We dissect AI-era technologies, tools, and trends with a hardcore perspective. Focused on large models, agents, MCP, function calling, and hands‑on AI development. No fluff, no hype—only actionable insights, source code, and practical ideas. Get a daily dose of intelligence to simplify tech and make efficiency tangible.
The article explains how the Hook model’s four‑step loop—trigger, action, variable reward, and investment—drives addictive behavior, and shows how to reverse‑engineer this cycle to create positive habits and break harmful ones.
The article explains that confusing urgency with importance leads to endless firefighting, cites a McKinsey 2020 study, and shows how the Eisenhower (four‑quadrant) matrix can help professionals shift 60‑80% of their effort to important but non‑urgent activities for lasting productivity.
The article breaks down the flywheel effect—why early effort feels futile, how momentum builds through four stages, illustrated by Amazon’s 20‑year growth, and offers three practical steps to keep your own flywheel turning toward lasting success.
The article explains that habit change failures stem from a design flaw, not personal weakness, and introduces BJ Fogg’s B=MAP model—Motivation, Ability, Prompt—showing how the weakest element blocks behavior and offering a three‑step method to redesign habits.
An interviewer's question about the difference between epoll and select reveals a tangled web of OS‑level IO mechanisms; this article unpacks user‑space vs kernel‑space, DMA, zero‑copy, buffering, and maps the five Java IO models (BIO, NIO, AIO, etc.) to their underlying system calls.
The article explains that repeatedly changing jobs, courses, or goals won’t fix recurring issues because the real root cause lies in higher‑order layers such as beliefs and identity, not in superficial effort or environment.
Many people get trapped in linear thinking, moving from one idea to the next without broader perspective; the article introduces the Mandala thinking method—a 3×3 grid that forces divergent, convergent, and deep‑dive thinking, turning a single goal into up to 64 concrete actions through structured expansion.
The article explains why amassed courses, books, and articles often remain unusable, introduces the SECI model’s four knowledge‑conversion modes, illustrates each with real‑world examples—including a Panasonic bread‑maker case—and offers practical steps to turn tacit insights into actionable expertise.
The article introduces the SOLO taxonomy, a five‑level framework for measuring how deeply you understand a book, contrasts it with Bloom's Taxonomy, and offers concrete self‑assessment tools and three practical steps to move from superficial recall to transferable insight.
After three years of drawing architecture diagrams, the author realized that a single diagram cannot convey a whole system; instead, a set of purpose‑specific diagrams—business, application, system, technical, data, and deployment—must be used, each tailored to its audience and clarified with the C4 modeling approach.
