Nanocode: The best Claude Code that $200 can buy in pure JAX on TPUs

1. 1
   Define Cost & Performance Targets

   Clearly outline your project's budget constraints (e.g., $200) and specific AI model performance goals (e.g., inference speed, training time per epoch).
2. 2
   Leverage LLM for Code Generation

   Use Anthropic's Claude (or similar advanced LLMs) to generate specialized and highly optimized JAX code snippets for your AI tasks, focusing on computational efficiency.
3. 3
   Implement Core Logic in Pure JAX

   Translate your AI model architectures, data processing pipelines, and numerical algorithms into pure JAX for its high-performance array computation and automatic differentiation capabilities.
4. 4
   Optimize JAX for TPU Execution

   Apply JAX transformations such as `jax.jit` for compilation, `jax.pmap` for data parallelism, and `jax.vmap` for automatic batching to maximize parallel processing and execution efficiency on TPU hardware.
5. 5
   Deploy & Run on Google Cloud TPUs

   Set up a Google Cloud environment, provision a TPU VM, and execute your optimized JAX models to achieve accelerated, cost-effective AI operations.

import jax
import jax.numpy as jnp

# This simple JAX function is JIT-compiled for performance.
# When run on a Google Cloud TPU VM, JAX automatically uses the TPU backend.

print(f"JAX backend: {jax.default_backend()}\n")

@jax.jit
def simple_tpu_op(x, y):
    # Perform a matrix multiplication and element-wise addition
    return jnp.dot(x, y) + x

# Initialize random matrices (e.g., 1000x1000) for demonstration
key = jax.random.PRNGKey(0)
size = 1000
matrix_a = jax.random.normal(key, (size, size), dtype=jnp.float32)
matrix_b = jax.random.normal(key, (size, size), dtype=jnp.float32)

print(f"Executing JAX operation with matrices of shape: {matrix_a.shape}")

# Execute the JIT-compiled function
result = simple_tpu_op(matrix_a, matrix_b)

# Trigger computation to see the result (usually done in a training loop)
_ = result.block_until_ready()

print("\nJAX operation completed successfully (JIT compiled for backend).")
print(f"Result shape: {result.shape}")

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