🏷️ #AI (10 articles)

Complete guide to Seedance 2.0 AI video generation — prompt formula, camera movement keywords, style references, audio prompts, material referencing system, and practical examples

Comprehensive research analysis of Seedance 2.0 (ByteDance/Jimeng) AI video generation model and major competitors, covering product features, competitive landscape, target users, market trends, and API integration

By early 2026, product management has entered the Agentic AI era. This comprehensive report analyzes the transformation from vibe coding to autonomous agents, examining synthetic user research, agentic workflows, and the evolution of Product Managers into AI Orchestrators. Discover how 85% of companies are customizing autonomous agents and the paradox of productivity gains concentrated in routine tasks while strategic work remains elusive.

This comprehensive analysis examines two dominant methodologies in autonomous software engineering: the Ralph Wiggum Loop (brute-force execution pattern) and Open Spec (structured requirements framework). Discover how these approaches address LLM limitations like "Context Rot" and the "Dumb Zone," and their role in the emerging "Autonomous Stack" that is reshaping software development in 2026.

Learn how to build intelligent AI agents with practical skills and tools. Complete beginner-friendly tutorial covering agent skills, tool integration, computer use, file operations, and real-world examples using Claude and OpenAI.

A comprehensive exploration of Parlant, an AI Agent framework specifically built for customer engagement scenarios. From architecture design and core features to practical applications and best practices, this article provides a complete guide to building high-quality conversational AI systems with Parlant.

In Agent model optimization, data is the core "lever" driving effect improvements. However, not all chat records have equal value. This article provides algorithm engineers and product teams with a detailed set of "effective question" filtering standards, teaching you how to accurately identify high-value samples from complex conversations—such as task failures, intent misunderstandings, negative emotions, and fallback responses. Mastering these filtering standards will help you pinpoint model weaknesses and efficiently use data to drive continuous improvement in Agent effectiveness and performance.

Transform your PocketFlow workflows from black boxes into fully observable, debuggable systems with just one line of code

A curated list of open-source projects related to Manus technology stack

A detailed introduction to Model Context Protocol (MCP), an open protocol that provides standardized context transmission for AI applications