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@kala shared a link, 4 months, 1 week 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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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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@kala shared a link, 4 months, 1 week 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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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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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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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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@devopslinks shared a link, 4 months, 1 week ago
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Scaling PostgreSQL to power 800 million ChatGPT users

OpenAI pushedPostgreSQLto handle millions of QPS across 800M users. How? Nearly 50 read replicas, heavy read offloading, and serious trimming on write pressure. Writes? Sent elsewhere. Sharded systems likeCosmosDB, lazy writes, and app-level tweaks helped sidestep PostgreSQL’sMVCCwrite amplification.. read more  

Scaling PostgreSQL to power 800 million ChatGPT users
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@devopslinks shared a link, 4 months, 1 week 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
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@devopslinks shared a link, 4 months, 1 week ago
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CloudBees CEO: Why Migration Is a Mirage Costing You Millions

A new CloudBees survey shows 57% of enterprises dropped over $1M on cloud migrations last year. Each effort blew past budget by an average of $315K. The kicker? Many teams still treatmodernization as migration- a shortcut that usually leads to drained budgets, burned-out devs, and delays in shipping.. read more  

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@devopslinks shared a link, 4 months, 1 week ago
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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  

Pulumi is an open-source infrastructure-as-code platform that allows you to define, deploy, and manage cloud resources using familiar general-purpose programming languages like Python, JavaScript, Go, and TypeScript.

Pulumi represents a major shift in the Infrastructure-as-Code (IaC) landscape by moving away from proprietary domain-specific languages (DSLs) and static configuration files like YAML or JSON. Instead, it leverages the power of standard programming languages, allowing engineers to use loops, functions, classes, and existing package managers to define their cloud environments. This means you can apply software engineering best practices—such as unit testing, modularity, and CI/CD integration—directly to your infrastructure setups on providers like AWS, Azure, Google Cloud, and Kubernetes.

The platform works by utilizing a "State" mechanism similar to Terraform, where it tracks the current deployment against your desired code. When you run a Pulumi program, it builds a resource graph to determine the most efficient way to provision or update your services. Because it uses real code, it provides superior IDE support, including auto-completion and type-checking, which significantly reduces the syntax errors and "trial-and-error" deployments common with text-based configuration tools.

Furthermore, Pulumi excels in hybrid and multi-cloud environments by providing a unified workflow for both infrastructure and application delivery. It bridges the gap between developers and platform engineers, as both can now speak the same language—literally.