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Personal GPT-4 AI document assistant

Hacker News

Personal GPT-4 AI document assistant

’m Zain Sheikh, co-founder @ Chat Dox. We are super excited to show you [ChatDox.com] With ChatDox.com Dox AI you can leverage the power of gpt-3.5 & gpt-4 and talk with your documents. Upload documents (.pdf, .txt, .docx, .csv) and ask questions from your personal document assistance. Why? What if you want to interact with your own documents? Chatgpt can't answer questions about your specific documents. For example: - The HR department of a company may want their new hires to train on the basic rules and regulations. - A researcher in a lab may need to quickly skim through hundreds of different documents or get statistics from them. - A book reader may want to know the plot of a book. - An analyst may need to analyze CSV files. Solution: Chat Dox allows you to upload documents in any format and converse with them to get results in any language using the powerful LLM models of OpenAI's chat-gpt. Your documents will be saved in Chat Dox's encrypted digital library, which you can access to ask questions and receive mind boggling answers. You can also ask question from group of documents using ChatDox.com Some notable features: Get answers from your Pdf, docx, txt and csv. Get answers from group of documents. Get 100x better answers using gpt-4. Your unlimited digital library. Support Chat GPT 3.5 turbo and GPT-4. How? We utilize the power of OpenAI's LLM models to embed each document using their APIs. Your questions will then be matched to these embeddings to provide you with results. Feature: Upload your documents (.pdf, .csv, .txt, .docx) Saving Conversations. Ask Question (individual | group of document). Delete Files. Live Support. gpt-4 supported. Get answers with their source. Future plans: We are continuously working to make it more accessible by integrating it with WhatsApp, Slack and other notable messengers. Additionally, we are building different use cases for various industries, such as creating questionnaires from documents, generating analysis from CSV files, making presentations from your documents and create your social media content for you from your data. Upcoming Features: Improve answer quality. Chat Dox in WhatsApp. Create content from documents. (presentations, analysis, questionnaire, social media content) We want you to try our service and make your life easier. Don't miss out on the opportunity to get your documents speak to you.

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Actual performance

5points
13comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
96%96% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, slack, new · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
68%68% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, answers · Missing: mobile apps, ios, entrepreneurs
62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% 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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