A
A Dashboard of Web Benchmarks
Hacker NewsA Dashboard of Web Benchmarks
wtx-bench ( https://github.com/c410-f3r/wtx-bench ) is a benchmark suite designed to measure the performance of the wtx project ( https://github.com/c410-f3r/wtx ) by comparing different implementations in various programming languages. Currently supports WebSockets and HTTP/2 but soon gRPC will also be available. Please feel free to indicate any related technologies for inclusion.
Share cardActual performance
3points
Did not reach leaderboard
Launch Intel predictions
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AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive62%62% predicted probability of success on AppSumo, 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created60%60% 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 released52%52% 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 · Missing: mobile apps, ios, personal43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos32%32% 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, revenue14%14% 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, crypto1%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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