Glossary page template with a built-in editor

Hacker News

This started out as a static HTML file, being used by some developers at my current employer to document "domain terminology" relevant to products we're building. Soon it became too much work to update the items in the HTML manually, so I added a JavaScript-based editor. It relies on a tiny Node.js server to write changes back to the HTML file. Not as user-friendly as a "normal" web-based application, but it does mean we can keep the HTML file under version control and follow our normal development process for making/tracking changes.

Visit product View on Hacker NewsJun 26, 2022

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52points

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Launch Intel predictions

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

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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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