Read source code together, write articles together, and enjoy spicy hot pot together.
This article evaluates Claude's ability to translate isolated Linux kernel modules from C to Rust, presenting a detailed analysis of translation granularity, token costs, experimental results on drivers, networking, and file‑system modules, and discussing the technical and economic challenges of a full kernel rewrite.
Harness Engineering is the emerging discipline that integrates Prompt Engineering, Context Engineering, and system-level controls to create reliable, maintainable AI‑generated code, and the article analyzes its origins, key components, real‑world performance data, and practical guidelines for building effective AI harnesses.
The article reveals that the perceived AI‑GPU shortage stems from misleading utilization metrics and wasted capacity, not actual supply constraints, and argues that better measurement and orchestration—not buying more hardware—will determine competitive advantage in the emerging AI infrastructure market.
The article walks through creating a compact 250‑line Rust gzip decompressor from scratch, explaining gzip’s header, DEFLATE block types, bit‑reading logic, Huffman coding, and LZ77 back‑references, and shows how to test it with real compressed data, highlighting key implementation challenges and trade‑offs.
This article compares Anthropic’s Claude Code skill and OpenAI’s Codex skill, detailing their design philosophies, structural methodologies, typography, motion strategies, content rules, anti‑patterns, and recommended use cases, and provides direct links to the open‑source repositories and blogs.
This article examines how Bun’s ultra‑fast package manager, integrated JavaScript runtime, native test runner, and built‑in database/S3 client together deliver a faster, lighter, and more streamlined development experience compared to traditional Node.js and npm workflows.
This guide shows how to extend the open‑source Opencode LLM coding tool with a custom agent and folder structure so it can store topic summaries, retain memory across sessions, and serve as a powerful terminal‑based research assistant.
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.
