still a major interruption. When an agent encounters paid data, an API limit, or premium content, it usually has to stop and ask a person to complete the transaction.

I m curious how makers think this should work in practice.

Should an agent:

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)?

While building an AI coach, we ran into an interesting problem: memory is useful, but some things shouldn t just be remembered - they should become rules.

If someone says, avoid overhead pressing, asking for a harder workout later shouldn t erase that constraint.

It made me wonder: what information in your AI product should survive every follow-up, no matter what?

I'm preparing for my first Product Hunt launch, and I've been trying to understand what actually makes the difference between a launch that gets some attention and one that reaches the top of the leaderboard.

There is a lot of advice online about launching on Product Hunt: building an audience beforehand, getting early supporters, posting at the right time, engaging with the community, getting comments, sharing on social media, etc.

But I'd rather hear from people who have actually done it.