Grok 3 is impressive. Maybe not the best, but among the best, and for many tasks the best that won't say no. Grok 3 trusts the prompter like no frontier model I've used since OpenAI's Davinci in 2022, and that alone gets it a place in my toolbox.
GENERATIVE AI
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Can AI Discover Relativity From Early 1900s Data Alone?
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I proposed a version of this at a dinner to some high level AI (train it only on data up to the early 1900s and see if it comes up with relativity) They said there wasn’t enough data. Not sure if that applies to high school too but it might
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AI-Generated Book Summaries Cannot Replace Quality Literature
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the books that are better read as AI responses weren’t very good books anyway says more about his taste in books than anything else imo saying this as a Deep Research DAU
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Fine-tuning’s Surprising Power: Knowledge Retention Through Style Change
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Wow it really has been that long 😐
The big thing I didn’t realize is that an assistant was just a finetune away. That is the surprising thing I was really missing. I still find it surprising today, that you can just change the style so dramatically but retain the knowledge. -
Avoid Common AI Prompt Mistakes and How to Fix Them for Better Results
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I help you avoid Common AI Prompt Mistakes and How to Fix Them They hinder clarity and effectiveness. • Be specific • Define goals • Test prompts Click below to read more: https://
buff.ly/3Xc3vXs -
GenAI Agents Hub: LangChain LangGraph Multi-Agent Systems
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GenAI Agents Hub A comprehensive collection of GenAI agent implementations, from simple bots to complex multi-agent systems. Built with LangChain and LangGraph, this repository empowers developers across business and creative applications. Start building your agents:
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Three Essential LLM Books Covering Different Perspectives
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Spotted this on Amazon, and it was such a nice coincidence and spot-on recommendation that I had to share it. As someone who wrote one and read the other two, what I like about this selection is how they cover LLMs on very different levels, with basically no overlap (besides them
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2025 LLM Roadmap: From Fundamentals to Production Applications
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Here’s the 2025 LLM roadmap 1. Code and train your own LLM to really understand the fundamentals
2. Train models more conveniently using production-ready libraries
3. Learn about the big-picture considerations for real-world LLM/AI apps -
Embedding Fine-Tuning Guide for RAG Systems
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RAG Embedding Fine-Tuning Guide A practical guide demonstrating embedding model fine-tuning for RAG systems, featuring LangSmith monitoring and RAGAS metrics for performance evaluation. Check out the complete implementation https://
github.com/apatti/AIEBoot
camp/blob/main/09_Finetuning_Embeddings/Fine_tuning_Embedding_Models_for_RAG_using_RAGAS.ipynb
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DeepSeek R1 Implementation with LangChain Azure Integration
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DeepSeek R1 Tutorial Learn to implement DeepSeek's reasoning model using LangChain and Azure AI. This hands-on guide shows you model integration and streaming responses in action. Key features:
– LangChain-Azure integration
– Real-time streaming Watch: