Many people ask what OpenClaw can actually be used for. Some people use it to make money—writing code, automating projects, and taking on freelance work. For me, it's mainly used to improve the workflow of running the company. I've split the Discord platform into different channels, each with different system prompts and skills corresponding to different projects. Article writing is for article writing, coding is for coding, and market research is for market research. For more complex projects, I inject the project folder path and a dedicated memory library into the system prompts to reduce wasted tokens from tool use. Then I integrated the company's Slack, Linear, GitHub repo, and the member group's Telegram group into it. The AI regularly scans the member group's chat history. When someone reports a bug or makes a feature request, it automatically assesses the severity and directly issues a ticket on Linear to the appropriate person. I also use Whisper to transcribe weekly meeting recordings into transcripts, which I then use to generate meeting summaries and action items. But for all this to work, there's a prerequisite: you must first feed it basic information. The most interesting thing is that once the AI has enough context, it starts doing things you hadn't designed in advance. It knows what decisions were made in last week's meetings, which tickets were assigned to whom, and what's delayed. So when a task is overdue, it proactively suggests that the PM catch up. It's not that I set a rule for it to do this. It judges what to do based on the context itself. That's the power of context. The more complete the information you feed it, the more it can do for you, and much of it you wouldn't have thought of beforehand. Besides company operations, I recently started a new channel category completely unrelated to work: parenting. My son was just born, and I want to seriously study how to raise a child. But parenting information is too scattered; the quality of content in the Chinese-speaking world varies greatly, and much of it is plagiarism. So I had OpenClaw crawl some high-yield parenting blogs from abroad, compiling the more systematic content sources. Then I used NotebookLM's Skills to input all this knowledge, asking it to output structured summary files. I built a Knowledge Base using these summaries. Now, for any parenting questions, I can simply ask on the Discord channel, and I can be sure that my questions aren't misleading, because everything is based on high-quality sources that I've carefully selected. None of these uses were planned before I installed it. When I installed OpenClaw, I didn't know I'd use it to manage tickets. I didn't know I'd use it to keep track of project manager progress. I certainly didn't know I'd use it to research how to raise children. Each scenario happened after I installed it, when I encountered a pain point, and then I thought, "Hey, maybe this can solve it." This "Hey, maybe I can give it a try" moment only happens when you have the tools at hand. Those who haven't installed it won't even have this thought. Most people's attitude towards AI tools is this: figure out what you want to do first, then decide whether to install it. But this logic has a fatal flaw: if you don't use it, you have no idea what it can do. If you don't actually play around with it, your understanding of the tool will be limited to other people's descriptions, screenshots, and tweets. Those are all secondhand information. The biggest problem with secondhand information is that it only tells you what others find useful. But what truly changes the way you work is often what you discover accidentally while playing around. No one will write tutorials about these things because they are too personal; you only learn by experiencing them yourself. When I was researching how to use Lobster in company operations, I found there was practically no information available because everyone was still figuring it out. Lobster was a project launched last November, but it truly went viral in mid-January of this year. That means it's only been in the public eye for a little over a month. All users are pioneers; everyone is feeling their way in the dark. In other words, most of the use cases that have emerged so far were things that most people didn't initially think of doing. Currently, 90% of people's understanding of AI is still stuck in the "GPT 3.5 era" of chatbots, but the tech world has undergone a complete transformation. Today everyone's discussing Context Engineering; if you haven't worked with AI agents, you won't understand. Tomorrow everyone's talking about multi-agent workflows; if you haven't run one, you'll understand even less. Last year, everyone was copying various prompt templates; then a bunch of people were researching MCPs; now everyone's talking about Skills. The trend changes every few months. Without heavy use, you have no idea what these things do, let alone why people are jumping from one to the next. Every time you choose "wait and see," you're widening this gap. And this gap has a terrifying characteristic: you don't feel it happening. Because you don't know what you don't know. You think you just haven't installed a tool yet. But in reality, you're missing out on an entire layer of cognitive updates. Those who have used it already think about problems differently. When they see a task, their minds automatically conjure up the idea that "AI can do this." This isn't a gap in knowledge; it's a gap in mindset. Knowledge can be acquired, but mindset cannot. Mindsets can only be developed through experience. So why insist on using it for a specific purpose? The first generation of internet users didn't use the internet for e-commerce or gaming; they simply thought it was cool. In this era of rapid technological iteration and no standard answers, "act first, then talk" is perhaps the most underrated principle of action.