276 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.

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.

Choose the right AI Agent platform by evaluating model flexibility, observability, tool integration, security compliance, and total cost. A complete decision framework to help technical leaders avoid vendor lock-in.

Choose an AI Agent platform by evaluating model flexibility, observability, tool integration, security compliance, and total cost. A complete decision framework to avoid vendor lock-in.

How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.

A developer added a DAW to their agentic dev environment with Claude, then paired with AI to finish music — experiencing a true AGI moment in creative collaboration.

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.

A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.

Ollama scales up for trillion-parameter open-source models like Kimi K3 and Qwen 3.8. Hugging Face demands $100M from OpenAI, Alibaba Coder goes mobile, and DeepSeek pauses fundraising.

A 7-year frontend engineer, fearing AI-driven job loss, builds a homelab to learn Docker, databases, and networking. A pragmatic roadmap for developers building breadth in the AI era.

GPT-5.6 fully launches with enhanced coding, computer operation, and long-horizon agent tasks, plus a dual quota reset. Meanwhile, ByteDance opens its C-Dance 2.5 API and Mistral debuts a single-RGB-camera natural language navigation model.

An in-depth look at an intelligent paper writing platform built on FastAPI + Vue 3, combining LLM, RAG, and multi-Agent collaboration for full-process automation—an excellent case study for AI developers.

DeepSeek founder Liang Wenfeng reveals a five-step AGI roadmap—from chain-of-thought to embodied intelligence—under a 20,000-GPU constraint, using the TileLang compiler to break domestic substitution challenges while API cash flow backs AGI exploration.

DeepSeek founder Liang Wenfeng reveals a five-step AGI roadmap from chain-of-thought to embodied intelligence. How does TileLang crack domestic GPU substitution under a 20,000-card constraint?

A full breakdown of DeepSeek founder Liang Wenfeng's 4-hour closed-door meeting: no KPIs, only reasonable profits, open-sourcing top models, and tackling AGI via continual learning—a rare AI strategy confession showing how restraint becomes a core edge.

Decoding DeepSeek's Liang Wenfeng 4-hour investor Q&A: 10-month-payback restrained pricing, why open source doesn't hurt revenue, the Agent-continual learning-self-iteration AGI roadmap, plus domestic chips, talent, and your moat.

An analysis of DeepSeek's Liang Wenfeng 4-hour investor meeting: restrained pricing with 10-month payback, why open source doesn't hurt revenue, the Agent–continual learning–self-iteration AGI roadmap, plus domestic chips, talent, and your moat.

DeepSeek founder Liang Wenfeng systematically explains: a KPI-free culture, 10-month break-even pricing, long-term open-source strategy, continual learning to break the AGI bottleneck, and his prediction of a mature domestic chip ecosystem within a year.

DeepSeek founder Liang Wenfeng shares his views on open source, pricing, computing power, and the five-stage roadmap to AGI in a 4-hour internal investor talk.

An in-depth analysis of the three-layer GTM Agent architecture—the Signal, Buyer Intelligence, and Action layers—revealing how context graphs identify anonymous visitors and capture purchase intent.