chore(security): enhance environment configuration, CI workflows, and wallet daemon with security improvements

- Restructure .env.example with security-focused documentation, service-specific environment file references, and AWS Secrets Manager integration
- Update CLI tests workflow to single Python 3.13 version, add pytest-mock dependency, and consolidate test execution with coverage
- Add comprehensive security validation to package publishing workflow with manual approval gates, secret scanning, and release

gpu_acceleration/benchmarks.md

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+# GPU Acceleration Benchmarks
+
+Benchmark snapshots for common GPUs in the AITBC stack. Values are indicative and should be validated on target hardware.
+
+## Throughput (TFLOPS, peak theoretical)
+| GPU | FP32 TFLOPS | BF16/FP16 TFLOPS | Notes |
+| --- | --- | --- | --- |
+| NVIDIA H100 SXM | ~67 | ~989 (Tensor Core) | Best for large batch training/inference |
+| NVIDIA A100 80GB | ~19.5 | ~312 (Tensor Core) | Strong balance of memory and throughput |
+| RTX 4090 | ~82 | ~165 (Tensor Core) | High single-node perf; workstation-friendly |
+| RTX 3080 | ~30 | ~59 (Tensor Core) | Cost-effective mid-tier |
+
+## Latency (ms) — Transformer Inference (BERT-base, sequence=128)
+| GPU | Batch 1 | Batch 8 | Notes |
+| --- | --- | --- | --- |
+| H100 | ~1.5 ms | ~2.3 ms | Best-in-class latency |
+| A100 80GB | ~2.1 ms | ~3.0 ms | Stable at scale |
+| RTX 4090 | ~2.5 ms | ~3.5 ms | Strong price/perf |
+| RTX 3080 | ~3.4 ms | ~4.8 ms | Budget-friendly |
+
+## Recommendations
+- Prefer **H100/A100** for multi-tenant or high-throughput workloads.
+- Use **RTX 4090** for cost-efficient single-node inference and fine-tuning.
+- Tune batch size to balance latency vs. throughput; start with batch 8–16 for inference.
+- Enable mixed precision (BF16/FP16) when supported to maximize Tensor Core throughput.
+
+## Validation Checklist
+- Run `nvidia-smi` under sustained load to confirm power/thermal headroom.
+- Pin CUDA/cuDNN versions to tested combos (e.g., CUDA 12.x for H100, 11.8+ for A100/4090).
+- Verify kernel autotuning (e.g., `torch.backends.cudnn.benchmark = True`) for steady workloads.
+- Re-benchmark after driver updates or major framework upgrades.