147 related articles

Computer Science or AI & Robotics—which is more stable and promising? This article analyzes major nature, job prospects, and risk hedging to help you plan wisely.

July 12 GitHub trending: Agent Skills/MCP ecosystem explodes with superpowers hitting ~900 stars, pgrust rewrites Postgres in Rust passing 100% tests, plus solid engineering foundations.

A complete guide to Dify's core features and 1.8.0 deployment. Covers 5 app types, Docker setup, Workflow vs Chatflow differences, and RAG knowledge bases for beginners.

Frugon is an MIT-licensed, local LLM cost analysis tool that helps developers identify which API calls can be switched to cheaper models for data-driven cost reduction — no log uploads, full privacy.

A 6-year electrical engineer from Brazil weighs transitioning to AI engineering. This deep-dive covers the stability vs. freedom tradeoff, transition advantages, and a practical roadmap for engineers with similar backgrounds.

9 battle-tested methods from hundreds of hours with Hermes Agent: model selection (Opus/ChatGPT/GLM), multi-agent failover, cross-device coordination via Tailscale, and reverse prompting workflows.

Step-by-step guide to deploying Dify locally: Docker setup, Docker Compose installation, source code configuration, .env file setup, and container startup for Windows, macOS, and Linux.

Kastor is an open-source project that brings IaC-style declarative specs to AI Agent management, inspired by Terraform — solving reproducibility, collaboration, and auditability challenges.

Abralo is a free, easy-to-use multi-agent coding tool that runs multiple Claude Code agents in parallel within a single window, solving task parallelism, solution comparison, and context isolation challenges.

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.

A detailed guide to Dify, the open-source LLM app development platform, covering its core features and full local deployment via VMware + Ubuntu + aaPanel + Docker. Supports 100+ models like DeepSeek and ChatGPT to build enterprise AI apps fast.

A systematic guide to Dify's three deployment methods (Docker/source/online), five application types, and hands-on workflow nodes—covering LLM integration, MySQL config, and app publishing.

An in-depth look at the difference between Vibe Coding and AI engineering-grade programming. Can AI replace programmers? From Claude Code and Codex to Cursor, revealing the real limits, enterprise pain points, and engineering solutions of AI coding tools.

LangChain's LangSmith Engine is an intelligent agent tool for tracking Agent failures, prioritizing issues, and auto-drafting fixes. Deep dive into its core capabilities, sandbox isolation, sub-Agent architecture, and continuous evaluation challenges.

How can beginners learn Python without getting lost? This guide outlines a 3-stage learning path covering basics, advanced topics, and hands-on practice in web scraping, data analysis, and office automation.

Harvard's open-source textbook cs249r (Machine Learning Systems) has 25,600+ GitHub stars. It covers ML systems engineering, TinyML, and MLOps — free for everyone.

In-depth look at DryFox v0.3.3's three core features: multi-role agent team collaboration, one-click reusable team templates, and block-style composable UI panels. With Stop Hook, file mailbox comms, and hot-reload plugins.

An in-depth analysis of the open-source LLM control plane tool Otari—covering unified multi-model access, cost observability, and security compliance governance to help teams build manageable, production-grade AI infrastructure.

Are your .io and .ai domains really safe? A deep dive into the sovereignty risks behind ccTLDs: political power transfers threaten .io's survival, while Anguilla controls the entire AI brand ecosystem. A must-read domain risk guide for founders and developers.
AI Engineer World's Fair Closing Day: …
AIEWF closing day recap: the agent loops debate, the State of AI Engineering report, and a keynote on what to build next — covering AI engineering's key divides and trends.