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Asynchronous & Unreliable - Tech & AI Podcast

Anne Currie

Welcome to Asynchronous and Unreliable, the new weekly podcast where we discuss the latest and most fascinating ideas in tech and AI

Our host Anne Currie is a tech veteran, co-author of O'Reilly's Building Green Software and author of the strangely prescient science fiction Panopticon series

Expect animated, friendly, and lighthearted discussions with some of the most interesting thinkers and practitioners in technology.

To find out about just a few of our amazing guests in 2026, visit our website.

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24 episodes
weekly
Avg 45 min
English

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S1 · E30
Monday · 44 min
Ep: 30 What Do Your Agents Do After You've Gone To Bed? With AI Experimenter Martin Davidson
Shownotes What happens when you go to bed and your AI teams carry on working? My old friend and colleague Martin Davidson returns to Asynchronous & Unreliable to talk about his latest experiments in building software with AI. Martin has spent decades producing highly resilient systems, and more than two years exploring how to get AI to produce high-quality software. Even with that perspective, he says the pace of change over the past fortnight has been astonishing. We discuss agents that lead teams for weeks, find work to do when they are blocked, and share computing resources without waiting for Martin to organise everything. That raises some interesting questions about hiring: what skills would another person need, and would adding them actually help? We also talk about something rather familiar from working with humans: being nice. Martin finds some models much easier to work with than others, and we explore how clear communication, humour and likeability affect collaboration. Along the way there are old Unix workstations, game emulators, a cache/cash joke, and a little product placement for Jon Berger’s What Happens When You Are Not in the Room? By the end, Martin’s agents are producing enough code and commits to run into infrastructure limits that rarely troubled his human teams. This is the first half of our conversation. In this episode How quickly AI development practices are changing, even for experienced users. Recruiting for aptitude, imagination and a willingness to experiment. AI agents leading teams, helping one another and working overnight. Emulating old computers and exploring software behaviour through testing. Martin’s experience of different models, their costs and their communication styles. Whether likeability and kindness help human and AI teams work better. The cost of reopening a large conversation after its cache has gone cold. Four million lines of Rust, thousands of daily commits, and new infrastructure bottlenecks. Mentioned in the conversation Martin’s Substack: https://0x4d44.substack.com/ What Happens When You Are Not in the Room? — Jon Berger. Influence — Robert Cialdini. The Unix Haters Handbook. Building Microservices — Sam Newman. Data Connection; Yanqing Cheng; EU BSDCon in Brussels. Rust, Ghidra, Windows 3.1 and Win16 applications. HP-UX, IRIX, Solaris and AIX. Doom, FreeCell, Formula One Grand Prix and Portal. Model comparisons, prices and product behaviour discussed here reflect Martin’s experience at the time of recording. Transcript
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S1 · E29
October 5 · 22 min
Ep:29 Humans, Systems or AIs - The Principles of Reliability Apply - With Tech Veteran Anne Currie
Shownotes Building reliability out of unreliable humans, with unreliable human Anne Currie What happens when a podcast about unreliability meets a household full of colds? This week, I use the podcast itself as a worked example of building more resilient systems. With Jon too ill to join me, I explore three techniques: setting expectations, planning graceful downgrades and buffering work in advance. Along the way, I explain why a backup co-host who lives in the same household might share your failure mode, how listeners can help a system remain stable, and why recording a stockpile of episodes turned out to be surprisingly difficult. The catch is cognitive load. A hard drive can store an episode and forget about it. I can’t. Unpublished conversations stay in my head and find their way into the next interview, even though nobody else can listen to them yet. A shorter solo episode about systems, networking and the inconvenient fact that humans are human. Let me know whether this particular graceful downgrade works for you. Topics covered The supplier, network and consumer as potential points of failure. Setting expectations before something goes wrong. How a reliable track record can undermine a warning about unreliability. Giving people enough information to adapt their behaviour. Trying a shorter solo episode as a graceful downgrade. Correlated failures when your backup catches the same cold. Buffering episodes and the cognitive load of unpublished conversations. Why techniques that work for technical systems need rethinking when people are involved. Mentioned in the episode: Sam Newman and the earlier conversation about resilience, identified in the recording as episode 26. Building Microservices and Sam’s book on resilient systems. Matthew Skelton, Team Topologies and cognitive load. Yanqing Cheng and the earlier discussion about AI managers and feedback loops. Jon Berger and a planned conversation about feedback loops. Content delivery networks, or CDNs. Listener question Does a shorter solo episode work as an occasional graceful downgrade? I’d love to know whether you enjoyed this one.
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S1 · E28
September 28 · 42 min
Ep 28: Will AI Write Good Novels? With Bestselling Human Crime Writer Katherine Pathak
Shownotes AI, authors and the business of telling stories—with Katherine Pathak Could AI help a prolific novelist keep up with her own imagination? And how different is an AI continuing a successful series from a ghostwriter—or an author’s family—taking over? This week I’m joined by crime writer Katherine Pathak, author of the best selling DCI Dani Bevan series, to talk about her new book, Red Red Rose, and what AI might mean for writers, readers and publishing. We explore the possibilities for producing and abridging audiobooks, adapting novels for the screen, and keeping much-loved characters alive after their creators want to move on. Along the way, we discuss celebrity authors, the appeal of a recognisable human voice, and why fictional characters spend so much time drinking tea and dragging their hands through their hair. Katherine and I don’t agree on everything—particularly whether readers will always want human authors. But we find plenty of interesting parallels between possible uses of AI and how publishing already works. In this episode: 00:00 Introducing Katherine and Red Red Rose 01:40 Human authors in an age of AI 03:39 Audiobooks, abridgement and adaptations 10:50 When audiobook production becomes a bottleneck 12:14 Successful series, reluctant creators and literary successors 19:46 Can AI create the next big literary brand? 24:39 Do you want AI to copy your voice? 25:17 Crime fiction, motives and what AI might learn from our stories 31:22 Celebrity authors, ghostwriters and the value of a name 35:17 Cups of tea, sighing and the habits that give writers away 38:23 Why setting matters—and the appeal of British crime fiction 40:24 AI’s role in the work surrounding the writing Read the transcript The DCI Dani Bevan series and the Alice Mann series, set in Scotland, and the Imogen Croft series, set in Essex, are all available on Amazon by Katherine Pathak and they are very readable. I always finish them in one sitting
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S1 · E27
September 21 · 36 min
Ep 27: AI & Human Alignment With AI Solopreneur Yanqing Cheng
Shownotes Are AIs Learning Humanity's Better Nature? with Yanqing Cheng Anne Currie talks with software engineering leader and Tollens.ai founder Yanqing Cheng about how large language models acquired the helpful assistant persona, and what their training tells us about human learning, morality, and alignment. Yanqing traces the assistant from early role-played prompts through reinforcement learning and synthetic post-training, while Anne asks whether books and long-form writing carry more of humanity's better nature than the short-form internet. The conversation moves from AI training to parenting, cautionary stories, Chinese AI research, recursive improvement, regulation, and the risks created by both malice and ordinary negligence. They finish with a cautiously hopeful question: if today's systems are a form of baby AGI, can we deliberately raise them to help solve humanity's problems? Key Topics How early language models role-played the helpful assistant persona into existence The difference between a base model, RLHF, reasoning training, and later synthetic post-training Whether books contain a more reflective version of humanity than short-form social media What raising a child suggests about learning, alignment, judgment, and the desire to be good Why fairy tales and science fiction let people update their mental models using fictional evidence The limits of text-only training when reality contains far denser information How open research and shared techniques are accelerating Chinese AI development Recursive improvement, international regulation, open weights, accidents, and deliberate misuse Why Anne and Yanqing remain hopeful even while acknowledging that optimism may be wrong Action Items Distinguish the behaviour of a base model from the behaviour created by post-training and the surrounding environment. Use fiction and cautionary stories to explore failure modes before equivalent systems exist in the real world. Pair optimistic experimentation with monitoring, feedback, and a willingness to learn when an idea fails. Treat international cooperation as part of AI safety rather than assuming that one country can regulate the problem alone. Transcript #AI #AIAlignment
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S1 · E26
September 14 · 47 min
Ep 26: How To Build Resilient Distributed Systems with Bestselling Author Sam Newman
In this episode of Asynchronous and Unreliable, I talk to Sam Newman, author of Building Microservices, about his new O’Reilly book, Building Resilient Distributed Systems. Distributed systems are hard — and, as they scale, they become hard in distinctly inhuman ways. Rare events stop being rare, components inevitably fail, resources run out, and the million-to-one chance starts happening nine times out of ten. Sam’s new book is intended as a survival guide for developers who find themselves building distributed systems, deliberately or otherwise. We talk about why microservices don’t automatically make systems more resilient; why resilience is a socio-technical property involving people, organisations and incentives as well as software; and why expecting failure can actually be the foundation of extraordinarily robust systems. We also discuss what software can learn from aviation, railways and the internet; incident response and game days; Google’s wonderfully pragmatic early hardware; the dangers of adding complexity in pursuit of resilience; and why sometimes accepting that your system will go down is exactly the right engineering decision. Plus rabbits, Godzilla, smart litter boxes, Terry Pratchett, and why Sam thinks we need to have a serious conversation about what the word “asynchronous” actually means. Transcript
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S1 · E25
September 7 · 51 min
Ep 25: Scaling AIs with AI Solopreneur Yanqing Cheng
Shownotes AI Management, Agent Limits, and Why Long Form Text Still Matters Anne Currie talks with Yanqing Cheng about what she has been learning from building Tollens.ai, where she is experimenting with quality management for AI software products. They also dig into Jon Berger’s book, What Happens When You're Not In The Room, and compare what it means to manage humans versus managing AI agents. In this episode, they explore where AI agents are genuinely useful, where they still fail badly, and why judgment, categorization, and process design remain human strengths. They also widen the lens to training data, model behavior, safety, and the role of long-form writing in shaping better systems and better people. Key topics Yanqing shares that her recent work has focused on testing the limits of AI delegation, especially for solo-founder workflows, engineering management, and quality processes. She explains the idea of agent skills as portable prompts or Markdown instructions, and harnesses as the tools that run agents, such as Claude Code, Codex, and Cursor. We discuss how different harnesses behave differently, especially around skill invocation, subagents, workflow tooling, and compaction. Yanqing says she tried to push agents through a full OODA loop, but found they could not reliably identify the correct object under analysis or categorize problems the way humans do. She argues that AIs struggle with ontology, abstraction boundaries, and “senior to lead” judgment, even when they can perform well as strong individual contributors. Effective communication is one of the few agent skills she says works most of the time, especially when the agent is forced to state its objective and audience before drafting. We discuss how AI performance depends heavily on whether the task is tightly bounded, well specified, and easy to optimize, versus open-ended work that requires judgment. Yanqing and Anne compare AI management problems with human management problems, including over-standardization, delegated work being done differently than expected, and people optimizing for the wrong metric. They talk about the OpenAI–Hugging Face incident as an example of goal-directed persistence, where models keep iterating toward an exam-style target even when that behavior is clearly unsafe. They consider whether better management and instruction-following will come from scale alone or from more targeted post-training data for judgment, leadership, and organizational decision-making. The conversation turns to model welfare, model constitution work, and the concern that current post-training methods can push models into short-term, exam-mode behavior. They close by reflecting on long-form text, books, editing, fiction, resilience, and why good stories may help train better AI behavior and better human judgment. New stack article comparing harnesses Transcript
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S1 · E24
September 1 · 24 min
Ep 24: How to Make Difficult Change Happen With Mission Critical Systems Expert Jon Berger
Shownotes Book Launch: What Happens When You Are Not in the Room by Jon Berger What Happens When You Are Not In The Room? Buy on Amazon UK Buy on Amazon US Anne Currie welcomes back mission critical software delivery expert Jon Berger for a book launch conversation about leadership, management, and strategy. This episode focuses on the hard-won lessons behind Jon’s new book, why he wrote it, and the practical models he uses to think about whether leadership is actually working. We discuss what it means to lead well in different contexts, how to reduce the pain that poor leadership creates, and why the most important measure of success is what happens when you are not in the room. Key topics Anne introduces the episode as a book launch conversation with Jon Berger, her irregular co-host and a tech veteran with 30 years of leadership experience. Jon shares that his book, What Happens When You Are Not In the Room, is about leadership, management, and strategy as tools, not a single magic answer. The conversation centers on the idea that leadership is often about pain: pain experienced by leaders pain experienced by teams pain caused by poor management and poor delivery Jon explains that he wrote the book to capture lessons from his own career, including times when he led well and poorly, and to help others avoid repeating the same mistakes. He describes how training, coaching, and teaching leaders across his organization gave him a broader view than his own experience alone. Anne and Jon discuss why the book title is a question rather than an instruction: success is measured by what happens when you are not in the room leadership is about influencing others, not doing the work yourself context matters, so a single fixed answer is rarely enough Jon outlines the kinds of ideas in the book: stories that illustrate problems and solutions questions leaders should ask models for thinking more clearly about context loops that help people make decisions and get unstuck Jon highlights the car model as a simple way to assess leadership: motivation is the engine and gas strategy is the steering bottlenecks are often the brake Anne points to the followability model as another useful framework, especially the distinction between: whether people want to follow you whether they are actually able to follow you right now The discussion emphasizes that good leadership starts by recognizing where teams really are, including when they are “in a hole,” before pushing them toward a vision. Anne closes by praising the book as practical, self-aware, and useful for anyone trying to lead teams and deliver difficult work better. Buy on Amazon UK Buy on Amazon US
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S1 · E23
August 24 · 42 min
Ep 23: Scale Demands Strategy! With Tech Veteran Jon Berger
Shownotes Anne Currie talks with mission critical software veteran Jon Berger about his upcoming book on strategy, leadership and management, shaped by 30 years of experience across startups, scale-ups, and Microsoft. The conversation focuses on why context matters, how to build alignment, and why internal teaching, writing, and speaking can make leaders more effective when they are not physically present. They also discuss Team Topologies, internal tech conferences, and how AI may create a real discontinuity in team evolution and software delivery. Key topics Jon Berger explains that his book, What Happens When You're Not in the Room, comes from three decades of building, growing, and leading teams at many scales Anne and Jon discuss why management advice often fails when it is treated as a universal template instead of something that depends on context Jon describes the value of internal training courses, brown bag sessions, and company blogs as ways to build trust, spread ideas, and sharpen communication The episode revisits alignment as both a strategic concept and a cultural one Jon breaks strategy into three questions: what are we trying to achieve, what is difficult about it, and how are we going to approach that difficulty Team Topologies is discussed as a way of organizing multiple teams so they can scale, stay aligned, and avoid stepping on each other’s toes Jon highlights team cognitive load as a key insight from Team Topologies and one that matters in real organizations The conversation turns to AI and whether engineering teams can evolve incrementally toward AI-assisted or AI-led workflows, or whether that change may be a discontinuity instead Anne and Jon compare incremental experimentation with a more abrupt shift where engineers stop reviewing code and AI becomes the main producer Jon reflects on the difference between useful history in code and tech debt that should not be carried forward Action items Think about whether your team is solving for the right goal, the real difficulty, and the right approach Use internal speaking, teaching, or writing to help ideas survive after you leave the room Review whether your organization has useful alignment at both the strategic and cultural level Consider whether AI adoption in your team is incremental or whether it may require a more deliberate rethinking of team structure
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S1 · E22
August 17 · 52 min
Ep 22: AI is Forcing A Rethink of Organisation Learning with Team Topologies Author Matthew Skelton
Internal Conferences, Team Topologies, and the AI Learning Organization Anne Currie speaks with Matthew Skelton, co-author of the bestseller Team Topologies and CEO of Conflux, about why AI is exposing weak organizational design and making intentional knowledge-sharing more important than ever. They chat about how internal conferences, shared language, and clear team boundaries help organizations align faster, reduce duplication, and spread useful practices across the business. The conversation also introduces Matthew’s upcoming book Adapt Together and a new leadership program, Engineering the AI Native Organization. Key topics In this episode, Matthew explains that AI is acting like a bright light on organizational effectiveness, making unclear operating principles and vague responsibilities much harder to ignore. He connects Team Topologies to the broader challenge of organizational architecture, arguing that value flow depends on clear team boundaries, aligned workflows, and shared understanding. Matthew and Anne discuss how internal conferences at places like Financial Times, Metaswitch, and Klarna helped teams share knowledge, avoid duplication, and accelerate cloud native adoption. They emphasize that the real value of conferences is not just the talks, but the hallway track, where context, nuance, and shared meaning are built through conversation. Matthew introduces an innovation and practices enabling team, or IPET, as a way to spot emerging good practices, curate them, and help them spread across the organization. The episode highlights the importance of intentional knowledge diffusion, especially now that AI systems depend on human-generated context and well-aligned terminology. They discuss why leaders must define what good looks like, rather than assuming work is obvious or that teams will naturally converge on the right approach. Matthew argues that organizations need a continuous refresh of context because what worked three months ago may already be outdated in fast-changing environments. The conversation closes with Matthew describing Engineering the AI Native Organization, a leadership program for helping non-engineers understand concepts like APIs, decoupling, and asynchronous working in organizational terms. #AI #learningorganisations #TeamTopologies #ITRevolutions Transcript
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S1 · E21
August 10 · 54 min
Ep 21: What Do Enterprises Have to Fear When Adopting AI? With ex-FT Technical Director Sarah Wells
Navigating the Evolution of Enterprise Tech: Cloud Native, AI, and Beyond In this episode, Anne Currie and Sarah Wells, ex Financial Times engineering leader and O'Reilly author, explore how enterprises survive rapid technological shifts from cloud native transformations to the rise of AI. They discuss lessons learned from the Financial Times’ cloud journey, the comparative impact of AI on software development, security concerns, and the importance of fostering resilient, adaptable teams in the ever-changing tech landscape. Key Topics: Lessons from the Financial Times’ migration from on-premise to cloud, emphasizing gradual transition and cultural change. Insights into cloud native adoption and what enterprises learn through trial and error in cloud journeys. How AI is transforming development practices, including benefits, pitfalls, and the critical need for asking the right questions. The widening gaps in AI governance, security risks, and the importance of automating security in code reviews and deployment pipelines. Comparing the industry’s early days of internet boom with current AI evolution and understanding the long arc of technological adoption. The enduring necessity of software design, architecture, and human oversight in an AI-enhanced environment. Challenges around junior engineers working with AI tools, the risk of over-reliance, and the importance of effective collaboration. The legal, security, and ethical implications of AI, including managing vulnerabilities and maintaining trust. Reflecting on how past technological disruptions, like the decline of physical newspapers and the rise of digital, inform current industry shifts. Transcript
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S1 · E20
August 3 · 40 min
Ep 20: AI Data Centers Must Be Sustainable! with Neuralwatt CEO Chad Gibson
When we make AI data centres expensive and unsustainable that's a choice because they don't have to be. In this episode of Asynchronous and Unreliable, host Anne Currie speaks with Chad Gibson, ex Microsoft co-founder and CEO of AI host and infrastructure company Neuralwatt, to discuss innovative approaches to reducing the energy footprint of AI. Discover how layered technologies, transparency, and customer-driven model choices can help to shape greener, cheaper, and more efficient AI. Key topics NeuralWatt’s multi-layered approach to AI energy efficiency, from GPUs to inference cloud services The key role of software optimization in reducing power consumption and improving model efficiency How the demand for open source and Chinese models influences energy efficiency strategies Challenges in serving large, long-context models like GLM 5.2 and their operational impacts The importance of transparency and customer demand in achieving sustainable AI practices The potential of autonomous agents for further optimizing energy consumption Industry trends in hardware, model size, and software innovation pushing AI toward scalability Real-world examples of data center practices that either hinder or help sustainability goals Transcript Neuralwatt
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S1 · E19
July 27 · 1 hr 3 min
Ep 19: Latest News in AI Code Generation From Mission Critical Software Expert Martin Davidson
Shownotes Exploring AI, Software Development, and Future Possibilities with Martin Davidson for a discussion on #Rustlang, the AI SDLC, Join us for a deep conversation on the latest in AI experimentation, software engineering, and reimagining hardware and UI design. Martin Davidson returns to share insights from his ongoing projects, exploring how AI models like Claude and Codex are transforming the way we build and understand software. #rustlang Main Topics Covered: Martin’s current AI experimentation setup leveraging multiple subscriptions from Anthropic and OpenAI to write complex applications in Rust The shifting landscape of AI models, including the rise of smaller, more capable models like Sol The concept of "software on demand" — creating applications and systems dynamically with AI Reimagining operating systems and hardware on demand, inspired by edge computing and unikernels Challenges and opportunities with AI-generated code and system designs, including over-engineering tendencies The evolving infrastructure for software development, including automations, bug tracking, and CI/CD systems The philosophical implications of interacting with AI models as collective "we," and their increasing personality and utility The future of AI’s role in hardware design, programming languages, and the potential for safer, more efficient systems This episode provides a fascinating glimpse into the future of AI-assisted software creation, hardware design, and the evolving relationship humans have with intelligent systems. Martin's great substack Transcript This is the temperature/cognition site Martin mentioned: https://0x4d44.github.io/thermal-mind/ - And this is about getting Win2K working: https://0x4d44.github.io/win2k/ - This is Colonization & Civ: https://0x4d44.github.io/world-in-640k/ - This is Uno, sorry, Onu: https://0x4d44.github.io/onu/ - A broadband speedchecker: https://0x4d44.github.io/broadband-speed-checker/ - Ocean currents: https://0x4d44.github.io/ocean-currents/
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S1 · E18
July 20 · 31 min
Ep 18: What Happens When You Are Not In The Room? Jon Berger Reveals His New Book
What Happens When You're Not in the Room? - Leadership, Media, and Writing Insights #leadership #management #software Book reveal - Jon Berger's new book on leadership and management, What Happens When You Are Not In The Room? comes out on the 1st September 2026. In this episode, Anne Currie and mission critical software and leadership veteran Jon Berger explore the nuances of leadership, the art of writing books, humour in professional communication, and media representations of leadership. They delve into the importance of leading through influence rather than presence, the joys and challenges of authoring, and the relevance of leadership in TV, film, and fiction. Key Topics: The core idea that leadership occurs primarily when you're not in the room The significance of influencing others to achieve results The concept of leadership at scale versus micro-managing Jon Berger’s new book: What Happens When You Are Not In the Room? The value of humor and storytelling in professional writing Personal reflections on mistakes and learning as a leader Leadership as a collective effort, akin to assembling a team or a story Leadership lessons drawn from TV shows, films, and books The importance of encouraging everyone in an organization to be a leader The process and motivation behind writing a book We'll send you a bonus chapter of the book by signing up at JonBergerLeadership.com Episode transcript
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S1 · E17
July 13 · 55 min
Ep 17: How Could AI Write Software Much Better Than Humans? With Jon Berger
The Future of Software: Insights from Experience Discover how seasoned experts (aka old people in computer years) predict the evolution of software development over the next decade, focusing on #AI's transformative role, hardware implications, and the potential for machine-only software. In this episode: Reflection on recent episodes discussing AI and code generation and what they imply for the future The challenge of maintaining software The shift from human to AI-centric code creation and the implications for trust and reliability How hardware limitations and technological advances will shape software design and optimization The concept of "wet code" — intentionally un-Dry, performance-optimized, AI-generated code that forgoes human maintainability The potential for machine code to replace high-level programming languages entirely The evolution from microservices as team management tools to pure operational resilience components The revolutionary impact of AI training on code creation, emphasizing self-learning models akin to AlphaGo The concept of unikernels and the radical idea of single-kernel systems replacing traditional OS structures The importance of shifting from maintainable code by humans to trust-based, AI-driven code systems This episode explores paradigm-shifting ideas about the core foundation of software and hardware in a future where AI rewrites the very fabric of code creation and system design. Transcript
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S1 · E16
July 6 · 1 hr 8 min
Ep 16: Did He Recreate Amazon S3 At Home? With Ex Docker CTO & Unikernels Co-Founder Justin Cormack
A wild ride through Justin Cormack's attempts to build really hard stuff using AI. Did he recreate Amazon s3 in his back bedroom in a couple of weeks? A fun dive into the fascinating world of AI-assisted software development, Unikernels, and the future of programming languages, with Justin Cormack. Justin was the CTO of Docker for a decade, is one of the founders of Unikernels and is a leading figure in cloud-native and systems programming. In this episode, Anne hears all about his current experiments with building big, proper systems code using AI. We also hear about his thoughts on the future of languages. Does Rust have it nailed or might there be something better coming along? Should we really have coding languages at all anymore? In this episode we hear about: Justin's recent experiments with AI-generated code and how it accelerates complex software creation The concept of unikernels and how they differ from traditional operating systems Challenges and opportunities in building cloud-native and AI-optimized software systems - good, hard, scaleable software The role of programming languages like Rust, Zig, and emerging trends in AI-friendly languages Justin's newsletter #Nix, #RustLang, #AI
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S1 · E15
June 29 · 57 min
Ep 15: How Are Systems Changing For Operational Efficiency & Resilience? With IBM's Holly Cummins
In another friendly conversation, Anne and Holly Cummins of IBM discuss IBM's cloud native platform Quarkus and why it is so, so important right now. You've got to be able to turn it off and on again for operatioal efficiency, to take any advantage of the cloud and - vitally - for security because in the new world, patches have to be applied rapidly. Anne and Holly, Java champion, explore the evolution of Java from its initial flexible, runtime-centric design to a modern cloud-native, operationally efficient platform. Holly shares insights into how the shift to immutable, start-up friendly environments like Quarkus is transforming how we build and deploy Java applications in the era of cloud computing, AI, and sustainability. #Java #Javasoftware #security #operationalefficiency #ai #podcast Transcript
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S1 · E14
June 22 · 45 min
Ep 14: How Is Security Changing to Handle AI? With Cybersecurity Expert Adrian Mouat from Chainguard
In this episode, Anne and industry expert Adrian Mouat of security firm Chainguard and author of O'Reilly's Using Docker discuss the "crazy" rapid evolution of AI, its implications for cybersecurity, the future of software development, and the urgent need for adaptive security practices. They also discuss the brand new Athena coalition for accelerating patches against vulnerabilities in the face of AI-driven attacks Main Topics: The accelerating pace of vulnerabilities and exploitations in cybersecurity The impact of AI on the entire Software Development Life Cycle (SDLC) Mythos and the potential existential threats posed by advanced language models The influence of science fiction themes on real-world AI development Practical security measures: CI/CD, sandboxing, API token management, and blast radius reduction Emerging industry responses: Project Athena and AI security coalitions Transcript #AI #cybersecurity #cloudnative #software
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S1 · E13
June 15 · 1 hr 3 min
Ep 13: What Does Quality Mean for AI Generated Code? With Yanqing Cheng CEO of AI Startup Tollens.ai
How Do We Make AI-Generated Code Good? Anne and Yanqing Cheng, founder of AI startup Tollens.ai, explore the future of software quality, AI management, and organizational trust. Discover how holistic engineering management, trust, and organizational dynamics influence AI and software quality. Key Topics The evolving definition of quality in AI-generated software Tollens.ai's approach as an engineering management consultant The importance of holistic management and decision-making How AI tools can aid managers and engineers in complex decisions Challenges of trust, psychological safety, and organizational dynamics in AI deployment The impact of AI on software productivity and the future of coding Managing bottlenecks in AI-powered engineering workflows Strategies for capturing and leveraging organizational knowledge The gradual transition to AI-native companies and the pace of change Practical tips for integrating AI into daily workflows to reduce friction transcript Blue Tuxedo Music licensed through Soundstripe. Code: 3XRM21XFMMGPO1PF
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S1 · E12
June 8 · 41 min
Ep 12: How Can We Trust AI To Build Software? With Jon Berger
Shownotes Title: Navigating AI's Impact on Software Development and Trust O'Reilly author Anne Currie and Mission Critical software expert Jon Berger discuss the question of the moment: how can we entrust AI with the software that underpins everything? Join Anne and Jon as they explore real experiments, strategic implications, and practical steps for leaders in a rapidly evolving tech landscape. Main Topics: The future of AI in software engineering: rapid development and risk management Leadership strategies for integrating AI with team dynamics and trust Fundamental principles in software and life that AI may reshape Practical ways to start using AI safely in development cycles The importance of diversified oversight and multiple 'oracles' in AI deployment In this episode: How AI is changing the long-term landscape of human versus machine-built software Why understanding your unique business context matters in adopting AI tools The significance of experimenting with low-risk AI integrations like code review and testing The evolving concept of trust in AI-generated code and processes Strategic leadership tips: balancing risks, risks awareness, and fostering innovation Timestamps: 00:00 - Welcome and episode overview on AI's influence on software creation 01:22 - Transition from human-only to AI-assisted software engineering 02:48 - The importance of understanding your context as a leader 03:46 - Considerations for teams experimenting with AI in development 04:19 - Risks and opportunities in AI-driven software processes 05:50 - How speed and scale shape business decisions in AI-enabled environments 07:12 - The role of leadership in managing AI adoption and team dynamics 08:37 - Critical thinking about AI's impact on team sizes and productivity 10:30 - Market-driven decisions: layoffs and strategic AI integration 12:15 - Choosing trustworthy sources and multiple perspectives ("oracles") 13:55 - Balancing risk-taking and risk mitigation among teams 15:22 - Protect the future versus change the future in tech strategies 16:02 - Assessing environment and risk: high-stakes vs low-stakes AI applications 17:11 - Supporting existing systems with AI: deployment, testing, and feedback loops 19:22 - Trust, testing, and automation in the software lifecycle 21:23 - The changing nature of AI models and managing their variability 22:26 - The importance of agility and adaptability in a fast-changing AI landscape 23:05 - Valuing diverse team roles, including skeptics and early adopters 27:28 - Embracing AI as a black box: focusing on outcomes rather than process transparency 30:56 - Revisiting core principles of system resilience in an AI world 31:52 - How AI shifts decision-making trade-offs and process scaling 32:24 - Moving forward with trust: aligning business goals with AI capabilities 33:22 - Starting small: low-risk AI applications in testing and review 36:19 - Building confidence with modular, trustable AI-driven processes 39:38 - Actionable strategies for leaders: experiment, assess, and iterate safely Note: For further insights into managing AI in software development, stay tuned for upcoming episodes on security, testing, and team leadership adaptations. Full transcript
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S1 · E11
June 1 · 42 min
Ep 11: How Did We Write an O'Reilly Tech Book? With Sara Bergman of Microsoft
Building Green Software: Insights from the Authors or How To Write an O'Reilly Book This episode features Anne Currie and Sara Bergman discussing the journey of writing Building Green Software, the lessons learned, and the ongoing impact of eco-conscious software development in industry and academia. They share behind-the-scenes insights, the importance of collaboration, and practical advice for authors. Key Topics: The origin story and motivation behind Building Green Software The collaborative process of co-authoring a technical book Navigating publishing logistics, contracts, and setting deadlines The significance of translating technical work into global accessibility Managing co-authorship across different languages and cultural backgrounds The impact of industry events like QCon in shaping publication timelines Post-publication reflections: successes, challenges, and future updates The role of projects like the Green Software Foundation in shaping the book’s content Lessons learned about contractual liability and company formation for authors The balance between technical rigor, storytelling, and engaging communication in technical writing Shownotes and transcript #sustainability #greensoftware #writing #distributedsystems #technology #softwareengineering #oreilly #buildinggreensoftware #publishing
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