Is AWS Lambda still serverless? With Managed Instances, MicroVMs, and Durable Functions, Lambda can now run workloads it was never designed for. AWS Serverless Hero Yan Cui explains what changed, what you give up, and why the core serverless promise still holds.
Romain sits down with Yan Cui, AWS Serverless Hero and independent consultant, to discuss how Lambda grew into general-purpose compute and where it fits for AI agents.
Key topics covered: • Why pay-per-use is no longer the whole story, and why that's fine • Lambda vs Fargate vs ECS: packaging a container image is not running a container • Lambda Managed Instances, the 90-minute timeout, and Durable Functions for jobs that run up to a year • Cold starts: why they were never as big a problem as the conversation around them • Lambda MicroVMs as a second sandbox for AI agents and CI/CD runners • AgentCore Runtime vs Lambda MicroVMs for agentic workloads • Observability for event-driven architectures, and what Lambda still gets wrong • Yan's rule: every box on your architecture diagram needs a reason to exist
Chapters: 00:00 Highlights 00:55 Meet Yan Cui, AWS Serverless Hero 05:23 Is Lambda still serverless? 14:36 Lambda vs Fargate vs ECS: where to draw the line 20:46 Lambda Managed Instances explained 24:10 Are cold starts finally dead? 29:45 Lambda MicroVMs, AI agents and S3 Files 37:51 AgentCore Runtime vs Lambda MicroVMs 45:28 What serverless still gets wrong, and observability 56:07 Yan's advice, book pick and AI security worriesWith <a href='https://theburningmonk.com/posts/'>Yan Cui</a>, AWS Serverless Hero and independent consultant<li><ul><a href="https://aws.amazon.com/lambda/lambda-managed-instances/">AWS Lambda Managed Instances</a></ul><ul><a href="https://aws.amazon.com/about-aws/whats-new/2026/09/aws-lambda-90-minute-function/">AWS Lambda adds 90-minute function timeout for Managed Instances</a></ul><ul><a href="https://aws.amazon.com/lambda/lambda-microvms/">AWS Lambda MicroVMs</a></ul><ul><a href="https://docs.aws.amazon.com/lambda/latest/dg/durable-functions.html">Lambda Durable Functions documentation</a></ul><ul><a href="https://docs.aws.amazon.com/lambda/latest/dg/snapstart.html">Lambda SnapStart documentation</a></ul><ul><a href="https://docs.aws.amazon.com/lambda/latest/dg/configuration-filesystem-s3files.html">Amazon S3 Files with Lambda documentation</a></ul><ul><a href="https://aws.amazon.com/products/developer-tools/agent-toolkit-for-aws/">Agent Toolkit for AWS</a></ul><ul><a href="https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agents-tools-runtime.html">Amazon Bedrock AgentCore Runtime documentation</a></ul><ul><a href="https://strandsagents.com/">Strands Agents</a></ul><ul><a href="https://edjgeek.com/blog/claude-code-sandbox-for-ipad/">Eric Johnson's cloud development environment on MicroVMs and S3 Files</a></ul><ul><a href="https://www.honeycomb.io/">Honeycomb</a></ul><ul><a href="https://architectelevator.com/book/">The Software Architect Elevator by Gregor Hohpe</a></ul><ul><a href="https://developers.podcast.go-aws.com/web/episodes/198/index.html">Episode 198: Evolving Lambda with Michael Gasch</a></ul><ul><a href="https://theburningmonk.com/hire-me/">Production-Ready Serverless workshop by Yan Cui</a></ul><ul><a href="https://theburningmonk.com/posts/">Yan Cui's blog</a></ul><ul><a href="https://theburningmonk.com/hire-me/">Work with Yan Cui</a></ul><ul><a href="https://www.linkedin.com/in/theburningmonk">Yan Cui on LinkedIn</a></ul></li>

Is AWS Lambda still serverless? With Managed Instances, MicroVMs, and Durable Functions, Lambda can now run workloads it was never designed for. AWS Serverless Hero Yan Cui explains what changed, what you give up, and why the core serverless promise still holds. Romain sits down with Yan Cui, AWS Serverless Hero and independent consultant, to discuss how Lambda grew into general-purpose compute and where it fits for AI agents. Key topics covered: • Why pay-per-use is no longer the whole story, and why that's fine • Lambda vs Fargate vs ECS: packaging a container image is not running a container • Lambda Managed Instances, the 90-minute timeout, and Durable Functions for jobs that run up to a year • Cold starts: why they were never as big a problem as the conversation around them • Lambda MicroVMs as a second sandbox for AI agents and CI/CD runners • AgentCore Runtime vs Lambda MicroVMs for agentic workloads • Observability for event-driven architectures, and what Lambda still gets wrong • Yan's rule: every box on your architecture diagram needs a reason to exist Chapters: 00:00 Highlights 00:55 Meet Yan Cui, AWS Serverless Hero 05:23 Is Lambda still serverless? 14:36 Lambda vs Fargate vs ECS: where to draw the line 20:46 Lambda Managed Instances explained 24:10 Are cold starts finally dead? 29:45 Lambda MicroVMs, AI agents and S3 Files 37:51 AgentCore Runtime vs Lambda MicroVMs 45:28 What serverless still gets wrong, and observability 56:07 Yan's advice, book pick and AI security worriesWith Yan Cui, AWS Serverless Hero and independent consultantAWS Lambda Managed InstancesAWS Lambda adds 90-minute function timeout for Managed InstancesAWS Lambda MicroVMsLambda Durable Functions documentationLambda SnapStart documentationAmazon S3 Files with Lambda documentationAgent Toolkit for AWSAmazon Bedrock AgentCore Runtime documentationStrands AgentsEric Johnson's cloud development environment on MicroVMs and S3 FilesHoneycombThe Software Architect Elevator by Gregor HohpeEpisode 198: Evolving Lambda with Michael GaschProduction-Ready Serverless workshop by Yan CuiYan Cui's blogWork with Yan CuiYan Cui on LinkedIn

Is AWS Lambda still serverless? With Managed Instances, MicroVMs, and Durable Functions, Lambda can now run workloads it was never designed for. AWS Serverless Hero Yan Cui explains what changed, what you give up, and why the core serverless promise still holds.
Romain sits down with Yan Cui, AWS Serverless Hero and independent consultant, to discuss how Lambda grew into general-purpose compute and where it fits for AI agents.
Key topics covered: • Why pay-per-use is no longer the whole story, and why that's fine • Lambda vs Fargate vs ECS: packaging a container image is not running a container • Lambda Managed Instances, the 90-minute timeout, and Durable Functions for jobs that run up to a year • Cold starts: why they were never as big a problem as the conversation around them • Lambda MicroVMs as a second sandbox for AI agents and CI/CD runners • AgentCore Runtime vs Lambda MicroVMs for agentic workloads • Observability for event-driven architectures, and what Lambda still gets wrong • Yan's rule: every box on your architecture diagram needs a reason to exist
Chapters: 00:00 Highlights 00:55 Meet Yan Cui, AWS Serverless Hero 05:23 Is Lambda still serverless? 14:36 Lambda vs Fargate vs ECS: where to draw the line 20:46 Lambda Managed Instances explained 24:10 Are cold starts finally dead? 29:45 Lambda MicroVMs, AI agents and S3 Files 37:51 AgentCore Runtime vs Lambda MicroVMs 45:28 What serverless still gets wrong, and observability 56:07 Yan's advice, book pick and AI security worriesWith <a href="https://theburningmonk.com/posts/" rel="noopener noreferrer">Yan Cui</a>, AWS Serverless Hero and independent consultant<li><ul><a href="https://aws.amazon.com/lambda/lambda-managed-instances/" rel="noopener noreferrer">AWS Lambda Managed Instances</a></ul><ul><a href="https://aws.amazon.com/about-aws/whats-new/2026/09/aws-lambda-90-minute-function/" rel="noopener noreferrer">AWS Lambda adds 90-minute function timeout for Managed Instances</a></ul><ul><a href="https://aws.amazon.com/lambda/lambda-microvms/" rel="noopener noreferrer">AWS Lambda MicroVMs</a></ul><ul><a href="https://docs.aws.amazon.com/lambda/latest/dg/durable-functions.html" rel="noopener noreferrer">Lambda Durable Functions documentation</a></ul><ul><a href="https://docs.aws.amazon.com/lambda/latest/dg/snapstart.html" rel="noopener noreferrer">Lambda SnapStart documentation</a></ul><ul><a href="https://docs.aws.amazon.com/lambda/latest/dg/configuration-filesystem-s3files.html" rel="noopener noreferrer">Amazon S3 Files with Lambda documentation</a></ul><ul><a href="https://aws.amazon.com/products/developer-tools/agent-toolkit-for-aws/" rel="noopener noreferrer">Agent Toolkit for AWS</a></ul><ul><a href="https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agents-tools-runtime.html" rel="noopener noreferrer">Amazon Bedrock AgentCore Runtime documentation</a></ul><ul><a href="https://strandsagents.com/" rel="noopener noreferrer">Strands Agents</a></ul><ul><a href="https://edjgeek.com/blog/claude-code-sandbox-for-ipad/" rel="noopener noreferrer">Eric Johnson's cloud development environment on MicroVMs and S3 Files</a></ul><ul><a href="https://www.honeycomb.io/" rel="noopener noreferrer">Honeycomb</a></ul><ul><a href="https://architectelevator.com/book/" rel="noopener noreferrer">The Software Architect Elevator by Gregor Hohpe</a></ul><ul><a href="https://developers.podcast.go-aws.com/web/episodes/198/index.html" rel="noopener noreferrer">Episode 198: Evolving Lambda with Michael Gasch</a></ul><ul><a href="https://theburningmonk.com/hire-me/" rel="noopener noreferrer">Production-Ready Serverless workshop by Yan Cui</a></ul><ul><a href="https://theburningmonk.com/posts/" rel="noopener noreferrer">Yan Cui's blog</a></ul><ul><a href="https://theburningmonk.com/hire-me/" rel="noopener noreferrer">Work with Yan Cui</a></ul><ul><a href="https://www.linkedin.com/in/theburningmonk" rel="noopener noreferrer">Yan Cui on LinkedIn</a></ul></li>

Is AWS Lambda still serverless?

Is Jev just a smarter if-statement? That's the question everyone's asking about TypeSafe AI's new "System One" model. In this episode, we dig into what Jev actually is: a model that makes fast, typed decisions your code can use directly — and why it points to a bigger shift: stop defaulting to one giant LLM for everything.
Romain sits down with Mike Chambers, Senior Developer Advocate for Generative AI at AWS, to discuss Jev, decision models, and how they complement the LLMs we already know.
Key topics covered: • What Jev is, and why it is not just another LLM • The three primitives: Noul, Choice, and Score • Putting gated decision points inside an agent with Strands • Fast model routing, and why cost and speed are the real win • Why the right tool for the right job is back
Chapters: 00:53 Welcome, and what is exciting in the Bay Area 04:13 What is Jev? 05:24 Why it is not just another model 09:53 A decision model: moving logic back into code 17:31 System One, and Thinking Fast and Slow 19:02 The three primitives: Noul, Choice, Score 22:29 Accessing Jev: API, OpenRouter, SDKs 26:22 Use cases: gated decision points in agents 35:19 Model routing, cost and speed 40:17 Can Jev drive a DeepRacer, and other models to considerWith <a href='https://www.coursera.org/instructor/~87498030'>Mike Chambers</a>, Senior Developer Advocate for Generative AI at AWS<li><ul><a href="https://typesafe.ai/blog/introducing-system-one-models-and-jev">Jev by TypeSafe AI</a></ul><ul><a href="https://sunilmallya.github.io/jev-control-loop-moving-back-into-code.html">Sunil Mallya — The Control Loop Is Moving Back Into Code</a></ul><ul><a href="https://strandsagents.com/">Strands Agents SDK</a></ul><ul><a href="https://github.com/strands-agents/harness-sdk">Strands Agents Harness SDK</a></ul><ul><a href="https://mistral.ai/news/ocr-4/">Mistral OCR</a></ul><ul><a href="https://huggingface.co/blog/reachy-mini">Hugging Face Reachy Mini</a></ul><ul><a href="https://techcrunch.com/2026/08/27/hugging-face-is-selling-a-cute-399-open-source-duck-robot-microduck/">Hugging Face Microduck</a></ul></li>

Is Jev just a smarter if-statement? That's the question everyone's asking about TypeSafe AI's new "System One" model. In this episode, we dig into what Jev actually is: a model that makes fast, typed decisions your code can use directly — and why it points to a bigger shift: stop defaulting to one giant LLM for everything. Romain sits down with Mike Chambers, Senior Developer Advocate for Generative AI at AWS, to discuss Jev, decision models, and how they complement the LLMs we already know. Key topics covered: • What Jev is, and why it is not just another LLM • The three primitives: Noul, Choice, and Score • Putting gated decision points inside an agent with Strands • Fast model routing, and why cost and speed are the real win • Why the right tool for the right job is back Chapters: 00:53 Welcome, and what is exciting in the Bay Area 04:13 What is Jev? 05:24 Why it is not just another model 09:53 A decision model: moving logic back into code 17:31 System One, and Thinking Fast and Slow 19:02 The three primitives: Noul, Choice, Score 22:29 Accessing Jev: API, OpenRouter, SDKs 26:22 Use cases: gated decision points in agents 35:19 Model routing, cost and speed 40:17 Can Jev drive a DeepRacer, and other models to considerWith Mike Chambers, Senior Developer Advocate for Generative AI at AWSJev by TypeSafe AISunil Mallya — The Control Loop Is Moving Back Into CodeStrands Agents SDKStrands Agents Harness SDKMistral OCRHugging Face Reachy MiniHugging Face Microduck

Is Jev just a smarter if-statement? That's the question everyone's asking about TypeSafe AI's new "System One" model. In this episode, we dig into what Jev actually is: a model that makes fast, typed decisions your code can use directly — and why it points to a bigger shift: stop defaulting to one giant LLM for everything.
Romain sits down with Mike Chambers, Senior Developer Advocate for Generative AI at AWS, to discuss Jev, decision models, and how they complement the LLMs we already know.
Key topics covered: • What Jev is, and why it is not just another LLM • The three primitives: Noul, Choice, and Score • Putting gated decision points inside an agent with Strands • Fast model routing, and why cost and speed are the real win • Why the right tool for the right job is back
Chapters: 00:53 Welcome, and what is exciting in the Bay Area 04:13 What is Jev? 05:24 Why it is not just another model 09:53 A decision model: moving logic back into code 17:31 System One, and Thinking Fast and Slow 19:02 The three primitives: Noul, Choice, Score 22:29 Accessing Jev: API, OpenRouter, SDKs 26:22 Use cases: gated decision points in agents 35:19 Model routing, cost and speed 40:17 Can Jev drive a DeepRacer, and other models to considerWith <a href="https://www.coursera.org/instructor/~87498030" rel="noopener noreferrer">Mike Chambers</a>, Senior Developer Advocate for Generative AI at AWS<li><ul><a href="https://typesafe.ai/blog/introducing-system-one-models-and-jev" rel="noopener noreferrer">Jev by TypeSafe AI</a></ul><ul><a href="https://sunilmallya.github.io/jev-control-loop-moving-back-into-code.html" rel="noopener noreferrer">Sunil Mallya — The Control Loop Is Moving Back Into Code</a></ul><ul><a href="https://strandsagents.com/" rel="noopener noreferrer">Strands Agents SDK</a></ul><ul><a href="https://github.com/strands-agents/harness-sdk" rel="noopener noreferrer">Strands Agents Harness SDK</a></ul><ul><a href="https://mistral.ai/news/ocr-4/" rel="noopener noreferrer">Mistral OCR</a></ul><ul><a href="https://huggingface.co/blog/reachy-mini" rel="noopener noreferrer">Hugging Face Reachy Mini</a></ul><ul><a href="https://techcrunch.com/2026/08/27/hugging-face-is-selling-a-cute-399-open-source-duck-robot-microduck/" rel="noopener noreferrer">Hugging Face Microduck</a></ul></li>

Jev: is it just a smarter if-statement?

Most developers say AI makes them more productive. Most companies say their teams aren't shipping faster. What's going on?
In this special solo episode, Romain unpacks the data behind the "acceleration whiplash," walks through a four-level AI maturity framework built from hundreds of customer conversations, and shares the practical lessons that separate teams getting incremental gains from those achieving 10x outcomes. This is a recording of his AI-DLC presentation, delivered at the AWS Summit in Zurich and iterated based on feedback from startups, enterprises, and digital-native companies across Europe.
Key topics covered: • The adoption paradox: individual productivity up, team delivery flat • Why vibe coding is a mirage for production systems • Theory of constraints applied to AI-era software development • The four-level AI maturity framework: Traditional, AI-Assisted, AI-Augmented, and AI-Native • From prompt engineering to context engineering to loop engineering • Cross-functional teams and the evolution of Amazon's two-pizza teams • AI-DLC workshops: mob elaboration, construction bolts, and continuous delivery • Building AI fluency across your organization • The technology stack for AI-native development • Werner Vogels' Renaissance Developer and T-shaped engineers
Chapters: 00:00 Introduction and why this episode exists 01:28 The evolution of coding assistants (2004–2026) 05:01 Systems of agents and software factories 06:27 The adoption paradox: faster developers, slower teams 09:05 Vibe coding is a mirage 10:12 Theory of constraints and The Phoenix Project 12:17 You can't bolt AI on existing workflows 13:03 Start with why: what are you optimizing for? 15:33 The four-level AI maturity framework 24:30 Level 3 and 4: loop engineering and frontier teams 26:57 People, process, and technology transformation 37:56 The AI-DLC technology stack 41:09 Mindset: the Renaissance Developer 43:24 Practical lessons learned at every level<li><ul><a href="https://www.faros.ai/research/ai-acceleration-whiplash">Faros AI Engineering Report 2026 — The Acceleration Whiplash</a></ul><ul><a href="https://aws.amazon.com/blogs/enterprise-strategy/business-value-of-developer-experience-improvements-amazons-159-breakthrough/">Amazon's Cost-to-Serve Software Metric</a></ul><ul><a href="https://www.amazon.science/blog/measuring-the-effectiveness-of-software-development-tools-and-practices">Amazon Science — Measuring Software Development Effectiveness</a></ul><ul><a href="https://www.ernestchiang.com/en/posts/2025/aws-reinvent-2025-special-closing-keynote-with-dr-werner-vogels/">Werner Vogels' re:Invent 2025 Keynote — The Renaissance Developer</a></ul><ul><a href="https://medium.com/prometheus-ags/stop-prompting-start-designing-loops-5216138e23b3">Boris Cherny on Loop Engineering</a></ul><ul><a href="https://www.lennysnewsletter.com/p/netflix-cpto-on-ai-and-the-future">Netflix CPTO Elizabeth Stone — Systems Thinkers in the AI Era</a></ul><ul><a href="https://github.com/strands-agents">Strands Agents — Open-Source Agentic Framework</a></ul><ul><a href="https://kiro.dev/">Kiro — Spec-Driven Development</a></ul><ul><a href="https://aws.amazon.com/bedrock/agentcore/">Amazon Bedrock AgentCore</a></ul><ul><a href="https://itrevolution.com/product/the-phoenix-project/">The Phoenix Project — Gene Kim, Kevin Behr, and George Spafford</a></ul><ul><a href="https://simonsinek.com/books/start-with-why/">Start with Why — Simon Sinek</a></ul><ul><a href="https://teamtopologies.com/book/">Team Topologies — Matthew Skelton and Manuel Pais</a></ul></li>

Most developers say AI makes them more productive. Most companies say their teams aren't shipping faster. What's going on? In this special solo episode, Romain unpacks the data behind the "acceleration whiplash," walks through a four-level AI maturity framework built from hundreds of customer conversations, and shares the practical lessons that separate teams getting incremental gains from those achieving 10x outcomes. This is a recording of his AI-DLC presentation, delivered at the AWS Summit in Zurich and iterated based on feedback from startups, enterprises, and digital-native companies across Europe. Key topics covered: • The adoption paradox: individual productivity up, team delivery flat • Why vibe coding is a mirage for production systems • Theory of constraints applied to AI-era software development • The four-level AI maturity framework: Traditional, AI-Assisted, AI-Augmented, and AI-Native • From prompt engineering to context engineering to loop engineering • Cross-functional teams and the evolution of Amazon's two-pizza teams • AI-DLC workshops: mob elaboration, construction bolts, and continuous delivery • Building AI fluency across your organization • The technology stack for AI-native development • Werner Vogels' Renaissance Developer and T-shaped engineers Chapters: 00:00 Introduction and why this episode exists 01:28 The evolution of coding assistants (2004–2026) 05:01 Systems of agents and software factories 06:27 The adoption paradox: faster developers, slower teams 09:05 Vibe coding is a mirage 10:12 Theory of constraints and The Phoenix Project 12:17 You can't bolt AI on existing workflows 13:03 Start with why: what are you optimizing for? 15:33 The four-level AI maturity framework 24:30 Level 3 and 4: loop engineering and frontier teams 26:57 People, process, and technology transformation 37:56 The AI-DLC technology stack 41:09 Mindset: the Renaissance Developer 43:24 Practical lessons learned at every levelFaros AI Engineering Report 2026 — The Acceleration WhiplashAmazon's Cost-to-Serve Software MetricAmazon Science — Measuring Software Development EffectivenessWerner Vogels' re:Invent 2025 Keynote — The Renaissance DeveloperBoris Cherny on Loop EngineeringNetflix CPTO Elizabeth Stone — Systems Thinkers in the AI EraStrands Agents — Open-Source Agentic FrameworkKiro — Spec-Driven DevelopmentAmazon Bedrock AgentCoreThe Phoenix Project — Gene Kim, Kevin Behr, and George SpaffordStart with Why — Simon SinekTeam Topologies — Matthew Skelton and Manuel Pais

Most developers say AI makes them more productive. Most companies say their teams aren't shipping faster. What's going on?
In this special solo episode, Romain unpacks the data behind the "acceleration whiplash," walks through a four-level AI maturity framework built from hundreds of customer conversations, and shares the practical lessons that separate teams getting incremental gains from those achieving 10x outcomes. This is a recording of his AI-DLC presentation, delivered at the AWS Summit in Zurich and iterated based on feedback from startups, enterprises, and digital-native companies across Europe.
Key topics covered: • The adoption paradox: individual productivity up, team delivery flat • Why vibe coding is a mirage for production systems • Theory of constraints applied to AI-era software development • The four-level AI maturity framework: Traditional, AI-Assisted, AI-Augmented, and AI-Native • From prompt engineering to context engineering to loop engineering • Cross-functional teams and the evolution of Amazon's two-pizza teams • AI-DLC workshops: mob elaboration, construction bolts, and continuous delivery • Building AI fluency across your organization • The technology stack for AI-native development • Werner Vogels' Renaissance Developer and T-shaped engineers
Chapters: 00:00 Introduction and why this episode exists 01:28 The evolution of coding assistants (2004–2026) 05:01 Systems of agents and software factories 06:27 The adoption paradox: faster developers, slower teams 09:05 Vibe coding is a mirage 10:12 Theory of constraints and The Phoenix Project 12:17 You can't bolt AI on existing workflows 13:03 Start with why: what are you optimizing for? 15:33 The four-level AI maturity framework 24:30 Level 3 and 4: loop engineering and frontier teams 26:57 People, process, and technology transformation 37:56 The AI-DLC technology stack 41:09 Mindset: the Renaissance Developer 43:24 Practical lessons learned at every level<li><ul><a href="https://www.faros.ai/research/ai-acceleration-whiplash" rel="noopener noreferrer">Faros AI Engineering Report 2026 — The Acceleration Whiplash</a></ul><ul><a href="https://aws.amazon.com/blogs/enterprise-strategy/business-value-of-developer-experience-improvements-amazons-159-breakthrough/" rel="noopener noreferrer">Amazon's Cost-to-Serve Software Metric</a></ul><ul><a href="https://www.amazon.science/blog/measuring-the-effectiveness-of-software-development-tools-and-practices" rel="noopener noreferrer">Amazon Science — Measuring Software Development Effectiveness</a></ul><ul><a href="https://www.ernestchiang.com/en/posts/2025/aws-reinvent-2025-special-closing-keynote-with-dr-werner-vogels/" rel="noopener noreferrer">Werner Vogels' re:Invent 2025 Keynote — The Renaissance Developer</a></ul><ul><a href="https://medium.com/prometheus-ags/stop-prompting-start-designing-loops-5216138e23b3" rel="noopener noreferrer">Boris Cherny on Loop Engineering</a></ul><ul><a href="https://www.lennysnewsletter.com/p/netflix-cpto-on-ai-and-the-future" rel="noopener noreferrer">Netflix CPTO Elizabeth Stone — Systems Thinkers in the AI Era</a></ul><ul><a href="https://github.com/strands-agents" rel="noopener noreferrer">Strands Agents — Open-Source Agentic Framework</a></ul><ul><a href="https://kiro.dev/" rel="noopener noreferrer">Kiro — Spec-Driven Development</a></ul><ul><a href="https://aws.amazon.com/bedrock/agentcore/" rel="noopener noreferrer">Amazon Bedrock AgentCore</a></ul><ul><a href="https://itrevolution.com/product/the-phoenix-project/" rel="noopener noreferrer">The Phoenix Project — Gene Kim, Kevin Behr, and George Spafford</a></ul><ul><a href="https://simonsinek.com/books/start-with-why/" rel="noopener noreferrer">Start with Why — Simon Sinek</a></ul><ul><a href="https://teamtopologies.com/book/" rel="noopener noreferrer">Team Topologies — Matthew Skelton and Manuel Pais</a></ul></li>

Building Software Will Never Be The Same

In this episode Dave and Emily talk to David Nalley, a Principal Advocate at AWS
				for Open Source, and President of the Apache Software Foundation. David has been involved in
				open-source projects for decades, and humbly shares the lessons he has learned.

				David Nalley on Twitter: twitter.com/ke4qqq
				Apache Software Foundation on Twitter: https://twitter.com/TheASF
				Innovating with Rust: https://aws.amazon.com/blogs/opensource/innovating-with-rust/

				Connect with Us on Twitter:
				Emily on Twitter: twitter.com/editingemily
				Dave on Twitter: twitter.com/thedavedev

In this episode Dave and Emily talk to David Nalley, a Principal Advocate at AWS for Open Source, and President of the Apache Software Foundation. David has been involved in open-source projects for decades, and humbly shares the lessons he has learned. David Nalley on Twitter: twitter.com/ke4qqq Apache Software Foundation on Twitter: https://twitter.com/TheASF Innovating with Rust: https://aws.amazon.com/blogs/opensource/innovating-with-rust/ Connect with Us on Twitter: Emily on Twitter: twitter.com/editingemily Dave on Twitter: twitter.com/thedavedev

Episode 002 - Open Source with David Nalley

In this episode Dave and Emily talk to Ken Collins, a Principal Engineer at
				Custom Ink, focused on DevOps and Ecommerce. Ken has done some interesting things with AWS
				Lambda and the Rails programming language when it comes to DevOps. Ken is also an AWS
				Serverless Hero and has created a blog series around real-world usage for Rails and Lambda.
				Ken's Info:
				Ken on Twitter: https://twitter.com/metaskills
				Custom Ink Technology https://twitter.com/custominktech
				Rails & Lambda with Lamby https://lamby.custominktech.com
				Ken's AWS Hero Page: https://aws.amazon.com/developer/community/heroes/ken-collins/

				AWS Services Discussed:
				AWS Lambda: https://aws.amazon.com/lambda/
				AWS CDK: https://aws.amazon.com/cdk/

				Connect with Us on Twitter:
				Emily on Twitter: https://twitter.com/editingemily
				Dave on Twitter: https://twitter.com/thedavedev

In this episode Dave and Emily talk to Ken Collins, a Principal Engineer at Custom Ink, focused on DevOps and Ecommerce. Ken has done some interesting things with AWS Lambda and the Rails programming language when it comes to DevOps. Ken is also an AWS Serverless Hero and has created a blog series around real-world usage for Rails and Lambda. Ken's Info: Ken on Twitter: https://twitter.com/metaskills Custom Ink Technology https://twitter.com/custominktech Rails & Lambda with Lamby https://lamby.custominktech.com Ken's AWS Hero Page: https://aws.amazon.com/developer/community/heroes/ken-collins/ AWS Services Discussed: AWS Lambda: https://aws.amazon.com/lambda/ AWS CDK: https://aws.amazon.com/cdk/ Connect with Us on Twitter: Emily on Twitter: https://twitter.com/editingemily Dave on Twitter: https://twitter.com/thedavedev

In this episode Dave and Emily talk to Ken Collins, a Principal Engineer at
				Custom Ink, focused on DevOps and Ecommerce. Ken has done some interesting things with AWS
				Lambda and the Rails programming language when it comes to DevOps. Ken is also an AWS
				Serverless Hero and has created a blog series around real-world usage for Rails and Lambda.
				Ken's Info:
				Ken on Twitter: https://twitter.com/metaskills
				Custom Ink Technology https://twitter.com/custominktech
				Rails &amp; Lambda with Lamby https://lamby.custominktech.com
				Ken's AWS Hero Page: https://aws.amazon.com/developer/community/heroes/ken-collins/

				AWS Services Discussed:
				AWS Lambda: https://aws.amazon.com/lambda/
				AWS CDK: https://aws.amazon.com/cdk/

				Connect with Us on Twitter:
				Emily on Twitter: https://twitter.com/editingemily
				Dave on Twitter: https://twitter.com/thedavedev

Episode 001 - AWS Lambda as a DevOps Tool with Ken Collins

Stay updated on the latest AWS news and insights for developers, wherever you are, whenever you want.

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