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A Storage-Agnostic Golang Message Queue

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A Storage-Agnostic Golang Message Queue

Hello, everyone. Three months ago, I started building my first GoLang package, VarMQ. In this short period, I gained a significant amount of traction from the community, and it resulted in over 140 stars on GitHub. A week ago, to check how it performs, I ran benchmarks with a similar package, Pond, which has been managed for years. Pond is not a message queue, though; however, it can be compared to VarmQ since it shares some similarities, except for its storage system. From the benchmarks, I got surprising results, which are that Varmq is taking 50%+ less memory than Pond and also has good performance metrics in IO tasks. which makes me glad and willing to share with everyone here. Benchmark chart site: https://varmq-benchmarks.netlify.app Overall, I would like to increase the adoption of this package and am willing to hear feedback from you. Regardless, if you like this project, don't forget to hit the Star button. It helps the project grow. Thank you!

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Actual performance

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
77%77% 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: tasks · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
51%51% 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: month · 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
22%22% 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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