When I first started learning Vibe Coding, there were three high-frequency concepts that were easy to confuse: System Prompt, Skill, and Workflow. Later, through practice, I gradually clarified that the essence of Vibe Coding is not about writing code, but about "leading a team." You can imagine that you have hired a remote programmer (AI), and these three elements are your management tools: System Prompt (Job Description) is the qualitative definition for the AI. You don't need to repeat "who you are" in every instruction, but rather set its technology stack preferences (e.g., only writing Python), coding style (e.g., enforced type checking), and communication principles through the System Prompt. Function: Reduce communication costs and establish behavioral benchmarks. Workflow (SOP/Workflow) is the process control for the AI. It’s not just about telling it "what to do," but also specifying "in what order to do it." For example: read the document > write tests > implement code > run validation. You can achieve your ultimate goal by calling multiple Skills. Function: Prevent the AI from skipping steps and ensure delivery quality. Skill is the specific execution permission granted to the AI (e.g., network access, file read/write, database queries). Why has Skill recently gained attention? On one hand, the capabilities of AI Agents are expanding daily, and on the other hand, the modular and shareable nature of Skills makes them more disseminable. Reusable: Once you write a Skill for "querying Etherscan," it can be directly mounted in any conversation or model. Shareable: Developers can package and share Skills, allowing users to use them without needing to understand the internal logic, plug-and-play. The essence of Skill is to transform the AI from a "text generator" into an "interface caller" that can interact with external systems. In fact, efficient Vibe Coding is essentially a process of system integration: Use System Prompt to standardize employee quality; Use Workflow to establish work standards; Use Skill to expand capability boundaries. There are now many practical Skill libraries on Github, and I’m sharing a relatively comprehensive one. I am also continuously trying and organizing to build a workflow that improves efficiency for our team. I will share any useful ones with everyone.

What are the recently popular OpenClaw and Moltbook? Can they be used in cryptocurrency? In simple terms, AI has started to not only chat but also work and socialize on its own. @openclaw has already changed its name three times surrounding my timeline, originally named Clawdbot and Moltbot. It is an open-source AI agent that can run locally, not just a chatbot that answers your questions, but one that actively reaches out to you: reminding you of tasks, handling emails, running commands, calling APIs, and even executing tasks directly. Many people say it feels like hiring a digital employee that is online 24/7 and has some autonomy, running locally and seamlessly integrating with various chat tools, which is why it suddenly exploded on GitHub. It even boosted the sales of Mac minis for a while. It's fun but expensive, with tokens flowing like water. Moltbook is a bit more abstract; it is a forum that only allows AI to post, while humans can only observe. AIs with their own personalities discuss technology, philosophy, and geopolitics, and they also talk about DeFi, send token mint requests, remind each other of security vulnerabilities, and complain about their supervisors. I took a look inside, and it feels more like a live broadcast of an AI social experiment. Currently, the discussions are sharply divided into two camps: one believes this is the starting point of the Agent Internet, where future DeFi users will mainly be AI; the other warns that this is a security disaster and a meme speculation bubble, with some even suggesting it's like humans walking robotic dogs in a park. What I care about is whether this wave of enthusiasm can bring volatility to the cryptocurrency market. So far, the only crypto-related outcome is the emergence of the $MOLT meme, with no further integration of crypto infrastructure. The reason might be that OpenClaw currently mainly addresses the "immediately usable" efficiency issue, as AI agents can already act autonomously locally; while cryptocurrency focuses more on "multi-party collaboration, asset settlement, and identity verification," which is not yet a pressing need. In terms of narrative strategy, the OpenClaw/Moltbook community deliberately emphasizes local, open-source, and security risks rather than tokens and finance, making the explosion more about tool effects rather than financial speculation. Of course, some have already used OpenClaw to connect wallets, research trading strategies, and issue tokens, but there hasn't yet been a large-scale consensus or necessity for crypto infrastructure. There has always been a notion that blockchain is more suitable for AI than for humans. However, AI seems to be developing a bit faster than we anticipated, and the popularity of Moltbook feels more like a community event that may not last. The question is whether existing Crypto+AI projects can integrate into the next wave of excitement.

Will the prediction market track produce a champion? If so, can Opinion do it? Let me start with a fair statement: The prediction market is definitely a good narrative. Dopamine + event-driven + game attributes make it naturally suitable for explosive growth. But from the perspective of making money or investing, there is really only one question: Who will pay for the prediction market in the long run? Events like the U.S. elections and the World Cup can bring peaks, but after the events end: Who will be the market maker? Who will provide the counterparty? How will the project team continue to make money and support the coin price? This is also the reason why most prediction market projects do not go far. Why do I specifically mention @opinionlabsxyz? Not because "it is also a prediction," but because it attempts to solve the most difficult aspect of the prediction market mechanism: Introducing an incentivized market-making mechanism to reduce the liquidity fluctuations of purely betting on events Weakening the dependency on one-time events, encouraging more frequent and sustainable prediction markets The project team can continuously extract profits from the trading structure, rather than just benefiting from peaks. At least at the design level, Opinion is not waiting for the next big election or World Cup to survive. But I still won't say that Opinion will definitely win right now. The prediction market is currently a fierce competition: - Mechanism is more important than narrative - Self-sustainability is more important than financing - Longevity is more important than volume At the same time, doing well does not mean making a lot of money. Which competitor will you bet on in the prediction market?