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@devopslinks shared a link, 1 month, 1 week ago
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Introducing Agentic Observability in NGINX: Real-time MCP Traffic Monitoring

NGINX ships an open-sourceAgentic ObservabilityJS module. It parsesMCPtraffic and extracts tool names, error statuses, and client/server identities. The module uses nativeOpenTelemetryto export spans. A Docker Compose reference wires upOTel collector,Prometheus, andGrafanafor realtime throughput, la.. read more  

Introducing Agentic Observability in NGINX: Real-time MCP Traffic Monitoring
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@devopslinks shared a link, 1 month, 1 week ago
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AWS RDS Cost Optimization Guide: Cut Database Costs in 2026

Amazon RDS costs are not fixed - they vary based on configuration and usage. Making informed configuration and governance decisions is key to optimizing costs. Graviton instances offer better price-performance for common databases, while storage costs can be reduced by decoupling performance from ca.. read more  

AWS RDS Cost Optimization Guide: Cut Database Costs in 2026
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@devopslinks shared a link, 1 month, 1 week ago
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Building a Database on S3

This paper from 2008 proposes a shared-disk design over Amazon S3 for cloud-native databases, separating storage from compute. Clients write redo logs to Amazon SQS instead of directly to S3 to hide latency. The paper presents a blueprint for serverless databases before the term existed... read more  

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@eon01 published a course, 1 month, 2 weeks ago
Founder, FAUN.dev

Learn Git in a Day

GitLab git Ubuntu

Everything you need, nothing you don't

Learn Git in a Day
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@shurup shared a post, 1 month, 2 weeks ago
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Kubernetes best practices for DevOps engineers

Kubernetes

Have to manage Kubernetes in production but don’t feel confident about its many moving parts, complex architecture, and configurations? Here’s a selection of technical guides from experienced engineers for Kubernetes beginners looking to master this orchestration tool for running containerised apps efficiently and reliably.

Best practices for Kubernetes
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@pramod_kumar_0820 shared a link, 1 month, 2 weeks ago
Software Engineer, Teknospire

⚡ Why Your Spring Boot API Takes 3 Seconds to Respond (And How to Fix It)

A practical breakdown of the most common Spring Boot performance bottlenecks — and how we optimized our API from 3 seconds to 200 ms.

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@devopslinks shared an update, 1 month, 2 weeks ago
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Microsoft Project Silica: Your Data, Stored in a Pyrex Dish, for 10,000 Years

Microsoft's Project Silica encodes data in borosilicate glass using femtosecond lasers, offering long-term storage for up to 10,000 years. This method overcomes traditional storage limitations and is cost-effective, though write speed remains a challenge. The research phase is complete, but no product release has been announced.

Microsoft Project Silica: Your Data, Stored in a Pyrex Dish, for 10,000 Years
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@varbear shared an update, 1 month, 2 weeks ago
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Operating Systems as Age Gatekeepers: The Law That Could Reshape the Internet

California's Digital Age Assurance Act mandates operating systems to share users' age data with app developers via a real-time API by 2027. The law faces criticism for depending on self-reported ages, potentially affecting its efficacy.

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@varbear shared a link, 1 month, 2 weeks ago
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The Great Developer Divide: How AI Is Reshaping the Software Job Market Into Three Tiers

AI hiring has split dev work into three camps:Apex Tier,Hybrid Middle, and a shrinkingAutomatable Tail. Demand now favorsAI orchestration,prompt engineering, fastcode reading, and platform roles likeplatform engineer,fleet supervisor, andAI QA. System shift:Organizations must rework career ladders, .. read more  

The Great Developer Divide: How AI Is Reshaping the Software Job Market Into Three Tiers
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 Intellect’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.