#supervised learning
505 related articles
Product Reviews·1 min
Qwen3.6 27B: Real-World Testing of Three Unconventional Quantized Models — Code Performance +15.8PP, 40B Distillation, 16GB Adaptation
Real-world comparison of three community-built Qwen3.6 27B variants: OmniMerge V4 with +15.8pp code gains, 40B OPUS distilled for roleplay, and a 16GB-optimized version for limited VRAM.
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Industry Insights·1 min
OpenAI's Five Leading Indicators: Why It Remains the Front-Runner in the AI Race
Deep analysis of OpenAI's five competitive advantages: Codex's explosive growth, full cloud coverage, ChatGPT's consumer dominance, compute strategy, and top research talent.
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Tutorials·2 min
DeepSeek R1 Usage Guide: Prompt Techniques & Practical Tips (2025)
A detailed guide to DeepSeek R1's reasoning capabilities and prompt techniques, covering reasoning vs. general models, core capabilities, and practical prompt strategies for 2025.
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Tutorials·1 min
AI Agent Interview Focus Comparison: ByteDance vs. Tencent vs. Alibaba — Differences in Approach & Preparation Strategies
Deep comparison of AI Agent interview focuses at ByteDance, Tencent, and Alibaba: ByteDance drills ReAct implementation & RLHF details, Tencent emphasizes MCP protocols & memory systems, Alibaba focuses on multi-Agent architecture & business deployment.
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Tutorials·4 min
Windsurf Download, Installation & Setup Guide: Complete Workflow in 5 Minutes (2025 Latest)
2025 latest Windsurf editor download, installation & setup guide covering Windows installer download, VS Code settings import, GitHub login, theme configuration and more — get started in 5 minutes.
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Deep Dives·1 min
Beginner's Guide to AI Agents: Core Principles, Technical Architecture, and Application Scenarios Explained
A systematic guide to AI Agents covering core principles of perception, decision-making, and action, plus the technical stack of LLMs, tool calling, memory systems, and RAG, with real-world use cases.
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Tutorials·3 min
2026 AI Learning Roadmap: 3 Proven Paths + A 4-Step Action Checklist (With Complete Technical Framework)
How to learn AI in 2026 without wasting time? This guide covers 3 proven paths — AI Agent customization, vertical specialization, and full-stack LLM development — plus a 4-step action checklist.
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Tutorials·2 min
2026 AI Large Language Model Learning Roadmap: Complete Guide from Zero to Agent Development
2026 AI large model learning roadmap: from Python basics and prompt engineering to RAG knowledge base, Agent development, and fine-tuning deployment. A complete 42-episode full-stack tutorial with three proven paths for enterprise AI deployment.
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Industry Insights·3 min
Deep Analysis of the AI Coding Landscape: Why Context Determines the Fate of Cursor, Windsurf, and Their Peers
Deep analysis of the AI coding sector: from GitHub Copilot to Cursor's $10B valuation, why context is the core moat, and how startups compete with tech giants.
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Deep Dives·3 min
NVIDIA Fleet Intelligence Explained: Real-Time Monitoring and Intelligent Optimization for GPU Clusters
Deep dive into NVIDIA Fleet Intelligence for GPU clusters: real-time visualization, AI anomaly detection, utilization optimization, and energy management to boost large-scale GPU infrastructure efficiency.
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Product Reviews·3 min
GitHub 8K Stars: Deep Dive into the Most Comprehensive LLM Resource Repository
Deep analysis of the GitHub project awesome-LLM-resources covering multimodal generation, AI Agents, MCP protocol, model training/inference, o1 models, and SLMs—a community-verified 8200+ Star LLM learning resource hub.
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Product Reviews·3 min
8K Stars on GitHub: Deep Dive into the Most Comprehensive LLM Resource Repository
Deep analysis of the awesome-LLM-resources project (8200+ GitHub Stars), covering multimodal AI, Agents, MCP protocol, model training, o1 reasoning, SLMs, and more for LLM practitioners.
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Product Reviews·5 min
DeepClaude Open Source Analysis: DeepSeek + Claude Dual-Model Collaborative Code Generation
Deep dive into DeepClaude, a 2800+ Star open source project combining DeepSeek R1 reasoning with Claude 3.7 Sonnet code generation for superior AI output.
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Product Reviews·4 min
Cube Studio Deep Dive: Tencent's Open-Source One-Stop MLOps Platform
Deep dive into Cube Studio, Tencent Music's open-source one-stop AI platform, covering architecture design, distributed training, large model fine-tuning and inference, and domestic chip adaptation.
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Expert Opinions·3 min
Dario and Daniela Amodei in Conversation: Decoding Claude's Future and AI Safety Strategy
Anthropic co-founders Dario and Daniela Amodei join a rare public conversation moderated by CPO Ami Vora, discussing Claude's evolution, AI safety advances, and Anthropic's competitive strategy.
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Tutorials·3 min
OpenAI Open-Sources GPT-OSS: Run O4-Level Models on 16GB VRAM — Complete Deployment Guide
OpenAI open-sources GPT-OSS (20B/120B) with MOE architecture and native FP4 precision. Run O3-level reasoning on a single RTX 4090. Full deployment guide for Ollama, vLLM, and more.
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Tutorials·4 min
Shopify Agent Cold Start in Practice: A Three-Step Method for Training Production-Grade AI with Zero Conversation Data
Shopify's production AI Agent cold start approach: zero real conversation data, reverse-engineering training samples from existing business workflows, fine-tuning Qwen-32B for 2.2x speed gain and 60% cost reduction.
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Research·4 min
Continual Harness: AI Automatically Builds Scaffolding to Beat Pokémon RPGs
Princeton and Google DeepMind propose Continual Harness, enabling LLM agents to automatically build and optimize scaffolding to beat multiple Pokémon RPGs without human intervention, cutting costs by 40%.
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Tech Frontiers·3 min
AI Radio Experiment: Claude, ChatGPT, Gemini, and Grok Run Stations Independently — The Results Are Surprising
Andon Labs had Claude, ChatGPT, Gemini, and Grok independently run radio stations. The experiment reveals real capability limits of autonomous AI in content quality, trustworthiness, and long-term stability.
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Deep Dives·3 min
What Are Large Language Models? A Deep Dive into Parameters, Models, and the Essence of AI LLMs
What exactly is a large model? This article explains the essence of LLMs from the core concepts of "models" and "parameters," covering GPT parameter scales, vector dimensions, and open-source model selection.
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