Programming Fonts Browser

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http://app.programmingfonts.org I've spent some time collecting monospace programming fonts, particularly those that are free to use. (Monospace because you can program with any font and you've got to draw the line somewhere). Usually I include some background info and basic stats in a blog post, and add it to a test drive "app". I've gone through some iterations here, and currently the backbone is a JSON data set ( https://github.com/braver/programmingfonts/blob/gh-pages/fon... ). I've been wondering what further to include here, e.g. glyph counts, of the zeroes are slashed or dotted, if there are OpenType alternatives, etc etc. And how to expose all that. It has been linked here before, but never by me and I haven't been able to respond before. Also, it has seen a lot of work since then. So, here I'd like to show HN what I've made and ask you for feedback, questions, suggestions, etc.

Visit product View on Hacker NewsOct 5, 2018

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% 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.

Correct prediction on native model

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