Explores the key traits of 'Expert Generalists'—professionals who bridge multiple specialties—and their growing importance in tech.
Key takeaways from Simon Willison's podcast interview on software architecture, plugins, and effectively using LLMs in development.
A course teaching how to code Large Language Models (LLMs) from scratch to deeply understand their inner workings and fundamentals.
A tutorial on building a React.js UI to stream and compare responses from multiple AI models simultaneously using the Vercel AI SDK.
A tutorial on building a React.js chat app that allows users to switch between multiple AI models like GPT-4 and Claude 3 using the AI SDK.
Explores building a web framework designed for AI-generated code, addressing LLM challenges like API mismatches and training data limitations.
An explanation of the Model Context Protocol (MCP), an open standard for connecting LLMs to data and tools, and why it's important for AI development.
Explores four main approaches to building and enhancing reasoning capabilities in Large Language Models (LLMs) for complex tasks.
A developer's experience with the Cline AI coding assistant, exploring its capabilities for bug fixing and the implications for programmers.
A curated list of 12 influential LLM research papers from 2024, highlighting key advancements in AI and machine learning.
A step-by-step guide to implementing the Byte Pair Encoding (BPE) tokenizer from scratch, used in models like GPT and Llama.
A guide on the pitfalls of blindly using cosine similarity with text embeddings and how to apply it more intentionally for better results.
Developer revives his old AI startup's brainstorming tool by building a GitHub Copilot Extension, using VS Code's speech features and LLMs.
A curated list of notable LLM and AI research papers published in 2024, providing a resource for those interested in the latest developments.
The article explores how the writing process of AI models can inspire humans to overcome writer's block by adopting a less perfectionist approach.
A 3-hour coding workshop teaching how to implement, train, and use Large Language Models (LLMs) from scratch with practical examples.
Analyzes the latest pre-training and post-training methodologies used in state-of-the-art LLMs like Qwen 2, Apple's models, Gemma 2, and Llama 3.1.
Analyzing if a Codenames bot can win using only card layout patterns, without understanding word meanings.
Analyzing if AI can replace humans using computational theory, comparing countable vs. uncountable problems and AI's inherent limitations.
A technical review of April 2024's major open LLM releases (Mixtral, Llama 3, Phi-3, OpenELM) and a comparison of DPO vs PPO for LLM alignment.
