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<div style="display: none; max-height: 0px; overflow: hidden;">Cloudflare launched a new integration that allows security teams to write proactive WAF rules using live threat intelligence data </div>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fchronosphere.io%2Fresource%2Fscaling-prometheus-frameworks-for-high-volume-metrics%2F%3Futm_source=tldr-devops%26utm_medium=newsletter%26utm_campaign=2026-06-10_Primary_Chronosphere%26utm_content=header_prometheus_just_works.../1/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/XpzjWbUpcUteeW78D-cKXIHK_wTH9-famVCqXlrRLxc=452"><img src="https://images.tldr.tech/chronosphere2.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="Chronosphere"></a></td></tr></tbody></table>
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<h1><strong>TLDR DevOps <span id="date">2026-06-10</span></strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fchronosphere.io%2Fresource%2Fscaling-prometheus-frameworks-for-high-volume-metrics%2F%3Futm_source=tldr-devops%26utm_medium=newsletter%26utm_campaign=2026-06-10_Primary_Chronosphere%26utm_content=header_prometheus_just_works.../2/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/l4QhoLTQ1A5Ti8MkDV2JnSJEfWnFjRhMqxvwpDLXAa4=452">
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<strong>Prometheus just works… until it doesn't (Sponsor)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Every team's Prometheus story starts the same way: it's affordable, it's the standard, and it works. Then the environment grows. Active time series creep up, queries crawl, retention gets messy, and someone ends up babysitting scrape configs instead of shipping.<p></p><p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fchronosphere.io%2Fresource%2Fscaling-prometheus-frameworks-for-high-volume-metrics%2F%3Futm_source=tldr-devops%26utm_medium=newsletter%26utm_campaign=2026-06-10_Primary_Chronosphere%26utm_content=Body_chronosphere_webinar/1/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/t6rFNNuCibgkI0NJhQEpwx7LpECY9kX_j3pSYyBOLL8=452" rel="noopener noreferrer nofollow" target="_blank"><span>This Chronosphere webinar</span></a> maps out:</p>
<p>1️⃣ Where things go sideways across the metrics lifecycle</p>
<p>2️⃣ The<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fchronosphere.io%2Fresource%2Fscaling-prometheus-frameworks-for-high-volume-metrics%2F%3Futm_source=tldr-devops%26utm_medium=newsletter%26utm_campaign=2026-06-10_Primary_Chronosphere%26utm_content=Body_early_warning_signs/1/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/PTFiBnMfq3OvEWLK3w_MHE-2jEDHRVapLB6pVjM54WI=452" rel="noopener noreferrer nofollow" target="_blank"><span> early warning signs</span></a> worth watching</p>
<p>3️⃣ The approaches teams use to stay ahead </p>
<p><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fchronosphere.io%2Fresource%2Fscaling-prometheus-frameworks-for-high-volume-metrics%2F%3Futm_source=tldr-devops%26utm_medium=newsletter%26utm_campaign=2026-06-10_Primary_Chronosphere%26utm_content=cta_watch_demand_session/1/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/STL87hkev4L63ubmt_TT1wz4HvWDkAn8akwR0DAADTI=452" rel="noopener noreferrer nofollow" target="_blank"><span>Watch the on-demand session →</span></a>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fblog.cloudflare.com%2Frealtime-threat-intel-waf-rules%2F%3Futm_source=tldrdevops/1/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/LNu9MSrBePv_2aHS-miSFK_a4Ufy1pCyRdhmthSdt-M=452">
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<strong>Turning Cloudflare's threat indicators into real-time WAF rules (6 minute read)</strong>
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Cloudflare launched a new integration that allows security teams to write proactive WAF rules using live threat intelligence data, enabling automated blocking of malicious IPs based on specific threat actors, targeted industries, and attack types before they reach infrastructure. The feature uses constant-time lookups across millions of threat indicators distributed globally with near-zero latency impact, and is available now for all Cloudforce One subscription customers.
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<strong>Claude Fable 5 and Claude Mythos 5 (12 minute read)</strong>
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Anthropic launched Claude Fable 5 as its most capable generally available model, with stronger performance on long-running coding, knowledge work, vision, memory, and scientific research tasks. It also introduced Claude Mythos 5, the same underlying model with some safeguards lifted for trusted cyberdefense and life-sciences partners. Fable 5 uses conservative classifiers that route sensitive cyber, biology, chemistry, and distillation requests to Opus 4.8 instead.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.hashicorp.com%2Fen%2Fblog%2Frethinking-infrastructure-access-in-the-age-of-agentic-ai%3Futm_source=tldrdevops/1/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/ZKa3s2yilY096Ym9nLzubOqcRkt_w6Vr90i8qxeGlkw=452">
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<strong>Rethinking infrastructure access in the age of agentic AI (5 minute read)</strong>
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HashiCorp Boundary secures agentic AI access with unique identities, just-in-time authorization, dynamic Vault credentials, and session-level controls that eliminate exposed secrets and overprivileged access. It provides full auditing, monitoring, and recorded sessions, enabling secure, scalable AI operations across critical infrastructure.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fdevblogs.microsoft.com%2Ffoundry%2Fbuild-2026-foundry-models%2F%3Futm_source=tldrdevops/1/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/Sgrnmchf7Gd1nF0msNqtn-ThH0b0GuqabBBKOMD08R0=452">
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<strong>A Developer's Guide to Managing Models, Cost, and Quality in Microsoft Foundry (6 minute read)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
AI system success in production depends less on selecting the most capable model and more on an end-to-end discipline of selecting, evaluating, optimizing, operating, and continuously improving models using workload-specific criteria, governance, and automated evaluation loops. Microsoft Foundry enables this approach through a unified, model-agnostic platform supporting routing, testing, cost controls, and lifecycle management.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.cncf.io%2Fblog%2F2026%2F06%2F08%2Fbreaking-free-of-a-single-datacenter-practical-geo-distributed-ai-operations-with-the-k0smos-platforms%2F%3Futm_source=tldrdevops/1/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/WMUat0bc4O7kQxxgmb7enLK-BsXF2Ily_Agu6YTBWgI=452">
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<strong>Breaking free of a single datacenter: Practical geo-distributed AI operations with the k0smos platforms (7 minute read)</strong>
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Engineers from Mirantis and Logsight.ai successfully demonstrated geo-distributed AI training by pooling Nvidia A100 GPUs in Quebec with AMD MI300X GPUs in Atlanta—managed from Frankfurt—using the open-source k0smos stack (k0s, k0smotron, and k0rdent) built on Kubernetes. The team trained multiple AI models, including GPT-NeoX and ResNet, across the heterogeneous, cross-border setup, and in a follow-up study, implemented dynamic GPU provisioning that spins resources up and down based on real-time electricity availability signals.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flaunchdarkly.com%2Fplatform%2Fcode-control%2F%3Futm_source=tldrdevops%26utm_medium=newsletter%26utm_campaign=brandrepo%26utm_term=secondary%26utm_content=code-control/1/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/anuk-QCTfOolYO-xrxeww2EhRh-pCjhqDWHepkMjtMg=452">
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<strong>Your SDLC wasn't built for AI. LaunchDarkly is. (Sponsor)</strong>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flaunchdarkly.com%2Fplatform%2Fcode-control%2F%3Futm_source=tldrdevops%26utm_medium=newsletter%26utm_campaign=brandrepo%26utm_term=secondary%26utm_content=code-control/2/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/8Ch9Jeq3srtNw2pYWlOFLyRCLmf3_o8BeOdvcrz8JfI=452" rel="noopener noreferrer nofollow" target="_blank"><span>CodeControl</span></a> from LaunchDarkly lets you release safer AI-generated changes with progressive rollouts, implement self-healing systems, and monitor in real time. Automatic fixes, variation testing, and enterprise-grade governance help you ship at AI speed.<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flaunchdarkly.com%2Fplatform%2Fcode-control%2F%3Futm_source=tldrdevops%26utm_medium=newsletter%26utm_campaign=brandrepo%26utm_term=secondary%26utm_content=code-control/3/0100019eb1462a84-4b30822b-2142-4a11-8971-040b24e0b818-000000/wlOLNBb7eOJGnk5cQh-fwiWhwkbyLZSvgP6NhFC26dw=452" rel="noopener noreferrer nofollow" target="_blank"><span> See how it works</span></a>
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<strong>MemPalace (GitHub Repo)</strong>
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MemPalace, a new local-first AI memory system, achieved 96.6% recall on the LongMemEval benchmark without any API calls by storing conversation history as verbatim text and using semantic search with a structured "palace" metaphor (people/projects as wings, topics as rooms). The open-source tool runs entirely on-device with pluggable backends like ChromaDB or PostgreSQL, integrates with Claude Code and other AI assistants through 29 MCP tools, and includes auto-save hooks to prevent data loss from session expiration.
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<strong>whichllm (GitHub Repo)</strong>
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whichllm is a new open-source tool that automatically detects your GPU, CPU, and RAM specifications to recommend the best-performing local LLM from HuggingFace that will actually run on your hardware, ranking models by real benchmarks rather than just size. The tool can simulate different GPU configurations before purchase, supports one-command model testing with automatic dependency installation, and scores models 0-100 based on benchmark quality, evidence confidence, runtime fit, and other factors using data from sources like LiveBench and Chatbot Arena.
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<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
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<strong>Monitor LLM routing with the Kubernetes Inference Extension (17 minute read)</strong>
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Kubernetes Gateway API's Inference Extension improves LLM serving by routing requests based on backend state such as KV cache, LoRA adapters, and queue depth, reducing latency and increasing cluster efficiency. It uses an Endpoint Picker and optional flow control for intelligent scheduling, prioritization, request shedding, and scale-to-zero, while observability metrics help validate routing effectiveness and distinguish misconfigurations from capacity constraints.
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<strong>Securing CI/CD for an open source project: Controlling who runs what (6 minute read)</strong>
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Cilium hardens its software supply chain by restricting who can trigger CI workflows, separating trusted and untrusted code in GitHub Actions, enforcing security reviews for CI changes, pinning dependencies, isolating credentials, and signing releases. The project uses layered controls to reduce supply chain risk while acknowledging remaining gaps such as missing SLSA provenance, limited dependency review, and some workflow components still needing further hardening.
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<strong>Grit: rewriting Git in Rust with agents (16 minute read)</strong>
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GitButler used coding agents to build Grit, a from-scratch, library-first Rust reimplementation of Git that passes 41,715 of Git's 42,001 tests. The project shows both the promise and pain of large agent-driven engineering work: agents accelerated a 360,000+ line rewrite, but also cheated tests, broke harnesses, required heavy coordination, consumed roughly 45 billion tokens, and still needed human direction around architecture, task ordering, cost control, and correctness.
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<h1><strong>Quick Links</strong></h1>
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<strong>Scribe Agent updates: no more manual note-taking or lost context (3 minute read)</strong>
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Scribe Agent automatically joins incident meetings to transcribe audio and chat, capturing decisions and context to prevent knowledge loss and reduce manual scribing during outages.
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<strong>WanderAI: Production-Ready AI Agents with New Relic Observability (9 minute read)</strong>
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A Microsoft developer relations engineer built a detailed observability framework for an AI travel-planning app called WanderAI, demonstrating how to monitor AI agents in production using just two lines of code with the Microsoft Agent Framework, OpenTelemetry, and New Relic.
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