[Doc] Update README (#617)

Co-authored-by: Cursor <cursoragent@cursor.com>

Author: hughperkins

README.md

@@ -25,59 +25,47 @@ This project is now fully independent and does not aim to maintain backward comp
 
 While the repository still resembles upstream in structure, major changes include:
 
-### Modernized infrastructure
+### Platform support
 
-* Revamped CI
-* Support for Python 3.10–3.13
-* Support for macOS up to 15
-* Significantly improved reliability (≥90% CI success on correct code)
+* LLVM 22, ARM (aarch64) support
 
-### Structural improvements
-
-* Added `dataclasses.dataclass` structs:
+### CI
 
-  * Work with both ndarrays and fields
-  * Can be passed into child `ti.func` functions
-  * Can be nested
-  * No kernel runtime overhead (kernels see only underlying arrays)
+* Kernel-level [code coverage](https://genesis-embodied-ai.github.io/quadrants/user_guide/kernel_coverage.html) — device-side branch coverage in standard `coverage.py` format, integrated with pytest-cov
+* AI-driven checks for line wrapping, deleted comments, test coverage, and feature factorization
 
-### Removed components
+### Structural improvements
 
-To focus the compiler and reduce maintenance burden, we removed:
+* `dataclasses.dataclass` structs — work with ndarrays and fields, nestable, passable to `qd.func`, zero kernel-runtime overhead
+* [`qd.Tensor`](https://genesis-embodied-ai.github.io/quadrants/user_guide/tensor.html) — unified API over fields and ndarrays with per-tensor layout control, pickle support, and a `backend=` switch
+* [`BufferView`](https://genesis-embodied-ai.github.io/quadrants/user_guide/buffer_view.html) — safe sub-range ndarray access with bounds checking in debug mode
 
-* GUI / GGUI
-* C-API
-* AOT
-* DX11 / DX12
-* iOS / Android
-* OpenGL / GLES
-* argpack
-* CLI
+### Removed components
 
-### Performance improvements
+To focus the compiler and reduce maintenance burden, we removed: GUI/GGUI, C-API, AOT, DX11/DX12, iOS/Android, OpenGL/GLES, argpack, CLI.
 
-#### Reduced launch latency
+### Performance
 
-* Release 4.0.0 improved non-batched ndarray CPU performance by **4.5×** in Genesis benchmarks.
-* Release 3.2.0 improved ndarray performance from **11× slower than fields** to **1.8× slower** (on a 5090 GPU, Genesis benchmark).
+* **Reduced launch latency** — ndarray CPU performance improved **4.5×**; ndarray GPU performance went from 11× slower than fields to ~30% slower (5090 GPU, Genesis benchmark)
+* **[Fastcache](https://genesis-embodied-ai.github.io/quadrants/user_guide/fastcache.html)** — opt-in source-level cache (`@qd.kernel(fastcache=True)`) that bypasses front-end AST parsing; reduces warm-cache kernel load from **7.2 s → 0.3 s** on Genesis benchmarks
+* **[GPU Graphs](https://genesis-embodied-ai.github.io/quadrants/user_guide/graph.html)** — `@qd.kernel(graph=True)` captures kernel sequences into a graph; `qd.graph_do_while` runs GPU-side iteration loops (hardware conditional nodes on CUDA SM 9.0+)
+* **[perf_dispatch](https://genesis-embodied-ai.github.io/quadrants/user_guide/perf_dispatch.html)** — auto-benchmarks multiple kernel implementations and selects the fastest at runtime
+* **[Zero-copy interop](https://genesis-embodied-ai.github.io/quadrants/user_guide/interop.html)** — `to_torch(copy=False)` / `to_numpy(copy=False)` via DLPack on CUDA, CPU, AMDGPU, and Metal; direct torch tensor pass-through into kernels
 
-#### Reduced warm-cache latency
+### SIMT primitives
 
-On Genesis simulator (Linux + NVIDIA 5090):
+* **[Tile16x16](https://genesis-embodied-ai.github.io/quadrants/user_guide/tile16.html)** — register-resident 16×16 matrix tiles with Cholesky, triangular solve, and rank-1 updates; 5× faster than shared-memory baselines on blocked linear algebra
+* **[Subgroup ops](https://genesis-embodied-ai.github.io/quadrants/user_guide/subgroup.html)** — cross-platform `shuffle`, `shuffle_down`, `reduce_add`, `reduce_all_add` across CUDA, AMDGPU, Metal and Vulkan
 
-* `single_franka_envs.py` cache load time reduced from **7.2s → 0.3s**
+### Autodiff
 
-#### Zero-copy Torch interop
+* [**Autodiff with dynamic loops**](https://genesis-embodied-ai.github.io/quadrants/user_guide/autodiff.html#autodiff-with-dynamic-loops) — computes the gradient of any kernel transparently using reverse-mode differentiation and runtime-based memory allocation
+* Forward-mode AD, custom gradients (`@qd.ad.grad_replaced`), `qd.ad.Tape`
 
-* Added `to_dlpack`
-* Enables zero-copy memory sharing between PyTorch and Quadrants
-* Avoids kernel-based accessors
-* Significantly improves performance
 
-### Compiler upgrades
+### Debugging & development
 
-* Upgraded to LLVM 22
-* Enabled ARM support
+* **[Python backend](https://genesis-embodied-ai.github.io/quadrants/user_guide/python_backend.html)** — `qd.init(qd.python)` interprets kernels as plain Python so they can be stepped through in a standard Python debugger
 
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