Why Beginning Developers Love Python

The New Stack Podcast

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#644: AI-augmented human recommendations for retail customers with Noah Zamansky, Stitch Fix 27:12

منذ 5 أيام27:12

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27:12

We are here at eTail Palm Springs and seeing and hearing the latest and greatest in e-commerce and retail. Question: Do you need to choose between AI and human recommendations as a customer? Why not have both? After all, don’t each have their strengths? AI in the retail experience is all the rage these days, but today I’m talking with someone from a brand that has been incorporating AI-personalized experiences and shopping combined with expert human recommendations for over 14 years, and continues to innovate today. Today we’re going to talk about how AI-based personalization plus human creativity and input makes an amazing customer experience at Stitch Fix. To help me discuss this topic, I’d like to welcome Noah Zamansky, Vice President of Product and Client Experience at Stitch Fix. About Noah Zamansky Noah Zamansky serves as the Vice President of Product and Client Experience at Stitch Fix, where he leads cross-functional teams spanning Product, Design, Engineering, Algorithms, and Platform Development. A seasoned leader, Noah has a proven track record of shaping product vision and strategy, designing exceptional user experiences, and spearheading the launch of new business ventures. Before joining Stitch Fix, Noah held the role of Senior Director of Product Management at eBay, overseeing Fashion and Vertical Experiences. Resources Stitch Fix: https://www.stitchfix.com eTail Palm Springs: https://etailwest.wbresearch.com/ Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstrom Listen to The Agile Brand without the ads. Learn more here: https://bit.ly/3ymf7hd Don't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.show Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company…

منذ عام واحد 29:15

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Deb Nicholson, executive director of the Python Software Foundation, attributes Python’s popularity to its minimal syntactical complexity, which appeals to beginners and seasoned developers alike. Python allows flexibility for those exploring coding without a specific focus, unlike purpose-built languages. Since her leadership began in 2022, Nicholson has overseen the foundation’s role in managing Python’s fiscal and operational needs, including the package index that hosts over half a million add-ons. This open ecosystem enables contributions from large corporations and individual developers while demanding vigilant security measures.

Nicholson envisions Python's future advancements, particularly in improving multi-threading and expanding usage in mobile development. She acknowledges Python’s critical role in AI and data science but remains cautious about AI’s pervasive application, likening it to a temporary trend. On open source in the enterprise, Nicholson critiques companies profiting from open-source tools while adopting restrictive licenses. Instead, she admires models like Red Hat’s, which leverage open source sustainably without compromising accessibility or innovation.

Learn more from The New Stack about Python:

Python 3.13: Blazing New Trails in Performance and Scale

The Top 5 Python Packages and What They Do

Python Mulls a Change in Version Numbering

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#Tech #News #Tech News #Technology #Thenewstack #New Stack #Development #DevOps #Joab Jackson #Alex Williams #Software Engineering #Heather Joslyn #Cloud Native #Software Development #Kubernetes #Open Source #Darry Taft

Why Beginning Developers Love Python

The New Stack Podcast

113 subscribers

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Learn more from The New Stack about Python:

Python 3.13: Blazing New Trails in Performance and Scale

The Top 5 Python Packages and What They Do

Python Mulls a Change in Version Numbering

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

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OAuth Works for AI Agents but Scaling is Another Question 25:36

منذ 6 أيام25:36

25:36

Maya Kaczorowski noticed that AI identity and AI agent identity concerns were emerging from outside the security industry, rather than from CISOs and security leaders. She concluded that OAuth, the open standard for authentication, already serves the purpose of granting access without exposing passwords. Kaczorowski, a respected technologist and founder of Oblique, a startup focused on self-serve access controls, recently wrote about OAuth and AI agents and shared her insights on this episode of The New Stack Makers. She noted that developers see AI agents as extensions of themselves, granting them limited access to data and capabilities—precisely what OAuth is designed to handle. The challenges with AI agent identity are vast, involving different approaches to authentication, such as those explored by companies like AuthZed. While existing authorization models like RBAC or ABAC may still apply, the real challenge lies in scale. The exponential growth of AI-related entities—from users to LLMs—could mean even small organizations manage hundreds of thousands of agents. Future solutions must accommodate this massive scale efficiently. For the full discussion, check out The New Stack Makers interview with Kaczorowski. Learn more from The New Stack about OAuth requirements for AI Agents: OAuth 2.0: A Standard in Name Only? AI Agents Are Redefining the Future of Identity and Access Management Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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LLMs and AI Agents Evolving Like Programming Languages 28:08

منذ 13 يومًا28:08

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The rise of the World Wide Web enabled developers to build tools and platforms on top of it. Similarly, the advent of large language models (LLMs) allows for creating new AI-driven tools, such as autonomous agents that interact with LLMs, execute tasks, and make decisions. However, verifying these decisions is crucial, and critical reasoning may be a solution, according to Yam Marcovitz, tech lead at Parlant.io and CEO of emcie.co. Marcovitz likens LLM development to the evolution of programming languages, from punch cards to modern languages like Python. Early LLMs started with small transformer models, leading to systems like BERT and GPT-3. Now, instead of mere text auto-completion, models are evolving to enable better reasoning and complex instructions. Parlant uses "attentive reasoning queries (ARQs)" to maintain consistency in AI responses, ensuring near-perfect accuracy. Their approach balances structure and flexibility, preventing models from operating entirely autonomously. Ultimately, Marcovitz argues that subjectivity in human interpretation extends to LLMs, making perfect objectivity unrealistic. Learn more from The New Stack about the evolution of LLMs: AI Alignment in Practice: What It Means and How to Get It Agentic AI: The Next Frontier of AI Power Make the Most of AI Agents: Tips and Tricks for Developers Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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Writing Code About Your Infrastructure? That's a Losing Race 31:21

منذ 20 يومًا31:21

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Adam Jacob, CEO of System Initiative, discusses a shift in infrastructure automation—moving from writing code to building models that enable rapid simulations and collaboration. In The New Stack Makers, he compares this approach to Formula One racing, where teams use high-fidelity models to simulate race conditions, optimizing performance before hitting the track. System Initiative applies this concept to enterprise automation, creating a model that understands how infrastructure components interact. This enables fast, multiplayer feedback loops, simplifying complex tasks while enhancing collaboration. Engineers can extend the system by writing small, reactive JavaScript functions that automate processes, such as transforming existing architectures into new ones. The platform visually represents these transformations, making automation more intuitive and efficient. By leveraging models instead of traditional code-based infrastructure management, System Initiative enhances agility, reduces complexity, and improves DevOps collaboration. To explore how this ties into the concept of the digital twin, listen to the full New Stack Makers episode. Learn more from The New Stack about System Initiative: Beyond Infrastructure as Code: System Initiative Goes Live How System Initiative Treats AWS Components as Digital Twins System Initiative Code Now Open Source Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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OpenTelemetry: What’s New with the 2nd Biggest CNCF Project? 30:14

منذ 27 يومًا30:14

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Morgan McLean, co-founder of OpenTelemetry and senior director of product management at Splunk, has long tackled the challenges of observability in large-scale systems. In a conversation with Alex Williams on The New Stack Makers , McLean reflected on his early frustrations debugging high-scale services and the need for better observability tools. OpenTelemetry, formed in 2019 from OpenTracing and OpenCensus, has since become a key part of modern observability strategies. As a Cloud Native Computing Foundation (CNCF) incubating project, it’s the second most active open source project after Kubernetes, with over 1,200 developers contributing monthly. McLean highlighted OpenTelemetry’s role in solving scaling challenges, particularly in Kubernetes environments, by standardizing distributed tracing, application metrics, and data extraction. Looking ahead, profiling is set to become the fourth major observability signal alongside logs, tracing, and metrics, with general availability expected in 2025. McLean emphasized ongoing improvements, including automation and ease of adoption, predicting even faster OpenTelemetry adoption as friction points are resolved. Learn more from The New Stack about the latest trends in Open Telemetry: What Is OpenTelemetry? The Ultimate Guide Observability in 2025: OpenTelemetry and AI to Fill In Gaps Honeycomb.io’s Austin Parker: OpenTelemetry In-Depth Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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What’s Driving the Rising Cost of Observability? 24:55

منذ عام واحد24:55

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Observability is expensive because traditional tools weren’t designed for the complexity and scale of modern cloud-native systems, explains Christine Yen, CEO of Honeycomb.io. Logging tools, while flexible, were optimized for manual, human-scale data reading. This approach struggles with the massive scale of today’s software, making logging slow and resource-intensive. Monitoring tools, with their dashboards and metrics, prioritized speed over flexibility, which doesn’t align with the dynamic nature of containerized microservices. Similarly, traditional APM tools relied on “magical” setups tailored for consistent application environments like Rails, but they falter in modern polyglot infrastructures with diverse frameworks. Additionally, observability costs are rising due to evolving demands from DevOps, platform engineering, and site reliability engineering (SRE). Practices like service-level objectives (SLOs) emphasize end-user experience, pushing teams to track meaningful metrics. However, outdated observability tools often hinder this, forcing teams to cut back on crucial data. Yen highlights the potential of AI and innovations like OpenTelemetry to address these challenges. Learn more from The New Stack about the latest trends in observability: Honeycomb.io’s Austin Parker: OpenTelemetry In-Depth Observability in 2025: OpenTelemetry and AI to Fill In Gaps Observability and AI: New Connections at KubeCon Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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How Oracle Is Meeting the Infrastructure Needs of AI 27:28

منذ عام واحد27:28

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Generative AI is a data-driven story with significant infrastructure and operational implications, particularly around the rising demand for GPUs, which are better suited for AI workloads than CPUs. In an episode of The New Stack Makers recorded at KubeCon + CloudNativeCon North America, Sudha Raghavan, SVP for Developer Platform at Oracle Cloud Infrastructure, discussed how AI’s rapid adoption has reshaped infrastructure needs. The release of ChatGPT triggered a surge in GPU demand, with organizations requiring GPUs for tasks ranging from testing workloads to training large language models across massive GPU clusters. These workloads run continuously at peak power, posing challenges such as high hardware failure rates and energy consumption. Oracle is addressing these issues by building GPU superclusters and enhancing Kubernetes functionality. Tools like Oracle’s Node Manager simplify interactions between Kubernetes and GPUs, providing tailored observability while maintaining Kubernetes’ user-friendly experience. Raghavan emphasized the importance of stateful job management and infrastructure innovations to meet the demands of modern AI workloads. Learn more from The New Stack about how Oracle is addressing the GPU demand for AI workloads with its GPU superclusters and enhancing Kubernetes functionality: Oracle Code Assist, Java-Optimized, Now in Beta Oracle’s Code Assist: Fashionably Late to the GenAI Party Oracle Unveils Java 23: Simplicity Meets Enterprise Power Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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Arm: See a Demo About Migrating a x86-Based App to ARM64 21:28

منذ عام واحد21:28

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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 of The New Stack Makers recorded 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 Subsystem Big Three in Cloud Prompts ARM to Rethink Software Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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Heroku Moved Twelve-Factor Apps to Open Source. What’s Next? 22:54

منذ عام واحد22:54

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Heroku has open-sourced its Twelve-Factor App methodology, initially created in 2011 to help developers build portable, resilient cloud applications. Heroku CTO Gail Frederick announced this shift at KubeCon + CloudNativeCon North America, explaining the move aims to involve the community in modernizing the framework. While the methodology inspired a generation of cloud developers, certain factors are now outdated, such as the focus on logs as event streams. Frederick highlighted the need for updates to address current practices like telemetry and metrics visualization, reflecting the rise of OpenTelemetry. The updated Twelve-Factor methodology will expand to accommodate modern cloud-native realities, such as deploying interconnected systems of apps with diverse backing services. Planned enhancements include supporting documents, reference architectures, and code examples illustrating the principles in action. Success will be measured by its applicability to use cases involving edge computing, IoT, serverless, and distributed systems. Heroku views this open-source effort as an opportunity to redefine best practices for the next era of cloud development. Learn more from The New Stack about Heroku: How Heroku Is Positioned To Help Ops Engineers in the GenAI Era The Data Stack Journey: Lessons from Architecting Stacks at Heroku and Mattermost Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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How Falco Brought Real-Time Observability to Infrastructure 19:27

منذ عام واحد19:27

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Falco, an open-source runtime observability and security tool, was created by Sysdig founder Loris Degioanni to collect real-time system events directly from the kernel. Leveraging eBPF technology for improved safety and performance, Falco gathers data like pod names and namespaces, correlating them with customizable rules. Unlike static analysis tools, it operates in real-time, monitoring events as they occur. In this episode of The New Stack Makers, TNS Editor-in-Chief, Heather Joslyn spoke with Thomas Labarussias, Senior Developer Advocate at Sysdig, Leonardo Grasso, Open Source Tech Lead Manager at Sysdig and Luca Guerra, Sr. Open Source Engineer at Sysdig to get the latest update on Falco. Graduating from the Cloud Native Computing Foundation (CNCF) in February 2023 after entering its sandbox six years prior, Falco’s maintainers have focused on technical maturity and broad usability. This includes simplifying installations across diverse environments, thanks in part to advancements from the Linux Foundation. Looking ahead, the team is enhancing core functionalities, including more customizable rules and alert formats. A key innovation is Falco Talon, introduced in September 2023, which provides a no-code response engine to link alerts with real-time remediation actions. Talon addresses a longstanding gap in automating responses within the Falco ecosystem, advancing its capabilities for runtime security. Learn more from The New Stack about Falco: Falco Is a CNCF Graduate. Now What? Falco Plugins Bring New Data Sources to Real-Time Security eBPF Tools: An Overview of Falco, Inspektor Gadget, Hubble and Cilium Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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How cert-manager Got to 500 Million Downloads a Month 23:18

منذ عام واحد23:18

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Jetstack’s cert-manager, a leading open-source project in Kubernetes certificate management, began as a job interview challenge. Co-founder Matt Barker recalls asking a prospective engineer to automate Let’s Encrypt within Kubernetes. By Monday, the candidate had created kube-lego, which evolved into cert-manager, now downloaded over 500 million times monthly. Cert-manager’s journey to CNCF graduation, achieved in September, began with its donation to the foundation four years ago. Relaunched as cert-manager, the project grew under engineer James Munnelly, becoming the de facto standard for certificate lifecycle management. The thriving community and ecosystem around cert-manager highlighted its suitability for CNCF stewardship. However, maintainers, including Ashley Davis, noted challenges in navigating differing opinions within its vast user base. With graduation achieved, cert-manager’s roadmap includes sub-projects like trust-manager, addressing TLS trust bundle management and Istio integration. Barker aims to streamline enterprise-scale deployments and educate security teams on cert-manager’s impact. Cert-manager has become integral to cloud-native workflows, promising to simplify hybrid, multicloud, and edge deployments. Learn more from The New Stack about cert-manager: Jetstack’s cert-manager Joins the CNCF Sandbox of Cloud Native Technologies Jetstack Secure Promises to Ease Kubernetes TLS Security Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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Why Are So Many Developers Out of Work in 2024? 21:10

منذ عام واحد21:10

21:10

The tech industry faces a paradox: despite high demand for skills, many developers and engineers are unemployed. At KubeCon + CloudNativeCon North America in Salt Lake City, Utah, Andela and the Cloud Native Computing Foundation (CNCF) announced an initiative to train 20,000 technologists in cloud native computing over the next decade. oss O'neill, Senior Program Manager at Andela and Chris Aniszczyk, CNCF’s CTO, highlighted the lack of Kubernetes-certified professionals in regions like Africa and emphasized the need for global inclusivity to make cloud native technology ubiquitous. Andela, operating in over 135 countries and founded in Nigeria, views this program as a continuation of its mission to upskill African talent, aligning with its partnerships with tech giants like Google, AWS, and Nvidia. This initiative also addresses the increasing employer demand for Kubernetes and modern cloud skills, reflecting a broader skills mismatch in the tech workforce. Aniszczyk noted that companies urgently seek expertise in cloud native infrastructure, observability, and platform engineering. The partnership aims to bridge these gaps, offering opportunities to meet evolving global tech needs. Learn more from The New Stack about developer talent, skills and needs: Top Developer Skills for AI and Cloud Jobs 5 Software Development Skills AI Will Render Obsolete Cloud Native Skill Gaps are Killing Your Gains Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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MapLibre: How a Fork Became a Thriving Open Source Project 25:50

منذ عام واحد25:50

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When open source projects shift to proprietary licensing, forks and new communities often emerge. Such was the case with MapLibre, born from Mapbox’s 2020 decision to make its map rendering engine proprietary. In conjunction with All Things Open 2024, Seth Fitzsimmons, a principal engineer at AWS and Tarus Balog, principal technical strategist for open source at AWS shared that this engine, popular for its WebGL-powered vector maps and dynamic customization features, was essential for organizations like BMW, The New York Times, and Instacart. However, Mapbox’s move disappointed its open-source user base by tying the upgraded Mapbox GL JS library to proprietary products. In response, three users forked the engine to create MapLibre, committing to modernizing and preserving its open-source ethos. Despite challenges—forking often struggles to sustain momentum—MapLibre has thrived, supported by contributors and corporate sponsors like AWS, Meta, and Microsoft. Notably, a community member transitioned the project from JavaScript to TypeScript over nine months, showcasing the dedication of unpaid contributors. Thanks to financial backing, MapLibre now employs maintainers, enabling it to reciprocate community efforts while fostering equality among participants. The project illustrates the resilience of open-source communities when proprietary shifts occur. Learn more from The New Stack about forking open source projects: Why Do Open Source Projects Fork? OpenSearch: How the Project Went From Fork to Foundation Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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OpenSearch: How the Project Went from Fork to Foundation 17:16

منذ عام واحد17:16

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At All Things Open in October, Anandhi Bumstead, AWS’s director of software engineering, highlighted OpenSearch's journey and the advantages of the Linux Foundation's stewardship. OpenSearch, an open source data ingestion and analytics engine, was transferred by Amazon Web Services (AWS) to the Linux Foundation in September 2024, seeking neutral governance and broader community collaboration. Originally forked from Elasticsearch after a licensing change in 2021, OpenSearch has evolved into a versatile platform likened to a “Swiss Army knife” for its broad use cases, including observability, log and security analytics, alert detection, and semantic and hybrid search, particularly in generative AI applications. Despite criticism over slower indexing speeds compared to Elasticsearch, significant performance improvements have been made. The latest release, OpenSearch 2.17, delivers 6.5x faster query performance and a 25% indexing improvement due to segment replication. Future efforts aim to enhance indexing, search, storage, and vector capabilities while optimizing costs and efficiency. Contributions are welcomed via opensearch.org. Learn more from The New Stack about deploying applications on OpenSearch AWS Transfers OpenSearch to the Linux Foundation From Flashpoint to Foundation: OpenSearch’s Path Clears Semantic Search with Amazon OpenSearch Serverless and Titan Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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Is Apache Spark Too Costly? An Amazon Engineer Tells His Story 25:26

منذ عام واحد25:26

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Is Apache Spark too costly? Amazon Principal Engineer Patrick Ames tackled this question during an interview with The New Stack Makers , sharing insights into transitioning from Spark to Ray for managing large-scale data. Ames, described as a "go-to" engineer for exabyte-scale projects, emphasized a goal-driven approach to solving complex engineering problems, from simplifying daily chores to optimizing software solutions. Initially, Spark was chosen at Amazon for its simplicity and open-source flexibility, allowing efficient merging of data with minimal SQL code. The team leveraged Spark in a decoupled architecture over S3 storage, scaling it to handle thousands of jobs daily. However, as data volumes grew to hundreds of terabytes and beyond, Spark’s limitations became apparent. Long processing times and high costs prompted a search for alternatives. Enter Ray—a unified framework designed for scaling AI and Python applications. After experimentation, Ames and his team noted significant efficiency improvements, driving the shift from Spark to Ray to meet scalability and cost-efficiency needs. Learn more from The New Stack about Apache Spark and Ray: Amazon to Save Millions Moving From Apache Spark to Ray How Ray, a Distributed AI Framework, Helps Power ChatGPT Join our community of newsletter subscribers to stay on top of the news and at the top of your game .…

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Codiac: Kubernetes Doesn't Need To Be That Complex 28:53

منذ عام واحد28:53

28:53

In this New Stack Makers, Codiac aims to simplify app deployment on Kubernetes by offering a unified interface that minimizes complexity. Traditionally, Kubernetes is powerful but challenging for teams due to its intricate configurations and extensive manual coding. Co-founded by Ben Ghazi and Mark Freydl, Codiac provides engineers with infrastructure on demand, container management, and advanced software development life cycle (SDLC) tools, making Kubernetes more accessible. Codiac’s interface streamlines continuous integration and deployment (CI/CD), reducing deployment steps to a single line of code within CI/CD pipelines. Developers can easily deploy, manage containers, and configure applications without mastering Kubernetes' esoteric syntax. Codiac also offers features like "cabinets" to organize assets across multi-cloud environments and enables repeatable processes through snapshots, making cluster management smoother. For experienced engineers, Codiac alleviates the burden of manually managing YAML files and configuring multiple services. With ephemeral clusters and repeatable snapshots, Codiac supports scalable, reproducible development workflows, giving engineers a practical way to manage applications and infrastructure seamlessly across complex Kubernetes environments. Learn more from The New Stack about deploying applications on Kubernetes: Kubernetes Needs to Take a Lesson from Portainer on Ease-of-Use Three Common Kubernetes Challenges and How to Solve Them Join our community of newsletter subscribers to stay on top of the news and at the top of your game.…

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