Release v1.0.0

.github/workflows/check.yml

@@ -0,0 +1,49 @@
+# CI: run the repo's own dependency-free gate (`make check` = clean + portable
+# CPU build + C unit suites + Python stdlib tests) on the three claimed
+# platforms. No model downloads, no CUDA, no external deps — by design (#140).
+name: check
+
+on:
+  push:
+    branches: [main, dev]
+  pull_request:
+    branches: [main, dev]
+
+jobs:
+  linux:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: make check
+        run: make -C c check
+
+  windows:
+    # The job that would have caught #68/#137 pre-merge: native MinGW-w64
+    # (MSYS2/UCRT64), the exact toolchain the README's Windows port targets.
+    runs-on: windows-latest
+    defaults:
+      run:
+        shell: msys2 {0}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: msys2/setup-msys2@v2
+        with:
+          msystem: UCRT64
+          update: false
+          install: >-
+            make
+            mingw-w64-ucrt-x86_64-gcc
+            mingw-w64-ucrt-x86_64-python
+      - name: make check
+        run: make -C c check
+
+  macos:
+    # clang; libomp for the threaded path (Makefile falls back to
+    # single-threaded automatically if it's ever missing).
+    runs-on: macos-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: install libomp
+        run: brew install libomp
+      - name: make check
+        run: make -C c check

.github/workflows/ci.yml

@@ -41,6 +41,78 @@ jobs:
             -Xcompiler=-Wall,-Wextra
           echo "CUDA syntax check passed"
 
+  windows-cuda-build:
+    # The Windows CUDA path has no coverage anywhere: `engine-cuda-syntax` above
+    # compiles with nvcc's *GCC* host on Linux, and check.yml's windows job is
+    # MinGW/UCRT64 CPU-only by design (#140). But nvcc on Windows requires MSVC
+    # as its host compiler — it does not accept MinGW — so `make cuda-dll` runs a
+    # toolchain nothing else in CI touches. That gap is not theoretical: every
+    # bug in #158 was a *build* failure on this path (MSVC rejects the GCC-style
+    # -Xcompiler=-Wall,-Wextra with "D8021 invalid numeric argument '/Wextra'",
+    # unresolvable CUDA_HOME/NVCC defaults, POSIX setenv in the kernel test), and
+    # #314 was CUDA_HOME with spaces — the layout the CUDA installer ships by
+    # default. Both classes are compile-time and need no GPU to catch.
+    #
+    # Build-only ON PURPOSE: GitHub's hosted runners have no NVIDIA device, so
+    # this job proves the Windows+MSVC CUDA build stays buildable, NOT that the
+    # kernels or the DLL loader behave on real silicon. That still needs hardware
+    # (see #157). Claiming otherwise would be the false confidence the
+    # engine-cuda-syntax comment above already warns about.
+    name: CUDA build (Windows, MSVC host)
+    # windows-2022, NOT windows-latest: the latest image now ships Visual Studio
+    # 18 (MSVC 14.5x), and CUDA's crt/host_config.h hard-errors on any host newer
+    # than VS 2022 ("Only the versions between 2017 and 2022 (inclusive) are
+    # supported"). That is a real constraint for every CUDA user on Windows, not
+    # a CI quirk — pinning tracks what the toolkit actually supports. Revisit when
+    # a CUDA release accepts VS 18; -allow-unsupported-compiler would only mask it.
+    runs-on: windows-2022
+    steps:
+      - uses: actions/checkout@v4
+      - name: MSVC environment (puts cl.exe on PATH for nvcc -ccbin)
+        uses: ilammy/msvc-dev-cmd@v1
+      - name: Install CUDA toolkit (compiler only)
+        uses: Jimver/cuda-toolkit@v0.2.19
+        with:
+          # Same pin as engine-cuda-syntax: v0.2.19's version table stops at
+          # 12.6.2. This installs to the default "C:\Program Files\NVIDIA GPU
+          # Computing Toolkit\..." path, so CUDA_HOME is space-bearing here —
+          # which is exactly the #314 regression this job would have caught.
+          cuda: '12.6.2'
+          method: network
+          # cudart as well as nvcc: unlike the Linux job (which only needs to
+          # *compile* backend_cuda.cu), cuda-dll links it, and on Windows the
+          # runtime headers/import lib ship as a separate installer component —
+          # with '["nvcc"]' alone this fails at `#include <cuda_runtime.h>`.
+          sub-packages: '["nvcc", "cudart"]'
+      - uses: msys2/setup-msys2@v2
+        with:
+          msystem: UCRT64
+          update: false
+          # inherit: cl.exe (msvc-dev-cmd) and nvcc (cuda-toolkit) are added to
+          # the *Windows* PATH by the steps above; without inheriting it the
+          # recipe's `command -v` guards fail inside the MSYS2 shell.
+          path-type: inherit
+          install: >-
+            make
+            mingw-w64-ucrt-x86_64-gcc
+      - name: make cuda-dll (nvcc + MSVC host)
+        shell: msys2 {0}
+        run: |
+          cd c
+          # CUDA_ARCH is pinned: the default is `native`, which asks the driver
+          # what card is present — there is none here, so it must be explicit.
+          # sm_80 matches engine-cuda-syntax and is supported by the 12.6 pin.
+          make cuda-dll CUDA_ARCH=sm_80
+          test -f coli_cuda.dll || { echo "cuda-dll reported success but produced no DLL" >&2; exit 1; }
+          echo "coli_cuda.dll built (MSVC host)"
+      - name: make glm CUDA_DLL=1 (host links backend_loader, not cudart)
+        shell: msys2 {0}
+        run: |
+          cd c
+          make glm CUDA_DLL=1
+          test -f glm.exe || { echo "glm CUDA_DLL=1 reported success but produced no exe" >&2; exit 1; }
+          echo "glm.exe built against the DLL loader"
+
   web:
     name: Web UI
     runs-on: ubuntu-latest

.github/workflows/release.yml

@@ -0,0 +1,99 @@
+name: Release
+
+on:
+  push:
+    tags: ['v*']
+
+permissions:
+  contents: write
+
+jobs:
+  build:
+    strategy:
+      matrix:
+        include:
+          - os: ubuntu-latest
+            name: linux-x86_64
+            ext: ""
+            make_args: "ARCH=x86-64-v3"
+          - os: macos-latest
+            name: macos-arm64
+            ext: ""
+            make_args: ""
+          - os: windows-latest
+            name: windows-x86_64
+            ext: ".exe"
+            make_args: ""
+            shell: msys2
+    runs-on: ${{ matrix.os }}
+    defaults:
+      run:
+        shell: ${{ matrix.shell || 'bash' }}
+    steps:
+      - uses: actions/checkout@v4
+
+      - if: matrix.shell == 'msys2'
+        uses: msys2/setup-msys2@v2
+        with:
+          msystem: UCRT64
+          update: false
+          install: >-
+            make
+            mingw-w64-ucrt-x86_64-gcc
+
+      - if: matrix.os == 'macos-latest'
+        run: brew install libomp
+
+      - name: Build engine
+        run: |
+          cd c
+          make glm ${{ matrix.make_args }}
+          ls -lh glm${{ matrix.ext }}
+
+      - name: Package
+        run: |
+          TAG=${GITHUB_REF#refs/tags/}
+          mkdir -p dist
+          cp c/glm${{ matrix.ext }} dist/colibri-${TAG}-${{ matrix.name }}${{ matrix.ext }}
+          cp c/coli dist/
+          cp c/version.py dist/
+          cp c/openai_server.py dist/
+          cp c/resource_plan.py dist/
+          cp c/doctor.py dist/
+          cp LICENSE dist/
+          cd dist
+          if [ "${{ matrix.ext }}" = ".exe" ]; then
+            7z a colibri-${TAG}-${{ matrix.name }}.zip *
+          else
+            tar czf colibri-${TAG}-${{ matrix.name }}.tar.gz *
+          fi
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: colibri-${{ matrix.name }}
+          path: dist/colibri-*.*
+
+  release:
+    needs: build
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/download-artifact@v4
+        with:
+          path: artifacts
+          merge-multiple: true
+
+      - name: Create GitHub Release
+        env:
+          GH_TOKEN: ${{ github.token }}
+        run: |
+          TAG=${GITHUB_REF#refs/tags/}
+          # Extract the latest version section from CHANGELOG
+          NOTES=$(awk "/^## \\[${TAG#v}\\]/{found=1;next} /^## \\[/{if(found)exit} found{print}" CHANGELOG.md)
+          if [ -z "$NOTES" ]; then
+            NOTES="Release ${TAG}"
+          fi
+          gh release create "$TAG" artifacts/* \
+            --title "colibrì ${TAG}" \
+            --notes "$NOTES"

CHANGELOG.md

@@ -0,0 +1,53 @@
+# Changelog
+
+All notable changes to colibrì are documented here.
+Format follows [Keep a Changelog](https://keepachangelog.com/).
+
+## [1.0.0] — 2026-07-19
+
+First tagged release. The engine has been running in production since late June
+2026; this tag marks the baseline for semantic versioning going forward.
+
+### Highlights
+
+- **GLM-5.2 (744B MoE)** runs on ~25 GB RAM in pure C, streaming experts from disk
+- **Three-tier placement**: VRAM (hot) / RAM (warm) / NVMe (cold), with a learning
+  cache that pins your workload's hottest experts automatically
+- **CUDA backend**: multi-GPU expert tier, dense tensor distribution, batched
+  ragged attention, resident pipeline (`COLI_CUDA_PIPE=2`)
+- **Metal backend** (Apple Silicon): batched expert SwiGLU + fused decode attention
+  on unified memory GPU
+- **MTP speculation**: native GLM-5.2 draft heads, grammar-forced drafts, kernel-
+  pinned verification (`SPEC_PIN=1`)
+- **OpenAI-compatible API**: `coli serve` with SSE streaming, KV slots, bounded
+  queue, web dashboard (`coli web`)
+- **Web UI**: chat with live metrics, expert cortex brain page, profiling breakdown,
+  expert atlas 3-D galaxy
+- **Cross-platform**: Linux, macOS, Windows 11 (native MinGW), PowerPC; CI on all three
+- **Auto-tune**: `coli plan --auto-tier` classifies the bottleneck and derives
+  MTP/PIPE/NUMA/PIN settings with explanations
+
+### Engine
+
+- Token-exact validation against `transformers` oracle (teacher-forcing 32/32)
+- Compressed MLA KV cache (576 floats/token, 57× smaller), persisted across
+  restarts (`.coli_kv`, zero re-prefill)
+- DSA sparse attention (lightning indexer), faithfully implemented
+- Router-lookahead prefetch (`PILOT=1`, 71.6% predictive)
+- Async expert I/O pool (`PIPE=1`), io_uring batching (`URING=1`)
+- NUMA-aware expert placement (`COLI_NUMA=1`, +13–40% on multi-socket)
+- AVX2 / AVX-512 / AVX-VNNI / ARM NEON / NEON-i8mm / POWER VSX kernels
+- int4 / int8 / int2 / grouped-int4 (fmt=4) quantization formats
+
+### Tools
+
+- `coli convert` — FP8→int4 one-shard-at-a-time converter
+- `coli doctor` — read-only setup diagnostics
+- `coli plan` — resource planner with auto-tune prescription
+- `coli bench` — MMLU / HellaSwag / ARC quality benchmarks
+- Expert atlas (`tools/analyze.py --web`) — measured topic affinity for 19,456 experts
+
+### Community
+
+- 30+ hardware datapoints in the benchmark tracker
+- Contributions from 20+ authors across engine, docs, tooling, and ports