I built a SaaS for pentesting web apps

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I recently built a SaaS called Barrion, used to help tech teams identify security issues in their websites and web apps. The service allows users to scan websites or really any deployed web application which is publicly accessible, such as portals, API's etc. There's support to run these scans on schedule, as well as to run instantly for a quick (almost instant) assessment. Each scan provides a summary with actionable insights about potential vulnerabilities. The service is currently completely free to use, including all pro features, as I’m looking for feedback to improve it. Feel free to check it out and share your thoughts! Thanks in advance :)

Visit product View on Hacker NewsJan 26, 2025

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Actual performance

1points

Did not reach leaderboard

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

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best fitHighest predicted score across all platforms for this description.

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

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released

55%55% 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: apps, users · Missing: mobile apps, ios, personal

46%46% 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

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

Acquire.comPre-revenue stage for this audience · Strong signals: arr, saas · Missing: mrr, revenue, profit

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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