A monthly podcast about the shift from Cloud Native to AI Native. Hosted by Deejay, each episode features a guest picked by our community—engineers, leaders, and thinkers sharing how they’re adapting, experimenting, and figuring it out as they go. Real stories, practical lessons, and things we’re all still learning.
Waves of Innovation by re:cinq
Claim This Podcastby Deejay
17 episodes
Updated Daily
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Podcast Overview
A monthly podcast about the shift from Cloud Native to AI Native. Hosted by Deejay, each episode features a guest picked by our community—engineers, leaders, and thinkers sharing how they’re adapting, experimenting, and figuring it out as they go. Real stories, practical lessons, and things we’re all still learning.
Language
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Publishing Since
6/13/2025
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Recent Episodes
May 19, 2026
72x Faster Software Delivery with a Former AI Skeptic
<p>The episode begins with a candid look at Dominic Warchalowski and his evolution from a staunch AI skeptic to a lead engineer driving a massive velocity increase at Odevo. Dominic recounts his early and frustrating experiences with isolated AI prompts, which frequently yielded hallucinations and eroded his trust. A significant pivot occurs when he is introduced to Claude Opus within a modern IDE environment, triggering an aha moment that fundamentally shifted his perspective on the capability of AI to handle complex engineering tasks.</p><p></p><p><b>The Technical Core</b><br />The heart of the episode explores the friction between legacy development methodologies and modern agentic speed. The duo laments the loss of traditional Kanban workflows, noting that when AI agents generate code at unprecedented speeds, conventional small batch pull requests quickly become massive bottlenecks. Dominic explains that you cannot simply fit AI into an existing process; the process must be redesigned around the AI. He details the ambitious project at Odevo to rebuild a five year old legacy system in just eleven months. This involves a rigorous and almost waterfall like upfront discovery phase where domain logic is extracted, meetings are transcribed, and AI structures the output into strict epics and stories. Dominic also dives into the repository architecture, explaining their use of a hub repo with submodules to perfectly scope context for agents, ensuring low entropy outputs regardless of whether an engineer uses SpecKit, a Ralph Wiggum loop, or raw terminal commands.</p><p></p><p><b>Philosophical/Human Implications</b><br />A fascinating philosophical discussion emerges around the nature of software complexity and human communication. Deejay and Dominic reflect on how rigid and upfront specification is now a superpower in the agentic era. Because the transaction cost of writing code has plummeted, the premium is now placed on clear communication, structured domain modeling, and human in the loop oversight to manage irreducible complexity.</p><p></p><p><b>Future Outlook</b><br />The conversation concludes with a look at the diversity of tooling. Rather than forcing a single automated factory pipeline, the approach at Odevo standardizes the inputs while allowing engineers the freedom to choose their preferred agentic tools. This flexibility, grounded in strict architectural rules, points to a future where developers act more like orchestrators and reviewers of highly capable autonomous systems.</p><p>Key Themes Explored</p><p></p><p><b>The Breaking Point of Agile</b><br />Traditional small batch workflows and Kanban boards create severe pull request bottlenecks when paired with agentic coding speeds. Engineering teams must redesign their entire software development lifecycle around AI velocity rather than forcing AI into legacy processes.</p><p></p><p><b>Upfront Discovery as an AI Superpower</b><br />While heavy upfront specification resembles outdated waterfall methodologies, it is essential for agentic coding. By investing time in structured domain modeling and precise acceptance criteria, teams can drastically reduce AI entropy and hallucination.</p><p></p><p><b>Architecting Codebases for Agents</b><br />Modern repositories must be designed from the ground up to support AI context management. Utilizing hub repositories, submodules, and strict architectural golden rules ensures that agents have the exact context needed to generate accurate and low entropy code.</p>
April 30, 2026
Scaling AI Enablement: How Odevo Upskilled Engineering
<p>The conversation begins with an inside look at Odevo, a massive Swedish property technology company managing residential assets globally. Tomasz outlines the early days of their AI adoption, characterized by an internal AI team building bespoke document sorting tools and a secure internal GPT instance. However, a significant pivot occurs when casual Slack debates about engineering productivity evolve into a structured realization: the rules of software development are fundamentally changing, and unstructured experimentation is no longer sufficient.</p><p></p><p><b>The Technical Core</b><br />The heart of the episode explores the mechanics of the comprehensive AI training rollout at Odevo. Tomasz and Deejay dissect the deliberate choices made during the enablement program, such as rejecting compressed three-day bootcamps in favor of spaced, bi-weekly modules. This pacing allowed engineers to digest concepts and test them in real-world scenarios. The duo delves into the tangible outcomes of this structured approach, noting that pull request volume increased by up to ten times while merge times plummeted by fifty percent. Importantly, they discuss how AI adoption actually reinforced foundational engineering practices, making tedious tasks like unit testing and documentation effortless rather than burdensome. Tomasz emphasizes that the training was not just about syntax, but about establishing a baseline of good and bad practices for utilizing tools like Claude Code and GitHub Copilot.</p><p></p><p><b>Philosophical and Human Implications</b><br />A fascinating shift in the narrative happens when the discussion moves beyond the engineering department. Tomasz highlights the democratization of software creation, detailing how financial managers, product owners, and HR professionals are leveraging platforms like Lovable to build functional applications. This introduces a new paradigm of ownership, encapsulated by the you build it, you own it philosophy. The duo explores the psychological impact of AI, noting a marked increase in developer boldness. Engineers are no longer paralyzed by the fear of starting; instead, they are proactively rewriting legacy applications and exploring new architectures because the barrier to entry has evaporated.</p><p></p><p><b>Future Outlook</b><br />Looking ahead, Tomasz advocates for a culture of enablement over restriction. He argues against heavy-handed governance and token rationing, comparing AI access to basic utilities like Microsoft Office. The focus remains on fostering curiosity, sharing wins and failures openly, and allowing teams to push the boundaries of what is possible as the industry accelerates toward an agentic future.</p><p></p><p><b>Key Themes Explored</b></p><p></p><ul><li><b>Structured Enablement Over Organic Adoption</b><br />Relying on developers to figure out AI tools independently leads to uneven adoption and amplified bad practices. A guided, paced training program establishes a baseline of quality and accelerates true organizational fluency.</li></ul><p></p><ul><li><b>The Rise of the Bold Developer</b><br />AI tools significantly lower the activation energy required to start complex tasks. This results in engineers taking more calculated risks, prototyping faster, and tackling legacy debt with renewed confidence.</li></ul><p></p><ul><li><b>Democratization of Software Creation</b><br />No-code and agentic tools are turning product managers and operational staff into functional developers. This shift requires a new approach to governance, emphasizing personal ownership and collaborative refinement over traditional IT gatekeeping.</li></ul><p></p><ul><li><b>Treating AI as a Fundamental Utility</b><br />Restricting token usage or imposing heavy red tape stifles innovation and limits ROI. Forward-thinking enterprises view access to frontier models as a basic operational necessity rather than a heavily audited expense.</li></ul>
April 10, 2026
Scaling Code Review When AI Writes the Software
<p>The episode begins by addressing a stark new reality for engineering teams: AI agents are writing code at an unprecedented pace, leading to pull requests that are 150 percent larger and review times that have doubled. Deejay and Jaime Jorge unpack this sudden shift, noting how the friction of software development is being removed faster than ever before. However, this frictionless environment introduces a dangerous side effect known as automation bias, where developers might blindly merge massive blocks of AI-generated code simply because a machine wrote it.</p><p></p><p><b>The Technical Core</b><br />A significant pivot occurs when the conversation moves from identifying the problem to exploring architectural solutions. Jaime introduces the cyborg approach to code analysis. He explains that while AI models are incredibly powerful, their non-deterministic nature means they cannot reliably enforce consistent coding standards. To counter this, engineering teams must maintain deterministic rules as a structural backbone. The duo explores how tools like Model Context Protocol servers are allowing AI agents to run local static analysis and security checks before a pull request is ever created. Instead of discarding traditional CI/CD pipelines, Jaime argues that these deterministic gates are becoming even more critical, acting as necessary friction to ensure that AI-generated software is actually secure.</p><p></p><p><b>Philosophical and Human Implications</b><br />The heart of the episode explores the evolving role of the software developer. Jaime likens managing modern AI coding tools to opening loot boxes, where the output is a gamble that requires constant supervision and orchestration. As coding becomes less about typing syntax and more about acting as an agent herder, the fundamental principles of software engineering—like rigorous test coverage and clear specifications—are proving more vital than ever. The discussion also touches on the anxiety surrounding software as a defensive moat. If anyone can spin up a prototype over a weekend, the true differentiator for a business becomes trust and reliability, rather than just the codebase itself.</p><p></p><p><b>Future Outlook</b><br />Looking ahead, the conversation shifts toward the concept of software factories and autonomous agents operating in isolated environments. Jaime anticipates a future where systemic failures in code quality will no longer be blamed on individual human error, but on poorly designed automated workflows. The episode concludes with a grounding reminder for tech leaders: while the pace of AI innovation is relentless and impossible to track hourly, embracing the change and implementing robust, automated guardrails will be the key to surviving and thriving in this new era.</p><p></p><p><b>Key Themes Explored</b></p><ul><li>The Cyborg Approach to Analysis. Combining deterministic security rules with non-deterministic AI models ensures consistent code quality without sacrificing development speed.</li><li>The Danger of Automation Bias. As AI generates massive pull requests, developers risk blindly trusting machine output, making rigorous and automated review gates essential.</li><li>Coding as Agent Orchestration. The developer role is shifting from writing syntax to guiding multiple AI agents, requiring a renewed focus on strict testing and clear specifications.</li></ul>
17 total episodes available
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- What is Waves of Innovation by re:cinq?
- How often does this podcast release new episodes?
This podcast updates daily.
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