Document next steps after broadcasting implementation

[Copilot]

User asked for guidance on next steps after completing broadcasting support for operators.

Analysis

Reviewed codebase and README roadmap to identify logical next features:

• Reduction operations (sum, mean, max, min) — Foundation for flexible loss computation, relatively simple to add
• Automatic differentiation — GradFunc.h placeholder exists, requires_grad commented out in tensor.h, zeroGrad() already implemented
• Batched operations — Enable mini-batch training workflows
• Performance enhancements — SIMD, OpenMP, expression templates, loop tiling

Recommended Priority

1. Reduction operations (unlocks loss flexibility)
2. Autograd infrastructure (core ML capability)
3. Batched ops (training efficiency)
4. SIMD/parallelization (performance layer)

No code changes in this PR — awaiting user direction on which feature to implement.

Original prompt

what should i do next after i complete adding broadcasting to the opreators

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