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.
Echoes: Customized Context on a Cadence
This week at Steady, we unveiled a new feature: Echoes. Echoes leverages AI to provide completely customized context for teams and people on a schedule, in the background. Echoes works by automatically assembling information relevant to a user’s role and delivering it exactly when it’s needed. For instance, it brief managers with everything they need to prepare for one-on-one meetings with their reports, right before the meeting. It can also be used to continuously keep track of shipped features, monitor the progress of team goals to prevent them from slipping, or prepare the bullets you need for that stakeholder powerpoint slide. Echoes breaks down cross-functional silos, providing briefs rooted in human input from different teams to ensure everyone is informed and aligned. Echoes is included for free for every customer.
— Steady, 5m, #product-update, #knowledge-management, #teamwork, #productivity, #ai, #agents
The AI Goldilocks Zone: Real ROI
Forget the all-knowing chatbots and the AI-powered paperclip removers—real AI impact is happening in the “forgotten middle.” While moonshot projects burn cash and simple automations plateau, mid-tier AI use cases—like fraud detection, demand forecasting, and hyper-personalized coordination briefs—deliver scalable, measurable ROI without the risk. The key? Treating AI investments as a network of operational improvements rather than a one-off miracle. (See above!)
— Dataiku, 6m, #ai, #agents, #roi, #productivity
Scaling AI Falls Short in Delivering AGI
Gary Marcus, NYU professor emeritus, argues that the current AI trend of pure scaling has failed to deliver Artificial General Intelligence (AGI). Despite the hype and high hopes, the brute force approach of simply amplifying existing AI technologies hasn’t produced the expected leap towards AGI, a level of machine intelligence that can understand, learn, and apply knowledge across a wide range of tasks. Marcus’s assessment serves as a reality check for those in the tech industry who believed that scaling alone would be the magic bullet for achieving AGI.
— Fortune, 8m, #artificial-intelligence, #tech-commentary, #machine-learning
1X Engineers, 10X Teams
Honeycomb CTO Charity Majors argues that the focus in software development should shift from hunting for mythical “10x engineers” to creating cohesive teams that can collectively deliver high-quality work. According to Majors, the concept of “10x engineers” - individuals who are ten times as productive as their peers - is flawed due to the difficulties in measuring productivity and the fact that it overlooks the importance of teamwork. Instead, she advocates for nurturing an environment where all engineers, regardless of their skill level, can contribute effectively, thereby turning “normal” engineers into high-performing teams.
— Refactoring, 8m, #engineering, #teamwork, #productivity
Designing for AI
As AI technologies grow in influence, designers play a crucial role in ensuring ethical, transparent, and human-centric solutions. Amid the rise of tools like ChatGPT, the balance of control is tilting away from the user, making the designer’s role in safeguarding user interests more vital than ever. The rapid deployment of powerful AI technologies is outpacing societal adaptation, impacting the economy, mental health, and culture. Designers are urged to take responsibility in this predictive landscape, shaping AI experiences that consider potential biases in data, the impact on user privacy, and the societal implications. Prioritising responsibility and accountability, designers can help create AI technologies that enhance human capabilities and improve quality of life. The challenge lies in the pressure to quickly adopt the latest trends without sufficient time for reflection or to consider ethical implications.
— UX Collective, 12m, #ai, #design, #ethics
A Dev Workflow with LLMs
Software builder Harper Reed shares his workflow for utilizing LLMs in the development process. He begins by employing a conversational LLM to flesh out an idea into a detailed project spec. This concrete document is then passed to a reasoning model, which drafts a comprehensive blueprint for building the project, broken down into easily implementable, iterative chunks. This approach, Reed explains, offers a solid foundation for code-generation LLMs to implement each step in a test-driven manner, ensuring incremental progress and avoiding large jumps in complexity. Reed’s method provides a robust, flexible, and efficient approach to project planning and execution, though he notes that the effectiveness of this system may vary as technology and best practices evolve.
— Harper Reed’s Blog, 8m, #software-development, #workflow, #ai, #llms
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