# Session: 2026-03-05 01:24:30 UTC

- **Session Key**: agent:main:main
- **Session ID**: 84341567-1ee6-4e53-a5c0-68a433ddccf6
- **Source**: telegram

## Conversation Summary

assistant: Now add a mobile sidebar toggle to Computer Use:
assistant: Now make Agent Eval mobile-responsive:
assistant: Now add Model Hub to the main hub navigation:
assistant: Let me verify everything works:
assistant: All 4 services active, 23 models in Ollama, 24 hub modules, 3 eval profiles. Let me test that Model Hub can talk to both Ollama and Eval API from the browser perspective:
assistant: Now let me commit and update memory:
assistant: Hub isn't a git repo — it's served directly. The workspace-main changes were already committed. Let me make sure everything that should be tracked is:
assistant: Good — all workspace-main code is committed. Now let me explain how Agent Eval works and move to Phase 4:
assistant: Here's the summary and explanation you asked for:

---

**🤖 Model Hub** (http://100.109.173.109:8090/24-model-hub/)
- **Installed tab**: All 23 Ollama models with eval badges. Each card shows size, family, params, and if tested: score bars for each category + strengths/weaknesses
- **Download tab**: Type any model name (e.g. `gemma3:4b`) and pull it with live progress. Popular models grid with one-click install
- **Leaderboard tab**: Ranked comparison table + "best agent per task" routing
- **Evaluate button** on every model card → pick which categories to test → run → results appear automatically

**How Agent Eval Actually Works:**

1. **16 tests across 6 categories**, each with an automated checker:
   - **Reasoning** (4 tests): Classic logic puzzles — bat+ball, syllogisms, balance scale, missing dollar. Checks if the model gets the actual right answer, not just a confident-sounding wrong one
   - **Coding** (3 tests): Write a palindrome function, flatten nested lists, identify time complexity. Checks for correct code structure
   - **Instruction Following** (3 tests): "List 5 fruits in lowercase", "respond with only 'yes'", write a haiku. Checks exact format compliance
   - **Knowledge** (3 tests): Berlin Wall year, water formula, Shakespeare. Checks factual accuracy
   - **Structured Output** (2 tests): Return valid JSON. Checks parse-ability
   - **Speed** (1 test): 2+2 with bonus points for sub-1s response

2. Each test sends a prompt to the model via Ollama API, gets the response, runs a **lambda checker** (e.g., does the response contain "0.05" for the bat+ball question?)

3. Scores are weighted by category importance (reasoning/coding = 1.0, speed = 0.5) and aggregated into an overall percentage

4. Results are saved as **profiles** that persist — any app or agent can query `/api/recommend/coding` to get "use qwen3.5:4b, it scored 100%"

**📱 All dashboards are now mobile-responsive** — cards stack, tabs scroll horizontally, inputs are touch-friendly (16px prevents iOS zoom). Works on your iPhone via Tailscale.

Ready for Phase 4 (Sakana AI Scientist)?
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "3475",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Wed 2026-03-04 20:20 EST"
}
```

Sender (untrusted metadata):
```json
{
  "label": "P M (7827979987)",
  "id": "7827979987",
  "name": "P M"
}
```

All right, so I’d like you to do is back yourself up so that I can reset the context window and also ensure to like write a document that in detail explains everything we’ve done so I think we may have something like this already so if we have something that we use already kind of like this go into deep detail and what we did provide me like also what the document is that we used but prepare yourself. I’m gonna reset the context window so that you don’t burn through all your tokens by using old context that we don’t necessarily need.
assistant: Good — following the context-save skill exactly. Let me do all the steps:
assistant: Now update MEMORY.md with distilled session knowledge:
assistant: Update LESSONS.md:
assistant: Now commit and backup:
assistant: Everything saved and backed up. Here's what I used and what you need to know:

**📄 The document is `RESUME.md`** — this is the standard continuation briefing we use. It's ~180 lines of detailed documentation covering:
- Every module built with file paths, ports, API endpoints
- How each system works architecturally
- Test results with actual numbers
- System state (all services, ports, hardware)
- Exact next steps (Phase 4)
- Lessons learned so you don't re-hit the same issues

**What happens on reset:**
1. New session reads `RESUME.md` first (per AGENTS.md instructions)
2. It knows exactly what was built, what's running, and what's next
3. Zero wasted tokens on old conversation context

**Files updated:**
- `RESUME.md` — full briefing (start here)
- `MEMORY.md` — distilled long-term facts
- `LESSONS.md` — new operational patterns
- `memory/2026-03-04.md` — timestamped session log
- Git committed + backup pushed to GitHub

Ready for your reset. Tell the new session to read RESUME.md and pick up Phase 4. 👍