850 related articles

A systematic breakdown of the AI LLM learning roadmap covering prompt engineering, AI Agent development, RAG knowledge bases, model fine-tuning, and hands-on projects for beginners.

How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.

Complete guide for backend developers transitioning to AI/LLM engineering. Covers the 4 core skills—Python, RAG, Fine-tuning, and Agents—with a phased learning roadmap and practical project advice.

A deep dive into enterprise RAG from setup to production, covering document chunking, vector search, query rewrite, reranking, and quality evaluation frameworks.

Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.

Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.

Build an enterprise RAG knowledge base Q&A system using Spring AI 2.0, Cursor AI programming, Ollama local deployment, and Redis vector storage. Runs on just 4GB VRAM.

Hands-on comparison of 7 Vibe Coding agents including Trae, Cursor, Claude Code, Codex, WorkBuddy & CoderWork, ranked by beginner-friendliness and performance.

Side-by-side review of 7 Vibe Coding agents including Trae, Cursor, Claude Code, Codex, WorkBuddy, and CoderWork, ranked by beginner-friendliness, performance, and ease of use.

Just $500 in RL fine-tuning enables a 9B open-source model to outperform frontier LLMs on catalog review tasks. Analysis of when small-model RL works and its enterprise implications.

Moonshot AI's Kimi K3 is now available on Telnyx Inference API. Explore how Chinese LLMs are entering global developer ecosystems through third-party inference platforms.

Anthropic releases Claude Opus 5 with near-frontier performance at lower prices. Same day, Jensen Huang co-signs open-weight letter with 20+ companies while DeepSeek fundraising rumors surface.

Chinese open-source AI models surged from under 10% to 58% of U.S. AI consumption. Kimi K3, DeepSeek, and Qwen are reshaping AI cost structures as DoorDash, Airbnb, and other Silicon Valley giants adopt them at scale.

Moonshot AI launches Kimi K3 reasoning model with performance rivaling Claude and OpenAI's top models at one-third the price. The US-China AI gap narrows from 6-12 months to just 3 months.

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.

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.

Compare Codex and Claude Code AI agent programming tools. Learn AI Agent concepts, tool selection, cost analysis, and GPT account setup in this complete beginner's guide.

Kimi K3 officially launches on Ollama Cloud as an "extra high usage" model. This guide covers free tier quotas, cloud inference experience, technical advantages, and how developers can seamlessly call this high-performance LLM.

Deep dive into Anthropic's Agent Skills mechanism, explaining how Progressive Disclosure solves MCP context bloat and tool calling accuracy issues in AI agents.

Deep breakdown of 4 core AI Agent engineer competencies: business decomposition, multi-Agent architecture, quantitative evaluation, and engineering delivery—bridging the gap from Demo to production.