LikePulse - See exactly where YouTube audiences react — instantly
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LikePulse analyzes YouTube comments in real time to show you the exact moments where audiences peaked.
Open any video and get:
• Engagement heatmap — comment spikes overlaid with Most Replayed data
• Key moments — the exact timestamps with the highest audience reaction
• AI analysis — Claude Haiku explains why each moment resonated
• Product detection — AI finds Amazon products mentioned in the video
Free. No account. No tracking. Works on any YouTube video instantly.
Replies
LikePulse
@adriancubas Congrats on the launch Adrian. I could see this being big with creator/brand partnerships.
LikePulse
@zolani_matebese Thanks so much! Really appreciate the support — it means a lot on launch day. Absolutely — that’s one of the directions I’m most excited about. Creators and brands both want clearer insight into what actually resonates, and LikePulse can surface those moments instantly.
@adriancubas
Thanks Adrián — will absolutely run it on a few of mine and report back. The pacing-vs-rewatch gap is exactly what I want to test: I have a hunch the spots where viewers comment are where the editorial sequence breaks expectations, while the rewatches concentrate where I deliver something concretely useful. Different feedback signals, different edits. Looking forward to seeing the heatmap.
this is great, looking forward to trying it out. the why is what most youtube analytics tools bet on! what does claude haiku get fed for the "why this resonated" explanation? comment text only, transcript at the timestamp, visual frame, or all three? congrats on the launch!
LikePulse
@hiyamojo Hi Keith,You actually decoded it perfectly — that’s exactly how it works. All 3 !
Best,
Adrian
Really like the framing of overlaying comment spikes with Most Replayed — the two signals say different things and the gap between them is where the interesting stuff lives. I run the Mod3Loop YouTube channel on financial modeling, and the moments people comment on are almost never the ones I'd predict from watch retention alone. Tools that surface that mismatch quickly would change how I edit. Following.
LikePulse
@samir_asadov Love this — and you articulated the core idea perfectly. Comment spikes and Most Replayed really are two different signals, and the gap between them is where the real editorial insight lives.
What you described about your channel is exactly the pattern I kept seeing: the moments people talk about are often not the moments they rewatch. Surfacing that mismatch instantly is what I built LikePulse for, because it changes how creators think about pacing, clarity, and emotional beats.
Really appreciate you following along — would love to hear what you discover if you try it on one of your videos.
knowing WHERE in the video people react is way more useful than just total view count. this is basically a free focus group for every video you publish
LikePulse
@tina_chhabra Exactly — that’s the whole point. Total view count tells you if people showed up, but knowing where they react tells you why the video works. When you can see those reaction spikes in context, it becomes a free focus group baked into every upload. That’s the kind of feedback loop creators rarely get, and it’s what I’m trying to make effortless with LikePulse.
the product detection feature is the one that feels slightly out of place with the rest. heatmaps and key moments are clearly for creators and researchers. amazon product detection feels like a different user entirely, affiliate marketers or brand analysts. are those actually the same person in your head or did that feature come from a different use case you're testing. curious because it changes who you're building for pretty significantly
LikePulse
@ansari_adin That’s a great point — and here’s why I still think product detection can add value for creators and researchers. A lot of high‑engagement moments on YouTube revolve around specific products: tech reviews, unboxings, tutorials, “Amazon favorites,” beauty routines, etc. When a spike in reactions is tied to a product, identifying it helps explain why that moment resonated.
For creators, it highlights what their audience is reacting to. For researchers, it adds context to emotional peaks. So while the feature came from a different experiment, it actually complements the core insight workflow more than it seems at first glance.
Stripo.email
Really interesting concept. Most analytics tools show views and retention, but LikePulse focuses on audience reaction moments, which feels much more actionable for creators and marketers. The engagement heatmap combined with AI explanations is a very smart touch. Congrats on the Product Hunt launch
LikePulse
@alina_tyslenok_ Hi Alina,
Thank you so much for the thoughtful feedback! I’m really glad you found the concept interesting — we wanted to make analytics feel more human and actionable, so hearing that it resonates means a lot.
Appreciate your kind words and support!
Best,
Adrian
Overlaying comment spikes against YouTube's own Most Replayed data is the smart move — those two signals don't always agree, and the gaps are probably the interesting part. When a moment is heavily Most-Replayed but the comments are quiet, or vice versa, does LikePulse surface that divergence, or just show both tracks and leave me to eyeball it? The disagreement is where the real insight usually hides.
Mailwarm
Does it work well on longer podcasts too, or is it mainly tuned for shorter Youtube videos?