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The invisible engineering behind Lambdaโ€™s network

AWS engineers explain how the Lambda team rebuilt VPC networking so they can keep per-invocation setup off the hot path and run dense microVM workers at scale... read more ย 

The invisible engineering behind Lambdaโ€™s network
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Shift Left Did Not Fix It

Shift left has become a buzzword, but merely moving testing earlier doesn't address the core issue of authority and decision-making in quality assurance. AI may offer quicker testing, but it doesn't comprehend risk like human testers do - beware the dangerous lie that AI can replace thorough, critic.. read more ย 

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Terraform is dead

Graham Gilbert argues Terraform is effectively dead, kept alive only by inertia: HCL forced engineers to translate intent (the diagrams, paragraphs, and constraints that actually describe systems) into a DSL that nobody naturally thinks in, while fragmenting infrastructure, application logic, polici.. read more ย 

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Finding zombies in our systems: A real-world story of CPU bottlenecks

After a network outage crisis, Pinterest's ML Platform team discovered high Kubernetes agent CPU usage was causing critical Ray training job failures. The team's deep profiling strategy revealed a rarely seen flaw in how Kubelet was handling memory cgroup iterations... read more ย 

Finding zombies in our systems: A real-world story of CPU bottlenecks
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AI in SRE: What's Actually Coming in 2026

AI in SRE is evolving, with true value in Root Cause Analysis and Pre-Change Impact Analysis, not autonomous remediation or AI replacing SREs - it's about collaboration and focus evolution... read more ย 

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@laura_garcia shared a post, 1ย month ago
Software Developer, RELIANOID

๐—Ÿ๐—ถ๐—ป๐˜‚๐˜… ๐—ธ๐—ฒ๐—ฟ๐—ป๐—ฒ๐—น ๐˜ƒ๐˜‚๐—น๐—ป๐—ฒ๐—ฟ๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐—ถ๐—ฒ๐˜€

๐Ÿ” ๐—Ÿ๐—ถ๐—ป๐˜‚๐˜… ๐—ธ๐—ฒ๐—ฟ๐—ป๐—ฒ๐—น ๐˜ƒ๐˜‚๐—น๐—ป๐—ฒ๐—ฟ๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐—ถ๐—ฒ๐˜€ are once again forcing enterprises to rethink ๐™ž๐™ฃ๐™›๐™ง๐™–๐™จ๐™ฉ๐™ง๐™ช๐™˜๐™ฉ๐™ช๐™ง๐™š ๐™จ๐™š๐™˜๐™ช๐™ง๐™ž๐™ฉ๐™ฎ ๐™ฅ๐™ง๐™ž๐™ค๐™ง๐™ž๐™ฉ๐™ž๐™š๐™จ. The recent disclosure of โ€œ๐—–๐—ผ๐—ฝ๐˜† ๐—™๐—ฎ๐—ถ๐—นโ€ and โ€œ๐——๐—ถ๐—ฟ๐˜๐˜† ๐—™๐—ฟ๐—ฎ๐—ดโ€ highlights how kernel-level flaws can rapidly evolve into major risks for cloud environments, containers, Kubernetes clusters, and cr..

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Magika is an open-source file type identification engine developed by Google that uses machine learning instead of traditional signature-based heuristics. Unlike classic tools such as file, which rely on magic bytes and handcrafted rules, Magika analyzes file content holistically using a trained model to infer the true file type.

It is designed to be both highly accurate and extremely fast, capable of classifying files in milliseconds. Magika excels at detecting edge cases where file extensions are incorrect, intentionally spoofed, or absent altogether. This makes it particularly valuable for security scanning, malware analysis, digital forensics, and large-scale content ingestion pipelines.

Magika supports hundreds of file formats, including programming languages, configuration files, documents, archives, executables, media formats, and data files. It is available as a Python library, a CLI, and integrates cleanly into automated workflows. The project is maintained by Google and released under an open-source license, making it suitable for both enterprise and research use.

Magika is commonly used in scenarios such as:

- Secure file uploads and content validation
- Malware detection and sandboxing pipelines
- Code repository scanning
- Data lake ingestion and classification
- Digital forensics and incident response