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ElectronCGI – Cross Platform .Net Core GUIs with Electron

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ElectronCGI – Cross Platform .Net Core GUIs with Electron

ElectronCGI is a library that enables making requests from a NodeJs application that are served by a .Net application. It uses the standard in/out streams for communication. To use it install the electron-cgi npm package on you node application and the ElectronCgi.DotNet nuget package on your .Net console app. Here's an example: In NodeJs/Electron: const { ConnectionBuilder } = require('electron-cgi'); const connection = new ConnectionBuilder() .connectTo('dotnet', 'run', '--project', 'DotNetConsoleProjectWithElectronCgiDotNetNugetPackage') .build(); connection.onDisconnect = () => { console.log('Lost connection to the .Net process'); }; connection.send('greeting', 'John', theGreeting => { console.log(theGreeting); // will print "Hello John!" }); connection.close(); And in the .Net Console Application: using ElectronCgi.DotNet; //... static void Main(string[] args) { var connection = new ConnectionBuilder() .WithLogging() .Build(); // expects a request named "greeting" with a string argument and returns a string connection.On<string, string>("greeting", name => { return $"Hello {name}!"; }); // wait for incoming requests connection.Listen(); } [Blog post with extra information about ElectronCGI]( https://www.blinkingcaret.com/2019/02/27/electron-cgi/ )

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4points
2comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · 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 · Strong signals: platform, builder · Missing: plus, intuitive, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
40%40% 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 · Strong signals: new, using · Missing: mac, agents, macos
32%32% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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