DashScope Pi Agent Configuration: Step-by-Step Guide to Token Plan Setup
17 days ago 高效码农How to Configure DashScope Token Plan in Pi Agent To connect Alibaba Cloud DashScope’s Token Plan to Pi Agent, you need to register [https://coding.dashscope.aliyuncs.com/v1](https://coding.dashscope.aliyuncs.com/v1) as an OpenAI-compatible service within Pi’s local configuration files. By default, Pi’s interactive terminal login menu (/login) only lists natively pre-configured providers. You won’t see an option to enter a custom API endpoint directly in that UI menu. The solution is straightforward: define your service configuration in a local models.json file. Once the base URL, authentication key, and model IDs are mapped correctly, restart Pi and select your custom model through the /model menu. Here is …
Harness Engineering: Why AI Writes Faster but Development Doesn’t Speed Up – And How We Fixed It
1 months ago 高效码农Harness Engineering: Why AI Writes Faster but Development Doesn’t Speed Up – And How We Fixed It Core question this article answers: If AI can write code faster than ever, why hasn’t our overall delivery velocity improved proportionally – and what can we do about it? Over the past two years, AI coding has made a staggering leap. It went from “producing runnable code” to “autonomously writing entire features.” But when we dropped this capability into real business scenarios, multi‑person collaboration, and legacy systems, we hit a strange paradox: the faster AI wrote code, the slower our overall development rhythm …
Loop Engineering Is Your Next Move: Stop Prompting, Start Designing Autopilot Systems
1 months ago 高效码农Stop Asking: What Loop Engineering Really Is — From Prompt to Loop, the Engineer’s Next Move Core question this article answers: What is Loop Engineering, and why does it fundamentally change how engineers work with AI? If you’re still prompting an AI one sentence at a time, you might be missing a massive shift. Over the past two years, we’ve moved from Prompt Engineering to Context Engineering to Harness Engineering. But Loop Engineering isn’t about helping you do the work better — it’s about removing you from the work entirely. In short: you stop prompting the agent. Instead, you design …
Local Shared Memory for Multi-Agent AI: Stop Losing Context Between Claude and Codex in 10 Minutes
2 months ago 高效码农Build a Local Shared Memory for Multi‑Agent AI: Stop Losing Context Between Claude and Codex in 10 Minutes Core question of this article: How do you stop each AI agent from forgetting everything the other just did when you switch between Claude Code and Codex? And how can you build a shared memory using nothing but a local folder? I’ve been using Claude Code for a long time. Inside it, a whole ecosystem has grown organically: memory, skills, vault, hooks. These pieces weren’t built overnight—they accumulated over months of daily use. Claude remembers my preferences, my active tasks, my user …
Why Your LLM API Is Slow in China Even With a Proxy: Cherry Studio, Claude Code, and NVIDIA API Fix
2 months ago 高效码农Why Your LLM API Is Slow in China Even With a Proxy (Cherry Studio, Claude Code, NVIDIA API Explained) Many developers working with large language model (LLM) tools such as Cherry Studio, Claude Code, or OpenAI-compatible APIs (for example NVIDIA NIM endpoints) encounter a frustrating situation: The API is reachable, but responses are slow—even after configuring a proxy. This issue is often misunderstood as a simple “network problem” or “bad VPN configuration.” In reality, it is a multi-layer architectural issue involving routing, SDK behavior, and model inference latency. This article breaks down the problem in a structured and practical way, …
AI Agent Harness Solutions: 4 Ways to Tame Claude Code and Stop Context Decay
2 months ago 高效码农Stop Letting Your AI Agent Run Naked: 4 Harness Solutions to Tame Claude Code (and Your Sanity) Core question this article answers: Why do powerful AI coding tools go off the rails the longer they run—and what can you do about it? Have you ever had this happen? You ask Claude Code to refactor a module. It writes 5 files, then tests fail. It starts debugging frantically, stuffing temporary patches, error logs, and abandoned solutions into the codebase. By the time it reaches the 10th file, it can’t tell the difference between the current task and the historical noise—it starts …
OpenClaw 2026.5.18: Why “Live Reliability” Is Now the Core AI Agent Framework Obsession
2 months ago 高效码农OpenClaw 2026.5.18: When an AI Agent Framework Starts Obsessing Over “Live Reliability” The core question this article answers: What engineering philosophy is reflected in over a hundred fixes and testing improvements in an open-source AI agent framework? What real value do ordinary users and developers get from this release? If you open the release page for OpenClaw 2026.5.18, the first thing you’ll likely notice is a change list that takes three scrolls to get through. The plus‑sign features, the minus‑sign fixes, the dozens of contributor names — this doesn’t look like a routine release. It looks like a systematic reckoning …
Claude Code Large Codebase Best Practices: Mastering AI Navigation in Million-Line Repos
3 months ago 高效码农How to Use Claude Code in Large Codebases: Best Practices from Real Deployments Claude Code is already running in production environments that include multi‑million‑line monorepos, legacy systems built over decades, distributed architectures with dozens of separate repositories, and organizations with thousands of developers. These environments create challenges that small, simple codebases simply don’t have – for example, build commands that differ from one subdirectory to the next, or legacy code scattered across folders without a shared root. This article summarises the patterns we have seen lead to successful adoption of Claude Code at scale. When we say “large codebase”, we …
Self-Improving AI Agents: How Closed-Loop Systems Fix Broken Prompts Automatically
4 months ago 高效码农Self-Improving Skills for AI Agents: Building Systems That Learn from Failure Core Question: How can AI agent skills automatically adapt and improve when environments change, instead of relying on manual maintenance of static prompts that quickly become outdated? In AI agent development, we face a fundamental challenge: skills are typically static, but the environment around them constantly changes. A skill that worked perfectly a few weeks ago can silently begin failing when the codebase updates, when model behavior shifts, or when the types of tasks users request evolve over time. In most systems, these failures remain invisible until someone notices …
Build AI Agent Team: Zero to Lobster Legion Mastery with OpenClaw
5 months ago 高效码农From Zero to AI Army: The Ultimate Guide to Building Your First OpenClaw Agent Team Core Question: How do you build an AI Agent team from scratch that possesses memory, programming skills, and collaboration capabilities? Building an efficient AI Agent team—often referred to as a “Lobster Legion” in the community—is about more than just creating chatbots. It is about injecting them with a soul, equipping them with memory and skills, and establishing a mechanism for efficient collaboration. This guide will walk you through the entire process: from setting up the environment to defining Agent personas, configuring core capabilities, and finally …
Human-Curated Agent Skills: The 52% Performance Edge AI Can’t Generate
5 months ago 高效码农SkillsBench Deep Dive: Why Human-Crafted Agent Skills Dramatically Outperform AI-Generated Ones Core Question: Of the tens of thousands of AI Agent Skills currently available on the market, how many actually deliver value? How can we distinguish a useful skill from a useless one, and what are the best practices for optimization? The capability boundaries of AI Agents are constantly being expanded by modular knowledge packages known as “Skills.” However, an awkward reality persists: among the tens of thousands of available skills, only a precious few are truly effective. A comprehensive new study, SkillsBench, involving 7,308 rigorous test trajectories, reveals that …
AI Agent Tool Design: How Claude Code Mastered Action Space Strategy
5 months ago 高效码农Thinking Like an Agent: Lessons in Tool Design from Building Claude Code As artificial intelligence continues to evolve at a breakneck pace, one of the most significant challenges facing developers today is how to design the “action space” for AI agents—the collection of tools and capabilities they can use to accomplish tasks. The team at Anthropic, particularly Thariq, has accumulated a wealth of practical experience while building the widely-discussed Claude Code. This article will guide you through the process of learning to “think like an agent” through careful observation, experimentation, and iteration, ultimately helping you design more effective and natural …
Claude Code Login Bypass: The 5-Minute Fix to Skip Mandatory Authentication
6 months ago 高效码农Complete Guide to Bypassing Claude Code’s Mandatory Login Requirement If you’ve recently tried installing or using Claude Code only to find that even with properly set API environment variables, you still can’t skip the login screen at startup, you’re not alone. Many developers and tech enthusiasts have encountered similar obstacles when using Claude Code. This article will explain the root cause of this issue in detail and provide a verified solution to help you smoothly use Claude Code for programming and development work. Background: Why Does Claude Code Force Login? Claude Code is an intelligent assistant tool for code writing …
Mastering AI in 2026: 6 Skills to Outperform 90% of the Workforce
7 months ago 高效码农Mastering AI in 2026: 6 Essential Skills to Transition from Chatbots to Intelligent Systems 2025 has been a year of massive leaps in artificial intelligence. Tasks that once seemed impossible are now achievable with a few clicks. However, a quick look around reveals a surprising reality: most people are still using AI the same way they did years ago—treating it like a slightly smarter search engine or a basic Q&A machine. If you want to truly excel in 2026, you need to move beyond simple chatting. To stay ahead of 90% of the workforce, you must transition from a “tool …
AI Agent Evaluations: The Complete 2025-2026 Guide to Bulletproof Testing
7 months ago 高效码农How to Build Reliable Evaluations for AI Agents: A Complete Practical Guide (2025–2026 Edition) If you’re building, shipping, or scaling AI agents in 2025 or 2026, you’ve probably already discovered one hard truth: The same autonomy, tool use, long-horizon reasoning, and adaptability that make powerful agents incredibly valuable… also make them extremely difficult to test and improve reliably. Without a solid evaluation system, teams usually fall into the same reactive cycle: users complain → engineers reproduce the bug manually → a fix is shipped → something else quietly regresses → repeat. Good evaluations break this loop. They turn vague feelings …
Essential Deep Agent Evaluation Strategies: A LangChain Case Study
7 months ago 高效码农LangChain on X: “Evaluating Deep Agents: Our Learnings” Over the past month at LangChain, we’ve launched four applications built on top of the Deep Agents framework: A coding agent LangSmith Assist: an in-app agent to assist with various tasks in LangSmith Personal Email Assistant: an email assistant that learns from each user’s interactions A no-code agent building platform powered by meta deep agents Developing and launching these agents required creating evaluations for each, and we gained valuable insights along the way! In this post, we’ll delve into the following patterns for evaluating deep agents. Deep agents demand custom test logic …
MobiAgent Framework: Transforming Mobile Automation with Cutting-Edge AI
8 months ago 高效码农MobiAgent: The Most Practical and Powerful Open-Source Mobile Agent Framework in 2025 As of November 2025, the mobile intelligent agent race has quietly entered a new stage. While most projects are still showing flashy demos on carefully selected screenshots, a research team from Shanghai Jiao Tong University’s IPADS laboratory has open-sourced a complete, production-ready mobile agent system that actually works on real phones — MobiAgent. This is not another proof-of-concept. It is a full-stack solution that includes specialized foundation models, an acceleration framework that makes the agent faster the more you use it, a brand-new real-world evaluation benchmark, and even …
HunyuanVideo-1.5: Lightweight AI Video Generation on Consumer GPUs
8 months ago 高效码农HunyuanVideo-1.5: The Lightweight Video Generation Model That Puts Professional AI Video Creation on Your Desktop How can developers and creators access state-of-the-art video generation without data-center-grade hardware? HunyuanVideo-1.5 answers this by delivering cinematic quality with only 8.3 billion parameters—enough to run on a single consumer GPU with 14 GB of VRAM. On November 20, 2025, Tencent’s Hunyuan team open-sourced a model that challenges the assumption that bigger is always better. While the industry races toward百亿级 parameters, HunyuanVideo-1.5 proves that architectural elegance and training efficiency can democratize AI video creation. This article breaks down the technical innovations, deployment practices, and real-world …
Claude Skills Explained: Ultimate Guide to Prompts, Projects, Subagents & MCP
9 months ago 高效码农★Claude Skills Explained: A Comprehensive Guide to Skills, Prompts, Projects, MCP, and Subagents★ Since the introduction of Skills, there’s been a growing interest in understanding how the various components of Claude’s agentic ecosystem work together. Whether you’re building sophisticated workflows in Claude Code, creating enterprise solutions with the API, or maximizing your productivity on Claude.ai, knowing which tool to reach for—and when—can fundamentally transform how you work with AI. This guide breaks down each core building block of Claude’s ecosystem, explains when to use each component, and demonstrates how to combine them to create powerful, intelligent workflows that go beyond …
VibeThinker-1.5B: Compact AI Model Achieves High Performance At Scale
9 months ago 高效码农Exploring VibeThinker-1.5B: A Compact AI Model That Thinks Like the Big Ones Have you ever wondered if a small AI model could tackle tough math problems or write code as well as those massive ones that take up server farms? It sounds counterintuitive—after all, the tech world often pushes for bigger models with billions or trillions of parameters to get better results. But what if the key isn’t just size, but smarter training? That’s where VibeThinker-1.5B comes in. This 1.5 billion-parameter model, developed by a team at Sina Weibo, flips the script. It uses a fresh approach to post-training that …