Asynchronous & Unreliable - Tech & AI Podcast

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.

AI Teams, Niceness and Who's Responsible — Martin Davidson, Part 2Martin 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 #AIdevelopmentTranscript

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

ShownotesBuilding reliability out of unreliable humans, with unreliable human Anne CurrieWhat 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 coveredThe 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 questionDoes a shorter solo episode work as an occasional graceful downgrade? I’d love to know whether you enjoyed this one.

ShownotesAI, authors and the business of telling stories—with Katherine PathakCould 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 transcriptThe 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