Day 2 for DocChat Updates: - Added user sign up and sign in with Supabase Auth - Landing page, Dashboard and Separated Doc Page for Chat - User can save his/her own docs safely. Getting better and more excited 🦾

Built the first prototype of DocChat app in two days. Upload a doc → chunked, embedded, and stored in Pinecone → Ask any question → Claude streams back an answer about your document. Next: auth, dashboard, per doc pages. Just getting back to building. 🫡 #buildinpublic

- Plastic case that has a much higher potential of breaking and awful rigidity - 16GB is more on paper but thanks to MicroSlop and "AI" literally in the name means you actually get this AI slop forced onto you, it isn't. - screen is terribly washed out - screen also only achieves 300 nits and lower res - Acer included an assistant called "DocChat" which sounds way too close to Dogshit to be a coincidence - according to tests it has a really annoying fan noise (Neo is silent) - MicroSlop - MicroSlop Windows - MicroSlop CoPilot This thing is shit. Just get a Neo.

DAY 18 DELIVERED 🎄 Day 18: I Built DocChat AI - Chat with Your Documents Using RAG 📄🤖 Spent Christmas Day building something powerful: an AI-powered document analysis system that actually reads ALL pages and gives you accurate answers with source citations. 𝗪𝗵𝗮𝘁'𝘀 𝗗𝗼𝗰𝗖𝗵𝗮𝘁 𝗔𝗜? Upload PDFs, DOCX, or TXT files. Ask questions. Get intelligent answers with clickable page references. No more ctrl F through 200-page documents. 𝗧𝗵𝗲 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: → Django PostgreSQL (Backend) → LangChain (RAG Framework) → OpenAI GPT-4o-mini (LLM) → FAISS (Vector Search) → Custom Sovereign Design System (Frontend) 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: ✅ Full document analysis (not just snippets) ✅ Multi-document chat support ✅ Source citations with page numbers ✅ AI-powered summaries ✅ Real-time processing status ✅ Beautiful dark mode interface 𝗛𝗼𝘄 𝗥𝗔𝗚 𝗪𝗼𝗿𝗸𝘀: 1️⃣ Documents → Text chunks 2️⃣ Chunks → Vector embeddings 3️⃣ Store in FAISS vector database 4️⃣ User question → Find similar chunks 5️⃣ GPT generates answer using relevant context 6️⃣ Return response with source citations 𝗪𝗵𝗮𝘁 𝗜 𝗟𝗲𝗮𝗿𝗻𝗲𝗱: - RAG implementation is more accessible than ever - Vector databases unlock powerful semantic search - Good design transforms technical tools into delightful experiences - Building in public accelerates learning 𝗣𝗲𝗿𝗳𝗲𝗰𝘁 𝗙𝗼𝗿: 📚 Students analyzing research papers 💼 Professionals reviewing contracts 🔬 Researchers working with large documents 📊 Anyone extracting insights from PDFs 𝗪𝗵𝗮𝘁'𝘀 𝗡𝗲𝘅𝘁: Planning to add user authentication, document folders, chat export, and voice input support. --- Check out the code: github.com/Princeeze744/DocC… What would YOU use an AI document assistant for? Drop your use case in the comments! 👇 #AI #Python #Django #LangChain #OpenAI #RAG #MachineLearning #FullStackDevelopment #BuildInPublic #SoftwareEngineering #TechInnovation #DocumentAI #VectorDatabase #Day18Challenge #FullStackDeveloper --- 👨‍💻 Prince Ochidi | Full Stack Developer Building AI-powered solutions one day at a time

Good morning, domainers! 👋 Happy Halloween 🎃 Drop the latest domain you’ve acquired this week! My latest are FightingBets in .com and DocChat in .net ⭐️ What you got? ⬇️

🚀 We believe that documentation isn’t just “files in a folder” it should know what you need when you need it. With DocsGPT, you can ask your docs anything, and get answers, not just pages. 🔒 100% private • Trusted by UK enterprises • Open-source with 17K stars! github.com/arc53/DocsGPT 💡 Why DocsGPT rocks: - You chat with your docs like it’s a smart teammate. - Perfect for teams drowning in specifications, APIs, SOPs. - Built for real world use: privacy enterprise scale. 🔥 Join the conversation: docsgpt.cloud #DocsGPT #DocChat #AIforDocs #EnterpriseAI #OpenSource #KnowledgeOps

Just Built a RAG DocCHAT AI Assistant 🚀 Upload PDFs/DOCs/TXTs → parse → chunk → embed → store → query → get smart answers in real-time. Built with Supabase, pgvector, and OpenRouter. 🔗 Live: lnkd.in/dn54Hawk 🧠 GitHub: lnkd.in/d_yR7iUb

Just Built a RAG DocCHAT AI Assistant 🚀 Upload PDFs/DOCs/TXTs → parse → chunk → embed → store → query → get smart answers in real-time. Built with Supabase, pgvector, and OpenRouter. 🔗 Live: lnkd.in/dn54Hawk 🧠 GitHub: lnkd.in/d_yR7iUb

8. Summarize or Chat with any pdf or doc using Merlin. Improve Your Study Experience with Accurate PDF or Doc Summaries using DocChat on Merlin AI Chatbot.

10. 使用Merlin摘要或与任何pdf或文档聊天 使用Merlin AI聊天机器人上的DocChat获取准确的PDF或文档摘要，提升你的学习体验。

10. Summarize or Chat with any pdf or doc using Merlin. Improve Your Study Experience with Accurate PDF or Doc Summaries using DocChat on Merlin AI Chatbot.

Thanks for always sharing such informative content, John! These tools are fantastic. I really enjoyed the video with @gregisenberg about building directories to create a paying user base. It got me thinking: I’m working on a couple of healthcare tech ideas, and I’m considering starting with a directory of the best doctors in the USA. The goal would be to establish a strong initial user base that could later transition into my main apps, DOCchat and InstaHealth. Would love to hear any thoughts you might have on this approach!

2/ Merlin : Free Chrome extension with: ✔️GPT-4 AI Chatbot ✔️Unlimited Google Search responses ✔️WebChat for websites ✔️DocChat for PDFs and docs

Cerebras DocChat Released: Built on Top of Llama 3, DocChat holds GPT-4 Level Conversational QA Trained in a Few Hours The release of DocChat by Cerebras marks a major milestone in document-based conversational question-answering systems. Cerebras, known for its deep expertise in machine learning (ML) and large language models (LLMs), has introduced two new models under the DocChat series: Cerebras Llama3-DocChat and Cerebras Dragon-DocChat. These models are designed to deliver high-performance conversational AI, specifically tailored for document-based question-answering tasks, and were developed with unprecedented speed using Cerebras’ cutting-edge technology. Cerebras Llama3-DocChat is built on the foundation of Llama 3 and incorporates advanced insights from recent research in the field, particularly Nvidia’s ChatQA model series. The development of this model involved leveraging extensive experience in LLM training and dataset curation alongside innovative techniques like synthetic data generation. This approach enabled Cerebras to address limitations that could not be fully resolved using available real-world data. Cerebras Dragon-DocChat is a multi-turn retriever model that is fine-tuned to improve recall rates. The model was trained on the ChatQA conversational Q&A dataset and enhanced using contrastive loss with hard negatives, leading to significant improvements in recall rates compared to its predecessors and competitors..... Read our full take on this: marktechpost.com/2024/08/24/… Model Card: huggingface.co/cerebras/Llam… Details: cerebras.ai/blog/train-a-gpt… @cerebras

Good Morning Designers 🤗✨️ I'm happy to share a few screens I've wrapped up for VaxTrack! Vaccination Tracker and Medication Tracker, Docchat (Video call Chatbox) More to come as I continue working on this project #UIDesign #UXDesign #MobileDesign #DesignInspiration #figma

Cerebras dropped DocChat: GPT-4 Level Conversational QA Model - designed for document-based conversational question answering. Cerebras Llama3-DocChat was built on top of Llama 3 base using insights from the latest research on document-based Q&A, most notably Nvidia’s ChatQA model series.

⚡ Cerebras Announces DocChat: GPT-4 Level Conversational QA Trained in a Few Hours ⚡ DocChat is our latest document-based conversational AI model series, that includes two models: 🦙 Cerebras Llama3-DocChat - An LLM that was trained to GPT-4 level document-based Q&A performance in a few hours 🐲 Cerebras Dragon-DocChat - A multi-turn retriever model that was fine-tuned in a few minutes DocChat models demonstrate top-of-the-line performance across major QA benchmarks and are fully open-source (model weights, training recipes, and datasets) Read more in our blog: cerebras.net/blog/train-a-gp… Check out the model here: huggingface.co/cerebras/Llam…

A.I. ไทยที่ทำงานคล้ายๆ ChatGPT หรือ Gemini ที่ สวทช. พัฒนาโดยรันโมเดลบนซูเปอร์คอมพิวเตอร์ Lanta คือ Docchat โดยสามารถสรุปเอกสารทั้ง doc pdf ภาษาไ ทย ลองใช้กันได้ที่ abdul.in.th/docchat

Ditch long waits! 🩺 DocChat brings healthcare straight to patients. See how we transformed a client's approach: bit.ly/3TM6OCh #healthcarechatbot #futureofhealthcare

Our BHM feature of the day is Dr. John Turner. He is the CEO of The Professor JT LLC, creating educational programs & content on LinkedIn & YouTube as the Host of The Doc Chat Show! youtube.com/@THEPROFESSORJT/… #BHM #AdjunctFaculty #DocChat #Author