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Incremental JSON parser for streaming LLM tool calls in Ruby

Hacker News

Incremental JSON parser for streaming LLM tool calls in Ruby

Built this for streaming AI tool calls. LLMs stream function arguments as JSON character-by-character. Most parsers reparse from scratch each time - O(n²) behavior that causes UI lag. This maintains parsing state, processing only new characters. True O(n) performance that stays imperceptible throughout the entire response. Ruby gem, MIT licensed. Would love feedback.

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Actual performance

13points
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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
60%60% 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: calls · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
15%15% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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