One clean, developer-focused page for every Unicode symbol

Hacker News

I’m building a Unicode reference where each symbol has its own dev-friendly page with all relevant encodings. Example: [ https://fontgenerator.design/symbol/almost-equal-to ]( https://fontgenerator.design/symbol/almost-equal-to ) Includes Unicode, HTML, CSS, JS, UTF-8/16 bytes, URL encoding, and usage examples. The same structure is used across thousands of symbols (math, arrows, currency, tech/UI, punctuation). Built because existing references are fragmented. Feedback welcome.

Visit product View on Hacker NewsDec 25, 2025

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Product HuntUnlikely to reach the leaderboard · Strong signals: coding, code · Missing: mac, agents, macos

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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