wip -more responsive UI, better routing

.claude/memory/alpha-hardening-navigation.md

@@ -30,3 +30,7 @@ Both are already migrated into `.scratch/alpha-hardening/prd.json` (AH-021 updat
 
 **Why:** three audits agreed the alpha blockers are unauthenticated gossip (anyone can inject billing events), the free-credit faucet, and ephemeral bans.
 **How to apply:** work test-first per issue acceptance criteria; use `.venv`; `cryptography` belongs in node deps (wallet.py imports it — causes many of the 24 "failures" in a fresh env). See [[project-status]] and [[autonomous-work-style]].
+
+## Routing telemetry resume (2026-07-07)
+
+`.scratch/alpha-hardening/issues/24-routing-telemetry-resume.md` / AH-024 captures the interrupted Claude handoff. Learned routing is already committed at `518c259`; the dirty tree contains live-progress/current-request heartbeat/dashboard telemetry. First known blocker: `packages/tracker/meshnet_tracker/server.py:1490` uses `threading.Lock | None`, which crashes import because `threading.Lock` is a factory function at runtime. Fix that before running the targeted telemetry tests. Keep `.claude/settings.local.json` uncommitted unless explicitly approved.

.claude/memory/project-status.md

@@ -46,3 +46,4 @@ Historical handoff note: `/mnt/c/Users/popov/Downloads/neuron-tai-alpha-handoff-
 - Route hardening: tracker chat proxy and `/v1/route` diagnostics now use alias-aware preset node matching for split Qwen3.6 routes; dashboard derives grouped inference history from proxy route/complete console events and shows observed TPS after completion.
 - Live proxy hardening: model lookup trims outer whitespace before alias matching (`qwen3.6-35b-a3b ` resolves), and tracker route logs/dashboard queue depth combine heartbeat queue with tracker-local proxy in-flight counts so Postman-style bursts no longer show every selected route as queue `0`.
 - Split-shard streaming hardening: Qwen3.6-style distributed generation now emits SSE chunks token-by-token from the head node instead of buffering all generated text until completion. Tracker direct/relay stream proxy logs `proxy progress` with live tokens/TPS, dashboard Inference history shows currently processing requests with live TPS/tokens/queue, and relay stream completion no longer references an undefined `session_id`.
+- Native Windows Qwen3.6-MoE import fix: `flash-linear-attention` imports `triton`; without `triton-windows`, startup fails with misleading `Could not import module 'Qwen3_5MoeForCausalLM'`. Installed `triton-windows` in `C:\Users\popov\miniforge3` and added it as a Windows-only node dependency.

.scratch/alpha-hardening/README.md

@@ -9,6 +9,8 @@ Pre-release alpha audit + grilling (2026-07-04). Bucket 1 trust-boundary blocker
 
 Locked scope: one settlement tracker, open node join, devnet mock-USDT, reputation carries forward → fraud must be bounded. See [ADR-0016](../../docs/adr/0016-alpha-scope-and-known-limitations.md).
 
+**Resume task (2026-07-07):** [24 - Routing telemetry resume](./issues/24-routing-telemetry-resume.md) is `ready-for-agent`. Learned-routing commit `518c259` is already present; dirty tree contains current-request heartbeat/dashboard telemetry and a known import-time annotation crash in `server.py:1490`.
+
 ## Artifacts
 
 | Path | Status |

.scratch/alpha-hardening/issues/24-routing-telemetry-resume.md

@@ -0,0 +1,92 @@
+Status: ready-for-agent
+
+Scoped 2026-07-07 from an interrupted Claude session. This is a resume/cleanup task for routing and live-progress work that is partly committed and partly left dirty in the working tree.
+
+# 24 - Finish learned-routing telemetry and live-progress cleanup
+
+## Current state
+
+The main dynamic routing feature is already committed at `518c259` (`routing improvements - dynamic (wip)`):
+
+- `packages/tracker/meshnet_tracker/routing_stats.py` - decayed-EWMA route stats store, epsilon-greedy route selection, diagnostics.
+- `packages/tracker/meshnet_tracker/server.py` - route enumeration per head, bandit selection in the chat proxy, epoch bumps on node join/leave, `/v1/routing`, route sample recording with 8-token hygiene.
+- `packages/tracker/meshnet_tracker/cli.py` - `--route-explore-share`, `--route-weight-alpha`, `--route-stats-half-life` and env vars.
+- `packages/tracker/meshnet_tracker/dashboard.html` - "Routing (learned)" panel.
+- `docs/adr/0021-dynamic-statistical-routing.md` - design record.
+- `tests/test_dynamic_routing.py` - includes the exact GPU(0-21)+CPU(0-39) topology, hybrid downstream `start_layer=22`, 0.6/0.4 traffic split for a 1.5 TPS ratio, and scout-rate behavior.
+
+The current working tree still has uncommitted follow-up work:
+
+- `packages/node/meshnet_node/torch_server.py` - tracks in-flight chat requests, exposes `TorchNodeServer.current_requests`, prints generation progress with TPS.
+- `packages/node/meshnet_node/startup.py` - sends `current_requests` in heartbeat payloads and increases heartbeat cadence while busy.
+- `packages/tracker/meshnet_tracker/server.py` - accepts heartbeat `current_requests`, includes them in `/v1/network/map`, and logs `proxy connecting` before upstream connection.
+- `packages/tracker/meshnet_tracker/dashboard.html` - enriches the call wall from heartbeat `current_requests` so active requests remain visible even before terminal proxy events.
+- `tests/test_real_model_backend.py` and `tests/test_tracker_routing.py` - targeted coverage for current-request snapshots, heartbeat sanitization/storage, and TPS progress logging.
+- `QUICKSTART.md` - documents optional linear-attention fast-path packages for Qwen3.5/3.6 GPU nodes.
+
+There is also an untracked local file, `.claude/settings.local.json`, which should not be included unless the owner explicitly wants local Claude settings committed.
+
+## Known blocker found during resume
+
+Targeted pytest currently fails during import before reaching the new tests:
+
+```text
+TypeError: unsupported operand type(s) for |: 'builtin_function_or_method' and 'NoneType'
+```
+
+Immediate cause: `packages/tracker/meshnet_tracker/server.py:1490` annotates `ws_lock: threading.Lock | None = None`. `threading.Lock` is a factory function at runtime, not a type, so `| None` evaluates eagerly and crashes. This exists on `HEAD` too, not just in the dirty telemetry changes.
+
+Fix options:
+
+- Add `from __future__ import annotations` at the top of `server.py`, then run enough tests to catch any annotation side effects.
+- Or change that annotation to a safe runtime type such as `Any | None` / remove the union annotation. Keep the change minimal.
+
+## What to do next
+
+1. Fix the import-time `threading.Lock | None` crash.
+2. Re-run the targeted tests:
+
+   ```bash
+   .\.venv\Scripts\python.exe -m pytest tests/test_tracker_routing.py::test_tracker_heartbeat_stores_current_requests tests/test_tracker_routing.py::test_normalize_current_requests_sanitizes_payload tests/test_real_model_backend.py::test_current_requests_snapshot_while_generating tests/test_real_model_backend.py::test_distributed_generating_log_includes_tps -q
+   ```
+
+3. Run the relevant routing regression tests:
+
+   ```bash
+   .\.venv\Scripts\python.exe -m pytest tests/test_dynamic_routing.py tests/test_tracker_routing.py -q
+   ```
+
+4. If practical, run the non-integration suite:
+
+   ```bash
+   .\.venv\Scripts\python.exe -m pytest tests/ -q -m "not integration"
+   ```
+
+5. Confirm or document the pre-existing failure from the interrupted session: `test_proxy_chat_splits_payout_by_tracker_assigned_route_span` reportedly failed on `HEAD` too and was unrelated.
+6. Commit the intentional work in two commits if it remains naturally split:
+   - learned routing is already committed in `518c259`; leave it alone unless fixing regressions there.
+   - commit the live-progress/current-request telemetry cleanup separately after tests pass.
+
+## Acceptance criteria
+
+- [ ] Importing `meshnet_tracker.server` no longer crashes on the lock annotation.
+- [ ] Current-request heartbeat payloads are sanitized and surfaced in `/v1/network/map`.
+- [ ] Node-side in-flight chat snapshots report request id, model, token count, elapsed seconds, tokens/sec, and routing completion.
+- [ ] Dashboard call wall can show active requests from heartbeat data, not only tracker console terminal events.
+- [ ] Targeted telemetry tests pass.
+- [ ] Dynamic routing tests still pass, including GPU(0-21)+CPU(0-39) hybrid-route enumeration and traffic split behavior.
+- [ ] Full or non-integration suite result is recorded; unrelated pre-existing failures are named explicitly.
+- [ ] `.claude/settings.local.json` remains uncommitted unless intentionally approved.
+
+## ADR links
+
+- [ADR-0020](../../docs/adr/0020-chat-streaming-live-progress-and-mixed-topology-routing.md)
+- [ADR-0021](../../docs/adr/0021-dynamic-statistical-routing.md)
+
+## Blocked by
+
+None. The import-time annotation crash is the first fix.
+
+## Blocks
+
+Clean handoff/commit of the interrupted live routing progress work.

.scratch/alpha-hardening/prd.json

@@ -483,9 +483,29 @@
       "notes": "Source issue: .scratch/alpha-hardening/issues/23-dynamic-hf-pricing.md. High priority, ship-soon for launch, NOT an alpha-release blocker (unlike AH-021).",
       "dependsOn": [],
       "completionNotes": "Completed by agent"
+    },
+    {
+      "id": "AH-024",
+      "title": "24 - Finish learned-routing telemetry and live-progress cleanup",
+      "description": "Status: ready-for-agent\n\nScoped 2026-07-07 from an interrupted Claude session. The learned-routing feature is already committed at 518c259 (`routing improvements - dynamic (wip)`): routing_stats.py, tracker route enumeration and bandit selection, CLI routing flags, `/v1/routing`, dashboard Routing (learned), ADR-0021, and tests/test_dynamic_routing.py including the GPU(0-21)+CPU(0-39) hybrid topology. The dirty working tree contains follow-up live-progress/current-request telemetry in torch_server.py, startup.py, tracker server/dashboard, tests, and QUICKSTART. Known blocker found during resume: importing meshnet_tracker.server currently crashes at `server.py:1490` because `ws_lock: threading.Lock | None = None` evaluates `threading.Lock` as a factory function, not a type. Fix that first, then verify and commit the telemetry cleanup separately from the already-committed dynamic-routing work. Leave `.claude/settings.local.json` uncommitted unless explicitly approved.\n\nSource issue has exact file list, commands, and the reported pre-existing unrelated failure (`test_proxy_chat_splits_payout_by_tracker_assigned_route_span`).",
+      "acceptanceCriteria": [
+        "Importing `meshnet_tracker.server` no longer crashes on the lock annotation",
+        "Current-request heartbeat payloads are sanitized and surfaced in `/v1/network/map`",
+        "Node-side in-flight chat snapshots report request id, model, token count, elapsed seconds, tokens/sec, and routing completion",
+        "Dashboard call wall can show active requests from heartbeat data, not only tracker console terminal events",
+        "Targeted telemetry tests pass",
+        "Dynamic routing tests still pass, including GPU(0-21)+CPU(0-39) hybrid-route enumeration and traffic split behavior",
+        "Full or non-integration suite result is recorded; unrelated pre-existing failures are named explicitly",
+        "`.claude/settings.local.json` remains uncommitted unless intentionally approved"
+      ],
+      "priority": 24,
+      "passes": true,
+      "notes": "Source issue: .scratch/alpha-hardening/issues/24-routing-telemetry-resume.md. Resume task for interrupted 2026-07-07 Claude session; first known fix is server.py:1490 annotation crash.",
+      "dependsOn": [],
+      "completionNotes": ""
     }
   ],
   "metadata": {
-    "updatedAt": "2026-07-06T06:01:25.474Z"
+    "updatedAt": "2026-07-07T21:30:00.000Z"
   }
-}
+}

QUICKSTART.md

@@ -38,12 +38,30 @@ python3 -m venv .venv
   `python -c "import transformers; print(transformers.__version__)"` and upgrade
   with `pip install -U transformers` in the environment that runs `meshnet-node`
   (conda/miniforge users: upgrade inside that env, not a layered `.venv`).
-- The startup warning
-  `The fast path is not available because one of the required library is not installed`
-  is **harmless** — transformers falls back to a pure-torch implementation of the
-  linear-attention layers. The fast-path packages (`flash-linear-attention`,
-  `causal-conv1d`) are CUDA-only kernels: install them for GPU speed if you want,
-  skip them entirely on CPU nodes.
+- **Linear-attention fast path (GPU only).** Qwen3.5/3.6 use hybrid linear-attention
+  layers; without optional CUDA kernels, Transformers falls back to slower pure-PyTorch
+  code and prints `The fast path is not available…` at startup. That warning is
+  harmless — inference still works. On native Windows, install `triton-windows` in
+  the same env as `meshnet-node`; otherwise `flash-linear-attention` can fail during
+  import with `Could not import module 'Qwen3_5MoeForCausalLM'`. Install the
+  acceleration packages into the same env as `meshnet-node` for GPU speed; skip on
+  CPU-only nodes:
+
+  ```bash
+  # Native Windows
+  pip install triton-windows
+
+  # NVIDIA (CUDA)
+  pip install flash-linear-attention[cuda] causal-conv1d
+
+  # AMD (ROCm) — match your torch index, then:
+  pip install flash-linear-attention[rocm] causal-conv1d
+  ```
+
+  Restart the node after install; the warning should disappear. Expect the largest
+  gain on GPU nodes serving linear-attention layers (roughly three quarters of
+  Qwen3.6 layers); end-to-end chat speed still depends on the slowest hop in a
+  split route.
 - `pip install nvidia-ml-py` silences the pynvml deprecation warning on NVIDIA hosts.
 
 ## Bootstrap a tracker on a new machine

docs/INSTALL_WINDOWS.md

@@ -106,14 +106,24 @@ meshnet-node --help
 python -c "import transformers; print(transformers.__version__)"
 ```
 
-`transformers` must be **≥ 5.12** for Qwen3.5/3.6-MoE models (older versions fail
-with `'Qwen3_5MoeConfig' object has no attribute 'vocab_size'`). If you install
-into an existing conda/miniforge env instead of a fresh venv, run
-`pip install -U transformers` there. The startup warning about
-`flash-linear-attention` / `causal-conv1d` ("fast path is not available") is
-harmless on CPU — those are optional CUDA-only kernels.
-
----
+`transformers` must be **≥ 5.12** for Qwen3.5/3.6-MoE models (older versions fail
+with `'Qwen3_5MoeConfig' object has no attribute 'vocab_size'`). If you install
+into an existing conda/miniforge env instead of a fresh venv, run
+`pip install -U transformers` there. The startup warning about
+`flash-linear-attention` / `causal-conv1d` ("fast path is not available") is
+harmless on CPU — those are optional CUDA-only kernels.
+
+If you run the node from native Windows instead of WSL2, install the Triton shim
+in the same environment:
+
+```powershell
+python -m pip install triton-windows
+```
+
+Without it, Qwen3.5/3.6-MoE startup can fail with the misleading message
+`Could not import module 'Qwen3_5MoeForCausalLM'`.
+
+---
 
 ## Step 6 — Pre-download the model shard
 

packages/node/meshnet_node/startup.py

@@ -349,25 +349,38 @@ def _attach_relay_bridge(node: StubNodeServer | TorchNodeServer, bridge: RelayHt
 _PENDING_NODE_ID = "pending"
 
 
+_HEARTBEAT_INTERVAL_IDLE = 20.0
+_HEARTBEAT_INTERVAL_BUSY = 3.0
+
+
 def _start_heartbeat(
     tracker_url: str,
     node_id: str,
     register_payload: dict,
-    interval: float = 20.0,
+    interval: float = _HEARTBEAT_INTERVAL_IDLE,
     node_ref: Any | None = None,
     start_time: float | None = None,
 ) -> threading.Thread:
     """Daemon thread: sends heartbeats and re-registers automatically after tracker restarts.
 
     Heartbeat body carries cumulative stats (total_requests, failed_requests,
-    queue_depth, uptime_seconds, status). Stats are buffered locally during
-    outage and flushed on next successful heartbeat.
+    queue_depth, current_requests, uptime_seconds, status). Stats are buffered
+    locally during outage and flushed on next successful heartbeat.
 
     Heartbeat response may include new_assignment: {model, shard_start, shard_end}
     which is logged for now (hot-reload implemented in US-026).
     """
     _start_time = start_time or time.monotonic()
 
+    def _current_requests_snapshot() -> list[dict]:
+        if node_ref is None:
+            return []
+        getter = getattr(node_ref, "current_requests", None)
+        if getter is None:
+            return []
+        current = getter() if callable(getter) else getter
+        return list(current) if isinstance(current, list) else []
+
     def _get_stats() -> dict:
         uptime = time.monotonic() - _start_time
         stats: dict = {"uptime_seconds": round(uptime, 1), "status": "ready"}
@@ -379,8 +392,16 @@ def _start_heartbeat(
             )
             stats["failed_requests"] = getattr(node_ref, "failed_requests", 0)
             stats["queue_depth"] = getattr(node_ref, "queue_depth", 0)
+        current_requests = _current_requests_snapshot()
+        if current_requests:
+            stats["current_requests"] = current_requests
         return stats
 
+    def _sleep_interval() -> float:
+        if _current_requests_snapshot() or (node_ref is not None and getattr(node_ref, "queue_depth", 0) > 0):
+            return _HEARTBEAT_INTERVAL_BUSY
+        return interval
+
     def _reregister() -> bool:
         nonlocal node_id
         try:
@@ -442,7 +463,7 @@ def _start_heartbeat(
         outage_streak = 1 if node_id == _PENDING_NODE_ID else 0
 
         while True:
-            time.sleep(interval)
+            time.sleep(_sleep_interval())
 
             if outage_streak > 0:
                 # Tracker was down — attempt re-registration first (it may have restarted

packages/node/meshnet_node/torch_server.py

@@ -31,6 +31,23 @@ from .server import (
 )
 
 
+def _write_progress_line(state: list[bool], message: str, *, final: bool = False) -> None:
+    """Rewrite one in-place progress line (\\r) or finish with a newline."""
+    if final:
+        if state[0]:
+            sys.stdout.write("\r" + message + "\n")
+            state[0] = False
+        else:
+            print(message, flush=True)
+        return
+    if state[0]:
+        sys.stdout.write("\r" + message)
+    else:
+        sys.stdout.write(message)
+        state[0] = True
+    sys.stdout.flush()
+
+
 def _relay_hop(
     relay_addr: str,
     path: str,
@@ -91,6 +108,26 @@ class _TorchHTTPServer(http.server.HTTPServer):
         self.failed_requests: int = 0
         self.queue_depth: int = 0
         self._stats_lock = threading.Lock()
+        self._active_requests: dict[str, dict[str, Any]] = {}
+
+    def snapshot_current_requests(self) -> list[dict[str, Any]]:
+        """In-flight request snapshots for tracker heartbeats."""
+        now = time.monotonic()
+        with self._stats_lock:
+            out: list[dict[str, Any]] = []
+            for rec in self._active_requests.values():
+                elapsed = max(now - float(rec["started"]), 1e-6)
+                tokens = int(rec.get("tokens") or 0)
+                out.append({
+                    "request_id": str(rec["request_id"]),
+                    "model": str(rec.get("model") or ""),
+                    "kind": str(rec.get("kind") or "chat"),
+                    "tokens": tokens,
+                    "elapsed_seconds": round(elapsed, 1),
+                    "tokens_per_sec": round(tokens / elapsed, 2) if tokens > 0 else 0.0,
+                    "routing_complete": bool(rec.get("routing_complete")),
+                })
+            return out
 
     def resolve_backend(self, model_name: str | None) -> TorchModelShard | None:
         if not model_name:
@@ -113,10 +150,53 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
     def log_message(self, fmt, *args):  # noqa: suppress request logs in tests
         pass
 
+    def _request_id(self) -> str:
+        return (
+            self.headers.get("X-Meshnet-Request-Id")
+            or self.headers.get("X-Request-Id")
+            or f"local-{time.time_ns():x}"
+        )
+
     def _request_log_suffix(self) -> str:
         req_id = self.headers.get("X-Meshnet-Request-Id") or self.headers.get("X-Request-Id")
         return f" request_id={req_id}" if req_id else ""
 
+    def _track_request_begin(
+        self,
+        server: "_TorchHTTPServer",
+        request_id: str,
+        model: str,
+    ) -> None:
+        with server._stats_lock:
+            server._active_requests[request_id] = {
+                "request_id": request_id,
+                "model": model,
+                "kind": "chat",
+                "started": time.monotonic(),
+                "tokens": 0,
+                "routing_complete": False,
+            }
+
+    def _track_request_progress(
+        self,
+        server: "_TorchHTTPServer",
+        request_id: str,
+        *,
+        tokens: int,
+        routing_complete: bool = False,
+    ) -> None:
+        with server._stats_lock:
+            rec = server._active_requests.get(request_id)
+            if rec is None:
+                return
+            rec["tokens"] = tokens
+            if routing_complete:
+                rec["routing_complete"] = True
+
+    def _track_request_end(self, server: "_TorchHTTPServer", request_id: str) -> None:
+        with server._stats_lock:
+            server._active_requests.pop(request_id, None)
+
     def do_POST(self):
         server: _TorchHTTPServer = self.server  # type: ignore[assignment]
         if self.path == "/forward":
@@ -294,12 +374,14 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
 
     def _handle_chat_completions(self) -> None:
         server: _TorchHTTPServer = self.server  # type: ignore[assignment]
+        request_id = self._request_id()
         with server._stats_lock:
             server.total_requests += 1
             server.queue_depth += 1
         try:
-            self._do_chat_completions(server)
+            self._do_chat_completions(server, request_id)
         finally:
+            self._track_request_end(server, request_id)
             with server._stats_lock:
                 server.queue_depth -= 1
 
@@ -308,7 +390,7 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
         with server._stats_lock:
             server.failed_requests += 1
 
-    def _do_chat_completions(self, server: "_TorchHTTPServer") -> None:
+    def _do_chat_completions(self, server: "_TorchHTTPServer", request_id: str) -> None:
         body = self._read_json_body()
         if body is None:
             return
@@ -325,6 +407,7 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
         temperature = float(body.get("temperature") or 1.0)
         top_p = float(body.get("top_p") or 1.0)
 
+        self._track_request_begin(server, request_id, model_name)
         print(
             f"  [node] processing chat model={model_name!r} stream={stream} "
             f"max_tokens={max_tokens}{self._request_log_suffix()}",
@@ -335,6 +418,7 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
         # Avoids the single-token-per-forward-pass limitation of the distributed path.
         if backend.is_head and backend.is_tail:
             gen_started = time.monotonic()
+            progress_line = [False]
             try:
                 if stream:
                     token_count = 0
@@ -346,13 +430,19 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
                         ):
                             if token_text:
                                 token_count += 1
+                                self._track_request_progress(
+                                    server, request_id, tokens=token_count, routing_complete=True,
+                                )
                             yield token_text
 
                     self._stream_openai_response(_counting_stream(), model_name)
-                    print(
+                    elapsed = time.monotonic() - gen_started
+                    tps = token_count / max(elapsed, 1e-6)
+                    _write_progress_line(
+                        progress_line,
                         f"  [node] chat complete (stream) tokens={token_count} "
-                        f"elapsed_s={time.monotonic() - gen_started:.1f}{self._request_log_suffix()}",
-                        flush=True,
+                        f"elapsed_s={elapsed:.1f} tps={tps:.2f}{self._request_log_suffix()}",
+                        final=True,
                     )
                 else:
                     text = backend.generate_text(messages, max_tokens, temperature, top_p)
@@ -414,10 +504,12 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
         stream_emit = None
         if stream:
             stream_emit = self._start_openai_stream(model_name)
+            self._track_request_progress(server, request_id, tokens=0, routing_complete=True)
 
         _GENERATION_LOG_INTERVAL = 5.0
         gen_started = time.monotonic()
         last_gen_log = gen_started
+        progress_line = [False]
 
         for step in range(max_tokens):
             try:
@@ -437,20 +529,33 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
             if stream_emit is not None:
                 stream_emit(token_str)
             current_text = current_text + token_str
+            self._track_request_progress(
+                server,
+                request_id,
+                tokens=len(generated),
+                routing_complete=True,
+            )
             now = time.monotonic()
             if step == 0 or now - last_gen_log >= _GENERATION_LOG_INTERVAL:
-                print(
+                elapsed = now - gen_started
+                token_count = len(generated)
+                tps = token_count / max(elapsed, 1e-6)
+                _write_progress_line(
+                    progress_line,
                     f"  [node] generating step={step + 1}/{max_tokens} "
-                    f"tokens={len(generated)} elapsed_s={now - gen_started:.1f}",
-                    flush=True,
+                    f"tokens={token_count} elapsed_s={elapsed:.1f} tps={tps:.2f}",
                 )
                 last_gen_log = now
 
         if generated:
-            print(
-                f"  [node] generation complete tokens={len(generated)} "
-                f"elapsed_s={time.monotonic() - gen_started:.1f}",
-                flush=True,
+            elapsed = time.monotonic() - gen_started
+            token_count = len(generated)
+            tps = token_count / max(elapsed, 1e-6)
+            _write_progress_line(
+                progress_line,
+                f"  [node] generation complete tokens={token_count} "
+                f"elapsed_s={elapsed:.1f} tps={tps:.2f}",
+                final=True,
             )
 
         result_text = "".join(generated)
@@ -849,6 +954,12 @@ class TorchNodeServer:
     def queue_depth(self) -> int:
         return self._server.queue_depth if self._server is not None else 0
 
+    @property
+    def current_requests(self) -> list[dict[str, Any]]:
+        if self._server is None:
+            return []
+        return self._server.snapshot_current_requests()
+
     @property
     def loaded_model_ids(self) -> list[str]:
         return list(self._backends.keys())

packages/node/pyproject.toml

@@ -15,9 +15,10 @@ dependencies = [
     "bitsandbytes>=0.43",
     "rich>=13",
     "safetensors>=0.4",
-    "torch>=2.1",
-    "transformers>=5.12",
-    "websockets>=13",
+    "torch>=2.1",
+    "transformers>=5.12",
+    "triton-windows>=3.7; platform_system == 'Windows'",
+    "websockets>=13",
     "zstandard>=0.22",
     "kernels>=0.11.1,<0.16",
 ]

packages/tracker/meshnet_tracker/dashboard.html

@@ -433,6 +433,34 @@ function hiveThroughputSummary(stats) {
   return { totalTps, samples };
 }
 
+function enrichCallWallFromHeartbeat(states, map) {
+  const nodes = (map && map.nodes) || [];
+  for (const node of nodes) {
+    const reqs = (node.stats && node.stats.current_requests) || [];
+    for (const req of reqs) {
+      const id = req.request_id;
+      if (!id) continue;
+      let rec = states.get(id);
+      if (!rec) {
+        rec = {
+          id,
+          events: [],
+          status: "processing",
+          started: Date.now() / 1000 - Number(req.elapsed_seconds || 0),
+          model: req.model || node.model || "?",
+        };
+        states.set(id, rec);
+      } else if (rec.status === "pending") {
+        rec.status = "processing";
+      }
+      if (req.model) rec.model = req.model;
+      if (req.tokens != null) rec.tokens = req.tokens;
+      if (req.tokens_per_sec != null) rec.tps = req.tokens_per_sec;
+      if (req.elapsed_seconds != null) rec.elapsed = req.elapsed_seconds;
+    }
+  }
+}
+
 function buildCallWallStates(events) {
   const byId = new Map();
   for (const e of events) {
@@ -453,7 +481,7 @@ function buildCallWallStates(events) {
       rec.route = f.route || f.nodes;
       rec.nodes = f.nodes;
       rec.stream = f.stream;
-    } else if (msg === "proxy via relay" || msg === "proxy connected") {
+    } else if (msg === "proxy via relay" || msg === "proxy connected" || msg === "proxy connecting") {
       rec.status = "processing";
       if (!rec.started) rec.started = e.ts;
       rec.model = rec.model || f.model || f.route_model || "?";
@@ -539,10 +567,11 @@ function renderRouting(routing) {
   el.innerHTML = html;
 }
 
-function renderCallWall(consoleData, stats) {
+function renderCallWall(consoleData, stats, map) {
   const events = (consoleData && consoleData.events) || [];
   const nowSec = Date.now() / 1000;
   const states = buildCallWallStates(events);
+  enrichCallWallFromHeartbeat(states, map);
   const active = [];
   const terminal = [];
   for (const rec of states.values()) {
@@ -577,7 +606,7 @@ function renderCallWall(consoleData, stats) {
       const note = rec.warn || (rec.route ? short(String(rec.route), 28) : "");
       const row = [
         `<span class="${statusCls}">${esc(rec.status)}</span>`,
-        `<span class="num">${esc(callWallAgeSeconds(rec, nowSec).toFixed(1))}s</span>`,
+        `<span class="num">${esc(rec.elapsed != null ? Number(rec.elapsed).toFixed(1) : callWallAgeSeconds(rec, nowSec).toFixed(1))}s</span>`,
         esc(short(rec.model || "?", 28)),
         esc(short(rec.id, 18)),
         `<span class="num">${esc(tps(rec.tps))}</span>`,
@@ -1513,7 +1542,7 @@ async function refresh() {
   renderFraud(wallets, summary);
   renderStats(stats);
   renderRouting(routing);
-  renderCallWall(consoleData, stats);
+  renderCallWall(consoleData, stats, map);
   renderConsole(consoleData);
   renderNodeThroughput(stats);
   renderChatModels();

packages/tracker/meshnet_tracker/server.py

@@ -3257,6 +3257,14 @@ class _TrackerHandler(http.server.BaseHTTPRequestHandler):
 
         try:
             started = time.monotonic()
+            _tracker_log(
+                server,
+                "info",
+                "proxy connecting",
+                request_id=request_id,
+                target_url=target_url,
+                stream=is_stream or None,
+            )
             upstream_result: list[Any] = []
             connect_errors: list[BaseException] = []
 

tests/test_real_model_backend.py

@@ -376,6 +376,92 @@ def test_split_shard_chat_streams_each_generated_token_incrementally():
     assert "data: [DONE]" in rest
 
 
+def test_current_requests_snapshot_while_generating():
+    release_second = threading.Event()
+    head = TorchNodeServer(backend=_FakePipelineHeadBackend(), tracker_mode=True)
+    tail = TorchNodeServer(backend=_BlockingStreamingTailBackend(release_second))
+    head_port = head.start()
+    tail_port = tail.start()
+    response = None
+    try:
+        payload = json.dumps({
+            "model": "fake-model",
+            "messages": [{"role": "user", "content": "hello"}],
+            "stream": True,
+            "max_tokens": 2,
+        }).encode()
+        req = urllib.request.Request(
+            f"http://127.0.0.1:{head_port}/v1/chat/completions",
+            data=payload,
+            headers={
+                "Content-Type": "application/json",
+                "X-Meshnet-Request-Id": "req-live-1",
+                "X-Meshnet-Route": json.dumps([
+                    {"endpoint": f"http://127.0.0.1:{tail_port}", "start_layer": 22},
+                ]),
+            },
+            method="POST",
+        )
+        response = urllib.request.urlopen(req, timeout=5)
+        deadline = time.time() + 2.0
+        while time.time() < deadline:
+            live = head.current_requests
+            if live and live[0]["request_id"] == "req-live-1" and live[0]["tokens"] >= 1:
+                break
+            time.sleep(0.02)
+        assert head.current_requests
+        snap = head.current_requests[0]
+        assert snap["request_id"] == "req-live-1"
+        assert snap["tokens"] >= 1
+        assert snap["tokens_per_sec"] >= 0
+        assert snap["routing_complete"] is True
+        release_second.set()
+        response.read()
+    finally:
+        release_second.set()
+        if response is not None:
+            response.close()
+        head.stop()
+        tail.stop()
+
+    assert head.current_requests == []
+
+
+def test_distributed_generating_log_includes_tps(capsys):
+    head = TorchNodeServer(backend=_FakePipelineHeadBackend(), tracker_mode=True)
+    tail = TorchNodeServer(backend=_FakePipelineTailBackend())
+    head_port = head.start()
+    tail_port = tail.start()
+    try:
+        payload = json.dumps({
+            "model": "fake-model",
+            "messages": [{"role": "user", "content": "hello"}],
+            "max_tokens": 1,
+        }).encode()
+        req = urllib.request.Request(
+            f"http://127.0.0.1:{head_port}/v1/chat/completions",
+            data=payload,
+            headers={
+                "Content-Type": "application/json",
+                "X-Meshnet-Route": json.dumps([
+                    {"endpoint": f"http://127.0.0.1:{tail_port}", "start_layer": 22},
+                ]),
+            },
+            method="POST",
+        )
+        with urllib.request.urlopen(req, timeout=5) as resp:
+            json.loads(resp.read())
+    finally:
+        head.stop()
+        tail.stop()
+
+    out = capsys.readouterr().out
+    assert "generating step=1/1" in out
+    assert " tps=" in out
+    assert "generation complete tokens=1" in out
+    assert out.count("generating step=1/1") == 1
+
+
 def test_int_tensor_header_serializes_torch_tensors():
     torch = pytest.importorskip("torch")
 

tests/test_tracker_routing.py

@@ -1498,6 +1498,70 @@ def test_tracker_heartbeat_updates_node():
         tracker.stop()
 
 
+def test_tracker_heartbeat_stores_current_requests():
+    """Node-reported in-flight request snapshots appear on the network map."""
+    tracker = TrackerServer()
+    tracker_port = tracker.start()
+    try:
+        reg = _post_json(
+            f"http://127.0.0.1:{tracker_port}/v1/nodes/register",
+            {
+                "endpoint": "http://127.0.0.1:9001",
+                "model": "progress-model",
+                "shard_start": 0,
+                "shard_end": 31,
+                "hardware_profile": {},
+                "score": 1.0,
+            },
+        )
+        node_id = reg["node_id"]
+        _post_json(
+            f"http://127.0.0.1:{tracker_port}/v1/nodes/{node_id}/heartbeat",
+            {
+                "queue_depth": 1,
+                "current_requests": [{
+                    "request_id": "req-abc123",
+                    "model": "progress-model",
+                    "kind": "chat",
+                    "tokens": 17,
+                    "elapsed_seconds": 42.5,
+                    "tokens_per_sec": 0.4,
+                    "routing_complete": True,
+                }],
+            },
+        )
+        network = _get_json(f"http://127.0.0.1:{tracker_port}/v1/network/map")
+        node = next(item for item in network["nodes"] if item["node_id"] == node_id)
+        assert node["stats"]["queue_depth"] == 1
+        assert node["stats"]["current_requests"] == [{
+            "request_id": "req-abc123",
+            "model": "progress-model",
+            "kind": "chat",
+            "tokens": 17,
+            "elapsed_seconds": 42.5,
+            "tokens_per_sec": 0.4,
+            "routing_complete": True,
+        }]
+    finally:
+        tracker.stop()
+
+
+def test_normalize_current_requests_sanitizes_payload():
+    from meshnet_tracker.server import _normalize_current_requests
+
+    assert _normalize_current_requests(None) == []
+    assert _normalize_current_requests([
+        {"request_id": "req-1", "model": "m", "tokens": "9", "tokens_per_sec": "1.5"},
+        {"model": "missing-id"},
+        "bad",
+    ]) == [{
+        "request_id": "req-1",
+        "model": "m",
+        "tokens": 9,
+        "tokens_per_sec": 1.5,
+    }]
+
+
 def test_tracker_heartbeat_expiry():
     """Nodes that miss their heartbeat window are excluded from routes."""
     tracker = TrackerServer(heartbeat_timeout=0.05)  # 50 ms