Balter, the Open Source, composable, distributed load testing framework

Hacker News

Balter, A Load TestER, is a new distributed load testing framework I've been working on for the past few months. It is Open Source, and designed from the ground up to support composability. This allows flexibility in the kinds of load tests you are able to run, such as fully stressing the system and then automatically turning off services; or the ability to run a background noise test, and then slam the service with traffic. Let me know what you think!

Visit product View on Hacker NewsApr 18, 2024

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Actual performance

5points

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Launch Intel predictions

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

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source · Missing: https docs, excited, just released

78%78% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% 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

25%25% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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