The AI tooling landscape shifted. Instead of configuring rules and triggers, the latest tools watch how you work and propose the automations. But the category is noisy, and most tools over-promise.
Here's a framework for evaluating what's real.
Three categories worth knowing
Screen-aware workflow tools
These record your screen plus system events (file changes, terminal commands, app switches), then extract structured workflows from the session.
Best for: documenting onboarding processes, creating SOPs from expert workflows, building a reusable knowledge base.
What to evaluate: Does it capture system events alongside video? Can it distinguish signal from noise in long sessions? Does it produce reusable output — not just a summary?
Context-learning coding assistants
These observe your coding patterns over time and adapt suggestions to your specific conventions. They build persistent context from your interactions, learning which suggestions you accept, reject, or modify.
Best for: developers who want their AI to understand project-specific patterns, naming conventions, and architectural decisions.
What to evaluate: Does context persist across sessions? Can it learn project-specific conventions? Does it improve measurably over time — or is day 30 identical to day 1?
Hybrid workflow + coding tools
These combine session recording with persistent coding context, creating a compounding knowledge base. You get documentation from each session and an improving AI assistant.
Best for: solo developers and small teams who want both.
Four things that matter
Privacy and data handling. Your workflow recordings contain API keys, credentials, proprietary code. Evaluate: what processes locally? What goes to cloud APIs? Can you redact sensitive content?
Compounding value. The whole point of workflow learning is that it gets better with use. If the tool is equally useful on day 1 and day 30, it's not learning — it's just a fancy recorder.
Integration cost. The best tools are invisible. They observe your existing workflow without requiring a different IDE, terminal, or browser. If the tool demands you change how you work, it's solving the wrong problem.
Output format. A tool that produces beautiful summaries but not reusable automations is a documentation tool, not an automation tool. Know which one you need.
Common traps
Evaluating on day 1. AI workflow tools need data. Give any tool 5–10 sessions before judging.
Choosing cloud-only for sensitive work. If you work with proprietary code or client data, local-first is non-negotiable.
Confusing features with categories. A coding assistant that added "memory" in a feature update is not the same as a tool built around behavioral learning from the ground up.
Where Distill fits
We're building in the hybrid category: real-time screen and workflow recording, structured skill extraction per session, compounding skill library over time. Everything processes locally on your Mac. After multiple sessions, the system proposes rules and detects drift from your patterns.
The best test: install a tool, use it for two weeks, then ask yourself one question — does it know something about my workflow that it couldn't have known on day one? If yes, keep it.