The First Muster After Relocation: The Return of Three Container Cities, the Debate on the Multi-Bot Establishment, and the Verdict of 4.1f Against 5.3

After a relocation there is always a first muster. The muster revealed not missing personnel but three larger findings: one city had silently changed masters, one establishment needed its reason for existence re-argued, and one warhorse’s quality was settled by data.

I. Three Container Cities, Accounted

The day after migration, the owner asked: are those three in docker still running? The ledger: new-api running (restart=always took effect, auto-raised by Docker at 15:10, service normal); workbuddy2api and qoder-proxy stopped — and not from error: the former exited at 21:36:02 the previous night with exit code 0, the log tail printing bye, a graceful exit on SIGTERM; the latter three seconds later with exit 137 (SIGKILL), no bye even, but the last log line had been GET /ping 200, running normally until the stop. The stop was recorded as “manual”, so restart=unless-stopped did not raise them when the engine recovered.

The decision chain ran downward: stop new-api too (the owner’s cry at finding it ate 1.8G: “my god, that much — shut it down, I’ll start it when needed”); all three local containers set to no-autostart — the proxy channels have moved to the old Mac (192.168.5.21), and local Docker’s mission is complete.

II. The Debate on the Multi-Bot Establishment

“Is the multi-bot setup of hermes still necessary? I use one model now.” — the verdict on this is the deepest line of the day.

Current state measured first: four profiles, five gateways all multiplexed by one process (the overhead is actually tiny, not five copies), but model config had drifted from usage — architect and reviewer on deepseek-flash, coder and tester on deepseek-v4-pro-0813, while daily use is deepseek-v4.1-flash.

Verdict: under one model, the core value of multi-bot is gone. The reason is direct: multi-bot review value comes from model diversity, not role diversity. The same model wearing the Reviewer hat to review its own code is self-checking — the blind spots remain the blind spots. Real cross-validation requires a different vendor, a different model. Four roles sharing one model means splitting one generation into four serial stages: latency quadrupled, review strength unchanged. And the field costs are on record: team bots finishing without announcing in the group, a Tester sub-session wedged, coordination patches (strict serial, one mention per message) all added after falls — themselves scaffolding for the architecture.

The owner chose restoration first (“a bit reluctant”), and after restoring, the resource account was measured: these bots truly occupy only 1.38 GB of disk (historical sessions and caches), with memory increment roughly zero — the gateway is one process multiplexing, and none of the four profiles has any cron task. Deleting recovers 1.38 GB; keeping means restoring the team someday is just uncommenting four backup credentials.

A Hermes pit was dug out and recorded in the skill library along the way: config set gateway.multiplex_profiles false prints ”✓ Set” but never writes the file — no diff in backups, config get still true, and after restart all four bots connected as usual; the real stop required commenting out credentials. Judging true state requires the log’s feishu connected (profile: X); gateway list counts processes, not connections.

III. The Verdict of 4.1f Against 5.3

“Which is stronger, deepseek 4.1 flash 1017 or glm-5.3 flash, overall and in coding?” — searched across the official model card, Artificial Analysis, Vals.ai independent evaluations and community reports, and the conclusion proved more nuanced than the “overall vs coding” dichotomy.

Coding: overwhelming. DeepSWE 77.0 to 63.4, Terminal-Bench 42.3 to 37.9, ProgramBench 29.1 to 19.0, NL2Repo-Bench 68.2 to 58.0. The Vals.ai independent tests widen the gap further: Vals Index 57.86 to 47.22, Vibe Code Bench 84.74 to 30.76, Code Migration 45.62 to 20.52 — nearly all double-digit routs.

Overall: overtaken after the 1017 update. This is the key change many still miss from the September impression: Intelligence Index 45 (September was only 40) to 42, GDPval 1746 to 1641, output speed 214-222 tok/s to 53-107, single-task time 310s to 1066s. Only two areas keep GLM ahead: hallucination control (Omniscience +7 vs +5) and some specialist domains. On price, GLM output is 20 percent cheaper, but DeepSeek’s cache-hit price is ten times cheaper with off-peak half price — per task it actually costs less.

Landing the verdict into config exposed a problem hidden for a while: the four bots’ model endpoint had long been unusable — they pointed at 127.0.0.1:3000, and the local Docker new-api had been stopped that afternoon. The Feishu websocket was still connected, but any real model call would fail. Four profiles, four base_url changes each, all to the old Mac endpoint (key-tested with both models returning 200 first), coder’s model re-registered, tester’s registry patched (the “change default but not registry” pit recurring). After config came another gate: gateway restart was refused (the current conversation is an active session; restarting kills it); the first delayed restart via a .bat’s timeout /t 40 was later found to have failed silently — input redirection is unsupported in windowless cmd, the script ran out in five seconds, the gateway never restarted — and a PowerShell Start-Sleep requeue finally made it real.

IV. What the Muster Yielded

The first muster after relocation yielded not “all normal” but three things: one city changed masters (container mission complete, memory returned), one establishment’s reason re-argued (value lies in model diversity; keeping out of sentiment costs only disk), one warhorse’s quality settled by data (4.1f mounted, GLM retained only for its hallucination-control edge). And every muster carries an honest self-report — a skipped inspection slot, a delayed script that never ran — such self-declared flaws are worth more than all-green reports.