#prompt robustness
125 related articles
·2 min
Modern Web Guidance: Bringing Modern Web Standards to Your AI Coding Assistant
Modern Web Guidance is an agent skill & CLI tool that injects modern Web standards into AI coding assistants like Claude Code and Copilot CLI, replacing outdated polyfills with Baseline-validated solutions.
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·3 min
Agent Infinite Loop Engineering Guide: Detection, Breaking, and Root Cause Analysis
How to handle Agent infinite loops? This guide covers three-layer loop detection, four strategy-switching techniques, root cause analysis, and multi-layer fallbacks for building stable, production-grade Agent systems.
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DeepTutor: An Open-Source AI Tutoring …
·2 min
DeepTutor: An Open-Source AI Tutoring System for Lifelong Personalized Learning
DeepTutor is an open-source lifelong personalized AI tutoring system from HKUDS with 26,000+ GitHub stars. Explore its knowledge tracing, RAG, and multi-agent architecture.
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·3 min
Automating Chrome Extension Development with AI Coding Agents: Antigravity & DevTools MCP Complete Workflow
A complete workflow from Google I/O: use Antigravity, Modern Web Guidance, and Chrome DevTools MCP to build Chrome extensions automatically — from prompt to publish.
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Getting Legal AI Right: Why the Coding…
·2 min
Getting Legal AI Right: Why the Coding Agent Paradigm Doesn't Translate
Most legal AI products are just general-purpose models wrapped in RAG and prompt engineering scaffolding. Learn why the coding agent paradigm fails in law and what real legal AI requires.
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·2 min
Text-Only Models Controlling a Linux Desktop: Inside PawFlow's Dual-Model Architecture
PawFlow decouples vision and reasoning into two models, letting a text-only LLM reliably control a Linux desktop. A deep dive into its dual-model architecture, pre-click guard, and caching strategy.
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5,000+ Kagglers Reveal What Actually W…
·2 min
5,000+ Kagglers Reveal What Actually Works for Improving AI Reasoning
5,000+ Kaggle participants in NVIDIA's Nemotron challenge validate test-time compute, self-consistency, and chain-of-thought as key techniques for boosting AI reasoning without bigger models.
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·3 min
MCP + Skills AI Reverse Engineering Toolchain: Technical Capabilities and Compliance Boundaries
A technical deep-dive into AI-assisted reverse engineering: how MCP, Skills libraries, and Frida toolchains work together, their real capability limits, and the legal boundaries of iOS/Android/Web reverse analysis.
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·2 min
TradingAgents-astock: A Practical Guide to the Multi-Agent Research Framework for A-Share Markets
TradingAgents-astock is a multi-agent A-share research framework with free domestic data sources, seven debating analyst agents, and support for DeepSeek, Qwen, and more.
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·2 min
AI Skill Auto-Generates Test Cases: Say Goodbye to Manually Transcribing PRDs
Test engineers: use the AI Skill 'Doc-based Test Case Generator' to auto-generate structured test cases from PRDs or screenshots, covering boundary values, negative scenarios, and more.
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·3 min
Adversarial Testing of AI Agents in the Terminal via MCP
Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.
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·1 min
Tailscale SSH Root Privilege Escalation Vulnerability: Impact, Fixes, and Defense Guide
Tailscale SSH has a critical root privilege escalation vulnerability affecting all ACL-based permission users. Learn the impact, how to patch, and how to use OpenSSH for defense in depth.
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·2 min
GPT-Red: OpenAI's Internal Red-Team Security Tool That Uses AI to Attack Its Own Models
GPT-Red is OpenAI's internal red-team tool that auto-generates prompt-injection attacks against AI agents, turning successful attacks into training data to harden future GPT models.
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·2 min
JEPA World Model Deep Dive: Promise and Controversy
JEPA is LeCun's world model architecture that predicts in abstract embedding space rather than pixels. This article analyzes JEPA's core ideas, differences from generative world models, and key controversies including representation collapse, decodability, and lack of empirical results.
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MIT Breakthrough: Detecting Illegal Tr…
·2 min
MIT Breakthrough: Detecting Illegal Training Data in AI Models Without Generating Any Content
MIT researchers propose a novel AI detection method that identifies models trained on CSAM and other illegal data by analyzing internal features — no content generation required.
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·2 min
QuantaMind: An Open-Source Tool for Testing AI Agent Reliability Locally
QuantaMind is a free, open-source local AI Agent reliability testing tool using pass^k scoring and deterministic evaluation, supporting Ollama, llama.cpp, vLLM, and more.
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·3 min
How to Test High-Risk Actions in AI Agents Before Going Live: A Complete Strategy Beyond Mocking
Mock testing can't cover the real side effects of high-risk, irreversible AI Agent actions. Learn sandbox environments, shadow mode, dry run, HITL, and more.
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·2 min
Getting Started with AI Agent Development: Master the Core Mindset and Clear the Zero-Experience Barrier
New to AI Agent development? Learn why core thinking—breaking down requirements, designing workflows, solving problems—matters more than memorizing APIs. Beginner roadmap included.
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·3 min
AI Agent Beginner's Guide: LLMs vs. Agents, and a Full Breakdown of Development Tools
What is an AI Agent? This guide explains the key differences between LLMs and Agents, breaks down the Agent formula (LLM + Workflow + Knowledge Base), and compares tools like Dify, Coze, LangChain, and LlamaIndex.
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·5 min
Complete Guide to the National Mathematical Modeling Contest: Modeling, Topic Selection, and Award-Winning Strategies in the AI Era
A comprehensive guide to preparing for the National Mathematical Modeling Contest: covering the essence of modeling, judging rules, topic selection, AI usage guidelines, and a four-day schedule to boost your chances of winning.
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