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95 related articles
·7 min
Context Engineering: A Systematic Approach to Stop AI Agents from 'Talking Nonsense'
Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.
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·3 min
Connecting Zotero to AI via MCP: A Complete Zero-Cost Guide to Generating Real Literature Reviews
Connect your local Zotero library to AI via MCP protocol to eliminate hallucinated citations. Full guide covering UV, Zotero setup, and OpenCode with free DeepSeek.
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·3 min
AI Manga Series Production: A Complete Guide from Script to Storyboard
A complete guide to AI manga production: from scriptwriting to storyboard generation. Learn the universal formula, avoid common pitfalls, and launch your first episode.
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·3 min
OpenAI's First AI Hardware Revealed: A Screenless, Animated Emotional Companion Speaker
A Reddit leak suggests OpenAI's first hardware is a screenless, motorized AI companion speaker with a camera and personality-driven design. Deep-dive analysis.
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·3 min
Cherry Studio + MCP: A Practical Guide to Building AI Agents with Local Deployment
Learn how to build an automated AI agent using Cherry Studio, MCP protocol, and locally deployed models — covering DeepSeek integration, web scraping, and private knowledge base setup.
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·3 min
The Complete Guide to Looping Engineering: Building Efficient Agent Loops from Scratch
A deep dive into Looping Engineering — covering the five core loop elements (Trigger, Goal, Judgment, Feedback, Memory), when to use loops, and a step-by-step guide to building a topic-selection loop with Claude Code.
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·3 min
Four Paths for Frontend Engineers to Level Up in AI: From RAG to Vertical Agents
How can frontend engineers transition into AI development? This guide covers four agent development directions: RAG, workflow agents, vertical agents, and general-purpose agents — with framework picks like LangChain.js.
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·3 min
GPU Rental vs. Buying: How Low-Usage Teams Should Actually Do the Math
Should low-usage teams rent or buy GPUs? Based on real billing data, we break down hardware costs, electricity, and hidden time losses to help small teams decide.
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·3 min
Chain of Thought (CoT) Explained: Teaching AI to Reason Step by Step
A deep dive into Chain of Thought (CoT) prompting: from prompt construction to reasoning chain generation, revealing the three mechanisms behind AI's improved reasoning. Covers math, medical, and financial use cases.
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There's No Best Agent Framework — Only…
·2 min
There's No Best Agent Framework — Only the Right One for Your Needs: A Deep Comparison of Leading Options
LangGraph, PydanticAI, OpenAI Agents SDK, CrewAI — a senior developer's practical guide to choosing the right AI Agent framework for your project.
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·2 min
4-Step Roadmap to Becoming an AI Agent Engineer: From Zero to a $50K LLM Role
A proven 4-step roadmap to becoming an AI Agent engineer: stable LLM calls, tool use (RAG + Function Calling), production engineering, and resume optimization.
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·4 min
From Engineer to Machine Learning Practitioner: A Practical Roadmap from Data Compliance to Deployment
Have an engineering or data background and want to transition to machine learning? This article covers data anonymization compliance essentials, knowledge base tech route selection (RAG/traditional ML/BI), and a phased practical learning path.
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·4 min
Hands-On Test of GPT Real-Time Voice: A Comprehensive Review of Dialect Recognition, Emotional Expression, and Role-Play
An in-depth hands-on test of GPT's real-time voice feature, covering Cantonese and Sichuanese dialect recognition, emotional tone switching, complex role-play, and cross-voice contextual memory—objectively presenting the true level and remaining gaps of AI voice interaction.
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·3 min
K-means vs Random Forest: How to Choose the Right Algorithm from First Principles
A machine learning exam question pitting K-means against Random Forest sparks debate. Learn the core difference between supervised and unsupervised learning, and how to choose the right algorithm for mixed-feature tasks.
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·6 min
GPT-5.6 Fully Available: A Deep Dive into the Sol/Terra/Luna Three-Tier Models and Four-Agent Parallelism
GPT-5.6 is now officially available to all users, launching the three-tier Sol, Terra, and Luna models with four-agent parallelism. An in-depth look at the official benchmarks, API pricing, safety, and Ultra mode.
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·6 min
Rust AI Agent System Prompt Engineering in Practice: A Seven-Dimensional Design Guide
An in-depth analysis of the four core roles of AI Agent system prompts and the four-step tool-calling method, covering behavioral boundaries, autonomous execution principles, and trigger pattern design—helping Rust developers build high-quality agents.
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·7 min
Tencent DiscoBench: The First Benchmark for Evaluating LLM Search Agents' Ambiguity Clarification Capabilities
Tencent Hunyuan and Tsinghua jointly release DiscoBench, the first benchmark evaluating search agents' dynamic ambiguity clarification. Covering 463 ambiguity instances across 11 domains, it reveals real weaknesses of mainstream LLMs.
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·7 min
HuggingFace Open-Sources ml-intern: An AI Engineer That Reads Papers and Trains Models Autonomously
Hugging Face's open-source ml-intern autonomously reads papers, writes training scripts, and finetunes LLMs, deeply integrating the HF ecosystem and smolagents. Explore its features and impact on ML careers.
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·5 min
Python Learning Roadmap for Beginners: A Three-Stage System to Avoid 90% of Detours
Learning Python from scratch? This article breaks down the three learning stages—Fundamentals, Intermediate, and Practice—covering variables, OOP, scraping, and data analysis to help you plan a systematic Python path.
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Building a Coding Agent with LLM: A De…
·3 min
Building a Coding Agent with LLM: A Deep Dive into Simon Willison's llm-coding-agent Experiment
Simon Willison built llm-coding-agent — an open-source Claude Code-style agent — using just two prompts and TDD. Explore its tool design, bootstrapped dev process, and real-world test results.
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