A developer frustrated by AI industry jargon and "AI washing" built an open-source AI Dictionary structured to serve both non-technical stakeholders and engineers, complete with Python code examples.
The resource emphasizes rigor in regulated domains like healthcare and legal technology, where precise definitions matter for compliance and safe deployment.
What happened
A developer created an open-source AI Dictionary (https://alexnubla.github.io/ai-dictionary/) designed to provide rigorous definitions of AI terms, structured with plain-English explanations for stakeholders, technical definitions for engineers, and Python code examples.
Why it matters
The creator observed widespread "AI washing"—people using terms like "Agentic AI" or "Vibe Coding" without understanding the mechanics—and built the dictionary to address this gap. The resource includes dedicated sections on Healthcare AI and Legal AI, reflecting the author's background in healthcare and compliance, since deploying AI in regulated fields requires rigor rather than marketing hype.
What to watch
The dictionary is open-source and publicly available online, including specialized categories like Digital Biomarkers, In Silico Trials for healthcare, and TAR (Technology-Assisted Review) and Algorithmic Risk Assessment for legal applications.
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The article captures frustration within the AI community over imprecise language and marketing claims that obscure technical reality. Terms like "Agentic AI" and "Vibe Coding" circulate in industry discourse without clear definitions, creating confusion among stakeholders trying to understand and deploy these technologies. The creator's solution—a structured, open-source reference—reflects a broader need for clarity in how AI concepts are communicated across different audiences: non-technical business leaders need intuitive explanations, while engineers require formal technical precision. By grounding each entry in code examples, the dictionary bridges this gap and ensures definitions are verifiable rather than aspirational. The emphasis on regulated domains (healthcare and legal) underscores why rigor matters: in these fields, vague or incorrect terminology can have real compliance and safety consequences, making a shared, vetted lexicon a practical tool rather than just an educational one.
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