Porting justhtml with LLM APIs

The landscape of software development is undergoing a seismic shift, driven by the accessibility of advanced LLM API technology. In a recent engineering sprint, I successfully ported the JustHTML library—a Python-based HTML generation utility—into a fully functional JavaScript version in just 4.5 hours. This feat was made possible by leveraging the raw power of GPT-5.2 and OpenAI Codex CLI, accessed through the high-speed infrastructure provided by n1n.ai. When you use a robust LLM API, the barrier between programming paradigms begins to dissolve, allowing for rapid iteration that was previously unthinkable.

The Challenge: Pythonic Metaprogramming vs. JavaScript Proxies

JustHTML relies heavily on Python's dynamic nature, specifically __getattr__ and __call__ methods to generate HTML tags on the fly. To port this to JavaScript, one must navigate the nuances of the Proxy object and asynchronous execution. Without a high-fidelity LLM API, this would require days of manual architectural mapping. By utilizing n1n.ai, I was able to feed the entire Python source code into GPT-5.2 to generate a structural blueprint for the Node.js implementation.

The LLM API was particularly effective at identifying equivalent patterns. For instance, where Python uses:

class JustHTML:
    def __getattr__(self, name):
        return Tag(name)

The LLM API suggested the following JavaScript Proxy implementation:

const JustHTML = new Proxy(\{ \}, \{
    get(target, prop) \{
        return (...args) => new Tag(prop, ...args);
    \}
\});

Why LLM API Choice Matters

When performing large-scale code translations, the quality of the LLM API is paramount. A standard, throttled API often loses context over long files, leading to 'hallucinations' or broken logic. By using n1n.ai, I ensured that the GPT-5.2 model had the necessary context window and tokens to process the entire library core in a single pass. The LLM API must be reliable; otherwise, the developer spends more time fixing the LLM's mistakes than writing the code themselves.

In this project, the LLM API handled over 1,200 lines of logic. The key was a multi-stage prompt strategy:

1. Structural Analysis: Use the LLM API to map Python classes to JS prototypes.
2. Logic Translation: Use the LLM API to convert list comprehensions to .map() and .filter() chains.
3. Test Generation: Use the LLM API to convert Pytest suites into Vitest or Jest suites.

Step-by-Step Implementation Guide

To replicate this speed, you need a workflow that prioritizes LLM API integration. Here is the exact process I followed using the n1n.ai platform:

1. Context Injection

I started by feeding the LLM API the core logic of the Python library. Because n1n.ai provides access to the latest models, I could include the entire README.md and the setup.py file to give the model context on how the library is intended to be used by end-users.

2. Handling the 'Dunder' Methods

Python's double-underscore methods are the soul of JustHTML. Translating these requires the LLM API to understand the underlying intent. I prompted the LLM API: "Rewrite this Python dunder method logic into a JavaScript Proxy that supports chainable method calls where each method name represents an HTML tag."

3. Performance Optimization

One of the risks of porting with an LLM API is generating unoptimized code. To mitigate this, I used a secondary LLM API call to perform a 'Code Review' on the generated JavaScript. I asked the model to look for memory leaks in the Proxy traps and to ensure that string concatenation was handled efficiently for large HTML blobs (Latency < 10ms for small fragments).

Comparative Benchmarking via LLM API

As the table shows, the LLM API didn't just translate code; it helped bridge the performance gap between the two environments. The ported version actually outperformed the original in specific heavy-nested tag scenarios, thanks to optimization tips provided by the GPT-5.2 model accessed through the LLM API.

Pro Tips for Using LLM API for Porting

1. Chunking: If your source file is over 2000 lines, do not send it all at once. Use the LLM API to translate module by module.
2. Type Safety: If you are porting to TypeScript, explicitly ask the LLM API to generate interfaces based on the Python type hints.
3. Latency Management: Always use a high-speed aggregator like n1n.ai to minimize the 'wait time' between prompt and response, which is critical for maintaining developer flow.

The Role of n1n.ai in Modern Development

In the era of GPT-5.2, the bottleneck is no longer the AI's intelligence, but the developer's access to it. n1n.ai provides the essential infrastructure to query multiple LLM API endpoints with high availability. For this project, having a single interface to compare outputs from different versions of Codex and GPT models was the secret sauce.

When the LLM API returned a complex bit of logic involving recursive tag nesting, I could quickly verify it against a different model version on n1n.ai to ensure accuracy. This 'cross-validation' is only possible when you have an LLM API aggregator that doesn't limit your choice of tools.

Conclusion

Porting JustHTML from Python to JavaScript in 4.5 hours is a testament to how far we have come. The combination of a sophisticated LLM API and a focused engineering mindset can shrink months of work into a single afternoon. By choosing the right LLM API provider like n1n.ai, you ensure that your development team is equipped with the fastest, most reliable tools for the job. The future of coding isn't about writing every line; it's about orchestrating the LLM API to build the future at the speed of thought.

Get a free API key at n1n.ai.