Don’t Paste the AI: Keep Your Judgment
Pasting chatbot output makes your message feel generic and unowned. Use AI as a drafting partner, then extract the useful core, rewrite in your voice, and quote only with an explanation of why it fits.
Tag: #llms
Learning Complex Topics with LLMs by Building Simulations
Instead of asking an LLM for a plain explanation, you can learn complex topics by building a structured knowledge base, auditing it for consistency, and turning it into a simulation you can step through. The result is interactive, model-driven learning that exposes gaps instead of hiding them behind fluent prose.
LLMs can’t jump: the abductive gap in AI science
A growing AI-for-science argument says LLMs excel at induction (pattern learning) and deduction (deriving consequences) but miss abduction: inventing the premises that make new theories work. The proposed fix points to physically consistent, multimodal world models that can translate simulated dynamics into formal axioms.
Measuring “Real” Intelligence: What Cognitive Benchmarks Try to Fix
Recent DeepMind × Kaggle “cognitive abilities” benchmarks push beyond recall by scoring metacognition (monitoring + belief update) and inference-time learning (adapting inside a run). This post explains how uncertainty, calibration, abstention, and decision control reshape what it means for a model to “really” know.
Don’t You Mean Extinct? Evolving Software Engineering in the Age of LLMs
LLMs change how code gets produced, but they don’t remove the need for engineering judgment. This post shows how to learn LLMs, review AI-generated changes, write commit messages that preserve intent, and manage context switching so quality stays high.