My dashboard is beautiful. Signups, activation, weekly actives, retention curves, the whole thing. And I've started to suspect it's telling me a story I want to hear.

I chose the definitions. I chose the date range. I chose which cohorts to compare. Every one of those choices was defensible on its own, and together they might add up to a portrait of a business that doesn't exist.

Dashboards, analytics, CRMs, most of what I use is great at reporting on the past. Very few actually tell me what needs doing today. Anyone found or built something that closes that gap?

It's funny how smart these models are, yet they will completely ignore the clean patterns right in front of them and mimic some weird workaround I wrote in a utility file two years ago.

The more context or files I attach the more it hallucinates random functions that don't even make sense for what I'm building today.

I'm starting to think manual context curation is the only way to keep the code clean. How do you feed your tools the right files without overwhelming them with noise?

It's honestly hilarious and frustrating at the same time. I asked my coding assistant to fix a tiny typo in a button text yesterday and it came back with a brand new component architecture split files and a custom state hook. It felt like dealing with an overly enthusiastic junior developer high on espresso who just wants to show off everything they can do. I just wanted a one line string change!

Have you found a reliable way to keep these agents lazy and focused strictly on the exact line you point to?

I ran into this exact headache while pulling company details from random websites.

Some pages have their logo hidden in weird spots or completely skip a clear description, leaving my script scrambling. At first, I tried forcing smart guesses just so the UI wouldn't look empty but it backfired when the tool confidently displayed the wrong info to users.

Now I'd rather just leave a clean "not found" or let the user fill it in manually than show fake confidence. How do you handle UI states when your scraper comes back empty handed?

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?

I let an agent auto-generate unit tests for a complex module. It created 15 test cases, all green! But when I actually looked at the assertions, it was mocking everything into oblivion and testing getters and setters. How do you prompt agents to write rigorous, edge-case-heavy tests instead of vanity metrics?

Launching on Product Hunt is no small feat it s a thrilling combination of strategy, creativity, and resilience. As I was reviewing some examples of launches on the platform in March 2025, I couldn t help but feel inspired by the determination of every team and individual who puts their product out there.

I deeply admire everyone who chooses to launch on Product Hunt it s not just about creating a great product, but also about having the courage to present it to the world. I respect those who make it to the top because I know firsthand the amount of work and preparation that goes into it. And I have even more admiration for those who don t give up after setbacks.