KS

KSON, a love-letter to the humans maintaining computer configuration

Hacker News

KSON, a love-letter to the humans maintaining computer configuration

Hi friends, I'm really excited to introduce KSON, which just entered public beta! Anywhere a human is reading or editing YAML/JSON/TOML, KSON may be used as a more effective interface on that data. If you are such a human, we invite you to participate in this beta. tl;dr Check out the website [1], play with the online playground [2], install the library for your programming language [3], edit in your favorite editor [4], discuss and give feedback [5], contribute to the project [6]. (A personal note about this project: I love software. Machines made of words! Such a wonder. KSON itself, as a collection of words that both make a machine and explain that machine, is an expression of a lot ideas I feel really passionately about around software and our relationship to it. I've put a lot of love into trying to make that expression eloquent and reliable. I hope some of that comes through clearly, and I look forward to discussing this more over time with anyone who's interested) One of the key things KSON wants to say is: let's keep everything that's great about YAML and JSON as "Configuration User Interfaces", and let's make those interfaces more toolable, robust, and fun. Here's some of the ways we do that: - KSON is a verified superset of JSON, has native JSON Schema support, transpiles cleanly to YAML (with comments preserved!), and is likely available wherever you want it—current supported platforms: JS/TS, Python, Rust, JVM, and Kotlin Multiplatform. - KSON is also widely available in developer tools, with support for VS Code, Jetbrains IDEs, and anywhere you can plug in an LSP. - KSON is fully open source, licensed under Apache-2.0, and you are invited to meet its words and tinker with how they make its machine. A lot of care, craft, attention and joy went into making the KSON project understandable and approachable for developers. We hope to see you around. PS. This is an HN-friendly version of the official announcement at < https://kson.org/docs/blog/2025/09/17/introducing-kson/ >. [1]: https://kson.org/ [2]: https://kson.org/playground/ [3]: https://kson.org/docs/install/#languages [4]: https://kson.org/docs/install/#editor-support [5]: https://kson-org.zulipchat.com/ [6]: https://github.com/kson-org/kson

Share card

Actual performance

34points
14comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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: excited, open source, ide · Missing: https docs, just released, exist
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, user, computer · Missing: agents, macos, agent
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, friendly, interface · Missing: plus, intuitive, reviews
38%38% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, introduce · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

A
A love letter to anagramatron41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A love letter to anagramatron

Hacker News2
Lo
Love Letter Generator for Dummies39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Love Letter Generator for Dummies

Hacker News1
Le
Letter to a Young Artist32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Letter to a Young Artist

Hacker News2
Ap
Apology Letter from American + 2500 miles54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Apology Letter from American + 2500 miles

Hacker News2
RealESALetter
RealESALetter27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ESA Letter from Licensed Therapists.

Indie Hackerscommitment-full-time
Th
The Most Open Letter Ever47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The Most Open Letter Ever

Hacker News1
mhappeal
mhappeal35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

MHPAEA Appeal Letter Generator

Indie Hackerscommitment-side-project
Ke
Keybone – GPG for humans62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Keybone – GPG for humans

Hacker News1
Sp
Sparser is string parsing for humans55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Sparser is string parsing for humans

Hacker News3
Ai
Aioserial, pyserial-asyncio for humans62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Aioserial, pyserial-asyncio for humans

Hacker News1