Python released synchronized updates for five versions (3.10.22, 3.11.17, 3.12.15, 3.13.16, 3.14.8) with shared security fixes for tar, ZIP, HTTPS, and TLS vulnerabilities, while marking Python 3.10 end-of-life after five years and urging immediate upgrades.
Cua provides a complete open-source computer-use stack with five modules—cross-platform driver, cloud desktops, local macOS/Linux VMs, a specialized decision model, and an evaluation benchmark—enabling end-to-end GUI agent training, evaluation, and deployment without piecing together fragmented tools.
This article introduces five mobile apps — c4droid for C/C++, AIDE for Java, QPython3 for Python, w3cschool for web development, and Termux for Linux — with installation steps and basic usage guides for learning programming on smartphones.
The September 2026 TIOBE index shows Julia one spot away from re-entering the top 20 while MATLAB drops two places; the article analyzes how Python's free ecosystem and Julia's performance are eroding MATLAB's dominance in scientific computing, though Simulink and legacy code keep MATLAB entrenched in industrial control.
A step-by-step Python tutorial demonstrating how to scrape book details (publisher, ISBN, ratings) from Douban using search suggestion API and XPath parsing, then merge with Excel data for statistical analysis and visualization with pandas and matplotlib.
This guide walks through resolving the 'No module named win32api' error when running Scrapy on Windows, covering failed pip installs, downloading the correct wheel from UCI's Python libs, a .whl-to-.zip workaround, and the final simple fix using pip install pywin32.
The article argues that creators of foundational software like Linux, Python, Redis, Nginx, and SQLite are true geniuses due to their exceptional abstraction skills and system-level boundary control, illustrated by real-world cases where their minimal, convergent designs outperform modern complex solutions, and warns that over-reliance on AI tools erodes deep system understanding essential for solving hard production issues.
This tutorial guides you through creating a Scrapy project on Windows, from activating a virtual environment and running scrapy startproject to examining the generated file structure and understanding the purpose of each core file like settings.py, pipelines.py, and spiders.
This tutorial walks through creating a Scrapy spider using the genspider command, verifying the project structure, importing the project into PyCharm, synchronizing the spiders folder, examining the generated spider file, and configuring the Python interpreter to use the project's virtual environment.
This article explains Scrapy's asynchronous architecture built on Twisted, details its five core components (Engine, Scheduler, Downloader, Spiders, Item Pipeline) and middleware, then walks through creating a project with scrapy startproject, the generated directory structure, and running a spider via scrapy crawl.
A step-by-step tutorial for Ubuntu beginners covering LTS version selection, post-install essentials (apt mirror switch, system update, GPU drivers, Chinese input method, fonts), development environment setup (Git, Python with venv, Node.js via nvm, Docker, VSCode), common pitfalls like DNS errors and driver black screens, and survival habits including Timeshift snapshots and config backups.
This article explains web crawler fundamentals, from the need for automated data collection amid information explosion to the URL-based queue crawling process, basic Python libraries, and the role of frameworks in boosting development efficiency.
The article analyzes Jev Ultrafast, a minimal browser agent combining Browser Use and TypeSafe Jev that completes Google Flights searches in 7 seconds by converting pages into dynamic action tables, and details the author's 12306 test revealing three compatibility gaps fixed with generic keyboard events, cursor-pointer elements, and tab-switching logic.
This tutorial walks through installing Scrapy on Windows, resolving frequent errors like missing lxml by using unofficial Windows binaries, fixing Microsoft Visual C++ 14.0 requirement via Build Tools, and a workaround for unsupported Twisted wheels by renaming .whl to .zip and extracting.
This tutorial covers Python's built-in numeric types including integers, floats, complex numbers, booleans, and fractions, with detailed examples of division behavior differences between Python 2 and 3, floor division, modulo operations, and the fractions module for precise rational arithmetic.
This tutorial walks through four practical methods to enter the correct Python interactive shell on Windows when both Python 2 and Python 3 are installed, covering PATH ordering, opening a prompt in the install folder, the py launcher shortcuts, and virtual environments.
This article explains how Python 3 treats all strings as Unicode by default, removing the need for encoding declarations required in Python 2, and demonstrates the behavior on Windows and Linux where the default environment encoding shifts from ASCII to UTF-8.
This tutorial shows how to scrape real-time weather data from Sohu Weather using BeautifulSoup and automatically send it via email with Python's smtplib, including code walkthroughs for both Sohu and 163 email servers, a reference list of common SMTP servers, and PM2.5 air quality standards.
OpenAI's Habitat storage system evolved from a Python library to a distributed platform handling 70M requests/second for 1B users, with engineers detailing scaling challenges, asyncio tuning, connection pool fixes, and a 2-engineer Rust rewrite using Codex and GPT-5.5 that boosted CPU efficiency 6x and memory efficiency 15x.
CrewAI is a 57k-star open-source multi-agent framework that orchestrates role-based AI agents (researcher, writer, reviewer) via sequential or hierarchical processes, adds tooling, memory, human-in-the-loop, observability, and introduces Flows for deterministic control alongside autonomous Crews.
The TIOBE September 2026 index shows Julia approaching the top 20 while MATLAB drops two spots; Perl and Ruby exit the top 20 as Ada and Objective-C re-enter. The article analyzes how Python's free ecosystem and Julia's performance are eroding MATLAB's scientific computing dominance, though Simulink and legacy industrial code keep MATLAB entrenched in control systems.
This tutorial explains Red-Black Trees, comparing them with AVL trees, detailing their five properties, three fix operations (recolor, left/right rotations), providing a Python implementation, and covering real-world uses in Java HashMap, Linux kernel, and databases, plus selection guidelines and common pitfalls.
This article reviews an open-source book 'Deep Understanding of AI Agent' that structures AI Agent engineering around the formula Agent = LLM + Context + Tools, offering 10 chapters and 103 runnable Python experiments covering context engineering, memory, tool use, multi-agent collaboration, and model post-training.
This article presents a complete Python workflow for Sentinel-2 L2A imagery, covering automated download, preprocessing, 47 remote sensing indices calculation, index applications, and GB/T 21010-2017 compliant 8-class land cover classification with a PySide6 visualization interface.
This article explains AVL balanced binary trees, covering the balance factor, four rotation types (LL, RR, LR, RL), a complete Python implementation with height updates, real-world use cases, and a comparison showing AVL's guaranteed O(log n) query performance versus BST's degradation to O(n).
This article explains binary search trees (BST), detailing their left‑small right‑large ordering rule, core properties, and why they outperform ordinary binary trees with O(log n) search, while covering insertion, search, deletion, in‑order traversal code in Python, common pitfalls, and real‑world use cases.
This article introduces binary trees, explains key terminology and four common tree shapes, details four traversal methods, provides a complete Python implementation with sample output, and highlights real‑world uses and beginner pitfalls.
The article explains the LRU (Least Recently Used) page‑replacement algorithm, its basis in the locality principle, hit‑rate example, various exact and approximate implementations—including counter, stack, linked‑list, Clock, Aging and Working‑Set methods—its hardware requirements, pros and cons, and practical uses such as a Python cache and Redis eviction policies.
This article explains how hash tables achieve near‑constant‑time lookups by hashing keys to direct storage slots, compares their speed to arrays, linked lists, stacks, and queues, shows real‑world uses, demonstrates a full Python implementation, and warns about common pitfalls.
CrewAI is an open‑source Python framework that lets you build role‑playing AI agent teams, breaking complex long‑running tasks into specialized agents with defined roles, goals and backstories, and combines autonomous Crews with deterministic Flows to overcome model forgetting, improve output stability, and provide enterprise‑grade features such as memory, private knowledge bases, human‑in‑the‑loop approvals, observability and checkpoint recovery.
This article introduces YOLO26, a new NMS‑free, lightweight computer‑vision model from Ultralytics, details its architecture, training innovations, performance benchmarks, multi‑task capabilities, and provides a step‑by‑step Python tutorial for installing the library, running a Hello‑World inference, and interpreting the detailed latency breakdown.
This tutorial explains the FIFO principle of queues with everyday analogies, compares stacks and queues, lists core operations, provides a full Python implementation, demonstrates real‑world scenarios like server requests and printer jobs, and highlights common beginner pitfalls.
This article introduces the stack data structure, explains its LIFO principle with everyday analogies like browser back and undo, compares it to arrays and linked lists, provides a complete Python implementation, demonstrates a simple undo simulation, and highlights common pitfalls for beginners.
This guide walks through installing the xmind-to-markdown MCP tool, configuring the local MCP service in CodeBuddy, handling encoding and path issues, executing the conversion commands, and verifying that the resulting Markdown preserves the original mind‑map hierarchy and content.
This tutorial explains linked lists with a vivid array‑vs‑list analogy, outlines their core structure, shows real‑world scenarios, provides fully commented Python code with sample output, and highlights common pitfalls and a quick decision guide for choosing between arrays and linked lists.
The article presents a curated list of ten Python libraries—Polars, Pandera, DuckDB, Rich, Pydantic, MLflow, tqdm, pyinstrument, RapidFuzz, and sqlite-utils—each chosen for its ability to eliminate specific bottlenecks, reduce errors, and turn guesswork into verifiable steps in data workflows, complete with concrete code examples and practical trade‑offs.
This article explains arrays using a locker analogy, outlines their core characteristics and typical use cases, provides a complete Python example for basic operations with output, and highlights common beginner pitfalls such as zero‑based indexing and fixed length.
This article explains how to replace static CPU usage thresholds in Zabbix with a lightweight Python script that computes adaptive thresholds based on real‑time host load, buffer offsets and a minimum protection value, eliminating alert storms and reducing manual maintenance.
Anthropic added an invisible watermark to Claude's outputs, prompting the community to create the open‑source watermarks‑remover tool that strips three layers of hidden marks—from zero‑width characters to statistical sampling watermarks and file metadata—supporting multiple AI models and file formats, and it quickly amassed over 15,000 GitHub stars.
FastMCP, a Python framework built on the Model Context Protocol (MCP), now powers over 70% of MCP servers thanks to its one‑line decorator API, automatic schema generation, production‑ready features, and a thriving ecosystem that turns the MCP standard into a practical development tool.
This article introduces Python's regular expression engine, explains the special characters ^, ., and * with clear examples, and demonstrates how they can be used to match and extract specific patterns in web‑scraped text.
FastMCP now powers over 70% of MCP servers, offering a Pythonic decorator‑based API that automates schema, validation and documentation, bridges prototype and production workloads, and enjoys a thriving ecosystem, which together explain its rapid adoption among AI developers.
This article explains Python's built‑in object model, focusing on simple numeric types—int, long, float, complex, bool—and their literal forms, division behavior differences between Python 2 and Python 3, as well as fractions, boolean values, and complex number operations.
macOS Harness is an open‑source project that gives a persistent Python process to a large language model, exposing six primitive actions so the model can see the screen, type, click, read accessibility trees, run AppleScript, and write custom Python logic to automate virtually any macOS task.
This guide shows how to use the virtualenv tool on Windows to create a Python 2 virtual environment by specifying the interpreter path, activate it, verify the Python version, and then deactivate it, with step‑by‑step commands and screenshots.
This tutorial explains how to install Graphify, generate a local knowledge graph of an entire codebase—including code, docs, PDFs, images, and videos—and then query the graph via AI assistants or the CLI, eliminating the need for manual full‑text file searches.
This guide walks you through installing virtualenv on Windows, creating a named virtual environment, locating its files, activating it with activate.bat, checking the Python version, and properly deactivating the environment to keep Python2 and Python3 projects isolated.
JoyAI‑Video‑Edit, an open‑source system from JD, enables real‑time, frame‑by‑frame video editing driven by natural‑language commands, achieving 30 FPS at 720p and offering capabilities such as subject replacement, background swapping, style transfer, and motion alteration, while contrasting sharply with traditional offline batch editors.
LangGraph is an open‑source framework that replaces linear LLM pipelines with graph‑based, stateful agents, offering loops, conditional branching, persistent checkpoints, human‑in‑the‑loop support, and built‑in monitoring, enabling complex multi‑step workflows that scale from simple chatbots to enterprise‑grade AI assistants.
The article compares Python, Clojure, and Elixir for building production‑ready AI agents, detailing their concurrency models, state isolation, fault tolerance, and distributed scaling, and provides concrete code samples, a feature matrix, and guidance on choosing the right language for different team and workload requirements.
Although most Python developers overlook Tkinter, the underlying Tcl/Tk engine has quietly powered EDA tools for decades, evolving from a simple GUI library into the de‑facto scripting language that drives modern chip‑design workflows.
The article explains that asyncio.gather only propagates the first exception without cancelling other awaitables, distinguishes fan‑out/fan‑in from a Supervisor role, and demonstrates a pure‑Python pattern for classifying required versus optional branches, handling failures, and merging results safely.
This guide shows how to launch Python's interactive environment on Windows via IDLE or the command prompt, explains executing statements, using the special '_' variable, handling errors, navigating command history, and switching to script editing for longer programs.
The hello_js_reverse_skill provides a comprehensive workflow for web reverse engineering and signature reconstruction, combining Camoufox anti‑detection browsing, dual‑language algorithm decryption, JS obfuscation analysis, anti‑crawling tactics, and seamless AI integration, with both AI‑chat and manual installation options.
The article analyses Google’s claim that Go is the ideal language for AI‑assisted software engineering, contrasting dynamic languages’ rapid prototyping strengths with Go’s static safety, readability, built‑in testing and profiling that make it suited for large‑scale, production‑grade AI code generation.
Mojo 1.0, the production‑ready language created by Chris Lattner, promises a unified, open‑source AI stack that combines Python simplicity, Rust safety, and C‑level performance via MLIR, breaking CUDA lock‑in and supporting any GPU, CPU or ASIC.
The article explains the historical need for Python environment isolation, details the directory layout and configuration files of a virtual environment, walks through its creation steps, shows how sys.path is built and used for module imports, compares venv, virtualenv and conda, and provides advanced tips and debugging techniques for managing Python environments.
Anydoc is an open‑source tool that instantly converts Word, PPT, Excel, PDF and other office files into clean Markdown, supporting 14 formats, running in a median of 4.7 ms versus LibreOffice’s 1.1 s, with local PDF parsing and easy CLI or library integration.
The article describes a self‑built K8s‑RAG‑AIOps tool that uses an offline vector knowledge base and the DeepSeek‑v4‑pro model to automatically retrieve internal SOPs, collect live cluster data via SSH, and generate a complete, executable fault‑diagnosis report, dramatically speeding up Kubernetes troubleshooting while keeping data secure.
The open‑source open‑kimi‑ppt‑skill reverses Kimi Slides' format so AI can generate a programmable PPT project that remains editable locally while also exporting a ready‑to‑use PPTX, solving the common issue of AI‑generated slides being locked images.
The article explains deadlock by describing two programmers who each hold a meeting room and wait for the other, defines deadlock and its four necessary conditions, provides real‑world analogies, shows a Python threading example that deadlocks, and outlines prevention, avoidance, detection, and best‑practice strategies.
The article analyzes the compliance shortcomings of traditional Retrieval‑Augmented Generation, introduces the open‑source Semantica framework (v0.6.0) that combines RDF triples with property graphs, and demonstrates how its context graph, W3C PROV‑O provenance, deterministic reasoning and multi‑agent sharing enable fully auditable AI decision pipelines for high‑regulation sectors.
A head‑to‑head benchmark on a 2.3 GB CSV (~1.2 million rows) shows Pandas exhausting memory, Polars completing the pipeline in 8.7 seconds with modest RAM, and DuckDB answering the same query in just 12 milliseconds, highlighting distinct trade‑offs for Python data processing.
AirLLM lets ordinary 4‑GB consumer GPUs run massive LLMs such as 70B Llama and the 2.8‑trillion‑parameter Kimi K3 MoE without quantisation, by streaming model layers from disk, offering a detailed comparison with traditional low‑VRAM tricks, step‑by‑step installation, pitfalls, scenario‑based guidance and an assessment of strengths and risks.
This article presents ten concrete Python techniques for handling APIs—automatic retries, response caching, environment‑based secrets, Pydantic schema validation, clean pagination, WebSocket streaming, rate‑limiting, concurrent requests, mock responses, and GraphQL—each illustrated with runnable code to make scripts more reliable and efficient.
Octop is a self‑hosted AI assistant platform that supports multiple users and agents in a single process, integrating a web console, CLI, IM channels, scheduled tasks, browser automation, and ACP integration while keeping all data and credentials on the local machine.
This article explains how to replace tedious manual conversion of Swagger/OpenAPI specifications into test steps by using Python and AI to automatically generate fully executable pytest test code, covering constraint extraction, intelligent planning, and three practical implementation paths.
This article dissects DeepAgents Code’s command architecture, explaining the five‑layer framework, slash‑command registration, priority handling via BypassTier, skill and startup commands, and the engineering safeguards that balance interaction efficiency with system safety in production‑grade AI agents.
API testing often involves repetitive string concatenation, inconsistent output formats, and costly token usage; this article shows how LangChain's PromptTemplate and ChatPromptTemplate turn prompts into reusable, composable components that generate assertions, test data, log analysis, multi‑turn debugging, and more, with concrete Python examples.
This guide explains how to capture the hidden $:LWCP_v1:$‑prefixed dingtalkid from DingTalk robot callbacks using a minimal Python HTTP service, then construct a dingtalk://jump‑robot link that pre‑fills a query, enabling users to launch an internal Agent with a single click.
ADK 2.0 introduces a graph‑based Workflow Runtime that separates routing from LLM inference, replacing Sequential/Parallel/Loop agents with deterministic nodes, cutting token usage by ~50% and latency by ~20%, while adding HITL, automatic retries, and a clear migration path for Python and Go users.
The article uses office analogies and Python examples to explain how a CPU’s cores, threads, and processes work together, showing why a single user can feel “exploited” when multiple tasks compete for limited processing resources.
OpenWorker, an open‑source AI coworker released by Andrew Ng, delivers finished work such as HTML briefings by integrating with tools like HubSpot and Slack, supports multiple models, offers 25+ connectors, and enforces approval for risky actions, positioning it as a self‑hosted alternative to Tencent's WorkBuddy.
The new MCP 2026-07-28 specification replaces the stateful session model with a stateless request‑response protocol, introduces nine breaking changes, splits the TypeScript SDK into multiple packages, upgrades the Python SDK to 2.0.0, and forces developers to lock SDK versions and verify gateway header handling before deployment.
The article walks through the pain of manually creating, validating, and cleaning test data for a user‑registration feature and shows how the test-data-manager Skill automates the entire workflow—producing dependency lists, SQL/API scripts, verification steps, and cleanup commands in minutes instead of hours.
This article provides a systematic deep‑dive into the Model Context Protocol (MCP), explaining its purpose, architecture, core capabilities, data and transport layers, and step‑by‑step guides for building both local and remote MCP servers with Python, while also covering security, permission, and best‑practice recommendations for real‑world AI applications.
Vibe‑Trading, an open‑source AI‑driven personal trading agent from HKU’s Data Intelligence Lab, lets users issue a natural‑language prompt to automatically fetch data, generate strategy code, run vectorized backtests across multiple markets, produce detailed reports, and simulate live orders, while offering self‑improving behavior, multi‑agent teams, a 460‑plus academic factor library, and extensive broker connectors.
The Python 3.15 beta introduces lazy imports, built‑in frozendict and sentinel types, a low‑overhead sampling profiler, JIT speed gains, unpacking syntax in comprehensions, UTF‑8 as the default encoding, smarter error messages, TypedDict enhancements, and a rollback of incremental GC, each backed by concrete examples and practical guidance for immediate adoption.
The open‑source bradautomates/claude-video project adds a /watch command that lets Claude Code and compatible AI agents download videos, extract subtitles and key frames, deduplicate them, and feed the visual context back to the model, enabling practical video analysis and note‑taking.
A GitHub repository named cve-2026-poc-collection, maintained by researcher XZ1r0, aggregates twelve high‑severity CVE‑2026 proof‑of‑concept exploits across web, Linux, Windows and other categories, detailing their impact, implementation languages, and offering a search script to help security professionals quickly locate and analyze the code.
This article reviews the four main Python selectors used for web scraping—regular expressions, BeautifulSoup, lxml/XPath, and CSS—detailing their installation, parsing behavior, advantages, drawbacks, and a performance comparison table to help developers choose the most suitable tool for their projects.
This tutorial walks through building a complete sentiment‑analysis pipeline for the IMDB movie‑review dataset using Scikit‑LLM and Groq, from environment setup and data sampling to custom text cleaning, zero‑shot LLM classification, model fitting, prediction, and evaluation with a 95% accuracy report.
This article demystifies maximum likelihood estimation by using a coin‑flip example to contrast probability and likelihood, introduces log‑likelihood for numerical stability, shows Python implementations for discrete and continuous cases, discusses historical origins, and highlights practical limitations such as small‑sample bias and over‑fitting.
This article presents a comprehensive collection of visual knowledge maps that outline data analysis steps, foundational concepts, technical tools, business processes, analyst skill frameworks, and related topics such as Python learning paths, Excel formulas, MySQL, and statistical methods.
Alien is an open‑source, modular Webshell client that centralizes file management, virtual shells, database access, pivoting, encrypted traffic and plugin support for multiple scripting languages, dramatically speeding up CTF Webshell challenges while remaining lightweight and extensible.
The author transformed the four‑hour Liang Wenfeng investment conference transcript into an open‑source AI Skill that answers uncovered questions, provides career advice, and can be duplicated for other industry figures using a RAG‑based design and a detailed repository layout.
This guide explains how to automate embedded software testing by controlling hardware interfaces (DI/DO, CAN, 485, Ethernet) with Python, building a pytest‑based framework, and integrating continuous integration to achieve fast, reliable, and maintainable test cycles.
In the AI era developers often copy‑paste full tracebacks into ChatGPT, but the most efficient way to diagnose Python errors is to read the traceback from the bottom up, focusing on the last three lines and applying a three‑step method to pinpoint the problem.
This article introduces FastAPI, compares it with Spring Boot and Flask, explains why its ASGI architecture delivers superior performance, provides step‑by‑step setup and code examples—including routing, validation, dependency injection, async support, database integration, unified responses, middleware, and automatic documentation—then evaluates its pros, cons, ideal use cases, and a real‑world benchmark against Spring Boot.
The article surveys seven popular open‑source AI PPT generators on GitHub, detailing each project's star count, output format (editable PPTX or HTML), key features, licensing, and recommending which tool fits specific presentation needs.
FastCode, an open‑source framework from HKU’s DS team, builds semantic maps and structural graphs of codebases to let AI assistants answer queries up to four times faster, cut token usage by up to 44 %, and achieve higher accuracy across benchmarks, supporting multiple languages and deployment options.
The author, with experience in embedded and web development, recommends C, Python, Shell, and Go as the four most valuable languages, explaining each language's strengths, typical use‑cases, and trade‑offs so readers can choose the right tool for their problems.
LLMVault, an open‑source offline sandbox released by GitHub user CyberSunil, offers 25 tiered labs that simulate all ten OWASP LLM Top 10 vulnerabilities, enabling security professionals and learners to practice prompt injection, data poisoning, model extraction, and other AI attacks without needing external API keys.
Vibe‑Trading is an MIT‑licensed open‑source AI‑driven quant platform that bundles 74 financial skills and 29 pre‑configured multi‑agent teams, offers zero‑token data access, one‑click Pine Script export, and Docker deployment, while warning about hallucinations, data latency, and token costs.
The article introduces five core NumPy array creation functions—np.array, np.zeros, np.ones, np.arange, and np.linspace—explaining their use cases in interface automation, performance testing, and large‑scale test data generation, and provides concrete code examples and best‑practice tips.
This article explains why AI agents need function calling, describes the structured tool‑call protocol between LLMs and runtimes, shows how to define and secure function tools, and provides best‑practice code and checklists for production deployments.
The author, drawing on experience with C, Python, Shell, and Go, explains why these four languages together cover most development scenarios—C for low‑level embedded work, Python for rapid scripting and data tasks, Shell for Linux automation, and Go for high‑performance cloud services—while noting each language’s trade‑offs.
This article explains what vectors are, how embedding models turn text into high‑dimensional vectors, the common distance metrics, why specialized vector databases are needed, and walks through a hands‑on Python demo with ChromaDB before covering production‑grade options, advanced tuning, and typical pitfalls.
In Python, reading the traceback from the bottom up—identifying the error type, location, and call chain—lets you diagnose most runtime errors within seconds, and the article explains this three‑step method, common exception types, and its limits.
FastAPI, a modern Python web framework, offers high‑performance APIs, automatic documentation, type‑safe validation and native async support, making it a compelling alternative to Spring Boot and Flask for AI model serving, microservices, and rapid prototyping, as demonstrated by benchmarks, code examples, and real‑world comparisons.
