Practical AI for solving real problems and creating value
The author has compiled the capability models of senior engineers (P7, P8, P9) from leading tech firms into three open‑source AI Skills, allowing users to submit their problems, plans, or projects and receive perspective‑specific feedback, with installation instructions, usage examples, and practical tips.
The article walks through assigning a unique @agent.qq.com mailbox to an AI Agent, explains why a separate email address is essential for identity, permission, and audit in enterprise workflows, and demonstrates the setup, testing, and automation possibilities with practical examples.
Many people adopt AI tools but see little impact because their mindset and methods are misaligned; this article breaks down ten common cognitive gaps—from poor business judgment and ROI misunderstanding to inadequate scenario analysis, tool awareness, and expectation management—that determine whether AI truly adds value.
The article argues that best‑practice guides that cannot be transformed into reusable AI Skills quickly become forgotten, and explains how converting such knowledge into Skills lets agents automatically recall and execute valuable methods within workflows.
The article reflects on an Alibaba Cloud AI productivity roundtable, summarizing speakers' insights on how AI can expand personal capability, automate expert workflows, and create lasting competitive advantages that depend on integrating industry knowledge, judgment, and execution rather than merely using tools.
The article identifies five groups—process‑followers, slow learners, tool‑resisters, uncritical thinkers, and poor communicators—who risk being outpaced by AI, and outlines the traits of a "super individual" who can thrive by continuously learning, thinking critically, and leveraging AI tools effectively.
The article explains how the author packaged repetitive photo‑album download tasks into two Skills—alltuu-downloader and photoplus-downloader—so that an AI Agent can automatically fetch original images from cloud albums, saving users from clicking each picture individually.
The article examines why a model’s advertised context window (e.g., 128 K or 1 M tokens) does not guarantee effective long‑context reasoning, summarizing the RULER framework that breaks long‑context ability into retrieval, interference resistance, multi‑hop tracking, aggregation, and multi‑answer recall, and offering practical guidance for evaluating and using such models.
This article walks through enabling Codex’s new desktop‑pet feature, selecting a built‑in pet, installing the Hatch Pet Skill from GitHub, creating a custom pet by describing or uploading an image, and using a generic cyber‑pet tool for other AI coding assistants.
The article recommends five versatile AI Skills—find-skills, skill-creator, skill-vetter, dokobot, and superpowers—explaining their use cases, installation commands, and how they help automate workflows, while encouraging readers to create custom Skills when none match their needs.
