Jeremy Unruh

@dallasmcgroarty Good question, and yeah, it's a real one. What it does is pull competitors from an AI-generated category answer first, not just match on the brand name, then search for threads mentioning those specific competitors within relevant subreddits. That anchoring is what actually solves the generic-name problem, since it's not "find every mention of this word," it's "find threads where these specific named competitors show up."

Where it gets more interesting is smaller or newer categories where the competitor set itself is thinner. That's less a Reddit problem and more an upstream one, get the competitor list right and the Reddit matching follows pretty cleanly from there.

@canooqcan Appreciate it, thanks! Yeah, agreed, it is a huge source that companies need to be active on.

@whitelabels Appreciate it — yeah, that's basically the whole challenge, finding threads that are actually still open and active, not just ones that existed at some point. Thanks for checking it out!

@digitalobtain2022 Thank you for checking it out! Appreciate it!

@stornoxmarketing Thank you Viabhav, it's becoming a well documented necessity.

@salonistio Great to hear, let me know what you find when you run it on your own company!

@simplereceiptmaker Appreciate it, thanks for checking it out!

@finn_furriq Good question — right now it's weighted more toward engagement (upvotes, comment count, recency) than raw subreddit size. A small, tightly-focused subreddit with real back-and-forth in the thread tends to score higher than a huge general subreddit with one dead comment, since the point is finding threads where a genuine conversation is actually happening, not just where the audience is biggest.

Haven't found a clean way to weigh subreddit size itself yet, mostly because a 500K-member subreddit and a 5K-member one can both produce a genuinely relevant thread, and size alone doesn't predict whether AI models will actually pull from it.

👋 Founder here. Built this after hearing the same thing over and over from people: they know Reddit matters for AI visibility now, they just have no idea which threads are worth showing up in.

Two things I learned building it that surprised me:

Archived threads are a trap. Reddit auto-archives posts after 6 months and archived threads can't take new comments. The data source I was using snapshots posts right after creation, before archiving happens, so the "archived" flag came back false even for long-dead threads. Had to use post age as the real signal instead. Means fewer results sometimes, occasionally zero — but a thread you can't reply to isn't an opportunity.

Fake data is worse than no data. Early version filled in placeholder upvote and comment counts when real numbers weren't available. That quietly poisoned the ranking, threads were getting scored on numbers I'd made up. Ripped it out entirely.

Genuinely curious what people find running it on their own company. Happy to answer anything about how it works. 🙏

This is a really clean niche to focus on — 'career readiness at scale' for institutions is a different buyer than the typical individual-job-seeker resume tool crowd. Curious how you're handling the ATS-compatibility piece specifically: are you validating formatting against real applicant tracking systems, or is it more rules-based best practice? That's usually where these tools live or die for users who don't realize their resume's getting filtered before a human ever sees it.