[new release] raven (11 packages) (1.0.0~alpha3)

CHANGES:

This release reshapes raven's foundations. Every package received API
improvements, several were rewritten, and two new packages — nx-oxcaml and
kaun-board — were built as part of our Outreachy internships.

### Highlights

- **Unified tensor type** — `Nx.t` and `Rune.t` are now the same type.
  Downstream packages no longer need to choose between them or convert at
  boundaries. Rune is now a pure transformation library (grad, vjp, vmap)
  over standard Nx tensors.
- **nx-oxcaml** (new, Outreachy) — Pure-OCaml tensor backend using OxCaml's
  unboxed types and SIMD intrinsics. Performance approaches the C backend —
  in pure OCaml.
- **kaun-board** (new, Outreachy) — TUI dashboard for monitoring training
  runs in the terminal. Live metrics, loss curves, and system stats.
- **quill** — Rewritten from the ground up with two interfaces: a terminal UI
  with syntax highlighting and code completion, and a web frontend via
  `quill serve` with a CodeMirror 6 editor, WebSocket-based execution,
  autocompletion, and diagnostics.
- **brot** — The tokenization library formerly known as saga. Complete rewrite
  with a cleaner API. [1.3-6x faster than HuggingFace Tokenizers](packages/brot/bench/)
  on most benchmarks.
- **nx** — Redesigned backend interface, RNG with effect-based scoping.
  Einsum **8-20x** faster, matmul dispatch at BLAS parity with NumPy.

### Breaking changes

- **nx**: Redesigned backend interface with new `Nx_buffer` type. Removed
  `nx.datasets` library. Moved NN functions to Kaun (use `Kaun.Fn`). Renamed
  `im2col`/`col2im` to `extract_patches`/`combine_patches`. RNG uses
  effect-based implicit scoping instead of explicit key threading. Removed
  in-place mutation operations (`ifill`, `iadd`, `isub`, `imul`, `idiv`,
  `ipow`, `imod`, `imaximum`, `iminimum` and `_s` variants). Removed
  `Symbolic_shape` module; shapes are concrete `int array` throughout.
  Removed `Instrumentation` module.
- **rune**: `Rune.t` no longer exists — use `Nx.t` everywhere. `Rune` no
  longer re-exports tensor operations; use `open Nx` for tensor ops and
  `Rune.grad`, `Rune.vjp`, etc. for autodiff. Remove any `Rune.to_nx` /
  `Rune.of_nx` calls. Removed `enable_debug`, `disable_debug`, `with_debug`;
  use `Rune.debug f x` instead.
- **rune**: Removed JIT/LLVM backend. This will come back in a future
  release with a proper ML compiler.
- **kaun**: Rewritten core modules API, datasets, and HuggingFace integration.
  Removed `kaun-models`.
- **brot**: Renamed from saga. Rewritten API focused on tokenization.

### Nx

- Unify `Nx.t` and `Rune.t` into a single tensor type. A new `nx.effect` library (`Nx_effect`) implements the backend interface with OCaml 5 effects: each operation raises an effect that autodiff/vmap/debug handlers can intercept, falling back to the C backend when unhandled. `Nx.t` is now `Nx_effect.t` everywhere — no more type conversions between Nx and Rune.
- Make transcendental, trigonometric, and hyperbolic operations (`exp`, `log`, `sin`, `cos`, `tan`, `asin`, `acos`, `atan`, `atan2`, `sinh`, `cosh`, `tanh`, `asinh`, `acosh`, `atanh`, `erf`, `sigmoid`) polymorphic over all numeric types including complex, matching the backend and effect definitions.
- Make `isinf`, `isfinite`, `ceil`, `floor`, `round` polymorphic (non-float dtypes return all-false/all-true or no-op as appropriate).
- Redesign backend interface with more granular operations (e.g. dedicated unary and binary kernels). This improves performance by letting backends optimize individual ops directly, and prepares for the JIT pipeline which will decompose composite operations at the compiler level instead of the frontend.
- Rewrite `Nx_buffer` module with new interface. The backend now returns `Nx_buffer.t` instead of raw bigarrays.
- Add new C kernels for unary, binary, and sort operations, and route new backend ops to C kernels.
- Add scipy-style `correlate`, `convolve`, and sliding window filters.
- Generalize `unfold`/`fold` to arbitrary leading dimensions.
- Remove neural-network functions from Nx (softmax, log_softmax, relu, gelu, silu, sigmoid, tanh). These now live in `Kaun.Fn`.
- Rename `im2col`/`col2im` to `extract_patches`/`combine_patches`.
- Remove `nx.datasets` module. Datasets are now in `kaun.datasets`.
- Simplify `Nx_io` interface. Inline vendor libraries (safetensors, and npy) directly into nx_io.
- Move the `Rng` module from Rune into Nx with effect-based implicit scoping. Random number generation uses `Nx.Rng.run` to scope RNG state instead of explicit key threading.
- Reduce matmul dispatch overhead to reach BLAS parity with NumPy.
- Fix Threefry2x32 to match the Random123 standard.
- Fix `save_image` crash on multi-dimensional genarray.
- Pre-reduce independent axes in einsum to avoid OOM on large contractions.
- Make Nx backends pluggable via Dune virtual libraries. The new `nx.backend` virtual library defines the backend interface, with the C backend (`nx.c`) as the default implementation. Alternative backends (e.g., `nx-oxcaml`) can be swapped in at link time. The `Nx_c` module is renamed to `Nx_backend`.
- Fix `.top` libraries failing to load in utop with "Reference to undefined compilation unit `Parse`".
- Fix OpenMP flag filtering in `discover.ml`: strip `-Xpreprocessor -fopenmp` as a pair on macOS to prevent dangling `-Xpreprocessor` from consuming subsequent flags and causing linker failures. (@Alizter)
- Add missing bool→low-precision cast support (f16/bf16/fp8) in the C backend.
- Add UInt32/UInt64 dtypes, rename complex dtypes to Complex64/Complex128, and drop Complex16/QInt8/QUInt8/Int/NativeInt as tensor element dtypes.
- Remove in-place mutation operations (`ifill`, `iadd`, `isub`, `imul`, `idiv`, `ipow`, `imod`, `imaximum`, `iminimum` and `_s` variants). Use functional operations instead.
- Remove `Symbolic_shape` module; shapes are now concrete `int array` throughout.
- Remove `Instrumentation` module. Nx no longer wraps operations in tracing spans. Debugging tensor operations is handled by Rune's effect-based debug handler.
- Fix critical correctness issue in fancy slicing (`L`) where permutations were ignored if the number of indices matched the dimension size (e.g., `slice [L [1; 0]] x` returned `x` unmodified).
- Rewrite `slice` implementation to use `as_strided` for contiguous operations, reducing overhead to **O(1)** for view-based slices and separating gather operations for better performance.
- Optimize `set_slice` by replacing scalar-loop index calculations with vectorized coordinate arithmetic, significantly improving performance for fancy index assignments.
- Improve `einsum` performance **8–20×** with greedy contraction path optimizer (e.g., MatMul 100×100 f32 207.83 µs → 10.76 µs, **19×**; BatchMatMul 200×200 f32 8.78 ms → 435.39 µs, **20×**)
- Rewrite `diagonal` using flatten + gather approach instead of O(N²) eye matrix masking, reducing memory from O(N²) to O(N)
- Improve error messages for shape operations (`broadcast`, `reshape`, `blit`) with per-dimension detail and element counts.

### nx-oxcaml (new)

New pure-OCaml tensor backend that can be swapped in at link time via Dune virtual libraries. Uses OxCaml's unboxed types for zero-cost tensor element access, SIMD intrinsics for vectorized kernels, and parallel matmul. Performance approaches the native C backend — in pure OCaml. Supports the full Nx operation set: elementwise, reductions, matmul, gather/scatter, sort/argsort, argmax/argmin, unfold/fold, pad, cat, associative scan, and threefry RNG. (@nirnayroy, @tmattio)

### Rune

- Unify tensor types: `Rune.t` is now `Nx.t`. Rune no longer re-exports the Nx frontend — it is a pure transformation library exporting only `grad`, `grads`, `value_and_grad`, `vjp`, `jvp`, `vmap`, `no_grad`, `detach`, and debugging/gradcheck utilities. All tensor creation and manipulation uses `Nx` directly.
- Remove `Tensor` module and `Nx_rune` backend. Effect definitions moved to the new `nx.effect` library shared with Nx.
- Remove `Rune.to_nx` / `Rune.of_nx` (no longer needed — types are identical).
- Remove `Rune.enable_debug`, `Rune.disable_debug`, `Rune.with_debug`. Use `Rune.debug f x` to run a computation with debug logging enabled.
- Remove JIT compilation support from Rune. The `Rune.Jit` module and LLVM/Metal backends have been removed and will be re-introduced later as a standalone package.
- Update to new `Nx_buffer.t` type.
- Propagate new backend operations through effects and autodiff.
- Rewrite `Autodiff` module to fix critical JVP correctness issues, enable higher-order derivatives (nested gradients), and introduce `vjp` as a first-class primitive.
- Fix pointer-based hashing in autodiff, correcting nested JVP handler behavior.
- Add autodiff support for `as_strided`, enabling gradients through slicing and indexing operations
- Add autodiff support for `cummax` and `cummin` cumulative operations
- Add autodiff support for FFT operations
- Add autodiff support for some linear algebra operations: QR decomposition (`qr`), Cholesky decomposition (`cholesky`), and triangular solve (`triangular_solve`).

### Kaun

- Simplify and redesign the core API for better discoverability and composability. Layers, optimizers, and training utilities now follow consistent patterns and compose more naturally.
- Add `Fn` module with `conv1d`, `conv2d`, `max_pool`, `avg_pool` — neural network operations that were previously in Nx now live here with a cleaner, more focused API.
- Redesign datasets and HuggingFace integration with simpler, more composable APIs.
- Remove `kaun-models` library. Pre-built models now live in examples.
- Reinitialize dataset each epoch to avoid iterator exhaustion (raven-ml/raven#147, @Shocker444, @tmattio)

### kaun-board (new)

TUI dashboard for monitoring training runs in the terminal. Displays live metrics, loss curves, and system stats. Extracted from kaun's console module into a standalone package. (raven-ml/raven#166, raven-ml/raven#167, raven-ml/raven#170, @Arsalaan-Alam)

### Brot

- Rename the library from saga to brot.
- Simplify brot to a tokenization-only library. Remove the sampler, n-gram models, and I/O utilities. The sampler is rewritten with nx tensors and moved to `dev/mimir` as the seed of an experimental inference engine.
- Merge `brot.tokenizers` sub-library into `brot`.
- Remove dependency on Nx.
- Use `Buffer.add_substring` instead of char-by-char loop in whitespace pre-tokenizer.
- Compact BPE symbols in-place after merges, avoiding an intermediate array allocation.
- Replace list cons + reverse with forward `List.init` in BPE `word_to_tokens`.
- Use pre-allocated arrays with `Array.blit` instead of `Array.append` in encoding merge and padding, halving per-field allocations.
- Avoid allocating an unused `words` array in post-processor encoding conversion.
- Reduce WordPiece substring allocations from O(n²) to O(n) per word by building the prefixed candidate string once per position.
- Add `encode_ids` fast path that bypasses `Encoding.t` construction entirely when only token IDs are needed.
- Add ASCII property table for O(1) character classification in pre-tokenizers, replacing O(log n) binary search for `is_alphabetic` (600 ranges), `is_numeric` (230 ranges), and `is_whitespace` (10 ranges). Yields 12-27% speedup on encode benchmarks with ~30% allocation reduction.
- Add inline ASCII fast paths in all pre-tokenizer loops, skipping UTF-8 decoding and using `Buffer.add_char` instead of `String.sub` for single-byte characters. Combined with the property table, yields 20-30% total speedup and 36-55% allocation reduction vs baseline.
- Parallelize batch encoding with OCaml 5 domains.
- Optimize BPE merge loop with open-addressing hash, flat arrays, and shift-based heap.
- Add trie-based WordPiece lookup and normalizer fast path.
- Remove dependency on `str` library.
- Generate unicode data offline, removing runtime dependency on `uucp`.
- Remove unused `Grapheme` module. Grapheme cluster segmentation is not needed for tokenization.
- Remove `uutf` dependency in favour of OCaml `Stdlib` unicode support.

### Fehu

- Simplify and redesign the core API. Environments and training utilities now follow consistent functional patterns that are easier to use and compose.
- Remove `fehu.algorithms` — fehu now only depends on rune, and users bring their own algorithms. Examples provided for well-known RL algorithms like DQN and REINFORCE.

### Sowilo

- Cleaner public API — internal implementation split into focused submodules while the public surface stays small.
- Faster grayscale conversion, edge detection, and gaussian blur.

### Quill

Rewritten from the ground up. Terminal UI with syntax highlighting, code completion, and a compact single-line footer. Web frontend via `quill serve` with a CodeMirror 6 editor, WebSocket-based execution, autocompletion, and diagnostics. Markdown notebook format shared across both interfaces.

### Hugin

- Fix potential bad memory access in rendering.
- Fix single-channel HWC image handling in `float32_to_cairo_surface`.

### Talon

- Remove `jsont`, `bytesrw`, and `csv` dependencies from Talon. CSV support is now built-in via the `talon.csv` sub-library with a minimal RFC 4180 parser.
- Remove `talon.json` sub-library.

Author: tmattio

packages/brot/brot.1.0.0~alpha3/opam

@@ -0,0 +1,50 @@
+opam-version: "2.0"
+synopsis: "Tokenization for OCaml"
+description:
+  "Fast, HuggingFace-compatible tokenization for language models. BPE, WordPiece, Unigram, word-level, and character-level algorithms with composable pipelines and training from scratch."
+maintainer: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+authors: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+license: "ISC"
+tags: [
+  "tokenization" "bpe" "wordpiece" "subword-tokenization" "language-models"
+]
+homepage: "https://github.com/raven-ml/raven"
+doc: "https://raven-ml.dev/docs/"
+bug-reports: "https://github.com/raven-ml/raven/issues"
+depends: [
+  "ocaml" {>= "5.2.0"}
+  "dune" {>= "3.21"}
+  "re"
+  "jsont"
+  "bytesrw"
+  "uunf" {>= "15.1.0"}
+  "uucp"
+  "windtrap" {with-test}
+  "mdx" {with-test}
+  "odoc" {with-doc}
+]
+build: [
+  ["dune" "subst"] {dev}
+  [
+    "dune"
+    "build"
+    "-p"
+    name
+    "-j"
+    jobs
+    "@install"
+    "@runtest" {with-test}
+    "@doc" {with-doc}
+  ]
+]
+dev-repo: "git+https://github.com/raven-ml/raven.git"
+x-maintenance-intent: ["(latest)"]
+url {
+  src:
+    "https://github.com/raven-ml/raven/releases/download/1.0.0_alpha3/raven-1.0.0.alpha3.tbz"
+  checksum: [
+    "sha256=6e78aa943959610b6a5c3439061959d8f228256df860bfb48eb573a7e79ffcd3"
+    "sha512=d59fc622f3b9fa11b6a065d89dc2425d14b1408cfc0fb9b9711e6ece70bb4ce6114ba177ce49766a9f7ae790023de77964f44f00e1a1e1068698415016ab0900"
+  ]
+}
+x-commit-hash: "67c327945fb7a1066e5262fbc3db1ca68410857e"

packages/fehu/fehu.1.0.0~alpha3/opam

@@ -0,0 +1,44 @@
+opam-version: "2.0"
+synopsis: "Reinforcement learning for OCaml"
+description:
+  "Type-safe RL environments, observation/action spaces, vectorized environments, trajectory collection, replay buffers, and generalized advantage estimation. Built on Nx."
+maintainer: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+authors: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+license: "ISC"
+tags: ["reinforcement-learning" "machine-learning" "environments"]
+homepage: "https://github.com/raven-ml/raven"
+doc: "https://raven-ml.dev/docs/"
+bug-reports: "https://github.com/raven-ml/raven/issues"
+depends: [
+  "ocaml" {>= "5.2.0"}
+  "dune" {>= "3.21"}
+  "nx" {= version}
+  "windtrap" {with-test}
+  "mdx" {with-test}
+  "odoc" {with-doc}
+]
+build: [
+  ["dune" "subst"] {dev}
+  [
+    "dune"
+    "build"
+    "-p"
+    name
+    "-j"
+    jobs
+    "@install"
+    "@runtest" {with-test}
+    "@doc" {with-doc}
+  ]
+]
+dev-repo: "git+https://github.com/raven-ml/raven.git"
+x-maintenance-intent: ["(latest)"]
+url {
+  src:
+    "https://github.com/raven-ml/raven/releases/download/1.0.0_alpha3/raven-1.0.0.alpha3.tbz"
+  checksum: [
+    "sha256=6e78aa943959610b6a5c3439061959d8f228256df860bfb48eb573a7e79ffcd3"
+    "sha512=d59fc622f3b9fa11b6a065d89dc2425d14b1408cfc0fb9b9711e6ece70bb4ce6114ba177ce49766a9f7ae790023de77964f44f00e1a1e1068698415016ab0900"
+  ]
+}
+x-commit-hash: "67c327945fb7a1066e5262fbc3db1ca68410857e"

packages/hugin/hugin.1.0.0~alpha3/opam

@@ -0,0 +1,47 @@
+opam-version: "2.0"
+synopsis: "Plotting for OCaml"
+description:
+  "Publication-quality 2D and 3D plots using Cairo rendering. Line plots, scatter, bar charts, contour plots, and image display from Nx tensors."
+maintainer: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+authors: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+license: "ISC"
+tags: ["visualization" "plotting" "charts" "data-science" "graphics"]
+homepage: "https://github.com/raven-ml/raven"
+doc: "https://raven-ml.dev/docs/"
+bug-reports: "https://github.com/raven-ml/raven/issues"
+depends: [
+  "ocaml" {>= "5.2.0"}
+  "dune" {>= "3.21"}
+  "dune-configurator" {build}
+  "conf-sdl2"
+  "cairo2"
+  "nx" {= version}
+  "windtrap" {with-test}
+  "mdx" {with-test}
+  "odoc" {with-doc}
+]
+build: [
+  ["dune" "subst"] {dev}
+  [
+    "dune"
+    "build"
+    "-p"
+    name
+    "-j"
+    jobs
+    "@install"
+    "@runtest" {with-test}
+    "@doc" {with-doc}
+  ]
+]
+dev-repo: "git+https://github.com/raven-ml/raven.git"
+x-maintenance-intent: ["(latest)"]
+url {
+  src:
+    "https://github.com/raven-ml/raven/releases/download/1.0.0_alpha3/raven-1.0.0.alpha3.tbz"
+  checksum: [
+    "sha256=6e78aa943959610b6a5c3439061959d8f228256df860bfb48eb573a7e79ffcd3"
+    "sha512=d59fc622f3b9fa11b6a065d89dc2425d14b1408cfc0fb9b9711e6ece70bb4ce6114ba177ce49766a9f7ae790023de77964f44f00e1a1e1068698415016ab0900"
+  ]
+}
+x-commit-hash: "67c327945fb7a1066e5262fbc3db1ca68410857e"

packages/kaun-board/kaun-board.1.0.0~alpha3/opam

@@ -0,0 +1,47 @@
+opam-version: "2.0"
+synopsis: "Training dashboard and logging for Raven"
+description:
+  "Lightweight training logger and terminal dashboard for monitoring runs. The core library (kaun-board) provides a Log API for writing JSONL events and a reader for consuming them. The TUI library (kaun-board.tui) provides a Mosaic-based dashboard."
+maintainer: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+authors: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+license: "ISC"
+tags: ["training-dashboard" "monitoring" "logging" "machine-learning"]
+homepage: "https://github.com/raven-ml/raven"
+doc: "https://raven-ml.dev/docs/"
+bug-reports: "https://github.com/raven-ml/raven/issues"
+depends: [
+  "ocaml" {>= "5.2.0"}
+  "dune" {>= "3.21"}
+  "jsont"
+  "bytesrw"
+  "cmdliner"
+  "mosaic"
+  "dune-configurator" {build}
+  "matrix"
+  "odoc" {with-doc}
+]
+build: [
+  ["dune" "subst"] {dev}
+  [
+    "dune"
+    "build"
+    "-p"
+    name
+    "-j"
+    jobs
+    "@install"
+    "@runtest" {with-test}
+    "@doc" {with-doc}
+  ]
+]
+dev-repo: "git+https://github.com/raven-ml/raven.git"
+x-maintenance-intent: ["(latest)"]
+url {
+  src:
+    "https://github.com/raven-ml/raven/releases/download/1.0.0_alpha3/raven-1.0.0.alpha3.tbz"
+  checksum: [
+    "sha256=6e78aa943959610b6a5c3439061959d8f228256df860bfb48eb573a7e79ffcd3"
+    "sha512=d59fc622f3b9fa11b6a065d89dc2425d14b1408cfc0fb9b9711e6ece70bb4ce6114ba177ce49766a9f7ae790023de77964f44f00e1a1e1068698415016ab0900"
+  ]
+}
+x-commit-hash: "67c327945fb7a1066e5262fbc3db1ca68410857e"

packages/kaun/kaun.1.0.0~alpha3/opam

@@ -0,0 +1,47 @@
+opam-version: "2.0"
+synopsis: "Neural networks for OCaml"
+description:
+  "Composable layers, parameter trees, optimizers, training loops, data pipelines, and HuggingFace Hub integration. Built on Rune."
+maintainer: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+authors: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+license: "ISC"
+tags: ["neural-networks" "machine-learning" "deep-learning"]
+homepage: "https://github.com/raven-ml/raven"
+doc: "https://raven-ml.dev/docs/"
+bug-reports: "https://github.com/raven-ml/raven/issues"
+depends: [
+  "ocaml" {>= "5.2.0"}
+  "dune" {>= "3.21"}
+  "rune" {= version}
+  "nx" {= version}
+  "jsont"
+  "bytesrw"
+  "windtrap" {with-test}
+  "mdx" {with-test}
+  "odoc" {with-doc}
+]
+build: [
+  ["dune" "subst"] {dev}
+  [
+    "dune"
+    "build"
+    "-p"
+    name
+    "-j"
+    jobs
+    "@install"
+    "@runtest" {with-test}
+    "@doc" {with-doc}
+  ]
+]
+dev-repo: "git+https://github.com/raven-ml/raven.git"
+x-maintenance-intent: ["(latest)"]
+url {
+  src:
+    "https://github.com/raven-ml/raven/releases/download/1.0.0_alpha3/raven-1.0.0.alpha3.tbz"
+  checksum: [
+    "sha256=6e78aa943959610b6a5c3439061959d8f228256df860bfb48eb573a7e79ffcd3"
+    "sha512=d59fc622f3b9fa11b6a065d89dc2425d14b1408cfc0fb9b9711e6ece70bb4ce6114ba177ce49766a9f7ae790023de77964f44f00e1a1e1068698415016ab0900"
+  ]
+}
+x-commit-hash: "67c327945fb7a1066e5262fbc3db1ca68410857e"

packages/nx/nx.1.0.0~alpha3/opam

@@ -0,0 +1,64 @@
+opam-version: "2.0"
+synopsis: "N-dimensional arrays for OCaml"
+description:
+  "Nx provides n-dimensional arrays with NumPy-like semantics and OCaml's type safety. 19 data types, broadcasting, slicing, linear algebra, FFT, and I/O. The numerical foundation for the Raven ecosystem."
+maintainer: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+authors: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+license: "ISC"
+tags: ["numerical-computation" "tensor-library" "machine-learning"]
+homepage: "https://github.com/raven-ml/raven"
+doc: "https://raven-ml.dev/docs/"
+bug-reports: "https://github.com/raven-ml/raven/issues"
+depends: [
+  "ocaml" {>= "5.2.0"}
+  "dune" {>= "3.21"}
+  "dune-configurator" {build}
+  "conf-pkg-config" {build}
+  "conf-zlib" {build}
+  "logs"
+  "windtrap" {with-test}
+  "mdx" {with-test}
+  "odoc" {with-doc}
+]
+build: [
+  ["dune" "subst"] {dev}
+  [
+    "dune"
+    "build"
+    "-p"
+    name
+    "-j"
+    jobs
+    "@install"
+    "@runtest" {with-test}
+    "@doc" {with-doc}
+  ]
+]
+dev-repo: "git+https://github.com/raven-ml/raven.git"
+x-maintenance-intent: ["(latest)"]
+depexts: [
+  ["libc-dev" "openblas-dev" "lapack-dev"] {os-distribution = "alpine"}
+  ["epel-release" "openblas-devel"] {os-distribution = "centos"}
+  ["libopenblas-dev" "liblapacke-dev"] {os-family = "debian"}
+  ["libopenblas-dev" "liblapacke-dev"] {os-family = "ubuntu"}
+  ["openblas-devel"] {os-family = "fedora"}
+  ["libopenblas_openmp-devel"] {os-family = "suse" | os-family = "opensuse"}
+  ["openblas" "lapacke" "cblas"] {os-distribution = "arch"}
+  ["openblas"] {os = "macos" & os-distribution = "homebrew"}
+  ["openblas" "lapacke"] {os = "freebsd"}
+  ["mingw64-x86_64-cblas" "mingw64-x86_64-lapack"] {os = "cygwin"}
+]
+x-ci-accept-failures: [
+  "oraclelinux-7"
+  "oraclelinux-8"
+  "oraclelinux-9"
+]
+url {
+  src:
+    "https://github.com/raven-ml/raven/releases/download/1.0.0_alpha3/raven-1.0.0.alpha3.tbz"
+  checksum: [
+    "sha256=6e78aa943959610b6a5c3439061959d8f228256df860bfb48eb573a7e79ffcd3"
+    "sha512=d59fc622f3b9fa11b6a065d89dc2425d14b1408cfc0fb9b9711e6ece70bb4ce6114ba177ce49766a9f7ae790023de77964f44f00e1a1e1068698415016ab0900"
+  ]
+}
+x-commit-hash: "67c327945fb7a1066e5262fbc3db1ca68410857e"

packages/quill/quill.1.0.0~alpha3/opam

@@ -0,0 +1,55 @@
+opam-version: "2.0"
+synopsis: "Notebooks as markdown files"
+description:
+  "Quill turns markdown files into interactive OCaml notebooks. Terminal UI for cell navigation and execution, batch evaluation with watch mode, and in-place output serialization. All Raven packages are pre-loaded."
+maintainer: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+authors: ["Thibaut Mattio <thibaut.mattio@gmail.com>"]
+license: "ISC"
+tags: ["notebooks" "interactive-computing" "literate-programming"]
+homepage: "https://github.com/raven-ml/raven"
+doc: "https://raven-ml.dev/docs/"
+bug-reports: "https://github.com/raven-ml/raven/issues"
+depends: [
+  "ocaml" {>= "5.2.0"}
+  "dune" {>= "3.21"}
+  "cmarkit"
+  "cmdliner"
+  "jsont"
+  "mosaic"
+  "nx" {= version}
+  "brot" {= version}
+  "talon" {= version}
+  "rune" {= version}
+  "kaun" {= version}
+  "sowilo" {= version}
+  "fehu" {= version}
+  "hugin" {= version}
+  "windtrap" {with-test}
+  "mdx" {with-test}
+  "odoc" {with-doc}
+]
+build: [
+  ["dune" "subst"] {dev}
+  [
+    "dune"
+    "build"
+    "-p"
+    name
+    "-j"
+    jobs
+    "@install"
+    "@runtest" {with-test}
+    "@doc" {with-doc}
+  ]
+]
+dev-repo: "git+https://github.com/raven-ml/raven.git"
+x-maintenance-intent: ["(latest)"]
+url {
+  src:
+    "https://github.com/raven-ml/raven/releases/download/1.0.0_alpha3/raven-1.0.0.alpha3.tbz"
+  checksum: [
+    "sha256=6e78aa943959610b6a5c3439061959d8f228256df860bfb48eb573a7e79ffcd3"
+    "sha512=d59fc622f3b9fa11b6a065d89dc2425d14b1408cfc0fb9b9711e6ece70bb4ce6114ba177ce49766a9f7ae790023de77964f44f00e1a1e1068698415016ab0900"
+  ]
+}
+x-commit-hash: "67c327945fb7a1066e5262fbc3db1ca68410857e"