Eno – A lightning fast, user-friendly YAML/TOML alternative

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eno [1] - A modern plaintext language w/ libraries [2] for JavaScript, Python, Ruby & soon more! We migrated a big relational research database to a file-based solution - requirements were: - Super fast and easy editability for users - Highest performance for parsing/validating >10K documents on every user change. Our trials with YAML/TOML showed us that we wanted something both faster [3][4] and easier [4], something tailored for file-based content management ... and after months of research & development it's now publicly available (under MIT license) for everyone! Last but not least I also want to mention eno's document introspection capabilities - with a few lines of code you can build intelligent relational suggestion UIs as shown in [4] below. [1] https://eno-lang.org/ [2] https://eno-lang.org/libraries/ [3] https://github.com/eno-lang/benchmarks/ [4] https://eno-lang.org/resources/introspection.mp4 PS. Your input for the Roadmap is highly welcome - what do you think should be in the next releases? More languages? (If so, which? Currently in progress: Rust/PHP, Currently planned: Go/Java) Additional IDE/editor support? (Currently supported: Atom/VSCode/Sublime) Or something else entirely? :) Looking forward to your feedback!

Visit product View on Hacker NewsAug 15, 2018

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

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.

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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: friendly, soon, users · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

38%38% 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.

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