#AI evaluation frameworks
1033 related articles
Tech Frontiers·2 min
Qwen3.7 Max Deep Dive: 1T Parameter MoE Architecture with Full Agent Framework Compatibility
Deep analysis of Alibaba's Qwen3.7 Max: 1T-parameter MoE architecture, 256K context window, leading agent programming capabilities, full framework compatibility, and the model-vs-harness industry debate.
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Deep Dives·2 min
How to Add Deep Research Skills to AI Agent Frameworks: NVIDIA's Skill-Based Architecture Explained
Explore NVIDIA's Deep Research Skill approach for embedding deep research capabilities as skill modules into AI Agent frameworks like Claude Code and LangChain, enabling goal decomposition, multi-source retrieval, and knowledge synthesis.
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Tutorials·2 min
Using AI to Debug AI: Three Battle-Tested Patterns from Incident.io
Incident.io shares three battle-tested patterns for using AI to debug AI: Agent-driven Eval workflows, debug UI as file systems, and AI analysis pipelines for complex system debugging.
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Deep Dives·1 min
Berkeley CS294 Course: A Complete Guide to Agentic AI Security — Attacks and Defenses
A deep dive into Berkeley CS294-196's agentic AI security lecture, covering prompt injection, indirect injection, AgentPoison backdoor attacks, defense-in-depth, least privilege, and runtime guardrails.
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Tutorials·3 min
OpenClaw Agent Development in Practice: A Complete Guide to the Skill Mechanism and Production-Ready Engineering
Deep dive into OpenClaw's Skill mechanism for AI Agent development, covering Function Calling, RAG, production deployment, and resume-worthy project design.
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Deep Dives·1 min
Ralph Loop: The New AI Programming Paradigm That Beats Multi-Agent Systems Through Iterative Trial and Error
Ralph Loop is a 2025 AI programming paradigm: a memoryless AI Agent repeatedly reads the same Prompt, accumulates experience via file systems, beating multi-agent architectures through iterative loops.
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Tutorials·1 min
AI Agent Open-Source Course: 36 Chapters from ReAct to Claude Code Full-Stack Practical Guide
Open-source GitHub project agent-study offers 36 chapters covering ReAct loops, Claude Code reverse engineering, MCP protocol, RAG, DSPy, and production observability as runnable Python code.
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Product Reviews·4 min
Claude Code vs Cursor vs Windsurf: In-Depth 2025 AI Coding Tool Comparison & Selection Guide
In-depth comparison of Claude Code, Cursor, and Windsurf — why the same model delivers different experiences. Includes hybrid workflow tips, project-building guide, and 2025 AI coding tool selection advice.
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Tech Frontiers·2 min
Malta Offers Free ChatGPT to All Citizens: A Deep Dive into OpenAI's First National AI Partnership
OpenAI partners with Malta to provide free ChatGPT Plus to all 570,000 citizens for one year. A deep analysis of this first-ever national AI adoption program and OpenAI's global government strategy.
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Product Reviews·2 min
GPT-5 vs Claude 4 Coding Test: In-Depth Full-Stack Project Comparison Results
Full-stack developer tests GPT-5 vs Claude 4 Sonnet on a real NestJS project covering architecture, UI, APIs, and multi-file collaboration with cross-platform validation.
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Tutorials·3 min
AI Agent Context Management in Practice: A Complete Guide from Death Loops to Intelligent Truncation
Deep dive into AI Agent context management: a three-step strategy from naive truncation to intelligent memory, covering sub-Agent architecture and long session evals to solve LLM context bloat.
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Tutorials·3 min
3 Prompting Strategies to Guide AI Toward Original Solutions | With Rocket Thruster Case Study
How to use prompt engineering to make AI break from conventional solutions and create unique paths? This article details three strategies—exclusion, constraint-driven, and multi-round iteration—with a real generative design rocket thruster case.
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Product Reviews·4 min
Manus Hands-On Review: Testing a General-Purpose AI Agent Across Three Real-World Scenarios
Hands-on testing of Manus general AI Agent across history report generation, Tesla stock analysis, and GAIA benchmarks. Compares vertical vs general agents with scoring data and limitation analysis.
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Product Reviews·5 min
Manus vs Google Deep Research vs Flowith: A Hands-On Comparison of AI Agents for Courseware Generation
Hands-on comparison of Manus, Google Deep Research, and Flowith generating Kafka courseware with the same prompt. Detailed scoring reveals which AI agent delivers the best results.
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Product Reviews·5 min
Nano AI Agent Hands-On Review: Real Experience and In-Depth Evaluation of Free MCP Tools
In-depth hands-on review of Nano AI agent with Guizhou travel guide and Pop Mart business analysis tasks. Free MCP tools, zero setup, 10-min reports with detailed pros/cons analysis.
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Product Reviews·4 min
Kimi OK Computer Hands-On Review: In-Depth Evaluation of AI Agent Performance Across Four Scenarios
In-depth hands-on review of Kimi's AI Agent 'OK Computer' across website building, data analysis, audio picture books, and PPT creation. Can an agent with 20+ built-in tools truly do your work?
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Product Reviews·3 min
Manus vs Tiangong vs Liaobots: AI Subtitle Translation Real-World Scoring Comparison
Real-world comparison of Manus, Tiangong, and Liaobots translating English tech presentation subtitles, scored across colloquial handling, terminology accuracy, and ASR error correction.
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Deep Dives·2 min
The MARVIS Project: A Complete Analysis of Embedded AI Agents Empowering Autonomous Space Exploration
Deep dive into how the MARVIS project deploys LLM agents on spacecraft, covering agent architecture, edge hardware token performance benchmarks, expert evaluations, and space AI benchmark planning.
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Deep Dives·3 min
NVIDIA Vera Rubin Platform Deep Dive: Solving the Scalability Challenge of Agentic AI Inference
Deep dive into NVIDIA's Vera Rubin platform Pod-level architecture and next-gen NVLink, revealing how it solves Agentic AI inference scalability bottlenecks and the industry shift from training-first to inference-first.
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Deep Dives·3 min
AI Agent Core Principles Explained: The Decision Loop and Three-Component Architecture
Deep dive into AI Agent core principles: the Sense-Plan-Act-Observe decision loop, and how Planning, Memory, and Tools components transform LLMs into autonomous digital employees.
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