Key Insights from Training Session Discussion
On 3D vs 4D / Operational Reasoning
Operational reasoning is NOT a new dimension - it's still 3D format (
On Gateway/MCP Architecture for LARS
Chris's Point (Valid): We shouldn't dismiss building Gateway for LARS. We've built 15+ MCP servers together. One more that mirrors gateway isn't a mountain.
Architecture Vision: - Copy gateway pattern to AI server (/opt/mcp-servers/) - Point at Nexus (100.73.67.78) over network - LARS gets tool access without SSH - Train LARS to use gateway.run() calls - same pattern Claude uses
Training Example for Tool Use:
<think>
Chris wants me to create a KB entry. I need to use my gateway to access Nexus tools.
The kb.create tool is what I need. Let me structure the call...
</think>
<answer>
Creating KB entry now via gateway...
</answer>
On Scaling to Larger Models
What We Proved: - Transformers + PEFT + BitsAndBytes works (no Unsloth bugs) - 4-bit quantization on 7B fits easily on one 3090 - 3D format trains effectively
Scaling Path: - 7B working ✓ - 14B should work on single 3090 with 4-bit - 30B needs both GPUs (sharding) - test after second 3090 installed
On Scenarios vs Questions - CRITICAL INSIGHT
It's not just 'Who are you?' - it's task-based:
'I need you to check the Nexus database and create a KB on some work we did'
This requires: 1. Understanding request (parsing rambling, ADHD style) 2. Breaking it down (check database, then create KB) 3. Tool selection (what tools needed?) 4. Execution planning (what order?) 5. Reasoning through ambiguity (which database? what work?)
On Self-Learning Vision - The Progression
- Level 1 (done): LARS knows who it is, thinks before answering
- Level 2 (next): LARS can use tools, reason about which ones
- Level 3: LARS can read data, comprehend, summarize
- Level 4: LARS can learn from what it reads, update knowledge
- Level 5: LARS picks up cues, asks clarifying questions, adapts
Each level builds on previous. 3D format is foundation - thinking is visible and trainable.
On Failure
Chris's philosophy: 'Failure shows us what doesn't work so it leads us to what will work.'
If test works = possibility confirmed, keep going. If test fails = recipe needs adjustment, re-cook.
Either outcome is data for the pathway.