AutoHarness: The Secret to 100% Legal LLM Agent Actions in Just 14.5 Iterations
1 hours ago 高效码农A Practical Guide to AutoHarness: Auto-Generating Code Harnesses for LLM Agents Using Tree Search and Sandboxing Getting large language models (LLMs) to behave is hard. In agent architectures, models routinely generate illegal actions or break the environment. We used to write manual rules to constrain them, which is tedious and full of blind spots. AutoHarness, a Rust library, takes a different approach: it uses tree search combined with Thompson sampling to automatically synthesize and optimize code harnesses for LLM agents. In testing, it hits a 100% legal action rate in just 14.5 iterations on average. Why Do We Need Auto-Generated …
Long-Running AI Agents: How LoopX Provides the Control Plane They Need to Stay on Track
1 days ago 高效码农The Local Control Plane for Long-Running AI Agents: LoopX Design Philosophy and Practical Guide An agent can complete a task in a single session – many developers do this daily. But long-running work is different: goals shift mid‑stream, human decisions interrupt, evidence goes stale, peer agents hand off to each other, and schedulers can keep burning resources long after the state has ceased to transition meaningfully. Chat memory and timers aren’t enough to govern these scenarios. LoopX offers a different approach: keep the long-term control state in a compact, durable layer, so that the loop keeps moving forward while critical …
Magika AI File Detection: Stop Guessing File Types – Google’s Open-Source Tool Is 99% Accurate
2 days ago 高效码农Stop Guessing File Types: Google’s Magika Uses AI to Identify Files Accurately Ever received a file with no extension? Or extracted a batch of .dat files from an old system and had no idea what’s inside? You try opening it with a text editor – garbled mess. You run the system file command – vague answer. Then you start dragging it into different applications, hoping one of them will open it. Minutes turn into hours. Google has open‑sourced a tool called Magika that solves exactly this problem. It uses a deep‑learning model to detect file types – not by looking …
Coding Agent Architecture: Inside the 792-Line While Loop That Powers 78k-Star Production Agents
3 days ago 高效码农What Is an Agent Really? After Reading 792 Lines of Code, It’s Just a While Loop Over the past year, “Agent” has become one of those terms that means everything and nothing. Orchestration, planning, reflection, multi-agent collaboration — it sounds like some revolutionary new paradigm. But when you actually open the source code of a coding agent that tens of thousands of people use daily, the core is one thing: A while loop. Call the LLM. The model says it needs to use a tool. Execute the tool. Put the result back into the conversation. Call the LLM again. Repeat …
Skill Recorder: Record a Task Once, Let Your AI Agent Repeat It Forever
4 days ago 高效码农Microsoft’s Skill Recorder: Record a Task Once, Let Your AI Agent Repeat It Do you ever find yourself doing the same sequence of clicks, forms, and file uploads every week? You know the steps by heart, but automating them feels like more work than just doing them manually again. Microsoft recently open‑sourced a tool called Skill Recorder that tackles this from a different angle. Instead of giving you another screen recorder or a fragile macro tool, Skill Recorder captures your screen and actions while you work, then uses GitHub Copilot to turn that recording into a structured procedure. That procedure …
LangChain OpenRouter: How to Access 400+ LLMs Through One API Without Rewriting Chains
5 days ago 高效码农LangChain + OpenRouter: A Practical Guide to Calling 400+ LLMs Through One API How to integrate OpenRouter’s unified model gateway into your LangChain applications without rewriting your chains. If you’re building AI apps with LangChain, you’ve probably faced the provider dilemma. Lock into OpenAI’s GPT family? Bet on Anthropic’s Claude? Keep a back door open for Google’s Gemini? Every choice means a separate API key, a different parameter schema, and its own failure modes. Worse, when you want to A/B test GPT-4 against Claude, or give your users in China a DeepSeek fallback, nearly every line of your chain code …
Codex DeepSeek Setup Errors: 5 Common Failures and How to Fix Them
6 days ago 高效码农Codex + DeepSeek Setup Failing? Here’s Every Error We Hit (And How to Fix Each One) If you’re routing Codex through CC Switch to run DeepSeek instead of GPT, you’ll likely run into five specific failures, in this order: a curl syntax mistake, a local proxy network mismatch, a provider config gap, an upstream protocol mismatch, and a malformed test request. This guide walks through each one, in the order they actually show up, with the reasoning behind each fix so you can diagnose new variants yourself. Why DeepSeek Doesn’t Just Work With Codex Out of the Box Codex talks …
ARC-AGI-3 Harness Fail: Why GPT-5.6 Sol Scored Only 7.8% – And the Simple Fix That Tripled Its Score
7 days ago 高效码农GPT-5.6 Sol Scored 7.8% on ARC-AGI-3. The Problem Was the Harness, Not the Model. When we first pulled up the ARC-AGI-3 leaderboard, our immediate reaction was confusion. GPT-5.6 Sol had already solved the cycle double cover conjecture, a longstanding open problem in mathematics. It beat Pokémon FireRed using a vision-only setup. Yet on ARC-AGI-3, a benchmark built from 2D puzzle games, it managed a mere 7.8%. GPT-5.5’s performance was even more extreme, scoring 0.4%, which essentially means it failed to play the games at all. How does a model capable of beating Pokémon get stuck on simple 2D puzzles? The …
WeChat Decryption: How to Export Your Encrypted macOS Chat History Using wechat-exporter
8 days ago 高效码农My WeChat Chat History, Finally Freely Queryable A lot of people don’t realize this, but the chat history we generate every day on WeChat actually sits on our computer’s hard drive—encrypted. Want to back it up? Export it? Analyze it? You’re stuck without the key that unlocks it. That’s exactly what wechat-exporter solves. It does one thing directly: it takes your macOS WeChat local chat history and decrypts the entire thing into plain SQLite databases. After that, you can query, export, or analyze however you like. This post walks through what it does, how to use it, and what’s happening …