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JavaScript+Tcl Remote REPL

Hacker News

JavaScript+Tcl Remote REPL

I was recently working in extending the Tcl Duktape extension to support calling Tcl procedures from the JavaScript side -- the result is the unholy amalgamation of Tcl+JavaScript which actually turned out to work quite well. I slapped a REPL and then a WebSocket-based access layer onto it and it can be accessed remotely at: https://rkeene.dev/js-repl/ All the Tcl commands are in the "runtime" object. js> runtime.package.require('sha1') => 2.0.3 js> runtime.sha1.sha1('Hello World') => 0a4d55a8d778e5022fab701977c5d840bbc486d0

Visit product View on Hacker NewsJun 17, 2019

Actual performance

6points

Did not reach leaderboard

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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.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

57%57% 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

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

37%37% 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

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

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

19%19% 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

12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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