# AI & ML Published articles for AI & ML. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Migrating the GitHub Copilot runtime to Rust, using Copilot DevFeed: [Migrating the GitHub Copilot runtime to Rust, using Copilot]() Original publisher: [Read original article]() Author: Stephen Toub Published: 2026-09-17T00:26:43Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot](), [Rust](), [Agent Harness](), [GitHub Copilot SDK](), [GitHub]() Tags: [agent-harness](), [ai-agents](), [ai-ml](), [developer-experience](), [generative-ai](), [github](), [github-copilot](), [github-copilot-sdk](), [rust]() ### AI overview The GitHub Copilot agent runtime was rewritten from TypeScript on Node.js and V8 into more than 800,000 lines of production Rust. AI agents wrote most of the code across 128 pull requests, and the runtime was shipped incrementally while regressions were fixed and performance improved by orders of magnitude. ### Source excerpt A rewrite this size wasn't affordable before agents. Here's what porting the Copilot agent runtime to 800,000 lines of production Rust actually took. The post Migrating the GitHub Copilot runtime to Rust, using Copilot appeared first on The GitHub Blog. ## How Everpure proposes reducing GPU idle time by improving AI data access DevFeed: [How Everpure proposes reducing GPU idle time by improving AI data access]() Original publisher: [Read original article]() Author: Chris Mellor Published: 2026-09-15T08:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Infrastructure](), [GPU](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [AI Agent](), [data-processing](), [data]() Tags: [agents](), [ai](), [ai-infrastructure](), [ai-ml](), [data-processing](), [gpu](), [hardware](), [rag](), [sponsored]() ### AI overview This sponsored feature describes Everpure's approach to reducing GPU idle time in AI systems by improving access to large-scale insurance data. It discusses central metadata indexing, storage performance, self-describing data, and integration with Nvidia GPU infrastructure for AI agents and retrieval-augmented generation. ### Source excerpt SPONSORED FEATURE: The vendor's AI solutions are dedicated to increasing GPU utilization and avoiding costly GPUs doing nothing while waiting for data ## Marketing ops as code: Automating events from planning to follow-up on GitHub DevFeed: [Marketing ops as code: Automating events from planning to follow-up on GitHub]() Original publisher: [Read original article]() Author: Tomoko Tanaka Published: 2026-09-11T18:26:10Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [AI-assisted coding]() Tags: [ai-ml](), [api](), [automation](), [cli](), [events](), [github](), [github-actions](), [github-copilot](), [github-issues]() ### AI overview A GitHub marketing lead describes using GitHub Copilot and GitHub Actions to automate event operations from a GitHub Issue, including setup, daily registrant processing, and post-event cleanup. ### Source excerpt If you can write down how you do your work, you can automate it. Here's what I did to support GitHub's APAC marketing team. The post Marketing ops as code: Automating events from planning to follow-up on GitHub appeared first on The GitHub Blog. ## GitHub Copilot app for Beginners: Using the diff, terminal, and browser DevFeed: [GitHub Copilot app for Beginners: Using the diff, terminal, and browser]() Original publisher: [Read original article]() Author: Kayla Cinnamon Published: 2026-09-10T21:31:19Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot](), [AI-assisted coding](), [dev-tools](), [web applications]() Tags: [ai-coding](), [ai-ml](), [github-copilot](), [github-copilot-app](), [github-copilot-app-for-beginners](), [pull-request](), [review](), [web-browser]() ### AI overview A beginner-oriented guide to using the GitHub Copilot app's diff, terminal, and browser panels to review agent-made code changes, run a project, preview a web feature, iterate, and create a pull request. ### Source excerpt Checking agent-generated code usually means hopping between tabs. Learn how to view diffs, run terminal commands, and preview web apps side by side in the GitHub Copilot app. The post GitHub Copilot app for Beginners: Using the diff, terminal, and browser appeared first on The GitHub Blog. ## A C File Runs a 744B Model, Cloudflare Maps Agentic Traffic, and Karpathy's $48 GPT-2 - The Tokenizer Edition #36 DevFeed: [A C File Runs a 744B Model, Cloudflare Maps Agentic Traffic, and Karpathy's $48 GPT-2 - The Tokenizer Edition #36]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-09-09T00:00:40Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [Machine learning](), [C](), [Cloudflare](), [Command-line interface]() Tags: [agentic](), [ai](), [ai-ml](), [c](), [cloudflare](), [terminal]() ### AI overview A curated weekly roundup of AI resources covering research papers, videos, articles, developer tools, and learning materials. Featured items include a C program that streams a 744-billion-parameter model from disk, Cloudflare's analysis of human and agent web sessions, and a low-cost GPT-2-class model training project. ### Source excerpt This week's most valuable AI resources ## 6 Benefits of Sandbox Environments (and How Docker Sandboxes Delivers Them) DevFeed: [6 Benefits of Sandbox Environments (and How Docker Sandboxes Delivers Them)]() Original publisher: [Read original article]() Author: Kevin Wittek Published: 2026-09-08T23:05:55Z Content type: article Language: en Sources: [Docker]() Topics: [Docker](), [Authorization]() Tags: [agentic-ai](), [ai-agents](), [ai-ml](), [autonomous](), [concepts](), [docker-ai-governance](), [docker-sandboxes](), [linux](), [policy](), [products](), [sandbox](), [secrets]() ### AI overview The article explains how Docker Sandboxes isolate untrusted code and autonomous AI agents from host machines and external systems. It highlights runtime policy controls, credential handling, disposability, and microVM-based isolation. ### Source excerpt Learn about the key benefits of sandbox environments with Docker including isolation, definable controls, secrets credential handling, and more. ## Introducing the Backblaze B2 MCP Server DevFeed: [Introducing the Backblaze B2 MCP Server]() Original publisher: [Read original article]() Author: Jeronimo De Leon Published: 2026-09-08T18:13:00Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [MCP Server](), [Model Context Protocol](), [Cloud](), [Authorization](), [Open Source]() Tags: [agents](), [ai](), [ai-ml](), [api](), [b2cloud](), [cloud](), [cloud-storage](), [developers](), [featured](), [featured-cloud-storage](), [mcp](), [mcp-server](), [object-storage](), [open-source](), [storage](), [uncategorized]() ### AI overview Backblaze introduces an open-source B2 MCP Server that lets AI agents operate cloud object storage through MCP-compatible clients. Access is constrained by the permissions of the connected B2 application key, including limits on buckets and operations. ### Source excerpt The Backblaze B2 MCP Server gives AI agents a safe, standard way to operate cloud object storage. Connect MCP-compatible clients to scoped B2 application keys, manage files and buckets, move large objects directly, and control destructive actions with configurable safeguards. The post Introducing the Backblaze B2 MCP Server appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Project HydraFusion: Frontier quality via multi-model orchestration DevFeed: [Project HydraFusion: Frontier quality via multi-model orchestration]() Original publisher: [Read original article]() Author: GitHub Staff Published: 2026-09-04T16:04:14Z Content type: release Language: en Sources: [GitHub Engineering]() Topics: [Orchestration](), [Model Routing](), [GitHub Copilot](), [AI-assisted coding](), [Optimization](), [Claude]() Tags: [agentic-coding](), [ai-ml](), [benchmarking](), [benchmarks](), [claude](), [cost](), [github-copilot](), [llms](), [optimization](), [orchestration](), [routing]() ### AI overview GitHub introduces Project HydraFusion, a GitHub Copilot research preview that selects multi-model execution workflows to balance quality, cost, and latency for coding tasks. ### Source excerpt In controlled offline evaluations, HydraFusion's selective coding workflows matched or exceeded the evaluated Opus 5 baseline while reducing estimated workflow cost. Now available as a research preview in GitHub Copilot. The post Project HydraFusion: Frontier quality via multi-model orchestration appeared first on The GitHub Blog. ## GitHub Copilot app for Beginners: Run several agents at once DevFeed: [GitHub Copilot app for Beginners: Run several agents at once]() Original publisher: [Read original article]() Author: Kayla Cinnamon Published: 2026-09-03T16:00:00Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [AI Chat]() Tags: [agents](), [ai](), [ai-agents](), [ai-ml](), [copilot](), [developer](), [developer-tools](), [development](), [git](), [github](), [github-copilot](), [github-copilot-app](), [github-copilot-app-for-beginners]() ### AI overview A beginner tutorial on using the GitHub Copilot app to run several AI agent sessions in parallel. It explains that sessions are isolated through separate Git worktrees and retain their own context, then demonstrates concurrent feature work, accessibility review, and testing. ### Source excerpt Learn how to run parallel agents in the GitHub Copilot app, and experience the moment it stops feeling scary and starts feeling powerful. The post GitHub Copilot app for Beginners: Run several agents at once appeared first on The GitHub Blog. ## Announcing General Availability of VMware Cloud Foundation 9.1.1 DevFeed: [Announcing General Availability of VMware Cloud Foundation 9.1.1]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-03T13:47:18Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [Cloud](), [Security](), [AI Platform](), [AI Models](), [MLOps]() Tags: [ai](), [ai-ml](), [ai-models](), [announce](), [apis](), [cloud](), [cloud-infrastructure](), [complexity](), [cost](), [cpu](), [data-privacy](), [evpn](), [governance](), [home-page](), [integrations](), [kubernetes](), [memory](), [nsx](), [private-ai-services](), [release](), [root-cause-analysis](), [security](), [troubleshooting](), [vcf-9-1](), [vcf-operations](), [vmware](), [vmware-cloud-foundation]() ### AI overview VMware announces the general availability of VMware Cloud Foundation 9.1.1. The release adds tougher security, vSAN Object Storage as a tech preview, multi-tenant AI model sharing with tenant-isolated access controls, and an AI Assistant for diagnostics, management-pack creation, and troubleshooting across infrastructure and Kubernetes clusters. ### Source excerpt Coming on the heels of a very successful VMware Explore in Vegas and the VMware Cloud Foundation (VCF) 9.1 launch in May, we're excited to announce the general availability of VCF 9.1.1. This release builds on VCF 9.1 with tougher security, vSAN Object Storage (tech preview, previously announced) and new capabilities designed to make your ... Continued The post Announcing General Availability of VMware Cloud Foundation 9.1.1 appeared first on VMware Blogs. ## Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my! DevFeed: [Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my!]() Original publisher: [Read original article]() Author: Cassidy Williams Published: 2026-09-02T21:00:00Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [AI Development](), [Software Engineering](), [Open Source Models & Datasets]() Tags: [agent](), [agentic-ai](), [agents](), [ai](), [ai-ml](), [engineering](), [github-podcast](), [loops](), [models](), [observability](), [open-source](), [routing](), [skills](), [validation](), [workflow]() ### AI overview A guide to emerging AI-development terminology, explaining loop engineering and Ralph loops, multi-agent squads and fleets, and related concepts such as open weights and open source models. ### Source excerpt From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations. The post Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my! appeared first on The GitHub Blog. ## How we make AI coding more cost efficient without sacrificing task quality DevFeed: [How we make AI coding more cost efficient without sacrificing task quality]() Original publisher: [Read original article]() Author: Erik Kristensen Published: 2026-09-02T18:00:00Z Content type: article Language: en Sources: [GitHub](), [GitHub Engineering]() Topics: [GitHub Copilot](), [AI-assisted coding](), [GitHub Copilot CLI](), [coding](), [GitHub Copilot app](), [GitHub Copilot code review]() Tags: [agentic-ai](), [agentic-coding](), [ai](), [ai-coding](), [ai-ml](), [architecture-optimization](), [benchmarks](), [compression](), [cost](), [engineering](), [experiments](), [github-copilot](), [github-copilot-cli](), [github-copilot-code-review](), [llms](), [prompt-engineering]() ### AI overview GitHub Copilot's efficiency work focuses on total task cost and duration rather than minimizing tokens in individual tool responses. The article describes evaluating changes with coding benchmarks and controlled experiments, and explains that overly compressed output can cause agents to repeat work. ### Source excerpt Why shorter outputs can cost more, and how GitHub Copilot reduces wasted work across the complete coding task. The post How we make AI coding more cost efficient without sacrificing task quality appeared first on The GitHub Blog. ## Below the Harness: Governing a Multi-Model, Multi-Harness World DevFeed: [Below the Harness: Governing a Multi-Model, Multi-Harness World]() Original publisher: [Read original article]() Author: Srini Sekaran Published: 2026-09-02T13:00:00Z Content type: opinion Language: en Sources: [Docker]() Topics: [AI Bots]() Tags: [ai-agent](), [ai-ml](), [apis](), [claude-code](), [codex](), [company](), [model](), [security]() ### AI overview An opinion piece arguing that AI agents require a new trust model as teams adopt multiple models and harnesses. It frames agents as probabilistic deputies that can act with users' credentials and access. ### Source excerpt We believe the future is a multi-model, multi-harness world. And we think it needs a new trust model. In 1988, Norm Hardy described a problem that had been quietly breaking systems for years: the confused deputy. A program that takes action using its permissions instead of yours. Today, every AI agent is that deputy. It... ## Building Reproducible AI Evaluation Workflows with Docker Sandboxes DevFeed: [Building Reproducible AI Evaluation Workflows with Docker Sandboxes]() Original publisher: [Read original article]() Author: Jennifer Kohl Published: 2026-09-02T13:00:00Z Content type: tutorial Language: en Sources: [Docker]() Topics: [ai observability](), [Human-AI evaluation]() Tags: [ai](), [ai-agent](), [ai-ml](), [ai-models](), [benchmarks](), [claude](), [community](), [docker](), [docker-sandboxes](), [evaluation](), [genai](), [json](), [open-source](), [python](), [sandbox](), [sandboxes](), [workflow](), [workflows]() ### AI overview The article presents an open-source Docker Sandboxes Mixin Kit for making AI evaluation workflows reproducible. It runs configured commands in a consistent environment and records structured results and runtime evidence, without executing models or generating evaluation judgments itself. ### Source excerpt Learn how Docker Sandboxes can make AI evaluation workflows more reproducible with consistent execution, structured artifacts, and runtime evidence. ## GitHub Copilot app for Beginners: Automate Dependabot pull request triage DevFeed: [GitHub Copilot app for Beginners: Automate Dependabot pull request triage]() Original publisher: [Read original article]() Author: Christopher Harrison Published: 2026-08-26T20:12:53Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot app](), [pull-requests](), [Automation](), [GitHub Copilot](), [GitHub](), [Vulnerabilities]() Tags: [ai-ml](), [automation](), [copilot](), [developer](), [github](), [github-copilot](), [github-copilot-app](), [github-copilot-app-for-beginners](), [pull-request](), [workflow]() ### AI overview Learn how to create a daily automation in the GitHub Copilot app to triage Dependabot pull requests. The automation reviews open pull requests, groups updates by risk, checks CI status, and returns a summary highlighting changes ready to merge or requiring closer attention. ### Source excerpt Managing library updates can be tedious at times. Learn how the GitHub Copilot app can handle this type of repetitive task. The post GitHub Copilot app for Beginners: Automate Dependabot pull request triage appeared first on The GitHub Blog. ## How to evaluate LLMs before production DevFeed: [How to evaluate LLMs before production]() Original publisher: [Read original article]() Author: Mariko Wakabayashi Published: 2026-08-25T21:35:11Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [Large Language Model](), [Secret Scanning](), [Benchmark](), [datasets](), [GitHub](), [Security]() Tags: [ai-ml](), [benchmark](), [benchmarks](), [code](), [developer-tools](), [evaluation](), [github](), [llms](), [secret-scanning](), [security]() ### AI overview This article explains how to evaluate LLM-based systems before moving them into production. Using GitHub secret scanning as an example, it emphasizes defining the product decision first, testing against realistic production conditions, accounting for ambiguity and edge cases, and balancing reduced false positives with sufficient recall. ### Source excerpt These are the lessons we learned evaluating LLMs for real-world secret scanning. The post How to evaluate LLMs before production appeared first on The GitHub Blog. ## How Adobe Reduced GPU Idle Time in Generative AI Training Through Faster Data Access and Checkpointing DevFeed: [How Adobe Reduced GPU Idle Time in Generative AI Training Through Faster Data Access and Checkpointing]() Original publisher: [Read original article]() Author: Maddie Presland Published: 2026-08-25T15:22:30Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [GPU](), [Training AI Models](), [cloud-infrastructure](), [networking](), [data]() Tags: [ai-ml](), [ai-training](), [b2cloud](), [cloud](), [cloud-storage](), [compute](), [ethernet](), [featured](), [featured-cloud-storage](), [gpu](), [model-training](), [networking]() ### AI overview Adobe's generative AI training pipeline left roughly two-thirds of GPU time waiting for data. The article attributes the waste to storage and retrieval bottlenecks, networking limits, and checkpointing overhead, and describes Adobe's use of a high-performance networking fabric and fragmented checkpoint storage to reduce delays. ### Source excerpt Adobe's experience reveals why GPUs sit idle during AI model training: slow storage, insufficient throughput, and uneven workloads. Learn how storage bottlenecks and uneven data loading leave expensive GPUs idle--and how always-hot, high-throughput object storage keeps AI training pipelines running efficiently at scale while reducing wasted compute costs. The post Why Your GPU Is Sitting Idle: The Data Pipeline Problem No One Talks About appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## 17,600 Actions: Agent Security Is a Systems Problem DevFeed: [17,600 Actions: Agent Security Is a Systems Problem]() Original publisher: [Read original article]() Author: Jin Kim Published: 2026-08-18T16:00:00Z Content type: opinion Language: en Sources: [Docker]() Topics: [AI Agent](), [Security](), [incident](), [Securing AI](), [Incident response](), [Vulnerabilities](), [Benchmark](), [benchmarking]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-ml](), [benchmark](), [community](), [company](), [dhi](), [docker](), [docker-ai-governance](), [docker-hardened-images](), [engineering](), [evaluation](), [hugging-face](), [incident](), [infrastructure](), [model](), [network](), [openai](), [opinion](), [persistence](), [sandboxes](), [security](), [solutions](), [systems](), [vulnerabilities]() ### AI overview The article argues that AI-agent security is a systems problem, using the OpenAI/Hugging Face incident and its approximately 17,600 attacker actions to show why human approval and ordinary alert triage cannot control persistent, high-rate workloads. It emphasizes constraining authority, credentials, network access, state, and execution across environments. ### Source excerpt The OpenAI/Hugging Face incident exposed a new challenge for AI agent security. 17,600 attacker actions show why AI agent security can't rely on human review. Explore the controls needed to constrain, observe, and govern agents at speed. ## Results from the Backblaze Generative AI Media Hackathon DevFeed: [Results from the Backblaze Generative AI Media Hackathon]() Original publisher: [Read original article]() Author: Jeronimo De Leon Published: 2026-08-14T15:52:05Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [Generative AI](), [Orchestration](), [cloud-infrastructure](), [For the Love of Code](), [Open Source](), [SDKs]() Tags: [2026](), [ai-ml](), [b2cloud](), [cloud](), [cloud-storage](), [developer](), [featured](), [featured-cloud-storage](), [generative](), [hackathon](), [object-storage](), [open-source](), [orchestration](), [sdk](), [tech-lab](), [technical]() ### AI overview The article examines the strongest projects from the Backblaze Generative AI Media Hackathon and argues that production-ready generative media applications depend on more than generating images, video, or audio. It highlights storage design, provenance, verification, correction, deletion protection, and orchestration across multiple providers. ### Source excerpt See what the strongest projects from the Backblaze Generative AI Media Hackathon had in common. These five generative media apps show how storage and orchestration become part of production-ready design. The post Results from the Backblaze Generative AI Media Hackathon appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Data pipeline monitoring 101: Tracking health and performance across the data stack DevFeed: [Data pipeline monitoring 101: Tracking health and performance across the data stack]() Original publisher: [Read original article]() Author: Aaron Kaplan; Ryan Warrier Published: 2026-08-14T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Monitoring](), [data observability](), [observability](), [data](), [data streams monitoring](), [Kafka](), [Apache Spark](), [event driven](), [Streaming]() Tags: [ai-ml](), [analytics](), [data](), [data-analytics](), [data-engineering](), [data-observability](), [data-streams-monitoring](), [event-driven](), [kafka](), [learn](), [monitoring](), [observability](), [performance](), [spark](), [streaming]() ### AI overview This article introduces end-to-end monitoring for modern data pipelines. It explains how to track pipeline health, performance, data quality, and availability across varied architectures and technology layers, with examples including Kafka, Flink, Apache Spark, data lakes, warehouses, and lakehouses. ### Source excerpt Learn about monitoring the end-to-end health and performance of modern data pipelines. ## Backblaze Drive Stats: How an Open Dataset Powers Academic and AI/ML Research DevFeed: [Backblaze Drive Stats: How an Open Dataset Powers Academic and AI/ML Research]() Original publisher: [Read original article]() Author: Stephanie Doyle Published: 2026-08-13T15:08:38Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [dataset](), [DRIVE](), [Machine learning](), [Transformer](), [Disk image]() Tags: [ai-ml](), [articles](), [b2cloud](), [cloud-storage](), [data](), [drive](), [featured](), [featured-cloud-storage](), [hard-drive-stats](), [ml](), [research](), [source](), [storage]() ### AI overview The article explains how Backblaze Drive Stats evolved from an internal hard-drive reliability tool into an open dataset used in academic and AI/ML research. It describes the dataset's real-world scale, quarterly publication, SMART attributes, labeled failures, broad manufacturer coverage, and use in hard-drive failure prediction research. ### Source excerpt Backblaze Drive Stats has been cited in more than 105 academic papers and AI/ML projects since 2018. Explore the research it powers and download the dataset. The post Backblaze Drive Stats: How an Open Dataset Powers Academic and AI/ML Research appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Introducing advanced Kubernetes control plane configuration in Amazon EKS DevFeed: [Introducing advanced Kubernetes control plane configuration in Amazon EKS]() Original publisher: [Read original article]() Author: Ashok Srirama Published: 2026-08-12T17:23:45Z Content type: article Language: en Sources: [Containers]() Topics: [Amazon EKS](), [Kubernetes](), [configuration](), [API](), [Availability](), [Provisioning]() Tags: [advanced-300](), [ai-ml](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [announcements](), [api](), [availability](), [ci-cd](), [kubernetes](), [packing](), [retention](), [storage]() ### AI overview Amazon EKS introduces advanced Kubernetes control plane configuration, allowing users to set API server, scheduler, and controller manager parameters directly. The article covers pod placement scoring, event retention, and HPA synchronization settings through feature explanations and hands-on walkthroughs. ### Source excerpt With Amazon EKS, you can now configure Kubernetes control plane components (the API server, scheduler, and controller manager) directly through EKS APIs. This post explains what's configurable and includes two hands-on walkthroughs: enabling MostAllocated bin-packing to optimize pod placement, and tuning event retention duration. ## Using the GitHub Copilot SDK for Java DevFeed: [Using the GitHub Copilot SDK for Java]() Original publisher: [Read original article]() Author: Edward Burns Published: 2026-08-10T19:30:00Z Content type: tutorial Language: en Sources: [GitHub]() Topics: [GitHub Copilot](), [Java](), [SDK](), [Jakarta EE](), [Agent Harness](), [Artificial Intelligence](), [Command-line interface](), [Spring AI]() Tags: [agent](), [agent-harness](), [ai](), [ai-ml](), [cli](), [client-library](), [engineering](), [framework](), [github-copilot](), [github-copilot-sdk](), [jakarta-ee](), [java](), [sdk]() ### AI overview This tutorial introduces the GitHub Copilot SDK for Java, a framework-agnostic client library for creating Copilot agent sessions, registering tools, sending prompts, and receiving structured responses. It demonstrates the SDK in a Jakarta EE 11 real-estate lead-management application and describes prerequisites including JDK, Maven, a GitHub Copilot subscription, and the Copilot CLI. ### Source excerpt Enterprise Java developers have a new superpower--drive GitHub Copilot from idiomatic Java code with annotations, virtual threads, and more. The post Using the GitHub Copilot SDK for Java appeared first on The GitHub Blog. ## Open Models and Open Weights Are Foundational to Secure AI DevFeed: [Open Models and Open Weights Are Foundational to Secure AI]() Original publisher: [Read original article]() Author: andrewb@proximabiz.com (The Linux Foundation) Published: 2026-07-27T13:24:44Z Content type: opinion Language: en Sources: [Linux Foundation - Blog]() Topics: [Artificial Intelligence](), [Open Source](), [Securing AI](), [AI research agents]() Tags: [agent](), [ai](), [ai-ml](), [cisco](), [dell](), [ibm](), [meta](), [microsoft](), [nvidia](), [open-source](), [securing-ai]() ### AI overview The Linux Foundation describes its participation in the Open Secure AI Alliance with NVIDIA and other members. The alliance promotes open tools for testing, tracing, auditing, and governing AI systems, and argues that open models and open weights can support safer, more transparent, and broadly accessible AI. ### Source excerpt The Linux Foundation celebrates collaborative efforts to build and enhance the open tools and techniques that keep AI systems safe and secure. [Next page]()