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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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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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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, 4ย days, 4ย hours 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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INTELLECT-3 is a frontier-class 100B+ Mixture-of-Experts language model developed by Prime Intellect and trained end-to-end using their large-scale asynchronous RL framework, PRIME-RL. Built on the GLM-4.5-Air base model, INTELLECT-3 combines supervised fine-tuning with long-horizon reinforcement learning across hundreds of verifier-backed environments spanning math, code, science, logic, and agentic tasks.

The model was trained on a high-performance cluster of 512 NVIDIA H200 GPUs across 64 nodes, supported by Prime Intellectโ€™s Sandboxes execution engine, deterministic compute orchestration, and Lustre-backed distributed storage. The result is a model that surpasses many larger systems in reasoning benchmarks while remaining fully open-source.

Prime Intellect released not only the model weights but also the full training recipe: PRIME-RL, Verifiers, the Environments Hub, datasets, and evaluation suites. INTELLECT-3 is positioned as a foundation for organizations seeking to post-train or customize their own frontier-grade models without relying on proprietary AI labs.