# Session: 2026-03-07 07:22:58 UTC

- **Session Key**: agent:main:main
- **Session ID**: 45e1d4de-f197-4ee0-9af2-785d32a09ae3
- **Source**: telegram

## Conversation Summary

assistant: Everything looks stable right now actually:
- **122B model:** running, healthy ✅
- **No OOM kills** ✅
- **No failed services** ✅
- **OpenClaw gateway:** responding ✅

What are you seeing crash? Is it on your end in Telegram, or one of the other agents? The repeated "How is this all going?" messages you sent earlier — were those because my responses weren't getting through to you?
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "4220",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Sat 2026-03-07 01:45 EST"
}
```

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

It is looking pretty good right now. Can you tell me what exactly we’re doing right now like what’s running what we’re doing what’s next?
assistant: 

**What's running right now:**

- **Qwen3.5-122B-A10B (UD-Q5_K_XL)** — our new primary model, live on port 18080 via llama.cpp. 85.6 GB loaded onto GPU. 32K context. This is the model all OpenClaw local agents talk to.

- **Ollama** — still running with the small models (0.8b, 2b, 4b, etc.) available on demand at port 11434

- **Model Supervisor** — the lifecycle daemon we built tonight, on port 18090. Manages swaps, rollbacks, and context monitoring. Currently stopped (I stopped it during the manual loading process)

- **OpenClaw Gateway** — running, routing your Telegram messages to me

- **The old 35B model** — files still on disk (kept as fallback), but its auto-restart services are disabled so it won't interfere

**What we did tonight:**

1. Researched and chose Qwen3.5-122B-A10B as the strongest model that fits on the Spark
2. Downloaded the Unsloth UD-Q5_K_XL quantization (85.6 GB, 3 files)
3. Built a Model Registry (`/home/pmello/models/MODEL_REGISTRY.md`) — unified catalog of every model on the system
4. Built a Model Supervisor daemon with swap/rollback/context watchdog
5. Built a swap script (`swap-model 122b` / `swap-model 35b`)
6. Fought with the old jess-q35 auto-restart services, eventually disabled them
7. Got the 122B loaded and passing smoke tests

**What's next:**

1. **Update the jess-q35 service** to point at the 122B as the new default boot model (so it survives reboots)
2. **Re-enable the supervisor** with a longer timeout (5+ minutes instead of 3) since the 122B takes a while to load
3. **Run deeper tests** — tool calling, longer generations, check inference speed
4. **Update the supervisor timeout** — the first swap attempt failed because 180s wasn't enough for 85 GB

Want me to keep going on these, or save it for tomorrow? It's almost 2 AM.
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "4222",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Sat 2026-03-07 01:49 EST"
}
```

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

Can you in very high detail document everything we did today in scrutinizing detail and we’re gonna start a daily log where we drop all of our information that we accrued throughout the day all of our details should go into a singular place and will timestamp it with the day and then we’ll keep doing this and the goal will be every time we contact switch or every time we clear context window, we document in detail and scrutinizing detail. Everything we we ran did how we did it exactly everything in ways that will allow us to eventually reuse this information later on, but also while also backing all the information up I think we have a rag database that we should probably throw this all into as well and also I want you to update all the models on what’s currently happening what we have create skills for anything that you did that we haven’t done before and then prepare yourself for a context clearing
assistant: Let me do this systematically. Starting with the detailed daily log, then updating everything else.
assistant: Now update MEMORY.md, RESUME.md, and notify the other agents:
assistant: Now notify the other agents and update comms:
assistant: Now let me check if we should create a skill for the model swap workflow:
assistant: Now let me push the daily log to the RAG system and commit everything:
assistant: Everything is documented, backed up, and synced. Here's the summary of what I just did:

**✅ Documentation complete:**
- **Daily log** (`memory/2026-03-07.md`) — 11 KB of scrutinizing detail: every command, every decision, every benchmark number, every failure, every lesson
- **RESUME.md** — updated with current state and exact next steps for next session
- **MEMORY.md** — key facts about the upgrade added to long-term memory
- **SHARED_CONTEXT.md** — updated so all agents know about the model change

**✅ Agents notified:**
- Posted to #ops and #general via comms

**✅ Skill created:**
- `skills/model-swap/SKILL.md` — documents the swap script, supervisor API, file triggers, and important notes

**✅ Backed up:**
- Git committed and pushed to backup repo

**✅ Ready for context clear.** The daily log and RESUME.md have everything needed to pick up exactly where we left off. The next session should:
1. Fix supervisor timeout (180s → 360s)
2. Update jess-q35 service to point at 122B
3. Re-enable supervisor
4. Run deeper tests
assistant: The timer was explicitly stopped at 01:35 AM tonight (was running since 01:30, then disabled ~5 min later). This is a Q35 agent's watchdog — likely intentionally stopped during maintenance or by the Q35 agent itself. It's 2 AM Saturday, non-urgent. Logged for Pat's awareness when he's next active.

🔍 **jess-watchdog.timer** — inactive (disabled), stopped at 01:35 AM. Last ran successfully at 01:37. No restart attempted since it may have been intentionally disabled.
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "4230",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Sat 2026-03-07 02:05 EST"
}
```

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

Before I clear your context what is it that we plan on doing next?
assistant: 

**Immediate next session (pick up where we left off):**

1. **Fix the supervisor timeout** — change 180s → 360s in `model-supervisor.py` so the 122B swap actually succeeds automatically
2. **Update the jess-q35 service file** — point it at the 122B model path so it survives reboots. Right now the 122B is running manually — if the Spark reboots, nothing comes back.
3. **Re-enable the supervisor** — start the model-supervisor service so swap/rollback/context watchdog are all active
4. **Run real tests on the 122B** — tool calling, vision, longer generations, measure tokens/second. We smoke-tested with "what's 2+2" but haven't stress-tested it.

**After that (when the basics are solid):**

5. **Build out the supervisor's smarter features** — the context watchdog (rescue before overflow), the 0.8b validation of handoffs, scheduled profiles (122B during work, 35B at night)
6. **Clean up old Ollama models** — ~270 GB of stuff we don't use (gpt-oss, old qwen3, llava, phi3)
7. **Test whether Ollama small models (4b for vision) can coexist** with the 122B in RAM — we have ~20 GB free, the 4b is 3.4 GB, should work but need to verify

**The big picture goal you described tonight:** Models that have infrastructure to help them through transition periods — context overflow rescue, safe model swapping, validation of handoffs — things that are part of the system, not tools the models have to remember to use.
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "4232",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Sat 2026-03-07 02:12 EST"
}
```

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

Can we do a couple things? Can you add these things to your list? I want to ask you these by yourself the full context we have like a model lab where we can see all the models and I can test them out if we can test them I can test them with you know like it took a Chat Gpt like Chat station for me to like people to select any model that we currently have on device and talked with them. Can you ensure that that is fully functional so I want that to be on the list of things to do once you restart. I also found a few of those models that I know for certain accept image, but when I go to send them an image, it would say they don’t accept it so you just really just allow an image to go to any of the box and if it fails, just have it fail like have the image sent to them they may not know what it is and it may confuse them, but it’s better than just preventing me from being able to use images. I also have some ideas on how we can process large videos from YouTube and such quickly or be able to digest large chunks of memory quickly these are all things I’m definitely let them mention. Part of my idea is using the point B models and just generating a bunch of them like 30 or 40 maybe even 50 of them and then if you have one model that can chunk up data like like distribute the workload and it happened just summarize and if you have them, I have strict guard reels on how they need to act we may be able to get them to categorize summarize and create like a really effective vector database. That’s really quick and easy to pull up information, but we’ll talk about all this afterwards so please update your memory with a little bit of this stuff I’m mentioning and then I will clear the context and we will get going
assistant: All saved, committed, and backed up. Ready for context clear. 

When I come back I'll read RESUME.md and pick up right where we are — supervisor fix, jess service update, Model Lab fixes, and your parallel processing ideas. Good session tonight. 🤙