release: version infrastructure + GitHub Release workflow

- c/version.py: single source of truth (__version__ = "1.0.0")
- coli: reads version.py, banner shows dynamic version, --version flag
- .github/workflows/release.yml: tag push triggers cross-platform build
  (Linux x86_64, macOS ARM64, Windows x86_64) and creates a GitHub
  Release with packaged binaries + changelog notes
- CHANGELOG.md: v1.0.0 baseline documenting all shipped features

To cut a release:
  1. bump c/version.py
  2. add a CHANGELOG section
  3. git tag v1.0.0 && git push --tags

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