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

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.

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

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.

• 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.

• 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

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.

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

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

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

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

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

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

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

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

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

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/

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

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

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

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

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

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

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

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

What Is Platform Engineering and how will it work in an AI world? An In-Depth Conversation with Paula Kennedy

In this episode, Anne Currie chats with old friend and colleague Paula Kennedy, COO of platform engineering startup Syntasso about the fundamentals of platform engineering, its evolution, and its impact on organizations of all sizes. They explore how platform engineering helps manage the growing cognitive load in modern software development and operational practices.

This episode is a great companion to episode 3 - Niki Manoledaki's on platform engineering at Grafana Labs

Key Topics Covered:

• The origins and definition of platform engineering, tracing back to the earliest days of software development
• How platform engineering bridges the gap between code creation and deployment infrastructure
• The role of platform teams as enablers and marketplace providers within organizations
• Ownership, responsibility, and the importance of platform as a product mindset
• The impact of AI on platform engineering, automation, and operational efficiency
• Avoiding bottlenecks: scaling platform capabilities to meet rising code generation and operational demands
• Practical insights into managing complexity, security, and costs across organization-wide platforms

Timestamps:

• (0:00) – Introduction to platform engineering and tools for easy editing of recordings
• (3:00) – Defining platform engineering as the space between code and infrastructure
• (6:00) – The evolution from traditional IT to DevOps and modern platform teams
• (9:00) – How cognitive load and responsibility are distributed across teams
• (12:00) – Historical perspective: from separate dev and ops teams to integrated platforms
• (15:00) – The role of ownership and product mindset in platform success
• (20:00) – The marketplace model for platform services, enabling internal contribution
• (23:00) – The importance of clear boundaries and ownership akin to cloud provider models
• (28:00) – The influence of AI and agents on future platform strategies
• (36:00) – Recognizing system bottlenecks and scaling horizontally as a key challenge
• (37:00) – The timeless nature of operational principles and organizational constraints

For transcript

Join us in this enlightening episode as Anne Currie chats with Jamie Dobson, co-founder of Container Solutions and author of Rebels, Visionaries, and Machines. They dive into the history of technology, the evolution of the tech industry, and the challenges and opportunities in communicating complex ideas in tech today. This conversation offers valuable insights for tech enthusiasts, educators, and communicators alike.

Key Topics Covered:

• The history of cloud computing and its cultural impact since 1799
• How tech conferences like Software Circus aimed to reshape industry conversations
• The ethical and social implications of AI and automation in the workforce
• The importance of understanding problem context before choosing tools
• Strategies for explaining complex technical ideas without oversimplification
• Lessons from Mary Shelley's Frankenstein applied to tech ethics and responsibility
• The challenge of capturing audience attention in storytelling and education
• How to communicate the essence of microservices, patterns, and cloud-native concepts effectively
• Reflecting on the role of stories and analogies in making tech more accessible

#tech #AI #techconferences #cloudnative #techcommunications

Will AI-driven software eliminate the need for traditional coding teams alogether? Is all the system software going to get re-written in Rust? What about maintenance and operations?

Shownotes & transcript

#tech #podcast #softwareengineering #AI #sustainability #green #software #distributedsystems #rustlang

- Martin Davidson’s career evolution from high-resilience telco software to AI and machine learning 

- The concept of oracle-driven development for AI-generated code quality 

- Practical experimentation with rewriting libraries and building emulators in Rust using AI

- The extension of traditional unit testing to fuzz testing, differential testing, and other AI-powered validation 

- The importance of defining success criteria ("what good looks like") upfront in AI projects 

- Parallelization strategies in AI and software architectures: from agents to cores and teams 

- Organizational implications of AI-driven productivity increases and automation 

- Future outlook for legacy software companies amid AI disruptions 

- The cultural and economic impacts of AI on software engineering careers and industry stability

#tech #podcast #softwareengineering #AI #sustainability #green #software #distributedsystems

shownotes and transcript: https://www.asynchronousunreliable.com/asynchronous-and-unreliable-e7

Discover how the rapid shift from fossil fuels to renewables, especially solar and wind, is shaping the tech industry and global economy. Hosted by Anne Currie and Sara Bergman, co author of O'Reilly's Building Green Software, this episode explores the exponential growth of renewable energy, the role of software in optimizing energy use, and the geopolitical and environmental implications of this transition.

Main insights:

• The exponential rise of solar energy generation and its implications
• The importance of grid management and energy storage solutions in a renewable-powered world
• How different countries are adopting strategies like East-West data centers in China and time-of-use tariffs in India to optimize renewable energy consumption
• The role of software in enabling carbon-aware computing and energy efficiency
• Challenges and opportunities of integrating variable renewable sources into the energy mix
• The historical context of fossil fuels as first gear in industrial development and the move towards more complex, resilient energy systems
• Innovations like balcony solar panels and plug-in solar devices, and their potential for widespread adoption
• The geopolitical considerations, including nuclear debates in Sweden and France's energy independence
• How increasing renewable infrastructure can accelerate economic development, especially in Africa

Shownotes and transcript

#tech #podcast #softwareengineering #AI #sustainability #green #software #distributedsystems

If you wonder why people have no real clue about the nines resilience scale, this is why.

In the fourth episode of Asynchronous and Unreliable, amongst many other subjects, Anne & writer, author, and editor Charles Humble discuss the past and future of tech communication.

Discover how communication methods in tech have evolved over decades, from early magazines and conferences to the challenges and opportunities presented by AI and large language models. This episode explores the importance of clear, honest, and varied communication for tech progress and societal impact.

In this episode:

• The historical shifts from magazine learning, to books, to conferences and open source, and finally even podcasts for tech education
• The role of conferences in motivating further reading and understanding complex topics
• How the reliability of spoken vs. written communication impacts knowledge sharing
• The significance of context, audience, and repetition in effective communication
• Challenges of AI-generated content: surface-level writing, fact-checking, and nuance
• The societal implications of transparency and responsible tech communication
• Strategies for engaging non-technical audiences in tech debates
• Personal stories and lessons from industry veterans about editing, clarity, humility and not giving up (all early drafts are rubbish)

#tech #AI #communcations #conferences #literacy #chatbots

Shownotes and transcript

#cloud #kubernetes #karpenter #kepler #VPA #CNCF #opensource #earthday #sustainability

In this episode of Asynchronous and Unreliable, host Anne Currie is joined by long-time tech veteran Jon Berger to explore the critical importance of code efficiency, especially in the networking software that underpins the internet’s performance and resilience. They delve into practical comparisons between operational and code efficiency, the impact of AI on high-performance software, and how scale influences software optimization strategies.

Key Topics:

• The necessity of ultra-efficient code in networking software managing billions of packets per second
• Differences between operational efficiency, systems design, and code efficiency
• The exponential scale of code efficiency impacts versus the linear scale of operational efficiency
• How high-frequency trading and networking code both push the limits of performance, often in assembler and machine language
• The influence of AI and automation on future high-performance software development
• Trade-offs between hardware reliance for speed versus software optimization
• The impact of scale: Large companies like Google vs small startups in operational and code efficiency
• Challenges of maintaining operational efficiency at scale in different sized businesses
• The role of human expertise versus AI in optimizing software for resilience, energy, and security
• The importance of aligning software performance strategies with business goals like growth or cost reduction

Shownotes and transcript

Key Topics:

• The balance between code efficiency and operational efficiency in sustainable software development
• How AI is increasingly capable of optimizing code performance, including rewriting for efficiency
• The shift from hardware solutions to smarter software and operational practices for sustainability
• The strategic use of multi-tenancy and hardware flexibility to enhance efficiency and sustainability
• The significance of face-to-face conferences, hallway track networking, and speaking opportunities for professional growth
• The impact of transitioning from traditional hardware dependence to chip-agnostic, multi-platform code
• The industry's response to energy constraints, energy transition, and the potential environmental benefits of energy-aware design
• The power of community, feedback, and continuous learning through conferences and meetups
• The evolving role and perception of tech investments in a changing economy

This episode emphasizes that success in tech sustainability involves not only technical innovation but also community engagement, face-to-face interactions, and continual learning. With tools advancing and energy concerns growing, the right operational practices and hardware flexibility are more vital than ever .

Shownotes and transcript

Anne on LinkedIn

It's hosted by Anne Currie, co-author of O'Reilly's Building Green Software, The Cloud Native Attitude, and the science fiction Panopticon series.

Expect regular guests and lighthearted discussion of some of the most fascinating & sometimes challenging ideas in tech.