This article introduces Codebase Navigator, a tool designed to simplify the process of understanding large, unfamiliar GitHub repositories. By pasting a repository URL, users can interact with an AI assistant that provides a live dependency graph built from actual import statements, a code viewer, and a full file tree. Unlike standard AI assistants that often hallucinate file paths, this tool uses real data to visualize connections between files in real time. Built with a modern tech stack including Next.js, CopilotKit, and React Flow, the project can be run entirely for free using local LLMs via Ollama. The author provides a deep dive into the architecture, the technical implementation of the dependency resolution, and how the tool maintains state across multiple UI panels.
This guide helps engineers build and ship LLM products by covering the full technical stack. It moves from core mechanics (tokenization, embeddings, attention) to training methodologies (pretraining, SFT, RLHF/DPO) and deployment optimizations (LoRA, quantization, vLLM). The focus is on managing critical production tradeoffs between accuracy, latency, memory, and cost
This repository focuses on the concept of an "agent" as a trained model, not just a framework or prompt chain. It emphasizes building a "harness" – the tools, knowledge, and interfaces that allow the model to function effectively in a specific domain. The core idea is that the model *is* the agent, and the engineer’s role is to create the environment it needs to succeed.
The content details a 12-session learning path, reverse-engineering the architecture of Claude Code to understand how to build robust and scalable agent harnesses. It highlights the importance of separating the agent (model) from the harness, and provides resources for extending this knowledge into practical applications.
The content details a 12-session learning path, reverse-engineering the architecture of Claude Code to understand how to build robust and scalable agent harnesses. It highlights the importance of separating the agent (model) from the harness, and provides resources for extending this knowledge into practical applications.
This article provides a comprehensive guide on implementing the Model Context Protocol (MCP) with Ollama and Llama 3, covering practical implementation steps and use cases.
A guide to setting up local LLMs on Linux using LLaMA.cpp, llama-server, llama-swap, and QwenCode for various workflows like chat, coding, and data analysis.
This blog post details how to build a natural language Bash agent using NVIDIA Nemotron Nano v2, requiring roughly 200 lines of Python code. It covers the core components, safety considerations, and offers both a from-scratch implementation and a simplified approach using LangGraph.
This tutorial provides a step-by-step guide on building an LLM router to balance the use of high-quality closed LLMs like GPT-4 and cost-effective open-source LLMs, achieving high response quality while minimizing costs. The approach includes preparing labeled data, finetuning a causal LLM classifier, and offline evaluation using the RouteLLM framework.
An article discussing the current state, recent approaches, and future directions of prompt engineering in data and machine learning. It includes several links to relevant articles and tutorials on the topic.
This is a hands-on guide with Python example code that walks through the deployment of an ML-based search API using a simple 3-step approach. The article provides a deployment strategy applicable to most machine learning solutions, and the example code is available on GitHub.
A tutorial showing you how how to bring real-time data to LLMs through function calling, using OpenAI's latest LLM GTP-4o.
