Innovating Developer Tools with AI: Insights from GitHub Next
How do you design AI developer tools that account for LLM hallucinations? Learn how GitHub Next is building controllable co-agents that prioritize structured, task-oriented workflows.
#1about 4 minutes
Investigating the future of software development at GitHub Next
GitHub Next focuses on creating AI-powered prototypes to enhance developer experience, productivity, and happiness.
#2about 6 minutes
Explaining how large language models work and why they hallucinate
Large language models function by predicting the most probable next word, a probabilistic nature that can lead to incorrect or fabricated outputs known as hallucinations.
#3about 5 minutes
How GitHub Copilot was designed to keep developers in flow
The original GitHub Copilot uses inline suggestions and a "ghost text" UI to automate boilerplate code without disrupting a developer's state of flow.
#4about 4 minutes
The impact of ChatGPT and the rise of chat interfaces
ChatGPT's mainstream success created a user expectation for chat-based AI interfaces, which are better suited for planning and exploration than for maintaining coding flow.
#5about 9 minutes
Using Copilot Workspace to turn GitHub issues into code
Copilot Workspace provides a structured workflow that uses AI to brainstorm, generate an implementation plan, and apply code changes directly from a GitHub issue.
#6about 5 minutes
Building and iterating on micro-applications with GitHub Spark
GitHub Spark is a tool and runtime that allows developers to rapidly generate and modify small applications using natural language prompts.
#7about 7 minutes
Designing cooperative and controllable AI agents for developers
Effective AI tools should function as controllable "co-agents" that enhance user capabilities and are designed defensively to handle inevitable failures gracefully.
#8about 4 minutes
Exploring the future of AI beyond simple code generation
The next frontier for AI developer tools includes creating UIs that sync natural language with code, focusing on code understanding, and carefully considering the ethics of AI application.
#9about 1 minute
Book recommendations for prompt engineering and LLM observability
Two recommended books cover the practical skills of prompt engineering for LLMs and the critical process of implementing observability for AI systems.
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