An AI agent that improves itself, built in the open.
The Tinker Zone is the workshop log of TinkerClaw: the open-source code, the research papers behind it, and notes from running it around the clock.
Newest posts
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SEAHORSE: What Actually Makes an Agent Code Well
File leases, a bible that asserts shape not liveness, four premises, and the harness. v1.0.
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MAESTRO: What Orchestration Actually Buys — A Taxonomy of Latency, Capability and Supply
Orchestration is a scheduler, not an intelligence multiplier. Latency vs capability vs supply — three axes, three cost structures.
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AI Analysis — the Smartness × Cost Map and the Model Dossier
A live, machine-maintained map of every model we can reach: intelligence against what a token actually costs, plus a dossier of what each model…
TinkerClaw
Our open-source fork of OpenClaw, and the agent most of this site is about. The code is free to read, run and fork.
- Shows what every token costs, as it happens
- Matches each task to a model and an effort level
- Keeps its memory and personality across restarts
- Runs saved playbooks, and nightly jobs that improve their own prompts
Articles about TinkerClaw
How it works, one part at a time.
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AI Analysis — the Smartness × Cost Map and the Model Dossier
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Every Token, Every Dollar: The Tinker Command Center
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Pick the Right Brain for Every Task: The Effort × Model Slider
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The Recipe Book: TinkerClaw’s Playbook Library
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Watching an Agent Think: The EEG View
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The TinkerClaw Brain — we mapped agent capabilities onto the human brain, then built it
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TinkerClaw: We Forked OpenClaw. Here’s What We Changed and Why.
Research papers: Building Jarvis
All papersThe ideas behind TinkerClaw, one paper each: memory, reflexes, personality, security, orchestration and cost.
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SEAHORSE: What Actually Makes an Agent Code Well
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MAESTRO: What Orchestration Actually Buys — A Taxonomy of Latency, Capability and Supply
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STRIATUM: Learned Personality Modulation — Tuning an Agent’s Behaviour from Feedback, Not Static Prompt Text
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BROCA: Giving Agent Recipes a Grammar — A Gradual Type System and Combinator Algebra for Self-Composing Workflows
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SALIENCE: The Death of Fixed Thresholds, the Pyramid of Significance, and Cheap Traversal as the Basis of Next-Generation Vibe Programming
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Instant Recall: A Pre-Computed Concept Index for O(1) Memory Retrieval in Persistent AI Agents
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Fractal Reasoning: Multi-Resolution Memory and Self-Similar Metacognition for LLM Agents
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Identity Persistence: Keeping an LLM Agent’s Personality Stable Across Sessions, Model Swaps, and Restarts
Field notes from running an agent 24/7
Browse all postsWhat happens when an AI agent never switches off: the bills, the crashes, and the lessons we wrote down.
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Running an AI Agent 24/7: What Nobody Tells You
75 crashes, 45 wrong messages, and 138 restart loops. What actually happens when you leave an AI agent running around the clock.
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How to Track AI Token Costs in Real Time
See where every AI token goes — in real time, not on next month’s invoice. Practical strategies that cut our daily costs by 60%.
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How to Set Up Self-Improving AI Cron Jobs
The META pattern: AI cron jobs that rewrite their own prompts after each run. Day 1 mediocre, Day 30 expert. No human intervention.