A developer's weekly update covering work on an LLM project, custom scripting, PICO-8 experimentation, and blog theme adjustments.
A software engineer explores using LLMs and R code to analyze GitHub activity to track and summarize their weekly work, addressing the challenge of remembering tasks.
A curated list of 12 influential LLM research papers from 2024, highlighting key advancements in AI and machine learning.
Summarizes key challenges and methods for evaluating open-ended responses from large language models and foundation models, based on Chip Huyen's book.
A summary and discussion of Chapter 1 of Chip Huyen's book, exploring the definition of AI Engineering, its distinction from ML, and the AI Engineering stack.
A step-by-step guide to implementing the Byte Pair Encoding (BPE) tokenizer from scratch, used in models like GPT and Llama.
A step-by-step educational guide to building a Byte Pair Encoding (BPE) tokenizer from scratch, as used in models like GPT and Llama.
A guide on using Anthropic's Model Context Protocol (MCP) to connect AI agents with tools and data sources using various LLMs like OpenAI or Gemini.
A guide to common mistakes developers make when building applications with generative AI, including overuse and poor UX integration.
A guide on the pitfalls of blindly using cosine similarity with text embeddings and how to apply it more intentionally for better results.
A summary of Chapter 6 from 'Prompt Engineering for LLMs', covering prompt structure, document templates, and strategies for effective context inclusion.
A developer builds an AI-powered reading companion called Dewey, detailing its features, design, and technical implementation.
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.
A tutorial on building a simple AI-powered chat client in Java using the Spring AI framework, covering setup, configuration, and provider abstraction.
Introducing Logfire, Pydantic's new observability tool for Python, with easy integration for OpenAI LLM calls, FastAPI, and logging.
Explores using Azure AI Inference Service to simplify LLM integration, focusing on Python SDK and GitHub Marketplace for experimentation.
Argues that building a good search engine is more critical for effective RAG than just using a vector database, as poor retrieval misleads AI.
Analyzes why building Large Language Models (LLMs) may be a poor business, comparing the AI industry's structure to historically unprofitable sectors like airlines.
Explores a method using a 'Judging AI' (like o1-preview) to evaluate the performance of other AI models on tasks, relative to human capability.
