Field Notes publishes original long-form writing from the engineers, researchers, operators, and analysts who build and deploy autonomous intelligence systems in mission-critical environments. Not press releases repackaged as thought leadership. Technical observations, operational analyses, research findings, and honest assessments — by practitioners, for practitioners.
Every piece must contain a specific, substantiated claim or observation useful to a reader with operational responsibility. We ask three questions of every submission: Does this contain specific, verifiable information? Does this treat the reader as an intelligent professional who can handle nuance? Will a reader who disagrees find the evidence and reasoning presented in good faith? If the answer is no, it goes back for revision.
The instinct to solve latency problems with faster hardware is understandable but insufficient. The latency gains from architectural decisions dwarf the gains available from hardware upgrades — and those decisions are irreversible once the system is built.
The gap between 'deployable in classified environments' (a marketing claim) and 'actually deployed in a classified environment' (an operational reality) is measured in dozens of specific technical decisions most vendors have never had to make.
Model accuracy on a holdout dataset is a necessary but deeply insufficient indicator of production performance. Three failure modes that only appear when the model meets real operational data — and how to design for them.
Most assessments of the EU AI Act focus on whether a use case is 'high-risk.' The more important question — what compliance with the high-risk provisions actually requires technically — gets far less attention.
The term zero-trust has been applied to enough products that it has begun to lose operational meaning. This piece attempts to restore precision — specifically in the context of AI systems that make autonomous decisions on sensitive operational data.
Most published cross-domain guidance was written for file transfers, not for continuous AI inference. The practical decisions that determine whether a CDS deployment will withstand operational scrutiny.