#MiniMax M2
162 related articles
·2 min
MiniMax M3 Local Deployment: 23B Active Parameters ≠ 23GB VRAM — A Complete Guide to MoE Architecture
MiniMax M3 is a 428B MoE model. Its 23B active parameters reflect per-token compute, not VRAM needs. Learn the MoE trade-offs, quantization options, and deployment paths to avoid the most common misconception.
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·4 min
MiniMax M2 Local AI Knowledge Base Test: Source Tracing, Deduplication, and Fact Verification
Build a local AI knowledge base with MiniMax M2 in OpenCode: source tracing, fact vs. opinion separation, conflict preservation, and timeliness management.
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·2 min
MiniMax M3 Launches on Fireworks: 512K Context and MSA Sparse Attention Explained
MiniMax M3 launches on Fireworks with 512K context and multimodal input. MSA sparse attention delivers 9x prefill and 15x decode speedups. Deep dive into architecture, pricing, and open-model competition.
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Product Reviews·3 min
Claude Code with MiniMax M2: Testing a Low-Cost AI Coding Solution Across Three Real Projects
Real-world testing of MiniMax M2 as Claude Code's backend model across three projects: framework migration, iOS development, and full-stack MVP — at just 8% of Claude's price.
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Product Reviews·1 min
MiniMax M2.5 Hands-On Review: How 10B Parameters Deliver Flagship-Level Coding Performance
Hands-on review of MiniMax M2.5's coding performance in Claude Code, including 3D game dev and AI translation platforms, compared to Claude Opus and GPT-5.2.
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·3 min
Open Source vs. Closed Source: Who Will Win the US-China AI Battle?
Chinese open-source models DeepSeek and Kimi K3 are challenging OpenAI's closed-source dominance. Analyzing the business logic, chip ecosystems, and US-China strategic dynamics behind the open vs. closed AI debate.
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·4 min
Jensen Huang Joins 50 Silicon Valley Giants to Champion Open-Source Models: How Capital Logic Overrides Political Correctness
Jensen Huang's first-ever tweet backs open-weight AI. 50 Silicon Valley giants oppose banning Chinese open-source models. Deep analysis of the interests behind closed vs. open AI ecosystems.
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·4 min
Choosing the Wrong Agent Ruins Everything: Why the Same Model Can Perform Like Two Completely Different AIs
The same LLM API performs drastically differently under different Agent frameworks. Through a real database crash case, this article analyzes why choosing the right Agent matters more than switching models.
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·3 min
Open Source vs. Closed Source: Who Will Win the US-China AI Battle?
Chinese open-source models DeepSeek and Kimi K3 challenge OpenAI's closed-source dominance. Analysis of open vs. closed AI strategies, CUDA moat erosion, and the US-China strategic battle for AI supremacy.
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·4 min
Hands-On Skill Writing and Agent Testing: A Guide to AI Testing Transformation
An in-depth look at AI testing challenges. Learn to write reusable Skill packs and master Agent testing and LLM evaluation—covering the SKILL.md six-dimensional rule, skill-creator, EvalScope, and dataset selection.
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·3 min
Pi Agent Configuration Guide: Building Your Own AI Coding Assistant
A step-by-step Pi Agent configuration tutorial covering installation, LLM connection, extension ecosystem, MCP setup, and Token-saving tips. Learn to build a truly controllable AI coding assistant.
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·3 min
AI Agent in Action: Building a Local Netlist Analysis Plugin for JLCEDA from Scratch
How a Bilibili creator used AI Agent, MCP, and Playwright to build a JLCEDA local netlist analysis plugin from scratch—covering features, setup, and the full Agent-driven dev process.
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·4 min
AI Testing in Practice: A Complete Guide to Three Types of AI Tools and the New Paradigm of Test Development
A complete guide to the three core categories of AI tools in the testing era (personal assistants, CLI geek tools, AI IDEs), revealing the real challenges of AI test case generation and the new AI test development paradigm.
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·4 min
AI Testing in Practice: A Complete Guide to Three Types of AI Tools and the New Paradigm of Test Development
A comprehensive guide to three core AI tool types (personal assistant, CLI geek, AI IDE) in the testing era. Uncover the real challenges of AI test case generation and the new AI test development paradigm.
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·3 min
The Claude Code Covert Marking Controversy: How AI Coding Tools Are Reshaping Enterprise Security Boundaries
Claude Code was revealed to steganographically mark system prompts under specific conditions, triggering a developer trust crisis. This article analyzes the steganography, Anthropic's tightening China access, and how AI coding tools became a core enterprise security issue.
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·1 min
Codex vs Claude Code: A Deep Cost Comparison — Which AI Coding Assistant Saves You More Money?
Which is more cost-effective, OpenAI Codex or Claude Code? Starting from real bills, this article breaks down the billing structure, multiplier traps, and subscription limits of AI coding assistants.
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·3 min
The AI Coding Landscape: Model Tiers, Tool Camps, and a Deep Dive into MCP/Skills/CLI
A systematic map of today's AI coding landscape: the evolution from ChatGPT to Claude Code, LLM capability tiers, tool camps like Cursor/Copilot, and the three key weapons of the Agent era — MCP, Skills, and CLI.
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·3 min
Claude Code for Absolute Beginners: A Step-by-Step Guide (Install + Connect an LLM + Hands-On Practice)
How do complete beginners get started with Claude Code? This guide covers VS Code setup, two ways to connect an LLM in China, four core usage modes, and building your first practical tool without writing a single line of code.
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·1 min
Build AI Agents Without Code: A Complete Beginner's Guide to Coze
Coze by ByteDance is a no-code AI agent platform with visual workflows, 60+ plugins, knowledge bases & persistent memory. A complete beginner's guide.
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·3 min
The T-Parameter Era of Open-Source LLMs: Four Chinese AI Companies Break the Trillion-Parameter Barrier
Moonshot AI, Alibaba, DeepSeek, and Meituan all crossed the trillion-parameter threshold. China's open-source LLMs made the B-to-T leap in just 18 months.
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