Foggy Software

  Or an AI Jargon Decoder Ring This post is a whirlwind tour of AI terminology and the key players. Given that this field moves at the speed of light, expect these facts to be stale by next Tuesday. Take any claims about "who’s winning" with a bucket of salt. Models, Tokens, and the Meaning of Life Consumer AI apps usually let you pick your "brain" by choosing a Model . These differ in Size —measured in Billions of Parameters (e.g., Llama 3B vs. 405B)—and Sophistication (e.g., GPT-4o vs. GPT-5 or Gemini 2.0). Generally, the smarter the model, the more it costs to run. While consumers pay a flat monthly "All You Can Eat" fee, the pros (Enterprise/API users) are billed per Token . A token is roughly a word’s worth of data. You’re metered on Input (what you ask), Output (what it says), and Thinking Tokens . These are the model's internal "reasoning" steps—essentially the AI talking to itself before giving you the final answer (which is, obvio...

  Or, The lessons of History. The Hype Curve (or by t he stuggey analyst name - technology adoption curve ) of adoption has become a cliche in technology circles, and like many other cliches, its anchored in experience and history. By Olga Tarkovskiy - Own work, CC BY-SA 3.0 , https://commons.wikimedia.org/w/index.php?curid=27546041 It inevitably raises questions about any new technology - is it pure hype? Where is it on the curve? Despite the fact that the curve itself is driven by history, discussions often rage for long times about how to answer these questions about any new technology wave. Obviously, the same is happening around AI. How can you tell that AI Adoption is maturing? What is a good parallel or pattern matching hint? That last big trend to flow across software development was Containerization . First there was Docker which was the new cool kid (parallel OpenAI ). Then there were competitors (ala R ancher ) and eventually standardization in the form of Open Con...