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Dew v1.0.0 – A lightweight, zero-dependency command bus for Go

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Dew v1.0.0 – A lightweight, zero-dependency command bus for Go

Dew is a pragmatic command bus library for Go that simplifies complex backend architectures. It provides a unified interface for handling operations and domain logic, promoting clean, maintainable code. Why Dew? - Lightweight (450 LOC) with zero dependencies - Fast performance (benchmarks in README) - Full middleware support for cross-cutting concerns - 100% test coverage, production-ready Dew implements the command-oriented interface pattern, helping separate concerns and reduce cognitive load in large Go projects. It's designed for developers who want a simple, efficient way to structure their application logic without heavy frameworks. We built Dew because we couldn't find a command bus library for Go that met all our needs. We'd love your feedback!

Visit productView on Hacker NewsAug 18, 2024
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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
68%68% 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: code · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: interface, efficient · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% 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 · Strong signals: way, para · Missing: mobile apps, ios, personal
40%40% 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
18%18% 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.

Correct prediction on native model

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