AI and agents
Draft, transform, inspect, and automate work that already has a definition of done.
- AI agents
- Hermes
- GPT models
- Local AI models
Show the manual process, acceptance criteria, review points, and human stop line.
Start with the job. Demand the evidence. Name the failure cost. Keep only the systems that improve a real decision without hiding their maintenance.
Test bench // grouped by job
Draft, transform, inspect, and automate work that already has a definition of done.
Show the manual process, acceptance criteria, review points, and human stop line.
Publish owned assets and connect repeatable steps without hiding maintenance.
Show what can fail, who owns the correction, and how the workflow is reversed.
Observe discovery, reader behavior, and page fit without turning dashboards into outcomes.
Name the decision each metric can change—and the metrics that cannot prove value alone.
Carry a useful asset to the right audience and connect it to a relevant offer.
Show channel fit, commercial incentives, platform risk, and the owned asset underneath.
Permission slip // before the free trial
What exact step becomes easier, faster, safer, or more accurate?
How is the step performed now, and what is actually wrong with it?
What new failure, lock-in, cost, or review burden does the tool create?
What condition means the trial stops or the tool is removed?
Which evidence would justify keeping it after the novelty disappears?
Bench verdicts
The job is recurring, the evidence is clear, and the maintenance is acceptable.
The job is real, but the benefit or failure cost still needs a bounded trial.
The tool adds a dashboard, dependency, or bill without improving a decision.
Continue through the desk
The Duff Lab
The Duff Lab focuses on implementation playbooks, documented builds, and member discussions around real operating constraints.