ExitPlanMode

Prompt: build Rust crates with cargo

Prompt: run cargo tests

Prompt: run Python scripts for smoke testing

Prompt: install Python dependencies with pip

Prompt: list directory contents

Prompt: run git commands for staging and committing

# Plan: Intel AI Edge Application Ready Verification -- Cubie Trust Compiler Integration

## Context

The Cubie Trust Compiler is a formally verified AI workload admission gate that compiles to native x86_64 machine code. It already:
- Cross-compiles to 3 bare-metal targets (RISC-V, x86_64, AArch64)
- Has a C-ABI entry point: `extern "C" fn cubie_admit(*const CubieRequest) -> AdmitResult`
- Runs in ~730 ns/call on Intel Core Ultra 9 285HX (~1.37M admits/sec)
- Is proven correct by 754 theorems across Coq 8.18.0, Lean 4.30.0, and Verus
- Has 62 passing integration tests and zero external dependencies
- Is backed by 3 patent families (US11956363B2, US20240195628A1)

Intel's AI Edge Application Ready Verification program (Rev 2.1, Jan 2026) is a 5-step ISV onboarding process. Nick has testbed access. The goal: get Cubie verified as an Intel AI Edge application, which unlocks technical support and co-marketing.

**What Intel needs from us:**
1. Application running on Intel edge hardware
2. Workload performance metrics at light/medium/heavy loads
3. Pipeline block diagram
4. Resource utilization screenshots (CPU/GPU/NPU)
5. Sizing tool report OR Appendix A alternative artifacts

**The gap:** The bare-metal library has no Python bindings, no C header, no container config, and no OpenVINO pipeline integration. We need a bridge from `libcubie_bare_metal.a` to a Python+OpenVINO demo pipeline.

---

## What a Real Intel Edge AI Workload Looks Like

The Intel Edge AI Sizing Tool (Node.js frontend + Python FastAPI workers, ports 5000-6000) processes workloads that carry these actual properties:

**Workload-level properties (what gets submitted to a worker):**
```
workload_id:           string    # unique identifier
workload_type:         enum      # "dlstreamer" | "text-generation" | "instance-segmentation" | ...
model_name:            string    # "yolo11n", HuggingFace ID, or custom .xml path
model_precision:       enum      # "FP16" | "FP32"
device:                string    # "CPU" | "GPU" | "NPU" | "AUTO" | "MULTI:GPU,CPU"
batch_size:            int       # frames or tokens per inference call
number_of_streams:     int       # parallel inference streams
input_source:          string    # file path, RTSP URL, /dev/videoN, or prompt text
```

**OpenVINO compile_model config (set at model load time):**
```
performance_mode:      LATENCY | THROUGHPUT
model_priority:        LOW(0) | MEDIUM(1) | HIGH(2)
num_streams:           int
inference_num_threads: int
num_requests (nireq):  int       # async request pool size
scheduling_core_type:  ANY_CORE | PCORE_ONLY | ECORE_ONLY
affinity:              NONE | CORE | NUMA | HYBRID_AWARE
execution_mode:        PERFORMANCE | ACCURACY
```

**DLStreamer pipeline element properties (per-stage in video pipelines):**
```
gvadetect / gvaclassify / gvainference:
  model:               .xml path
  device:              CPU | GPU | NPU
  nireq:               parallel inference requests
  inference_interval:  process every N-th frame
  batch_size:          frames batched per call
  threshold:           detection confidence cutoff
  pre_process_backend: ie | opencv | va | va-surface-sharing
```

**KServe V2 inference request (OVMS REST API):**
```json
{
  "id": "request-42",
  "inputs": [{ "name": "input", "shape": [1,3,224,224], "datatype": "FP32" }],
  "outputs": [{ "name": "detection_output" }]
}
```

### Mapping CubieRequest to Real Workload Properties

The existing `CubieRequest` (40 bytes, `bare-metal/src/types.rs:4-25`) has abstract booleans. Here is how each field maps to real Intel Edge workload properties:

| CubieRequest field | Real-world meaning on Intel Edge | How it gets populated |
|---|---|---|
| `who_pass` | **Identity**: Is the requesting entity (user/service/container) authenticated? | Check API key, JWT, or mTLS cert against allow-list |
| `what_pass` | **Capability**: Is this model+precision allowed on this device? | Validate `model_name` + `model_precision` + `device` against policy |
| `how_pass` | **Method**: Is the execution config (batch_size, nireq, streams) within bounds? | Check `batch_size <= max_batch`, `nireq <= max_nireq`, `num_streams <= max_streams` |
| `where_pass` | **Location**: Is this edge node authorized for this workload type? | Check node ID / testbed system ID against deployment policy |
| `when_pass` | **Lease**: Is the time-bounded execution lease still valid? | Check `lease_expiry > current_time` |
| `why_pass` | **Policy**: Does the workload comply with governance rules? | Check `model_priority`, `performance_mode`, resource quotas |
| `has_valid_lease` | Lease not expired | `lease_expiry_epoch > now()` |
| `epoch_fresh` | Request not stale/replayed | `request_epoch` within freshness window |
| `request_hash` | Hash of (model_name + device + batch_size + input_shape) | SHA-256 of workload config fields, truncated to u64 |
| `capability_hmac` | HMAC-SHA256 of request_hash with eFuse key | Proves the request was authorized by the platform root of trust |
| `key_slot` | Which eFuse key slot (0 = platform root) | Always 0 for single-tenant edge; multi-tenant uses slots 0-7 |
| `peer_id` | NVLink peer to check for deadlock | Maps to GPU device index in multi-GPU edge systems |
| `adapter_id` | PCIe adapter for bandwidth check | Maps to GPU/NPU PCIe device ID |

### The Pipeline Integration Point

The Cubie gate sits at **workload submission time** -- BEFORE `core.compile_model()` or DLStreamer pipeline launch. It does NOT gate individual inference frames. It gates whether the entire workload is allowed to start.

```
Workload Request (model, device, config)
    |
    v
[Policy Evaluator] -- populates CubieRequest bools from workload config
    |
    v
[cubie_admit()] -- ~730ns, HMAC + NVLink + PCIe hardware checks
    |
    +-- Allow --> core.compile_model(model, device, config) --> inference loop
    |
    +-- Deny --> HTTP 403, workload never touches GPU/NPU
```

---

## Phase 1: FFI Bridge (Python-callable shared library)

**Why:** Intel's ecosystem is Python+OpenVINO. We need `cubie_admit()` callable from Python.

**Why ctypes over PyO3:** Zero Python build tooling. Load `.so`, define struct, call function. Minimal moving parts for a verification submission.

**Why mock feature:** Non-mock builds use `core::ptr::read_volatile()` on hardware MMIO addresses -- this will segfault in Linux userspace. The `mock` feature replaces MMIO with AtomicU32 cells. The admission logic is identical. The formal proofs hold regardless of MMIO backend.

### Files to create:

- [ ] **`cubie-ffi/Cargo.toml`** -- New workspace member, `crate-type = ["cdylib"]`, depends on `cubie-bare-metal` with `features = ["std", "mock"]`
- [ ] **`cubie-ffi/src/lib.rs`** -- Re-exports `cubie_admit`, adds `cubie_mock_setup()` (configures eFuse key + NVLink + PCIe mock state) and `cubie_create_valid_request()` (returns a CubieRequest with correct HMAC)
- [ ] **`cubie-ffi/include/cubie.h`** -- C header documenting the ABI. Uses `_Bool` (not `bool`). Includes `static_assert(sizeof(CubieRequest) == 40)`
- [ ] **`pipeline/cubie_gate.py`** -- Python ctypes wrapper. `CubieRequest` as `ctypes.Structure`, `CubieGate` class with `admit()`, `benchmark()`, `create_test_request()` methods

### Modify:

- [ ] **`Cargo.toml`** (workspace root) -- Add `"cubie-ffi"` to `members` list

### Build:

```bash
cargo build --release --manifest-path cubie-ffi/Cargo.toml
# Output: target/release/libcubie_ffi.so (Linux) or cubie_ffi.dll (Windows)
```

### Verify:

```bash
cargo test --manifest-path bare-metal-tests/Cargo.toml  # 62 tests still pass
python3 -c "from pipeline.cubie_gate import CubieGate; g = CubieGate(); print(g.benchmark(100000))"
```

### Critical detail -- CubieRequest struct layout:

```
Offset  Field              Type    Size
0       who_pass           bool    1
1       what_pass          bool    1
2       how_pass           bool    1
3       where_pass         bool    1
4       when_pass          bool    1
5       why_pass           bool    1
6       has_valid_lease    bool    1
7       epoch_fresh        bool    1
8       request_hash       u64     8
16      capability_hmac    u64     8
24      key_slot           u8      1
25      peer_id            u8      1
26-31   (padding)                  6
32      adapter_id         u64     8
Total: 40 bytes
```

Source: `bare-metal/src/types.rs:4-25`

---

## Phase 2: OpenVINO Pipeline (Cubie as workload admission gate)

**Where the gate sits:** At workload submission time, BEFORE `core.compile_model()`. Cubie decides whether a workload is allowed to load onto the GPU/NPU at all. It does NOT gate individual frames -- it gates the entire workload lifecycle.

**Architecture:**

```
Workload Request {model: "yolo11n", device: "GPU", batch_size: 2, nireq: 4}
    |
    v
[Policy Evaluator]  -- evaluates workload config against admission rules
    |                  who: API key valid?  what: model+device allowed?
    |                  how: batch/nireq within bounds?  where: node authorized?
    |                  when: lease valid?  why: policy compliant?
    v
[cubie_admit()]     -- ~730ns: HMAC proof-of-authorization + NVLink + PCIe check
    |
    +-- Allow --> ov.Core().compile_model(model, "GPU", config) --> inference loop --> results
    |
    +-- Deny  --> 403 Forbidden, workload never touches GPU/NPU, zero accelerator waste
```

**Workload type:** Video Processing (DLStreamer + OpenVINO object detection). Fallback: NLP text generation.

### Files to create:

- [ ] **`pipeline/policy.py`** -- Admission policy evaluator. Takes a workload request dict (model_name, device, batch_size, nireq, num_streams, model_precision, performance_mode, model_priority) and evaluates it against configurable rules to produce the 6 face booleans + HMAC. This is the bridge between real workload properties and CubieRequest fields. Example rules:
  - `who`: API key in allow-list
  - `what`: `(model_name, device)` pair in approved model registry
  - `how`: `batch_size <= 8`, `nireq <= 16`, `num_streams <= 4`
  - `where`: `node_id` matches deployment target
  - `when`: `lease_expiry > time.time()`
  - `why`: `model_priority != HIGH` or caller has elevated privilege

- [ ] **`pipeline/cubie_openvino_demo.py`** -- Main demo application. FastAPI server (mirrors Intel sizing tool worker pattern). Routes:
  - `POST /admit` -- accepts workload config JSON, runs through policy.py -> cubie_admit(), returns Allow/Deny with timing
  - `POST /launch` -- admit + compile model + run inference loop (the full pipeline)
  - `GET /metrics` -- current admit/deny counts, latencies, resource utilization
  - `GET /health` -- service health

- [ ] **`pipeline/load_profiles.py`** -- Load generator for Intel's 3-level requirement:
  - Light: 1 workload submission/sec, single model, CPU-only
  - Medium: 5 concurrent workloads, mix of CPU+GPU, varied batch sizes
  - Heavy: max concurrent workloads, all devices, stress test admission gate throughput

- [ ] **`pipeline/resource_monitor.py`** -- Mirrors Intel sizing tool's `custom-application-profiling` worker pattern. Uses `psutil` for CPU/memory, `xpumcli` for GPU, sysfs for NPU. 1s sampling interval, CSV output, 5s idle baseline subtraction.

- [ ] **`pipeline/requirements.txt`** -- `openvino>=2024.0`, `fastapi>=0.110`, `uvicorn>=0.29`, `psutil>=5.9`, `matplotlib>=3.7`, `numpy>=1.24`

- [ ] **`pipeline/models.json`** -- Approved model registry (policy config). Lists allowed (model_name, device, max_batch, max_nireq) tuples. The `what_pass` face checks workload requests against this registry.

### Model setup:

```bash
# Object detection (DLStreamer-compatible)
pip install openvino-dev
omz_downloader --name yolo-v8 --output_dir models/
omz_converter --name yolo-v8 --output_dir models/

# Or text generation
pip install openvino-genai
# Download TinyLlama in OpenVINO format
```

### How policy.py populates CubieRequest from real workload properties:

```python
def evaluate_workload(workload: dict, policy: dict, gate: CubieGate) -> AdmitResult:
    # 6-face evaluation against real workload properties
    who = workload["api_key"] in policy["allowed_keys"]
    what = (workload["model_name"], workload["device"]) in policy["approved_models"]
    how = (workload["batch_size"] <= policy["max_batch"]
           and workload["nireq"] <= policy["max_nireq"])
    where = workload.get("node_id", "local") in policy["authorized_nodes"]
    when = workload.get("lease_expiry", float("inf")) > time.time()
    why = (workload.get("model_priority", 1) <= policy["max_priority"]
           or workload.get("elevated", False))

    # Compute request hash from workload config
    config_str = f"{workload['model_name']}:{workload['device']}:{workload['batch_size']}"
    request_hash = int.from_bytes(hashlib.sha256(config_str.encode()).digest()[:8], "big")

    # Build CubieRequest with real data
    req = CubieRequest(
        who_pass=who, what_pass=what, how_pass=how,
        where_pass=where, when_pass=when, why_pass=why,
        has_valid_lease=when,
        epoch_fresh=True,  # checked against request timestamp
        request_hash=request_hash,
        capability_hmac=gate.compute_hmac(request_hash),  # FIPS 198-1
        key_slot=0, peer_id=0,
        adapter_id=get_pcie_adapter_id(workload["device"]),
    )
    return gate.admit(req)
```

---

## Phase 3: Intel Edge Testbed Deployment

**Prereqs:** Testbed access (confirmed), Ubuntu 22.04/24.04, Rust toolchain, Python 3.10+, OpenVINO, Node.js 22+

### Steps:

- [ ] **Clone repo and build FFI** on testbed
- [ ] **Run speedtest** -- `cargo run --release --manifest-path bare-metal-tests/Cargo.toml --bin speedtest` -- captures Intel CPU brand string + TSC-calibrated ns/call for all gate components
- [ ] **Install OpenVINO + download model** on testbed
- [ ] **Run demo pipeline at 3 load levels** -- capture metrics CSVs
- [ ] **Capture resource utilization** -- CPU/GPU/NPU screenshots at each load level

### Intel Edge AI Sizing Tool (primary method):

```bash
git clone https://github.com/intel/edge-ai-sizing-tool
cd edge-ai-sizing-tool && ./install.sh && ./start.sh
# Browse to http://localhost:8080
# Configure workload, generate PDF report
```

### Appendix A fallback (if sizing tool doesn't support custom pipelines):

```bash
sudo dmidecode > artifacts/hardware_info.txt                    # System info
lsof -p $(pgrep -f cubie_openvino_demo) > artifacts/runtime_libs.txt  # Runtime libraries
# Resource utilization captured by resource_monitor.py at each load level
```

---

## Phase 4: Generate Submission Artifacts

### Files to create:

- [ ] **`pipeline/generate_diagrams.py`** -- Produces pipeline block diagram (PNG) + formal verification architecture diagram using matplotlib
- [ ] **`artifacts/capture_system_info.sh`** -- Captures CPU, RAM, GPU, NPU, OS, OpenVINO version, Rust version
- [ ] **`artifacts/pipeline_description.md`** -- Narrative for Intel form: "First formally verified AI workload admission gate for Intel edge hardware. 754 theorems, sub-microsecond decisions, 3 patent families, native x86_64 machine code, FIPS 198-1 HMAC-SHA256."
- [ ] **`artifacts/build_submission_report.py`** -- Compiles system info + metrics CSVs + diagrams into a single PDF

### Required artifacts checklist:

| Artifact | Source | Format |
|----------|--------|--------|
| System info | `capture_system_info.sh` | TXT |
| Pipeline description | `pipeline_description.md` | Text (pasted into form) |
| Block diagram | `generate_diagrams.py` | PNG |
| Sizing tool report | Edge AI Sizing Tool or Appendix A | PDF |
| CPU utilization (light/med/heavy) | `resource_monitor.py` | Screenshots/CSV |
| GPU utilization (light/med/heavy) | `intel_gpu_top` or resource_monitor | Screenshots/CSV |
| System photo | Testbed screenshot | Image |

---

## Phase 5: Submit

- [ ] **Fill Intel form** at `https://app.smartsheet.com/b/form/019a3bfa6a0b7623876994c2950fc14f`
- [ ] **Upload all artifacts** from Phase 4
- [ ] **Application name:** "Cubie Trust Compiler -- Formally Verified AI Admission Gate"

**Narrative for Intel review meeting:**

> The Cubie Trust Compiler is the first formally verified AI workload admission gate. It runs as a sub-microsecond pre-inference filter on Intel edge hardware, making admission decisions in ~730 nanoseconds -- proven correct by 754 independent theorems across three proof assistants (Coq, Lean 4, Verus). Every AI inference request passes through the gate before consuming GPU or NPU compute. Denied requests use zero accelerator resources. The entire stack compiles to native x86_64 machine code with zero external dependencies. Protected by 3 patent families (US11956363B2, US12073439B2, US20240195628A1).

Support: intel.edge.ai.application.qualification@intel.com

---

## File Summary

| Phase | New Files | Modified |
|-------|-----------|----------|
| 1: FFI Bridge | `cubie-ffi/Cargo.toml`, `cubie-ffi/src/lib.rs`, `cubie-ffi/include/cubie.h`, `pipeline/cubie_gate.py` | `Cargo.toml` (workspace) |
| 2: Pipeline | `pipeline/policy.py`, `pipeline/models.json`, `pipeline/cubie_openvino_demo.py`, `pipeline/load_profiles.py`, `pipeline/resource_monitor.py`, `pipeline/requirements.txt` | -- |
| 3: Deploy | -- (commands only) | -- |
| 4: Artifacts | `pipeline/generate_diagrams.py`, `artifacts/capture_system_info.sh`, `artifacts/pipeline_description.md`, `artifacts/build_submission_report.py` | -- |
| **Total** | **14 new files** | **1 modified** |

**Zero existing files touched** (all bare-metal, Coq, Lean, Verus source preserved).

---

## Critical Source Files (read-only dependencies)

| File | What the plan uses from it |
|------|---------------------------|
| `bare-metal/src/types.rs:4-25` | `CubieRequest` #[repr(C)] layout -- Python ctypes must mirror exactly |
| `bare-metal/src/admit.rs:20-32` | `cubie_admit` C-ABI signature -- FFI crate re-exports this |
| `bare-metal/src/mmio.rs` | Mock setter functions (`mock_set_efuse_key`, etc.) -- exposed through FFI |
| `bare-metal/src/hmac.rs` | `hmac_sha256` -- used by FFI to compute valid test HMACs |
| `bare-metal-tests/src/speedtest.rs` | CPUID + RDTSC benchmarks -- run on testbed for Intel-specific numbers |
| `Cargo.toml` | Workspace members list -- add `cubie-ffi` |

---

## Risks and Mitigations

| Risk | Mitigation |
|------|-----------|
| CubieRequest struct layout mismatch (Rust vs Python) | `static_assert(sizeof == 40)` in C header + round-trip test |
| Sizing tool doesn't support custom pipelines | Use Appendix A alternative (explicitly supported by Intel Rev 2.1) |
| NPU utilization is zero (model not compiled for NPU) | Frame Cubie as CPU-side filter that *reduces* unnecessary NPU load |
| Mock MMIO means "not real hardware" | HMAC-SHA256 computation is identical; formal proofs hold regardless of MMIO source; document clearly |
| Bool representation differs C vs Rust | Use `_Bool` in header (not `bool`); `ctypes.c_bool` maps to `_Bool` correctly |

---

## Verification (how to test end-to-end)

1. `cargo test --manifest-path bare-metal-tests/Cargo.toml` -- 62 tests pass (no regression)
2. `cargo build --release --manifest-path cubie-ffi/Cargo.toml` -- .so/.dll builds
3. `python3 -c "from pipeline.cubie_gate import CubieGate; g = CubieGate(); print(g.admit(g.create_test_request()))"` -- prints True
4. Policy evaluation test -- submit a workload with `model_name="yolo11n", device="GPU", batch_size=2` against a policy that allows it -> returns Allow. Change `device="INVALID"` -> returns Deny (what_pass fails). Change `batch_size=999` -> returns Deny (how_pass fails)
5. `python3 pipeline/cubie_openvino_demo.py --mode light --duration 10` -- runs without error, logs admit/deny decisions with ns latencies
6. `python3 pipeline/generate_diagrams.py --output artifacts/` -- produces PNGs
7. All artifacts in `artifacts/` directory are non-empty and submission-ready


Error: The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). To tell you how to proceed, the user said:
save as doc to my downloads folder your last output with text based diagrams as well in the doc

Note: The user's next message may contain a correction or preference. Pay close attention — if they explain what went wrong or how they'd prefer you to work, consider saving that to memory for future sessions.


1	# Plan: Intel AI Edge Application Ready Verification -- Cubie Trust Compiler Integration
2
3	## Context
4
5	The Cubie Trust Compiler is a formally verified AI workload admission gate that compiles to native x86_64 machine code. It already:
6	- Cross-compiles to 3 bare-metal targets (RISC-V, x86_64, AArch64)
7	- Has a C-ABI entry point: `extern "C" fn cubie_admit(*const CubieRequest) -> AdmitResult`
8	- Runs in ~730 ns/call on Intel Core Ultra 9 285HX (~1.37M admits/sec)
9	- Is proven correct by 754 theorems across Coq 8.18.0, Lean 4.30.0, and Verus
10	- Has 62 passing integration tests and zero external dependencies
11	- Is backed by 3 patent families (US11956363B2, US20240195628A1)
12
13	Intel's AI Edge Application Ready Verification program (Rev 2.1, Jan 2026) is a 5-step ISV onboarding process. Nick has testbed access. The goal: get Cubie verified as an Intel AI Edge application, which unlocks technical support and co-marketing.
14
15	**What Intel needs from us:**
16	1. Application running on Intel edge hardware
17	2. Workload performance metrics at light/medium/heavy loads
18	3. Pipeline block diagram
19	4. Resource utilization screenshots (CPU/GPU/NPU)
20	5. Sizing tool report OR Appendix A alternative artifacts
21
22	**The gap:** The bare-metal library has no Python bindings, no C header, no container config, and no OpenVINO pipeline integration. We need a bridge from `libcubie_bare_metal.a` to a Python+OpenVINO demo pipeline.
23
24	---
25
26	## What a Real Intel Edge AI Workload Looks Like
27
28	The Intel Edge AI Sizing Tool (Node.js frontend + Python FastAPI workers, ports 5000-6000) processes workloads that carry these actual properties:
29
30	**Workload-level properties (what gets submitted to a worker):**
31	```
32	workload_id:           string    # unique identifier
33	workload_type:         enum      # "dlstreamer" | "text-generation" | "instance-segmentation" | ...
34	model_name:            string    # "yolo11n", HuggingFace ID, or custom .xml path
35	model_precision:       enum      # "FP16" | "FP32"
36	device:                string    # "CPU" | "GPU" | "NPU" | "AUTO" | "MULTI:GPU,CPU"
37	batch_size:            int       # frames or tokens per inference call
38	number_of_streams:     int       # parallel inference streams
39	input_source:          string    # file path, RTSP URL, /dev/videoN, or prompt text
40	```
41
42	**OpenVINO compile_model config (set at model load time):**
43	```
44	performance_mode:      LATENCY | THROUGHPUT
45	model_priority:        LOW(0) | MEDIUM(1) | HIGH(2)
46	num_streams:           int
47	inference_num_threads: int
48	num_requests (nireq):  int       # async request pool size
49	scheduling_core_type:  ANY_CORE | PCORE_ONLY | ECORE_ONLY
50	affinity:              NONE | CORE | NUMA | HYBRID_AWARE
51	execution_mode:        PERFORMANCE | ACCURACY
52	```
53
54	**DLStreamer pipeline element properties (per-stage in video pipelines):**
55	```
56	gvadetect / gvaclassify / gvainference:
57	  model:               .xml path
58	  device:              CPU | GPU | NPU
59	  nireq:               parallel inference requests
60	  inference_interval:  process every N-th frame
61	  batch_size:          frames batched per call
62	  threshold:           detection confidence cutoff
63	  pre_process_backend: ie | opencv | va | va-surface-sharing
64	```
65
66	**KServe V2 inference request (OVMS REST API):**
67	```json
68	{
69	  "id": "request-42",
70	  "inputs": [{ "name": "input", "shape": [1,3,224,224], "datatype": "FP32" }],
71	  "outputs": [{ "name": "detection_output" }]
72	}
73	```
74
75	### Mapping CubieRequest to Real Workload Properties
76
77	The existing `CubieRequest` (40 bytes, `bare-metal/src/types.rs:4-25`) has abstract booleans. Here is how each field maps to real Intel Edge workload properties:
78
79	| CubieRequest field | Real-world meaning on Intel Edge | How it gets populated |
80	|---|---|---|
81	| `who_pass` | **Identity**: Is the requesting entity (user/service/container) authenticated? | Check API key, JWT, or mTLS cert against allow-list |
82	| `what_pass` | **Capability**: Is this model+precision allowed on this device? | Validate `model_name` + `model_precision` + `device` against policy |
83	| `how_pass` | **Method**: Is the execution config (batch_size, nireq, streams) within bounds? | Check `batch_size <= max_batch`, `nireq <= max_nireq`, `num_streams <= max_streams` |
84	| `where_pass` | **Location**: Is this edge node authorized for this workload type? | Check node ID / testbed system ID against deployment policy |
85	| `when_pass` | **Lease**: Is the time-bounded execution lease still valid? | Check `lease_expiry > current_time` |
86	| `why_pass` | **Policy**: Does the workload comply with governance rules? | Check `model_priority`, `performance_mode`, resource quotas |
87	| `has_valid_lease` | Lease not expired | `lease_expiry_epoch > now()` |
88	| `epoch_fresh` | Request not stale/replayed | `request_epoch` within freshness window |
89	| `request_hash` | Hash of (model_name + device + batch_size + input_shape) | SHA-256 of workload config fields, truncated to u64 |
90	| `capability_hmac` | HMAC-SHA256 of request_hash with eFuse key | Proves the request was authorized by the platform root of trust |
91	| `key_slot` | Which eFuse key slot (0 = platform root) | Always 0 for single-tenant edge; multi-tenant uses slots 0-7 |
92	| `peer_id` | NVLink peer to check for deadlock | Maps to GPU device index in multi-GPU edge systems |
93	| `adapter_id` | PCIe adapter for bandwidth check | Maps to GPU/NPU PCIe device ID |
94
95	### The Pipeline Integration Point
96
97	The Cubie gate sits at **workload submission time** -- BEFORE `core.compile_model()` or DLStreamer pipeline launch. It does NOT gate individual inference frames. It gates whether the entire workload is allowed to start.
98
99	```
100	Workload Request (model, device, config)
101	    |
102	    v
103	[Policy Evaluator] -- populates CubieRequest bools from workload config
104	    |
105	    v
106	[cubie_admit()] -- ~730ns, HMAC + NVLink + PCIe hardware checks
107	    |
108	    +-- Allow --> core.compile_model(model, device, config) --> inference loop
109	    |
110	    +-- Deny --> HTTP 403, workload never touches GPU/NPU
111	```
112
113	---
114
115	## Phase 1: FFI Bridge (Python-callable shared library)
116
117	**Why:** Intel's ecosystem is Python+OpenVINO. We need `cubie_admit()` callable from Python.
118
119	**Why ctypes over PyO3:** Zero Python build tooling. Load `.so`, define struct, call function. Minimal moving parts for a verification submission.
120
121	**Why mock feature:** Non-mock builds use `core::ptr::read_volatile()` on hardware MMIO addresses -- this will segfault in Linux userspace. The `mock` feature replaces MMIO with AtomicU32 cells. The admission logic is identical. The formal proofs hold regardless of MMIO backend.
122
123	### Files to create:
124
125	- [ ] **`cubie-ffi/Cargo.toml`** -- New workspace member, `crate-type = ["cdylib"]`, depends on `cubie-bare-metal` with `features = ["std", "mock"]`
126	- [ ] **`cubie-ffi/src/lib.rs`** -- Re-exports `cubie_admit`, adds `cubie_mock_setup()` (configures eFuse key + NVLink + PCIe mock state) and `cubie_create_valid_request()` (returns a CubieRequest with correct HMAC)
127	- [ ] **`cubie-ffi/include/cubie.h`** -- C header documenting the ABI. Uses `_Bool` (not `bool`). Includes `static_assert(sizeof(CubieRequest) == 40)`
128	- [ ] **`pipeline/cubie_gate.py`** -- Python ctypes wrapper. `CubieRequest` as `ctypes.Structure`, `CubieGate` class with `admit()`, `benchmark()`, `create_test_request()` methods
129
130	### Modify:
131
132	- [ ] **`Cargo.toml`** (workspace root) -- Add `"cubie-ffi"` to `members` list
133
134	### Build:
135
136	```bash
137	cargo build --release --manifest-path cubie-ffi/Cargo.toml
138	# Output: target/release/libcubie_ffi.so (Linux) or cubie_ffi.dll (Windows)
139	```
140
141	### Verify:
142
143	```bash
144	cargo test --manifest-path bare-metal-tests/Cargo.toml  # 62 tests still pass
145	python3 -c "from pipeline.cubie_gate import CubieGate; g = CubieGate(); print(g.benchmark(100000))"
146	```
147
148	### Critical detail -- CubieRequest struct layout:
149
150	```
151	Offset  Field              Type    Size
152	0       who_pass           bool    1
153	1       what_pass          bool    1
154	2       how_pass           bool    1
155	3       where_pass         bool    1
156	4       when_pass          bool    1
157	5       why_pass           bool    1
158	6       has_valid_lease    bool    1
159	7       epoch_fresh        bool    1
160	8       request_hash       u64     8
161	16      capability_hmac    u64     8
162	24      key_slot           u8      1
163	25      peer_id            u8      1
164	26-31   (padding)                  6
165	32      adapter_id         u64     8
166	Total: 40 bytes
167	```
168
169	Source: `bare-metal/src/types.rs:4-25`
170
171	---
172
173	## Phase 2: OpenVINO Pipeline (Cubie as workload admission gate)
174
175	**Where the gate sits:** At workload submission time, BEFORE `core.compile_model()`. Cubie decides whether a workload is allowed to load onto the GPU/NPU at all. It does NOT gate individual frames -- it gates the entire workload lifecycle.
176
177	**Architecture:**
178
179	```
180	Workload Request {model: "yolo11n", device: "GPU", batch_size: 2, nireq: 4}
181	    |
182	    v
183	[Policy Evaluator]  -- evaluates workload config against admission rules
184	    |                  who: API key valid?  what: model+device allowed?
185	    |                  how: batch/nireq within bounds?  where: node authorized?
186	    |                  when: lease valid?  why: policy compliant?
187	    v
188	[cubie_admit()]     -- ~730ns: HMAC proof-of-authorization + NVLink + PCIe check
189	    |
190	    +-- Allow --> ov.Core().compile_model(model, "GPU", config) --> inference loop --> results
191	    |
192	    +-- Deny  --> 403 Forbidden, workload never touches GPU/NPU, zero accelerator waste
193	```
194
195	**Workload type:** Video Processing (DLStreamer + OpenVINO object detection). Fallback: NLP text generation.
196
197	### Files to create:
198
199	- [ ] **`pipeline/policy.py`** -- Admission policy evaluator. Takes a workload request dict (model_name, device, batch_size, nireq, num_streams, model_precision, performance_mode, model_priority) and evaluates it against configurable rules to produce the 6 face booleans + HMAC. This is the bridge between real workload properties and CubieRequest fields. Example rules:
200	  - `who`: API key in allow-list
201	  - `what`: `(model_name, device)` pair in approved model registry
202	  - `how`: `batch_size <= 8`, `nireq <= 16`, `num_streams <= 4`
203	  - `where`: `node_id` matches deployment target
204	  - `when`: `lease_expiry > time.time()`
205	  - `why`: `model_priority != HIGH` or caller has elevated privilege
206
207	- [ ] **`pipeline/cubie_openvino_demo.py`** -- Main demo application. FastAPI server (mirrors Intel sizing tool worker pattern). Routes:
208	  - `POST /admit` -- accepts workload config JSON, runs through policy.py -> cubie_admit(), returns Allow/Deny with timing
209	  - `POST /launch` -- admit + compile model + run inference loop (the full pipeline)
210	  - `GET /metrics` -- current admit/deny counts, latencies, resource utilization
211	  - `GET /health` -- service health
212
213	- [ ] **`pipeline/load_profiles.py`** -- Load generator for Intel's 3-level requirement:
214	  - Light: 1 workload submission/sec, single model, CPU-only
215	  - Medium: 5 concurrent workloads, mix of CPU+GPU, varied batch sizes
216	  - Heavy: max concurrent workloads, all devices, stress test admission gate throughput
217
218	- [ ] **`pipeline/resource_monitor.py`** -- Mirrors Intel sizing tool's `custom-application-profiling` worker pattern. Uses `psutil` for CPU/memory, `xpumcli` for GPU, sysfs for NPU. 1s sampling interval, CSV output, 5s idle baseline subtraction.
219
220	- [ ] **`pipeline/requirements.txt`** -- `openvino>=2024.0`, `fastapi>=0.110`, `uvicorn>=0.29`, `psutil>=5.9`, `matplotlib>=3.7`, `numpy>=1.24`
221
222	- [ ] **`pipeline/models.json`** -- Approved model registry (policy config). Lists allowed (model_name, device, max_batch, max_nireq) tuples. The `what_pass` face checks workload requests against this registry.
223
224	### Model setup:
225
226	```bash
227	# Object detection (DLStreamer-compatible)
228	pip install openvino-dev
229	omz_downloader --name yolo-v8 --output_dir models/
230	omz_converter --name yolo-v8 --output_dir models/
231
232	# Or text generation
233	pip install openvino-genai
234	# Download TinyLlama in OpenVINO format
235	```
236
237	### How policy.py populates CubieRequest from real workload properties:
238
239	```python
240	def evaluate_workload(workload: dict, policy: dict, gate: CubieGate) -> AdmitResult:
241	    # 6-face evaluation against real workload properties
242	    who = workload["api_key"] in policy["allowed_keys"]
243	    what = (workload["model_name"], workload["device"]) in policy["approved_models"]
244	    how = (workload["batch_size"] <= policy["max_batch"]
245	           and workload["nireq"] <= policy["max_nireq"])
246	    where = workload.get("node_id", "local") in policy["authorized_nodes"]
247	    when = workload.get("lease_expiry", float("inf")) > time.time()
248	    why = (workload.get("model_priority", 1) <= policy["max_priority"]
249	           or workload.get("elevated", False))
250
251	    # Compute request hash from workload config
252	    config_str = f"{workload['model_name']}:{workload['device']}:{workload['batch_size']}"
253	    request_hash = int.from_bytes(hashlib.sha256(config_str.encode()).digest()[:8], "big")
254
255	    # Build CubieRequest with real data
256	    req = CubieRequest(
257	        who_pass=who, what_pass=what, how_pass=how,
258	        where_pass=where, when_pass=when, why_pass=why,
259	        has_valid_lease=when,
260	        epoch_fresh=True,  # checked against request timestamp
261	        request_hash=request_hash,
262	        capability_hmac=gate.compute_hmac(request_hash),  # FIPS 198-1
263	        key_slot=0, peer_id=0,
264	        adapter_id=get_pcie_adapter_id(workload["device"]),
265	    )
266	    return gate.admit(req)
267	```
268
269	---
270
271	## Phase 3: Intel Edge Testbed Deployment
272
273	**Prereqs:** Testbed access (confirmed), Ubuntu 22.04/24.04, Rust toolchain, Python 3.10+, OpenVINO, Node.js 22+
274
275	### Steps:
276
277	- [ ] **Clone repo and build FFI** on testbed
278	- [ ] **Run speedtest** -- `cargo run --release --manifest-path bare-metal-tests/Cargo.toml --bin speedtest` -- captures Intel CPU brand string + TSC-calibrated ns/call for all gate components
279	- [ ] **Install OpenVINO + download model** on testbed
280	- [ ] **Run demo pipeline at 3 load levels** -- capture metrics CSVs
281	- [ ] **Capture resource utilization** -- CPU/GPU/NPU screenshots at each load level
282
283	### Intel Edge AI Sizing Tool (primary method):
284
285	```bash
286	git clone https://github.com/intel/edge-ai-sizing-tool
287	cd edge-ai-sizing-tool && ./install.sh && ./start.sh
288	# Browse to http://localhost:8080
289	# Configure workload, generate PDF report
290	```
291
292	### Appendix A fallback (if sizing tool doesn't support custom pipelines):
293
294	```bash
295	sudo dmidecode > artifacts/hardware_info.txt                    # System info
296	lsof -p $(pgrep -f cubie_openvino_demo) > artifacts/runtime_libs.txt  # Runtime libraries
297	# Resource utilization captured by resource_monitor.py at each load level
298	```
299
300	---
301
302	## Phase 4: Generate Submission Artifacts
303
304	### Files to create:
305
306	- [ ] **`pipeline/generate_diagrams.py`** -- Produces pipeline block diagram (PNG) + formal verification architecture diagram using matplotlib
307	- [ ] **`artifacts/capture_system_info.sh`** -- Captures CPU, RAM, GPU, NPU, OS, OpenVINO version, Rust version
308	- [ ] **`artifacts/pipeline_description.md`** -- Narrative for Intel form: "First formally verified AI workload admission gate for Intel edge hardware. 754 theorems, sub-microsecond decisions, 3 patent families, native x86_64 machine code, FIPS 198-1 HMAC-SHA256."
309	- [ ] **`artifacts/build_submission_report.py`** -- Compiles system info + metrics CSVs + diagrams into a single PDF
310
311	### Required artifacts checklist:
312
313	| Artifact | Source | Format |
314	|----------|--------|--------|
315	| System info | `capture_system_info.sh` | TXT |
316	| Pipeline description | `pipeline_description.md` | Text (pasted into form) |
317	| Block diagram | `generate_diagrams.py` | PNG |
318	| Sizing tool report | Edge AI Sizing Tool or Appendix A | PDF |
319	| CPU utilization (light/med/heavy) | `resource_monitor.py` | Screenshots/CSV |
320	| GPU utilization (light/med/heavy) | `intel_gpu_top` or resource_monitor | Screenshots/CSV |
321	| System photo | Testbed screenshot | Image |
322
323	---
324
325	## Phase 5: Submit
326
327	- [ ] **Fill Intel form** at `https://app.smartsheet.com/b/form/019a3bfa6a0b7623876994c2950fc14f`
328	- [ ] **Upload all artifacts** from Phase 4
329	- [ ] **Application name:** "Cubie Trust Compiler -- Formally Verified AI Admission Gate"
330
331	**Narrative for Intel review meeting:**
332
333	> The Cubie Trust Compiler is the first formally verified AI workload admission gate. It runs as a sub-microsecond pre-inference filter on Intel edge hardware, making admission decisions in ~730 nanoseconds -- proven correct by 754 independent theorems across three proof assistants (Coq, Lean 4, Verus). Every AI inference request passes through the gate before consuming GPU or NPU compute. Denied requests use zero accelerator resources. The entire stack compiles to native x86_64 machine code with zero external dependencies. Protected by 3 patent families (US11956363B2, US12073439B2, US20240195628A1).
334
335	Support: intel.edge.ai.application.qualification@intel.com
336
337	---
338
339	## File Summary
340
341	| Phase | New Files | Modified |
342	|-------|-----------|----------|
343	| 1: FFI Bridge | `cubie-ffi/Cargo.toml`, `cubie-ffi/src/lib.rs`, `cubie-ffi/include/cubie.h`, `pipeline/cubie_gate.py` | `Cargo.toml` (workspace) |
344	| 2: Pipeline | `pipeline/policy.py`, `pipeline/models.json`, `pipeline/cubie_openvino_demo.py`, `pipeline/load_profiles.py`, `pipeline/resource_monitor.py`, `pipeline/requirements.txt` | -- |
345	| 3: Deploy | -- (commands only) | -- |
346	| 4: Artifacts | `pipeline/generate_diagrams.py`, `artifacts/capture_system_info.sh`, `artifacts/pipeline_description.md`, `artifacts/build_submission_report.py` | -- |
347	| **Total** | **14 new files** | **1 modified** |
348
349	**Zero existing files touched** (all bare-metal, Coq, Lean, Verus source preserved).
350
351	---
352
353	## Critical Source Files (read-only dependencies)
354
355	| File | What the plan uses from it |
356	|------|---------------------------|
357	| `bare-metal/src/types.rs:4-25` | `CubieRequest` #[repr(C)] layout -- Python ctypes must mirror exactly |
358	| `bare-metal/src/admit.rs:20-32` | `cubie_admit` C-ABI signature -- FFI crate re-exports this |
359	| `bare-metal/src/mmio.rs` | Mock setter functions (`mock_set_efuse_key`, etc.) -- exposed through FFI |
360	| `bare-metal/src/hmac.rs` | `hmac_sha256` -- used by FFI to compute valid test HMACs |
361	| `bare-metal-tests/src/speedtest.rs` | CPUID + RDTSC benchmarks -- run on testbed for Intel-specific numbers |
362	| `Cargo.toml` | Workspace members list -- add `cubie-ffi` |
363
364	---
365
366	## Risks and Mitigations
367
368	| Risk | Mitigation |
369	|------|-----------|
370	| CubieRequest struct layout mismatch (Rust vs Python) | `static_assert(sizeof == 40)` in C header + round-trip test |
371	| Sizing tool doesn't support custom pipelines | Use Appendix A alternative (explicitly supported by Intel Rev 2.1) |
372	| NPU utilization is zero (model not compiled for NPU) | Frame Cubie as CPU-side filter that *reduces* unnecessary NPU load |
373	| Mock MMIO means "not real hardware" | HMAC-SHA256 computation is identical; formal proofs hold regardless of MMIO source; document clearly |
374	| Bool representation differs C vs Rust | Use `_Bool` in header (not `bool`); `ctypes.c_bool` maps to `_Bool` correctly |
375
376	---
377
378	## Verification (how to test end-to-end)
379
380	1. `cargo test --manifest-path bare-metal-tests/Cargo.toml` -- 62 tests pass (no regression)
381	2. `cargo build --release --manifest-path cubie-ffi/Cargo.toml` -- .so/.dll builds
382	3. `python3 -c "from pipeline.cubie_gate import CubieGate; g = CubieGate(); print(g.admit(g.create_test_request()))"` -- prints True
383	4. Policy evaluation test -- submit a workload with `model_name="yolo11n", device="GPU", batch_size=2` against a policy that allows it -> returns Allow. Change `device="INVALID"` -> returns Deny (what_pass fails). Change `batch_size=999` -> returns Deny (how_pass fails)
384	5. `python3 pipeline/cubie_openvino_demo.py --mode light --duration 10` -- runs without error, logs admit/deny decisions with ns latencies
385	6. `python3 pipeline/generate_diagrams.py --output artifacts/` -- produces PNGs
386	7. All artifacts in `artifacts/` directory are non-empty and submission-ready
387

# Plan: Intel AI Edge Application Ready Verification -- Cubie Trust Compiler Integration

## Context

The Cubie Trust Compiler is a formally verified AI workload admission gate that compiles to native x86_64 machine code. It already:
- Cross-compiles to 3 bare-metal targets (RISC-V, x86_64, AArch64)
- Has a C-ABI entry point: `extern "C" fn cubie_admit(*const CubieRequest) -> AdmitResult`
- Runs in ~730 ns/call on Intel Core Ultra 9 285HX (~1.37M admits/sec)
- Is proven correct by 754 theorems across Coq 8.18.0, Lean 4.30.0, and Verus
- Has 62 passing integration tests and zero external dependencies
- Is backed by 3 patent families (US11956363B2, US20240195628A1)

Intel's AI Edge Application Ready Verification program (Rev 2.1, Jan 2026) is a 5-step ISV onboarding process. Nick has testbed access. The goal: get Cubie verified as an Intel AI Edge application, which unlocks technical support and co-marketing.

**What Intel needs from us:**
1. Application running on Intel edge hardware
2. Workload performance metrics at light/medium/heavy loads
3. Pipeline block diagram
4. Resource utilization screenshots (CPU/GPU/NPU)
5. Sizing tool report OR Appendix A alternative artifacts

**The gap:** The bare-metal library has no Python bindings, no C header, no container config, and no OpenVINO pipeline integration. We need a bridge from `libcubie_bare_metal.a` to a Python+OpenVINO demo pipeline.

---

## What a Real Intel Edge AI Workload Looks Like

The Intel Edge AI Sizing Tool (Node.js frontend + Python FastAPI workers, ports 5000-6000) processes workloads that carry these actual properties:

**Workload-level properties (what gets submitted to a worker):**
```
workload_id:           string    # unique identifier
workload_type:         enum      # "dlstreamer" | "text-generation" | "instance-segmentation" | ...
model_name:            string    # "yolo11n", HuggingFace ID, or custom .xml path
model_precision:       enum      # "FP16" | "FP32"
device:                string    # "CPU" | "GPU" | "NPU" | "AUTO" | "MULTI:GPU,CPU"
batch_size:            int       # frames or tokens per inference call
number_of_streams:     int       # parallel inference streams
input_source:          string    # file path, RTSP URL, /dev/videoN, or prompt text
```

**OpenVINO compile_model config (set at model load time):**
```
performance_mode:      LATENCY | THROUGHPUT
model_priority:        LOW(0) | MEDIUM(1) | HIGH(2)
num_streams:           int
inference_num_threads: int
num_requests (nireq):  int       # async request pool size
scheduling_core_type:  ANY_CORE | PCORE_ONLY | ECORE_ONLY
affinity:              NONE | CORE | NUMA | HYBRID_AWARE
execution_mode:        PERFORMANCE | ACCURACY
```

**DLStreamer pipeline element properties (per-stage in video pipelines):**
```
gvadetect / gvaclassify / gvainference:
  model:               .xml path
  device:              CPU | GPU | NPU
  nireq:               parallel inference requests
  inference_interval:  process every N-th frame
  batch_size:          frames batched per call
  threshold:           detection confidence cutoff
  pre_process_backend: ie | opencv | va | va-surface-sharing
```

**KServe V2 inference request (OVMS REST API):**
```json
{
  "id": "request-42",
  "inputs": [{ "name": "input", "shape": [1,3,224,224], "datatype": "FP32" }],
  "outputs": [{ "name": "detection_output" }]
}
```

### Mapping CubieRequest to Real Workload Properties

The existing `CubieRequest` (40 bytes, `bare-metal/src/types.rs:4-25`) has abstract booleans. Here is how each field maps to real Intel Edge workload properties:

| CubieRequest field | Real-world meaning on Intel Edge | How it gets populated |
|---|---|---|
| `who_pass` | **Identity**: Is the requesting entity (user/service/container) authenticated? | Check API key, JWT, or mTLS cert against allow-list |
| `what_pass` | **Capability**: Is this model+precision allowed on this device? | Validate `model_name` + `model_precision` + `device` against policy |
| `how_pass` | **Method**: Is the execution config (batch_size, nireq, streams) within bounds? | Check `batch_size <= max_batch`, `nireq <= max_nireq`, `num_streams <= max_streams` |
| `where_pass` | **Location**: Is this edge node authorized for this workload type? | Check node ID / testbed system ID against deployment policy |
| `when_pass` | **Lease**: Is the time-bounded execution lease still valid? | Check `lease_expiry > current_time` |
| `why_pass` | **Policy**: Does the workload comply with governance rules? | Check `model_priority`, `performance_mode`, resource quotas |
| `has_valid_lease` | Lease not expired | `lease_expiry_epoch > now()` |
| `epoch_fresh` | Request not stale/replayed | `request_epoch` within freshness window |
| `request_hash` | Hash of (model_name + device + batch_size + input_shape) | SHA-256 of workload config fields, truncated to u64 |
| `capability_hmac` | HMAC-SHA256 of request_hash with eFuse key | Proves the request was authorized by the platform root of trust |
| `key_slot` | Which eFuse key slot (0 = platform root) | Always 0 for single-tenant edge; multi-tenant uses slots 0-7 |
| `peer_id` | NVLink peer to check for deadlock | Maps to GPU device index in multi-GPU edge systems |
| `adapter_id` | PCIe adapter for bandwidth check | Maps to GPU/NPU PCIe device ID |

### The Pipeline Integration Point

The Cubie gate sits at **workload submission time** -- BEFORE `core.compile_model()` or DLStreamer pipeline launch. It does NOT gate individual inference frames. It gates whether the entire workload is allowed to start.

```
Workload Request (model, device, config)
    |
    v
[Policy Evaluator] -- populates CubieRequest bools from workload config
    |
    v
[cubie_admit()] -- ~730ns, HMAC + NVLink + PCIe hardware checks
    |
    +-- Allow --> core.compile_model(model, device, config) --> inference loop
    |
    +-- Deny --> HTTP 403, workload never touches GPU/NPU
```

---

## Phase 1: FFI Bridge (Python-callable shared library)

**Why:** Intel's ecosystem is Python+OpenVINO. We need `cubie_admit()` callable from Python.

**Why ctypes over PyO3:** Zero Python build tooling. Load `.so`, define struct, call function. Minimal moving parts for a verification submission.

**Why mock feature:** Non-mock builds use `core::ptr::read_volatile()` on hardware MMIO addresses -- this will segfault in Linux userspace. The `mock` feature replaces MMIO with AtomicU32 cells. The admission logic is identical. The formal proofs hold regardless of MMIO backend.

### Files to create:

- [ ] **`cubie-ffi/Cargo.toml`** -- New workspace member, `crate-type = ["cdylib"]`, depends on `cubie-bare-metal` with `features = ["std", "mock"]`
- [ ] **`cubie-ffi/src/lib.rs`** -- Re-exports `cubie_admit`, adds `cubie_mock_setup()` (configures eFuse key + NVLink + PCIe mock state) and `cubie_create_valid_request()` (returns a CubieRequest with correct HMAC)
- [ ] **`cubie-ffi/include/cubie.h`** -- C header documenting the ABI. Uses `_Bool` (not `bool`). Includes `static_assert(sizeof(CubieRequest) == 40)`
- [ ] **`pipeline/cubie_gate.py`** -- Python ctypes wrapper. `CubieRequest` as `ctypes.Structure`, `CubieGate` class with `admit()`, `benchmark()`, `create_test_request()` methods

### Modify:

- [ ] **`Cargo.toml`** (workspace root) -- Add `"cubie-ffi"` to `members` list

### Build:

```bash
cargo build --release --manifest-path cubie-ffi/Cargo.toml
# Output: target/release/libcubie_ffi.so (Linux) or cubie_ffi.dll (Windows)
```

### Verify:

```bash
cargo test --manifest-path bare-metal-tests/Cargo.toml  # 62 tests still pass
python3 -c "from pipeline.cubie_gate import CubieGate; g = CubieGate(); print(g.benchmark(100000))"
```

### Critical detail -- CubieRequest struct layout:

```
Offset  Field              Type    Size
0       who_pass           bool    1
1       what_pass          bool    1
2       how_pass           bool    1
3       where_pass         bool    1
4       when_pass          bool    1
5       why_pass           bool    1
6       has_valid_lease    bool    1
7       epoch_fresh        bool    1
8       request_hash       u64     8
16      capability_hmac    u64     8
24      key_slot           u8      1
25      peer_id            u8      1
26-31   (padding)                  6
32      adapter_id         u64     8
Total: 40 bytes
```

Source: `bare-metal/src/types.rs:4-25`

---

## Phase 2: OpenVINO Pipeline (Cubie as workload admission gate)

**Where the gate sits:** At workload submission time, BEFORE `core.compile_model()`. Cubie decides whether a workload is allowed to load onto the GPU/NPU at all. It does NOT gate individual frames -- it gates the entire workload lifecycle.

**Architecture:**

```
Workload Request {model: "yolo11n", device: "GPU", batch_size: 2, nireq: 4}
    |
    v
[Policy Evaluator]  -- evaluates workload config against admission rules
    |                  who: API key valid?  what: model+device allowed?
    |                  how: batch/nireq within bounds?  where: node authorized?
    |                  when: lease valid?  why: policy compliant?
    v
[cubie_admit()]     -- ~730ns: HMAC proof-of-authorization + NVLink + PCIe check
    |
    +-- Allow --> ov.Core().compile_model(model, "GPU", config) --> inference loop --> results
    |
    +-- Deny  --> 403 Forbidden, workload never touches GPU/NPU, zero accelerator waste
```

**Workload type:** Video Processing (DLStreamer + OpenVINO object detection). Fallback: NLP text generation.

### Files to create:

- [ ] **`pipeline/policy.py`** -- Admission policy evaluator. Takes a workload request dict (model_name, device, batch_size, nireq, num_streams, model_precision, performance_mode, model_priority) and evaluates it against configurable rules to produce the 6 face booleans + HMAC. This is the bridge between real workload properties and CubieRequest fields. Example rules:
  - `who`: API key in allow-list
  - `what`: `(model_name, device)` pair in approved model registry
  - `how`: `batch_size <= 8`, `nireq <= 16`, `num_streams <= 4`
  - `where`: `node_id` matches deployment target
  - `when`: `lease_expiry > time.time()`
  - `why`: `model_priority != HIGH` or caller has elevated privilege

- [ ] **`pipeline/cubie_openvino_demo.py`** -- Main demo application. FastAPI server (mirrors Intel sizing tool worker pattern). Routes:
  - `POST /admit` -- accepts workload config JSON, runs through policy.py -> cubie_admit(), returns Allow/Deny with timing
  - `POST /launch` -- admit + compile model + run inference loop (the full pipeline)
  - `GET /metrics` -- current admit/deny counts, latencies, resource utilization
  - `GET /health` -- service health

- [ ] **`pipeline/load_profiles.py`** -- Load generator for Intel's 3-level requirement:
  - Light: 1 workload submission/sec, single model, CPU-only
  - Medium: 5 concurrent workloads, mix of CPU+GPU, varied batch sizes
  - Heavy: max concurrent workloads, all devices, stress test admission gate throughput

- [ ] **`pipeline/resource_monitor.py`** -- Mirrors Intel sizing tool's `custom-application-profiling` worker pattern. Uses `psutil` for CPU/memory, `xpumcli` for GPU, sysfs for NPU. 1s sampling interval, CSV output, 5s idle baseline subtraction.

- [ ] **`pipeline/requirements.txt`** -- `openvino>=2024.0`, `fastapi>=0.110`, `uvicorn>=0.29`, `psutil>=5.9`, `matplotlib>=3.7`, `numpy>=1.24`

- [ ] **`pipeline/models.json`** -- Approved model registry (policy config). Lists allowed (model_name, device, max_batch, max_nireq) tuples. The `what_pass` face checks workload requests against this registry.

### Model setup:

```bash
# Object detection (DLStreamer-compatible)
pip install openvino-dev
omz_downloader --name yolo-v8 --output_dir models/
omz_converter --name yolo-v8 --output_dir models/

# Or text generation
pip install openvino-genai
# Download TinyLlama in OpenVINO format
```

### How policy.py populates CubieRequest from real workload properties:

```python
def evaluate_workload(workload: dict, policy: dict, gate: CubieGate) -> AdmitResult:
    # 6-face evaluation against real workload properties
    who = workload["api_key"] in policy["allowed_keys"]
    what = (workload["model_name"], workload["device"]) in policy["approved_models"]
    how = (workload["batch_size"] <= policy["max_batch"]
           and workload["nireq"] <= policy["max_nireq"])
    where = workload.get("node_id", "local") in policy["authorized_nodes"]
    when = workload.get("lease_expiry", float("inf")) > time.time()
    why = (workload.get("model_priority", 1) <= policy["max_priority"]
           or workload.get("elevated", False))

    # Compute request hash from workload config
    config_str = f"{workload['model_name']}:{workload['device']}:{workload['batch_size']}"
    request_hash = int.from_bytes(hashlib.sha256(config_str.encode()).digest()[:8], "big")

    # Build CubieRequest with real data
    req = CubieRequest(
        who_pass=who, what_pass=what, how_pass=how,
        where_pass=where, when_pass=when, why_pass=why,
        has_valid_lease=when,
        epoch_fresh=True,  # checked against request timestamp
        request_hash=request_hash,
        capability_hmac=gate.compute_hmac(request_hash),  # FIPS 198-1
        key_slot=0, peer_id=0,
        adapter_id=get_pcie_adapter_id(workload["device"]),
    )
    return gate.admit(req)
```

---

## Phase 3: Intel Edge Testbed Deployment

**Prereqs:** Testbed access (confirmed), Ubuntu 22.04/24.04, Rust toolchain, Python 3.10+, OpenVINO, Node.js 22+

### Steps:

- [ ] **Clone repo and build FFI** on testbed
- [ ] **Run speedtest** -- `cargo run --release --manifest-path bare-metal-tests/Cargo.toml --bin speedtest` -- captures Intel CPU brand string + TSC-calibrated ns/call for all gate components
- [ ] **Install OpenVINO + download model** on testbed
- [ ] **Run demo pipeline at 3 load levels** -- capture metrics CSVs
- [ ] **Capture resource utilization** -- CPU/GPU/NPU screenshots at each load level

### Intel Edge AI Sizing Tool (primary method):

```bash
git clone https://github.com/intel/edge-ai-sizing-tool
cd edge-ai-sizing-tool && ./install.sh && ./start.sh
# Browse to http://localhost:8080
# Configure workload, generate PDF report
```

### Appendix A fallback (if sizing tool doesn't support custom pipelines):

```bash
sudo dmidecode > artifacts/hardware_info.txt                    # System info
lsof -p $(pgrep -f cubie_openvino_demo) > artifacts/runtime_libs.txt  # Runtime libraries
# Resource utilization captured by resource_monitor.py at each load level
```

---

## Phase 4: Generate Submission Artifacts

### Files to create:

- [ ] **`pipeline/generate_diagrams.py`** -- Produces pipeline block diagram (PNG) + formal verification architecture diagram using matplotlib
- [ ] **`artifacts/capture_system_info.sh`** -- Captures CPU, RAM, GPU, NPU, OS, OpenVINO version, Rust version
- [ ] **`artifacts/pipeline_description.md`** -- Narrative for Intel form: "First formally verified AI workload admission gate for Intel edge hardware. 754 theorems, sub-microsecond decisions, 3 patent families, native x86_64 machine code, FIPS 198-1 HMAC-SHA256."
- [ ] **`artifacts/build_submission_report.py`** -- Compiles system info + metrics CSVs + diagrams into a single PDF

### Required artifacts checklist:

| Artifact | Source | Format |
|----------|--------|--------|
| System info | `capture_system_info.sh` | TXT |
| Pipeline description | `pipeline_description.md` | Text (pasted into form) |
| Block diagram | `generate_diagrams.py` | PNG |
| Sizing tool report | Edge AI Sizing Tool or Appendix A | PDF |
| CPU utilization (light/med/heavy) | `resource_monitor.py` | Screenshots/CSV |
| GPU utilization (light/med/heavy) | `intel_gpu_top` or resource_monitor | Screenshots/CSV |
| System photo | Testbed screenshot | Image |

---

## Phase 5: Submit

- [ ] **Fill Intel form** at `https://app.smartsheet.com/b/form/019a3bfa6a0b7623876994c2950fc14f`
- [ ] **Upload all artifacts** from Phase 4
- [ ] **Application name:** "Cubie Trust Compiler -- Formally Verified AI Admission Gate"

**Narrative for Intel review meeting:**

> The Cubie Trust Compiler is the first formally verified AI workload admission gate. It runs as a sub-microsecond pre-inference filter on Intel edge hardware, making admission decisions in ~730 nanoseconds -- proven correct by 754 independent theorems across three proof assistants (Coq, Lean 4, Verus). Every AI inference request passes through the gate before consuming GPU or NPU compute. Denied requests use zero accelerator resources. The entire stack compiles to native x86_64 machine code with zero external dependencies. Protected by 3 patent families (US11956363B2, US12073439B2, US20240195628A1).

Support: intel.edge.ai.application.qualification@intel.com

---

## File Summary

| Phase | New Files | Modified |
|-------|-----------|----------|
| 1: FFI Bridge | `cubie-ffi/Cargo.toml`, `cubie-ffi/src/lib.rs`, `cubie-ffi/include/cubie.h`, `pipeline/cubie_gate.py` | `Cargo.toml` (workspace) |
| 2: Pipeline | `pipeline/policy.py`, `pipeline/models.json`, `pipeline/cubie_openvino_demo.py`, `pipeline/load_profiles.py`, `pipeline/resource_monitor.py`, `pipeline/requirements.txt` | -- |
| 3: Deploy | -- (commands only) | -- |
| 4: Artifacts | `pipeline/generate_diagrams.py`, `artifacts/capture_system_info.sh`, `artifacts/pipeline_description.md`, `artifacts/build_submission_report.py` | -- |
| **Total** | **14 new files** | **1 modified** |

**Zero existing files touched** (all bare-metal, Coq, Lean, Verus source preserved).

---

## Critical Source Files (read-only dependencies)

| File | What the plan uses from it |
|------|---------------------------|
| `bare-metal/src/types.rs:4-25` | `CubieRequest` #[repr(C)] layout -- Python ctypes must mirror exactly |
| `bare-metal/src/admit.rs:20-32` | `cubie_admit` C-ABI signature -- FFI crate re-exports this |
| `bare-metal/src/mmio.rs` | Mock setter functions (`mock_set_efuse_key`, etc.) -- exposed through FFI |
| `bare-metal/src/hmac.rs` | `hmac_sha256` -- used by FFI to compute valid test HMACs |
| `bare-metal-tests/src/speedtest.rs` | CPUID + RDTSC benchmarks -- run on testbed for Intel-specific numbers |
| `Cargo.toml` | Workspace members list -- add `cubie-ffi` |

---

## Risks and Mitigations

| Risk | Mitigation |
|------|-----------|
| CubieRequest struct layout mismatch (Rust vs Python) | `static_assert(sizeof == 40)` in C header + round-trip test |
| Sizing tool doesn't support custom pipelines | Use Appendix A alternative (explicitly supported by Intel Rev 2.1) |
| NPU utilization is zero (model not compiled for NPU) | Frame Cubie as CPU-side filter that *reduces* unnecessary NPU load |
| Mock MMIO means "not real hardware" | HMAC-SHA256 computation is identical; formal proofs hold regardless of MMIO source; document clearly |
| Bool representation differs C vs Rust | Use `_Bool` in header (not `bool`); `ctypes.c_bool` maps to `_Bool` correctly |

---

## Verification (how to test end-to-end)

1. `cargo test --manifest-path bare-metal-tests/Cargo.toml` -- 62 tests pass (no regression)
2. `cargo build --release --manifest-path cubie-ffi/Cargo.toml` -- .so/.dll builds
3. `python3 -c "from pipeline.cubie_gate import CubieGate; g = CubieGate(); print(g.admit(g.create_test_request()))"` -- prints True
4. Policy evaluation test -- submit a workload with `model_name="yolo11n", device="GPU", batch_size=2` against a policy that allows it -> returns Allow. Change `device="INVALID"` -> returns Deny (what_pass fails). Change `batch_size=999` -> returns Deny (how_pass fails)
5. `python3 pipeline/cubie_openvino_demo.py --mode light --duration 10` -- runs without error, logs admit/deny decisions with ns latencies
6. `python3 pipeline/generate_diagrams.py --output artifacts/` -- produces PNGs
7. All artifacts in `artifacts/` directory are non-empty and submission-ready


come up with a plan for integration into this . i do have access to the edge api testbed "C:\Users\NickV\Downloads\intel-isbc-2025-application-ready-verification (4).pdf"


Conversation compacted


This session is being continued from a previous conversation that ran out of context. The summary below covers the earlier portion of the conversation.

Summary:
1. Primary Request and Intent:

   **Completed in this session:**
   - Create `bare-metal-tests/tests/dual_ledger.rs` — 15 integration tests for the dual-ledger module
   - Run `cargo test` — all 62 tests pass (21 admit + 26 consent + 15 dual-ledger)
   - Cross-compile to all 3 bare-metal targets (RISC-V, x86_64, AArch64)
   - Update manifest.json with SHA-256 hashes for all new files
   - Commit with granular summary, text flowcharts, and mermaid diagrams
   - Push to origin/feat/executive-presentation
   - Update existing PR #1 with new title and comprehensive description

   **In progress (most recent):**
   - User asked: "come up with a plan for integration into this" referencing the Intel AI Edge Application Ready Verification PDF
   - I created a detailed 5-phase integration plan
   - User challenged: "how do you even know what an incoming workload looks like?" — forcing me to research real Intel Edge AI workload formats
   - I researched the Intel Edge AI Sizing Tool, OpenVINO API, DLStreamer, KServe V2 protocol
   - Updated the plan with real workload property mappings and a policy evaluator bridge
   - User then asked: "save as doc to my downloads folder your last output with text based diagrams as well in the doc" — this is the ACTIVE pending request

2. Key Technical Concepts:
   - **Intel AI Edge Application Ready Verification** (Rev 2.1, Jan 2026): 5-step ISV onboarding — acquire system, install sizing tool, configure workload, submit form, get reviewed
   - **Intel Edge AI Sizing Tool**: Node.js 14 frontend (port 8080) + Python FastAPI workers (ports 5000-6000) + MongoDB. Workers: dlstreamer, text-generation, instance-segmentation, text-to-image, text-to-speech, ASR, custom-application-profiling
   - **OpenVINO API**: `ov.Core().compile_model(model, device, config)` → `InferRequest` → `infer()` or `start_async()`. Config properties: performance_mode (LATENCY/THROUGHPUT), model_priority (LOW/MEDIUM/HIGH), num_streams, nireq, scheduling_core_type (ANY/PCORE/ECORE), affinity
   - **DLStreamer**: GStreamer-based video analytics. Elements: `gvadetect`, `gvaclassify`, `gvainference`. Properties: model (.xml), device (CPU/GPU/NPU), nireq, batch_size, inference_interval, threshold, pre_process_backend
   - **KServe V2 Protocol**: REST API for model serving. `POST /v2/models/{name}/infer` with inputs (name, shape, datatype, data)
   - **CubieRequest to Real Workload Mapping**: The 6 abstract boolean faces map to real properties: who=identity/auth, what=model+device capability, how=batch/nireq bounds, where=node authorization, when=lease validity, why=policy compliance
   - **Policy Evaluator pattern**: A Python bridge (`policy.py`) that evaluates real workload properties against configurable rules and populates CubieRequest fields, including computing request_hash as SHA-256 of workload config and capability_hmac via FIPS 198-1
   - **Gate integration point**: cubie_admit() sits at workload SUBMISSION time, BEFORE core.compile_model(). It gates whether the entire workload is allowed to start, NOT individual inference frames
   - **FFI Bridge**: cubie-ffi crate (cdylib) → libcubie_ffi.so → Python ctypes. Uses mock feature (AtomicU32 cells replace MMIO) because real MMIO would segfault in Linux userspace
   - **Cross-compilation**: 3 targets — riscv32imc-unknown-none-elf (5.2 MB), x86_64-unknown-none (6.8 MB), aarch64-unknown-none (8.5 MB). All 12 modules compiled on all targets
   - **Bare-metal architecture**: no_std, no alloc, #[repr(C)] structs, panic = infinite loop, zero external dependencies

3. Files and Code Sections:

   - **`bare-metal-tests/tests/dual_ledger.rs`** (CREATED)
     - 15 integration tests covering CUB-0734 through CUB-0740
     - Tests: dual_ledger_ok_when_all_valid, dual_ledger_requires_linkage, anon_id_linked_when_matching, anon_id_not_linked_hash_mismatch, anon_id_not_linked_epoch_mismatch, no_consent_no_access, no_capability_no_access, public_hides_identity_when_hashes_differ, public_leaks_identity_when_hashes_match, identity_leak_breaks_dual_ledger, policy_filter_active_regardless_of_consent, policy_filter_inactive, epoch_mismatch_breaks_linkage, consent_valid_requires_both, full_admission_requires_all_three
     - Test fixtures: `linked_anon_id()` (AnonID { anon_hash: 0xABCD_1234, epoch: 100 }), `valid_public_entry()`, `valid_private_entry()` (priv_real_id_hash: 0x9999_8888, different from pub_policy_hash: 0x1111_2222), `valid_dual_ledger()`

   - **`bare-metal-tests/Cargo.toml`** (MODIFIED)
     - Added two new `[[test]]` sections for consent_hierarchy and dual_ledger test targets

   - **`manifest.json`** (MODIFIED)
     - Added Coq entries: CubieDualLedger.v (sha256: 412066cb..., V2.5-DL, 7 theorems, US11956363B2), CubieQuantumConsentAdvanced.v (sha256: df877637..., V2.5-HC, 14 theorems, US20240195628A1)
     - Added Lean entries: CubieDualLedger.lean (sha256: 6370a62c...), CubieQuantumConsentAdvanced.lean (sha256: 2ee1d624...)
     - Added verus section: cubie_consent_hierarchy_spec.rs (sha256: e238f186..., 10 spec_fns, 14 proof_fns, 5 exec_fns)
     - Added bare_metal section: consent.rs (sha256: 4fa4fccf..., 10 functions), dual_ledger.rs (sha256: cfc93359..., 5 functions)
     - Added bare_metal_tests section: consent_hierarchy.rs (26 tests), dual_ledger.rs (15 tests)
     - Updated crosswalk_summary: VERIFIED_EXACT: 30 (was 15), FUTURE_OBLIGATION: 0 (was 15)
     - Updated counts: total_coq_files: 32, total_lean_files: 31, total_verus_files: 3, total_bare_metal_modules: 8, total_host_tests: 62

   - **`bare-metal/src/types.rs`** (READ, not modified)
     - CubieRequest struct layout (40 bytes): 8 bools (who/what/how/where/when/why/lease/epoch) + request_hash(u64) + capability_hmac(u64) + key_slot(u8) + peer_id(u8) + padding(6) + adapter_id(u64)
     - AdmitResult: #[repr(u32)] enum, Allow=1, Deny=0

   - **`bare-metal/src/admit.rs`** (READ, not modified)
     - C-ABI entry: `#[no_mangle] pub extern "C" fn cubie_admit(req: *const CubieRequest) -> AdmitResult`
     - admit_inner checks: gpu_corner_pass && deployment_corner_pass && has_valid_lease && epoch_fresh && capability_hmac_verify && nvlink_waiter_safe && adapter_swap_ok

   - **`Cargo.toml`** (workspace root, READ)
     - members = ["bare-metal", "bare-metal-tests"], resolver = "2"
     - release profile: opt-level = "s", lto = true, codegen-units = 1, panic = "abort"

   - **`.git/PR_BODY.md`** (CREATED for PR update)
     - Full PR description with summary, architecture diagram, file tables, verification stats, test plan

   - **Plan file `C:\Users\NickV\.claude\plans\harmonic-nibbling-pony.md`** (OVERWRITTEN)
     - Complete 5-phase Intel Edge integration plan with real workload mapping

4. Errors and Fixes:

   - **AArch64 cross-compilation failed** ("can't find crate for `core`"):
     - Fixed by running `rustup target add aarch64-unknown-none`, then retried successfully

   - **Commit heredoc broke on single quotes** in mermaid diagram syntax:
     - The `cat <<'EOF'` heredoc couldn't handle nested single quotes in mermaid labels
     - Fixed by writing commit message to `.git/COMMIT_MSG.txt` file and using `git commit -F .git/COMMIT_MSG.txt`

   - **PII filter blocked commit message file**:
     - Hook flagged hex values like `0x9999`, `0xABCD`, `0x1111` in the mermaid dual-ledger diagram as potential PII
     - Fixed by replacing specific hex values with abstract labels (`H_real`, `H_anon`, `H_policy`, `N`)

   - **MCP update_pull_request returned 404**:
     - `mcp__MCP_DOCKER__update_pull_request` for iamdatanick/cubie-math PR #1 returned 404
     - Fixed by using `gh pr edit 1 --title "..." --body-file .git/PR_BODY.md` via CLI instead

   - **User feedback "how do you even know what an incoming workload looks like?"**:
     - I was designing an admission gate pipeline without understanding what real Intel Edge AI workloads carry
     - Fixed by launching a deep research effort: researcher agent + WebFetch to study Intel Edge AI Sizing Tool architecture, OpenVINO API, DLStreamer pipeline elements, KServe V2 protocol
     - Updated the plan with concrete mappings of CubieRequest fields to real workload properties (model_name, device, batch_size, nireq, etc.) and added a policy evaluator bridge (policy.py)

5. Problem Solving:

   - **Bare-metal test completion**: Created dual_ledger tests following the same pattern as consent_hierarchy tests (fixtures, per-theorem test grouping with CUB-ID comments). All 62 tests pass.
   - **Cross-compilation verification**: Confirmed all 12 bare-metal modules compile on all 3 targets. AArch64 output landed in workspace-level target/ dir rather than bare-metal/target/ due to Cargo workspace resolution.
   - **PR management**: Existing PR #1 was for the earlier PowerPoint work. Updated its title and body to reflect the full scope of changes (now includes formal proofs + bare-metal + cross-compilation).
   - **Real workload understanding**: Deep research revealed that the Intel Edge AI ecosystem uses specific workload properties (model_name, device, batch_size, nireq, performance_mode, model_priority, scheduling_core_type) that must map to CubieRequest's abstract boolean fields through a policy evaluator. The gate sits at workload submission time, not per-frame.

6. All User Messages:
   - "did we do risc?" — asking whether RISC-V cross-compilation was done
   - "did all of the codebase and historical work go through risc-v" — asking whether ALL modules (not just new ones) compiled for RISC-V
   - "ok commit all of this and in the summary write a granular level overview as well as text based flowchart system architecture and mermaids the more granular the better. start out with the big wows like risc-v" — requesting commit with detailed documentation
   - "what language is this in? bytecode" — asking whether output is bytecode or native code
   - "push it" — push to remote
   - "open a PR" — create/update pull request
   - "come up with a plan for integration into this. i do have access to the edge api testbed" + attached Intel PDF — requesting integration plan for Intel AI Edge verification program
   - "how do you even know what an incoming workload looks like?" — critical feedback challenging my assumptions about workload format
   - "save as doc to my downloads folder your last output with text based diagrams as well in the doc" — requesting the plan be saved as a document to Downloads folder

7. Pending Tasks:
   - **ACTIVE**: Save the Intel Edge integration plan as a document to `C:\Users\NickV\Downloads\` with text-based diagrams included. User explicitly asked: "save as doc to my downloads folder your last output with text based diagrams as well in the doc"
   - The actual implementation of the 5-phase integration plan (FFI bridge, OpenVINO pipeline, testbed deployment, artifacts, submission) is designed but NOT started

8. Current Work:
   The plan for Intel AI Edge integration was completed and written to the plan file at `C:\Users\NickV\.claude\plans\harmonic-nibbling-pony.md`. The user rejected ExitPlanMode and instead asked: **"save as doc to my downloads folder your last output with text based diagrams as well in the doc"**

   This means the user wants the full integration plan document (including the text-based architecture diagrams, workload mapping tables, phase descriptions, and mermaid diagrams) saved as a document file in their Downloads folder (`C:\Users\NickV\Downloads\`).

9. Optional Next Step:
   Write the complete Intel Edge integration plan to a document file at `C:\Users\NickV\Downloads\` (e.g., `cubie_intel_edge_integration_plan.md` or similar). The document should contain the full plan content from the plan file including all text-based architecture diagrams, the workload property mapping table, the CubieRequest-to-real-workload mapping, the policy evaluator code example, phase descriptions, file summaries, and mermaid diagrams.

   User's exact words: "save as doc to my downloads folder your last output with text based diagrams as well in the doc"

If you need specific details from before compaction (like exact code snippets, error messages, or content you generated), read the full transcript at: C:\Users\NickV\.claude\projects\C--Users-NickV\bfc2bfbf-6683-4826-bf18-edd758f725bb.jsonl
Continue the conversation from where it left off without asking the user any further questions. Resume directly — do not acknowledge the summary, do not recap what was happening, do not preface with "I'll continue" or similar. Pick up the last task as if the break never happened.


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