I built a load testing tool so you can develop load test in minutes

Hacker News

Hello HN, I spent the last week building a k6 alternatives supporting Web APIs (fetch, localStorage, and more), TypeScript and ESNext. It enables performance engineer to just import client libraries and write a load test in minutes. The repository includes an example load test of GoTrue (supabase's authentication server) using '@supabase/gotrue-js' npm package. Like k6 tests, 'denoload' (that's the name of the tool) tests can scale to infinity (if virtual users are distributed across computing ressources). Let me know what you think and if you have any questions. Thanks!

Visit product View on Hacker NewsOct 14, 2023

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3points

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

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 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.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

16%16% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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