301cc75de9
3 browser-native chat surfaces (atlas-chat / atlas-hud / atlas-command, no build step) + 11 Python automation scripts (cluster, tray, finance, reminders, autonomy loops) + 2 patterns documented (cluster-then-polish, anti-impulse R1-R10) MIT licensed, anonymized from production stack.
71 lines
2.8 KiB
Markdown
71 lines
2.8 KiB
Markdown
# Pattern: Cluster-then-Polish
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> Local 7B-8B models generate volume. Claude / human polishes signal.
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## Problem
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Open-source LLMs (qwen2.5:7b, llama3.1:8b, hermes3:8b on consumer hardware) are good at:
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- Classification / categorization
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- Embeddings / RAG retrieval
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- Code completion (within trained languages)
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- Structured output (JSON, tables)
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- Triage of inbox / queues at volume
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They are **bad** at:
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- Narrative copywriting for clients
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- Strategic analysis without grounded data
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- Tone / voice consistency
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- Factual claims — they will hallucinate numbers, dates, names
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- Anything where wrong output sent externally has cost
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## Pattern
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1. Cluster generates a first draft with explicit `polish_status: pending` YAML frontmatter
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2. Output saved as Markdown file with full metadata (node, model, elapsed_s, prompt, draft_id)
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3. Polish review queue tracked in `~/.config/atlas/drafts/_registry.json`
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4. Operator OR a stronger model (Claude, GPT-4) reviews + edits + flips frontmatter to `polish_status: polished`
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5. Only `polished` items are usable externally (sent to clients, published, etc.)
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## Reference implementation
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`scripts/cluster_then_polish.py` — simple wrapper around any Ollama-compatible HTTP API.
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```python
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from cluster_then_polish import draft, save_draft
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d = draft(
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prompt="Schrijf cold-WhatsApp voor Tilburg lunchroom",
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task='chat',
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node_pref='pc',
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purpose='cold_outreach',
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)
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save_draft(d, '~/.config/atlas/drafts/lunchroom_outreach_001.md')
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# operator reviews -> mark_polished(d['draft_id'])
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```
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## Real-world data (production)
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After 7.5 hours of perpetual loop across 3 nodes:
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- 1172 autonomy outputs generated
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- ~5-10% immediately useful (operator polishes light)
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- ~30% spark a useful follow-up question
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- ~60% are noise (still saved for pattern detection later)
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- 0% should be sent externally without review
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The pattern is not "less work" — it is "different work". Operator becomes editor, not writer. The cluster does the cold-start text generation that's the worst part of writing.
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## When NOT to use this pattern
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- When you have Claude API access and the task is small enough to just call Claude directly
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- When the output must be perfect (legal documents, financial figures, contracts)
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- When you don't have a polish step set up — generating drafts you never review is worse than no drafts
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## Anti-pattern to avoid
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Don't trust cluster output because it "looks confident". 7B models will produce well-formatted Dutch with hallucinated company names, wrong euro amounts, invented words. Production example:
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> qwen2.5:7b output: "Bisogno's marktshare neemt toe met €4.578 miljoen, Inbrengking bleef stabiel"
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>
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> Reality: revenue is €4,578 (not millions), "Inbrengking" is not a Dutch word, "marktshare" was confused with revenue concentration percentage.
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The `polish_status: pending` flag exists because of this exact failure mode.
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