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Mark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic's Claude, Google's Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies. Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals testing tactics for non-deterministic AI outputs, configuration tips for custom authentication, and the highlights of the new v0.4 release, including “content parts” support for thinking-style models. Key topics discussed in this episode: • Abstracting LLM APIs behind a unified Elixir interface • Building and managing conversation chains across multiple models • Exposing application functionality to LLMs through tool integrations • Automatic retries and fallback chains for production resilience • Supporting a variety of LLM providers • Tracking and optimizing token usage for cost control • Configuring API keys, authentication, and provider-specific settings • Handling rate limits and service outages with degradation • Processing multimodal inputs (text, images) in Langchain workflows • Extracting structured data from unstructured LLM responses • Leveraging “content parts” in v0.4 for advanced thinking-model support • Debugging LLM interactions using verbose logging and telemetry • Kickstarting experiments in LiveBook notebooks and demos • Comparing Elixir LangChain to the original Python implementation • Crafting human-in-the-loop workflows for interactive AI features • Integrating Langchain with the Ash framework for chat-driven interfaces • Contributing to open-source LLM adapters and staying ahead of API changes • Building fallback chains (e.g., OpenAI → Azure) for seamless continuity • Embedding business logic decisions directly into AI-powered tools • Summarization techniques for token efficiency in ongoing conversations • Batch processing tactics to leverage lower-cost API rate tiers • Real-world lessons on maintaining uptime amid LLM service disruptions Links mentioned: https://rubyonrails.org/ https://fly.io/ https://zionnationalpark.com/ https://podcast.thinkingelixir.com/ https://github.com/brainlid/langchain https://openai.com/ https://claude.ai/ https://gemini.google.com/ https://www.anthropic.com/ Vertex AI Studio https://cloud.google.com/generative-ai-studio https://www.perplexity.ai/ https://azure.microsoft.com/ https://hexdocs.pm/ecto/Ecto.html https://oban.pro/ Chris McCord's ElixirConf EU 2025 Talk https://www.youtube.com/watch?v=ojL_VHc4gLk Getting started: https://hexdocs.pm/langchain/gettingstarted.html https://ash-hq.org/ https://hex.pm/packages/langchain https://hexdocs.pm/igniter/readme.html https://www.youtube.com/watch?v=WM9iQlQSFg @brainlid on Twitter and BlueSky Special Guest: Mark Ericksen.

Container-based Linux distributions are gaining traction, especially for edge deployments that demand lightweight and secure operating systems. Talos Linux, developed by Sidero Labs, is purpose-built for Kubernetes with security-first features like a fully immutable file system and disabled SSH access. In a demo, Sidero CTO Andrew Rynhard and Head of Product Justin Garrison explained Talos's design philosophy, highlighting its minimalism and focus on automation. Inspired by CoreOS, Talos removes traditional tools like systemd and Bash, replacing them with machineD, a custom process manager written in Go.Talos emphasizes API-driven management rather than SSH, making Kubernetes cluster operations more scalable and consistent. Its design supports cloud, bare metal, Docker, and edge devices like Raspberry Pi. Kernel immutability is reinforced by ephemeral signing keys. Through Sidero's Omni SaaS, Talos nodes connect securely via WireGuard. The operating system handles all certificates and network connectivity internally, streamlining security and deployment. As Garrison notes, Talos delivers a portable API for “big iron, small iron—no matter what.”Learn more from The New Stack about Sidero Labs:  Is Cluster API Really the Future of Kubernetes Deployment? Choosing a Linux Distribution Join our community of newsletter subscribers to stay on top of the news and at the top of your game. https://thenewstack.io/newsletter/ 

Connor Rigby joins the Elixir Wizards to talk about Blue Heron BLE (Bluetooth Low Energy) support for Elixir apps. Blue Heron implements the BLE specs in pure Elixir, leveraging binary pattern matching and concurrent message processing to handle Bluetooth protocols. Unlike most solutions that require C ports or NIFs, Blue Heron runs entirely in user space, so it works seamlessly in both Nerves-based embedded projects and (eventually) desktop Elixir applications. We discuss how Nerves development differs from building Phoenix apps. Connor shares challenges he's experienced with hardware compatibility, where some chips only partially implement the spec, and he discusses the surprisingly deep (but sometimes incomplete) world of BLE device profiles. His tip for anyone entering the BLE space: read the official spec instead of trusting secondhand blog posts. Tools like Nerves LiveBook give you hands-on examples, so you can get a BLE prototype running on a Raspberry Pi and your phone in no time. Key topics discussed in this episode: Blue Heron origins and “bird” naming convention BLE vs. Bluetooth Classic: core differences Pure Elixir implementation—no C dependencies Binary pattern matching for packet parsing Hardware transport options: UART, SPI, USB, SDIO GenServer patterns in Nerves vs. Phoenix Linux requirement and power-consumption trade-offs GATT (Generic Attribute Table) implementation patterns SQLite integration for Nerves apps Hardware chip quirks and spec compliance Manufacturer-specific commands and workarounds BLE device profiles and spec gaps Security Management Profile (SMP) for encryption Device connection and pairing workflows Web vs. embedded development differences Where to get started: hardware recommendations and docs Links mentioned: https://github.com/ConnorRigby/ https://github.com/blue-heron/ https://nerves-project.org/ BLE (Bluetooth Low Energy) https://en.wikipedia.org/wiki/BluetoothLowEnergy https://developer.apple.com/ibeacon/ https://learnyousomeerlang.com/building-otp-applications Linux https://www.linux.org/ HCI (Host Controller Interface) https://en.wikipedia.org/wiki/Hostcontrollerinterface Circuits UART Library https://hexdocs.pm/circuitsuart/readme.html SPI (Serial Peripheral Interface) https://github.com/elixir-circuits/circuitsspi SDIO (Secure Digital Input Output https://en.wikipedia.org/wiki/SDIO Raspberry Pi https://www.raspberrypi.com/ Coral SoM Dev Board https://coral.ai/products/dev-board/ BeagleBone Single-Board Linux Computer https://www.beagleboard.org/boards/beaglebone-black https://www.bluetooth.com/bluetooth-resources/intro-to-bluetooth-gap-gatt/ Genservers https://hexdocs.pm/elixir/1.12/GenServer.html https://hexdocs.pm/ecto/Ecto.html https://github.com/elixir-sqlite/ectosqlite3 https://github.com/nerves-livebook/nerveslivebook Special Guest: Connor Rigby.

AI agents hold the promise of continuously testing, scanning, and fixing code for security vulnerabilities, but we're still progressing toward that vision. Startups like Aptori are helping bridge the gap by building AI-powered security engineers for enterprises. Aptori maps an organization's codebase, APIs, and cloud infrastructure in real time to understand data flows and authorization logic, allowing it to detect and eventually remediate security issues. At Google Cloud Next, Aptori CEO Sumeet Singh discussed how earlier tools merely alerted developers to issues—often overwhelming them—but newer models like Gemini 2.5 Flash and Claude Sonnet 4 are improving automated code fixes, making them more practical. Singh and co-founder Travis Newhouse previously built AppFormix, which automated OpenStack cloud operations before being acquired by Juniper Networks. Their experiences with slow release cycles due to security bottlenecks inspired Aptori's focus. While the goal is autonomous agents, Singh emphasizes the need for transparency and deterministic elements in AI tools to ensure trust and reliability in enterprise security workflows.Learn more from The New Stack about the latest insights in AI application security: AI Is Changing Cybersecurity Fast and Most Analysts Aren't ReadyAI Security Agents Combat AI-Generated Code RisksDevelopers Are Embracing AI To Streamline Threat Detection and Stay AheadJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

RJJ Software's Software Development Service This episode of The Modern .NET Show is supported, in part, by RJJ Software's Software Development Services, whether your company is looking to elevate its UK operations or reshape its US strategy, we can provide tailored solutions that exceed expectations. Show Notes "If your app has a backend, it's Aspire-able. And so it's tools, templates, and packages for really any type of app… So just being able to walk up to a repo, clone it, and hit F5. When was the last time we were able to do that? Like, ten years ago, maybe?"— Maddy Montaquila Welcome friends to The Modern .NET Show; the premier .NET podcast, focusing entirely on the knowledge, tools, and frameworks that all .NET developers should have in their toolbox. We are the go-to podcast for .NET developers worldwide, and I am your host: Jamie “GaProgMan” Taylor. In this episode, we talk with Maddy Montaquila about .NET Aspire, what it is, how it's not just for .NET developers, and how it can help you to run a repo by simply hitting F5, regardless of what's in there. "To me, it really is just a dev tool in a bunch of different ways. It makes you just hit F5 again, no matter how many containers, or local, or deployed services you have to deal with, or projects, or languages, or if you're in VS, or VS Code, or on a Mac, or on a command line, or on a Linux machine. Like Aspire just makes all that magical without replatforming"— Maddy Montaquila Along the way, we also talk about the importance of reducing the complexity of going from, "I have an idea," to, "my app is running in the cloud." And Maddy drops a wonderful metaphor for .NET Aspire using a Logo-based metaphor. And we address the community invented elephant in the room: that .NET Aspire, somehow, locks you into using one vendor. Spoiler alert: it can deploy to any cloud vendor, and even to on-prem servers. Anyway, without further ado, let's sit back, open up a terminal, type in `dotnet new podcast` and we'll dive into the core of Modern .NET. Supporting the Show If you find this episode useful in any way, please consider supporting the show by either leaving a review (check our review page for ways to do that), sharing the episode with a friend or colleague, buying the host a coffee, or considering becoming a Patron of the show. Full Show Notes The full show notes, including links to some of the things we discussed and a full transcription of this episode, can be found at: https://dotnetcore.show/season-7/net-aspire-how-maddy-montaquila-and-the-net-team-are-revolutionizing-development/ Maddy's Links: Maddy on Bluesky Other Links: CNCF OpenTelemetry Helm Codespaces Podman Devcontainers Vim GDB FreeBSD Jail .NET Aspire Community Toolkit CORS MCP Phi-4 Four stages of competence dot.net Cloud features of .NET Customer Stories: customers.microsoft.com dot.net/customers Ollama Supporting the show: Leave a rating or review Buy the show a coffee Become a patron Getting in Touch: Via the contact page Joining the Discord Remember to rate and review the show on Apple Podcasts, Podchaser, or wherever you find your podcasts, this will help the show's audience grow. Or you can just share the show with a friend. And don't forget to reach out via our Contact page. We're very interested in your opinion of the show, so please get in touch. You can support the show by making a monthly donation on the show's Patreon page at: https://www.patreon.com/TheDotNetCorePodcast. Music created by Mono Memory Music, licensed to RJJ Software for use in The Modern .NET Show

In the Season 14 premiere, hosts Dan Ivovich and Sundi Myint chat with Isaac Yonemoto, creator of the Zigler library, to explore how Zigler brings Zig's performance and safety to Elixir through Native Implemented Functions (NIFs). Isaac walks through the core design of Zigler and how it auto-generates the Elixir-to-Zig bridge, enforces type safety, and exposes multiple execution modes (normal, dirty, threaded). The conversation covers real-world applications, from SIMD-powered token selection for LLM hardware acceleration to OTP-style fault tolerance in low-level code. Isaac shares his own journey: stepping back from professional software work to launch a biotech startup focused on reducing drug manufacturing costs while continuing to maintain Zigler and even leveraging Elixir for bioinformatics pipelines. Topics discussed in this episode: What is the Zigler library and what does it do? What does it mean to run a "dirty NIF"? Async mode is temporarily removed from Zig (therefore, yielding NIFs is temporarily deprecated in Zigler) Zigler's three execution modes (normal, dirty, and threaded) and how you switch modes with a single config change Isaac's journey from professional software work to launching a biotech startup How Isaac leverages Elixir in bioinformatics pipelines at his startup LLM hardware acceleration using Zigler NIFs and SIMD-powered token picking Fault-tolerant load balancing of NIF workloads via OTP principles Transparent handling and recovery from hardware failures through monitoring Potential future memory-safety features in Zig and their implications The Elixir-based borrow-checker prototype: purpose and design Unit-checking for scientific computations to enforce correctness New OS support in Zigler 0.14: macOS, Windows, and FreeBSD Inline Zig code authoring directly within Elixir modules Isaac's commitment to maintain Zigler through its 1.0 release (...and beyond?) Links mentioned: https://github.com/E-xyza/zigler https://github.com/ziglang/zig https://vidalalabs.com/ Zig Programming Language: https://ziglang.org/ https://obsidian.md/ https://hexdocs.pm/elixir/macros.html https://erlang.org/documentation/doc-4.7.3/doc/extensions/macros.html A Deep Dive Into the Elixir AST: https://dorgan.ar/posts/2021/04/theelixirast/ https://www.erlang.org/doc/system/nif.html https://nodejs.org/en Llama Open-Source LLM: https://www.llama.com/ Mixtral Open-Source LLM: https://mistral.ai/news/mixtral-of-experts https://Fly.io SIMD: https://en.wikipedia.org/wiki/Singleinstruction,multiple_data https://opentrons.com/ CI/CD: https://en.wikipedia.org/wiki/CI/CD https://hexdocs.pm/zigler/Zig.html http://www.x.com/DNAutics https://bsky.app/profile/dnautics.bsky.social

In this episode ofThe New Stack Makers, Nitric CEO Steve Demchuk discusses how the frustration of building frontend apps within rigid FinTech environments led to the creation of the Nitric framework — a tool designed to eliminate the friction between developers and cloud infrastructure. Unlike traditional Infrastructure as Code (IaC), where developers must manage both app logic and infrastructure definitions separately, Nitric introduces “Infrastructure from Code.” This approach allows developers to focus solely on application logic while the platform infers and automates infrastructure needs using SDKs and CLI tools across multiple languages and cloud providers.Demchuk emphasizes that Nitric doesn't remove platform team control but enforces it consistently. Guardrails defined by platform teams guide infrastructure provisioning, ensuring security and compliance — even as developers use AI tools to rapidly generate code. The result is a streamlined workflow where developers move faster, AI enhances productivity, and platform teams retain oversight. This episode offers engineering leaders insight into a paradigm shift in how cloud infrastructure is managed in the AI era.Learn more from The New Stack about the latest insights about Nitric:  Building a Serverless Meme Generator With Nitric and OpenAIWhy Most Companies Are Struggling With Infrastructure as Code Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

CodeRabbit, led by founder Harjot Gill, is tackling one of software development's biggest bottlenecks: the human code review process. While AI coding tools like GitHub Copilot have sped up code generation, they've inadvertently slowed down shipping due to increased complexity in code reviews. Developers now often review AI-generated code they didn't write, leading to misunderstandings, bugs, and security risks. In an episode of The New Stack Makers, Gill discusses how Code Rabbit leverages advanced reasoning models—OpenAI's o1, o3 mini, and Anthropic's Claude series—to automate and enhance code reviews. Unlike rigid, rule-based static analysis tools, Code Rabbit builds rich context at scale by spinning up sandbox environments for pull requests and allowing AI agents to navigate codebases like human reviewers. These agents can run CLI commands, analyze syntax trees, and pull in external context from Jira or vulnerability databases. Gill envisions a hybrid future where AI handles the grunt work of code review, empowering humans to focus on architecture and intent—ultimately reducing bugs, delays, and development costs.Learn more from The New Stack about the latest insights about AI code reviews: CodeRabbit's AI Code Reviews Now Live Free in VS Code, Cursor AI Coding Agents Level Up from Helpers to Team Players Augment Code: An AI Coding Tool for 'Real' Development WorkJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Agentic AI represents the next phase beyond generative AI, promising systems that not only generate content but also take autonomous actions within business processes. In a conversation recorded at Google Cloud Next, Kevin Laughridge of Deloitte explains that businesses are moving from AI pilots to production-scale deployments. Agentic AI enables decision-making, reasoning, and action across complex enterprise environments, reducing the need for constant human input. A key enabler is Google's newly announced open Agent2Agent (A2A) protocol, which allows AI agents from different vendors to communicate and collaborate securely across platforms. Over 50 companies, including PayPal, Salesforce, and Atlassian, are already adopting it. However, deploying agentic AI at scale requires more than individual tools—it demands an AI platform with runtime frameworks, UIs, and connectors. These platforms allow enterprises to integrate agents across clouds and systems, paving the way for AI that is collaborative, adaptive, and embedded in core operations. As AI becomes foundational, developers are transitioning from coding to architecting dynamic, learning systems.Learn more from The New Stack about the latest insights about Agent2Agent Protocol: Google's Agent2Agent Protocol Helps AI Agents Talk to Each OtherA2A, MCP, Kafka and Flink: The New Stack for AI AgentsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

Aja Hammerly, director of developer relations at Google, sees AI as the always-available coding partner developers have long wished for—especially in those late-night bursts of inspiration. In a conversation with Alex Williams at Google Cloud Next, she described AI-assisted coding as akin to having a virtual pair programmer who can fill in gaps and offer real-time support. Hammerly urges developers to start their AI journey with tools that assist in code writing and explanation before moving into more complex AI agents. She distinguishes two types of DevEx AI: using AI to build apps and using it to eliminate developer toil. For Hammerly, this includes letting AI handle frontend work while she focuses on backend logic. The newly launched Firebase Studio exemplifies this dual approach, offering an AI-enhanced IDE with flexible tools like prototyping, code completion, and automation. Her advice? Developers should explore how AI fits into their unique workflow—because development, at its core, is deeply personal and individual.Learn more from The New Stack about the latest AI insights with Google Cloud:Google AI Coding Tool Now Free, With 90x Copilot's OutputGemini 2.5 Pro: Google's Coding Genius Gets an UpgradeQ&A: How Google Itself Uses Its Gemini Large Language ModelJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

At Google Cloud Next '25, the company introduced Ironwood, its most advanced custom Tensor Processing Unit (TPU) to date. With 9,216 chips per pod delivering 42.5 exaflops of compute power, Ironwood doubles the performance per watt compared to its predecessor. Senior product manager Chelsie Czop explained that designing TPUs involves balancing power, thermal constraints, and interconnectivity. Google's long-term investment in liquid cooling, now in its fourth generation, plays a key role in managing the heat generated by these powerful chips. Czop highlighted the incremental design improvements made visible through changes in the data center setup, such as liquid cooling pipe placements. Customers often ask whether to use TPUs or GPUs, but the answer depends on their specific workloads and infrastructure. Some, like Moloco, have seen a 10x performance boost by moving directly from CPUs to TPUs. However, many still use both TPUs and GPUs. As models evolve faster than hardware, Google relies on collaborations with teams like DeepMind to anticipate future needs.Learn more from The New Stack about the latest AI infrastructure insights from Google Cloud:Google Cloud Therapist on Bringing AI to Cloud Native InfrastructureA2A, MCP, Kafka and Flink: The New Stack for AI AgentsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

At Google Cloud Next, Bobby Allen, Group Product Manager for Google Kubernetes Engine (GKE), emphasized GKE's foundational role in supporting AI platforms. While AI dominates current tech conversations, Allen highlighted that cloud-native infrastructure like Kubernetes is what enables AI workloads to function efficiently. GKE powers key Google services like Vertex AI and is trusted by organizations including DeepMind, gaming companies, and healthcare providers for AI model training and inference. Allen explained that GKE offers scalability, elasticity, and support for AI-specific hardware like GPUs and TPUs, making it ideal for modern workloads. He noted that Kubernetes was built with capabilities—like high availability and secure orchestration—that are now essential for AI deployment. Looking forward, GKE aims to evolve into a model router, allowing developers to access the right AI model based on function, not vendor, streamlining the development experience. Allen described GKE as offering maximum control with minimal technical debt, future-proofed by Google's continued investment in open source and scalable architecture.Learn more from The New Stack about the latest insights with Google Cloud: Google Kubernetes Engine Customized for Faster AI WorkKubeCon Europe: How Google Will Evolve Kubernetes in the AI EraApache Ray Finds a Home on the Google Kubernetes EngineJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Without this, developers waste time managing infrastructure instead of focusing on code. VMware addresses this with VCF, a pre-integrated Kubernetes solution that includes components like Harbor, Valero, and Istio, all managed by VMware. While some worry about added complexity from abstraction, Turner dismissed concerns about virtualization overhead, pointing to benchmarks showing 98.3% of bare metal performance for virtualized AI workloads. He emphasized that AI is driving nearly half of Kubernetes deployments, prompting VMware's partnership with Nvidia to support GPU virtualization. Turner also highlighted VMware's open source leadership, contributing to major projects and ensuring Kubernetes remains cloud-independent and standards-based. VMware aims to simplify Kubernetes and AI workload management while staying committed to the open ecosystem.Learn more from The New Stack about the latest insights with VMware Has VMware Finally Caught Up With Kubernetes?VMware's Golden PathJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  

Prequel is launching a new developer-focused service aimed at democratizing software error detection—an area typically dominated by large cloud providers. Co-founded by Lyndon Brown and Tony Meehan, both former NSA engineers, Prequel introduces a community-driven observability approach centered on Common Reliability Enumerations (CREs). CREs categorize recurring production issues, helping engineers detect, understand, and communicate problems without reinventing solutions or working in isolation. Their open-source tools, cre and prereq, allow teams to build and share detectors that catch bugs and anti-patterns in real time—without exposing sensitive data, thanks to edge processing using WebAssembly.The urgency behind Prequel's mission stems from the rapid pace of AI-driven development, increased third-party code usage, and rising infrastructure costs. Traditional observability tools may surface symptoms, but Prequel aims to provide precise problem definitions and actionable insights. While observability giants like Datadog and Splunk dominate the market, Brown and Meehan argue that engineers still feel overwhelmed by data and underpowered in diagnostics—something they believe CREs can finally change.Learn more from The New Stack about the latest Observability insights Why Consolidating Observability Tools Is a Smart MoveBuilding an Observability Culture: Getting Everyone Onboard Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

At Arm, open source is the default approach, with proprietary software requiring justification, says Andrew Wafaa, fellow and senior director of software communities. Speaking at KubeCon + CloudNativeCon Europe, Wafaa emphasized Arm's decade-long commitment to open source, highlighting its investment in key projects like the Linux kernel, GCC, and LLVM. This investment is strategic, ensuring strong support for Arm's architecture through vital tools and system software.Wafaa also challenged the hype around GPUs in AI, asserting that CPUs—especially those enhanced with Arm's Scalable Matrix Extension (SME2) and Scalable Vector Extension (SVE2)—are often more suitable for inference workloads. CPUs offer greater flexibility, and Arm's innovations aim to reduce dependency on expensive GPU fleets.On the AI framework front, Wafaa pointed to PyTorch as the emerging hub, likening its ecosystem-building potential to Kubernetes. As a PyTorch Foundation board member, he sees PyTorch becoming the central open source platform in AI development, with broad community and industry backing.Learn more from The New Stack about the latest insights about Arm: Edge Wars Heat Up as Arm Aims to Outflank Intel, Qualcomm Arm: See a Demo About Migrating a x86-Based App to ARM64Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

In today's uncertain economy, businesses are tightening costs, including for Kubernetes (K8s) operations, which are notoriously difficult to optimize. Yodar Shafrir, co-founder and CEO of ScaleOps, explained at KubeCon + CloudNativeCon Europe that dynamic, cloud-native applications have constantly shifting loads, making resource allocation complex. Engineers must provision enough resources to handle spikes without overspending, but in large production clusters with thousands of applications, manual optimization often fails. This leads to 70–80% resource waste and performance issues. Developers typically prioritize application performance over operational cost, and AI workloads further strain resources. Existing optimization tools offer static recommendations that quickly become outdated due to the dynamic nature of workloads, risking downtime. Shafrir emphasized that real-time, fully automated solutions like ScaleOps' platform are crucial. By dynamically adjusting container-level resources based on real-time consumption and business metrics, ScaleOps improves application reliability and eliminates waste. Their approach shifts Kubernetes management from static to dynamic resource allocation. Listen to the full episode for more insights and ScaleOps' roadmap.Learn more from The New Stack about the latest in scaling Kubernetes and managing operational costs: ScaleOps Adds Predictive Horizontal Scaling, Smart Placement ScaleOps Dynamically Right-Sizes Containers at Runtime Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Heroku has been undergoing a major transformation, re-platforming its entire Platform as a Service (PaaS) offering over the past year and a half. This ambitious effort, dubbed “Fir,” will soon reach general availability. According to Betty Junod, CMO and SVP at Heroku (owned by Salesforce), the overhaul includes a shift to Kubernetes and OCI standards, reinforcing Heroku's commitment to open source. The platform now features Heroku Cloud Native Buildpacks, which let developers create container images without Dockerfiles. Originally built on Ruby on Rails and predating Docker and AWS, Heroku now supports eight programming languages. The company has also deepened its open source engagement by becoming a platinum member of the Cloud Native Computing Foundation (CNCF), contributing to projects like OpenTelemetry. Additionally, Heroku has open sourced its Twelve-Factor Apps methodology, inviting the community to help modernize it to address evolving needs such as secrets management and workload identity. This signals a broader effort to align Heroku's future with the cloud native ecosystem. Learn more from The New Stack about Heroku's approach to Platform-as-a-Service:Return to PaaS: Building the Platform of Our DreamsHeroku Moved Twelve-Factor Apps to Open Source. What's Next?How Heroku Is Positioned To Help Ops Engineers in the GenAI EraJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

In this episode of The New Stack Makers, recorded at KubeCon + CloudNativeCon Europe, Alex Williams speaks with Ville Aikas, Chainguard founder and early Kubernetes contributor. They reflect on the evolution of container security, particularly how early assumptions—like trusting that users would validate container images—proved problematic. Aikas recalls the lack of secure defaults, such as allowing containers to run as root, stemming from the team's internal Google perspective, which led to unrealistic expectations about external security practices.The Kubernetes community has since made strides with governance policies, secure defaults, and standard practices like avoiding long-lived credentials and supporting federated authentication. Aikas founded Chainguard to address the need for trusted, minimal, and verifiable container images—offering zero-CVE images, transparent toolchains, and full SBOMs. This security-first philosophy now extends to virtual machines and Java dependencies via Chainguard Libraries.The discussion also highlights the rising concerns around AI/ML security in Kubernetes, including complex model dependencies, GPU integrations, and potential attack vectors—prompting Chainguard's move toward locked-down AI images.Learn more from The New Stack about Container Security and AIChainguard Takes Aim At Vulnerable Java LibrariesClean Container Images: A Supply Chain Security RevolutionRevolutionizing Offensive Security: A New Era With Agentic AI Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

In a candid episode of The New Stack Makers, Kubernetes pioneer Kelsey Hightower and AWS's Eswar Bala explored the evolving relationship between enterprise cloud providers and open source software at KubeCon+CloudNativeCon London. Hightower highlighted open source's origins as a grassroots movement challenging big vendors, and shared how it gave people—especially those without traditional tech credentials—a way into the industry. Recalling his own journey, Hightower emphasized that open source empowered individuals through contribution over credentials.Bala traced the early development of Kubernetes and his own transition from building container orchestration systems to launching AWS's Elastic Kubernetes Service (EKS), driven by growing customer demand. The discussion, recorded at KubeCon + CloudNativeCon Europe, touched on how open source is now central to enterprise cloud strategies, with AWS not only contributing but creating projects like Karpenter, Cedar, and Kro.Both speakers agreed that open source's collaborative model—where companies build in public and customers drive innovation—has reshaped the cloud ecosystem, turning former tensions into partnerships built on community-driven progress.Learn more from The New Stack about the relationship between enterprise cloud providers and open source software:The Metamorphosis of Open Source: An Industry in TransitionThe Complex Relationship Between Cloud Providers and Open SourceHow Open Source Has Turned the Tables on Enterprise SoftwareJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

In a rare show of collaboration, Google, Amazon, and Microsoft have joined forces on Kro — the Kubernetes Resource Orchestrator — an open source, cloud-agnostic tool designed to simplify custom resource orchestration in Kubernetes. Announced during KubeCon + CloudNativeCon Europe, Kro was born from strong customer demand for a Kubernetes-native solution that works across cloud providers without vendor lock-in. Nic Slattery, Product Manager at Google and Jesse Butler, Principal Product Manager, AWS shared with The New Stack that unlike many enterprise products, Kro didn't stem from top-down strategy but from consistent customer "pull" experienced by all three companies. It aims to reduce complexity by allowing platform teams to offer simplified interfaces to developers, enabling resource requests without needing deep service-specific knowledge. Kro also represents a unique cross-company collaboration, driven by a shared mission and open source values. Though still in its alpha stage, the project has already attracted 57 contributors in just seven months. The team is now focused on refining core features and preparing for a production-ready release — all while maintaining a narrowly scoped, community-first approach.Learn more from The New Stack about KRO:One Mighty kro; One Giant Leap for Kubernetes Resource OrchestrationKubernetes Gets a New Resource Orchestrator in the Form of KroOrchestrate Cloud Native Workloads With Kro and KubernetesJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

OpenSearch has evolved significantly since its 2021 launch, recently reaching a major milestone with its move to the Linux Foundation. This shift from company-led to foundation-based governance has accelerated community contributions and enterprise adoption, as discussed by NetApp's Amanda Katona in a New Stack Makers episode recorded at KubeCon + CloudNativeCon Europe. NetApp, an early adopter of OpenSearch following Elasticsearch's licensing change, now offers managed services on the platform and contributes actively to its development.Katona emphasized how neutral governance under the Linux Foundation has lowered barriers to enterprise contribution, noting a 56% increase in downloads since the transition and growing interest from developers. OpenSearch 3.0, featuring a Lucene 10 upgrade, promises faster search capabilities—especially relevant as data volumes surge. NetApp's ongoing investments include work on machine learning plugins and developer training resources.Katona sees the Linux Foundation's involvement as key to OpenSearch's long-term success, offering vendor-neutral governance and reassuring users seeking openness, performance, and scalability in data search and analytics.Learn more from The New Stack about OpenSearch: Report: OpenSearch Bests ElasticSearch at Vector ModelingAWS Transfers OpenSearch to the Linux Foundation OpenSearch: How the Project Went From Fork to FoundationJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

AI applications are evolving beyond chatbots into more complex and transformative solutions, according to Marco Palladino, CTO and co-founder of Kong. In a recent episode of The New Stack Makers, he discussed the rise of AI agents, which act as "virtual employees" to enhance organizational efficiency. For instance, AI can now function as a product manager for APIs—analyzing documentation, detecting inaccuracies, and making corrections.However, reliance on AI agents brings security risks, such as data leakage and governance challenges. Organizations need observability and safeguards, but developers often resist implementing these requirements manually. As GenAI adoption matures, teams seek ways to accelerate development without rebuilding security measures repeatedly.To address these challenges, Kong introduced AI Gateway, an open-source plugin for its API Gateway. AI Gateway supports multiple AI models across providers like AWS, Microsoft, and Google, offering developers a universal API to integrate AI securely and efficiently. It also features automated retrieval-augmented generation (RAG) pipelines to minimize hallucinations.Palladino emphasized the need for consistent security in AI infrastructure, ensuring developers can focus on innovation while leveraging built-in protections.Learn more from The New Stack about Kong's AI GatewayKong: New ‘AI-Infused' Features for API Management, Dev ToolsFrom Zero to a Terraform Provider for Kong in 120 HoursJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Platform engineering was meant to ease the burdens of Devs and Ops by reducing cognitive load and repetitive tasks. However, building internal development platforms (IDPs) has proven challenging. Despite this, Gartner predicts that by 2026, 80% of software engineering organizations will have a platform team.In a recent New Stack Makers episode, Mallory Haigh of Humanitec and Nathen Harvey of Google discussed the current state and future of platform engineering. Haigh emphasized that many organizations rush to build IDPs without understanding why they need them, leading to ineffective implementations. She noted that platform engineering is 10% technical and 90% cultural change, requiring deep introspection and strategic planning.AI-driven automation, particularly agentic AI, is expected to shape platform engineering's future. Haigh highlighted how AI can enhance platform orchestration and optimize GPU resource management. Harvey compared platform engineering to generative AI—both aim to reduce toil and improve efficiency. As AI adoption grows, platform teams must ensure their infrastructure supports these advancements.Learn more from The New Stack about platform engineering:  Platform Engineering on the Brink: Breakthrough or Bust?Platform Engineers Must Have Strong OpinionsThe Missing Piece in Platform Engineering: Recognizing ProducersJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

AI agents are set to transform software development, but software itself isn't going anywhere—despite the dramatic predictions. On this episode of The New Stack Makers, Mark Hinkle, CEO and Founder of Peripety Labs, discusses how AI agents relate to serverless technologies, infrastructure-as-code (IaC), and configuration management. Hinkle envisions AI agents as “dumb robots” handling tasks like querying APIs and exchanging data, while the real intelligence remains in large language models (LLMs). These agents, likely implemented as serverless functions in Python or JavaScript, will automate software development processes dynamically. LLMs, leveraging vast amounts of open-source code, will enable AI agents to generate bespoke, task-specific tools on the fly—unlike traditional cloud tools from HashiCorp or configuration management tools like Chef and Puppet. As AI-generated tooling becomes more prevalent, managing and optimizing these agents will require strong observability and evaluation practices. According to Hinkle, this shift marks the future of software, where AI agents dynamically create, call, and manage tools for CI/CD, monitoring, and beyond. Check out the full episode for more insights. Learn more from The New Stack about emerging trends in AI agents: Lessons From Kubernetes and the Cloud Should Steer the AI RevolutionAI Agents: Why Workflows Are the LLM Use Case to Watch Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

The transition from SaaS to Services as Software with AI agents is underway, necessitating new orchestration methods similar to Kubernetes for containers. AI agents will require resource allocation, workflow management, and scalable infrastructure as they evolve. Two key trends are driving this shift: Data Evolution – From spreadsheets to AI agents, data has progressed through relational databases, big data, predictive analytics, and generative AI. Computing Evolution – Starting from mainframes, the journey has moved through desktops, client servers, web/mobile, SaaS, and now agentic workflows. Janakiram MSV, an analyst, notes on this episode of The New Stack Makers that SaaS depends on data—without it, platforms like Salesforce and SAP lack value. As data becomes more actionable and compute more agentic, a new paradigm emerges: Services as Software. AI agents will automate tasks previously requiring human intervention, like emails and sales follow-ups. However, orchestrating them will be complex, akin to Kubernetes managing containers. Unlike deterministic containers, AI agents depend on dynamic, trained data, posing new enterprise challenges in memory management and infrastructure. Learn more from The New Stack about evolution to AI agents: How AI Agents Are Starting To Automate the Enterprise Can You Trust AI To Be Your Data Analyst? Agentic AI is the New Web App, and Your AI Strategy Must Evolve Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  

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

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. 

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 fullNew Stack Makers episode.Learn more from The New Stack about System Initiative:Beyond Infrastructure as Code: System Initiative Goes LiveHow System Initiative Treats AWS Components as Digital TwinsSystem Initiative Code Now Open SourceJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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 onThe 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 GuideObservability in 2025: OpenTelemetry and AI to Fill In GapsHoneycomb.io's Austin Parker: OpenTelemetry In-DepthJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  

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-DepthObservability in 2025: OpenTelemetry and AI to Fill In GapsObservability and AI: New Connections at KubeConJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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 EraThe Data Stack Journey: Lessons from Architecting Stacks at Heroku and MattermostJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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 SecurityeBPF Tools: An Overview of Falco, Inspektor Gadget, Hubble and CiliumJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

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 TechnologiesJetstack Secure Promises to Ease Kubernetes TLS SecurityJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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 Jobs5 Software Development Skills AI Will Render ObsoleteCloud Native Skill Gaps are Killing Your GainsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  

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

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 OpenSearchAWS Transfers OpenSearch to the Linux FoundationFrom Flashpoint to Foundation: OpenSearch's Path ClearsSemantic Search with Amazon OpenSearch Serverless and TitanJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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 RayHow 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. 

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. 

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 ScaleThe Top 5 Python Packages and What They DoPython Mulls a Change in Version NumberingJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

Platform engineering will be a key focus at KubeCon this year, with a special emphasis on AI platforms. Priyanka Sharma, executive director of the Linux Foundation, highlighted the convergence of platform engineering and AI during an interview on The New Stack Makers with Adobe's Joseph Sandoval. KubeCon will feature talks from experts like Chen Goldberg of CoreWeave and Aparna Sinha of CapitalOne, showcasing how AI workloads will transform platform operations.Sandoval emphasized the growing maturity of platform engineering over the past two to three years, now centered on addressing user needs. He also discussed Adobe's collaboration on CNOE, an open-source initiative for internal developer platforms. The intersection of platform engineering, Kubernetes, cloud-native technologies, and AI raises questions about scaling infrastructure management with AI, potentially improving efficiency and reducing toil for roles like SRE and DevOps. Sharma noted that reference architectures, long requested by the CNCF community, will be highlighted at the event, guiding users without dictating solutions. Learn more from The New Stack about Kubernetes: Cloud Native Networking as Kubernetes Starts Its Second DecadePrimer: How Kubernetes Came to Be, What It Is, and Why You Should Care How Cloud Foundry Has Evolved With Kubernetes Join our community of newsletter subscribers to stay on top of the news and at the top of your game. game. https://thenewstack.io/newsletter/

النهاردة حيشرفني بشمهنىس محمد مشرف, و هو من أقدم ضيوف البودكاست و من الناس اللي ليهم بصمة في مجال التقنية في مصر, و له خبرات كثير في مجالات مختلفة و أدوار مختلفة في شركات التقنية, و مقدم محتوى تقني بأشكال مختلفة. حنتكلم النهاردة سوا عن ازاي تفرق بين كورس برمجة أو تقنية حقيقي و مفيد, و بين كورسات السبوبة, و تبعد عن النصب و النصابين.

Rohit Choudhary, co-founder and CEO of Acceldata, placed an early bet on data observability, which has proven prescient. In a New Stack Makers podcast episode, Choudhary discussed three key insights that shaped his vision: First, the exponential growth of data in enterprises, further amplified by generative AI and large language models. Second, the rise of a multicloud and multitechnology environment, with a majority of companies adopting hybrid or multiple cloud strategies. Third, a shortage of engineering talent to manage increasingly complex data systems.As data becomes more essential across industries, challenges in data observability have intensified. Choudhary highlights the complexity of tracking where data is produced, used, and its compliance requirements, especially with the surge in unstructured data. He emphasized that data's operational role in business decisions, marketing, and operations heightens the need for better traceability. Moving forward, traceability and the ability to manage the growing volume of alerts will become areas of hyper-focus for enterprises.Learn more from The New Stack about data observability: What Is Data Observability and Why Does It Matter?The Looming Crisis in the Observability MarketThe Growth of Observability Data Is Out of Control!Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  

Rust has maintained its place among the top 15 programming languages and has been the most admired language for nine consecutive years. In a New Stack Makers podcast, Joel Marcey, director of technology at the Rust Foundation, discussed the language's growing importance, including initiatives to improve its security, performance, and adoption in various domains. While Rust is widely used in systems and backend programming, it's also gaining traction in embedded systems, safety-critical applications, game development, and even the Linux kernel.Marcey highlighted Rust's strengths as a safe and fast systems language, noting its use on the web through WebAssembly (Wasm), though adoption there is still early. He also addressed Rust vs. Go, explaining that Rust excels in performance-critical applications. Marcey discussed recent updates, such as Rust 1.81, and project goals for 2024, which include a new edition and async improvements.He also touched on government interest in Rust, including DARPA's initiative to convert C code to Rust, and the Rust Security Initiative, aimed at maintaining the language's strong security reputation.Learn more from The New Stack about Rust Could Rust be the Future of JavaScript Infrastructure?Rust Growing Fastest, But JavaScript Reigns SupremeRust vs. Zig in Reality: A (Somewhat) Friendly DebateJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

In a New Stack Makers episode, Ashley Williams, founder and CEO of axo, highlights how the software world depends on open-source code, which is largely maintained by unpaid volunteers. She likens this to a CVS relying on volunteer-run shipping companies, pointing out how unsettling that might be for customers. The conversation focuses on open-source maintainers' reluctance to be seen as "suppliers" of software, an idea explored in a 2022 blog post by Thomas Depierre. Many maintainers reject the label, as there is no contractual obligation to support the software they provide. Williams critiques the industry's response to this, noting that instead of involving maintainers in software supply chain security, companies have relied on third-party vendors. However, these vendors have no relationship with the maintainers, leading to increased vulnerabilities. Williams advocates for better engagement with maintainers, especially at build time, to improve security. She also reflects on the growing pressures on maintainers and the underappreciation of release teams.Learn more from The New Stack about open source software supply chain2023: The Year Open Source Security Supply Chain Grew UpFortifying the Software Supply ChainThe Challenges of Securing the Open Source Supply ChainJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

In this New Stack Makers podcast, Xun Wang, CTO of Bloomreach, brings insights from his time at Nvidia, particularly lessons from its founder, Jensen Huang, to his current role in e-commerce personalization. Wang emphasizes structuring organizations to reflect the architecture of the products they build, applying a hands-on, detail-oriented approach that encourages deep understanding of engineering challenges. He credits Huang for teaching him the importance of focusing on fundamental architecture rather than relying on iterative testing alone. Wang highlights the impact of generative AI (GenAI) on Bloomreach, explaining how AI-driven search is essential to understanding human language and user intent. As GenAI reshapes application development, Wang stresses the need for engineers to adopt new skills in AI manipulation, while still maintaining traditional coding expertise. He advocates for continuous learning, acknowledging the challenge of staying updated in a rapidly evolving field. Wang, himself, reads extensively to keep pace with innovations, underscoring the importance of staying curious and adaptable in today's tech landscape. Learn more from The New Stack about Entrepreneurship for Engineers: How to Grow into Leadership Engineering Leaders: Switch to Wartime Management Now How Teleport's Leader Transitioned from Engineer to CEO  Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Bob Wise, CEO of Heroku, discussed the impact of generative AI (GenAI) coding tools on software development in a recent episode of The New Stack Makers. He compared the rise of these tools to adding an "infinite number of interns" to development teams, noting that while they accelerate code writing, they don't yet simplify testing, deployment, or production operations. Wise likened this to the early days of Kubernetes, which focused on improving operations rather than the frontend experience. He emphasized that Kubernetes' success was due to its focus on easing the operational burden, something current GenAI tools have yet to achieve.Heroku, acquired by Salesforce in 2010, is positioned to benefit from these changes by helping teams transition to more automated systems. Wise highlighted Heroku's strategic bet on Postgres, a database technology that's gaining traction, especially for GenAI workloads. He also discussed Heroku's ongoing migration to Kubernetes, aligning with industry standards to enhance its platform.Learn more from The New Stack about HerokuThe Data Stack Journey: Lessons from Architecting Stacks at Heroku and MattermostKubernetes and the Next Generation of PaaS Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

After the XZ Utils backdoor vulnerability was uncovered in March, the OpenJS Foundation saw a surge in inquiries from potential open source JavaScript contributors. Robin Ginn, executive director of the foundation, noted that volunteer-led JavaScript communities often face challenges in managing these contributions. The discovery that a single contributor, "Jia Tan," planted the backdoor heightened vigilance, especially when new contributors requested admin privileges. Ginn emphasized that trust is not synonymous with security, especially in open source projects where maintainers must be vigilant about who can access their repositories.The XZ vulnerability highlighted broader concerns about the security of open source software, particularly in projects with only a single maintainer. Despite receiving a significant grant from Germany's Sovereign Tech Fund, the foundation remains under-resourced, with just two full-time staffers supporting 35 projects. Ginn urged companies that rely on open source software to invest in it by hiring maintainers, ensuring these critical projects are properly supported.Learn more from The New Stack about open source vulnerabilityLinux xz Backdoor Damage Could Be Greater Than Feared Unzipping the XZ Backdoor and Its Lessons for Open Source Linux xz and the Great Flaws in Open Source Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  

Anne Currie, a leading expert in sustainable tech and part of the Green Software Foundation, discusses practical steps for building resilient, sustainable software in an episode of The New Stack Makers. With 30 years of experience, Currie co-authored Building Green Software, emphasizing the tech industry's role in the energy transition. She highlights the complexity of adapting technology to renewable energy, involving extensive research and debunking misinformation. Currie discusses the importance of energy proportionality—the idea that increased utilization improves a computer's energy efficiency—and how this concept aligns with modern DevOps practices that reduce carbon emissions while enhancing speed, cost efficiency, and security.Currie also emphasizes architecting systems to operate on renewable power and draws parallels between managing variable grid power and internet bandwidth. Using examples like video conferencing, she illustrates how software can adapt to fluctuating resources. The episode also touches on potential pitfalls like greenwashing and the challenges in accurately naming concepts like energy proportionality.Learn more from The New Stack about sustainability: Sustainability: How Did Amazon, Azure, Google Perform in 2023? Sustainability Focus: Cloud Efficiency, Not Carbon Emissions Developers Should Press Cloud Providers on Sustainability Join our community of newsletter subscribers to stay on top of the news and at the top of your game. https://thenewstack.io/newsletter/

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