Just used CoWork to Audit my Books and Prepare my Schedule C Told Claude it was a CPA and that this was for a Realtor SoleP. Then it did a GAP analysis and cross referenced all charges and paper receipts by the last 4 digits of the account. ($20 plan was painful in the 4 hour block prison) (ChatGPT did the OCR work for shoebox of paper receipts / Claude tried and lost in this category) Last year 2024 it took me 40+ hours, this 2025 year 10ish hours (#winning)

what is your use case for the Box? RAM Amount?

OAuth openai-codex/gpt-5.4 has been my GoTo for operations Working on Coding outside of OC and then giving it REPOs for new applications. moving slowly. Next Week Ill have a 64GB QWEN 3.5 i7 Machine LLM for local coding calls (dedicated to coding)

GAP ----------- Here is a verified action plan based solely on confirmed OpenClaw config keys, Anthropic prompt caching docs, and OpenAI prompt caching docs. Every item here can be cross-referenced in official sources. OpenClaw Cost & Efficiency Action Plan (Claude / OpenAI models only) Priority 1 — Immediate Config Changes (Low Risk, High Impact) 1. Set a cheaper model for compaction Why: By default, compaction uses the same model as your main session. If you're running Claude Sonnet or GPT-5.x as your primary, every context compaction runs on that expensive model. Confirmed config key: agents.defaults.compaction.model json{ "agents": { "defaults": { "compaction": { "model": "anthropic/claude-haiku-3-5" } } } } For OpenAI setups, use "openai/gpt-5-mini" or equivalent. This is the only legitimate way to pin a cheaper model for compaction — it's a real, documented field. The BiClaw blog's summaryModel in a plugin does not exist; this key does. 2. Enable context pruning Why: OpenClaw keeps full tool results in context by default. Long browser/exec tool outputs re-read on every turn add up fast. contextPruning trims old tool result content before sending to the LLM without touching the disk history. Confirmed config key: agents.defaults.contextPruning json{ "agents": { "defaults": { "contextPruning": { "mode": "cache-ttl", "ttl": "1h", "keepLastAssistants": 3, "softTrimRatio": 0.3, "hardClearRatio": 0.5, "minPrunableToolChars": 50000, "softTrim": { "maxChars": 4000, "headChars": 1500, "tailChars": 1500 }, "hardClear": { "enabled": true, "placeholder": "[Old tool result content cleared]" }, "tools": { "deny": ["browser", "canvas"] } } } } } The tools.deny list exempts browser and canvas results from hard-clearing (since those are often large but needed for next-turn context). Adjust minPrunableToolChars upward if you want to be more conservative. 3. Set agents.defaults.timeoutSeconds to a sane value Why: The default is 600 seconds. A runaway or hung agent turn holds a slot for 10 minutes and burns tokens. For most conversational tasks, 120–180s is sufficient. Set longer only for explicit long-running tasks.

MD File for the chat (or for implementation of action steps 1-11)