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Convert OpenAPI spec to gRPC, Protobuf definitions in 3 steps

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Convert OpenAPI spec to gRPC, Protobuf definitions in 3 steps

To convert OpenAPI spec (v2, v3) to gRPC, Protobuf definitions, please follow 3 simple steps below: 1. Download the Java JAR (https://oss.sonatype.org/content/repositories/snapshots/org/openapitools/openapi-generator-cli/4.1.2-SNAPSHOT/openapi-generator-cli-4.1.2-20190904.113123-33.jar) 2. Rename the JAR as "openapi-generator-cli.jar" 3. Run the following command to generate an gRPC, Protobuf definitions 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 grpc-schema -i https://raw.githubusercontent.com/OpenAPITools/openapi-generator/master/modules/openapi-generator/src/test/resources/2_0/petstore.yaml -o /var/tmp/grpc-schema/ Windows: java -jar openapi-generator-cli.jar generate -g grpc-schema -i https://raw.githubusercontent.com/OpenAPITools/openapi-generator/master/modules/openapi-generator/src/test/resources/2_0/petstore.yaml -o C:\tmp\grpc-schema Project page: https://github.com/OpenAPITools/openapi-generator

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% 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, open · Missing: agents, macos, agent
21%21% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
20%20% predicted probability of success on BetaList, 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.

Incorrect prediction on native model

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