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The AWS Developers Podcast

The AWS Developers Podcast

Written by: Amazon Web Services
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Episodes
  • Jev: is it just a smarter if-statement?

    Jev: is it just a smarter if-statement?
    Sep 23 2026
    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 consider
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    50 mins
  • Building Software Will Never Be The Same

    Building Software Will Never Be The Same
    Sep 16 2026
    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
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    51 mins
  • Kiro, Strands Agents & MCP: 10 Updates You Missed

    Kiro, Strands Agents & MCP: 10 Updates You Missed
    Sep 2 2026
    The MCP specification just went stateless. Clare Liguori — Senior Principal Engineer at AWS and core MCP maintainer — explains what changed, why it matters, and what it unlocks for agent developers building with Strands Agents and Kiro. In this episode, Romain sits down with Clare Liguori, Senior Principal Engineer at AWS, to discuss the July 28 MCP spec release, new MCP extensions (Skills, Tasks, Events), Strands Agents Harness SDK, Strands Shell, Physical AI in Strands Labs, and the latest Kiro updates across Web, CLI, IDE 1.0, and iOS. Key takeaways: • MCP goes stateless — the July 28 spec release removes the need for stateful streaming in remote MCP servers, so SaaS providers can drop sticky sessions and load-balancer gymnastics and run each request anywhere. Expect a new wave of remote MCP servers over HTTP. • New MCP extensions framework — features now start as stable extensions before graduating into the official spec: Skills over MCP (bundle a workflow and its tools together), long-running Tasks (kick off builds or jobs without blocking the agent), and Events (trigger always-on agents from external signals like Slack or an earthquake feed). • Strands Agents Harness SDK & TypeScript 1.0 — a more batteries-included harness with context management, compaction, and excellent out-of-the-box file tools, plus the TypeScript SDK reaching 1.0. Upgrade the model ID and your agent gets better. • Strands Shell — a lightweight, in-process agent sandbox written in Rust (cross-platform, Python SDK today). It gives an agent a virtual file system and minimal bash/Lua scripting without a heavyweight VM — great as a safe scratch pad or for scripting tools together. • Strands Labs & Physical AI — an experimental space for bleeding-edge agent ideas, including combining low-latency local VLA models on robots with the long-range, multi-task reasoning of frontier models in the cloud. • AgentCore Managed Harness — a configuration-based way to run agents (prompt, model, Lambda tools, context and session management) that is Strands under the hood, no Python or TypeScript required. AgentCore Gateway added MCP 2026-07-28 support day one, with version negotiation for backward compatibility. • One unified Kiro harness — Kiro Web, iOS, CLI, and IDE 1.0 now share one harness, bringing spec-driven development, hooks, skills, and powers to every client and letting new features ship across clients on the same day. • Automated reasoning in specs — property-based testing plus ambiguity and conflict detection in requirements help you express intent clearly; the permission system is built on Cedar with policy presets like dev shell, trust all, and read all. • Right-sizing specs and collaborating — check specs into code as a snapshot of intent, watch design and task-list length as a signal to split into multiple specs, add per-task validation steps, and collaborate on specs with comments in Kiro Web.
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    1 hr and 3 mins
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