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.

  1. 4 days ago

    Ep 33: Will Open Source Maintainers Survive AI? With OpenUK and the CNCF's Liz Rice

    Liz Rice, O'Reilly author and open source expert joins Anne to talk about the problems faced by OSS maintainers in the face of a tsunami of AI-generated PRs. When does being helpful become more like an attack? What is the purpose of seeking contributions to an open source project? Why are huge PRs from folk you don't know a problem not a solution? In this short episode, which follows on from their discussion of EBPF in episode 32, Anne and old friend Liz explore the future of open source in a software world that will increasingly be dominated by the bots. Will AI save OSS or destroy it? Transcript

  2. 25 Oct

    Ep 32: AI, Security and eBPF: Buying Time Against the Hackers with AI & Security Expert Liz Rice

    What happens when attackers can exploit vulnerabilities faster than organisations can patch them — sometimes before the vulnerabilities have even been publicly disclosed? Anne talks to Liz Rice, Chief Open Source Officer at Isovalent (Cisco), CNCF governing board member, O’Reilly author and eBPF security expert, about how AI is changing the economics of cyberattacks, why conventional patching and firewalls are no longer sufficient on their own, and how eBPF provides another line of defence. Liz explains how Cilium and Tetragon use the Linux kernel to observe activity, enforce policies and deploy highly targeted protections against known exploits — without restarting running applications. Along the way, they revisit their early cloud-native startup, Microscaling, discuss AI agents behaving like hackers inside their own organisations, explore the eBPF verifier and Linux containers, and examine Cisco Hypershield Live Protect. They finish with a memorable example of supermarket refrigeration systems being compromised through poor access configuration: a reminder that not every security failure is a software vulnerability.

  3. 19 Oct

    Ep 31: Is Being Nice a Useful AI Mgmt Tool? With AI Expert Martin Davidson

    AI Teams, Niceness and Who's Responsible — Martin Davidson, Part 2 Martin Davidson returns for the second half of our conversation about working with AI agents. We discuss niceness, delegation and agents that negotiate with one another, as well as code optimisation, rewriting Windows 3.1 in Rust, and the growing gap between what AI can do and what people think it can do. And when automation goes wrong, who's responsible? #rustlang #AIdevelopment Transcript

  4. 12 Oct

    Ep: 30 What Do Your Agents Do After You've Gone To Bed? With AI Experimenter Martin Davidson

    ShownotesWhat 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 episodeHow 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 conversationMartin’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

  5. 5 Oct

    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.

  6. 28 Sept

    Ep 28: Will AI Write Good Novels? With Bestselling Human Crime Writer Katherine Pathak

    ShownotesAI, 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 Rose01:40 Human authors in an age of AI03:39 Audiobooks, abridgement and adaptations10:50 When audiobook production becomes a bottleneck12:14 Successful series, reluctant creators and literary successors19:46 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 stories31:22 Celebrity authors, ghostwriters and the value of a name35:17 Cups of tea, sighing and the habits that give writers away38:23 Why setting matters—and the appeal of British crime fiction40:24 AI’s role in the work surrounding the writingRead 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

  7. 21 Sept

    Ep 27: AI & Human Alignment With AI Solopreneur Yanqing Cheng

    ShownotesAre 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 existenceThe difference between a base model, RLHF, reasoning training, and later synthetic post-trainingWhether books contain a more reflective version of humanity than short-form social mediaWhat raising a child suggests about learning, alignment, judgment, and the desire to be goodWhy fairy tales and science fiction let people update their mental models using fictional evidenceThe limits of text-only training when reality contains far denser informationHow open research and shared techniques are accelerating Chinese AI developmentRecursive improvement, international regulation, open weights, accidents, and deliberate misuseWhy Anne and Yanqing remain hopeful even while acknowledging that optimism may be wrongAction 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

  8. 14 Sept

    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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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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