Quick Demo - @coder AI Pack

Here is a quick AI coding demo using the AIPACK runtime and my JC Coder AI Pack for production coding.

While this demo is very simple and might fall into the "snake game" AI coding category, it demonstrates the flexibility of the JC Coder pack—which I am using to code AIPACK itself and other production projects.

Video chapters:

• 00:00 - AIPACK installation and initialization

• 00:10 - Install and run jc coder pack

• 00:37 - Creating the initial Pong game

• 02:34 - Refactor 1 – Separating JS and CSS into their own files

• 03:24 - Refactor 2 – Using the type module and splitting JS files

• 04:47 - Adding Particle explosion

• 06:07 - The final result

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Key Concepts for the jc@coder Pack:

• Works with any IDE, as it is just a command line tool (Tip: map "cmd + r" to send 'r' to the terminal).

• File-based – the prompt is a single Markdown file, with the top section containing the instructions and the bottom containing the AI response. So, no copy/paste code into the terminal or get lost in chats.

• Fully parametric – thanks to AIPACK parametric agent support, JC Coder allows the prompt to customize the model, knowledge/source context files, and even concurrency and temperature.

• AI response info – returns usage and cost details in the prompt file.

• The JC Coder AIPACK is extremely small (currently under 8 KB zipped) and implements all of the agent coding logic.

• Once installed, the JC Coder Pack can be found at ~/.aipack-base/pack/installed/jc/coder. For very advanced users, it can be copied into your ~/.aipack-base/pack/custom/my/coder directory so that you can customize it and run it with aip run my@coder.

Key Concepts/Benefits for AIPACK:

• AIPACK is built with Rust, ensuring efficiency and high concurrency.

• It leverages the Rust genai crate, which supports all major AI providers and models.

• AIPACK is an agentic runtime offering a multi-stage, Markdown-based model and Lua as the efficient embedded scripting language, providing full flexibility and simplicity.

• AIPACK features an innovative parametric agent model that allows agents to extract deterministic parameters directly from the prompt—or even from the AI response.

• AIPACK is designed for concurrency, allowing agent packs like JC Coder to parallelize agent tasks across multiple input files (demo video coming).

• AIPACK can run any multi-stage agent Markdown file using a command like: aip run path/my-agent-aip

• It can also pack, install, and run AI Packs whenever users are ready to share their agents.

• See intro video at

Subscribe to this Substack for news and updates about AIPACK. It's just the beginning – things are about to pick up speed, thanks to AIPACK.

Until next time – happy running!