---
type: reference
title: "hashfita — Chronobreaker Project"
description: "Bitcoin hash analysis system: RingTransformer neural network on MLX, OpenCL SHA-256 feature extraction, QUIC distributed workers, .NET/C# compute layer."
tags: [hashfita, bitcoin, mlx, opencl, transformer, distributed, compute-optimization]
timestamp: 2026-07-25
---

# hashfita — Chronobreaker

A Bitcoin hash analysis system that attempts to predict nonce locations using a custom transformer neural network (RingTransformer) trained via reinforcement on SHA-256 ring features extracted by OpenCL GPU kernels. Targets Apple Silicon (M3 Pro) as primary compute platform.

**Repository:** `/Users/alefita/workdir/hashfita/`
**Version:** v0.1.0
**Python:** 3.13
**Package Manager:** uv (pyproject.toml)

---

## Architecture

| Layer | Technology | Purpose |
|-------|-----------|---------|
| Model | MLX (`mlx.nn`) | RingTransformer — 272k params, 4D RoPE |
| Training | MLX autograd + MuonClip | Custom optimizer with Newton-Schulz ortho |
| Feature extraction | PyOpenCL | SHA-256 carrier/ring/trace extraction on GPU |
| Nonce validation | PyOpenCL | Double SHA-256 batch validation |
| Networking | aioquic (QUIC) | Distributed scheduler-worker protocol |
| Workers | .NET/C# + Python IPC | Hardware-adaptive compute (NVIDIA/AMD/Apple) |

## Compute Stack

- **MLX ≥ 0.31.1** — Primary framework (model, training, autograd, optimizers)
- **PyOpenCL ≥ 2024.1** — GPU kernel execution for SHA-256 operations
- **NumPy ≥ 1.26** — Bridge for PyOpenCL buffer transfer only
- **No PyTorch** — Zero imports, zero references

## Key Components

| Component | File | Purpose |
|-----------|------|---------|
| RingTransformer | `src/chronobreaker/model/transformer.py` | Neural network: 17 rings → nonce prediction |
| AttentionBlock4D | `src/chronobreaker/model/attention.py` | Multi-head attention with 4D RoPE |
| MuonClip | `src/chronobreaker/train/muonclip_mlx.py` | Newton-Schulz + QK-Clip optimizer |
| ReinforcementTrainer | `src/chronobreaker/train/trainer.py` | 3-pass training: forward → GPU validation → autograd |
| OpenCL Context | `src/chronobreaker/core/opencl_context.py` | Kernel loading, buffer management, GPU dispatch |
| Holographic Projector | `src/chronobreaker/holographic/projector.py` | PHIN Scatter candidate generation |

## Related Pages

- [[ring-transformer]] — Full architecture deep-dive
- [[muonclip-optimizer]] — Optimizer internals
- [[mlx-opencl-bridge]] — Buffer transfer patterns
- [[opencl-kernel-design]] — Kernel inventory and loading
- [[gpu-async-pipelines]] — Async architecture

## Status

- ✅ RingTransformer training functional
- ✅ OpenCL feature extraction pipeline
- ✅ QUIC distributed protocol
- ⚠️ 3-copy buffer chain (OpenCL→numpy→MLX) — known bottleneck
- ⚠️ No TDR recovery
- ⚠️ float32 exclusively — bfloat16 opportunity
- 🔜 .NET Silk.NET.OpenCL migration pending