Intermediate

A practical workflow model for Estonian companies working across Estonian, English, Russian, Finnish, and other customer languages without losing tone, terminology, privacy, or accountability.

Human review is not a vague safety blanket. A practical guide to deciding what humans approve, sample, audit, escalate, or never delegate in AI workflows.

Evals — systematic measurement of AI output quality — are usually treated as an engineering concern. But every team running AI workflows needs them, and the basics are accessible without code. The how-to.

A practical AI sales stack that handles research, personalization, sequencing, and follow-up — without becoming the spam everyone deletes. The architecture, the tools, the prompts, and the guardrails that separate effective from annoying.

A practical, end-to-end AI marketing stack for content, SEO, and social — the tools, the workflows, the prompts, and the discipline that separates real automation from spam. Built for teams of one to small teams, not enterprise.

Most teams fail at AI adoption not because the technology doesn't work, but because the rollout doesn't. A practical playbook: how to pick use cases, train people, set policy, measure impact, and avoid the common failures.

Using one model for everything is the rookie move. Production AI systems route different requests to different models — and save 60-90% on cost while improving quality. The patterns, the routing logic, and the trade-offs.

Browser agents and computer-use AI promise to operate your computer the way you do. The reality in 2026 is more useful and more limited than the demos suggest. A grounded guide to what works, what doesn't, and where to apply them.

An automated daily briefing or newsletter that arrives in your inbox, with content actually worth reading, is one of the highest-leverage AI builds. The architecture, the prompts, and the discipline that makes it sustainable.

Running AI locally has matured. With Ollama or LM Studio and a modern Mac, you can run capable models offline, free, and private. What works, what doesn't, and the use cases that actually benefit.

Non-developers can now build real software with AI. A practical guide to using Cursor and Claude Code as a non-engineer — what's realistic, what's not, and the discipline that separates useful tools from broken ones.

MCP is the new standard for connecting AI to your tools. You don't need to write one to benefit. A non-engineer's guide to what MCP is, which servers to install, and what becomes possible once your AI can actually act.

Most RAG implementations work poorly because they get three things wrong. A practical guide to chunking documents, reranking results, and combining keyword with semantic search — without becoming a search engineer.

Build your own document-grounded chat in under an hour, with no code. The three no-code options worth using in 2026, the tradeoffs, and the patterns that distinguish a useful RAG from a frustrating one.

Connecting AI to your real tools — email, calendar, CRM — is the productivity unlock and the risk. A practical guide to the integrations that work in 2026, the patterns that are safe, and the lines you should not cross.

A realistic design for an AI customer support agent that resolves the common cases, escalates the hard ones, and doesn't make the kind of mistake that ends up on Hacker News. The architecture, the prompts, the guardrails.

A complete walk-through of building a real AI agent in n8n — one that triages incoming leads, enriches them, scores them, and routes them. Every node, every prompt, every gotcha.

An honest comparison of the three main automation platforms for AI workflows in 2026. What each is good at, where each breaks, and the decision rules for choosing without regret.

Reasoning models are not fast models with extra steps. They reward different prompting, ignore some conventional patterns, and have their own pitfalls. A practical guide to working with them well.

Three reasoning techniques that genuinely improve AI output on hard problems — and the cost-benefit math of using them. With concrete prompts, side-by-side comparisons, and the gotchas modern reasoning models introduce.

Most people use one AI tool for everything. Intermediate users orchestrate four or five — each for the part it does best. A practical guide to building multi-tool workflows that compound.

Once you use AI seriously, you write the same kinds of prompts over and over. A practical system for building, organising, and sharing a prompt library — what to capture, how to version, and what infrastructure to use.