Advanced AI Application Practice
The author walks through using the open‑source paper‑collage‑ad‑codex skill with Codex to generate a Vox‑style paper‑cut video about Chinese art history, detailing prompt setup, multi‑round interactions, generated storyboard and video, and the practical limitations encountered.
This guide walks through the complete workflow of using the open‑source TestHub platform to automate a mobile app—splitting a business process, connecting a device, creating a project and elements, arranging test cases, executing them, viewing reports, and troubleshooting common issues.
This guide walks through TestHub’s open‑source UI automation module, explaining its POM‑based low‑code design, step‑by‑step project creation, collection and element management, test‑case assembly, execution options (Playwright/Selenium, headless or headed browsers), common troubleshooting, and important operational considerations.
The article recounts a costly misdiagnosis caused by confirmation bias, then introduces the defect‑analyzer skill that enforces an 11‑step, evidence‑driven workflow—covering phenomenon description, impact assessment, fact collection, multiple hypotheses, verification methods, prioritised troubleshooting, and post‑fix validation—to help teams locate and resolve bugs accurately and efficiently.
The June 27 industry roundup covers OpenAI’s limited preview of the three‑tier GPT‑5.6 models and the Daybreak security toolset, a critical Codex logging bug, US regulatory constraints on frontier AI, DeepSeek’s 51‑billion‑yuan funding and hiring surge, major semiconductor IPOs, AI‑driven robotics advances, AI drug‑discovery competitions, and rising AI‑related job trends.
The article presents a step‑by‑step AI‑driven defect‑analyzer skill that combats confirmation bias by forcing analysts to separate facts from hypotheses, enumerate multiple possible causes with evidence, rank investigations by likelihood and cost, and produce verifiable, repeatable bug‑fix reports for backend incidents.
The June 28 report covers Anthropic’s Claude Mythos 5 partial unblocking for over 100 trusted partners, Nvidia’s Q1 Ethernet‑switch revenue jump to $2.1 billion, Apple’s line‑wide price hikes driven by a 700% DRAM surge, Meituan’s extensive LongCat open‑source releases, a narrowing gap between open‑source and closed‑source LLMs, Google’s MTP‑frozen Gemini Nano speedup, a zero‑click WebKit RCE vulnerability, and major AI‑related hiring and layoff trends.
The article presents eleven striking GPT‑5.6 design examples—from a voxel‑style Manhattan and Blender‑driven scenes to city‑floating islands, a 3D globe dashboard, procedural terrain, a Google‑Earth clone, UI replica, Kyoto street view, a 3D watch, a GTA‑style world, and a Xiaohongshu clone—highlighting the model's design power, cost, token usage, and code size compared to earlier versions.
The article explains that writing many test cases does not guarantee bug reduction because missing test points create blind spots, and introduces the AI‑powered test‑point‑generator skill that systematically scans requirements across 14 dimensions to produce comprehensive, verifiable test points and reduce P1 incidents.
The Requirement‑Decomposition skill transforms vague PRDs into a 12‑dimension, structured analysis that automatically highlights concurrency, boundary, and permission risks, generates ready‑to‑use test‑case reports, and enforces four safety red‑lines to keep testing effort accurate and reusable.
