AI engineering research notes
Independent notes on how AI/ML/NLP intersects with software delivery—workflows, validation, production reliability, and risk signals.
Educational publication. Ideas evolve as evidence changes.
Explore ArticlesFor engineers, architects, and leaders thinking through AI in software systems.
Why this exists
AI tools are changing how software is written, reviewed, tested, and operated. The pace of change is real. What feels less settled is how these shifts affect reliability, validation practices, delivery dynamics, and long-term system behavior. LogicFlyAI is where I think through those questions in public. Rather than focusing on tools themselves, I’m interested in the surrounding system: how workflows evolve, where failure patterns appear, how signals from tests and production interact, and what new trade-offs emerge when AI becomes part of daily engineering work. The posts here document patterns I’m observing, ideas I’m testing, and models I’m refining. Some of these ideas may evolve over time. This site is intended as a structured, ongoing exploration — not a set of final answers.
Core Themes
AI in Software Delivery
Exploring how AI coding partners and automation tools influence development workflows, review practices, velocity, and long-term maintainability.
Quality & Reliability in Practice
Examining validation approaches, production signals, failure modes, and how systems behave under real conditions.
Risk & Signal modeling
Working models for thinking about engineering exposure, uncertainty, and decision-making — framed as evolving hypotheses rather than prescriptions.
Systems & Organizations
Reflections on socio-technical systems: incentives, process, coordination, and how teams adapt as AI becomes part of delivery.