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Templon – A strict, modern template string engine for JavaScript/TS

Hacker News

Templon – A strict, modern template string engine for JavaScript/TS

Hi HN, I built [Templon]( https://github.com/amirfarzamnia/templon ), a lightweight template string engine for JavaScript and TypeScript. It’s designed to solve a simple but surprisingly messy problem: safely replacing placeholders in a string, with features you usually have to hack together yourself. Templon supports: - Deep variable access (`{{user.name}}`) - Custom delimiters - Escape characters for literal brackets - Strict mode (error if missing keys) - Circular reference protection - Full TypeScript types It’s useful when you want more than `string.replace`, but don’t need a full EJS/Handlebars engine. I was frustrated by older libraries like `string-template` not supporting nested objects or strict validation, and wanted something modern, strict, and flexible. Repo: https://github.com/amirfarzamnia/templon Would love feedback — good, bad, brutal. Thanks!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
86%86% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
47%47% 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: io · Missing: https docs, excited, just released
37%37% 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
22%22% 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
14%14% 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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