#Pytest
47 related articles
·4 min
AI-Powered Automated Testing: Using Pytest + MCP to Tackle Non-Determinism
Deep dive into AI-era automated testing: using Pytest + Playwright + MCP for stable automation, constraining code conventions with Skills, avoiding non-determinism and high token costs. Includes real debugging war stories.
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·4 min
pytest Testing Patterns in Practice: Building a Systematic Automated Testing Framework
A deep dive into pytest patterns: layered fixture management, parameterized coverage, mock isolation, coverage gates, and CI integration — upgrade your team from scattered scripts to a maintainable automated testing framework.
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·3 min
In-Depth Analysis of Three AI Test Automation Approaches: Code Scripting, DOM Parsing, and Visual Models
A systematic breakdown of the three mainstream test automation approaches in the AI era: AI-generated code scripts, DOM parsing driven, and LVM visual model driven. In-depth comparison of principles, pros/cons, and use cases.
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·8 min
A Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
In-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing and the transition path for test engineers.
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·8 min
The Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
An in-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing methods and the transition path for test engineers.
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·2 min
AI Skills in Practice: How Test Engineers Can Automate API Testing and Generate Test Cases at Scale
Learn how AI Skills are transforming software testing. This guide covers Skill architecture, learning paths, and real-world applications in API automation and WebApp testing.
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·3 min
LangGraph Production Template in Practice: A Complete Guide to Budget Control and Canary Routing
Deep dive into langgraph-agent-stack: per-run dollar budget control, canary traffic routing, Mock testing mode, and 800+ test cases to safely deploy AI Agents from demo to production.
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·3 min
AI Testing End-to-End in Practice: A Complete Guide from Test Case Generation to Agent Automation
A deep dive into AI testing workflows: API capture, test case generation, Agent orchestration, and automated execution. Learn the two core challenges — incomplete information and mandatory human review.
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·3 min
Open-Source Dynamic Workflows: How awman Enables Multi-Model Collaborative AI Programming
awman's --dynamic flag enables cross-framework dynamic workflows with multi-model collaboration. Explore its leader agent architecture, shared context design, and auto fault-tolerance mechanisms.
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·3 min
Adversarial Testing of AI Agents in the Terminal via MCP
Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.
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·3 min
GPT-5.6 Sol in Action: A 3D Interactive London Website Generated in 5 Minutes
A Reddit user tested GPT-5.6 Sol with a single prompt. In the Row-Bot framework, the model built a 3D interactive London website in 5 minutes—complete with browser verification and vision analysis. A deep dive into AI coding Agents.
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·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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·7 min
Spec-Driven Development: Andrew Ng Teaches You to Master AI Coding Agents Efficiently
Andrew Ng partners with JetBrains on a new course systematically teaching Spec-Driven Development. By writing high-quality specs, developers can precisely control AI coding agents, eliminate context decay, and boost intent fidelity.
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·7 min
HuggingFace Open-Sources ml-intern: An AI Engineer That Reads Papers and Trains Models Autonomously
Hugging Face's open-source ml-intern autonomously reads papers, writes training scripts, and finetunes LLMs, deeply integrating the HF ecosystem and smolagents. Explore its features and impact on ML careers.
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·6 min
Semi-AI Mode: A Practical Guide to Implementing API Automation Testing
Can't make pure AI work? This guide explores the Semi-AI approach to API automation testing, covering key challenges, enterprise framework design, and how AI and frameworks work together for maximum impact.
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·6 min
10 Billion Tokens in Practice: An In-Depth Review of Codex's Long-Horizon Engineering Capabilities
Creator Ajiang burned 10B Tokens on Codex to migrate cc-haha from Tauri 2 to Electron. A deep dive into Codex's long-horizon engineering, Computer Use, costs, and practical advice for developers.
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Building a Coding Agent with LLM: A De…
·3 min
Building a Coding Agent with LLM: A Deep Dive into Simon Willison's llm-coding-agent Experiment
Simon Willison built llm-coding-agent — an open-source Claude Code-style agent — using just two prompts and TDD. Explore its tool design, bootstrapped dev process, and real-world test results.
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·4 min
Getting Started with Software Testing: A Complete Guide to Core Concepts and Classification Systems
A comprehensive guide to software testing fundamentals covering definitions, purposes, classification by phase, technique, and method, plus core concepts like smoke testing and regression testing.
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·3 min
Agent + Skill Architecture: A Practical Guide to AI-Driven End-to-End Automated Testing
Learn how to build an AI-driven API automation testing framework using Agent+Skill architecture with Claude Code, covering test case generation, script execution, and report output.
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·4 min
The Four Stages of AI Engineering Evolution: From Prompt to Loop Engineering Explained
A systematic breakdown of the four stages of AI engineering: Prompt Engineering, Context Engineering, Runtime Environment Engineering, and Loop Engineering — with core logic, bottlenecks, and real-world use cases.
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