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DankGPT – Chat with Your Documents

Hacker News

DankGPT – Chat with Your Documents

Uses hybrid semantic search (combination of dense embeddings and sparse vectors) to retrieve high quality answers across your documents. Features - Significantly faster than competition (Process a 200 page PDF in <5s) - Much better answer quality - Fast summarization tool - Beta API for end to end extractive document QA (hello@dankgpt.com) Try it out (no login) - Llama 2 paper https://www.dankgpt.com/chat/346f444d-e286-4671-b157-540f4cb... - Scott Aaronson Quantum Information Science lectures https://www.dankgpt.com/chat/cc491d72-dc7b-4ace-8e26-60026ae... - Berkshire 2022 Annual Report https://www.dankgpt.com/chat/068bf85f-b372-46a4-a164-6096f8c... Why not host it yourself? - You definitely can! DankGPT is intended as a quick way to ask questions about a research paper, or help students with answering questions from their lecture slides, with an easy way to share your chatbot.

Visit productView on Hacker NewsJul 26, 2023
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Actual performance

17points
9comments
Made the leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released
74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
70%70% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: answers, 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 · Strong signals: active · Missing: arr, mrr, revenue
11%11% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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