I built a small AI tool to analyze YouTube titles — looking for honest feedback from creators & builders
Hey Indie Hackers 👋
I’m a solo builder and YouTube enthusiast. Over the last few weeks I’ve been working on a small side project to solve a problem I kept running into myself:
Before publishing a YouTube video, it’s hard to know if a title is actually good — or if you’re just guessing.
So I built a lightweight tool that:
Analyzes a YouTube title
Scores it for performance potential
Breaks down SEO vs CTR vs clarity
Suggests improved title variations ranked by strength
It’s still early, free to use, and very much in learning mode.
I’m not trying to sell anything right now — I’m genuinely looking for feedback on:
Whether this is actually useful
What feels confusing or unnecessary
What you’d expect from a tool like this before trusting it
Here’s the link if you want to try it:
👉 https://gethooksy.online
If you’ve built creator tools, analytics tools, or AI utilities before, I’d especially love to hear:
How you validated usefulness early on
What signals told you “this is worth continuing”
Thanks for reading, and appreciate any honest feedback
I’d start general but explainable, then add optional niche profiles once you’ve got enough real usage to validate them. A simple path could be: user picks niche + goal (browse vs search) → keep the same transparent heuristics but reweight. That keeps trust while you gather data for true niche conditioning.
This is a nice wedge — “title QA” is a real pain because you’re always guessing.
If you’re looking to validate usefulness early, two things I’d want as a creator/builder:
For trust: I’d show (even roughly) what the model is optimizing for (CTR vs search intent) and add a disclaimer that it’s directional, not a guarantee.
How are you thinking about grounding the suggestions — training on high-performing titles in a niche, or using general heuristics + LLM rewriting?
This is super helpful — and honestly aligned with how I’ve been thinking about it.
The “black box score” problem is real. That’s why I’m trying to always pair the score with explicit reasoning like:
“Too long for mobile truncation”
“Low specificity — lacks concrete outcome”
“Keyword not front-loaded”
“No curiosity gap or tension”
So it feels more like QA than magic.
I also agree comparison mode is probably the real wedge. Ranking 3–5 candidate titles side-by-side (with explanation) feels much more actionable than scoring one in isolation. That’s something I’m actively iterating on — especially for creators who batch-produce and test multiple angles.
On grounding:
Right now it’s mostly structured heuristics (mobile length, clarity, specificity, keyword positioning, tension patterns) combined with LLM-assisted rewriting.
I’m not training on niche-specific performance data yet — mostly because I don’t want to imply predictive certainty I can’t back up. My goal is to keep it directional and transparent rather than claim “this will get you 2x CTR.”
Long term, niche conditioning + channel-size context is interesting — but I’d only add that once I have enough usage data to avoid it being cosmetic.
Curious — if you were building this, would you lean more into niche-specific optimization or keep it general but explainable?
This is a great “builder-to-creator” problem — the hard part is distribution, not the model.
Two tactical suggestions:
If you want, I can put together a quick discovery brief: which communities/threads are highest-signal for your niche, the language creators use, and a short engagement playbook.
Really appreciate this — and I agree 100%.
The model is the easy part now. Distribution and positioning are the real game.
I’ve started testing exactly what you described — manually doing mini title teardowns in creator threads instead of just dropping links. It’s been interesting how often the language is emotional (“my title sucks”, “no one is clicking”, “CTR is dead”) rather than technical.
Your second point about mirroring real creator phrases on the landing page is 🔥. I’ve been writing more feature-focused copy, but the pain-language angle probably converts better.
And yes — I’d absolutely be interested in that discovery brief.
If you’re open to it, I’d love to see:
Which communities you think are highest-signal
What recurring title problems show up most
Whether this skews more beginner-creator or agency-level
Distribution is the part I want to get right early instead of just shipping features.
Thanks again — this kind of tactical feedback is gold.