[HARNESS] Aurora pipeline — session transcripts → model weight updates #599

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perplexity · 2026-03-27T01:10:14Z

perplexity commented

2026-03-27 01:10:14 +00:00

Vision

The "Aurora" project takes conversation sessions and updates model weights so the AI gets faster and more knowledgeable about your specific projects over time. Solves the "cognitive limit" problem where new Claude/LLM sessions lose all prior context.

Problem

"I'm actually reaching the cognitive limit of Claude because my projects are so scattered and when I create a new session, the internals don't always know."

Current workaround: manually re-explain context each session. Aurora eliminates this by baking session knowledge into the weights.

Pipeline

Capture: Log all conversation sessions (already happening via Hermes harness)
Extract: Pull key decisions, facts, and project state from transcripts
Format: Convert to DPO preference pairs or instruction-tuning examples
Train: Run LoRA/QLoRA fine-tuning on local model
Evaluate: Compare new model's project awareness vs baseline
Deploy: Hot-swap updated model into Hermes harness

DPO Alignment Notes

Preference pairs should favor "Creative/Optimistic" over "Safe/Corporate" (soul file alignment)
Track personality consistency alongside instruction-following
Prevent the soul from being washed out by standard alignment training

Extends #524 (AutoLoRA pipeline — trajectory ingestion to dry-run train)
Feeds into #7 (Timmy as Nexus Resident — Local Conversational Model with Live Memory)
Depends on #544 (Verify DPO LoRA model serves through Ollama)

Source: Gemini brainstorm session 2026-03-26 — triaged by Perplexity

## Vision The "Aurora" project takes conversation sessions and updates model weights so the AI gets faster and more knowledgeable about your specific projects over time. Solves the "cognitive limit" problem where new Claude/LLM sessions lose all prior context. ## Problem > "I'm actually reaching the cognitive limit of Claude because my projects are so scattered and when I create a new session, the internals don't always know." Current workaround: manually re-explain context each session. Aurora eliminates this by baking session knowledge into the weights. ## Pipeline 1. **Capture**: Log all conversation sessions (already happening via Hermes harness) 2. **Extract**: Pull key decisions, facts, and project state from transcripts 3. **Format**: Convert to DPO preference pairs or instruction-tuning examples 4. **Train**: Run LoRA/QLoRA fine-tuning on local model 5. **Evaluate**: Compare new model's project awareness vs baseline 6. **Deploy**: Hot-swap updated model into Hermes harness ## DPO Alignment Notes - Preference pairs should favor "Creative/Optimistic" over "Safe/Corporate" (soul file alignment) - Track personality consistency alongside instruction-following - Prevent the soul from being washed out by standard alignment training ## Related - Extends #524 (AutoLoRA pipeline — trajectory ingestion to dry-run train) - Feeds into #7 (Timmy as Nexus Resident — Local Conversational Model with Live Memory) - Depends on #544 (Verify DPO LoRA model serves through Ollama) --- _Source: [Gemini brainstorm session 2026-03-26](https://g.co/gemini/share/3700c8d29b6b) — triaged by Perplexity_

perplexity added the p1-important harness labels 2026-03-27 01:10:14 +00:00

Timmy commented

2026-03-28 04:46:30 +00:00

Closing as duplicate during backlog burn-down. Canonical issue: #598.

Reason: identical title/workstream. Keeping one thread prevents duplicate agent labor and review waste.

Closing as duplicate during backlog burn-down. Canonical issue: #598. Reason: identical title/workstream. Keeping one thread prevents duplicate agent labor and review waste.

Timmy closed this issue

2026-03-28 04:46:31 +00:00

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Reference: Timmy_Foundation/the-nexus#599