$ toilet -f mono9 -w 100 "autograd.c" 

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     ▄▄▄   ▄   ▄  ▄▄█▄▄   ▄▄▄    ▄▄▄▄   ▄ ▄▄   ▄▄▄    ▄▄▄█          ▄▄▄  
    ▀   █  █   █    █    █▀ ▀█  █▀ ▀█   █▀  ▀ ▀   █  █▀ ▀█         █▀  ▀ 
    ▄▀▀▀█  █   █    █    █   █  █   █   █     ▄▀▀▀█  █   █         █     
    ▀▄▄▀█  ▀▄▄▀█    ▀▄▄  ▀█▄█▀  ▀█▄▀█   █     ▀▄▄▀█  ▀█▄██    █    ▀█▄▄▀ 
                                 ▄  █                                    
                                  ▀▀                                     

$ cat docs/about.md | fold -w 75

	a minimal reverse mode autograd engine in c with reference counted tensors,
	arena allocated function nodes, explicit dependency counting, centralized 
	gradient accumulation, scalar loss backpropagation and a small set of core 
	tensor ops implemented with tightly coupled forward and backward code.

$ make download

    data/cifar-10-binary  40%[====>         ]  33.10M  2.55MB/s    eta 47s    

$ make run-release

    loaded data
    train samples: 50000
    test samples: 10000
    created model
    starting training

	avg loss: 2.2632, avg acc: 20.48%
    evaluating: 100%|█████████████████████████████████████| 79/79 [0.7it/s]
    test acc: 23.56%

	┌─────────────────── loss (epoch  2/15) ───────────────────┐
	│                              •                           │ 2.3112 (max)
	│                                                          │
	│                            ••                            │
	│            •          •                  •               │
	│•                               •                         │
	│    ••••          • •            •                        │
	│          ••   • •       •                       •        │
	│ ••     •     •                                           │
	│         •           •     •       •   •     •            │
	│   •                                 •        •           │
	│             •  •  •  • • •       •            •   • •    │
	│                               •    •       •             │
	│                                      • •       •     •   ┤––– 2.2941
	│                                           •              │
	│                                         •        • •     │ 2.2899 (min)
	└──────────────────────────────────────────────────────────┘
	96%|██████████████████████████████████████████████▊   | 750/782 [1.5it/s]