Repid v2 – open-source async Python task queue with AsyncAPI docs

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Python's async web ecosystem is great, but background task processing is still mostly sync-first. Repid is an async-native task queue that generates AsyncAPI documentation from your type annotations - like FastAPI does for REST. v2 ships with AMQP 1.0, Kafka, SQS, GCP Pub/Sub, Redis Streams, and NATS support. It is the fastest framework that I've benchmarked, and there are lots of cool features, so let me know if it's useful. Repo: https://github.com/aleksul/repid Docs: https://repid.aleksul.space Benchmarks: https://github.com/aleksul/repid-benchmarks

Visit product View on Hacker NewsApr 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

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