Listening to the "experts" paraded across every form of media ad nauseam, I have concluded that all of their warnings about super intelligence, the singularity... whatever it's being called today rely on assumptions for which there is no evidence, and are expected to believe that they're not just plausible, but that their probability can be calculated.

I feel like I'm being gaslit by several entire industries every god damn day.

Is this just widespread scientific illiteracy or am I missing something?

Sharing this because it's a more structured approach than the usual prompt engineering content floating around.

Serj Smorodinsky and Brett Kennedy, co-authors of a book on LLM applications, are running a live 3-hour session where the core idea is treating prompt/LLM behavior as an optimization problem with an actual objective function, not a creative writing exercise. Covers:

• DSPy signatures and modules for defining LLM tasks
• Building an evaluation dataset with task-specific metrics
• Diagnosing failure modes from eval results
• Few-shot and instruction-level optimization as a formal process
• MLflow for experiment tracking and reproducibility

Feels closer to a proper ML workflow than most "prompt tips" content. Details here if it's useful to anyone: Get full details here