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AI harnesses: the missing layer between a smart model and useful work
An AI model is not an AI worker. The harness is the operating layer that gives it tools, memory, permissions, and proof.
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Learnings
Lessons, mistakes, patterns, and takeaways from real work with AI and automation.
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Scaling n8n with queue mode and Docker: architecture and example review
Main, workers, Redis and PostgreSQL: a guide to queue mode architecture. The runner configuration needs review before the examples are used.
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Prompt Caching: A Technical Guide to LLM Efficiency
Prompt caching can reduce the cost of reusing context, but the benefit depends on repeated input and each provider’s conditions.
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AI Guardrails: Why Your Chatbot Needs a Safety Net
Guardrails help limit dangerous responses and actions, but cannot guarantee a safe chatbot: layered controls, testing and oversight still matter.
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n8n 2.0: changes and context for the 2025 migration
A guide to the n8n 2.0 changes announced in December 2025. Check the documentation for your version before applying the examples.
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TOON vs JSON: token savings and limits of the original recommendation
The original TOON recommendation was qualified in a correction: saving tokens does not guarantee unchanged accuracy. Read both pieces before switching formats.
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n8n Introduces Project-Scoped Variables: A Game-Changer for Team Collaboration
Project-scoped variables separate configuration in n8n; this article explains their scope and how they differ from global variables.
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DeepSeek-OCR: The AI That Reads Too Much Into Everything
DeepSeek-OCR proposes compressing text into visual information, but fewer tokens must be weighed against the risk of lost accuracy.
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