Read source code together, write articles together, and enjoy spicy hot pot together.
This guide walks you through installing, configuring, and using OpenCode—a terminal‑first, open‑source AI coding agent—covering CLI commands, provider authentication, headless server mode, SDK integration, sample workflows, and a minimal JSONC configuration for seamless AI‑driven development.
The “lobster” style in the AI community describes long‑running AI agents that stay on a server, receive commands via chat tools, and automate tasks such as code fixing, CI repair, and information summarization, turning AI from a occasional assistant into a continuous digital worker.
In early 2026, the Chinese AI community was swept by the OpenClaw "lobster" phenomenon, prompting major cloud providers and tech firms to launch services that turn idle compute into revenue, battle for next‑generation interaction points, and ride policy‑driven hype, while exposing significant cost, security, and regulatory risks.
Claude’s API now automatically caches static parts of prompts—system instructions, tool definitions, and context—so repeated calls reuse these sections at only 10% of the standard token price, dramatically reducing costs for multi‑turn agents, but developers must manage prefixes and avoid cache‑breaking changes.
Although Python 2 is officially deprecated, many legacy systems still use it, so understanding the key syntax, arithmetic, string handling, loop constructs, exception syntax, input functions, dictionary iteration, and library support differences between Python 2 and Python 3 is crucial for maintaining and migrating code.
This guide explains how the Claude.md (or agent.md) file lets you embed disciplined engineering rules—planning, validation, sub‑agents, self‑improvement loops, and autonomous error fixing—into Claude interactions, dramatically improving code quality and reliability for serious development projects.
The article examines which parts of a React code review can be reliably automated by AI—such as detecting missing dependencies, unused variables, and test gaps—while emphasizing that architectural decisions, state‑management trade‑offs, and performance reasoning must remain under human control.
This tutorial walks readers through the fundamentals of YOLOv12, covering model variants, dataset preparation with Roboflow, optional FlashAttention acceleration, installation, model selection, training commands, post‑training tasks such as tracking, validation, inference, exporting to ONNX, and benchmarking, all with concrete code snippets and practical tips.
This article provides a comprehensive overview of the YOLO (You Only Look Once) algorithm, explaining its core principles, architecture, version history, training workflow, real‑world applications, strengths, and current limitations for modern computer‑vision tasks.
This guide explains how to combine Figma, Cursor, and the Model Context Protocol (MCP) to create realistic, interactive front‑end prototypes that match design files, reduce hand‑off ambiguity, and generate near‑production code without replacing the engineering process.
