Awesome Go·46wBest Programming Language for 2026
Python remains the top choice for AI/ML and automation, while Rust gains momentum for systems programming and blockchain development. JavaScript/TypeScript continues dominating web development, Go excels for backend microservices, and Swift/Kotlin rule mobile development. The guide provides career-specific language recommendations and emphasizes choosing based on your goals rather than popularity alone.
Medium·48wHow to Build Production Ready AI Agents in 5 Steps
A comprehensive 5-step guide for building production-ready AI agents, covering Python foundations with FastAPI and async programming, implementing robust testing and logging, mastering RAG for knowledge retrieval, designing scalable agent architectures with frameworks like LangGraph, and establishing continuous monitoring and improvement processes. The guide emphasizes moving beyond prototype demos to create reliable, maintainable systems that can handle real-world production environments.
SwirlAI·49wBreaking into AI Engineering in 2025.
A comprehensive roadmap for becoming an AI Engineer in 2025, covering essential skills from Python fundamentals and LLM APIs to advanced topics like AI agents, RAG systems, and observability. The guide emphasizes learning fundamentals while building practical skills, starting with basic LLM integration and progressing through vector databases, prompt engineering, agentic systems, infrastructure deployment, and security considerations. Key recommendations include mastering FastAPI and Pydantic, understanding different LLM types and structured outputs, implementing RAG with proper data preprocessing, and learning agent design patterns like ReAct and task decomposition.
Towards Data Science·50wHow to Design My First AI Agent
A comprehensive guide to designing AI agents covering model selection, tooling choices, and reliability strategies. Explores different LLM options including OpenAI GPT-4, DeepSeek, Claude, and Mistral, each suited for specific use cases. Discusses infrastructure considerations, frameworks like LangGraph and Pydantic-AI, and security aspects. Emphasizes the importance of structured prompting techniques like Chain-of-Thought and ReAct, output validation, and failure handling to build reliable production-ready agents.
YouTube·47wSTOP Taking Random AI Courses - Read These Books Instead
A comprehensive guide to learning AI and machine learning through structured resources rather than random courses. Covers five key areas: programming fundamentals with Python, mathematics and statistics foundations, traditional machine learning concepts, deep learning and LLMs, and AI engineering for production deployment. Emphasizes practical application over theoretical study, recommending specific books like 'Hands-On ML with Scikit-Learn and TensorFlow' and courses like Andrew Ng's specializations. Highlights the importance of understanding both foundational concepts and modern deployment practices for current AI engineering roles.
Machine Learning Mastery·46wYour First Local LLM API Project in Python Step-By-Step
A comprehensive guide for setting up a local large language model API using Python, Ollama, and FastAPI. The tutorial covers downloading and running LLMs locally, creating a REST API endpoint, and testing the setup through a web interface. This approach enables developers to interact with language models without relying on external cloud services, providing complete control over the inference process.
freeCodeCamp·50wThe Open Source LLM Agent Handbook: How to Automate Complex Tasks with LangGraph and CrewAI
LLM agents are proactive AI systems that can break down complex tasks, make decisions, and use tools autonomously, unlike traditional reactive chatbots. The guide demonstrates building agents using open-source frameworks LangGraph and CrewAI to automate daily tasks like email summarization and schedule generation. LangGraph provides graph-based workflows for single agents, while CrewAI enables multi-agent collaboration with specialized roles. The tutorial includes practical code examples for creating an email processing agent that extracts meetings and deadlines, then formats them into organized daily schedules. Both frameworks integrate with OpenAI's models and offer structured approaches to agent development without requiring extensive custom code.
Nick Janetakis·48wSwitching pip to uv in a Dockerized Flask
A practical guide to replacing pip with uv in Dockerized Python applications, showing how to achieve 10x faster package installation speeds. Covers migrating from requirements.txt to pyproject.toml, configuring Docker environment variables, and using uv commands for dependency management. Includes specific examples for Flask and Django projects with detailed Dockerfile modifications and shell scripts for managing dependencies.
Machine Learning Mastery·49wImplementing Vector Search from Scratch: A Step-by-Step Tutorial
A comprehensive tutorial demonstrating how to build a vector search engine from scratch using Python. Covers the three core steps of vector search: converting text to numerical vectors, calculating similarity using cosine similarity, and retrieving the most relevant results. Includes practical code examples with NumPy and Matplotlib, visualization of vector spaces, and explains the connection to RAG systems. The implementation uses simplified word embeddings and averaging techniques to make concepts accessible while maintaining the fundamental principles of semantic search.
Community Picks·47wmicrosoft/magentic-ui: A research prototype of a human-centered web agent
Magentic-UI is a research prototype from Microsoft that provides a human-centered interface for web automation using multi-agent systems. Built on AutoGen, it features specialized agents (Orchestrator, WebSurfer, Coder, FileSurfer) that work together to browse websites, execute code, and handle files. Key features include collaborative planning, real-time task monitoring, action guards for sensitive operations, and parallel task execution. The system supports OpenAI, Azure OpenAI, and Ollama models, requires Docker for operation, and has been evaluated on benchmarks like GAIA (42.52%) and WebVoyager (82.2%). Installation is available via PyPI or from source, with comprehensive documentation for setup and configuration.
Indently·47wI'm a huge Zed fan
A developer shares their experience switching from PyCharm to Zed editor after 2 months of use. They highlight Zed's superior performance on older hardware, smooth user experience, and easy JSON-based configuration. Key features discussed include the new debugger, custom snippets, multi-language support, and integrated AI capabilities. The switch was motivated by PyCharm being overkill for simple development tasks, while Zed provides a lightweight yet feature-rich alternative with excellent customization options.
swizec.com·48wWhy utils are bad, an example
Utils-based code organization creates circular import problems that break applications unexpectedly. When payment utils depend on email utils and vice versa, Python cannot resolve the circular dependencies. The solution is vertical module organization based on domain context rather than technical function - group everything about payments (including emails) in a payment module, while keeping low-level operations like email sending in separate atomic libraries.
TestDriven.io·46wDeveloping a Real-time Dashboard with FastAPI, MongoDB, and WebSockets
A comprehensive tutorial demonstrating how to build a real-time order dashboard using FastAPI for the backend API, MongoDB with Change Streams for database operations, and WebSockets for live data streaming. The guide covers setting up MongoDB replica sets, implementing CRUD operations for order management, configuring Change Streams to monitor database events, and creating a responsive frontend that displays real-time updates as orders are created, modified, or deleted.
Learn Python·49w🔥 FastAPI in Production: Build, Scale & Deploy – Series A: Codebase Design
A comprehensive 3-part series covering production-ready FastAPI development, from project structure and configuration to performance optimization and deployment strategies. The content addresses real-world challenges beyond basic tutorials, focusing on scalable system design for data scientists, backend engineers, and developers transitioning from scripts to production applications.
Towards Data Science·47wBuilding A Modern Dashboard with Python and Taipy
Taipy is a Python web framework designed for data scientists and engineers to build production-ready dashboards without web development expertise. The tutorial demonstrates creating an interactive sales dashboard with filtering capabilities, key metrics display, multiple chart types, and raw data tables using 100,000 synthetic sales records from a CSV file. Taipy excels over Streamlit and Gradio when building complex, high-performance, enterprise-grade applications that require scalability and maintainability.
Lobsters·48wThe fastest way to detect a vowel in a string
A comprehensive performance analysis of 11 different methods to detect vowels in strings using Python. The study reveals that regex methods significantly outperform traditional loops due to CPython's interpreter overhead and optimized C implementations. Through bytecode analysis and CPython source code examination, the author demonstrates how regex engines use bitmap lookups for character matching, making them surprisingly faster than simple Python loops, especially for longer strings.
Machine Learning Mastery·50w10 Python One-Liners That Will Simplify Feature Engineering
Ten practical Python one-liners for feature engineering tasks including standardization, min-max scaling, polynomial features, one-hot encoding, discretization, logarithmic transformation, ratio creation, low variance removal, multiplicative interactions, and outlier tracking. Each technique uses popular libraries like scikit-learn and pandas to transform raw data into meaningful features for machine learning models.
Community Picks·47weli64s/readme-ai: README file generator, powered by AI.
ReadmeAI is a Python-based CLI tool that automatically generates comprehensive README files for software projects using AI language models. It supports multiple LLM providers (OpenAI, Anthropic, Google Gemini, Ollama) and offers extensive customization options including header styles, badges, logos, and navigation layouts. The tool analyzes codebases from various platforms (GitHub, GitLab, Bitbucket, local files) and creates structured documentation with project overviews, feature tables, installation guides, and usage instructions. It includes an offline mode for generating READMEs without API calls and supports containerized deployment via Docker.
Hacker News·47wPython can run Mojo now
Mojo can now be called from Python code, offering significant performance improvements for computationally intensive functions. The setup involves installing the modular package and creating .mojo files that export functions to Python. Benchmarks show Mojo delivering substantial speedups compared to pure Python and even NumPy implementations, though the technology is still in early development with some limitations like integer overflow issues.
Niklas Heidloff·48wBuilding agentic Applications with Langflow and MCP
Langflow is an open-source visual tool for building agentic applications using reusable UI components and Python code. The tutorial demonstrates creating an agent that uses watsonx.ai LLM to search news and generate charts by integrating MCP (Model Context Protocol) tools. It shows how to set up custom components, connect MCP servers for chart generation, and deploy Langflow applications as MCP servers for integration with other agentic systems.
Towards Dev·49wvLLM: A Quick Start
vLLM is an open-source library optimized for high-throughput serving of large language models in production. Its core innovation, PagedAttention, manages memory more efficiently by breaking the key-value cache into fixed-size pages instead of contiguous buffers, similar to virtual memory in operating systems. The tutorial covers installation on macOS M1, serving models via OpenAI-compatible API, using the native Python API, and integrating with LangChain for enhanced tooling capabilities.
Towards Data Science·49wBuilding a Modern Dashboard with Python and Gradio
A comprehensive guide to building an interactive sales performance dashboard using Gradio, a Python library for creating web applications. The tutorial covers setting up Gradio, processing CSV data with Pandas, implementing filtering capabilities, generating visualizations with Matplotlib, and creating a responsive interface with key metrics, charts, and data tables. The dashboard allows users to filter by date ranges and product categories while displaying revenue trends, top products, and raw data dynamically.
Planet Python·48wWill AI Replace Junior Developers? I Asked Experts at Pycon US
A survey of Python community experts at PyCon US reveals mixed opinions on AI's impact on programming jobs. Most experts, including Guido van Rossum and other prominent Python developers, believe AI won't replace programmers but will change how they work. While some see AI as useful for boilerplate code and productivity enhancement, others emphasize the continued importance of understanding code fundamentals. The consensus suggests AI will augment rather than replace developers, with junior developers needing to focus on learning and adaptation to remain competitive.
