No. 01Systems~7 min read
Why a capable model isn’t a working system
The distance between a promising demo and daily operations, and the parts that actually close it.
The argument +
- A model answers once. A system has to keep answering, on your data, every day.
- The missing parts: context, memory, tools, evaluation and oversight.
- Where most pilots stall, and how to plan past that point.
No. 02Reliability~6 min read
Evaluations before opinions
How we decide an AI system truly works, and prove it keeps working once it is live.
The argument +
- Agree on what “working” means before anything is built.
- Test on your real cases, not on public benchmarks.
- Keep measuring after launch, because data and models drift.
No. 03Responsible AI~5 min read
Keeping people in the loop on purpose
Designing human oversight in from the start. A deliberate choice, not something bolted on at the end.
The argument +
- Where a person must approve, and where they shouldn’t have to.
- Making the review step fast, so it never becomes a rubber stamp.
- Who is accountable when the system acts.
