Playing with {lambda talk}

Hacker News

Hi you, The {lambda way} project is a workshop built on a wiki, {lambda tank}, coming with a true functional programming language, {lambda talk}. http://lambdaway.free.fr/workshop/ http://lambdaway.free.fr/workshop/?view=oxford_slides The archive is small (50kb) and easy to install in your local server or in any web account running PHP. http://lambdaway.free.fr/workshop/?view=download http://lambdaway.free.fr/workshop/?view=installation You can try {lambda talk} in any modern web browser. Your opinion is welcome Alain Marty

Visit product View on Hacker NewsSep 13, 2017

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

48%48% 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

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

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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