I keep running into this while building.

There s always another feature that sounds useful on its own.

Another setting.
Another metric.
Another automation.
Another smart layer.

But at some point, the product starts asking the user to understand the system instead of the system understanding the user.

One habit I am trying to build: for any irreversible product decision, write down the constraint it answered, not only the decision itself.

Months later, the code may remain while the reason has vanished. That makes revisiting it far more expensive.

What lightweight practice has genuinely helped you preserve decision context as your product evolves?

Yesterday, a report circulated on X-com about how an Australian entrepreneur used data and AI to design a custom mRNA cancer vaccine from scratch.

So what does this mean for us?

AI video is becoming good enough that I m starting to think its most interesting use case may not be replacing traditional production.

It may be prototyping it.

I recently created a short fashion/editorial concept with Seedance 2.5.

I've noticed a shift in how I work over the past year, and I'm not 100% sure it's a good one.

A year ago, I'd use AI to speed up execution like writing boilerplate code, drafting an email or summarizing a doc. The thinking was still mine. Now, more and more often, I catch myself asking AI to decide things too: which architecture to pick, how to frame a pricing page, even how to respond to a tricky customer email. The line between "helping me think" and "thinking for me" has gotten blurry.

Here's what worries me a bit: the moments where I learn the most are usually the moments where I struggle first. If AI removes the struggle, does it also remove the learning? Or is that just an old-fashioned way of looking at it - the same way people worried calculators would ruin math skills, and mostly they didn't?

Do you think AI has made you better at thinking, or just faster at producing? And any moment where relying on AI backfired because you skipped the "struggle" step?

Hey, Product Hunt!

I've been a software engineer for almost 8 years (started in Android, then moved to .NET backend). Lately, I've been building my own SaaS project.

Let me start from where i'm sitting: i talk to founders, hiring managers, and recruiters every week, and the same conversation keeps coming up. nobody trusts their pipeline anymor

Application volumes have doubled since 2021. Cheating on technical assessments doubled in just six months last year, from 15% to 35% of candidates. One in three hiring managers has personally caught a fake identity in an interview. @Anthropic had to rewrite their interview questions after so many people cheated during the interview with Claude.

I built a benchmark that runs nine WordPress backup plugins head to head on identical sites. One of the nine is mine, and it currently sits second. That is an obviously compromised position, so I have spent more time on what makes a vendor-run benchmark believable than on the measuring.

Three things I landed on, and I am not sure any of them are enough.

Publish the runs you threw away. 581 completed runs, 180 scored, 401 excluded, and every excluded one is browsable with its reason attached. Most are boring reruns rather than embarrassing failures, and saying so is part of the point: 360 superseded, 41 excluded for cause. The honest number includes the boring ones.

Say when a lead is not a lead. Mine sits five points above third place with plus or minus 113 error bars. That is a tie, and the page says tie rather than letting me quote the higher number.