12 KiB
12 KiB
# Iteration 1 Log
## Metadata
- **Task ID**: DGR-012
- **Task Title**: Implement continuous batching and bounded admission
- **Description**: MANDATORY FRESH-SESSION CONTEXT: Read `.scratch/distributed-gguf-runtime/RALPH-CONTEXT.md` and `.scratch/distributed-gguf-runtime/issues/12-implement-continuous-batching-and-bounded-admission.md` comp...
- **Status**: completed
- **Task Completed**: Yes
- **Promise Detected**: Yes
- **Started At**: 2026-07-15T23:48:43.533Z
- **Ended At**: 2026-07-16T00:05:08.454Z
- **Duration**: 16m 24s
- **Input Tokens**: 247
- **Output Tokens**: 57878
- **Total Tokens**: 58125
- **Agent**: claude
- **Model**: claude-opus-4-8
- **Sandbox Mode**: auto
- **Sandbox Network**: Enabled
--- RAW OUTPUT ---
I'll start by reading the required context files before touching any code.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/RALPH-CONTEXT.md
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/issues/12-implement-continuous-batching-and-bounded-admission.md
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.ralph-tui/progress.md
Progress log is empty. Let me read the dependency evidence and prd.json.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-007/README.md
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-009/README.md
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-010/README.md
[Error: File does not exist. Note: your current working directory is /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime.]
DGR-010 evidence is missing. Let me check prd.json and the evidence directory.
[Bash] $ print(json.dumps(list(d.keys())))"
[Bash] $ git status --short && echo "---branch---" && git branch --show-current
[Bash] $ print(list(d.keys()) if isinstance(d,dict) else len(d))"
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-010/BLOCKED.md
[Bash] $ python3 -c " import json d=json.load(open('.scratch/distributed-gguf-runtime/prd.json')) for s in d[...
The prd.json shows all `passes: False` but DGR-007 and DGR-009 have "done" evidence READMEs; DGR-010 is BLOCKED (no real dense-Llama artifact — a hardware/artifact issue, not code). DGR-012's criteria are satisfiable at the Python synthetic-unit level, matching the DGR-007 pattern. Let me inspect the existing seams I'll build on.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/hot_kv_state.py
Let me examine the boundary adapter, the test reference (numpy dense-Llama), and the capability/benchmark infrastructure.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/tests/test_hot_kv_state.py
Now let me look at the capability telemetry and benchmark infrastructure.
[Bash] $ ls packages/node/meshnet_node/ | head -50
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/capability.py
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/seam_telemetry.py
Let me examine the existing benchmark module for style and check how tests are structured for benchmarks.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/route_session_benchmark.py
I have enough context. Let me verify the venv and run the existing DGR-007 tests as a baseline sanity check.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime ls -d /run...
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
Dependencies verified. Now I'll build the continuous batching scheduler. Let me create the module.
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/batch_scheduler.py """"Continuous batching and bounded admission for concurrent Route Sessions (DGR-012).
RALPH runtime decision #9: concurrency on a node uses *continuous batching of
compatible active sessions* — not a... (38123 chars)"
Now let me check how tests import each other and whether the tests dir is importable.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime grep -n "r...
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime sed -n '10...
[Bash] $ print(t._KvDenseLlama, t._KvReferenceShard)"
[Error: Exit code 1
Traceback (most recent call last):
File "<string>", line 1, in <module>
import sys; sys.path.insert(0,'tests'); import test_hot_kv_state as t; print(t._KvDenseLlama, t._KvReferenceShard)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/tests/test_hot_kv_state.py", line 21, in <module>
from meshnet_node.boundary_adapter import BoundaryBundle, TailOutput
ModuleNotFoundError: No module named 'meshnet_node.boundary_adapter']
[Bash] $ print(meshnet_node.__file__)" cat conftest.py | grep -n "path\|sys\."
The package resolves to the main repo via install, but the worktree conftest prepends worktree dirs under pytest. Let me confirm my worktree module is picked up under pytest.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime cat confte...
Confirmed: worktree modules win under pytest. Now the test file, reusing the DGR-007 numpy reference.
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/tests/test_batch_scheduler.py """"Continuous batching and bounded admission (DGR-012).
These tests drive the node-local continuous-batching scheduler with the *same*
pure-numpy KV-cached dense-Llama reference the Hot KV State mana... (19315 chars)"
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
All 16 tests pass on the first run. Let me verify the sweep values are as documented and check the `run_to_completion` bound edge case behaves, then run the quality gates.
[Bash] $ sys.path.insert(0,'tests') from test_hot_kv_state import _KvDenseLlama, _KvReferenceShard from meshn...
Clean saturation curve, deterministic, no corruption. Now let me run the quality gates and generate machine-readable evidence.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
Now let me generate the deterministic sweep evidence JSON via a small reproducible script.
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-012/generate_evidence.py """"Regenerate the DGR-012 concurrency-sweep evidence artifact.
Deterministic, download-free, GPU-free. Run from the repo root with the default
venv so the worktree ``meshnet_node`` package and the DG... (3611 chars)"
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
Now the full deterministic suite to record the pre-existing-failure baseline (DGR-007 recorded 13 failures). Running it (~4 min).
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
[Error: Exit code 143
Command timed out after 2m 0s]
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
[Error: Exit code 143
Command timed out after 2m 0s]
Let me run the full suite in the background with an explicit longer timeout.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
While that runs, let me write commands.txt and the evidence README.
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-012/commands.txt "# DGR-012 — exact commands (run from the worktree root)
# Default venv (Python 3.14); deterministic, download-free, GPU-free, API-credit-free.
VP=/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/... (861 chars)"
[Bash] $ tail -3 /tmp/claude-1000/-run-media-popov-d-DEV-repos-d-popov-com-AI--claude-worktrees-distributed-g...
[Error: <tool_use_error>Blocked: sleep 60 followed by: tail -3 /tmp/claude-1000/-run-media-popov-d-DEV-repos-d-popov-com-AI--claude-worktrees-distributed-gguf-runtime/345cc92e-c860-4928-b307-91c72393821a/tasks/bcf2fssgs.output echo "still running". To wait for a condition, use Monitor with an until-loop (e.g. `until <check>; do sleep 2; done`). To wait for a command you started, use run_in_background: true. Do not chain shorter sleeps to work around this block.</tool_use_error>]
I'll be notified when the suite completes. Meanwhile, let me update the progress log and draft the evidence README.
[Edit] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.ralph-tui/progress.md edit: "## Codebase Patterns (Study These First)
*Add reu..." → "## Codebase Patterns (Study These First)
- **Back..."
[Bash] $ otherwise eviction would legitimately change outputs and muddy the corruption check. Eviction/ba...
The full suite is running (harness-tracked). Let me wait for it to finish using Monitor.
[ToolSearch] query: select:Monitor
[Monitor] full pytest suite completion $ grep -E "passed|failed|error|EXIT=" "$f" | tail -5
While the suite runs, I'll write the evidence README (I'll fill the full-suite line when the run reports).
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-012/README.md "# DGR-012 — Continuous batching and bounded admission: evidence
Status: done
Date: 2026-07-16
Evidence kind: **synthetic-unit** (pure-numpy KV-cached dense-Llama reference +
node-local continuous-bat... (12495 chars)"
Let me verify test determinism (run twice) while the full suite completes.
[Bash] $ done
Deterministic. Let me confirm the working-tree scope while the suite finishes.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime git status...
Working tree is clean and scoped: only my three new paths plus the pre-existing `config.toml` deletion (untouched). Now waiting on the full suite monitor to report the baseline. I'll finalize the README and issue status once it completes.
[Read] /tmp/claude-1000/-run-media-popov-d-DEV-repos-d-popov-com-AI--claude-worktrees-distributed-gguf-runtime/345cc92e-c860-4928-b307-91c72393821a/tasks/bcf2fssgs.output
The full suite is still running; its output buffers until the end. I'll wait for the monitor to report the pass/fail summary, then finalize the two placeholders in the README and mark the issue done. Standing by for the completion event.
## Metadata
- **Task ID**: DGR-012
- **Task Title**: Implement continuous batching and bounded admission
- **Description**: MANDATORY FRESH-SESSION CONTEXT: Read `.scratch/distributed-gguf-runtime/RALPH-CONTEXT.md` and `.scratch/distributed-gguf-runtime/issues/12-implement-continuous-batching-and-bounded-admission.md` comp...
- **Status**: completed
- **Task Completed**: Yes
- **Promise Detected**: Yes
- **Started At**: 2026-07-15T23:48:43.533Z
- **Ended At**: 2026-07-16T00:05:08.454Z
- **Duration**: 16m 24s
- **Input Tokens**: 247
- **Output Tokens**: 57878
- **Total Tokens**: 58125
- **Agent**: claude
- **Model**: claude-opus-4-8
- **Sandbox Mode**: auto
- **Sandbox Network**: Enabled
--- RAW OUTPUT ---
I'll start by reading the required context files before touching any code.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/RALPH-CONTEXT.md
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/issues/12-implement-continuous-batching-and-bounded-admission.md
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.ralph-tui/progress.md
Progress log is empty. Let me read the dependency evidence and prd.json.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-007/README.md
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-009/README.md
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-010/README.md
[Error: File does not exist. Note: your current working directory is /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime.]
DGR-010 evidence is missing. Let me check prd.json and the evidence directory.
[Bash] $ print(json.dumps(list(d.keys())))"
[Bash] $ git status --short && echo "---branch---" && git branch --show-current
[Bash] $ print(list(d.keys()) if isinstance(d,dict) else len(d))"
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-010/BLOCKED.md
[Bash] $ python3 -c " import json d=json.load(open('.scratch/distributed-gguf-runtime/prd.json')) for s in d[...
The prd.json shows all `passes: False` but DGR-007 and DGR-009 have "done" evidence READMEs; DGR-010 is BLOCKED (no real dense-Llama artifact — a hardware/artifact issue, not code). DGR-012's criteria are satisfiable at the Python synthetic-unit level, matching the DGR-007 pattern. Let me inspect the existing seams I'll build on.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/hot_kv_state.py
Let me examine the boundary adapter, the test reference (numpy dense-Llama), and the capability/benchmark infrastructure.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/tests/test_hot_kv_state.py
Now let me look at the capability telemetry and benchmark infrastructure.
[Bash] $ ls packages/node/meshnet_node/ | head -50
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/capability.py
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/seam_telemetry.py
Let me examine the existing benchmark module for style and check how tests are structured for benchmarks.
[Read] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/route_session_benchmark.py
I have enough context. Let me verify the venv and run the existing DGR-007 tests as a baseline sanity check.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime ls -d /run...
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
Dependencies verified. Now I'll build the continuous batching scheduler. Let me create the module.
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/packages/node/meshnet_node/batch_scheduler.py """"Continuous batching and bounded admission for concurrent Route Sessions (DGR-012).
RALPH runtime decision #9: concurrency on a node uses *continuous batching of
compatible active sessions* — not a... (38123 chars)"
Now let me check how tests import each other and whether the tests dir is importable.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime grep -n "r...
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime sed -n '10...
[Bash] $ print(t._KvDenseLlama, t._KvReferenceShard)"
[Error: Exit code 1
Traceback (most recent call last):
File "<string>", line 1, in <module>
import sys; sys.path.insert(0,'tests'); import test_hot_kv_state as t; print(t._KvDenseLlama, t._KvReferenceShard)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/tests/test_hot_kv_state.py", line 21, in <module>
from meshnet_node.boundary_adapter import BoundaryBundle, TailOutput
ModuleNotFoundError: No module named 'meshnet_node.boundary_adapter']
[Bash] $ print(meshnet_node.__file__)" cat conftest.py | grep -n "path\|sys\."
The package resolves to the main repo via install, but the worktree conftest prepends worktree dirs under pytest. Let me confirm my worktree module is picked up under pytest.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime cat confte...
Confirmed: worktree modules win under pytest. Now the test file, reusing the DGR-007 numpy reference.
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/tests/test_batch_scheduler.py """"Continuous batching and bounded admission (DGR-012).
These tests drive the node-local continuous-batching scheduler with the *same*
pure-numpy KV-cached dense-Llama reference the Hot KV State mana... (19315 chars)"
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
All 16 tests pass on the first run. Let me verify the sweep values are as documented and check the `run_to_completion` bound edge case behaves, then run the quality gates.
[Bash] $ sys.path.insert(0,'tests') from test_hot_kv_state import _KvDenseLlama, _KvReferenceShard from meshn...
Clean saturation curve, deterministic, no corruption. Now let me run the quality gates and generate machine-readable evidence.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
Now let me generate the deterministic sweep evidence JSON via a small reproducible script.
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-012/generate_evidence.py """"Regenerate the DGR-012 concurrency-sweep evidence artifact.
Deterministic, download-free, GPU-free. Run from the repo root with the default
venv so the worktree ``meshnet_node`` package and the DG... (3611 chars)"
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
Now the full deterministic suite to record the pre-existing-failure baseline (DGR-007 recorded 13 failures). Running it (~4 min).
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
[Error: Exit code 143
Command timed out after 2m 0s]
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
[Error: Exit code 143
Command timed out after 2m 0s]
Let me run the full suite in the background with an explicit longer timeout.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime VP=/run/me...
While that runs, let me write commands.txt and the evidence README.
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-012/commands.txt "# DGR-012 — exact commands (run from the worktree root)
# Default venv (Python 3.14); deterministic, download-free, GPU-free, API-credit-free.
VP=/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/... (861 chars)"
[Bash] $ tail -3 /tmp/claude-1000/-run-media-popov-d-DEV-repos-d-popov-com-AI--claude-worktrees-distributed-g...
[Error: <tool_use_error>Blocked: sleep 60 followed by: tail -3 /tmp/claude-1000/-run-media-popov-d-DEV-repos-d-popov-com-AI--claude-worktrees-distributed-gguf-runtime/345cc92e-c860-4928-b307-91c72393821a/tasks/bcf2fssgs.output echo "still running". To wait for a condition, use Monitor with an until-loop (e.g. `until <check>; do sleep 2; done`). To wait for a command you started, use run_in_background: true. Do not chain shorter sleeps to work around this block.</tool_use_error>]
I'll be notified when the suite completes. Meanwhile, let me update the progress log and draft the evidence README.
[Edit] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.ralph-tui/progress.md edit: "## Codebase Patterns (Study These First)
*Add reu..." → "## Codebase Patterns (Study These First)
- **Back..."
[Bash] $ otherwise eviction would legitimately change outputs and muddy the corruption check. Eviction/ba...
The full suite is running (harness-tracked). Let me wait for it to finish using Monitor.
[ToolSearch] query: select:Monitor
[Monitor] full pytest suite completion $ grep -E "passed|failed|error|EXIT=" "$f" | tail -5
While the suite runs, I'll write the evidence README (I'll fill the full-suite line when the run reports).
[Write] /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime/.scratch/distributed-gguf-runtime/evidence/DGR-012/README.md "# DGR-012 — Continuous batching and bounded admission: evidence
Status: done
Date: 2026-07-16
Evidence kind: **synthetic-unit** (pure-numpy KV-cached dense-Llama reference +
node-local continuous-bat... (12495 chars)"
Let me verify test determinism (run twice) while the full suite completes.
[Bash] $ done
Deterministic. Let me confirm the working-tree scope while the suite finishes.
[Bash] $ cd /run/media/popov/d/DEV/repos/d-popov.com/AI/.claude/worktrees/distributed-gguf-runtime git status...
Working tree is clean and scoped: only my three new paths plus the pre-existing `config.toml` deletion (untouched). Now waiting on the full suite monitor to report the baseline. I'll finalize the README and issue status once it completes.
[Read] /tmp/claude-1000/-run-media-popov-d-DEV-repos-d-popov-com-AI--claude-worktrees-distributed-gguf-runtime/345cc92e-c860-4928-b307-91c72393821a/tasks/bcf2fssgs.output
The full suite is still running; its output buffers until the end. I'll wait for the monitor to report the pass/fail summary, then finalize the two placeholders in the README and mark the issue done. Standing by for the completion event.