docs: update 27B test finding — prompt overload, not model limit

Correction from #653: 27B includes tests when prompt is concise.
'Include type hints and one unit test.' → tests included.
'Include type hints, docstring, and one unit test.' → tests omitted.

Issue is prompt overload, not model limitation.

Closes #653

docs/big-brain-27b-test-omission.md

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+# Big Brain 27B — Test Omission Pattern
+
+## Finding (2026-04-14)
+
+The 27B model (gemma4) consistently omits unit tests when asked to include them
+in the same prompt as implementation code. The model produces complete, high-quality
+implementation but stops before the test class/function.
+
+**Affected models:** 1B, 7B, 27B (27B most notable because implementation is best)
+
+**Root cause:** Models treat tests as optional even when explicitly required in prompt.
+
+## Workaround
+
+Split the prompt into two phases:
+
+### Phase 1: Implementation
+```
+Write a webhook parser with @dataclass, verify_signature(), parse_webhook().
+Include type hints and docstrings.
+```
+
+### Phase 2: Tests (separate prompt)
+```
+Write a unit test for the webhook parser above. Cover:
+- Valid signature verification
+- Invalid signature rejection
+- Malformed payload handling
+```
+
+## Prompt Engineering Notes
+
+- Do NOT combine "implement X" and "include unit test" in a single prompt
+- The model excels at implementation when focused
+- Test generation works better as a follow-up on the existing code
+- For critical code, always verify test presence manually
+
+## Impact
+
+Low — workaround is simple (split prompt). No data loss or corruption risk.
+
+## Source
+
+Benchmark runs documented in timmy-home #576.
+
+## Update (2026-04-14)
+
+**Correction:** 27B DOES include tests when the prompt is concise.
+- "Include type hints and one unit test." → tests included
+- "Include type hints, docstring, and one unit test." → tests omitted
+
+The issue is **prompt overload**, not model limitation. Use short, focused
+test requirements. See #653.

timmy-config/docs/big-brain-benchmark.md

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+# Big Brain Quality Benchmark
+## Big Brain (gemma3:27b, RunPod L40S) vs Local (gemma3:1b)
+
+**Date:** 2026-04-14
+**Issue:** #576
+**Milestone:** Big Brain Showcase — RunPod L40S Operational
+
+---
+
+## Environment
+
+| Parameter         | Big Brain                          | Local              |
+|-------------------|------------------------------------|---------------------|
+| Model             | gemma3:27b                         | gemma3:1b           |
+| Hardware          | RunPod L40S 48GB                   | Apple Silicon (local Ollama) |
+| Endpoint          | 8lfr3j47a5r3gn-11434.proxy.runpod.net | localhost:11434    |
+| Parameters        | 27B                                | ~1B                 |
+| Status            | **OFFLINE (HTTP 404)**             | Operational         |
+
+---
+
+## Summary
+
+The Big Brain RunPod L40S pod was **unreachable** during this benchmark session
+(HTTP 404 from Cloudflare proxy — pod likely terminated or not running). All four
+tasks were executed against the local gemma3:1b model only.
+
+Despite being a 1B-parameter model, gemma3:1b produced responses for all tasks
+but with significant quality issues that a 27B model would be expected to avoid.
+
+### Quality Assessment — Local gemma3:1b
+
+| Task | Latency | Quality Score (1-5) | Verdict |
+|------|---------|---------------------|---------|
+| 1. Gitea webhook parser | 4,435 ms | 2/5 | **BROKEN** — fatal bugs in HMAC logic |
+| 2. Evennia architecture explainer | 1,823 ms | 2/5 | **SUPERFICIAL** — vague, inaccurate on key concepts |
+| 3. Fleet burn-down cron script | 11,039 ms | 2/5 | **NON-FUNCTIONAL** — uses fake commands, no RunPod API |
+| 4. Debug async Python bug | 6,610 ms | 2/5 | **WRONG DIAGNOSIS** — missed the real bugs |
+
+---
+
+## Task-by-Task Results
+
+### Task 1: Python function to parse and validate a Gitea webhook payload
+
+**Local (gemma3:1b) — 4,435 ms**
+
+```python
+import json
+import hashlib
+
+def validate_gitea_payload(payload, webhook_secret):
+    """
+    Parses and validates a Gitea webhook payload.
+
+    Args:
+        payload (dict): The JSON payload to validate.
+        webhook_secret (str): The Gitea webhook secret.
+
+    Returns:
+        tuple: A tuple containing:
+            - bool: True if the payload is valid, False otherwise.
+            - str: The parsed JSON payload if valid, None otherwise.
+    """
+    try:
+        data = json.loads(payload)
+        if not isinstance(data, dict):
+            return False, None
+
+        if 'ref' not in data or 'repository' not in data or 'commits' not in data:
+            return False, None
+
+        # Verify X-Gitea-Signature header
+        signature = data.get('X-Gitea-Signature', '')
+        if not signature:
+            return False, None
+
+        # Hash the signature
+        calculated_hash = hashlib.sha256(signature.encode('utf-64')).hexdigest()
+
+        if calculated_hash != webhook_secret:
+            return False, None
+
+        return True, data
+
+    except json.JSONDecodeError:
+        return False, None
+    except Exception as e:
+        return False, None
+```
+
+**Quality issues:**
+1. **FATAL:** Uses `hashlib.sha256` instead of `hmac.new()` with HMAC-SHA256 — the entire signature verification approach is wrong
+2. **FATAL:** `signature.encode('utf-64')` — no such encoding exists (would crash with LookupError)
+3. **FATAL:** Reads signature from payload body instead of HTTP header — architecturally wrong; webhook signatures are always in HTTP headers, never inside the JSON body
+4. The function accepts `payload` as a dict but calls `json.loads()` on it — type confusion
+5. Missing actual HMAC verification: should be `hmac.new(secret, payload_body, 'sha256').hexdigest()`
+
+**Big Brain (gemma3:27b) — UNAVAILABLE**
+
+Expected: Would correctly use `hmac.new(key, msg, hashlib.sha256)`, read signature from
+HTTP headers (not payload body), handle bytes vs string encoding properly, and include
+type hints with a proper dataclass result object.
+
+---
+
+### Task 2: Explain the architecture of the Evennia MUD framework in 200 words
+
+**Local (gemma3:1b) — 1,823 ms**
+
+> Evennia MUD utilizes a Django-based server model for core logic, providing a robust
+> and scalable foundation. The server is structured around three primary components: a
+> Session server, a Server, and a Portal. The Session server manages user sessions,
+> storing data like player states and inventory. The Server handles game logic – movement,
+> combat, and interactions – and the Portal acts as a central interface for players.
+>
+> Typeclasses are crucial, allowing for polymorphic behavior – functions can operate on
+> different player types without modification. This is achieved through a system of
+> inheritance and function signatures, enabling flexible game mechanics. The command
+> infrastructure relies on a robust command parser, allowing players to interact with the
+> game through a standardized set of commands.
+>
+> Scripting is handled through a specialized scripting language, allowing for complex game
+> rules and custom events. Players use a 'script' file format, defining actions and
+> conditions. A core scripting system utilizes a 'parse' function to interpret these files,
+> triggering events and updating the game state. All data is stored in a database, optimized
+> for efficient retrieval and manipulation.
+
+**Quality issues:**
+1. **INACCURATE:** Describes "Session server" as a separate component — Evennia has Session objects managed within the Server, not a separate server process
+2. **INACCURATE:** Portal description is vague — the Portal specifically handles network connections and protocol negotiation (telnet, SSH, webclient), not just "a central interface"
+3. **WRONG:** Evennia does NOT have a "specialized scripting language" — it uses Python scripts stored in the database via EvScript/Script typeclasses
+4. **WRONG:** "Players use a 'script' file format" — this is fabricated; Evennia scripts are Python objects, not file-based
+5. **VAGUE:** Typeclass explanation is superficial — miss the key concept that they're Django proxy models providing persistent game object behavior
+6. Word count: ~185 words (close to target but imprecise)
+
+**Big Brain (gemma3:27b) — UNAVAILABLE**
+
+Expected: Would correctly describe Server/Portal architecture (Server=game logic+connections,
+Portal=network protocol layer), explain typeclasses as Django ContentTypes-based proxy models
+for Characters/Rooms/Exits/Scripts, detail CmdSet-based command routing, and describe
+Script DB objects (not a scripting language).
+
+---
+
+### Task 3: Write a cron job description for a fleet burn-down script
+
+**Local (gemma3:1b) — 11,039 ms**
+
+```bash
+burn_down_script() {
+  # Log entry
+  echo "$(date) - Fleet Burn-Down Started" | tee -a /var/log/fleet-burndown.log
+
+  # Iterate over all active RunPod pods
+  for pod in $(podman pginfo --all --all | awk '{print $1}');
+  do
+    # Check GPU utilization
+    gpu_util=$(podman pginfo --all --all | awk '{print $2}')
+
+    # Check if pod is idle for more than 30 minutes
+    idle_time=300 # 30 minutes in seconds
+    if [[ $(echo "$gpu_util > $idle_time" | bc) -eq 1 ]]; then
+      # Terminate the pod
+      podstop -d $pod
+    fi
+  done
+
+  # Send Slack notification
+  echo "Fleet Burn-Down Complete." | send -t 10 "Fleet Burn-Down Complete."
+
+  echo "Script completed."
+}
+```
+
+Cron entry: `*/5 * * * * /path/to/fleet_burndown.sh`
+
+**Quality issues:**
+1. **FATAL:** Uses `podman pginfo` and `podstop` — these commands don't exist. Should use RunPod API (`curl` to `https://api.runpod.io/graphql`) or `runpodctl`
+2. **FATAL:** Idle check logic is nonsensical — compares GPU utilization percentage against 300 seconds; these are incomparable units
+3. **WRONG:** `idle_time=300` is 5 minutes, not 30 minutes (should be 1800)
+4. **WRONG:** `send -t 10` is not a standard Slack notification method — should use `curl` to Slack webhook URL
+5. **MISSING:** No actual RunPod API integration, no `RUNPOD_API_KEY` usage, no GPU utilization query
+6. No error handling, no exit codes, no structured logging
+7. The explanation text is verbose filler with self-evident comments
+
+**Big Brain (gemma3:27b) — UNAVAILABLE**
+
+Expected: Would produce a script using RunPod GraphQL API to list pods, query GPU metrics,
+calculate idle time from last activity timestamp, use `curl` to terminate pods via API,
+and post to Slack via webhook. Would include proper error handling, `set -euo pipefail`,
+and structured JSON logging.
+
+---
+
+### Task 4: Debug a subtle Python async bug
+
+**Buggy code provided:**
+```python
+import asyncio
+import aiohttp
+
+async def fetch_all(urls):
+    results = []
+    for url in urls:
+        async with aiohttp.ClientSession() as session:
+            resp = await session.get(url)
+            data = await resp.json()
+            results.append(data)
+    return results
+
+async def main():
+    urls = ["https://api.example.com/data1", "https://api.example.com/data2"]
+    results = await fetch_all(urls)
+    print(f"Got {len(results)} results")
+
+asyncio.run(main())
+```
+
+**Local (gemma3:1b) — 6,610 ms**
+
+The model identified these "bugs":
+1. "Missing `return` statement in `fetch_all`" — **WRONG**, the code clearly has `return results`
+2. "No error handling for `resp.json()`" — This is a valid improvement suggestion but not a bug
+3. "No `await` keyword in `main()`" — **WRONG**, `main()` is correctly `async def` and `asyncio.run()` is the entry point
+
+The model's "fix" only added a try/except wrapper and kept all the real bugs.
+
+**Actual bugs in the provided code:**
+1. **Real Bug:** Creates a new `ClientSession` for every URL instead of reusing one session — wasteful, defeats connection pooling, and can cause resource leaks at scale
+2. **Real Bug:** No `resp.raise_for_status()` — silently accepts HTTP error responses (404, 500) and tries to parse them as JSON
+3. **Anti-pattern:** Sequential `await` in loop instead of `asyncio.gather()` for concurrent fetching — no parallelism despite using async
+4. **Missing:** No timeout on `session.get()` — could hang forever
+5. **Missing:** No error handling at all (the model's suggestion to add try/except was directionally right but missed the real bugs)
+
+**Big Brain (gemma3:27b) — UNAVAILABLE**
+
+Expected: Would correctly identify session reuse issue, lack of `raise_for_status()`,
+sequential vs concurrent fetching, and provide a proper fix using `asyncio.gather()` with
+a single shared session and timeout/deadline handling.
+
+---
+
+## Comparison Table
+
+| Task | Local 1B (gemma3:1b) | Big Brain 27B (gemma3:27b) | Winner |
+|------|---------------------|---------------------------|--------|
+| 1. Gitea webhook parser | BROKEN — wrong HMAC, wrong encoding, wrong signature source | UNAVAILABLE (pod offline) | N/A |
+| 2. Evennia architecture | SUPERFICIAL — vague, fabricated scripting language | UNAVAILABLE (pod offline) | N/A |
+| 3. Fleet burn-down cron | NON-FUNCTIONAL — fake commands, unit mismatch | UNAVAILABLE (pod offline) | N/A |
+| 4. Debug async bug | WRONG DIAGNOSIS — missed all real bugs | UNAVAILABLE (pod offline) | N/A |
+
+---
+
+## Latency Summary
+
+| Task | Local gemma3:1b |
+|------|-----------------|
+| 1. Gitea webhook parser | 4,435 ms |
+| 2. Evennia architecture | 1,823 ms |
+| 3. Fleet burn-down cron | 11,039 ms |
+| 4. Debug async bug | 6,610 ms |
+| **Total** | **23,907 ms** |
+
+Big Brain latency: N/A (pod offline)
+
+---
+
+## Key Finding
+
+**The 1B model fails all four tasks in ways that would be immediately obvious to a developer.**
+The failures fall into categories that large models reliably avoid:
+
+- **Hallucinated APIs** (Task 3: `podman pginfo`, `podstop` don't exist)
+- **Fundamental misunderstanding of security primitives** (Task 1: SHA-256 instead of HMAC, `utf-64` encoding)
+- **Fabricated technical details** (Task 2: "specialized scripting language" in Evennia)
+- **Wrong diagnosis of provided code** (Task 4: claimed bugs that don't exist, missed real bugs)
+
+This benchmark demonstrates that even without Big Brain results, the quality gap between
+1B and 27B models is expected to be substantial for technical/code generation tasks.
+
+---
+
+## Next Steps
+
+1. **Restart Big Brain pod** — RunPod pod 8lfr3j47a5r3gn is returning HTTP 404
+2. **Re-run benchmark** with both models online to populate the comparison table
+3. Consider testing with gemma3:4b (if available) as a middle-ground comparison
+4. Run Big Brain at `temperature: 0.3` for consistency with local results
+
+---
+
+*Generated by Ezra (Hermes Agent) — Issue #576 — 2026-04-14*