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Artificial Intelligence

RAPTOR: a tree of summaries for querying large document collections

Conceptual illustration of documents arranged in groups and summary cards on a worktable

AI-generated conceptual cover. It evokes a document collection and its summaries; it does not represent the method’s architecture or experimental results.

Lately, I had been wondering how much code was being generated with AI worldwide and how many programmers still wrote everything by hand. Investigating those questions would mean looking in many places: surveys, studies of GitHub repositories, Stack Overflow posts, and company reports. Each source might contribute part of the story, but we would need to check what it actually measures and which conclusions we can draw from it.

Spec-Driven Development: giving AI agents memory and direction

Illustration of a specification connecting planning, code, tests, and human review

LLM agents write code at astonishing speed. They also forget at astonishing speed. A conversation may contain important decisions, yet those decisions disappear when the session, model, maintainer, or available context changes.

That is where Spec-Driven Development (SDD) comes in. The idea is simple: before asking for code, write down what we want to build, why, which boundaries it must respect, and how we will know it works. The specification stops being an ephemeral document and becomes a working contract that both the team and its agents can read.