FalsoAI – Detect influence/manipulation patterns in content

Hacker News

I built this tool because I believe AI can be used to protect people from social engineering and influence/manipulation patterns. In a way, it is similar to what an antivirus does for a computer, but applied to human cognition. This simple project is mainly an MVP proof of concept. I want to turn this project into an entire ecosystem to give people more control, detect PSYOPS, election manipulation, and give people more awareness. Right now all the marketing companies are getting very good an influencing people, and this is going to get worst with LLMs. All the innovation is going into marketing, and nothing is going into giving people more control over their devices and their lives. I want to build this ecosystem to serve as a counterweight, ensuring we don't live in a future where big companies control people's behavior. (not gonna let them do that) this is the website: https://www.falsoai.com/ these are some test examples that you can try: News https://edition.cnn.com/2026/04/22/us/epstein-files-sex-traf... https://www.bbc.com/news/articles/cn0ep28drllo https://edition.cnn.com/2026/04/21/economy/us-retail-sales-m... YouTube https://www.youtube.com/watch?v=2sQbK6EH9yg&t=2379s https://www.youtube.com/watch?v=pDxCC5-C9Tg&t=128s https://www.youtube.com/watch?v=MZPVPCIeUpg Thanks for taking a look. I would really appreciate any feedback and suggestions!!! :)

Visit product View on Hacker NewsApr 24, 2026

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

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.

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.

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

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

42%42% 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

18%18% 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.

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