I have automated a lot over the years, invoicing, scheduling, reporting, most of it now runs itself. But there is always one thing left that I still check manually, every single day, because the one time I trusted a tool to catch it, it did not. For me it is new leads going quiet after the first reply. I open that list myself every morning instead of trusting a dashboard to flag it. Curious what that one thing is for you, and whether you have ever found something that actually earned your trust enough to stop checking it by hand.
Not a vacation with your laptop open, an actual week off. I tried this last year and found out fast which parts of the business only worked because I was quietly patching them every day without noticing. For me it was follow ups, nothing dramatic broke, a few leads just went cold because nobody but me knew they were waiting on a reply. Curious what it would be for you, and whether you already know the answer or would have to find out the hard way like I did.
I went back through my own numbers expecting the writing to explain the gap. It doesn't.
Same account, same week, roughly the same effort on each. The one that hit 221 is not a better comment than the one that got 9. I reread them. If anything the 9 is tighter.
A confident wrong answer is obvious. A plausible answer assembled from stale, duplicated, or weak sources is harder to catch.
For research workflows, I care less about a giant stack of models than about source identity, retrieval time, exact transformations, and a way to reproduce the result. If the evidence trail vanishes, the polished summary is mostly decoration.
What provenance do you preserve in your AI research workflow today?
A launch can tell you whether a message earns attention. It cannot tell you whether the product becomes useful enough to return to. Those are different tests, and mixing them produces very flattering dashboards.
I would rather trace a small cohort from promise to first value to an unprompted return than celebrate a large spike that disappears by Friday.
What is the first post-launch behavior you treat as real evidence?
Shipping a feature creates a weird emotional tax. The code exists, the screenshots look good, and suddenly sunk cost starts impersonating customer value.
The strongest deletion evidence I have seen is not a low click count by itself. It is watching the feature add decisions, setup, or confusion without improving the user s actual outcome. Sometimes the cleanest roadmap move is subtraction.
What convinced you to remove something you had already invested in?
Building a browser extension for fashion shoppers taught me something I didn't expect: the tech isn't the hard part.
I'm a solo founder working on a shopping tool. My target users are women aged 15-35 who want to look good and save money. They're not on Product Hunt. They don't hang out in founder communities. They discover products through TikTok hauls and Instagram reels.
Someone writes a text, and it's encoded by their writing style, knowledge, thinking patterns, and so on. Then I need to read it through, decode it, and understand if it was worth the effort.
I ve been thinking a lot about the gap between having ideas and actually turning them into consistent content.
While building an AI content tool for creators, I noticed something interesting: a lot of people don t seem stuck because they lack creativity. They re stuck because they don t have a clear content direction.
They may have interests, personality, skills, and stories but they don t know how to turn that into a niche, a content plan, or repeatable posts.
For founders building creator tools, marketing tools, or AI products:
