# Release Notes — v0.0.6 (May 2026)

This release marks the shift from proof-of-concept to production architecture. The agent now runs on a **Dynamic Knowledge Architecture** and ships alongside **IoT-FirmwareDebugBench v0.1** — the first open, hardware-in-the-loop benchmark for embedded AI debugging tools.

---

## Architecture

### Dynamic Knowledge & Tool-Skill Loading (`iot-knowledge/`)
The agent no longer carries a static bundle of domain knowledge into every session. Domain expertise is now structured as discrete, composable modules — each scoped to a specific chip family, protocol stack, or debug capability — loaded on demand based on the project and task at hand.

- **Workflows** (`log-analyzer`, `log-generator`, `debug-loop`) define structured debugging sequences
- **Platform rules** enforce tool routing, device identity constraints, and context budget protection
- **Board specs** and **SDK references** load only when the task requires them

This architecture is designed to scale: new chip families, protocols, and tool integrations add as modules, not core changes.

---

## Stability & Bug Fixes

### Markdown Table Rendering
Implemented a custom, dependency-free Markdown table parser for the webview UI. Error codes, register maps, and configuration comparisons now render as clean, formatted tables.

### Notification Stability (Linux)
- Truncated long task completion messages to prevent `E2BIG` errors from the OS notification system
- Added `ENOENT` guards for systems missing `notify-send`

### Tool Naming Consistency
Unified internal tool naming between the backend and the webview UI, resolving a regression where native hardware actions were incorrectly routed as MCP tool calls.

---

## Benchmark

This release includes the full **IoT-FirmwareDebugBench v0.1** report and evaluation data.

- 6 BLE firmware bugs across 3 difficulty levels
- Evaluated on physical nRF52840 DK and nRF52832 DK hardware
- Same-model methodology (Claude Haiku 4.5) isolates architectural contribution

**Result:** Adsum IoT Coder resolved **5/6 tasks (83%)** vs Claude Code's **3/6 (50%)**, with **3.8× better token efficiency**.

👉 [Full benchmark report](../docs/benchmarks/v0.1-report.md)

---

*Built by [Adsum Networks](https://github.com/adsumnetworks). Apache 2.0.*
