llm-as-computerlisted

# LLM-as-Computer: Compiled Transformer Stack Machine A working computer built from transformer primitives. Every instruction fetch and stack read is a parabolic attention head (dot-product → argmax → value extraction). The transformer's weights ARE the interpreter — compiled analytically, not trained. ## What This Proves Attention is lookup; feed-forward is routing. A vanilla transformer with compiled weights can execute arbitrary programs: loops, recursion, arithmetic, memory access. 55 opcodes covering WASM i32 semantics. 21M+ steps/second via the Mojo executor. ## Setup (once per session) ```bash cd /mnt/skills/user/llm-as-computer/src && bash setup.sh ``` This installs Mojo (~20s) and compiles the executor binary (~6s). If Mojo is unavailable, the skill falls back to a pure-Python executor (slower but functional). ## Usage ```python import sys sys.path.insert(0, '/mnt/skills/user/llm-as-computer/src') from programs import make_fibonacci, make_factorial, make_gcd, make_multiply from runner import run, setup # Ensure Mojo is compiled (idempotent) setup() # Run a program — shows instructions, trace, result prog, expected = make_fibonacci(10) print(run(prog)) # Benchmark mode — measures throughput print(run(prog, benchmark=True, repeat=200)) ``` ## Available Programs From `programs.py` — all return `(program, expected_result)`: | Generator | Description | Example | |-----------|-------------|---------| | `make_fibonacci(n)` | Iterative fib via SWAP+OVER+ADD+RO