J4K is virtual this year!

‪J4K was a superb conference last year. This year, it’s virtual and free! Some of the world’s best speakers will present. Register here: http://j4k.io/ They say tickets will go fast. #j4k ‬

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Happy 10th year, JCertif!

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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.

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InfoQ Reading List

Presentation: Taming the Configuration Beast with Pkl!

Dan Chao explains how Pkl streamlines infrastructure as code workflows. By defining schemas and constraints, Pkl enables robust configuration management, catching errors early and providing clear feedback. The demo showcases Pkl's ability to generate YAML for Kubernetes and its advantages over manual YAML complexity.

By Dan Chao

Why We Should Care about Accessible Websites and How to Do It

Web accessibility ensures content is usable by people with disabilities. According to Joanna Falkowska it can give a competitive edge, improve SEO, and support basic human rights. She emphasizes using WCAG standard and making accessibility a shared team responsibility from the start of development, to prevent costly fixes later in the process.

By Ben Linders

Azure Logic Apps Introduces 'Agent Loop' for Building AI Agents in Enterprise Workflows

Microsoft's Build conference unveiled Agent Loop, a transformative feature in Azure Logic Apps enabling developers to embed AI agents into enterprise workflows. Leveraging over 1,400 connectors, it allows for creating autonomous and conversational agents for tasks like loan approvals and customer support, streamlining operations, and enhancing decision-making.

By Steef-Jan Wiggers

HashiCorp Releases Terraform MCP Server for AI Integration

HashiCorp has released the Terraform MCP Server, an open-source implementation of the Model Context Protocol designed to improve how large language models interact with infrastructure as code.

By Matt Foster

Prime Intellect Releases INTELLECT-2: A 32B Parameter Model Trained via Decentralized Reinforcement

Prime Intellect has released INTELLECT-2, a 32 billion parameter language model trained using fully asynchronous reinforcement learning across a decentralized network of compute contributors. Unlike traditional centralized model training, INTELLECT-2 is developed on a permissionless infrastructure where rollout generation, policy updates, and training are distributed and loosely coupled.

By Robert Krzaczyński

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