F#

F# Server stub generator for Azure functions

Hacker News

F# Server stub generator for Azure functions

4 months after we added the F# Giraffe server stub generator to OpenAPI Generator (https://twitter.com/oas_generator/status/1125309026558926848), we're glad to see another F# server stub generator for Azure Functions by the same author: Nick Fisher (https://twitter.com/NickFisherAU) To generate the F# server stub for Azure Functions given an OpenAPI/Swagger specification file, please follow 3 simple steps below: 1. Download the Java JAR (https://oss.sonatype.org/content/repositories/snapshots/org/openapitools/openapi-generator-cli/4.1.3-SNAPSHOT/openapi-generator-cli-4.1.3-20190924.112244-39.jar) 2. Rename the JAR as "openapi-generator-cli.jar" 3. Run the following command to generate server stub for F#/Azure functions for the Petstore API (https://raw.githubusercontent.com/OpenAPITools/openapi-generator/master/modules/openapi-generator/src/test/resources/2_0/petstore.yaml): Mac/Linux: $ java -jar openapi-generator-cli.jar generate -g fsharp-functions -i https://raw.githubusercontent.com/OpenAPITools/openapi-generator/master/modules/openapi-generator/src/test/resources/2_0/petstore.yaml -o /var/tmp/fsharp-functions/ Windows: $ java -jar openapi-generator-cli.jar generate -g fsharp-functions -i https://raw.githubusercontent.com/OpenAPITools/openapi-generator/master/modules/openapi-generator/src/test/resources/2_0/petstore.yaml -o C:\tmp\fsharp-functions If you've any feedback or question, please let us know via https://github.com/OpenAPITools/openapi-generator/issues/new Thanks again for the new generator by Nick Fisher (https://twitter.com/NickFisherAU) Ref: https://github.com/OpenAPITools/openapi-generator/pull/3933

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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
59%59% 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
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, user, new · Missing: agents, macos, agent
22%22% 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
18%18% 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
10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

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