Simon Willison discusses his experience using Large Language Models (LLMs) for coding, providing detailed advice on how to effectively use LLMs to augment coding abilities, set reasonable expectations, manage context, and more.
Zero Human Code: What I Learned from Forcing AI to Build (and Fix) Its Own Code for 27 Straight DaysAn experiment in agentic AI development, where AI tools were tasked with building and maintaining a full-service product, ObjectiveScope, without direct human code modifications. The process highlighted the challenges and constraints of AI-driven development, such as deteriorating context management, technical limitations, and the need for precise prompt engineering.
Dolphin 3.0 R1 is an instruct-tuned model designed for general-purpose reasoning, coding, math, and function calling. It is designed to be a local model that businesses can control, including setting the system prompt and alignment.
The Palo Alto City Library offers a series of online tutorials and resources to guide absolute beginners in learning coding, robotics, and computational thinking through various projects and initiatives including the use of robots such as Elsie and Dewey, and the agricultural robot FarmBot.
A beginner-friendly guide to AI development with Python, covering basics and sharing a concrete example with code.
01.AI's Yi-Coder, an open-source AI coding assistant
Key Features:
- 9B and 1.5B parameter versions
- 52 programming languages
- context length of 128,000 tokens
- code editing, completion, debugging, and mathematical reasoning
Cody is an AI coding assistant that uses advanced search and codebase context to help you understand, write, and fix code faster. It supports autocomplete, code generation, and explanation in various IDEs and code hosts.
Unblocked is an AI tool that augments code with knowledge from systems like GitHub, Slack, Confluence, and Jira to provide quick, accurate answers about your application.
This article provides four key concepts for writing modern Python, including type hinting, Python virtual environments and package management, new Python syntax, and Python testing.
Discover how GitHub engineers leverage GitHub Copilot to automate repetitive tasks, stay focused, and even explore new technologies.
The GitHub Blog article "4 ways GitHub engineers use GitHub Copilot" discusses how different GitHub engineers leverage GitHub Copilot to enhance efficiency and productivity in their work. The main use cases highlighted in the article are:
Semi-automating repetitive tasks: A GitHub engineer uses GitHub Copilot to automate the process of incrementing ID numbers in protobuf definitions, significantly reducing manual work.
Avoiding distractions: A colleague develops a regular expression to capture a Markdown code block and extract the language identifier. By prompting GitHub Copilot with a code comment, the regular expression is generated swiftly and accurately.
Structuring data-related notes: A support engineer at GitHub uses GitHub Copilot Chat to structure and compile their notes into Markdown tables, enabling efficient documentation of a complex troubleshooting process.
Exploring and learning: A colleague uses GitHub Copilot to develop a program in Rust, a programming language he was not previously familiar with. In just 23 minutes, he successfully created a functional program that converts numerical input into written English equivalents.
