name: llm-as-computer description: Execute programs on a compiled transformer stack machine where every instruction fetch and memory read is a parabolic attention head. Demonstrates that transformer attention + FF layers can implement a working computer. Use when user mentions "llm-as-computer", "lac", "stack machine", "compiled transformer", "percepta", "parabolic attention", "execute program", or asks to run/trace programs on the transformer executor. metadata: version: 1.0.1 repo: oaustegard/llm-as-computer

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)

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

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):

Writing Custom Programs

from isa_lite import program

# Assembly tuples
prog = program(
    ('PUSH', 10),
    ('PUSH', 20),
    ('ADD',),
    ('DUP',),
    ('ADD',),  # (10+20)*2 = 60
    ('HALT',),
)
print(run(prog))

# Loops: countdown from 5
prog = program(
    ('PUSH', 5),    # 0: counter
    ('PUSH', 1),    # 1: decrement
    ('SUB',),       # 2: counter - 1
    ('DUP',),       # 3: copy for JNZ test
    ('JNZ', 1),     # 4: loop if non-zero
    ('HALT',),      # 5: done, top = 0
)
print(run(prog))

ISA Reference (55 opcodes)

Stack: PUSH n, POP, DUP, SWAP, OVER, ROT Arithmetic: ADD, SUB, MUL, DIV_S, DIV_U, REM_S, REM_U Comparison: EQZ, EQ, NE, LT_S/U, GT_S/U, LE_S/U, GE_S/U Bitwise: AND, OR, XOR, SHL, SHR_S/U, ROTL, ROTR Unary: CLZ, CTZ, POPCNT, ABS, NEG, SELECT Control: JZ addr, JNZ addr, CALL addr, RETURN, HALT, NOP Locals: LOCAL.GET idx, LOCAL.SET idx, LOCAL.TEE idx Memory: I32.LOAD, I32.STORE, I32.LOAD8_U/S, I32.LOAD16_U/S, I32.STORE8, I32.STORE16

All arithmetic is 32-bit signed with WASM i32 semantics (wrap on overflow).

Architecture

The key insight: parabolic encoding k = (2j, -j²) makes dot-product attention peak sharply at a target position. Same encoding addresses program memory, stack, locals, and heap without interference. Each attention head is a compiled W_Q @ state → query, W_K @ memory → keys, scores = K @ q, output = V[argmax(scores)].

Updating from Repo

To pull latest source from the repository:

cd /mnt/skills/user/llm-as-computer/src
GH_TOKEN=$(grep GH_TOKEN /mnt/project/GitHub.env 2>/dev/null | cut -d= -f2)
for f in executor.mojo; do
  curl -sL -H "Authorization: token $GH_TOKEN" -H "Accept: application/vnd.github.v3.raw" \
    "https://api.github.com/repos/oaustegard/llm-as-computer/contents/src/$f?ref=main" > $f
done
for f in isa_lite.py programs.py runner.py; do
  curl -sL -H "Authorization: token $GH_TOKEN" -H "Accept: application/vnd.github.v3.raw" \
    "https://api.github.com/repos/oaustegard/llm-as-computer/contents/skill/src/$f?ref=main" > $f
done
rm -f percepta_exec  # force recompile
bash setup.sh