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.
This guide walks you through creating custom Claude skills—from defining a precise problem and naming conventions to crafting detailed descriptions, writing structured instructions, uploading via the UI or API, testing with realistic scenarios, iterating based on usage, and applying best‑practice tips with concrete skill examples.
The article explains how Domain‑Driven Design addresses the pitfalls of bloated services, unclear boundaries, and inconsistent terminology by introducing bounded contexts, aggregates, and domain events, and outlines a step‑by‑step adoption process while debunking common misconceptions.
At Tencent's May 13 shareholder meeting, CEO Ma Huateng admitted the company's AI efforts were "leaking," outlined a three‑stage diagnosis, detailed a massive 360 billion‑yuan investment and organizational overhaul, and explained the "parasitic" WeChat strategy while questioning whether the ship can now sail faster.
The article analyzes why AI agents lose progress across discrete context windows, identifies two failure patterns, and presents a dual‑harness solution—an initialization agent and a coding agent—that uses init scripts, progress files, and Git to enable incremental, test‑driven development over hours or days.
A Java team rebuilt the popular Node.js AI‑Agent platform OpenClaw from scratch, replacing AI‑generated “vibe code” with a carefully refactored architecture that leverages Spring AI, JobRunr, and Spring Modulith, and demonstrates how to run the new Java version with just a few commands.
The article explains that the classic 800‑page software engineering textbook is not a list of methods to memorize but a decision‑making coordinate system that guides readers through process, requirements, architecture, testing, and evolution, helping them understand why and how to apply each practice in real projects.
The article explains how AI agents that operate in a reasoning‑action‑observation loop outperform single‑shot LLM inference by continuously observing, planning, and correcting errors, illustrated through a ticket‑booking example and detailed analyses of ReAct, Plan‑Execute, OODA, and Steering Loop architectures.
Elon Musk announced on X that xAI will cease to operate independently, becoming part of SpaceX, leading to the departure of its entire founding team, the repurposing of its 220,000 Nvidia GPUs for Anthropic’s Claude, and revealing strategic motives behind the move.
WorldClaw's new WorldRouter lets AI agents settle model‑calling fees on Solana or BNB Chain using the USD1 stablecoin, offering a unified gateway to 300+ models at 30% lower cost while introducing programmable wallets and on‑chain auditability to solve the agent‑authorization bottleneck.
The article explains that large‑model agents differ from traditional chatbots by perceiving goals, planning steps, invoking tools, and executing actions autonomously, covering their definition, core modules, ReAct reasoning‑acting loop, single‑ versus multi‑agent systems, current industry trends, and the reliability, safety, observability, and cost challenges they face.
