#waterfall-strategy

#waterfall-strategy

Lydia noticed the machine's battery was running low and told two other team members. The more senior went to fetch the backup battery, while the junior team member suggested a quicker method that Lydia firmly rejected.

Most of these companies start the journey from a functional standpoint, avoiding extra layers that may "divert users' attention", such as refined flows, potential edge cases, and, sometimes, proper visual design foundations and user experience. Here, the goal is to ship the product first to validate its value, then address other considerations.

Capacity Planning is the process of right-sizing the 'Total Project Demand' with the forecasted Team Capacity. Most UX teams have no idea what their capacity is. Fewer still have a process for calculating it and using it during quarterly planning activities with their counterparts in Product Management & Engineering to ensure teams don't commit to more work than they can handle.

"I've never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between. I have a sense that I could be 10X more powerful if I just properly string together what has become available over the last ~year and a failure to claim the boost feels decidedly like skill issue."

Her payment form wasn't connecting to the payment processor, and every attempt ended in an error message that made no sense. I understood her frustration. As a founder myself, I was acutely aware of the pain of trying to run a business and feeling like nothing was going your way. When I dug into her form, I found the problem a few minutes later: a mismatch between test mode and live credentials.

One of the challenges teams face when working with large boards or displaying multiple fields on work item cards is limited screen space. This became even more noticeable with the rollout of the New Boards hub, which introduced additional spacing and padding for improved readability. While this enhances clarity, it can also reduce the number of cards visible at once.

Well, our guest today argues that the best way is by moving to a more project-driven model of work, up and down the organization from the corporate level to individual teams. He wants us to both ruthlessly prioritize as well as stay fluid so that we're identifying strategic goals, assembling teams to go after them, evaluating as we go, and then either continuing, shifting, or disbanding based on our outcomes.

The real cost of poor observability isn't just downtime; it's lost trust, wasted engineering hours, and the strain of constant firefighting. But most teams are still working across fragmented monitoring tools, juggling endless alerts, dashboards, and escalation systems that barely talk to one another, which acts like chaos disguised as control. The result is alert storms without context, slow incident response times, and engineers burned out from reacting instead of improving.

For decades, the to-do list has been a catalog of debt, a deceptively thin list of items to do, with icebergs of work hidden beneath the surface. AI transforms tasks to work that has already been done. Vibe Kanban, Gastown, & Conductor are the first instantiations of this for software developers. They have jargon-laden descriptions like "multi-agent orchestrator" or "visualizer," but they are, at heart, simple & beautiful Kanban boards of done & dusted work.

Your AI pilot showed 94% accuracy improvements. The LLM is yielding solid results. You're getting defunded anyway. The reason? You solved a problem AI can solve. Your budget-holder needed you to solve theirs. Companies launch AI pilots that produce results, then stall at scale. The team's diagnosis: "They don't get it." What's really going on: These projects never earned budget-holder buy-in.

During my eight years working in agile product development, I have watched sprints move quickly while real understanding of user problems lagged. Backlogs fill with paraphrased feedback. Interview notes sit in shared folders collecting dust. Teams make decisions based on partial memories of what users actually said. Even when the code is clean, those habits slow delivery and make it harder to build software that genuinely helps people.

Much of the conversation about how to work effectively with generative AI has focused on prompt engineering or, more recently, context engineering: the semi-technical skill of crafting inputs so that large language models produce useful outputs. These skills are helpful, but they are only part of the story.

This extends to the software development community, which is seeing a near-ubiquitous presence of AI-coding assistants as teams face pressures to generate more output in less time. While the huge spike in efficiencies greatly helps them, these teams too often fail to incorporate adequate safety controls and practices into AI deployments. The resulting risks leave their organizations exposed, and developers will struggle to backtrack in tracing and identifying where - and how - a security gap occurred.

Scrum has a bad reputation in some organizations. In many cases, this is because teams did something they called Scrum, it didn't work, and Scrum took the blame. To counter this, when working with organizations, we like to define a small set of rules a team must follow if they want to say they're doing Scrum. Enforcing this policy helps prevent Scrum from being blamed for Scrum-like failures.

To find the typical example, just observe an average stand-up meeting. The ones who talk more get all the attention. In her article, software engineer Priyanka Jain tells the story of two colleagues assigned the same task. One posted updates, asked questions, and collaborated loudly. The other stayed silent and shipped clean code. Both delivered. Yet only one was praised as a "great team player."

The recently updated SWEBOK Guide v4.0a represents a needful industry standard, following a thorough peer review and a consensus-based approach. With the rise of AI, a significant skills gap in IT and cybersecurity is emerging alongside changes in the global workforce. There has never been a greater need for a consensus-based framework. This guide, created and thoroughly reviewed by industry professionals, serves as a dynamic and evolving resource.

A secure software development life cycle means baking security into plan, design, build, test, and maintenance, rather than sprinkling it on at the end, Sara Martinez said in her talk Ensuring Software Security at Online TestConf. Testers aren't bug finders but early defenders, building security and quality in from the first sprint. Culture first, automation second, continuous testing and monitoring all the way; that's how you make security a habit instead of a fire drill, she argued.

Olimpiu Pop: Hello everybody. I'm Olimpiu Pop, an InfoQ editor, and I have in front of me Erica Pisani, one of the track hosts of QCon London 2025, and a very important track in my opinion. One that is important in general, but even more important these days. And the name of the track was performance and sustainability, which seems to be two opposing words. So, Erica, please introduce yourself.