637 related articles

Prompt Engineering is the core skill for harnessing LLMs. This article covers principles and design methods through real cases like translation role-setting and DeepSeek image generation.

An exclusive look at the AI Engineer Summit dress rehearsals, decoding the paradigm shift from research to production. A deep dive into AI Engineer challenges, RAG, agent systems, and AI engineering as a distinct discipline.

Prompt engineering is more than messaging AI. This guide breaks down the four core functions of prompts, the six-step prompt engineering process, and key limitations to help you build the right foundation.

Behind every hackathon lies a deeper story about AI innovation ecosystems. This article examines why hackathons are surging in the generative AI era, their core value, and key lessons for AI developers and founders.

Why can humans "see" the world even under blur and occlusion? This article analyzes bidirectional feedforward-feedback circuits in visual cortex, revealing how predictive coding fuses perception with cognition and its implications for AI.

Want to become an Agent engineer? This article systematically covers three core skill tracks—LLM fundamentals, LangChain architecture development, and enterprise deployment—to help you avoid detours.

OpenAI previews the GPT-5.6 series — Soul, Terra, and Luna — with a massive 1.5M-token context. In-depth analysis of coding leaps, the Fable 5 national security game, the heating U.S.-China AI race, and workflow economics.

OpenAI releases the GPT-5.6 series with flagship Sol, balanced Terra, and lightweight Luna. An in-depth look at each model's positioning, use cases, pricing, and the multi-agent Ultra architecture.

Embedded Linux or AI Agent development? This in-depth comparison covers salary, job availability, and career stability to help developers pick the right path.

Want to switch careers into LLM development but don't know where to start? This guide breaks down a four-level skill roadmap — from basics and API calls to RAG, fine-tuning, Agent development, and multimodal — to help you build real AI career value.

Andrew Ng partners with JetBrains on a new course systematically teaching Spec-Driven Development. By writing high-quality specs, developers can precisely control AI coding agents, eliminate context decay, and boost intent fidelity.

A must-read for test engineers: use Cursor's custom Skills to auto-generate test cases covering positive, negative, and boundary values from PRDs, output as Markdown linked to XMind, adapting to functional, interface, and performance testing.

Security researchers disclose the GitLost attack, which uses prompt injection to trick GitHub AI agents into leaking private repository source code. An in-depth analysis of the attack principle, AI agent security risks, and developer defense recommendations.

OSWorld 2.0 benchmark tests 108 long-horizon computer tasks (median 1.6 hrs for humans). Claude Opus tops out at 20.6% completion, exposing critical AI Agent weaknesses in state maintenance and self-correction.

OSWorld 2.0 benchmark tests 108 long-horizon computer tasks. Claude Opus tops at only 20.6% completion, exposing critical AI weaknesses in state tracking and error self-correction.

How developer Theo used Anthropic's Fable model to rebuild his AI coding workflow — controlling reasoning levels, multi-model routing with Codex, and sub-agent orchestration to cut costs from thousands to $150.

Hands-on guide: Use Anthropic's Fable model to optimize AI coding workflows — control reasoning levels, leverage Claude-Codex multi-model collaboration, and cut costs from thousands to $150.

As one of the world's largest car marketplaces, AutoScout24 is going AI-native with OpenAI Codex and agents. It built a CapEx agent in 48 hours, saving ~$1M/year, and explores hands-off coding.

When "AI-powered" becomes a magic phrase for valuation premiums, are companies paying for technology or for a story? A deep analysis of AI hype cycles, the gap between narrative and reality, and how to identify genuine AI value.

Block-sparse featurizers remap dense vision model activations into block-sparse representations, making the internal feature spaces of ViT, CNN, and other models readable and interpretable. This article explores their core principles, links to mechanistic interpretability, and applications.