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@varbear shared a link, 1 month, 1 week ago
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When upserts don't update but still write: Debugging Postgres performance at scale

The Datadog team introduced a new upsert query to track inactive hosts, but it unexpectedly increased disk writes and WAL syncs due to row locking. By digging into Postgres's Write-Ahead Logging (WAL) and rewriting the query using a Common Table Expression (CTE), they avoided unnecessary overhead an.. read more  

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@varbear shared a link, 1 month, 1 week ago
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How We Reduced Median Memory Estimation Error by 99%, With the Help of AI

The compaction pipeline at Mixpanel ran into memory estimation issues causing OOMKills. By implementing AI-assisted analysis, they were able to reduce median estimation errorby 99%, leading to a significant improvement in memory estimation accuracy. Through thorough analysis and exploration of alter.. read more  

How We Reduced Median Memory Estimation Error by 99%, With the Help of AI
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@varbear shared a link, 1 month, 1 week ago
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How To Make a Fast Dynamic Language Interpreter

Zef's AST-walking interpreter posts a 16.6× speed-up. The gains come from surgical changes:64-bit tagged values,AST node & RMW specialization,symbol hash-consing,inline caches, and a shapedobject model. Developers built it onFil-C++and later ported it toYolo-C++. The Yolo build adds ~4x speed, at th.. read more  

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@kaptain shared a link, 1 month, 1 week ago
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v1.36: Tiered Memory Protection with Memory QoS

Kubernetes v1.36 rolls out Memory QoS (alpha). Opt-inmemory reservation. Tiered protection by QoS class. Kubelet observability metrics. Kernel-version warnings. It separatesthrottlingfromreservation. A feature gate enables throttling. A kubelet config field controls tieredcgroup v2protection:Guarant.. read more  

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@kaptain shared a link, 1 month, 1 week ago
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v1.36: In-Place Vertical Scaling for Pod-Level Resources Graduates to Beta

Kubernetes v1.36 moves In-Place Pod-Level Resources Vertical Scaling to Beta and flips the feature gate on by default. Operators can patch a Pod's aggregate resource to resize running Pods. Often no container restart is needed. Kubelet breaks the Pod-level change into per-container resize events. It.. read more  

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@kaptain shared a link, 1 month, 1 week ago
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From Ingress NGINX to Higress: migrating 60+ resources in 30 minutes with AI

With the March 2026 retirement ofIngress NGINX, teams face an urgent compliance mandate. They must replace unpatched controllers. EnterHigress. Built onEnvoyandIstio. It unifies LLM protocols, enforces token rate limits, caches prompts, hostsMCP, and usesxDSfor zero-downtime. AnAI agentpaired withhi.. read more  

From Ingress NGINX to Higress: migrating 60+ resources in 30 minutes with AI
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@kaptain shared a link, 1 month, 1 week ago
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Auto-Diagnosing Kubernetes Alerts with HolmesGPT and CNCF Tools

STCLab built an AI investigation pipeline withHolmesGPT, a 200-linePythonplaybook, andOpenTelemetry. It streamedMimir,Loki, andTempointo Slack threads. Metadata-driven markdownrunbookslimited tools per namespace, cut wasted tool calls from 16 to 2, and let the same model resolve alerts faster... read more  

Auto-Diagnosing Kubernetes Alerts with HolmesGPT and CNCF Tools
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@kaptain shared a link, 1 month, 1 week ago
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v1.36: Staleness Mitigation and Observability for Controllers

Kubernetes v1.36 shipsclient-goatomicFIFOprocessing and cache-introspection APIs. Controllers detect stale informer state and skip acting on it. kube-controller-managerenables the capability by default for four high-contention pod controllers. It addsalpha metricsfor skipped syncs and informer resou.. read more  

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@kala shared a link, 1 month, 1 week ago
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Monitoring LLM behavior: Drift, retries, and refusal patterns

Traditional software is predictable due to determinism, while generative AI is unpredictable. Engineers need a new infrastructure layer, the AI Evaluation Stack, to ship enterprise-ready AI products. The stack includes deterministic assertions and model-based assertions to ensure structural integrit.. read more  

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@kala shared a link, 1 month, 1 week ago
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The AI engineering stack we built internally - on the platform we ship

Cloudflare wired AI into the engineering stack. LLM traffic funnels through aproxy WorkerandAI Gateway. It shippedWorkers AIand theAgents SDK. Daily users hit 3,683 (93% R&D). MR throughput climbed to ~10,952/week.Workers AIhandled 51B input tokens and cut a security agent's inference spend by 77%... read more  

The AI engineering stack we built internally - on the platform we ship
OpenAI is an independent artificial intelligence research organization that was founded in 2015 with the goal of promoting the development and safe use of advanced AI technologies. The organization's research focuses on a wide range of AI applications, including natural language processing, computer vision, and robotics. OpenAI's research is driven by a team of world-class researchers and engineers who work to develop cutting-edge AI technologies that are both powerful and safe.

One of the key goals of OpenAI is to promote responsible AI development. To this end, the organization works closely with policymakers, industry leaders, and other stakeholders to ensure that AI is developed in a way that is ethical and beneficial for society. OpenAI also provides resources and training to help people better understand AI and its potential impacts.

OpenAI's research has led to numerous breakthroughs in the field of AI, including the development of advanced language models like GPT-3, which can generate coherent and human-like text. The organization has also developed AI technologies that are used in a variety of applications, from self-driving cars to medical diagnostics.

Overall, OpenAI is at the forefront of AI research and development, and is working to ensure that AI is developed in a way that benefits everyone.