Building a Modern C64 Assembly AI Toolchain using Google Gemini 3

Source: Dev.to

Introduction

In the current AI landscape, it is easy to be impressed by the sheer volume of working code models produce. We see them generating Python scripts, React components, and complex SQL queries with apparent ease. However, these successes often occur within modern, forgiving development environments that mask fundamental inefficiencies. They offer abundant memory, standard libraries that abstract away complex logic, and garbage collection that forgives sloppy resource management.

Real problem‑solving, however, shows up best when resources are scarce and the safety nets are removed. For the past few months, I have been working on a personal benchmark I call The Commodore 64 Constraint.

The question is straightforward but brutal: Can an AI generate a functional game for a 1982 home computer with only 64 KB of RAM, a 1 MHz processor, and no native sprite handling in the language itself?

Recently, Gemini 3 became the first model to successfully pass my “Tetris Test” — a creativity‑constraint challenge I designed to filter out models that rely on rote memorization. This was a significant milestone; previous models (like Claude 4.0 and GPT‑4) frequently stumbled into what I call “stochastic archaeology” — producing code that was a broken pastiche of forum snippets, often hallucinating commands that never existed.

But BASIC, while constrained, is still high‑level. It is slow and interpreted. To truly test the limits of AI engineering capabilities, I decided to take a steep step up. I moved from high‑level logic to the bare metal: Snake in 6510 Assembly, wrapped in a modern, custom‑built Python AI toolchain.

The Benchmark: Why Gemini 3 Changed the Game

When I tested models on my C64 Tetris challenge (in BASIC), the failures fell into two distinct categories:

• Stochastic Archaeology: The model found a similar script in its training data (perhaps an Apple II or VIC‑20 game) and tried to force‑fit it to the C64. This often resulted in obscure variable names like A1 or Z9 and logic that simply didn’t compile.
• Hallucination: The model attempted to use “logical” commands that simply don’t exist on the platform, assuming the hardware was more capable than it actually is.

Gemini 3 demonstrated a different mode of operation. It didn’t just recall code; it appeared to reason through the problem from first principles. The evidence was in the implementation details:

• Algorithmic Choice: Instead of using lookup tables (the historical standard for 8‑bit rotation to save cycles), it derived the mathematical rotation matrix (x' = -y) directly, prioritizing logical correctness over historical optimization patterns.
• Modern Architecture: It used descriptive variable names (px for player x, py for player y) and structured GOSUB routines, treating the ancient BASIC interpreter like a modern structured language rather than writing spaghetti code.
• Constraint Awareness: It pre‑calculated memory offsets for screen and color RAM to save CPU cycles during the render loop, showing an understanding of the 1 MHz bottleneck.

If my Tetris challenge in BASIC was the test of logical reasoning, Snake in Assembly is the ultimate test of systems engineering.

The Architecture

To make this leap, Gemini 3 built a Python‑based AI toolchain that treats the emulated Commodore 64 not as a black box, but as an embedded device it can probe and control programmatically.

The stack consists of four key components:

Part 1: The Metal (6510 Assembly)

Writing Snake in Assembly forces you to think about memory layout immediately. Unlike modern development where malloc handles allocation invisibly, here every byte must be manually accounted for.

Gemini 3 mapped the memory to optimize for the 6510’s strengths:

• $0400 (Screen RAM): The visual grid. The C64 screen is a matrix of 40 × 25 characters. Writing the byte 81 (a solid block) to address $0400 puts the snake’s head in the top‑left corner.
• $0002 — $00FF (Zero Page): The “fast lane” of memory. The 6510 has special instructions for accessing the first 256 bytes of RAM that are faster (3 cycles vs 4) and smaller (2 bytes vs 3). The model stored the critical state—head X/Y, direction, and pointers—here to maximize game‑loop performance.

Modern Engineering in 6510 Assembly

This is where the “Stochastic Memory” theory falls apart. If the model were simply regurgitating artifacts from its training dataset—copy‑pasting from old magazines or forums—the output would look like 1980s code.

Code from that era was notoriously “write‑only.” To save every precious byte of RAM and squeeze performance out of a 1 MHz CPU, developers often used spaghetti logic (endless JMP and GOTO), single‑letter labels (L1, VAL), and “magic numbers” hard‑coded throughout the file.

The Assembly generated here is fundamentally different. It is 2025 code written for 1982 hardware:

• Clean Separation of Concerns: The architecture separates the Input, Update, and Render phases of the game loop. This mirrors the standard pattern in modern game engines (like Unity or Unreal) but is rarely formalized in simple 8‑bit games.
• Input Buffering (Debouncing): An intermediate input_buf variable captures the user’s joystick command but only commits it to the physics engine (dir) at the start of the next frame. This prevents the classic “suicide turn” bug—where a player inputs two direction changes within a single frame (e.g., Down then Left), causing the snake to 180° turn into its own neck.
• Semantic Naming: Instead of cryptic labels like chk_c, the code uses descriptive identifiers such as check_collision, move_timer, and head_idx. It prioritizes maintainability and readability over obfuscation, treating Assembly with the same respect as a high‑level language.

This proves the model isn’t just retrieving a “Snake” script from its weights; it is engineering a solution from scratch, applying modern best practices to the constraints of the 6510 instruction set.

The Challenge: 8‑Bit Arithmetic

In Python, calculating a pixel position is a trivial one‑liner:

index = y * width + x

On a 6510, there is no multiplication instruction—only addition (ADC). Implementing the same calculation requires careful use of registers, zero‑page addressing, and looped addition or shift‑and‑add techniques, all while staying within the tight cycle budget of a 1 MHz CPU.