1/ ChatGPT What to do this weekend: Let’s see what ChatGPT tells us to do… Prompt 1: Result
TOOLS
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Replacing Google Search with AI Tools
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Google search is dead. It's been 2 months since I last used Google for search. Instead, I've relied on ChatGPT, Claude, and Perplexity. Here's how I use AI for search:
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Streamlit Demo App Using Claude Web Search Integration
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Working Application demo using Streamlit
— Shubham Saboo (@Saboo_Shubham_) 2 juillet 2024
Paste the above code in vscode or pycharm and run the following command: 'streamlit run claude_websearch.py' pic.twitter.com/I9iF3KEQUfWorking Application demo using Streamlit Paste the above code in vscode or pycharm and run the following command: 'streamlit run claude_websearch.py'
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Full Application Code for Generative AI Search Assistant
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Full Application code for Generative AI Search Assistant
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Building Streamlit User Interfaces with Python Code
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3. Set up the Streamlit App Streamlit lets you create user interface with just python code, for this app we will:
• Add a title to the app using 'st.title()'
• Add a description for the app using 'st.caption()' -
Essential Libraries for Building AI Agents with Streamlit
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2. Import necessary libraries • Streamlit for building the web app
• Phidata for building AI agents
• Anthropic for using Claude via API
• Duckduckgo for the search functionality -
Amazon Proposes New AI Benchmark to Measure RAG
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Amazon proposes a new AI benchmark to measure RAG Choosing the right algorithm for RAG could yield more AI improvements than scaling to larger and larger language models, say AWS researchers. https://
zdnet.com/article/amazon
-proposes-a-new-ai-benchmark-to-measure-rag/
… $AMZN -
Glasp: Import Kindle highlights into your second brain
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You might like @_Glasp
! Import all of your Kindle highlights into a second brain that you can ask questions too. -
LoRA: Efficient LLM Fine-tuning Technique for Custom Applications
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Building with #LLMs? Then you owe it to yourself to know about #LoRA. LoRA is an #efficient technique for #finetuning pre-trained LLMs for your use case. It's the same technique employed by @Apple and what we use to train small models that beat #GPT4.