The New Stack Podcast

Amazon Q Developer is streamlining the software development lifecycle by integrating AI-powered tools into AWS. In an interview at AWS in Seattle, Srini Iragavarapu, director of generative AI Applications and Developer Experiences at AWS, discussed how Amazon Q Developer enhances the developer experience. Initially focused on inline code completions, Amazon Q Developer evolved by incorporating generative AI models like Amazon Nova and Anthropic models, improving recommendations and accelerating development. British Telecom reported a 37% acceptance rate for AI-generated code.Beyond code completion, Amazon Q Developer enables developers to interact with Q for code reviews, test generation, and migrations. AWS also developed agentic frameworks to automate undifferentiated tasks, such as upgrading Java versions. Iragavarapu noted that internally, AWS used Q Developer to migrate 30,000 production applications, saving $260 million annually. The platform offers code generation, testing suites, RAG capabilities, and access to AWS custom chips, further flattening the SDLC by automating routine work. Listen to The New Stack Makers for the full discussion.Learn more from The New Stack about Amazon Q Developer: Amazon Q Developer Now Handles Your Entire Code Pipeline Amazon Q Apps: AI-Powered Development for All Amazon Revamps Developer AI With Code Conversion, Security Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Maya Kaczorowski, a respected technologist and founder of Oblique, dives into the intersection of OAuth, AI agents, and identity management. She reveals how developers perceive AI agents as extensions of themselves, thus relying on OAuth for secure access. Kaczorowski highlights the growing challenges in managing vast numbers of AI identities, emphasizing the need for scalable solutions. The discussion addresses the complexities of integrating AI with SaaS, the balance of AI autonomy versus security safeguards, and ongoing evolution in access control methods.

Yam Marcovitz, tech lead at Parlant.io and CEO of emcie.co, dives into the evolution of large language models (LLMs) and their comparison to programming languages. He discusses how LLMs have progressed from simple text generation to more sophisticated reasoning and decision-making capabilities. Marcovitz highlights the importance of attentive reasoning queries for maintaining accuracy and consistency in AI interactions. He also addresses the subjectivity inherent in AI interpretation, emphasizing the need for nuanced approaches in developing AI agents, especially for customer service.

Adam Jacob, Co-founder and CEO of System Initiative, dives into the future of enterprise automation. He discusses the shift from writing code to building models for quick simulations, likening it to Formula One racing for optimization. The conversation highlights how this approach enhances collaboration among engineers and simplifies complex tasks. Adam also touches on the role of digital twins in model accuracy and emphasizes the importance of evolving organizational culture to leverage these technologies effectively.

Morgan McLean, co-founder of OpenTelemetry and senior director at Splunk, shares insights from his extensive experience in observability for large-scale systems. He discusses the evolution of OpenTelemetry, born from OpenTracing and OpenCensus, and its crucial role in simplifying distributed tracing and application metrics. McLean anticipates that profiling will emerge as a key observability signal by 2025. He also explores the integration of AI with observability and the community's collaborative efforts aimed at improving adoption and standardization across diverse environments.

Christine Yen, Co-founder and CEO of Honeycomb.io, discusses the rising costs of observability in modern cloud-native systems. She highlights how traditional logging and monitoring tools struggle to cope with today's software complexities, leading to inefficiencies. Yen emphasizes the need for innovative solutions that prioritize user experience, like Service Level Objectives (SLOs). She also explores the role of AI and OpenTelemetry in addressing these challenges, showcasing the potential for enhanced insights in software management.

Sudha Raghavan, SVP for Developer Platform at Oracle Cloud Infrastructure, discusses the seismic shift in infrastructure needs driven by AI's rapid adoption. She highlights the explosive demand for GPUs and the challenges posed by continuous workloads like large language model training. Raghavan elaborates on Oracle's innovative GPU superclusters and improvements in Kubernetes for better job management and observability. The conversation also touches on the evolution of AI pipelines and the critical trade-offs in AI model selection to meet specific business demands.

The hardware industry is surging, driven by AI's demanding workloads, with Arm—a 35-year-old pioneer in processor IP—playing a pivotal role. In an episode ofThe New Stack Makersrecorded at KubeCon + CloudNativeCon North America, Pranay Bakre, principal solutions engineer at Arm, discussed how Arm is helping organizations migrate and run applications on its technology.Bakre highlighted Arm’s partnership with hyperscalers like AWS, Google, Microsoft, and Oracle, showcasing processors such as AWS Graviton and Google Axion, built on Arm’s power-efficient, cost-effective Neoverse IP. This design ethos has spurred wide adoption, with 90-95% of CNCF projects supporting native Arm binaries.Attendees at Arm’s booth frequently inquired about its plans to support AI workloads. Bakre noted the performance advantages of Arm-based infrastructure, delivering up to 60% workload improvements over legacy architectures. The episode also features a demo on migrating x86 applications to ARM64 in both cloud and containerized environments, emphasizing Arm’s readiness for the AI era.Learn more from The New Stack about Arm: Arm Eyes AI with Its Latest Neoverse Cores and SubsystemBig Three in Cloud Prompts ARM to Rethink SoftwareJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  

Gail Frederick, CTO at Heroku Salesforce, champions developer productivity and community-driven innovation. She reveals the open-sourcing of the Twelve-Factor App methodology, aimed at modernizing cloud development practices. Gail discusses updating outdated elements like logging, while introducing new concepts for telemetry and metrics visualization. The talk highlights the significance of diverse contributions in open source and Heroku's adoption of Kubernetes to enhance performance. Plus, they explore AI integration, ensuring that the methodology evolves with emerging technologies.

Leonardo Grasso, Open Source Tech Lead Manager at Sysdig and a core maintainer of Falco, dives into the evolution of this innovative open-source runtime observability tool. The discussion highlights Falco’s integration with eBPF technology, enabling real-time event monitoring from the kernel. Grasso reveals the journey of Falco from its early days to its recent graduation from the Cloud Native Computing Foundation. He also discusses Falco Talon, a no-code response engine that enhances security automation, making runtime security more efficient than ever.