Blog

Learnings

Lessons, mistakes, patterns, and takeaways from real work with AI and automation.

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.

LearningsMay 13, 202612 min read

LearningsJan 13, 20268 min read

Scaling n8n: queue mode, workers and task runners

How main, workers, Redis and external task runners fit together in n8n queue mode, with routing, configuration responsibilities and validation checks.

LearningsJan 8, 20263 min read

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.

LearningsDec 24, 20256 min read

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.

LearningsNov 27, 202510 min read

n8n 2.0: changes and context for the 2025 migration

The n8n 2.0 changes announced in December 2025: runner modes, node permissions and migration checks. Use the official guidance for your chosen version.

LearningsNov 11, 20253 min read

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.

LearningsNov 10, 20253 min read

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.

LearningsNov 8, 20253 min read

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.