I tested Gemini 3 against my own “Commodore 64 Constraint.”, after it conquered my Tetris challenge in BASIC, we pushed harder: Snake in 6510 Assembly with a Python-powered AI toolchain using Gemini on Github Copilot.

It all starts at the base, one of the first AI-generated assembler games for the Commodore 64?

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, often 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 64KB of RAM, a 1MHz 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

Before diving into the Assembly project, it is crucial to understand the significance of the shift I observed. When I tested models on my C64 Tetris challenge (in BASIC), the failures were usually categorized into two distinct types:

1. 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.
2. 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:

1. 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. It prioritized logical correctness over historical optimization patterns.
2. 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.
3. 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 1MHz 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 didn’t want to develop like it was 1982, it wanted to bring modern engineering toolkit into the 8-bit world. It built a Python-based AI toolchain that treats the emulated Commodore 64 not as a black box, but as an embedded device it could probe and control programmatically.

The stack consists of four key components:

• Target: Commodore 64 (MOS 6510 CPU). A deterministic environment where every cycle counts.
• Compiler: cc65 (specifically ca65 and ld65). Unlike simple monolithic assemblers, this allows for a modular project structure with linker configurations, essential for complex memory management.
• Emulator: VICE (x64). Crucially, we utilize the binary monitor interface , which opens a TCP port allowing external tools to freeze execution and inspect RAM.
• The Brain: Python 3. Used to script the build process, test the game logic, and run the AI agent that plays the game.

Part 1: The Metal (6510 Assembly)

Writing Snake in Assembly forces to think about memory layout immediately. Unlike modern development where malloc handles the allocation details 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 40x25 characters. Writing the byte 81 (a solid ball) to address $0400\ puts the snake’s head in the top-left corner.
• $0002 — $00FF (Zero Page): The “fast lane” of memory. The 6510 processor 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 1MHz CPU, developers often used spaghetti logic (endless JMP and GOTO), single-letter labels (L1, VAL), and "magic numbers" hardcoded throughout the file.

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

1. Clean Separation of Concerns: The architecture separates the Input, Update, and Render phases of the game loop. This is a standard pattern in modern game engines (like Unity or Unreal) but was rarely formalized in simple 8-bit games.
2. Input Buffering (Debouncing): The code introduces an intermediate input_buf variable. It 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-degree turn into its own neck. This is a robust engineering solution to a race condition.
3. Semantic Naming: Instead of cryptic labels like chk_c, the code uses descriptive identifiers like 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, we don’t have a multiplication instruction. We only have addition (ADC) and bit-shifting (ASL/LSR).

To calculate the memory address of the snake’s head, The model implemented a routine that performs Y * 40 + X using purely logical shifts. This is the kind of low-level optimization that keeps the game running smoothly at 60Hz, a massive performance step up from the sluggish BASIC interpreter used in the Tetris test.

; Calculating Screen Address: Base + Y*40 + X
; 40 = 32 + 8. So we calculate (Y*32) + (Y*8)

calc_screen_pos:
lda #0
sta ptr_hi
lda head_y
asl ; Y * 2 (Shift left 1 bit)
asl ; Y * 4
asl ; Y * 8
sta ptr_lo ; Save the (Y*8) result for later
asl ; Y * 16
asl ; Y * 32
adc ptr_lo ; Add (Y*8) to (Y*32) -> Result is Y*40

; Add Base Address ($0400) and X offset
; ... (Handle carry bit propagation to high byte)

Part 2: The Bridge (Python <-> VICE)

This is where the project gets interesting. VICE has a feature called -remotemonitor. When enabled, it opens a socket on localhost:6510. This transforms the emulator from a standalone application into a server we can query.

Gemini 3 wrote a Python script, ai_toolchain.py, that acts as a wrapper around the emulator. It uses a binary protocol to send commands and receive raw memory dumps.

The “Bridge” performs four key actions in a tight loop:

1. Halt: Pauses the emulator CPU. This is critical — it allows us to inspect the state of the machine atomically, ensuring that the screen memory doesn’t change while we are reading it.
2. Dump Memory: Sends the command m 0400 07e7 to read the entire 1000-character screen buffer in one go.
3. Inject Input: Instead of simulating a keypress (which introduces latency and debouncing issues), we write directly to the Zero Page variable $04 (Direction). This gives us zero-latency control.
4. Resume: Unpauses the emulator for a set number of frames, allowing the game physics to advance exactly one step.

Part 3: The AI Loop

With the screen data available in Python, Gemini 3 Agent could write an demo to play the game without user interaction.

The AI uses a heuristic approach driven by the Manhattan Distance , prioritizing survival over path optimization:

1. Perception: The script halts VICE and parses the memory dump. It identifies the coordinates of the Head (Char 81), the Apple (Char 83), and all Obstacles (Char 160 walls or the snake’s own tail).
2. Pathfinding: It calculates the distance to the apple for all 4 possible neighbor cells.
3. Safety Check: It simulates the next move to ensure it doesn’t result in a collision. This prevents the “suicide” moves common in simple greedy algorithms.
4. Action: It writes the optimal new direction to the C64 memory and advances the frame.

Here is what the AI “sees” in the terminal — a direct translation of the C64 screen memory into a Python-friendly grid, complete with obstacles (T for Trees/Spades), the Snake (O), and the Apple (A):

|###################04###################|
|#                                      #|
|#                          T           #|
|#                                      #|
|#                        OOOOO         #|
|#                  T          O        #|
|#              T              O        #|
|# T                    T      O        #|
|#                                      #|
|#                            A         #|
|#                                      #|
|#                                      #|
|#                                      #|
|#T T                                   #|
|#                                      #|
|#                                      #|
|#              T                       #|
|#                                      #|
|#                                      #|
|#   T                                  #|
|#                                      #|
|#                                    T #|
|#                                      #|
|#                                      #|
|#                                      #|
|########################################|

Part 4: A Modern Workflow for Retro Dev

The most painful part of retro development is usually the iteration cycle. In 1982, testing a change meant saving to a slow floppy disk, waiting for the drive to spin up, and typing LOAD "*",8,1.

By wrapping cl65 and VICE in Python toolchain, Gemini 3 achieved a Hot Reload workflow similar to React or Webpack and can edit the Assembly code in VS Code, hit a key, and within milliseconds:

1. The code recompiles into a .prg binary.
2. Python connects to the running emulator.
3. It performs a soft-reset of the virtual CPU.
4. It injects the new binary directly into the emulated RAM.
5. The game restarts instantly with the new logic.

This allows for a velocity of experimentation that was physically impossible on the original hardware.

Conclusion

The Commodore 64 remains a solid tool for vetting how well AI systems actually reason. It strips away the bloat of modern computing and forces models to deal with hard constraints.

If Gemini 3’s success with my Tetris challenge proved it could handle logic under constraint, this Snake project proves it can handle systems engineering. By treating the C64 as an embedded device and applying modern principles — automated testing, hot reloading, and memory inspection — we pushed the boundaries of what is possible on 8-bit hardware.

The 6510 teaches you to be frugal with resources. Python teaches you to be efficient with your time. Combining them gives you the best of both worlds.

Resources

• GitHub Repository: C64AIToolChain
• The Commodore 64 Constraint: Why Gemini 3 Is the First AI to Beat the Tetris Test