938 related articles
In-Memory Layer Mapping: How to Effect…
Context overload is a core pain point for LLM deployment. This article breaks down In-Memory Layer Mapping, compares it with RAG, and offers practical architecture insights for AI engineering teams.

Redis creator runs 284B-parameter DeepSeek model on a MacBook Pro at 26 tokens/sec using a pure C engine, asymmetric quantization, and MoE architecture.

How to learn LLMs from scratch? This guide covers personalized learning paths for 3 types of learners, hardware tips (16GB RAM is enough), Python prep, and cloud GPU options.

Zhipu AI's GLM open-source model approaches top closed-source models at 15% of GPT-5.5's cost. HBM shortages fuel AI inflation, and space data centers emerge as a serious compute play.

Why did DeepSeek go open-source? How does a 300-person team beat tech giants? A deep dive into Liang Wenfeng's logic: flat org, algorithmic efficiency, and open-source disruption.

AMD GPU black screens running local LLMs? This post-mortem covers Ollama's 3 fatal flaws and how switching to LM Studio boosted token speed from 5 to 36, with ROCm setup, Speculative Decoding, and GFX version tips.

Full hands-on test of Short Drama Agent: from scriptwriting and character three-view sheets to AI video generation. We break down the workflow for cute-style and xianxia dramas and analyze three key pain points: cost, rigidity, and visual inconsistency.

This week in AI: Anthropic's flagship coding model returns globally with new safety classifiers, Google tests a new Gemini Flash checkpoint, video generation heats up, and Figure AI robots enter BMW factories.

Too hard to become an algorithm engineer? Too basic to just use AI tools? This guide breaks down the three levels of AI adoption for programmers, with a focus on Agent development and large model engineering — including salaries, timelines, and window risks.
Should Frontier AI Models Like GPT-5.6…
Should frontier AI models be open-sourced? This deep dive explores the key debates around democratization, misuse risks, commercial sustainability, and governance — and the middle paths between open and closed.

"Claude Fable 5" doesn't exist. This guide exposes the AI scam's core red flags: fake version numbers, fabricated demos, "no-VPN China access" lies, and traffic funnel tactics.

A detailed four-stage competency model for AI Agent development: from Python/RAG basics (15K) to workflow orchestration (20K), inference optimization (30K), and Agent cluster governance (40K RMB).

A complete guide to reproducing GitHub projects from scratch: due diligence, virtual environments, dependency installation, script interpretation, and breakpoint debugging — helping grad students and junior developers run others' code efficiently.

A systematic guide to OpenCode, the open-source terminal AI coding tool: installation methods (including WSL), model configuration, rules files, Agent types, custom commands, and MCP tool extensions.
Decoding xAI's Open Partnership Strate…
A deep dive into xAI's open partnership strategy, Grok model strengths, and Elon Musk's ecosystem expansion logic in the competitive AI landscape.

In-depth comparison of five AI Agent code execution sandbox solutions—E2B, Daytona, Modal, Cloudflare Sandbox, and Vercel Sandbox—across isolation, cold start latency, state management, and pricing.

The core of enterprise AI isn't calling general models—it's building a self-reinforcing "model-harness-sandbox-eval" flywheel. This article analyzes the four components, tacit knowledge moats, and the "token value per watt" efficiency metric.

In-depth comparison of Codex APP vs. Claude Code and Cursor: pricing, stability, and capability differences. Discover Codex's unique strengths in frontend UI development and how to choose the right AI coding tool.

In-depth analysis of AI aggregator platforms claiming free access to GPT, DeepSeek, and Gemini. Reveals hidden data risks, business logic, and recommends legitimate alternatives like OpenRouter and Poe.

Deep analysis of a viral Bilibili video claiming to show Claude Fable 5 — examining naming conventions, business logic, and demo effects to help users spot fake AI products.