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<div style="display: none; max-height: 0px; overflow: hidden;">Bainbridge's 1983 paper on automation ironies applies to AI agent supervision, revealing that effective human oversight requires better UI design </div>
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<h1><strong>TLDR DevOps <span id="date">2026-01-07</span></strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fkubernetes.io%2Fblog%2F2026%2F01%2F05%2Fkubernetes-v1-35-numeric-toleration-operators%2F%3Futm_source=tldrdevops/1/0100019b986b9606-d1bf8a6b-9859-4572-ac5a-c640b502b517-000000/-uI33zll1atNz5D2McrufmwVphnXl5p_n4F9JzYOiFg=439">
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<strong>Kubernetes v1.35: Extended Toleration Operators to Support Numeric Comparisons (Alpha) (4 minute read)</strong>
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Kubernetes v1.35 introduces Extended Toleration Operators as an alpha feature, adding Gt (Greater Than) and Lt (Less Than) operators to `spec.tolerations`. This enhancement enables threshold-based scheduling, unlocking new possibilities for SLA-based placement, cost optimization, and performance-aware workload distribution in Kubernetes clusters.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fplatformengineering.org%2Fblog%2F10-finops-tools-platform-engineers-should-evaluate-for-2026%3Futm_source=tldrdevops/1/0100019b986b9606-d1bf8a6b-9859-4572-ac5a-c640b502b517-000000/Zzzjt0gfWwZgjXIlQfaVqqd14vwShDt2WZAe0dmuf8U=439">
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<strong>10 FinOps tools platform engineers should evaluate for 2026 (6 minute read)</strong>
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Platform engineers require specialized tools integrated into their existing workflows for effective cloud cost optimization, diverging from traditional FinOps dashboards. This article evaluates ten FinOps platforms, including IBM's suite, CloudZero, and Datadog, highlighting the necessity of Kubernetes-native visibility, API-first architecture, and GitOps compatibility to address complex challenges like container cost attribution.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.ufried.com%2Fblog%2Fironies_of_ai_2%2F%3Futm_source=tldrdevops/1/0100019b986b9606-d1bf8a6b-9859-4572-ac5a-c640b502b517-000000/0AY3Zb6OE1SoHR9YbSAJ4jpgbJeXVJn71l8orMFk__M=439">
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<strong>AI and the ironies of automation (15 minute read)</strong>
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Lisanne Bainbridge's 1983 paper on automation ironies applies to AI agent supervision, revealing that effective human oversight requires better UI design, continuous training for rare interventions, and leadership skills to direct agent fleets. The paradox intensifies as AI agents improve because successful automation demands greater investment in human operator training and interface design to handle exceptional situations under time pressure.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.cncf.io%2Fblog%2F2026%2F01%2F06%2Fusing-istio-to-manage-high-traffic-services%2F%3Futm_source=tldrdevops/1/0100019b986b9606-d1bf8a6b-9859-4572-ac5a-c640b502b517-000000/P-bDrTrMT1oSVZWf9OoyH5FOD9lGhjLFzaKQYOepT6Y=439">
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<strong>Using Istio to manage high-traffic services (3 minute read)</strong>
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STCLab implemented Istio to manage its high-traffic SaaS platforms, achieving real-time traffic control and bot mitigation. The deployment utilized features like Proxy Protocol for client IP preservation, AuthorizationPolicy for access control, and automated Outlier Detection for enhanced resilience.
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<strong>Change is the root of all (evil) bugs (3 minute read)</strong>
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Bugs usually don't come from “bad code,” but from change—shifting dependencies, configurations, environments, assumptions, or system conditions that invalidate what once worked. Most good engineering practices exist to limit, isolate, or make change visible, because unmanaged change is what ultimately surprises and breaks systems.
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<strong>See how BuzzFeed fixed mobile CI and cut QA time by 95% (Sponsor)</strong>
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Mobile breaks standard DevOps patterns: code signing complexity, simulator sprawl, toolchains that fight your infrastructure. Learn from BuzzFeed's principal engineer and see how they ditched costly anti-patterns, scaled to 1,000+ builds a month, and made releases reliable and see how you can do the same. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwatch.getcontrast.io%2Fregister%2Fbitrise-bringing-devops-to-mobile-avoiding-pitfalls-and-unlocking-velocity%3Futm_medium=paid_other%26utm_source=tldr%26utm_campaign=all_webinar_bringing-devops-to-mobile_all_2025-06-30%26utm_content=secondary-placement-sponsorship/2/0100019b986b9606-d1bf8a6b-9859-4572-ac5a-c640b502b517-000000/Heb2UhKdoSp-RYyrG8p29Ybfgq41cFn8La2dI2ZSSIY=439" rel="noopener noreferrer nofollow" target="_blank"><span>Watch for free →</span></a>
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<strong>Claude-Mem (GitHub Repo)</strong>
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Claude-Mem automatically captures and AI-compresses Claude's coding session activities, seamlessly injecting relevant context into future sessions for improved knowledge continuity. This plugin features intelligent memory search via four MCP tools and offers a beta channel for experimental features like "Endless Mode."
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<strong>Beads (GitHub Repo)</strong>
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Beads is a distributed, git-backed graph issue tracker that provides AI coding agents with a persistent, structured, and dependency-aware memory. It aims to improve agents' ability to handle long-horizon tasks without losing context. Beads supports features like hierarchical IDs and a local "stealth mode."
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fcilium.io%2Fblog%2F2025%2F12%2F31%2Ftetragon-2025-review%2F%3Futm_source=tldrdevops/1/0100019b986b9606-d1bf8a6b-9859-4572-ac5a-c640b502b517-000000/h2TU-0lNw7XvW9n5lkef83IYzr9HNEubS2xu-OlRP5k=439">
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<strong>Tetragon: An End-of-Year 2025 Reflection (3 minute read)</strong>
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Tetragon closed the year by highlighting community growth, major features like Windows preview support, persistent enforcement, flexible policy modes, attribute resolution, and userspace hooks, while reinforcing its role as a high-performance runtime security standard built on eBPF.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgrafana.com%2Fblog%2Fhow-to-use-ai-to-analyze-and-visualize-can-data-with-grafana-assistant%2F%3Futm_source=tldrdevops/1/0100019b986b9606-d1bf8a6b-9859-4572-ac5a-c640b502b517-000000/4xjwECFQIgvsLxkUFa5VvFyPB64hZ1m-3nc0QV-y-Io=439">
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<strong>How to use AI to analyze and visualize CAN data with Grafana Assistant (9 minute read)</strong>
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CSS Electronics uses Grafana Assistant to analyze CAN bus data from vehicles and machinery, enabling engineers to explore terabytes of data through natural language prompts and generate customized dashboards without SQL knowledge. The tool integrates seamlessly with existing cloud data sources, drastically reducing analysis time compared to manual methods or ChatGPT's file size limitations.
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