Comparegpt.io – Trustworthy Mode to reduce LLM hallucinations

Hacker News

Hi HN , I’m Tina. Like many of you, I’ve been frustrated with how often LLMs hallucinate — confident answers with no real basis. That’s why I’ve been building CompareGPT.io, a tool to make outputs more trustworthy. The new Trustworthy Mode works by: Cross-verifying every answer with our TrustSource, combining our own model with multiple leading LLMs (ChatGPT-5, Gemini, Claude, Grok) and authoritative sources. Delivering each response with a Transparency Score and full references. Helping users quickly see where to trust the output, and where to double-check. It currently works best on knowledge-heavy domains (finance, law, science), and we’re looking for early users to test it out. Would love your feedback, suggestions, or even criticism on whether this feels useful and how we can improve.

Visit product View on Hacker NewsSep 24, 2025

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

94%94% 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 HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, 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

38%38% 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 · Strong signals: users · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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