This document demonstrates that a non-trivial Python application — in this case a full SN76489 sound chip emulator — can be built purely from structured text, without writing code by hand.
No Python knowledge is required to follow the process.
The key is not “AI magic”, but disciplined specification:
- clear functional intent
- explicit technical contracts
- deterministic tests
- controlled iteration
Large Language Models (LLMs) are used only as implementation accelerators.
A fully working SN76489 PSG emulator in Python, featuring:
- audio output
- MIDI input
- VGM file playback
- deterministic debug & regression tooling
All functionality lives in one Python file and runs on macOS.
Public release: v0.06
Repository:
https://github.com/pappavis/SN76489_emulator
This HOWTO is intended for:
- engineers curious about AI-assisted development
- technical managers evaluating LLM productivity claims
- architects interested in specification-first workflows
- hardware/software hobbyists validating emulation concepts
No Python knowledge is required to follow the process.
If you can describe a system precisely enough,
an AI can implement it — even if you cannot code.
The quality of the output is directly proportional to:
- the quality of the specifications
- the clarity of constraints
- the strictness of acceptance criteria
Open any modern LLM, for example:
- ChatGPT
- GitHub Copilot Chat
- Google Gemini
Copy-paste the full contents of: docs/02_Technical_Specification.md into the chat window.
Do not summarize it.
Do not paraphrase it.
After pasting, enter:
Build sn76489_emulator.py immediately, exactly according to this specification.Wait for the code to be generated.
- Install Python (free, from python.org)
- Install dependencies as instructed
- Run the generated script
If the specification was followed correctly:
- audio will play
- tests will pass
- debug output will match the contract
This project was completed in one evening:
-
Start: 2-Feb-2026, 17:30
Initial idea + first functional description -
Iteration:
Functional Specs → Technical Specs → Tests → Corrections -
Public release: 2-Feb-2026, 22:00
Version v0.06 (VGM playback, MIDI, full debug contracts)
Total elapsed time: ~5 hours
This experiment shows that:
- LLMs do not replace engineering thinking
- They amplify well-structured reasoning
- Poor specs lead to poor code, regardless of AI
In other words:
The bottleneck is no longer typing speed —
it is clarity of thought.
- Text is an executable artifact when structured correctly
- Specifications are more valuable than code
- AI works best when treated as a compiler, not an author
- Rollback, tests and determinism still matter
- Not a claim that “anyone can code anything instantly”
- Not a replacement for engineering judgement
- Not an endorsement of unreviewed AI output
Human intent remains the controlling factor.
Possible follow-ups include:
- expanding the specification to new features
- comparing multiple AI implementations against the same spec
- applying the method to non-audio domains
- bridging the emulator to real SN76489 hardware
See the main repository README for the current roadmap.
- Idea, execution: Michiel Erasmus