Bookmark with Scheme Lisp REPL

Hacker News

You can use it to run Scheme interpreter on any website, if it don't have content security policy (like Hacker News). You can use it while learning Lisp (Scheme) or when testing some code from online book or PDF (at least in Chrome). The link to Bookarklet can be found in this link: https://jcubic.github.io/lips/#bookmark The bookmark use devel version of LIPS Scheme, it will be released version after 1.0.0 is finished. Latest version is very old, devel and beta versions have lot of changes. LIPS scheme have almost fully working lisp macros and hygienic macros systems (syntax-rules) but still no call/cc and TCO.

Visit product View on Hacker NewsSep 8, 2020

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

48%48% predicted probability of success on AppSumo, 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.

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

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, new, code · Missing: agents, macos, agent

39%39% 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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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