Going to Production

Everyone's talking about spec driven development — and almost everyone is thinking about it too narrowly. If your spec only describes what your engineering team needs to build, you're leaving most of its power on the table. In this episode, David Dieruf and Wael Rabadi challenge the AI coding community's definition of "spec" and make the case for holistic specification thinking that drives your entire business. What You'll Learn: Why spec driven development as it's currently practiced is too narrow How LLMs fill context gaps the same way humans do — and why that's not hallucination Why BRD thinking and agile aren't mutually exclusive How a holistic spec drives backlogs, architecture, marketing, and sales from a single source of truth The regeneration power of AI when product direction changes from Feature A to Feature B Why ubiquitous language across all teams is more critical than ever in the AI era How the "multiplier effect" of centralized business context accelerates everything Key Insight: A spec isn't a technical artifact — it's a business artifact. When your entire organization shares the same context, AI doesn't just help you write code faster. It helps you change direction, regenerate plans, and move the whole business forward in days instead of months.

Is computer science a dying field now that AI can generate code? Are developers just becoming "code monkeys" who paste AI output into repositories? In this episode, David Dieruf and Wael Rabadi tackle these questions head-on and explore what true development actually means when AI handles the implementation grunt work. What You'll Learn: What true development means beyond writing code Why context and documentation are critical for effective AI pairing How AI exposes the difference between code monkeys and real developers The importance of shared understanding and ubiquitous language Why interdisciplinary teams and collaboration matter more in the AI age How to provide AI with the context it needs to be effective The future of software development careers when AI handles implementation Key Insight: AI doesn't eliminate the need for skilled developers—it raises the bar. The question isn't whether AI will replace developers, but whether you're doing true development or just being a code monkey. If you're the latter, AI is coming for your job. If you're the former, AI is your new superpower. Hosted by engineering leaders David Dieruf and Wael Rabadi, co-founders of Kisasa.

Most engineering teams treat staging environments as a safety net—a place to test changes before they hit production. But what if that safety net is actually slowing you down and creating more problems than it solves? In this episode, David Dieruf and Wael Rabadi challenge the conventional wisdom around staging environments and make the case for production-first development. What You'll Learn: Why staging environments create false confidence and environment parity problems How infrastructure as code (CDKTF) enables production-first development Blue-green deployment strategies for safe production deployments Why moving the edge to deployment eliminates the need for staging How to simplify cloud security audits with infrastructure as code Practical approaches to deploying directly to production safely Key Insight: If your staging environment isn't identical to production, it's not protecting you—it's just slowing you down. Invest in deployment practices that make production safe, not in maintaining a staging environment that lies to you. Hosted by engineering leaders David Dieruf and Wael Rabadi, co-founders of Kisasa.

The rise of AI coding assistants is fundamentally changing what it means to be a software developer. But contrary to popular fear, AI isn't replacing engineers—it's transforming their roles from code writers to context managers and decision-makers. In this episode, David Dieruf and Wael Rabadi explore how AI pair programming is reshaping development at every level, from entry-level developers to senior architects. What You'll Learn: How AI is transforming developer roles at entry, mid, and senior levels Why context management is the new critical skill for developers The economics of AI pair programming and productivity gains How to structure development workflows around AI collaboration Why documentation and shared understanding matter more than ever The future of software development careers in an AI-augmented world Key Insight: AI doesn't eliminate the need for skilled developers—it raises the bar. The question isn't "Can AI write code?" but "Can you provide the context and make the decisions that turn AI-generated code into production-quality software?" Hosted by engineering leaders David Dieruf and Wael Rabadi, co-founders of Kisasa.

Most engineering teams struggle with backlog grooming because they focus on estimating story points and debating technical implementation details rather than defining user value and thin slices. In this episode, David Dieruf and Wael Rabadi discuss why traditional backlog grooming often becomes a solutioning session—and how to shift toward grooming that enables rapid iteration, effective testing, and better product outcomes. What You'll Learn: Why story points create false precision and slow teams down How to identify true thin slices that deliver vertical value The difference between grooming (defining what) and solutioning (planning how) Practical techniques for journey-based testing How to maintain ubiquitous language across product and engineering Why premature technical planning hurts iteration speed Key Insight: The best backlog grooming sessions ask "What does the user need to accomplish?" not "How should we build this?" Save the technical planning for after you've clearly defined the user value you're delivering. Hosted by engineering leaders David Dieruf and Wael Rabadi, co-founders of Kisasa.

Why do talented engineers with good intentions end up building problematic architectures? In this episode, we explore how smart teams create bad systems through local optimization, unintended consequences, and the gap between component thinking and systems thinking. What You'll Learn: Why smart teams create problematic architectures The danger of local optimization without global context Unintended consequences of well-intentioned technical decisions Systems thinking vs component thinking How technical debt accumulates in successful systems Patterns that lead good engineers to build bad systems Key Insight: Good engineers build bad systems when they optimize locally without understanding the global delivery system. The problem isn't skill or intent—it's perspective. Systems thinking requires seeing beyond your component to understand the whole. Hosted by engineering leaders David Dieruf and Wael Rabadi, co-founders of Kisasa.

Your technology stack isn't just a list of tools—it's the foundation of your entire delivery system. In this episode, we introduce the Modern Delivery Stack methodology: how to connect business goals to technical decisions through a unified platform that enables sustainable velocity. What You'll Learn: The Modern Delivery Stack framework: Customer Value → Domain Applications → Domain Services → Runtime Platform How to connect business goals to customer outcomes through technology Why platform is the foundation for delivery, not just infrastructure Strategic technology planning that aligns with business objectives The difference between tooling and delivery systems How to build platforms that enable teams rather than constrain them Key Insight: Most teams focus on tools and frameworks. Great teams build complete delivery systems that connect business strategy to customer value. The Modern Delivery Stack provides the framework for making that connection real. Hosted by engineering leaders David Dieruf and Wael Rabadi, co-founders of Kisasa.

Your system architecture mirrors your organizational structure—whether you planned it that way or not. In this episode, we explore Conway's Law in practice: how team boundaries, communication patterns, and organizational design directly shape the technical systems you build. What You'll Learn: How team structure inevitably dictates system architecture Why organizational design is a technical decision Real-world examples of Conway's Law in action How to align teams with your desired architecture The relationship between communication patterns and system boundaries Strategies for restructuring teams to enable better systems Key Insight: If you want to change your architecture, you have to change your team structure first. Conway's Law isn't a suggestion—it's an organizational reality that shapes every system you build. Hosted by engineering leaders David Dieruf and Wael Rabadi, co-founders of Kisasa.

Why do organizations end up with multiple platform teams building the same infrastructure? In this episode, we explore the platform multiplication problem—how well-intentioned teams create duplicated effort, fragmented systems, and wasted resources across the organization. What You'll Learn: Why platform teams naturally duplicate effort across organizations The hidden costs of fragmented infrastructure and tooling How team autonomy leads to platform proliferation The difference between platform teams and platform engineering Why unified delivery systems matter for scale Practical approaches to consolidating platform efforts Key Insight: Platform multiplication happens when teams optimize locally without understanding the global delivery system. The result? Multiple teams solving the same problems, fragmented infrastructure, and wasted resources that could be invested in actual business value. Hosted by engineering leaders David Dieruf and Wael Rabadi, co-founders of Kisasa.