This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Not just another visitor counter. Lolicount splits Galgame-style sprites into layers — eyes, mouth, expression, face — and recomposes them per request. 71 images → 311,040 poses. Every refresh is a new pose. 🎨 In-browser theme editor — drag layers, live preview, export ZIP, zero toolchain ⚡ Real-time SVG — crisp vector, counts never stale 📦 Single binary — Go + Nuxt SSG, Docker one-liner 🌐 Trilingual (EN/JP/CN) One line of Markdown, a living character on your page: lolicount.top
I built Lolicount because every visitor counter I found was a static number image — boring. I wanted my GitHub profile to feel alive, like a Galgame character that changes expression every time you look at her.
Split a character into sprite layers (eyes ×18, mouth ×20, face ×6) and composite randomly per request. 71 images → 311,040 poses. Minimal storage, maximum variety.
Also built a full in-browser editor — drag layers, live preview, export ZIP. No toolchain needed.
Open source, single binary, Docker one-liner. Star? 🙏
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About Lolicount on Product Hunt
“A moe counter where 71 sprites spawn 311K poses.”
Lolicount was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #92 on the daily leaderboard. Not just another visitor counter. Lolicount splits Galgame-style sprites into layers — eyes, mouth, expression, face — and recomposes them per request. 71 images → 311,040 poses. Every refresh is a new pose. 🎨 In-browser theme editor — drag layers, live preview, export ZIP, zero toolchain ⚡ Real-time SVG — crisp vector, counts never stale 📦 Single binary — Go + Nuxt SSG, Docker one-liner 🌐 Trilingual (EN/JP/CN) One line of Markdown, a living character on your page: lolicount.top
Lolicount was featured in Open Source (68.8k followers), Comics & Graphic Novels (4.1k followers), GitHub (41.4k followers) and Data Visualization (3.6k followers) on Product Hunt. Together, these topics include over 46k products, making this a competitive space to launch in.
Who hunted Lolicount?
Lolicount was hunted by mld. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Lolicount stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
mld