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@kaptain shared a link, 1 week, 6 days ago
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How Kubernetes Learned to Resize Pods Without Restarting Them

Kubernetes v1.35 introduces in-place Pod resizing, allowing dynamic adjustments to CPU and memory limits without restarting containers. This feature addresses the operational gap of vertical scaling in Kubernetes by maintaining the same Pod UID and workload identity during resizing. With this breakt.. read more  

How Kubernetes Learned to Resize Pods Without Restarting Them
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@kaptain shared a link, 1 week, 6 days ago
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How GKE Inference Gateway improved latency for Vertex AI

Vertex AI now plays nice withGKE Inference Gateway, hooking into the Kubernetes Gateway API to manage serious generative AI workloads. What’s new:load-awareandcontent-aware routing. It pulls from Prometheus metrics and leverages KV cache context to keep latency low and throughput high - exactly what.. read more  

How GKE Inference Gateway improved latency for Vertex AI
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@kala shared a link, 1 week, 6 days ago
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My AI Adoption Journey

A dev walks through the shift from chatbot coding toagent-based AI workflows, think agents that read files, run code, and double-check their work. Things only clicked once they built outcustom tools and configsto help agents spot and fix their own screwups. That’s the real unlock... read more  

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@kala shared a link, 1 week, 6 days ago
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Generative Pen-trained Transformer

MeetGPenT, an open-source, wall-mounted polargraph pen plotter with a flair for generative art. It blends custom hardware, Marlin firmware, a Flask web UI running on Raspberry Pi, and Gemini-generated drawing prompts. The stack? Machina + LLM. Prompts go in, JSON drawing commands come out. That driv.. read more  

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@kala shared a link, 1 week, 6 days ago
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Towards self-driving codebases

OpenAI spun up a swarm of GPT-5.x agents - thousands of them. Over a week-long sprint, they cranked out runnable browser code and shipped it nonstop. The system hit 1,000 commits an hour across 10 million tool calls. The architecture? A planner-worker stack. Hierarchical. Recursive. Lean on agent ch.. read more  

Towards self-driving codebases
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@kala shared a link, 1 week, 6 days ago
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Self-Optimizing Football Chatbot Guided by Domain Experts on

Generic LLM judges and static prompts fail to capture domain-specific nuance in football defensive analysis. The architecture for self-optimizing agents built on Databricks Agent Framework allows developers to continuously improve AI quality using MLflow and expert feedback. The agent, such as a DC .. read more  

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@kala shared a link, 1 week, 6 days ago
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Nathan Lambert: Open Models Will Never Catch Up

Open models will be the engine for the next ten years of AI research, according to Nathan Lambert, a research scientist at AI2. He explains that while open models may not catch up with closed ones due to fewer resources, they are still crucial for innovation. Lambert emphasizes the importance of int.. read more  

Nathan Lambert: Open Models Will Never Catch Up
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@devopslinks shared a link, 1 week, 6 days ago
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Demystifying : Why You Shouldn’t Fear Observability in Traditional Environments

OpenTelemetry is friendly with the past. It now pipesreal-time observability into legacy systems- no code rewrite, no drama. Pull structured metrics straight from raw logs, Windows PDH counters, or SQL Server stats. It doesn’t stop there. Got MQTT-based IoT gear? OTLP export or lightweight adapters .. read more  

Demystifying : Why You Shouldn’t Fear Observability in Traditional Environments
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How GEICO lowered its $300M cloud spend and decoupled security from the network

GEICO's IT infrastructure transformation journey highlights the shift from legacy network-centric security model to a more modern, identity-first approach. By centralizing identity and secrets management using HashiCorp Vault, GEICO improved security, reliability, and compliance across their hybrid .. read more  

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@devopslinks shared a link, 1 week, 6 days ago
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The only Terraform pipeline you will ever need: GitHub Actions for Multi-Environment Deployments

A sharp new GitHub Actions pipeline can now sniff out which Terraform environments changed - anywhere in the repo, no matter how nested - and run them in parallel. Fast, clean, and automatic. It leans onmatrix jobs,Checkovfor static analysis,Workload Identity Federationfor secure cloud access (no ha.. read more  

The only Terraform pipeline you will ever need: GitHub Actions for Multi-Environment Deployments
FastMCP is an open-source Python framework designed to simplify the development of Model Context Protocol servers. It allows developers to define MCP components such as tools, resources, and prompts using decorators, and to organize them through a modular architecture built around providers and transforms. Providers determine where components originate, including local code, directories, OpenAPI specifications, or remote MCP servers. Transforms modify components as they flow to clients, enabling namespacing, filtering, versioning, and visibility control.

The framework supports component versioning, per-component authorization, and middleware for cross-cutting concerns such as authentication and logging. It includes a built-in command-line interface for listing, calling, discovering, and installing MCP servers. FastMCP also supports session-scoped state, background task execution, OpenTelemetry tracing, pagination for large component sets, and transport options including stdio and HTTP-based protocols.

FastMCP is intended for developers building agent-compatible backends and structured tool interfaces for large language model systems that implement the Model Context Protocol.