BestBlogs Issue #80: From Chat to Action

The Alibaba Qwen team has officially released the Qwen3-VL-Embedding and Qwen3-VL-Reranker model series, addressing a critical gap in high-performance open-source multimodal retrieval tools. Built upon Qwen3-VL, these models enable a unified semantic space for text, images, videos, and visual documents like charts or UI components. By leveraging a two-stage workflow—utilizing a dual-tower Embedding model for efficient recall and a single-tower Reranker with cross-attention for precision—this series has set new benchmarks on leaderboards like MMEB-v2. It serves as an essential infrastructure for developers building multimodal RAG systems or tackling complex cross-modal search challenges.

In a recent keynote, Yang Zhilin, founder of Moonshot AI, unveiled Kimi's 2025 roadmap, centered on "Agentic Intelligence." He emphasizes that beyond the standard Scaling Law, improving "Token Efficiency" and "Long Context" capabilities is vital for evolving models from passive tools into active agents. Key technical breakthroughs include a new second-order optimizer that doubles token efficiency and the KimiLinear architecture, which solves the performance degradation of linear attention in long sequences. Yang also shares his philosophy on "technical taste," viewing AGI as a key to expanding the limits of human civilization.

This article provides a profound retrospective of AI technological evolution in 2025 and an outlook for 2026. The author argues that 2025 marked the end of the "Brute Force" era dominated by simple Scaling Laws, shifting focus toward four pillars: Fluid Reasoning, Long-term Memory, Spatial Intelligence, and Meta-learning. By analyzing cutting-edge research such as Test-Time Compute (TTC), Titans architecture, and world models, the text illustrates AI's transition from being "encyclopedic" to truly "intelligent." Whether you are interested in RL engineering optimizations or the next generation of non-frozen model architectures, this systematic overview and its extensive bibliography offer invaluable insights.

This in-depth article by a Trae technical expert explores the first principles of Agentic Coding, shifting the focus from "larger context windows" to optimized human-AI collaboration. It dissects the autoregressive nature of LLMs, the constraints of attention mechanisms, and how Reinforcement Learning enables models to "act." The author advocates for "Short Dialogues" and "Compounding Engineering"—the practice of accumulating project-specific knowledge into reusable assets. By emphasizing that good developer experience (DX) benefits AI as much as humans, the article provides a robust framework for mastering AI coding tools. It is an essential read for developers aiming to transition from AI users to AI orchestrators.

This article explores the paradigm shift from Spec-Driven Development (SDD) to advanced "AI Engineering." By analyzing the limitations of tools like speckit and openspec in complex corporate environments, the author proposes a new architecture centered on Context Engineering and Compounding Engineering. The core focus is decoupling Agents and Skills to enable automated knowledge accumulation and retrieval, effectively breaking the cycle of constant marginal costs in AI-assisted programming to achieve "compound interest" in R&D efficiency.

This article provides a comprehensive guide on transitioning from single-agent setups to multi-agent architectures. It categorizes and compares four fundamental patterns—Subagents, Skills, Handoffs, and Routers—analyzing their performance in terms of latency, token cost, and parallelization. The piece emphasizes a pragmatic approach: start with a single agent and only graduate to complex multi-agent systems when facing clear context management or team coordination constraints. By providing quantitative benchmarks and decision frameworks, it serves as an essential resource for AI developers looking to build scalable and efficient agentic applications.

This article provides a deep dive into Anthropic's newly released "Skills" feature, offering a comprehensive implementation guide for developers. By comparing Skills (logic/best practice encapsulation) with MCP (tool/API connectivity), the author clarifies their distinct roles and introduces core methodologies like "progressive disclosure" and "AI-driven development." The piece goes beyond summarizing official best practices by showcasing a real-world "Prompt Optimizer" use case, demonstrating how to leverage context engineering to generate high-quality skills rapidly. It serves as an essential manual for developers looking to enhance agent autonomy within the Claude desktop, Claude Code, or API environments.

This comprehensive guide from the Cursor Team outlines a systematic approach to collaborating with programming agents. Moving beyond basic prompting, it introduces the "Plan-before-Code" strategy and details how to leverage .cursor/rules for global constraints and SKILL.md for dynamic capabilities. The article also explores advanced features like automated hooks, parallel multi-agent worktrees, and cloud-based agents for asynchronous tasks.

Anthropic’s Claude Cowork brings general-purpose agent capabilities to the desktop, allowing users to automate file tasks and research within a secure sandbox. While Simon Willison highlights its impressive productivity gains, he warns that the threat of prompt injection remains a critical concern for users handling sensitive data in this new agentic era.

Alibaba's Qwen App has evolved into a full-stack Agent, integrating 400+ services including Taobao and Fliggy. Leveraging the Qwen3-Max model and MCP/A2A protocols, it now handles complex end-to-end tasks like booking travel and bulk shopping via simple voice commands. This marks a shift from AI as a talker to a doer, creating the first large-scale "search-to-fulfillment" AI assistant in the industry.

This episode features AI experts Aishwarya Reganti and Kiriti Badam, who dive deep into why traditional software playbooks fail in the age of LLMs. They introduce the Agency-Control Trade-off, explaining that increasing an agent's autonomy inevitably reduces human control, necessitating a gradual deployment strategy. The conversation outlines the CC/CD (Continuous Calibration, Continuous Development) framework, moving beyond "Vibes vs. Evals" to a data-driven feedback loop. With insights from deploying 50+ AI products at companies like OpenAI and Google, this is an essential guide for teams struggling with reliability and seeking a realistic path toward autonomous agents.

This article provides a detailed sharing of the complete workflow and insights behind The Louvre Kitten AI video creation, presented by the two main creators Haixin and Aweng. It covers the entire process from initial concept development, character selection (why white and orange cats were ultimately chosen), film tone setting, music production (using Suno AI for generation and variations), storyboard design (emphasizing information density, rhythm, and emotional progression), to art style control (application of Islamic art style, AI model selection Nano Banana Pro, prompt engineering), as well as techniques for iterating complex scenes and live-action compositing. The creators emphasize the importance of pre-production human planning such as hand-drawn storyboarding in AI-assisted creation, and the refined use of AI tools, such as how to strategically utilize different AI functions to optimize workflow, reduce unnecessary prompt length, and efficiently iterate and correct errors through AI, ultimately achieving high-quality visual effects. The article not only shares technical details but also reveals the artistic creation thought process.

This comprehensive review captures the strategic insights from China's AI pioneers at the AGI-Next summit. Featuring leaders from Zhipu, Moonshot, and Alibaba, the article explores the pivotal shift from "Chat" to "Actionable Agents." It delves into technical breakthroughs such as Muon optimizers and Linear Attention mechanisms, designed to enhance Scaling Law efficiency under compute constraints. The text provides a high-density roadmap for 2025, addressing model architecture evolution, industry bifurcation, and the competitive landscape between China and the US. It is an essential read for professionals seeking to understand the underlying logic and future trajectory of LLMs.

In this insightful podcast episode, Zhang Jinjian, Partner at Oasis Capital, conducts a comprehensive retrospective of the three-year AI investment cycle since 2023. Triggered by the landmark public listings of Zhipu and MiniMax, the discussion explores the technical convergence of LLMs and Embodied AI—described as the "North and South Slope" paths to AGI. Moving beyond mere market analysis, Zhang introduces "Subjectivity" as the central theme for the next decade. He argues that as AI amplifies individual traits, "living as oneself" shifts from a romantic ideal to the only viable survival strategy.

This episode explores how AI Coding turns software into a disposable commodity. Key insights: AI-driven "dialogue-to-execution" workflows will eliminate middle-layer tools and shrink organizations into 3-5 person powerhouses. As production becomes effortless, the true competitive moats shift from technical building capacity to distribution channels and established user trust.