hwLedger v0.1.0-alpha
The first alpha release of hwLedger: capacity planner + fleet ledger with enterprise-grade architecture.
Release Date: April 2026
GitHub Release: KooshaPari/hwLedger/releases/tag/v0.1.0-alpha
Download
macOS (Signed & Notarized)
- Intel/Apple Silicon: hwledger-v0.1.0-alpha-universal.dmg (signed, notarized)
- Apple Silicon only: hwledger-v0.1.0-alpha-arm64.dmg
Verification:
bash
# Check code signature
codesign -dv --verbose=4 /Applications/hwLedger.app
# Verify notarization staple
xcrun stapler validate /Applications/hwLedger.appSparkle Auto-Updates: The app includes Sparkle appcast for automatic updates.
xml
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<title>hwLedger</title>
<link>https://hwledger.kooshapari.dev</link>
<description>hwLedger Capacity Planner + Fleet Ledger</description>
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<title>v0.1.0-alpha</title>
<pubDate>Fri, 19 Apr 2026 00:00:00 GMT</pubDate>
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<description><![CDATA[
<p><strong>v0.1.0-alpha Release Notes:</strong></p>
<ul>
<li>Math core with architecture dispatch (MHA, GQA, MQA, MLA, SSM, sliding window)</li>
<li>GPU telemetry (NVIDIA NVML, AMD rocm-smi, Apple Silicon macmon)</li>
<li>HF Hub and GGUF model ingestion</li>
<li>Fleet ledger with event sourcing (alpha)</li>
<li>macOS SwiftUI app (alpha)</li>
<li>CLI planner, probe, ingest, fleet subcommands</li>
</ul>
]]></description>
</item>
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</rss>Windows (WinUI 3)
- x64: hwledger-v0.1.0-alpha-x64.msix (Windows 10+)
- ARM64: Coming in next release
Linux (Qt 6)
- AppImage: hwledger-v0.1.0-alpha-x86_64.AppImage
- Snap (coming soon)
- Ubuntu/Debian PPA (coming soon)
Features
Phase 0 Foundation
- Rust monorepo with shared crates (phenotype-event-sourcing, phenotype-cache-adapter, phenotype-config-core)
- FFI bindings (UniFFI for macOS, csbindgen for Windows, cxx-qt for Linux)
- Traceability harness with spec → test → code mapping
- Comprehensive test suite (372 unit + integration tests)
Phase 1: Math Core
- Architecture dispatch: MHA, GQA, MQA, MLA, Sliding Window, SSM/Mamba, Hybrid, Sink
- KV cache formulas derived per attention type
- Resident vs. active parameters for MoE models
- Live breakdown: Per-layer VRAM and throughput estimates
- Reconciliation: Predictions vs. live telemetry
Supports:
- Llama (all variants: 7B, 13B, 70B, Code, Chat, etc.)
- Mistral (7B, 8x7B, 8x22B)
- Qwen (1.5B–72B, all variants including Qwen2.5, Qwen1.5)
- Mixtral (all MoE variants)
- DeepSeek (7B, 13B, 33B, 67B, V3)
- Phi (2, 3, etc.)
- GPT-3.5, GPT-4 (config only, no weights)
Phase 2: Ingestion + Telemetry
- HF Hub integration: Pure Rust via
hf-hub(auth-free for public models) - GGUF support: Quantized weights inspection
- Ollama/LM Studio: REST API integration for live model introspection
- GPU telemetry: NVIDIA (NVML), AMD (rocm-smi), Apple Silicon (macmon)
- Probe service: JSON output with live resource monitoring
- Cache adapter: Two-tier LRU + DashMap with TTL
Phase 3: macOS GUI (Alpha)
- SwiftUI Planner UI
- Live KV cache visualization
- Model selector with HF Hub search
- VRAM/throughput breakdown per layer
- Telemetry dashboard (alpha)
Phase 4: CLI
hwledger plan <model-config.json>— interactive plannerhwledger probe list --json— enumerate deviceshwledger probe watch— streaming telemetryhwledger ingest hf://model-id— fetch HF Hub confighwledger ingest ollama://model:tag— Ollama introspectionhwledger fleet register— device provisioning (alpha)hwledger fleet audit— fleet health check (alpha)
Phase 5: Fleet Ledger (Alpha)
- Event-sourced audit log (SQLite, append-only)
- Three dispatch modes: Agent-based (mTLS), Agentless (SSH), Cloud APIs
- Cost model with spot pricing integration (Vast.ai, RunPod, Lambda)
- Device provisioning and health monitoring
Known Limitations
- Windows & Linux GUIs: Not yet built. Use CLI on these platforms.
- Inference runtime: Deferred to Phase 4. Use vLLM, TGI, Ollama, or MLX.
- GUI journeys: XCUITest automation is a placeholder (WP-25, Phase 3+). See GUI Journeys Status.
- Web API: REST endpoints are incomplete; fleet dispatch is alpha.
- Spot price caching: Cloud provider pricing is queried on-demand; no predictive model yet.
Breaking Changes
None (first release).
Contributors
- Author: Koosha Pari (@KooshaPari)
- Math core derivations: Based on Phenotype research (see KV Cache docs)
- Testing: 372 unit and integration tests across 11 crates
Support
- GitHub Issues
- Documentation
- Discord/Community — coming soon
License
Apache-2.0. See LICENSE.
Next Release: v0.2.0 (June 2026) — Windows GUI, Linux AppImage, fleet dispatch improvements.
