WiFi-deauth –> A pentesting deauth attack tool against WiFi networks

Hacker News

I built WiFi-Deauth, a high-performance tool for automating WiFi deauthentication attacks. Features: Supports 5GHz channels (not just 2.4GHz) * Ability for better precision by targeting specific clients inside a network * Includes a mode that targets an access point on all channels (effective against a common protection mechanism of switching channels) * Designed for automation, using the correct filters and settings It’s lightweight, written in Python, and very straightforward to use. Let me know what you think or if you have feature suggestions!

Visit product View on Hacker NewsFeb 24, 2025

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83%83% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

69%69% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, 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

47%47% 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, personal

33%33% 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

12%12% 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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