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FastStream - A powerful library for building services with event streams

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FastStream - A powerful library for building services with event streams

FastStream (https://github.com/airtai/faststream) simplifies the process of writing producers and consumers for message queues, handling all the parsing, networking and documentation generation automatically. It is a new package based on the ideas and experiences gained from FastKafka and Propan. By joining our forces, we picked up the best from both packages and created a unified way to write services capable of processing streamed data regardless of the underlying protocol. We'll continue to maintain both packages, but new development will be in this project. Making streaming microservices has never been easier. Designed with junior developers in mind, FastStream simplifies your work while keeping the door open for more advanced use cases. Here's a look at the core features that make FastStream a go-to framework for modern, data-centric microservices. - Multiple Brokers: FastStream provides a unified API to work across multiple message brokers (Apache Kafka, RabbitMQ, NATS and Redis) - Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages - Automatic Docs: Stay ahead with automatic AsyncAPI documentation - Intuitive: Full-typed editor support makes your development experience smooth, catching errors before they reach runtime - Powerful Dependency Injection System: Manage your service dependencies efficiently with FastStream's built-in DI system - Testable: Supports in-memory tests, making your CI/CD pipeline faster and more reliable - Extendable: Use extensions for lifespans, custom serialization and middleware - Integrations: FastStream is fully compatible with any HTTP framework you want (FastAPI especially) - Observability: Add OpenTelemetry or Prometheus support to your services.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created, efficiently · Missing: reddit linkedin, podcasting, latex
95%95% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: intuitive, efficient · Missing: plus, platform, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
45%45% 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
10%10% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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