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AI Engineering
AI Engineering
May 28, 2026 · Artificial Intelligence

Webwright Lets Browser Agents Move Beyond Guessing the Next Click

Microsoft's newly open‑sourced Webwright framework replaces the traditional step‑by‑step LLM decision loop with code‑generated Playwright scripts, stores all state locally, achieves SOTA benchmark results on Online‑Mind2Web and Odysseys, integrates with major agent ecosystems, and offers auditability and reusable automation.

LLM automationMicrosoftPlaywright
0 likes · 9 min read
Webwright Lets Browser Agents Move Beyond Guessing the Next Click
Black & White Path
Black & White Path
Apr 22, 2026 · Information Security

One‑Line Skill that Instantly Analyzes WeChat Mini‑Programs

This article presents a hybrid script‑plus‑LLM Skill that automates decompilation, interface extraction, sensitive data discovery, encryption analysis, and vulnerability assessment for WeChat mini‑programs, showing a step‑by‑step workflow, agent architecture, example results, and a GitHub implementation that reduces analysis time to about 20‑30 minutes.

GitHubLLM automationWeChat Mini Program
0 likes · 7 min read
One‑Line Skill that Instantly Analyzes WeChat Mini‑Programs
Tech Verticals & Horizontals
Tech Verticals & Horizontals
Apr 16, 2026 · Artificial Intelligence

Harness Engineering Explained: From Concept to Real‑World Implementation

Leveraging Harness Engineering—a control‑system framework for AI agents—requires defining constraints, feedback loops, memory, and acceptance mechanisms, then integrating tools, execution environments, orchestration, and gating layers, enabling engineers to turn tacit knowledge into enforceable rules that guide AI safely from design to production.

AI control systemsHarness EngineeringLLM automation
0 likes · 18 min read
Harness Engineering Explained: From Concept to Real‑World Implementation
Full-Stack Cultivation Path
Full-Stack Cultivation Path
Mar 1, 2025 · Fundamentals

How Two Prompts Enable Cursor to Batch‑Generate Unit Tests

The article details a step‑by‑step workflow that uses two carefully crafted prompts with Cursor to automatically locate source files in a large monorepo, record tasks, iteratively generate Vitest unit tests, track progress, and handle failures, turning a 11 k‑line codebase into a semi‑automated test suite.

CursorLLM automationmonorepo
0 likes · 8 min read
How Two Prompts Enable Cursor to Batch‑Generate Unit Tests