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While current large language models (LLMs) can generate syntactically correct Terraform HCL code, they often miss critical elements like permissions, event triggers, and best practices. Iterative refinement with developer input is necessary to produce deployable, functional stacks. The article suggests using tools like Nitric to provide application context and enforce security, dependencies, and best practices.
SHREC is a physics-based unsupervised learning framework that reconstructs unobserved causal drivers from complex time series data. This new approach addresses the limitations of contemporary techniques, such as noise susceptibility and high computational cost, by using recurrence structures and topological embeddings. The successful application of SHREC on diverse datasets highlights its wide applicability and reliability in fields like biology, physics, and engineering, improving the accuracy of causal driver reconstruction.
The article discusses the future of observability in 2025, highlighting the significant role of OpenTelemetry and AI in improving observability and reducing costs.
An article on building an AI agent to interact with Apache Airflow using PydanticAI and Gemini 2.0, providing a structured and reliable method for managing DAGs through natural language queries.
Find and experiment with AI models for free, then switch to a paid Azure account when you're ready to bring your application to production.
GitHub Models now allows developers to retrieve structured JSON responses from models directly in the UI, improving integration with applications and workflows. Supported models include OpenAI (except for o1-mini and o1-preview) and Mistral models.
The article discusses the use of AI agents for automating and optimizing tasks in the networking industry, including network deployment, configuration, and monitoring. It outlines a workflow with four agents that collectively achieve the setup and verification of network connectivity within a Linux and SR Linux container environment.
The author demonstrates a workflow involving four AI agents designed to deploy, configure, and monitor a network:
Document Specialist Agent: This agent extracts installation, topology deployment, and node connection instructions from a specified website.
Ollogger is a powerful, flexible logging application that helps users create custom AI-powered logging assistants. Built with React, TypeScript, and modern web technologies.
This article discusses how traditional machine learning methods, particularly outlier detection, can be used to improve the precision and efficiency of Retrieval-Augmented Generation (RAG) systems by filtering out irrelevant queries before document retrieval.
The article discusses the challenges and strategies for load testing and infrastructure decisions when self-hosting Large Language Models (LLMs).
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