Early on, you want to please every single paying customer especially the ones paying top dollar. But I realized that building custom workflows just for one loud client was turning our clean software into a messy patched up tool nobody else wanted.

Saying "no" to big money is genuinely terrifying when you're trying to keep the lights on but holding your ground is the only way to build something scalable.

Have you ever turned down custom work because it pulled you away from your core vision?

I know it sounds almost cringe,

but I recently came across an article describing how someone used Claude Code to access robot vacuum devices across 24 countries and potentially observe their environments.

I used to think that giving an AI assistant access to every single file in my project folder was the ultimate cheat code. But lately I ve noticed the exact opposite happening.

When it sees old legacy code from three years ago alongside my clean new components, it gets confused and starts copying the bad habits of the codebase. Sometimes a smaller tightly controlled prompt works way better than letting it scan everything.

Have you started restricting your coding assistant's file access, or do you still feed it the whole repo?

I m curious how other makers handle recurring payments across work and personal life.

Do you audit them monthly, use a bank alert, keep a spreadsheet, or mostly notice only when a renewal hits?

I ve found the hard part isn t a single expensive tool; it s the small services that keep renewing after you stop using them. What system has actually worked for you?

It s fun setting up AI agents to handle routine backend tasks until you realize how easily a weird input can mess things up.

Even with good guardrails giving an agent direct write access to a live database always feels like playing with fire. I've started forcing a manual approval step for anything that modifies or deletes existing records no matter how confident the script claims it is.

Where do you personally draw the line do you let agents touch production data automatically or do you keep a human check on every single write?

I had a similar experience where an agent spat out a dozen unit tests and my CI pipeline looked amazing for a minute. Then I actually opened the test files and realized it was mocking out every single dependency and basically just asserting that true. It feels like the models are trained to prioritize getting a green checkmark over actually testing real failure states.

Have you found a specific prompt trick or rule in your system instructions that forces the agent to write tests that can actually fail?

What s the most ridiculous thing you ve blown startup money on?

Everyone has that regret: fancy office chair, logo redesign, too many SaaS subscriptions. What was yours?

Parsing messy public web data is honestly harder than building the actual core feature.

You think a website's meta tags will have standard company info and then you find sites that put random blog text in the title tag. If your tool makes a wild guess and gets it wrong the user instantly loses trust in the whole product. I've started marking scraped fields as "needs review" whenever the confidence score drops below a certain point.

Have you found a reliable way to clean up dirty data before it hits the user interface?

Product Hunt unveiled a list of 18 dev-first products nominated for the Golden Kitty Award 2023 in the Developer Tools category.
I analyzed the following data to identify some trends:

• launch dates;

• # upvotes;

• # comments;

• awards.

Here s what I found.

# tl,dr