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<div style="display: none; max-height: 0px; overflow: hidden;">Kubernetes v1.35 shipped with stable in-place Pod resource resizing, gang scheduling for distributed AI workloads, and a new default to KYAML format </div>
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<h1><strong>TLDR DevOps <span id="date">2026-02-25</span></strong></h1>
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<strong>Over 50% of AI SRE PoCs get abandoned. Most test in the wrong environment. 🔍 (Sponsor)</strong>
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Most AI SRE evaluations are set up to fail before they start:<p></p><ul><li>80% of AI projects fail, often due to misaligned expectations and undefined metrics</li><li>PoCs scoped under 3 months are 3X more likely to reach production</li><li>Most PoCs run in sandbox environments, so anything the tool learns won't transfer to production</li></ul><p>The <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.runllm.com%2Fbuyers-guide%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=Primary02252026/3/0100019c94c38837-5ba0b3e0-345b-4228-a4e6-e3907290761b-000000/bzDBRyuVg33H9appVRhDKDIVmhKpDhG0JqCaMhrNdfM=446" rel="noopener noreferrer nofollow" target="_blank"><span>RunLLM AI SRE Buyer's Guide</span></a> covers what to measure, what to avoid, and six criteria that separate tools that work in production from tools that only look good in demos. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.runllm.com%2Fresearch-report%3Futm_source=tldr%26utm_medium=newsletter%26utm_campaign=Primary02252026/1/0100019c94c38837-5ba0b3e0-345b-4228-a4e6-e3907290761b-000000/xL2mlf6XmSYBfXzGeJb2xgfbYOp-sbjoSHfJyv1Ifms=446" rel="noopener noreferrer nofollow" target="_blank"><span>Free to download.</span></a>
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<strong>Kubernetes as AI's operating system: 1.35 release signals (4 minute read)</strong>
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Kubernetes v1.35 ("Timbernetes") shipped with stable in-place Pod resource resizing, gang scheduling for distributed AI workloads, and a new default to KYAML output format designed to reduce configuration errors. The release addresses operational pain points for teams running mixed production workloads—particularly AI/ML jobs—by enabling coordinated Pod placement, CPU/memory adjustments without container restarts, and stricter YAML formatting guardrails.
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<strong>New in Pulumi IaC: `onError` Resource Hook (2 minute read)</strong>
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Pulumi released a new onError hook feature that lets developers automatically retry failed cloud infrastructure operations or handle errors with custom code, useful for transient failures like DNS propagation delays or resource readiness issues. The feature is available in Node, Python, Go, and .NET SDKs as of version 3.219.0.
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<strong>Choosing between Amazon ECS Blue/Green Native or AWS CodeDeploy in AWS CDK (7 minute read)</strong>
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Amazon Elastic Container Service now offers native blue-green deployments, enabling zero-downtime releases with built-in lifecycle hooks, bake time, and rollback without requiring AWS CodeDeploy. While CodeDeploy still supports canary and linear traffic shifting and CodePipeline integration, ECS native is the recommended default for simpler, service-centric deployments in CDK.
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<strong>The End of CI/CD Pipelines: The Dawn of Agentic DevOps (9 minute read)</strong>
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Agentic DevOps tools like GitHub Copilot and Microsoft's Azure SRE Agent shift CI CD from deterministic pipelines to AI-driven judgment that can autonomously debug tests, remediate incidents, and reduce toil. While increasing velocity, they introduce opaque decision-making, novel failure modes, and new operational risks that require guardrails, shadow deployments, and strong oversight.
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<strong>Lines of Code Are Back (10 minute read)</strong>
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AI has revived lines of code as a success metric. When code is nearly free to generate, volume measures nothing and correlates with more duplication, churn, and false productivity gains. Instead, engineering leaders should track outcomes like time-to-value, code durability, defect rates, and team comprehension, which reflect real impact rather than output.
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<strong>Get reserved instance pricing on-demand to save 50% on AWS costs (Sponsor)</strong>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.milkstraw.ai%2F%3Futm_campaign=DevOps2252025%26utm_source=tldr%26utm_medium=newsletter/2/0100019c94c38837-5ba0b3e0-345b-4228-a4e6-e3907290761b-000000/eXfVhmQzZbUmYmcgmtkzLBvM6KoFEEFOtox5D8rtV7I=446" rel="noopener noreferrer nofollow" target="_blank"><span>Milkstraw</span></a> syncs your AWS into a single layer and reserves instances for you so you get commitment-based discounts…without the commitment. Too good to be true? That's what Harsh, CEO of Kingpin, thought too. Now hundreds of cloud engineers use Milkstraw every day to save up to 50% on AWS costs. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.milkstraw.ai%2F%3Futm_campaign=DevOps2252025%26utm_source=tldr%26utm_medium=newsletter/3/0100019c94c38837-5ba0b3e0-345b-4228-a4e6-e3907290761b-000000/f5euJTUXJ8hL82D500rJDOppQvbfXmP2qhqvAiebEEQ=446" rel="noopener noreferrer nofollow" target="_blank"><span>See how</span></a>
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<strong>Ruvector (GitHub Repo)</strong>
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RuVector is an open-source Rust-based vector database that combines HNSW search, graph neural networks, and self-learning capabilities into a unified engine. It replaces multiple tools like Pinecone, Neo4j, and PyTorch in a single package. The platform introduces "cognitive containers" that can boot as Linux microservices in 125ms and powers production deployments.
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<strong>Pageindex (GitHub Repo)</strong>
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PageIndex, a new vectorless RAG system that uses hierarchical tree indexing and LLM reasoning instead of traditional vector databases, achieved 98.7% accuracy on the FinanceBench benchmark for financial document analysis. The open-source framework mimics human expert navigation by building semantic tree structures from long PDFs and using multi-step reasoning to retrieve relevant information, particularly excelling with complex professional documents like regulatory filings and technical manuals.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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Azure Quick Review azqr was completely refactored using GitHub Copilot plan and agent modes under architectural supervision, eliminating major technical debt and 72 scanner packages. The overhaul removed 16,506 net lines across 560 files and improved scan performance by up to 90 percent.
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A Ruby developer spent 300 days introducing RuboCop into a 1M+ line legacy Rails monolith, enabling cops incrementally and fixing offenses through hundreds of small, reviewable PRs instead of a big-bang migration. The effort reduced PR review noise, uncovered real bugs and risky patterns, led to upstream contributions to RuboCop, and concluded once the remaining cops offered diminishing returns relative to ROI.
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The Rust compiler team has launched an anonymous 5-minute Debugging Survey (open until March 13) to better understand developers' struggles with debugging Rust.
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