feat(scripts): add uptime-check retry + Obsidian variant, add LLM benchmark docs

check_uptime.js gets a fetchWithRetry wrapper (3 attempts, 2s backoff)
for transient failures against the status page/heartbeat API.
check_uptime_to_obsidian.js is a variant that logs results into the
Obsidian vault instead of stdout. Also adds two benchmark writeups
(gpt-oss-20b on Ollama, 73-node Ollama fleet).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

docs/2026-06-28-gpt-oss-20b-ollama-benchmark.md

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+# gpt-oss-20b — `.73` Ollama box vs valhalla P100 (2026-06-28)
+
+**TL;DR:** The same model (`gpt-oss-20b`) runs **~1.3–1.5× faster on generation** and
+**~1.6–2× faster on prefill** on ginnoir's `192.168.1.73` Ollama box than on valhalla's
+Tesla P100 llama-swap backend — *despite* the `.73` box partially offloading to CPU at 64K
+context. Tool-calls and code outputs are correct on both. For interactive Hermes use the
+`.73` box is the better backend whenever it's powered on; the P100 remains the always-on
+fallback. **Caveat:** at very deep context (~43K tokens) `.73` generation drops to ~8 tok/s
+(KV no longer fully GPU-resident).
+
+## Setup
+
+- **Endpoint:** `http://192.168.1.73:11434` — Ollama **0.30.11**, model `gpt-oss:20b`,
+  quant **MXFP4** (OpenAI's native gpt-oss 4-bit format), reported 20.9B params.
+- **Serving config:** `num_ctx 65536` (matches the P100's 64K and Hermes' hard ≥64K
+  requirement), sampling `temperature 0.6 / top_p 0.95 / top_k 20` (identical to the
+  2026-06-27 Ornith/P100 run).
+- **Measured via** Ollama's native `/api/chat` (exact `prompt_eval`/`eval` token counts +
+  durations). Cold model load at 64K ctx took **~65 s** (one-time).
+- **P100 baseline** = the gpt-oss-20b column from `docs/2026-06-27-ornith-9b-benchmark.md`
+  (llama-swap, q8/q8 KV, `--parallel 1`, `--jinja`, 64K).
+- GPU on `.73` **could not be identified** — no SSH (port 22 filtered). Inferred from VRAM
+  behavior (below): a fast but VRAM-limited card (or unified-memory host).
+
+## Results
+
+| Test | Metric | **.73 Ollama (MXFP4)** | **valhalla P100 (GGUF)** | `.73` advantage |
+|---|---|---|---|---|
+| Tool call | valid `tool_calls`? | ✅ `get_weather({"city":"Tokyo"})` | ✅ identical | tie (both correct) |
+| Codegen (`merge_intervals`) | correct? | ✅ correct, non-mutating, type-hinted | ✅ correct (mutates input) | `.73` slightly cleaner |
+| Debug (`second_largest`) | correct? | ✅ correct single-pass, handles dup max | ✅ correct | tie |
+| Deep-prefill | answered? | ✅ "quick brown fox" | ✅ (capped probe) | tie |
+| **Gen speed** | tok/s — tool_call | **33.1** | 23.0 | **1.44×** |
+| **Gen speed** | tok/s — codegen | **27.4** | 21.7 | **1.26×** |
+| **Gen speed** | tok/s — debug | **27.6** | 18.9 | **1.46×** |
+| **Prefill** | tok/s — shallow (~100–135 tok) | **240–287** | 133–188 | ~1.5–1.8× |
+| **Prefill** | tok/s — deep | **2542** (n=43.8k) | 155 (n=23.4k) | far faster (diff depths) |
+| Gen @ deep ctx | tok/s | 8.4 (n=43.8k ctx) | 12.7 (n=23.4k ctx) | **P100 wins at depth** |
+| Reasoning verbosity | think chars (codegen/debug) | 1760 / 1317 | 1671 / 2118 | comparable |
+| VRAM @ 64K | on-GPU / total | **8.82 / 14.16 GB** (partial CPU offload) | ~12.6 GB (100% GPU) | — |
+
+## Reading the numbers
+
+- **Shallow/typical depth is where `.73` wins decisively.** All three real tasks (tool-call,
+  codegen, debug) run at shallow context, and `.73` generates at **27–33 tok/s vs the P100's
+  ~19–23** — roughly the difference between "comfortable" and "sluggish" for an interactive
+  agent loop. Prefill is also ~1.5–1.8× faster, so first-token latency improves too.
+- **The `.73` box is VRAM-limited, not compute-limited.** At 64K ctx only **8.82 GB of the
+  14.16 GB** working set sits in VRAM — the rest (weights tail + deep KV) spills to system
+  RAM. It still beats the full-GPU P100, which means the card itself is much faster than the
+  P100; with more VRAM (or a smaller `num_ctx`) it would pull further ahead.
+- **The one place the P100 wins: very deep context.** At ~43K resident tokens, `.73`
+  generation falls to **8.4 tok/s** because the KV cache is partly in CPU RAM (memory-
+  bandwidth-bound attention). The P100 holds its whole 64K KV in VRAM and degrades more
+  gracefully (12.7 tok/s at 23K). In practice Hermes' steady-state prompt is ~16K, so this
+  rarely bites — but long sessions on `.73` will slow down more than on the P100.
+- **Quant differs**, so this isn't a pure hardware A/B: `.73` runs MXFP4 (gpt-oss's native,
+  near-lossless 4-bit) while the P100 GGUF quant is whatever llama-swap pulled. Both are
+  genuine gpt-oss-20b and both produced correct outputs; no quality regression observed.
+
+## Verdict for the Hermes backend
+
+- **Prefer `.73` when it's up.** It's the faster daily driver for gpt-oss-20b at the depths
+  Hermes actually runs at. Switch in-session with `/model --provider ollama --model gpt-oss:20b`.
+- **Keep the P100 (`valhalla-p100`) as the always-on default.** It's a container on the
+  24/7 server; the `.73` box may be a desktop/workstation that isn't always powered. The
+  P100 also degrades more gracefully at very deep context.
+- **If you want `.73` to be strictly better,** drop its `num_ctx` toward what Hermes needs
+  (it hard-requires ≥64K, so you can't go below that for Hermes) **or** put gpt-oss on a
+  bigger-VRAM card there — eliminating the CPU spill would lift both prefill and deep-context
+  generation.
+
+## Caveats
+
+- Small hand-written suite (4 tasks), not SWE-bench — measures latency/throughput and basic
+  correctness, not deep code quality.
+- The codegen prompt lost its back-ticked tokens to shell quoting during the run (prompt_n 95
+  vs the P100's 113); the model still produced a correct `merge_intervals`, and gen tok/s is
+  prompt-content-independent, so the speed comparison stands.
+- Deep-prefill rows use different context depths (43.8K on `.73` vs 23.4K on P100), so the
+  prefill-tok/s cells aren't directly comparable — read them as "each box's deep-prefill rate
+  at that depth," not a head-to-head ratio.
+- Raw responses saved on valhalla at `/tmp/ollama-bench/` (one `.json` per task); P100
+  baselines at `/tmp/ornith-bench/`.

docs/2026-06-28-ollama-73-fleet-benchmark.md

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+# `.73` Ollama fleet benchmark — all 9 models (2026-06-28)
+
+**TL;DR:** Throughput across every model on `192.168.1.73`. Generation speed spans an
+**~5× range** — from `gemma4:e4b` at **~93 tok/s** down to the big `qwen3:30b-a3b` at
+**~18 tok/s**. **All 9 models emit valid tool-calls.** For an interactive agent backend the
+sweet spot is **`gpt-oss:20b` (~29 tok/s)** or **`gemma4:12b` (~48 tok/s)** if 12B quality
+suffices; the 30B-class Qwen MoEs are the slowest here (heavy CPU offload at 64K on this
+VRAM-limited box). **Caveat:** code-correctness for the heavy *thinking* models is
+indeterminate — they used the whole 768-token gen cap reasoning and never emitted code (see
+Caveats); re-run with a bigger budget to judge quality.
+
+## Setup
+- Endpoint `http://192.168.1.73:11434`, Ollama 0.30.11. Each model served at **`num_ctx
+  65536`** (Hermes' ≥64K requirement), sampling `temp 0.6 / top_p 0.95 / top_k 20`.
+- Native `/api/chat` timings. 4 tasks: tool-call, codegen (`merge_intervals`), debug
+  (`second_largest`), deep-prefill (~16K-token filler). Gen capped: 256 / 768 / 768 / 128.
+- Same `.73` box as the gpt-oss head-to-head in
+  `docs/2026-06-28-gpt-oss-20b-ollama-benchmark.md` (GPU still unidentified — no SSH).
+
+## Generation speed (tok/s) — the headline
+
+Average of the three real tasks (tool-call / codegen / debug), sorted fastest first:
+
+| Model | avg gen t/s | tool | codegen | debug | deep-ctx gen | cold load s | tool-call? |
+|---|--:|--:|--:|--:|--:|--:|:--:|
+| **gemma4:e4b** | **92.7** | 91.1 | 93.3 | 93.6 | 82.6 | 24.8 | ✅ |
+| **gemma4:12b** | **48.2** | 46.8 | 49.0 | 48.8 | 45.7 | 10.6 | ✅ |
+| **gpt-oss:20b** | **29.4** | 32.7 | 27.9 | 27.7 | 29.9 | 0.4¹ | ✅ |
+| **qwen3.6:35b-a3b** | **27.3** | 29.1 | 26.4 | 26.4 | 28.2 | 35.9 | ✅ |
+| **gemma4:26b** | **25.7** | 27.6 | 25.0 | 24.5 | 26.5 | 53.8 | ✅ |
+| **glm-4.7-flash** | **21.2** | 24.2 | 19.7 | 19.7 | 21.4 | 34.8 | ✅ |
+| **qwen3-vl:30b-a3b** | **19.1** | 22.7 | 16.5 | 18.2 | 20.6 | 29.8 | ✅ |
+| **qwen3-coder:30b** | **19.0** | 22.8 | 17.1 | 17.2 | 20.4 | 25.3 | ✅ |
+| **qwen3:30b-a3b** | **17.6** | 19.5 | 16.6 | 16.6 | 18.5 | 25.0 | ✅ |
+
+¹ gpt-oss was already resident from the prior run; real cold load is ~65 s.
+
+## Prefill speed (tok/s)
+
+| Model | shallow (~100 tok) | deep (~16K tok) |
+|---|--:|--:|
+| gemma4:e4b | 1193–1799 | 7283 |
+| gemma4:12b | 667–1078 | 3318 |
+| gpt-oss:20b | 281–379 | 2655 |
+| qwen3:30b-a3b | 63–149 | 1030 |
+| qwen3-coder:30b | 76–270 | 979 |
+| gemma4:26b | 110–127² | 977 |
+| qwen3.6:35b-a3b | 78–209 | 620 |
+| glm-4.7-flash | 70–163 | 649 |
+| qwen3-vl:30b-a3b | 68–127 | 588 |
+
+² gemma4:26b's first request after load measured 11.7 t/s (cold-cache artifact); ignore.
+
+## What stands out
+- **The two small gemmas are in a different league.** `gemma4:e4b` (~93 t/s) and
+  `gemma4:12b` (~48 t/s) are dense but small, so they sit fully on GPU and fly. If a 4B/12B
+  is smart enough for the job, they're the most responsive options by far.
+- **gpt-oss:20b is the best "big-brain, still-fast" pick** (~29 t/s) — MoE ~3.6B active keeps
+  it quick despite 20B total. `qwen3.6:35b-a3b` nearly matches it (~27 t/s) and may be
+  stronger; worth A/B-ing on real tasks.
+- **The 30B-a3b Qwen trio is the slowest** (~17–19 t/s). Same "3B-active" MoE label, but
+  larger total weights → more spills to CPU RAM at 64K on this VRAM-limited box, dragging
+  generation below gpt-oss. `qwen3-coder` being this slow undercuts it as a *fast* coding
+  model here.
+- **Every model tool-calls.** All 9 emitted a valid `get_weather({"city":"Tokyo"})`, so any
+  of them can drive Hermes' tool loop.
+
+## Correctness (partial — see caveat)
+- **Confirmed correct** code on the non-/light-thinking models that finished within the cap:
+  `gpt-oss:20b`, `gemma4:12b`, `qwen3-vl:30b-a3b`, `qwen3-coder:30b` (clean `def`,
+  `reason=stop` or code present), plus `gemma4:26b` & `qwen3:30b-a3b` on the task each
+  finished.
+- **Indeterminate** (truncated mid-reasoning, `content=0`, `reason=length`): `glm-4.7-flash`
+  (both), `gemma4:e4b` (both), `qwen3.6:35b-a3b` (both), `gemma4:26b` (codegen),
+  `qwen3:30b-a3b` (debug). These spent all 768 gen tokens in the `thinking` channel — **not
+  wrong, just unfinished.** A re-run at `num_predict ~3072` is needed to grade their output.
+
+## Caveats
+- The 768-token gen cap was too low for heavy chain-of-thought models — it bounds runtime but
+  truncates their answers. Speed (tok/s) is unaffected and valid; code *quality* for the
+  truncated set is not measured here.
+- Per-model VRAM split not captured (models unload after 2 min `keep_alive`); only
+  gpt-oss-20b is known (8.82 GB on-GPU / 14.16 GB total at 64K → partial CPU offload). The
+  slow 30B-class numbers are consistent with heavier offload.
+- Small hand-written suite, not SWE-bench. Quants are each model's Ollama default.
+- Raw per-task responses on valhalla at `/tmp/ollama-bench-all/` (`summary.json` + one JSON
+  per model/task); progress log `/tmp/ollama-bench-all/progress.txt`.