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Generate F# Giraffe server stub using OpenAPI Generator in 3 steps

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Generate F# Giraffe server stub using OpenAPI Generator in 3 steps

Hi all, We've just added an F# Giraffe server stub generator (beta) to the OpenAPI Generator project ( https://github.com/OpenAPITools/openapi-generator ) and would appreciate if you can try it out and let us know if you've any feedback or question for us ( http://github.com/OpenAPITools/openapi-generator/issues/new ). To try it out, just follow 3 steps: 1. Download the Java JAR ( https://oss.sonatype.org/content/repositories/snapshots/org/... ) 2. Rename the JAR as "openapi-generator-cli.jar" 3. Run the following command to generate a F# Giraffe server stub for Petstore API ( https://raw.githubusercontent.com/OpenAPITools/openapi-gener... ): Mac/Linux: - java -jar openapi-generator-cli.jar generate -g fsharp-giraffe-server -i https://raw.githubusercontent.com/OpenAPITools/openapi-gener... -o /var/tmp/fsharp-giraffe-petstore/ Windows: - java -jar openapi-generator-cli.jar generate -g fsharp-giraffe-server -i https://raw.githubusercontent.com/OpenAPITools/openapi-gener... -o C:\temp\fsharp-giraffe-petstore I hope you will find the new generator useful. A special thanks to Nick Fisher (founder of https://Lexico.IO ) who contributes the generator. Thanks, William

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
58%58% 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
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, user, new · Missing: agents, macos, agent
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% 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
15%15% 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.

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