docs: consolidate all docs under docs/ — single source of truth

Move issues (01–29) and PRD from .scratch/distributed-inference-network/
into docs/issues/ and docs/. Update ralph_progress.py DEFAULT_PRD path
and rewrite docs/agents/issue-tracker.md to reflect the new layout.

The distributed_inference_network.egg-info/docs/ mirror is a build
artifact already covered by *.egg-info/ in .gitignore — not committed.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

docs/issues/11-binary-wire-format.md

@@ -0,0 +1,50 @@
+# US-011 — Binary activation wire format
+
+Replace the base64-encoded JSON activation payload from US-002 with a binary HTTP body, zstd compression, and chunked prefill. This is a protocol migration that must be applied to all nodes and the gateway before US-012 (real model backend) can be built on a sane foundation.
+
+## Context
+
+The current wire format encodes activation tensors as base64 strings inside a JSON dict:
+```json
+{"shape": [1, 1, 64], "dtype": "float32", "data": "<base64>", "context": {"prompt": "..."}}
+```
+This works for stub nodes (tiny tensors) but is unsuitable for real models:
+- base64 adds 33% size overhead
+- A 2048-token prefill at hidden_dim=16384 generates 64MB of activations per boundary — 85MB base64
+- JSON parsing overhead grows with payload size
+
+The new format per ADR-0008:
+```
+POST /forward HTTP/1.1
+Content-Type: application/octet-stream
+X-Meshnet-Shape: 1,128,16384
+X-Meshnet-Dtype: bfloat16
+X-Meshnet-Session: <uuid>
+X-Meshnet-Chunk-Index: 0
+X-Meshnet-Chunk-Total: 16
+X-Meshnet-Encoding: zstd
+Content-Length: <n>
+
+<raw zstd-compressed little-endian bfloat16 bytes>
+```
+
+Stub nodes still emit zeroed tensors — just in binary now. No real model required for this story.
+
+## Acceptance Criteria
+
+- `packages/node` `/forward` endpoint reads `X-Meshnet-Shape`, `X-Meshnet-Dtype`, `X-Meshnet-Session`, `X-Meshnet-Chunk-Index`, `X-Meshnet-Chunk-Total`, and `X-Meshnet-Encoding` headers; reads raw body bytes; decompresses if `zstd`; reconstructs tensor as numpy array
+- Node forward handler returns a binary response with the same header set reflecting output shape
+- The gateway sends binary chunked activations to the first node; reassembles binary responses from the last node
+- Chunked prefill: the gateway splits input sequences longer than `MESHNET_CHUNK_TOKENS` (default 128) into N chunks and sends them sequentially through the pipeline; stub nodes pass through each chunk unchanged
+- An integration test sends a 512-token stub activation (4 chunks of 128) through a two-node pipeline and asserts all 4 chunk responses are received with correct headers
+- `zstd` Python package added as a dependency to `packages/node` and `packages/gateway`
+- The old `_make_stub_activations` function in `server.py` is replaced with `_make_stub_binary_activation(shape, dtype) -> bytes`
+- `python -m pytest` passes from repo root
+- Commit only this story's changes
+
+## Implementation Notes
+
+- Use `numpy` to pack/unpack tensor bytes: `np.frombuffer(body, dtype=np.float16).reshape(shape)` (bfloat16 → numpy uses `np.dtype('bfloat16')` in numpy >= 1.20, or load as uint16 and reinterpret)
+- zstd: `import zstandard as zstd; cctx = zstd.ZstdCompressor(level=1); compressed = cctx.compress(raw_bytes)`
+- Chunk index + total allow the receiving node to know whether this is a mid-sequence chunk (no special handling needed for stub; real model in US-012 will need to manage KV cache across chunks)
+- Wire version header `X-Meshnet-Wire: 2` can be added for debugging mismatches between old and new nodes