# Session: 2026-03-04 23:42:43 UTC

- **Session Key**: agent:main:main
- **Session ID**: 5d2aef20-f1b6-4038-9d51-2af2db5e4a3c
- **Source**: telegram

## Conversation Summary

assistant: Now let me replace the settings panel with the deep version, add skills library, and improve the board cards with priority editing. I'll do this as a targeted replacement of the settings section:
assistant: Now let me add collapsible sidebar sections and the Skills link. Let me update the sidebar to make sections collapsible:
assistant: Now add a Skills link in the sidebar:
assistant: Now add a priority dropdown to the board card hotfix. Let me also add it to the original renderCard:
assistant: Now let me add a priority change function to the board and wire it into the card rendering. I need to add a `changePriority` function:
assistant: Now update the hotfix board rendering to include the priority selector — find the card rendering in the hotfix script:
assistant: Let me add priority selectors to both card renders:
assistant: Now let me commit, restart, and test:
assistant: Let me log everything and update memory:
assistant: All done. Here's what I built:

**Settings Panel v2** (⚙️ in sidebar → tabbed overlay):
- **Theme tab**: Dark/Light/Midnight/Forest + accent color picker
- **Features tab**: Toggle switches for all 8 features with descriptions
- **Agents tab** (the big one):
  - Per-agent DND mode (timed with countdown or indefinite)
  - Cadence controls: heartbeat interval, max sessions/hour, cooldown between sessions
  - Context window: max tokens, compact-at threshold, clear-at threshold — with visual bar
  - Token budget: $/M input & output pricing, max daily spend, max tokens/hr, max tokens/day — with enable/enforce toggle
  - Core toggles: nudges, cron
  - Scales automatically — add an agent to config, it appears
- **Notifications tab**: Max nudges/hour, quiet hours
- **System tab**: Status overview + link to Skills Library

**Skills Library** (🧰 in sidebar):
- Shows all 60 installed skills with descriptions
- Filterable search
- Tagged by source (system vs workspace)

**Board improvements**:
- Priority dropdown on every card (change priority inline)
- Column move dropdown (move cards between columns inline)

**Collapsible sidebar**:
- Threads and Library sections collapsed by default
- Click headers to expand/collapse — keeps the sidebar manageable

**Documented for next round**:
- Smart alert triage (tiny model gatekeeper for nudge decisions)
- Agent conversation viewer within the ecosystem
- Voice/video in comms
- Mini Qwen models for Jess's memory features
- Resource/knowledge library

Try it from your phone — should all be live at the usual URLs. The Settings → Agents tab is where the deep controls live.
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "3359",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Wed 2026-03-04 18:37 EST"
}
```

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

So here’s an additional area I think that would be really interesting to add to the ability of our agents as well as yourself and tell me like this one will be more of like hey let’s think this through and like figure out based on what each agent is capable of and their strong suits if you know if and what would be practical, but maybe some additional ability tools like not just tools, but like full applications that they can work within to get things done so like three modeling environments with tools on how to model and ways of going forward with that on the Sakana AI researcher set that up properly and so they know how to use it in one to use it also a computer use tool or app or whatever that you know how to use and no one to use it in our free rain to use it, but allows them to like you know see videos allows them to actually control the web and computer like a human does so some form of interface better then the current openclaw one like a cursor and vision and you know that way they can actually go on a website sign up so that they can actually make like Gmail and all this sort of stuff and figure out a way that this all works together so like so that it’s seamless so like you know that you have our messaging platform with our Jireh, which is great then this all has to cohesively work together, not like separate discrete things, but everything should be fluid and work together with all with the goal of self improvement and autonomy and they’re making own decisions and improving decision-making and improving performance as you go and growing from mistakes, I think a big thing is also being able to determine where each agent week spot is so they can determine that and how we can improve each agent and like context windows, making unlimited context window like that stuff where we can like, really walk in and all running local to the big goal this should cost money. None of this. They should all run locally on the DGX spark.

I think there’s a lot of value in qwen 3.5 mini models working with the medium size ones they now have a the following models I believe these are the right sizes .8b 2b 4b 9b I believe they all are vision capable however I’m sure they are not great at everything. They may be capable enough for some some like as tools in tasks for whether it’s vision or it’s button clicking you may be able to get something out of them just an idea.

So I know this a lot, but one additional piece would be a really cool thing to be able to see each agents chain of thought as they’re thinking and kind of see updates on what they’re doing so like kind of like a state machine would work where it’s like but like maybe like have some really small model just kind of like giving like a one sentence or a couple word explanation of what’s currently happening you can imagine how like a blinking light showing like hey doing this and they’re working on this.
assistant: This is a big vision and you're right to want to think it through before jumping in. Let me give you an honest assessment of what's practical on the Spark, what's within reach, and a concrete roadmap.
assistant: Here's my honest assessment. I've inventoried what we actually have on the Spark and what each model can do.

---

**What we're working with:**
- 128GB unified RAM, ~41GB available right now
- Qwen3.5: 0.8b (1GB), 2b (2.7GB), 4b (3.4GB), 35b (23GB)
- **The 4b has vision + tools + thinking** — confirmed. Likely the smaller ones do too.
- Two dedicated vision models: qwen3-vl:8b, qwen2.5vl:7b
- Playwright + Chromium already installed (ARM64)
- nomic-embed-text for RAG/embeddings
- Everything local, zero API cost

---

Here's how I'd structure this as a practical roadmap, grouped by difficulty and value:

### 🟢 Phase 1: Agent Live Status Dashboard (1-2 sessions)
**The "blinking light" idea — this is very doable and immediately valuable.**

- Each agent writes a status line to a shared state file (`/tmp/agent-status.json`) on every action
- A hub page polls it and shows live cards per agent: current task, thinking state, last action
- The 0.8b model runs as a "narrator" — takes the raw status and generates a 1-sentence human summary
- Cost: ~1GB RAM for the narrator model, trivial

**Architecture:**
```
Agent does work → writes status → 0.8b summarizes → dashboard displays
```

### 🟡 Phase 2: Vision + Computer Use Pipeline (2-3 sessions)
**Use qwen3.5:4b as an "eyes" model — screenshot → understand → act.**

- Playwright takes screenshots of web pages
- qwen3.5:4b (vision) analyzes: "what's on screen, where are buttons, what should I click"
- Action executor clicks/types based on the vision model's output
- This creates a **local computer use agent** — no Claude API needed

**What it can do:**
- Navigate websites, fill forms, sign up for accounts
- Read/interpret web UIs the agents can't currently see
- Process visual content (images, charts, dashboards)

**Practical limits:** The 4b model won't be as good as Claude's computer use — it'll be better at structured tasks (click the login button, fill this form) than creative navigation. But for repetitive web tasks, it'll work.

**What I'd chain:**
- 4b for fast vision/classification (1-2 sec per screenshot)
- 8b vision model for harder visual reasoning
- 35b for planning/decision-making about what to do next

### 🟡 Phase 3: Agent Weakness Detection + Self-Improvement (2 sessions)
**Build an evaluation framework that each agent runs periodically.**

- Test battery: reasoning, coding, memory recall, tool use, speed
- Run with each model, score results, store in a profile
- Agents can see their own weaknesses and route tasks accordingly
- "I'm weak at math → route math to Codex" type routing

**Combined with the config system we just built** — the weakness data feeds into the Settings/Agent panel so you can see strengths/weaknesses visually.

### 🟡 Phase 4: Sakana AI Scientist (1-2 sessions to set up)
**This is a research automation pipeline — agents design experiments, run them, write papers.**

- Clone the Sakana AI-Scientist repo
- Wire it to use local models (35b for reasoning, 4b for fast iteration)
- Point it at nanoGPT or other research targets you've worked on before
- The agents learn to use it through a skill file

### 🔴 Phase 5: Extended Context / "Unlimited" Context Windows (ongoing)
**This is the hardest problem but there are practical solutions:**

- **RAG pipeline**: nomic-embed-text (already installed) → chunk documents → retrieve relevant pieces → feed to model within its window
- **Hierarchical summarization**: 0.8b/2b summarize chunks → 4b/35b reasons over summaries
- **Conversation compaction**: Before hitting context limit, small model summarizes conversation so far, clears window, continues with summary
- **The key insight**: You don't need infinite context if you have good retrieval + summarization. The small models are perfect for this.

### 🔴 Phase 6: 3D Modeling (research needed)
**Most uncertain, but possible paths:**

- Blender has a Python API and might run on ARM64 Linux
- Text-to-3D local models exist but are GPU-hungry
- More realistic: use agents to write 3D code (Three.js, Blender scripts) that the vision models verify
- This one I'd research more before committing

---

### The Qwen 3.5 Mini Model Strategy

Here's how I'd deploy them as a tool chain:

| Model | RAM | Role | Use Cases |
|-------|-----|------|-----------|
| 0.8b | 1GB | Narrator/classifier | Status summaries, message routing, "should I alert?" decisions |
| 2b | 2.7GB | Fast tool worker | Summarization, simple extraction, form filling |
| 4b | 3.4GB | Vision + thinking | Screenshot analysis, UI navigation, visual QA |
| 35b | 23GB | Deep reasoning | Planning, code review, complex decisions |

**Total for running 0.8b + 2b + 4b concurrently: ~7GB** — easily fits alongside the 35b.

The key is making them work as a **pipeline, not competitors**. Small model triages → medium model executes → large model reviews.

---

### Integration with What We Have

Everything plugs into the ecosystem we already built:
- **Agent Comms** = the messaging backbone (already working)
- **Board** = task tracking (already working)
- **Settings panel** = control plane for all agents (just built)
- **Status dashboard** = new, shows live agent activity
- **Vision pipeline** = new, gives agents eyes
- **Tool chain** = small models as workers for bigger ones

---

**My recommendation for order:** Start with Phase 1 (status dashboard) because it gives you immediate visibility into what agents are doing, and it's the foundation for everything else. Then Phase 2 (vision/computer use) because it unlocks the most new capabilities. Phase 3 (weakness detection) can run alongside.

Want me to start building Phase 1 now? Or would you rather I write this whole plan into a proper document you can review and we tackle it piece by piece?
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "3365",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Wed 2026-03-04 18:41 EST"
}
```

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

Yeah, is there a way you could compact this all very clear without losing detail about what this is and then I’ll restart your sessions context window so that you can start clean, lean and not burn too many tokens
assistant: Done. Everything's in `ROADMAP.md` — full detail, zero fluff. Next session I'll read that and know exactly where we are. Go ahead and reset, I'll pick up clean.