Best of Python — August 2025

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   Article

   DEV·41w

   Programming Is Becoming Prompting

   The programming landscape is shifting as AI tools transform coding from writing functions to crafting prompts. While AI assistance can scaffold codebases, generate tests, and speed up routine tasks, it risks diminishing creativity and problem-solving skills. Developers need to balance leveraging AI for efficiency while maintaining deep coding knowledge for debugging, scaling, and handling complex edge cases. The key is knowing when to use AI and when to code manually, as understanding fundamentals remains crucial when AI-generated solutions break or need customization.

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   Article

   Serdarcan Buyukdereli·39w

   How a Senior DevOps Interview Became a Sophisticated Hacking Attempt

   A developer shares their experience with a sophisticated phishing attack disguised as a legitimate job interview for a Senior DevOps Engineer position at Revolut. The fake recruiter sent a technical task containing malicious Python code that would create admin users and establish remote server connections. The attack demonstrates how cybercriminals are exploiting recruitment processes to target developers with seemingly innocent coding challenges.

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   Article

   Medium·39w

   Build Your First Agent in 5 Days

   A practical 5-day guide for building AI agents from scratch, covering essential tools like OpenAI GPTs, n8n for automation, CrewAI for multi-agent systems, Cursor for AI-powered coding, and Streamlit for user interfaces. The guide provides a structured approach with a reusable recipe framework breaking agents into brain, tools, orchestration, interface, and hosting components, plus a complete Python code example for a basic agent implementation.

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   Article

   Hacker News·41w

   I spent 6 years building a ridiculous wooden pixel display

   A maker spent six years building Kilopixel, a 1000-pixel wooden display that changes one pixel at a time using a CNC gantry system. The project combines hardware fabrication, CNC programming, web development, and live streaming to create an interactive art installation where anyone can submit pixel art through a web interface. The display uses custom wooden pixels, stepper motors, Raspberry Pi control, and streams live to YouTube with automated timelapse generation.

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   Article

   Hacker News·41w

   KittenML/KittenTTS: State-of-the-art TTS model under 25MB 😻

   KittenTTS is an ultra-lightweight open-source text-to-speech model with only 15 million parameters and under 25MB size. It runs on CPU without GPU requirements, offers multiple voice options, and is optimized for real-time speech synthesis. The model is currently in developer preview with plans for full release, mobile SDK, and web version.

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   Article

   Medium·38w

   5 Agent Workflows You Need to Master (And Exactly How to Use Them)

   Five structured AI agent workflows are presented to replace ad-hoc prompting: prompt chaining breaks complex tasks into sequential steps, routing directs queries to appropriate models based on complexity, parallelization runs independent tasks simultaneously, orchestrator-workers use a planning model to coordinate specialized workers, and evaluator-optimizer creates feedback loops for quality improvement. Each workflow includes Python code examples and addresses specific use cases like code generation, content creation, and data analysis to achieve more consistent and production-ready results.

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   Article

   Programming Digest·39w

   Inside Netflix’s $1 Billion Algorithm - How Recommendations Predict Your Next Binge

   Netflix's recommendation algorithm uses matrix factorization and collaborative filtering to analyze user behavior and predict preferences, saving the company over $1 billion annually. The system breaks down sparse user-item rating matrices into dense feature matrices that capture hidden patterns in viewing habits. The article explains the mathematical concepts behind recommendations, provides Python code examples for building a basic recommender system, and covers advanced techniques like neural collaborative filtering and real-time learning systems that adapt to changing user preferences.

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·38w

   Build a 100% local MCP Server and Client

   Learn to build a completely local Model Context Protocol (MCP) server and client setup for enterprise-grade AI applications. The tutorial covers creating MCP servers using FastMCP, building secure local clients with mcp-use library, and integrating with Stagehand for browser automation. This approach keeps data on your own servers while enabling AI agents to perform tasks like web scraping and form filling through natural language commands.

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   Video

   Tech With Tim·38w

   How to Build an Advanced AI Agent with Search (LangGraph, Python & More)

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    Article

    Hugging Face·39w

    MCP for Research: How to Connect AI to Research Tools

    Model Context Protocol (MCP) enables AI systems to automate academic research discovery by connecting to tools that search across platforms like arXiv, GitHub, and Hugging Face. The approach progresses through three abstraction layers: manual research, scripted automation, and AI-orchestrated natural language workflows. MCP allows researchers to use natural language requests to gather comprehensive information about papers, implementations, and related resources, though it requires human oversight for quality control.

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    Article

    Getting started with AI·38w

    AutoGen and MCP

    Learn how to enhance AutoGen agents by connecting them to MCP (Model Context Protocol) servers, giving them access to external tools and capabilities. The tutorial demonstrates setting up a Python application with three agents that can communicate with both local and remote MCP servers, including a web fetching server and a custom C# server. Using the autogen-ext[mcp] extension, developers can easily integrate any MCP-compliant server to expand their agents' functionality beyond basic chat interactions.

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    Article

    Telerik·40w

    Async Programming in Python

    Async programming in Python enables handling multiple tasks concurrently without creating multiple threads by using the asyncio library and event loops. Key concepts include coroutines defined with async/await keywords, asyncio.gather() for running multiple tasks simultaneously, and asyncio.Queue() for producer-consumer patterns. This approach is particularly effective for I/O-bound operations like API calls and file operations, differing from multithreading by executing coroutines on a single thread.

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    Article

    Weaviate·40w

    Elysia: Building an end-to-end agentic RAG app

    Elysia is an open-source agentic RAG framework that goes beyond traditional text-only AI assistants by using decision tree architecture, dynamic data display formats, and intelligent data analysis. Built with Python and powered by Weaviate, it features transparent decision-making processes, chunk-on-demand document processing, personalized feedback learning, and multi-model routing. The framework can be used as both a web application and Python library, offering customizable tools and real-time observability of AI reasoning processes.

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    Article

    Lobsters·39w

    sabrinas.space -

    A data scientist analyzed 2,671 website screenshots from popular sites across different countries using AI and machine learning to investigate whether Japanese web design is truly more maximalist than other regions. The study used ResNet models and t-SNE visualization to cluster websites by visual similarity, confirming that Japanese sites tend to favor lighter colors and denser layouts. The research explores three potential causes: writing system constraints (CJK characters), cultural differences, and Japan's unique technology adoption patterns, particularly their separate smartphone evolution that bypassed the iPhone-driven minimalism trend that influenced Western web design.

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    Article

    Towards Data Science·39w

    LangGraph 101: Let’s Build A Deep Research Agent

    A comprehensive tutorial on building AI research agents using LangGraph, Google's open-source framework. Covers core concepts including graph-based workflow modeling with nodes and edges, state management for agent memory, structured outputs for reliable LLM responses, tool calling for web searches, conditional routing for decision-making, and parallel processing for concurrent operations. Uses Google's Deep Research Agent implementation as a practical example, demonstrating how to create agents that can autonomously search the web, evaluate results, and generate comprehensive reports with citations.

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    Article

    Hacker News·40w

    Talk to Your AI Agents from Anywhere!

    Omnara is an open-source platform that provides real-time monitoring and control for AI agents like Claude Code and GitHub Copilot. It offers mobile and web dashboards for tracking agent activities, receiving notifications when input is needed, and responding to agent questions remotely. The platform supports both monitoring existing agent sessions and launching agents remotely via a Python SDK, REST API, or CLI commands. Built with FastAPI backend and React/React Native frontends, it uses PostgreSQL for data storage and implements the Model Context Protocol for agent communication.

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    Article

    Claudette·40w

    Hack Smarter, Not Harder: Sitadel Revolutionizes Web App Security

    Sitadel is an updated version of WAScan, a web application security scanner compatible with Python 3.4+. It offers enhanced flexibility for writing custom modules, includes interface framework detection, CDN detection, configurable risk levels, an add-on system, and Docker support for easy deployment.

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    Article

    Infostrux·38w

    Building a React AI Agent: A Practical Guide for Developers

    A comprehensive guide to building a ReAct AI agent using Python, LangChain, and LangGraph for automating article writing workflows. The tutorial covers setting up the project structure, implementing file management and web search tools, creating the agent workflow, and practical challenges encountered during development. The author shares lessons learned about AI agent performance, consistency issues, and future improvements including memory systems and research capabilities.

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    Video

    Tech With Tim·40w

    Learn Pandas in 30 Minutes - Python Pandas Tutorial

    A comprehensive beginner tutorial covering pandas fundamentals including dataframe creation, data loading from CSV files, basic operations like head/tail/info, column and row indexing with iloc/loc, filtering data with conditions, updating and deleting entries, data cleaning methods, and basic analysis functions like groupby and value_counts. The tutorial demonstrates both regular Python files and Jupyter notebooks for data manipulation workflows.

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    Article

    Hacker News·38w

    sping Documentation

    Sping is a modern terminal-based HTTP/TCP latency monitoring tool that provides real-time visualization with interactive charts, outlier detection using Median Absolute Deviation analysis, and comprehensive phase timing breakdowns. It supports HTTP, HTTPS, and TCP protocols with features like customizable color palettes, authentication, threshold alerts, and multiple output formats including JSON for automation. The tool offers detailed connection phase analysis (DNS, connect, TLS, request, response) and includes advanced monitoring capabilities with percentile statistics and export functionality.

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    Article

    Daily Dose of Data Science | Avi Chawla | Substack·38w

    4 Layers of Agentic AI Systems

    Agentic AI systems are built on four distinct layers: LLMs as the foundation providing tokenization and inference capabilities, AI Agents that add autonomous behavior through tool usage and reasoning, Agentic Systems that coordinate multiple agents through communication protocols and orchestration frameworks, and Agentic Infrastructure that ensures production readiness with observability, security, and scalability features. Each layer builds upon the previous one to create robust, enterprise-ready AI systems.

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    Article

    Javarevisited·41w

    10 Best Udemy Courses to Learn Autonomous AI Agents and Auto-GPT in 2025

    A curated list of 10 Udemy courses for learning autonomous AI agents and Auto-GPT in 2025. The courses cover various frameworks including LangChain, LangGraph, CrewAI, and AutoGen, ranging from building agents from scratch with Python to creating multi-agent systems and RAG-integrated workflows. Each course focuses on hands-on projects and real-world applications, targeting developers who want to build production-ready AI agents for automation, business workflows, and agentic architectures.

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    Article

    Hacker News·38w

    roryclear/clearcam: Add object detection, tracking, and mobile notifications to any RTSP Camera or iPhone.

    ClearCam transforms RTSP cameras or old iPhones into AI-powered security systems with object detection, tracking, and mobile notifications. The open-source project offers both a Python-based NVR server and iOS app, featuring YOLOv8 integration, real-time inference, and premium cloud features including remote viewing and end-to-end encryption. Installation is available via Homebrew or from source code.

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    Article

    Javarevisited·38w

    Generative AI Study Plan: Essential Keywords & Concepts for Beginners

    A comprehensive beginner's guide to generative AI covering foundational concepts, mathematical prerequisites, key models like GPT and DALL-E, development stack including Python and frameworks, training workflows, AI agents, computer vision applications, and recommended learning resources. The guide breaks down complex topics into digestible sections with practical examples and code snippets.

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    omg! ubuntu!·37w

    NetPeek is a New, User-Friendly Network Scanner for Linux

    NetPeek is a new open-source network scanner for Linux that provides a user-friendly GTK4/libadwaita interface as an alternative to command-line tools like nmap. Built in Python, it offers basic network scanning features including device discovery, port scanning, multi-threaded operations, and support for various IP input formats. The tool aims to make network scanning accessible to users who prefer GUI applications over terminal-based solutions.

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