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JSON-io – Java library for JSON, JSON5, and TOON (40% fewer LLM tokens)

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JSON-io – Java library for JSON, JSON5, and TOON (40% fewer LLM tokens)

I built TOON support into json-io, a Java serialization library. TOON is an indentation-based format designed for LLM applications where every token costs money and eats context window. Same data as JSON, ~40-50% fewer tokens. No braces, brackets, commas, or unnecessary quoting. Arrays of uniform objects become CSV-like tables automatically. JSON: {"team":"Rockets","players":[{"name":"John","age":30},{"name":"Sue","age":27}]} TOON: team: Rockets players: name, age John, 30 Sue, 27 json-io reads/writes JSON, JSON5, and TOON with 60+ built-in Java types, cycle support, and zero config. There's also a Spring Boot starter and Spring AI module. GitHub: https://github.com/jdereg/json-io TOON spec: https://toonformat.dev Baeldung article: https://www.baeldung.com/java-json-toon-format-libraries

Visit productView on Hacker NewsMar 24, 2026
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
68%68% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, 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
50%50% 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 HuntUnlikely to reach the leaderboard · Strong signals: context · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
22%22% 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.

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