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<div style="display: none; max-height: 0px; overflow: hidden;">Amazon S3 Files is a new capability that allows any S3 bucket to be mounted and accessed as a fully-featured file system directly </div>
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<h1><strong>TLDR DevOps <span id="date">2026-04-10</span></strong></h1>
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<h1><strong>News & Trends</strong></h1>
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Faws.amazon.com%2Fblogs%2Faws%2Flaunching-s3-files-making-s3-buckets-accessible-as-file-systems%2F%3Futm_source=tldrdevops/1/0100019d77125c69-63614933-2cab-4619-a731-2da1c43142bf-000000/9GLhrYz5gxpJIv7HWbw0xxihLsWukDVim3KM755ufxQ=452">
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<strong>Launching S3 Files, making S3 buckets accessible as file systems (6 minute read)</strong>
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Amazon S3 Files is a new capability that allows any S3 bucket to be mounted and accessed as a fully-featured file system directly from EC2 instances, containers, or Lambda functions, with ~1ms latencies for active data and automatic bidirectional synchronization between the file system and S3 bucket. The service is now available in all commercial AWS Regions and eliminates the traditional tradeoff between S3's object storage benefits and the interactive capabilities of file systems, making it particularly useful for collaborative workloads like AI agents and ML training pipelines that need shared, low-latency access to S3 data.
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<strong>Introducing Bun as a Runtime for Pulumi (2 minute read)</strong>
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Pulumi now supports Bun as a fully supported runtime (not just a package manager) as of version 3.227.0, allowing developers to execute entire Pulumi programs by setting `runtime: bun` in their Pulumi.yaml with no Node.js required. The integration offers faster package installation and native TypeScript support, though it currently doesn't support policy packs, function serialization, or multi-language components that some projects may still need Node.js for.
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<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.databricks.com%2Fblog%2Fnext-era-open-lakehouse-apache-icebergtm-v3-public-preview-databricks%3Futm_source=tldrdevops/1/0100019d77125c69-63614933-2cab-4619-a731-2da1c43142bf-000000/b6uTIakBRzNvCC5FmPeJAYUUaTtPgZf-ouSWISiVqIY=452">
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<strong>The Next Era of the Open Lakehouse: Apache Iceberg™ v3 in Public Preview on Databricks (6 minute read)</strong>
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Databricks launched public preview support for Iceberg v3, which introduces Row Lineage for tracking data changes, Deletion Vectors for 10x faster updates and deletes, and a VARIANT type for querying semi-structured data without schema enforcement. The update eliminates the performance tradeoff between Delta Lake and Iceberg through Unity Catalog's cross-engine governance, allowing teams to write data once and read it across multiple platforms like Snowflake, BigQuery, and Redshift without replication.
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<strong>Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API (12 minute read)</strong>
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Ingress NGINX reached end-of-life in March, forcing organizations to urgently migrate to Kubernetes Gateway API due to critical vulnerabilities like CVE-2026-24512 and CVE-2026-3288 that now have no supported fixes. This article provides a technical migration guide that covers controller selection, baseline performance tracking, parallel deployment strategies, and DNS cutover procedures to help teams safely transition away from the deprecated ingress controller.
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<strong>Understanding Traceroute (16 minute read)</strong>
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Traceroute works by sending packets with increasing TTL values so each router drops them in sequence and returns an ICMP “Time Exceeded” message, revealing each hop's IP until the destination responds with a different ICMP message. By repeating this process with multiple probes and measuring response times, it maps the path and latency of network traffic, though gaps occur when routers don't send replies.
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“Why is my spend 5% higher this month?” <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fnoros.ai%2F%3Futm_source=sponsorship%26utm_medium=tldrdevops%26utm_campaign=nmrk-tldrdevops-awareness-devops-us-noros2%26utm_content=lp_north/2/0100019d77125c69-63614933-2cab-4619-a731-2da1c43142bf-000000/caBeWCvBZA17ZkvX8YPoPjNrrZJKXuxvf35-05Fmq3U=452" rel="noopener noreferrer nofollow" target="_blank"><span>Noros</span></a> is the AI agent that answers in seconds. Ask about anomalies, overprovisioned instances, cost spikes, and more across AWS, GCP, and Azure.
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<strong>Archon (GitHub Repo)</strong>
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Archon is the first open-source workflow engine for AI coding agents. It allows developers to define development processes like planning, testing, and PR creation as deterministic YAML workflows rather than relying on unpredictable AI behavior. The tool, which works with platforms like Claude Code, includes 17 default workflows, a web dashboard for monitoring, and aims to bring the same standardization to AI coding that Dockerfiles brought to infrastructure.
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<strong>Tailslayer (GitHub Repo)</strong>
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Tailslayer is a C++ library that reduces tail latency in RAM reads caused by DRAM refresh stalls.
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<strong>Veralang (Tool)</strong>
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<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Veralang is a programming language designed for LLMs to write, not humans. Vera's principles include prioritizing explicitness over convenience and checkability over correctness.
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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%2Fengineering.fb.com%2F2026%2F04%2F09%2Fdeveloper-tools%2Fescaping-the-fork-how-meta-modernized-webrtc-across-50-use-cases%2F%3Futm_source=tldrdevops/1/0100019d77125c69-63614933-2cab-4619-a731-2da1c43142bf-000000/CWQAstVhuPAz7mHaCXjyDnf-Dy-d3hkg7nZVwuZ0TeM=452">
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Meta avoided the “forking trap” by replacing a divergent WebRTC fork with a dual-stack architecture using a shim layer that lets legacy and upstream versions run side-by-side, enabling safe A/B testing and continuous upgrades. Combined with a feature-branch system for managing patches, this approach improved performance, reduced binary size, enhanced security, and allowed ongoing alignment with upstream releases without repeated costly migrations.
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<strong>Stop Answering the Same Question Twice: Interval-Aware Caching for Druid at Netflix Scale (10 minute read)</strong>
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