Manual to Magical: AI in Developer Tooling

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Learn how I built an AI-assisted codemod tool, exploring AI performance and automation. See its impressive ease and speed, inspiring you to create your own AI-driven solutions.

This talk has been presented at JSNation US 2024, check out the latest edition of this JavaScript Conference.

FAQ

RedwoodJS is a JavaScript and TypeScript framework designed for productivity in one-person projects or projects at smaller companies, providing a quick setup for full stack applications.

RedwoodJS uses code mods to help users upgrade from one version to the next, ensuring they can benefit from the latest features and security updates.

Code mods are important because they help users keep their RedwoodJS applications updated with the latest features and security patches without manual effort.

Toby uses the OpenAI SDK to generate code mods, leveraging AI to make the process quicker and more efficient.

Toby found that some AI frameworks like LangChain and Llama Index had limited TypeScript support, leading him to choose OpenAI for better documentation and quicker iteration.

The temperature setting in AI models affects the creativity of the output. For programming, a setting of 1 is recommended for balanced results.

Static analysis tools like ESLint, Prettier, and the TypeScript compiler are used to verify the correctness of AI-generated code mods, helping to identify errors.

The key strategy is to iterate and collaborate with AI, using static analysis tools and reinforcement techniques to refine outputs.

Users can contact Toby via his website tobi.dev or on Twitter as @tobi_dev.

Codemodder is a CLI tool developed by Toby to assist with generating code mods using AI, particularly through the OpenAI SDK.

Tobbe Lundberg
Tobbe Lundberg
18 min
21 Nov, 2024

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Video Summary and Transcription
RedwoodJS is a productive JavaScript TypeScript framework that uses code mods to help users upgrade and ensure they have the latest features and security updates. The speaker developed a CLI called Codemodder, using OpenAI's excellent documentation and SDK. They experimented with reinforcement techniques to improve AI understanding and generated test case descriptions. The AI's creativity control parameter didn't work well for programming, so the speaker asked the AI to generate other possible inputs and let the user verify them. Verifying the code mod is done through static code analysis tools like ESLint and TypeScript compiler. Iterating between generating and testing, the speaker often ends up with an error-free code mod. Automating the evaluation process and following standard research and experimentation rules is key to improving the output. Settling for 'good enough' and measuring the impact of changes through error count is important. Collaboration with the AI using available tools, iterating, and aiming for objective performance evaluation is recommended. Codebots are great for developer experience but time-consuming to write. The speaker encourages using an AI framework with good documentation, iterating quickly, and using clear prompts. The temperature setting is not necessary for code-related outputs. Connecting with the speaker and getting inspired to build AI-powered developer tools is also mentioned.
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