I made Confetti: a configuration language file format

Hacker News

Hello everyone, I created Confetti: a simple, typeless, and localization-friendly configuration language designed for human-editable configuration files. In my opinion, JSON works well for data interchange, but it's overused for configuration, it's not localization-friendly, and it's too syntactically noisy. INI is simple but lacks hierarchical structures and doesn't have a formal specification. Confetti is intended to bridge the gap. I aim to keep Confetti simple and minimalistic, while encouraging others to extend it. Think of it like Markdown for configuration files: there's a core specification, but your welcome to create your own variations that suit your needs.

Visit product View on Hacker NewsMar 31, 2025

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

71%71% 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 · Missing: mac, agents, macos

70%70% 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

49%49% 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 · Missing: plus, platform, intuitive

39%39% 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

39%39% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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