Deep dives into production AI systems. Architecture decisions, token economics, failure modes, and the engineering behind every build.
The full architecture behind our n8n + Claude onboarding automation. 14 nodes, $0.08 per run, 99.7% uptime. Every error handler, every fallback, every edge case — documented. This is the system running in the terminal mockup on our homepage.
Read the full build log →Most people guess at their API costs. Here's how to calculate exact token spend per workflow run, per client, and per month — with a spreadsheet template you can use today.
Read more →Feed in a video transcript, get back an X thread, standalone tweets, YouTube description, newsletter block, and TikTok captions — all in your voice. Here's how I built it and what broke.
Read more →I ran the same 10 prompts through both models 50 times each. Measured: JSON compliance rate, hallucination frequency, token cost per correct output, and latency. The results aren't what you'd expect.
Read more →Your workflow will fail. The question is whether it fails gracefully or takes your client's trust with it. Five error handling patterns I use in every production n8n workflow.
Read more →A Claude Code agent that monitors 5 competitors, scrapes public pages, analyzes changes, and sends a structured Telegram digest every Monday. Total cost: $0.23/run.
Read more →I've sold AI automations from $500 to $8,000. Here's the pricing framework: how to calculate COGS, set margins, and position value. Includes three real client proposals.
Read more →This week: deployed the monitoring system, hit a rate limit bug in the competitive research agent, and the content pipeline saved 6 hours. Full transparency on wins and losses.
Read more →System prompts aren't magic strings. They're architecture. Here's how to structure them for consistent, production-grade output — with templates for 5 common use cases.
Read more →A meta-system that monitors all your other automations. Checks health, tracks token cost spikes, and alerts via Telegram when something needs attention. The production reliability layer nobody builds.
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