Focused on hardcore practical AI technologies (OpenClaw, ClaudeCode, LLMs, etc.) and HarmonyOS development. No hype—just real-world tips, pitfall chronicles, and productivity tools. Follow to transform workflows with code.
This tutorial shows how to replace hardcoded values with a schema-driven configuration system for DeepSeek Harness plugins, covering TypeScript interfaces, Schemastery validation, cordis.yml setup, environment variable secrets, load-time validation, tool integration, common pitfalls, and HMR behavior.
This tutorial demonstrates how to create a functional publish_article tool for AI agents using DeepSeek Harness's defineTool, covering the five core components, output schema versus render separation, execution pipeline hooks, and real-world WeChat API permission constraints.
This article explains why the Web UI becomes a bottleneck for automation, introduces the DeepSeek Harness Python SDK, walks through installation, credential setup, running built‑in and custom agent scripts, and highlights key configuration and safety considerations for programmatic agent orchestration.
This guide walks you through installing DeepSeek Harness, launching its Web UI with a single command, configuring your DeepSeek API key, selecting a workspace, running your first Agent task, and understanding the sandbox permission levels and approval mechanism, all within five minutes.
The article introduces DeepSeek Harness, an open-source Agent framework built on a fully plug-in architecture where tools, models, and even the agent loop are interchangeable, explains its core concepts, plugin system, configuration via cordis.yml, and why it offers developers a flexible alternative to Claude Code and Codex.
On August 13, DeepSeek simultaneously released the flagship V4‑Pro model and open‑sourced its Harness runtime, illustrating the “Agent = Model + Harness” paradigm; the article details the model’s pricing, performance, new features, the plugin‑centric design of Harness, usage steps, community ecosystem, and broader AI‑agent implications.
After a 25% price cut, the author evaluated Seedance 2.0 Fast and MiniMax H3 across six challenging video generation scenarios, showing that the true cost of a video model depends on the number of attempts until a usable clip appears, not just the per‑generation price.
This article evaluates ByteDance’s Doubao-Seed-Evolving model, highlighting its weekly‑level updates, 1 M‑token context window, improved stability for long‑chain agent tasks, cost‑effective inference, and real‑world performance on a full‑project Android code‑refactoring case.
The article traces the evolution from Prompt Engineering to Context and Harness Engineering, introduces Loop Engineering as the next stage where AI runs autonomous cycles, explains its components, benefits, limitations, and offers guidance on when and how developers should adopt it.
The article evaluates Xiaomi’s open‑source MiMo Code, comparing its persistent memory, Compose mode, and /dream command against Claude Code, and provides a step‑by‑step hands‑on test that shows how MiMo Code’s engineering‑focused workflow can reduce rework despite a slower start.
