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Dart API client generator now supports Dart 2.3

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Dart API client generator now supports Dart 2.3

Thanks to the contribution by "ircecho" ( https://github.com/OpenAPITools/openapi-generator/pull/2894 ), the Dart API client generator in the OpenAPI Generator project now supports Dart 2.3 which was released recently. Here are a few steps to generate Dart API client given an OpenAPI specification file: 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 Dart API client for the Petstore API ( https://raw.githubusercontent.com/OpenAPITools/openapi-gener... ): Mac/Linux: - java -jar openapi-generator-cli.jar generate -g dart -i https://raw.githubusercontent.com/OpenAPITools/openapi-gener... -o /var/tmp/dart-api-client/ Windows: - java -jar openapi-generator-cli.jar generate -g dart -i https://raw.githubusercontent.com/OpenAPITools/openapi-gener... -o C:\tmp\dart-api-client Please give it a try and let us know if you've any feedback.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
75%75% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
47%47% 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
45%45% 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
34%34% 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
34%34% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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