Coding agents are remarkably capable, and yet how do we treat them? By giving them stream-of-consciousness descriptions of half-baked ideas: vague feature requests, underspecified tickets, or maybe just good old vibes. We've got better implementation assistants than we ever could have imagined, but we're not always doing a great job thinking through what we want them to build. Would a lightweight spec document be so bad?
OpenSpec doesn't think so. Born from the recognition we've never really come to consensus on how to capture features—some do PRDs in Notion, some make epics in Jira, some still write user stories on index cards—it uses some Agent Skills and a CLI to guide you through structured feature exploration for greenfield and brownfield projects alike, producing detailed designs and todo lists precise enough to direct a coding agent even in the presence of some complexity. We'll trace how it emerged, see how it positions itself against approaches like Spec Kit and traditional design tooling, and examine the recent developments that have extended its capabilities.
The specification problem is older than agents, having bedeviled us since the first time someone handed a one of our grandparents a napkin sketch and called it a design doc. OpenSpec is surely not the final answer, but it's the right one for agentic engineering right now.
Up until early 2023, I regularly said AI would always have a bright future—and I didn't mean that as a compliment. Sure, deep learning had made us good at building very impressive classifiers in the decade or so prior, but for so long, human-like intelligence was just five years in the future—and that made me a skeptic. Things are different now, but how much skepticism is still warranted? What is it that we've got on our hands? What changes is modern AI bringing with it? Like with so many other questions, the answers are easier if we understand where we've come from.
Beginning with the Turing Test itself, the famous Dartmouth Conference of 1956, and the Perceptron of 1957, we'll trace decades of disappointment and broken promises as we tried to realize the true potential of computing, at the same time grasping for an understanding of what it means to be human. Taking a close look at various technologies along the way, we'll arrive in the 21st century, the revival of neural networks, the advent of the Transformer, and a revolutionary new technology category that has prompted oracles of weal and woe from our most optimistic and apocalyptic technology prophets.
Standing on the edge of the unknown, what elements of the 75-year-old promise have we realized? A close examination gives us a better view of our immediate future as technologists and insight into what it means to be human.
Build and test software written in Java and many other languages with Gradle, the open source project automation tool that’s getting a lot of attention. This concise introduction provides numerous code examples to help you explore Gradle, both as a build tool and as a complete solution for automating the compilation, test, and release process of simple and enterprise-level applications.
Discover how Gradle improves on the best ideas of Ant, Maven, and other build tools, with standards for developers who want them and lots of flexibility for those who prefer less structure.