LeoQin

After completing BlueDot Impact AI Safety Fundamentals and receiving the certificate, I have a clearer view of agent safety evals, tool-use safety, and reliable AI infrastructure.

Completed BlueDot AI Safety Fundamentals

AI safety requires distinguishing saints from sycophants and schemers during training.

Distinguishing Saints from Sycophants and Schemers

MCP standardizes how AI apps connect to tools, resources, prompts, and external systems. It is useful infrastructure for reducing integration glue, but it does not replace permissions, confirmations, logging, threat modeling, or tool-safety design.

MCP Is Infrastructure, Not the Ultimate AI Solution

A personal, practical guide to robots.txt and data-use boundaries, choosing between requests, aiohttp, Scrapy, and Playwright, and handling anti-bot measures and HTML parsing.

Web Scraping 101: Permissions, Tools, and Anti-Bot Lessons

Shaders, GPU machine code, graphics APIs, and cache invalidation: why shader compilation takes so long.

What Does “Compiling Shaders” Mean in Games?

To learn algorithms, first draw the structure, simulate the process, and explain the invariant; only then turn the idea into code.

Learning Algorithms: Understand First, Code Second

Auto-scroll in an AI chat is not just scrollTop. It needs intent signals, a follow/free state machine, handling for programmatic scroll events, virtualization, and mobile edge cases.

Auto-Scrolling AI Chats: How to Read User Intent

Using OpenAI’s move from Next.js to Remix as the case study, this post compares the two React frameworks around data loading, server rendering, hydration costs, and when each one is a better fit.

Next.js or Remix: What OpenAI’s Migration Suggests

useEffect is useful for side effects outside rendering, but too many effects turn a component into a dependency maze. This post uses a concrete dashboard example to show when to extract custom hooks or move data fetching elsewhere.

Avoid Overusing React useEffect

Based on hands-on use of Copilot, Cursor, and Claude Code, this post separates where AI assistants help most, such as search, boilerplate, and error diagnosis, from where they still struggle, such as architecture and edge cases.

How AI Coding Assistants Actually Improve Developer Efficiency

A grounded overview of AI as systems that perceive, understand, decide, and learn, with a short history from Turing and Dartmouth to deep learning and generative AI, plus a practical view of how it affects daily life.

What Is AI, and How Is It Changing Everyday Life?

A product and architecture walkthrough for Statsify Finance, covering its analysis, strategy, article, search, and finance-term pages, plus the Next.js, Tailwind, Ant Design, ECharts, MongoDB, and TypeScript stack behind it.