You’re reading The Steady Beat, a weekly round-up of hand-picked articles and resources for people who make software products: designers, engineers, product managers, and organizational leaders. Brought to you by the team at Steady.
By the Numbers - Split Second
- 24 — The ancient Egyptians and Babylonians divided the day into 24 hours, likely influenced by their base-12 numbering system.
- 9,192,631,770 — The definition of a second in an atomic clock is “the duration of 9,192,631,770 periods of the radiation corresponding to the transition between the two hyperfine levels of the ground state of the cesium-133 atom.”
- 0.9 — Leap seconds are occasional one-second adjustments added to UTC to keep it in sync with Universal Time. These adjustments ensure that UTC remains within 0.9 seconds of Universal Time.
- 86,400 — The time system in JavaScript, as defined by the ECMAScript standard, is based on POSIX time. This approach treats every day as having exactly 86,400 seconds (24 hours), ignoring both leap seconds and astronomical variations in the Earth’s rotation.
— TimeTime, 7m, #javascript, #timekeeping, #programming
Agile and the Product Model
The Product Model, which has been around for over 20 years, encompasses three dimensions: deciding what problems to solve, figuring out how to solve them, and building/testing/deploying the solutions. Agile can significantly aid in the third dimension, but with various interpretations of what being “Agile” means, the connection isn’t always clear. Furthermore, as author Marty Cagan explains, while Agile principles are valuable and relevant, they primarily relate to building software, not figuring out what to build. Lastly, Agile’s principle that “working software is the primary measure of progress” may not suffice for commercial product work, as the Product Model emphasizes solving the underlying problem and achieving necessary results.
— Silicon Valley Product Group, 8m, #agile, #product-model, #product-strategy
AI Coding - Not So Fast
Despite the rise of AI for coding tasks, the need for human software developers remains paramount. The myth of AI producing flawless, ready-to-use code crumbles when faced with the realities of modern software development. Contrary to popular belief, coders spend a significant portion of their time not on writing code, but on deciding and agreeing on what to code. This process involves understanding and interpreting the often ambiguous nature of business requirements, an area where humans remain crucial.
— Secret Developer, 4m, #software-development, #ai, #programming
Decoding Bad Design
Scott Berkun’s article paints a vivid picture of the pitfalls that often lead to poor design in software products. He argues that bad design isn’t usually a result of laziness or incompetence, but rather a symptom of deeper organizational issues. In many cases, companies fail at good design because they fail at most things, with a lack of organizational efficiency to blame. Sometimes, the focus on affordability and convenience over quality leads to subpar design. Other times, the problem lies in the leadership, with executives unwilling to take the risk of prioritizing design. Even in organizations with many talented individuals, goals can diverge and authority can become too distributed, leading to a poorly designed product.
— Why Design is Hard, 16m, #design, #product-management, #organizational-leadership
DIY RAG: A Recipe for Disaster
IT departments with ambitions of crafting their own RAG-based chat systems are only setting themselves up for failure, according to Alden Do Rosario. Despite the appeal of in-house development, the reality is far from simple. Projects that seem straightforward initially can quickly balloon into resource-draining nightmares, with issues ranging from accuracy problems and integration difficulties to security vulnerabilities and data leakages. Furthermore, the costs of these ventures involve not only infrastructure expenses, but also personnel, operational costs, and the ever-looming potential for compliance audits. In contrast, bought solutions have been tested across numerous customers, which significantly reduces cost while increasing efficiency.
— Towards AI, 11m, #IT, #AI-development, #project-management
Autonomous Team Coordination
The average knowledge worker loses 23 hours weekly to coordination overhead. Fix that with Steady today.
Steady is an agentic AI coordination layer that runs in the background, distilling plans and progress from tools, teams, & people into forward-looking tailored summaries, giving everyone the clarity they need to build outstanding products together. Teams using Steady typically see 10X speed-to-impact increases in the first 30 days.
Learn more at steady.space.
