#Python automation testing
270 related articles
·7 min
21-Day Python Automation Testing Crash Course: A Complete Breakdown of the Three-Stage Learning Path
Learn automation testing from scratch! This article breaks down a three-stage path: Selenium/Appium tools, Requests+PyTest API testing, performance testing and CI/CD, with real projects—build a complete skill set in 21 days.
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·3 min
Cloud GPU Platform Selection: A Practical Guide from HuggingFace to Cloud Reproduction
In-depth analysis of five key dimensions for cloud GPU platform selection, covering RunPod, Lambda, Paperspace, Vast.ai, and more to solve environment setup challenges for open-source model reproduction.
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·3 min
Velane: A Deep Dive into the Cloud Platform Designed Specifically for AI Agent Tool Execution
Deep dive into how Velane provides dedicated cloud infrastructure for AI Agents through zero cold start sandboxes, version control, multi-environment management, and 800+ integrations.
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·3 min
Deep Analysis and Hardening Practices for 16 Critical Vulnerabilities in an AI Security Platform
An AI security platform was found to have 16 critical vulnerabilities spanning prompt injection, privilege escalation, and auth bypass. A deep dive into hardening methodologies.
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·4 min
Transitioning from DevOps to MLOps: A Complete Learning Roadmap
How can DevOps engineers efficiently transition to MLOps? This guide covers MLOps core concepts, standard workflows, essential tools, and Azure practices with a progressive learning roadmap.
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·4 min
A Guide to Full-Stack Machine Learning Project Repository Structure: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and externalized configuration to help ML developers move from experimental code to production-grade engineering standards.
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·4 min
Full-Stack Machine Learning Project Repository Structure Guide: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and configuration externalization to help ML developers move from experimental code to production-grade engineering.
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·3 min
Shell Colon Command Explained: The Art of "Doing Nothing" in Bash
Deep dive into Bash's colon command (:) and its practical uses: no-op placeholders, infinite loops, default value assignment. Explores Bash design philosophy and PowerShell comparison.
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·4 min
What It Feels Like When Production Actually Runs Like Localhost
Why does production never match local? This article analyzes root causes like config gaps and dependency drift, and explores how Docker, Twelve-Factor App, and IaC practices bridge the dev-prod divide.
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·6 min
Harness Engineering: From Context Engineering to the Third Phase of AI Programming
Explore Harness Engineering: the next evolution beyond context engineering for AI programming. Learn how to build enterprise-grade Skill systems and deliver real projects with mid-tier models.
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·3 min
Practical Guide to Agent Development: The Complete Path from Zero to Commercial Deployment
A systematic guide to AI Agent development from beginner to deployment, covering task planning, tool calling, memory management, learning paths, and realistic commercial monetization considerations.
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·3 min
The Real Daily Life of MLE and MLOps Engineers: Key Differences Between Three Roles and Career Paths
An in-depth look at the real daily work of data scientists, MLEs, and MLOps engineers — covering responsibilities, essential tools, and career paths to help you find your direction in AI.
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·4 min
Claude Code Tutorial: Environment Setup, Command Reference & Git Workflow in Practice
Complete guide to Claude Code covering CLI installation, domestic model switching, core commands, Git automation workflows, and automated code review and fix loops for enterprise projects.
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·4 min
From Solo Analyst to Team Data Platform: A Practical Guide to Leveling Up Your Tech Stack
How should a data scientist upgrade their tech stack when transitioning from IC to team lead? A phased roadmap covering Git, dbt, Snowflake, modern data stack, and generative AI.
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·4 min
Claude Code from Scratch: A Complete Guide from Installation to Enterprise-Grade Development
A complete guide to Claude Code from beginner to enterprise practice: covering CLI installation, connecting domestic LLMs via CC Switch, basic commands, Git workflow integration, automated bug fixing, and engineering capabilities like MCP and SubAgents.
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·3 min
Getting Started with Claude Code: A Practical Guide to Environment Setup and Git Workflow Automation
A systematic guide to Claude Code's core capabilities and environment setup, covering CLI installation, switching to domestic LLMs, project analysis, Git workflow automation, and automated bug fixing to help developers get started fast.
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·3 min
Getting Started with Claude Code: A Practical Guide to Environment Setup and Git Workflow Automation
A systematic guide to Claude Code's core capabilities and setup, covering CLI installation, switching to domestic models, project analysis, Git workflow automation, and automated bug fixing.
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·2 min
Getting Started with Claude Code: Environment Setup, Switching to Domestic Models, and Git Workflow Explained
A complete beginner's guide to Claude Code: from CLI installation and switching to domestic models like DeepSeek via CC Switch, to conversational Git operations, multi-branch management, and automated bug fixing.
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·2 min
Becoming a Computer Whiz: The Three-Stage Training Method from Beginner to Breakthrough
Why do some still feel lost after 4 years of coding? Break down the three-stage computer learning method: build fundamentals, pick a direction, learn by doing.
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·4 min
Office CLI: The Open-Source Tool That Lets AI Agents Generate and Deliver Office Files
Office CLI is an open-source GitHub Trending tool that lets AI Agents create Word, Excel, and PowerPoint files directly — with preview rendering and auto-checks, no Microsoft Office required.
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