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A practical guide to building agents

This guide is designed for product and engineering teams exploring how to build their first agents, distilling insights from numerous customer deployments into practical and actionable best practices. It includes frameworks for identifying promising use cases, clear patterns for designing agent logic and orchestration, and best practices to ensure your agents run safely, predictably, and...

https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
Stop Writing

Avoid writing custominitmethods in Python; use @dataclass for attribute definition and @classmethod factories for object creation. This approach ensures valid instances, simplifies testing, and supports async/error-prone operations, while maintaining type safety with NewType

https://blog.glyph.im/2025/04/stop-writing-init-methods.html
How to Add Blazing Fast Search to Your Django Site with Meilisearch

Step-by-step guide to integrating Meilisearch with Django, complete with automatic indexing, typo tolerance, and relevant filtering capabilities.

https://www.revsys.com/tidbits/how-to-add-blazing-fast-search-to-your-django-site-with-meilisearch/
cua

A framework that enables AI agents to control full operating systems within high-performance, lightweight virtual containers. It delivers up to 97% native speed on Apple Silicon and works with any vision language models.

https://github.com/trycua/cua
I Built a Bot to Chat with Our Team’s Wiki Using Azure OpenAI Service

The author built an AI-powered Teams bot using Azure OpenAI Service to query their team's dynamic Azure DevOps Wiki, automating data ingestion via Python scripts and Azure AI Search Indexers. The solution includes custom metadata integration for source linking and highlights challenges like SDK limitations and deployment security considerations.

https://medium.com/microsoftazure/i-built-a-bot-to-chat-with-our-teams-wiki-using-azure-openai-service-96bf67878302
How To Automate Your Finances with Python

Learn to build a Python tool using Pandas, Streamlit, and Plotly to automate personal finance tracking by analyzing bank statements, categorizing transactions, and generating visual dashboards. The tutorial includes file upload handling, editable dataframes, and interactive charts for expense analysis.

https://www.youtube.com/watch?v=wqBlmAWqa6A