AI assistants confidently get SaaS product facts wrong

Hacker News

While building this, I found that ChatGPT, Claude, Gemini, and Perplexity regularly describe SaaS products with specific but incorrect details — wrong pricing tiers, deprecated features, misattributed integrations — with no uncertainty expressed. The tool runs a consistent set of buyer-style prompts across all four and shows verbatim output. Not trying to fix anything, just make it visible. One free scan. https://www.bersyn.com/

Visit product View on Hacker NewsMar 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, perplexity, chatgpt · Missing: mac, agents, macos

76%76% 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 HackersIH features products with proven revenue · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

35%35% predicted probability of success on Acquire.com, 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

28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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

Correct prediction on native model

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