Sensible AI, Coding, and Everything In-Between

We keep asking prompts to do the job of an operating system. This idea is about the real control surfaces in agentic software - schemas, tool permissions, approvals, workflow boundaries, and runtime policies - and why the best guardrails usually live outside the model.

The core training objective behind LLMs sounds almost embarrassingly simple. So why does it produce systems that can reason, translate, code, and chat? I want to build the intuition from the ground up without turning it into a graduate seminar.

Beneath all the hype, every large language model runs the same tiny trick over and over: predict the next token. I want to unpack that loop visually and intuitively so the whole machine suddenly makes sense.

Agents don't just need more context. They need memory that survives the prompt. I want to explore what useful memory actually looks like in agentic systems, why most implementations are glorified note-taking, and how to build something that helps an agent get meaningfully better over time.

In a field that reinvents itself every six months, Prompty v2 does something almost suspiciously sane: it keeps the prompt format and upgrades the runtime. The result is a two-year-old .prompty file format that now speaks tools, tracing, structured output, streaming, and Python, TypeScript, C#, and Rust.

If you understand basic neural nets but still feel like transformers are magic, this is the bridge. I want to connect the math from perceptrons, hidden layers, and representation learning to the ideas that make LLMs work.