LC

LCON 1.0 – For reals this time (Ludicrously Compact Object Notation)

Hacker News

LCON 1.0 – For reals this time (Ludicrously Compact Object Notation)

When I posted LCON before (https://github.com/ar-nelson/lcon), it was a horribly broken prerelease version. This version actually works (it has unit tests and everything!), and it has a slightly modified syntax that takes into account some of the criticisms from the last HN thread. In particular: * Lists are now bulleted with - instead of . * Comments are now delimited with # instead of ; * Block strings are now delimited with """ instead of `` * Stringify functions are now available, including one that stringifies to JSON with key order preserved.

Visit product View on Hacker NewsSep 6, 2014

Actual performance

1points

3comments

Did not reach leaderboard

Launch Intel predictions

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

55%55% 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 · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: including · Missing: supports, reddit linkedin, podcasting

36%36% 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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

27%27% 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

17%17% 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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