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Go-SQS-Worker – A Simple and Scalable Async Job Library for AWS SQS

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Go-SQS-Worker – A Simple and Scalable Async Job Library for AWS SQS

I recently built go-sqs-worker, a Go library for managing asynchronous jobs using AWS SQS. It’s designed to strike a balance between simplicity and scalability, addressing common backend needs like background email processing or task retries without the complexity of Kafka or AWS Step Functions. Features: - Clean producer/consumer separation - Exponential backoff and retry management - Dead Letter Queue (DLQ) support To make monitoring easier, I also built a job viewer, inspired by Sidekiq, with features to inspect job details visually. GitHub: https://github.com/mickamy/go-sqs-worker Demo: https://github.com/mickamy/go-sqs-worker/tree/main/example Would love your feedback and thoughts on improving it!

Visit productView on Hacker NewsJan 22, 2025
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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
64%64% 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: ide, io · Missing: https docs, excited, just released
57%57% 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
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · 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: email, visual, using · Missing: mac, agents, macos
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
15%15% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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