Writing a sophisticated computer program often requires a lot of detailed knowledge. If we do this in Java, we need to know the syntax of the language, the wide range of libraries available to assist us in the work, the various tools required to verify and build our programs. If we do this in Python
Writing a sophisticated computer program often requires a lot of detailed knowledge. If we do this in Java, we need to know the syntax of the language, the wide range of libraries available to assist us in the work, the various tools required to verify and build our programs. If we do this in Python
Ever since AI coding assistance started gaining traction I’ve heard people say, “Oh, so at some point we might not even have to care about the code anymore, it’s like Assembly, we certainly don’t look at THAT anymore.” Recently, with enhanced agentic capabilities and the coinage of “vibe coding” there has been a new spike
Three articles I enjoyed yesterday: Stephen O’Grady talks about how Gen AI tools break two common constants with developer tools: they are willing to flit between Gen AI tools and they are willing to pay for them. This implies that it’s not too late for new tools to appear, and that enterprise adoption will be
Korny Sietsma has a great example of how using an LLM for coding is very helpful but with limitations… and a thoughtful general essay on why the hype and the immovable skeptics are both missing the train. While here, a professor of poetry ponders (gift link) on the value and limits of AI with writing:
It’s become a common habit for developers to give Large Language Models (LLMs) a persona when working with them. I describe four of them, a stubborn donkey, a genie, a slot machine, and Uriah Heep. more… Source link
Summary: a personal experience on how AI helped complete a non-trivial programming tasks. A difficult task This month I spent a whole week working on a really difficult algorithmic problem, made more difficult by complicated business rules, which I wasn’t able to get the client to agree to simplify. The problem was an API endpoint
OKRs—Objectives and Key Results—have become a popular goal-setting framework in tech and beyond. They were designed to bridge the gap between strategy and execution, promising focus, alignment, and accountability. But too often, they’ve turned into something else entirely: a quarterly ritual of checklists, dashboards, and performance metrics that smother the original intent. I’ve seen it
When people ask about the future of Generative AI in coding, what they often want to know is: Will there be a point where Large Language Models can autonomously generate and maintain a working software application? Will we be able to just author a natural language specification, hit “generate” and walk away, and AI will
Impact Intelligence is the title of my latest book. It explains how to improve awareness of the business impact of new initiatives. The Classic Enterprise thinks of the expenditure on these initiatives as discretionary spend. A software business might account for it as R&D expenditure. Written with a framing of investment governance, the book is