#Depth Anything V2
195 related articles
·4 min
The Secret of AI Power Users: Understanding the Underlying Logic Matters More Than Prompt Tricks
The gap between AI power users and everyone else isn't about prompt tricks — it's about understanding LLMs, multimodal models, workflows, and agents. Build your complete AI mental model here.
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·4 min
AI Agent Learning Path for Beginners: From Theory to Real-World Practice
A complete AI Agent learning roadmap covering BDI theory, core components (Perception/Planning/Execution), AutoGen multi-agent frameworks, and DeepSeek RAG projects for beginners.
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·4 min
The OpenAI Codex Product Philosophy: How AI Engineers Are Shifting from Writing Code to Managing Agents
OpenAI Codex isn't about replacing engineers—it's about empowering them. A deep dive into the AI Engineer conference talk: from code completion to long-horizon agents, Value Maxing, and managing agent teams.
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·2 min
Google Publishes Top 10 Gemini User Requests: Workspace Integration Pain Points and Community Reactions
Google publicly shared its top 10 Gemini user requests, revealing pain points around Workspace integration and unsolicited image generation. Community reactions are mixed.
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·6 min
Getting Started with Coze Agents: Core Concepts, Platform Positioning, and a Practical Implementation Guide
A systematic guide to Coze's core positioning, its differences from Dify/n8n, and its full capability system covering agents, workflows, and multi-agent modes—helping beginners get started fast.
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·6 min
Grok 4.5 Deep Dive: Coding on Par with Top Models, at a Fraction of the Cost
A senior developer's 24-hour deep test of Grok 4.5: a 1.5T-param MoE model at $2/M input tokens, with coding benchmarks rivaling GPT-5.5. Real performance, token efficiency, and limits explained.
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·6 min
OpenAI's Head of Evals: We've Been Consistently Underestimating What AI Models Can Really Do
OpenAI Frontier Evals lead Tejal Patwardhan reveals AI models are systematically underestimated — reasoning breakthroughs, wet lab records, the internal AGI Index, and a progress curve far steeper than most realize.
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·7 min
Fable AI Coding in Practice: Optimizing Reasoning Levels and Multi-Model Collaboration to Save Money
Hands-on guide: Use Anthropic's Fable model to optimize AI coding workflows — control reasoning levels, leverage Claude-Codex multi-model collaboration, and cut costs from thousands to $150.
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·7 min
Fable AI Coding in Practice: Optimizing Reasoning Levels and Multi-Model Collaboration to Cut Costs
How developer Theo used Anthropic's Fable model to rebuild his AI coding workflow — controlling reasoning levels, multi-model routing with Codex, and sub-agent orchestration to cut costs from thousands to $150.
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·4 min
The AI Story Bubble: Cutting Through the Hype to Find Real Value
When "AI-powered" becomes a magic phrase for valuation premiums, are companies paying for technology or for a story? A deep analysis of AI hype cycles, the gap between narrative and reality, and how to identify genuine AI value.
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·7 min
Block-Sparse Featurizers: Opening the Black Box of Vision Model Neural Geometry
Block-sparse featurizers remap dense vision model activations into block-sparse representations, making the internal feature spaces of ViT, CNN, and other models readable and interpretable. This article explores their core principles, links to mechanistic interpretability, and applications.
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·6 min
AI Agent Practical Guide: From Chatting to Directing — Building Fully Automated Workflows
AI use has three levels: Chat, Automation, and Agent. Learn how to use tools like Manus AI with a "director mindset" to build fully automated workflows — no technical background required.
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·6 min
AI for Beginners: A Five-Step Practical Path That Skips the Math and Python Syntax Traps
Struggling with math and Python when learning AI from scratch? This article lays out a five-step entry path: grasp the concepts, learn Python lightly, master ML and deep learning principles, get hands-on with PyTorch, then deepen understanding through real projects.
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·6 min
Hands-On with Mixar: An AI-Native Blender Fork That Handles Baking and LOD with a Single Prompt
Mixar is an AI-native fork of Blender 5.0 that embeds AI into the kernel layer. This hands-on review tests texture baking, LOD generation, mood boards, image-to-3D, and more, comparing it to MCP. Fully open source and free.
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·4 min
Claude Code /cd Command: The Hidden Trick for Switching Directories Without Losing Context
Learn how Claude Code's /cd command lets you switch working directories without losing conversation context — perfect for microservices, monorepos, and multi-module development.
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·7 min
The Complete Codex Guide: From Installation to Real-World Practice
Master OpenAI Codex fast, even from scratch! Learn Codex vs ChatGPT differences, four versions, interface tips, plugins & skills, browser automation, plus six best practices.
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·3 min
DeepSeek Open-Sources DiSpark: Up to 85% Faster AI Inference Without Changing Hardware
DeepSeek and Peking University release DiSpark, an open-source framework that speeds up LLM inference by 60–85% using speculative decoding and hierarchical verification — no hardware changes or retraining required.
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·7 min
The C Readability Crisis: Engineering Reflections Behind Clever Tricks
Why do C programmers keep creating readability disasters? An in-depth look at macro abuse, fancy pointer tricks, and over-nesting—and how coding standards, static analysis, and code review protect maintainability.
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·6 min
Step-by-Step Guide: Power Codex with DeepSeek for Users Without ChatGPT Access
No ChatGPT account? No problem! Learn how to power Codex with DeepSeek API using the Codex++ management tool. Five easy steps, starting at just $1.40.
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·3 min
Harness Architecture Explained: The Next-Generation Design Paradigm for Agent Development
A deep dive into Harness Architecture — the next-gen Agent design paradigm. Covers its evolution from prompt engineering and context engineering, multi-agent collaboration, sandbox security, feedback loops, and why it's a must-have for LLM developer interviews.
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