crucible v0.1: CPU inference library for PyTorch .pt state dicts, in pure Zig

- restricted pickle VM (whitelisted globals — cannot execute arbitrary
  Python, unlike torch.load)
- zip container reader (store + raw-deflate)
- Tensor views: dtype/offset/sizes/strides, f32 materialization
- layers: conv2d f32x8 FMA, linear, relu, maxpool2, adaptiveAvgPool2d,
  softmax, padInput
- examples/stripsolver: real .pt forward, 3777 @ 1.0

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Co-Authored-By: Letta Code <noreply@letta.com>

README.md

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+# crucible
+
+CPU-only inference library for PyTorch `.pt` state dicts, in pure Zig.
+No Python, no venv, no torch — the archive reader and the math both live here.
+
+```zig
+const crucible = @import("crucible");
+
+var sd = try crucible.loadStateDict(alloc, io, "model.pt");
+defer sd.deinit();
+
+const weights = try (sd.get("trunk.0.weight") orelse return error.Missing).toF32(alloc);
+try crucible.layers.conv2d(alloc, &input, h, w, in_c, weights, bias, &out, out_c);
+```
+
+## What it is
+
+- **Restricted pickle VM** — decodes the data-loading subset of pickle
+  (protocol 2) used by `.pt` state dicts. GLOBAL resolution is whitelisted:
+  it reads tensors, storages and OrderedDicts, and refuses everything else.
+  Unlike `torch.load`, it structurally cannot execute arbitrary Python.
+- **ZIP container reader** — store + raw-deflate entries via `std.zip`.
+- **Tensor views** — dtype (f32/f64/i64/i32/u8), storage offset, sizes,
+  strides; contiguous and strided materialization to f32.
+- **Layers (CPU, inference)** — `conv2d` (f32x8 FMA over output width),
+  `linear`, `relu`, `maxpool2`, `adaptiveAvgPool2d`, `softmax`, `padInput`.
+
+## What it is not
+
+- Not a training library. No autograd, no CUDA, no NPU.
+- Not a full pickle implementation — unsupported opcodes and globals are
+  errors, not imports.
+- Not a model format converter. state_dict-style checkpoints only.
+
+## Usage
+
+Add to your `build.zig.zon`:
+
+```sh
+zig fetch --save git+https://git.chaosmith.systems/pierre/crucible
+```
+
+```zig
+const crucible = b.dependency("crucible", .{});
+exe_mod.addImport("crucible", crucible.module("crucible"));
+```
+
+See `examples/stripsolver.zig` for a complete CNN: loads a real trained
+`.pt`, runs a 3×conv + pose-conditioned multi-head forward.
+
+## Performance
+
+On a 132k-parameter CNN (44×100 input, 3× conv3x3, pose-conditioned
+4-head FC): ~5 ms per forward pass with f32x8 FMA (`-Dcpu=x86_64_v3`),
+2.9 MB RSS. Validated at 99.95% digit accuracy against the reference
+PyTorch implementation — identical predictions.
+
+## Status
+
+v0.1 — working for real state dicts (Conv2d/Linear/MaxPool2d/
+AdaptiveAvgPool2d/ReLU/Softmax). Layer set grows on demand.
+AVX-512 path: when hardware that has it does.