Weekend project, real-time conversation fact-checking using Perplexity

Hacker News

Hi HN! I recently discovered Perplexity.ai and I was amazed! I built a product this past weekend that I felt should just exist in the world. With a knowledge engine such as Perplexity, a realtime fact checking platform is now finally possible. DeepFact runs in the background as you speak and actively extracts claims and fact checks them. The app is built using NextJS, Deepgram API(nova-2 model) for speech to text, and Perplexity API(llama-3.1-sonar-small-128k-chat) for real-time fact checking. It is completely free, just add your own API keys to give it a try. There's also a simulated demo using Elon-Trump's interview on X if you just want to see how it works. Ideally this product would be used to fact check any conversation in realtime: public speeches/presentations, lectures, meetings, debates, podcasts. The use cases seem endless. There are times where it fails and hallucinates source URLs or it cites itself. There is plenty of room to improve the fact-checking algs. Please try it out and I would love to hear any feedback or comments on how this could be used and ways to improve it. Thanks!

Visit product View on Hacker NewsAug 19, 2024

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

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

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

40%40% 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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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