Add v0.3.0 release notes and gitignore benchmark archives

- Create comprehensive release notes documenting all v0.3.0 features
- Add *.tar.gz to gitignore for benchmark archives
- Remove tracked benchmarks_results_20260110_213051.tar.gz

Co-Authored-By: Warp <agent@warp.dev>

Author: Mjboothaus

.gitignore

@@ -57,3 +57,4 @@ htmlcov/
 
 # Benchmark results - timestamped outputs
 benchmarks/*/results/
+*.tar.gz

benchmarks_results_20260110_213051.tar.gz

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+# Release Notes - v0.3.0 Community Release
+
+**Release Date:** 11 January 2026  
+**Status:** Ready for community feedback
+
+## Overview
+
+v0.3.0 represents a major expansion of the MAX Learning repository, growing from 3 examples to 6 progressive examples with comprehensive testing, benchmarking, and documentation. This release transforms the repository from a focused DistilBERT demonstration into a complete learning path for MAX Graph API.
+
+## What's New
+
+### 🎯 Six Progressive Examples
+
+1. **Element-wise Operations** - Basic ops (mul, add, relu)
+   - ✅ Works on CPU and Apple Silicon GPU
+   - Includes minimal and full versions
+
+2. **Linear Layer** - Matrix operations (matmul, transpose, bias)
+   - ✅ Works on CPU
+   - ❌ Apple Silicon GPU (matmul kernel missing)
+   - Includes minimal and full versions
+
+3. **DistilBERT Sentiment** - Full transformer (66M parameters)
+   - 🚀 5.58x speedup vs PyTorch on M1 CPU
+   - Production-quality inference
+
+4. **MLP Regression** - Multi-layer perceptron
+   - 3 hidden layers
+   - Housing price prediction
+   - NEW in v0.3.0
+
+5. **CNN MNIST** - Convolutional neural network
+   - 2 conv + 2 dense layers
+   - Digit classification
+   - NEW in v0.3.0
+
+6. **RNN Forecast** - Recurrent network
+   - 🚧 WIP - Parked due to MAX Graph API limitations
+   - NEW in v0.3.0
+
+### ⭐ Minimal Examples
+
+New self-contained minimal examples for element-wise and linear layer operations:
+- No configuration files
+- No helper functions
+- Pure MAX Graph API
+- Clear 4-step structure: Build → Compile → Run → Results
+- ~120-140 lines each
+- Perfect starting point for learning
+
+### 🧪 Comprehensive Testing
+
+- **49 tests** (up from 30)
+- Correctness validation for all examples
+- Tests mirror example structure:
+  - `tests/python/01_elementwise/`
+  - `tests/python/02_linear_layer/`
+  - `tests/python/03_distilbert/`
+  - `tests/python/03_mlp/`
+  - `tests/python/04_cnn_mnist/`
+
+### 📊 Enhanced Benchmarking
+
+- **MAX vs PyTorch comparisons** for all neural network models
+- Correctness verification (max absolute error checks)
+- Reports in MD/JSON/CSV formats
+- Machine ID in filenames for multi-system tracking
+- New benchmarks:
+  - `benchmarks/03_mlp/` - MLP vs PyTorch
+  - `benchmarks/04_cnn/` - CNN vs PyTorch
+
+### 📦 Package Restructuring
+
+- All Python modules now installable via `src/python/pyproject.toml`
+- Eliminated ALL `sys.path` manipulation from codebase
+- Created `utils` package with:
+  - `paths.py` - Project root detection via pixi.toml/.git search
+  - `benchmark_utils.py` - Shared benchmarking utilities
+- Editable installs via pixi: `[pypi-dependencies]`
+- Version synchronised: pyproject.toml and pixi.toml both at 0.3.0
+
+### 📚 Documentation Overhaul
+
+- **README.md** completely rewritten:
+  - Clear "Why This Repository?" section
+  - All 6 examples documented in order (1️⃣-6️⃣)
+  - Performance highlights (successes AND limitations)
+  - Progressive learning path articulated
+  - Repository structure expanded
+
+- **Example READMEs updated**:
+  - Document minimal vs full versions
+  - Explain when to use each
+  - Include expected output
+
+- **Community announcements**:
+  - Long version: comprehensive feature documentation
+  - Short version: 120-word summary
+  - Moved to `docs/planning/`
+
+## Performance Results
+
+### ✅ Where MAX Excels
+
+**DistilBERT (M1 CPU, batch=1)**
+- MAX: 45.88ms mean
+- PyTorch: 255.85ms mean
+- **Result: 5.58x speedup** 🚀
+
+### ⚠️ Where PyTorch is Faster
+
+**MLP Regression (M1 CPU, batch=2048)**
+- MAX: 142ms
+- PyTorch: 0.56ms
+- Result: PyTorch ~253x faster
+
+**CNN MNIST (M1 CPU, batch=256)**
+- PyTorch: ~5x faster than MAX
+- Note: Both produce identical predictions
+
+**Takeaway:** MAX shines on transformers but PyTorch is faster on smaller workloads (MLP, CNN) on Apple Silicon.
+
+### 🍎 Apple Silicon GPU Status
+
+- ✅ **Element-wise operations working** (first reported MAX Graph GPU inference)
+- ❌ **Matrix operations blocked** - matmul kernel not available
+- Blocks: transformers, MLP, CNN on GPU
+
+## Breaking Changes
+
+None - this is a purely additive release.
+
+## Migration Guide
+
+If upgrading from v0.2.0:
+1. Run `pixi install` to install new package structure
+2. No code changes required - all examples remain compatible
+3. New examples are opt-in
+
+## What's Next (v0.4.0 candidates)
+
+- Larger models: LLaMA, Mistral via MAX Pipeline API
+- Batch inference optimisation
+- Quantisation experiments (INT8/INT4)
+- More GPU work when matmul kernels available
+
+## Installation
+
+```bash
+git clone https://github.com/DataBooth/max-learning
+cd max-learning
+pixi install
+
+# Start with minimal examples
+pixi run python examples/python/01_elementwise/elementwise_minimal.py
+pixi run python examples/python/02_linear_layer/linear_layer_minimal.py
+```
+
+## Requirements
+
+- MAX 25.1.0 or later
+- Pixi package manager
+- Python 3.11+
+
+## Contributors
+
+This release was made possible by thorough testing, benchmarking, and documentation work.
+
+## Acknowledgements
+
+- [Modular](https://modular.com) for MAX Engine
+- [DataBooth](https://www.databooth.com.au) for sponsoring this learning exploration
+- Community for early feedback
+
+## Licence
+
+MIT Licence
+
+---
+
+**Full Changelog:** https://github.com/DataBooth/max-learning/compare/v0.2.0...v0.3.0