币圈空投项目
币圈空投项目
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数字货币空投的基本概念、参与方式和获得潜在收益的机会,撸毛党集中营在这里🙌
andrew chen
andrew chen
Crypto Newbie
19h ago
Web 1.0 introduced new channels: - Email, search, link sharing, etc. Web 2.0 also introduced new channels: - News feeds, creators, viral marketing, etc. Mobile: - App stores, SMS invitations, vertical videos, mobile ads So what about artificial intelligence? I've always complained that AI hasn't brought about much substantial change. But we're now seeing a huge growth channel opening up: products built as APIs/CLIs can be instantly integrated into new projects by tools like Codex/Claude. Perhaps a "native AI hotel app" doesn't refer to a mobile booking app with an AI chat panel, but rather a CLI that helps you book hotels, which AI agents can integrate into custom answers, projects, or code. Simply adding an AI chat panel is a weak form of this generation of AI. Perhaps the real innovation lies in being agent-first, not human-first. Once you start looking at AI this way, many existing products suddenly seem poorly designed. They were designed as destinations, but agents don't need destinations; they need functionality. Composable, callable, and reliable functionality. Therefore, the way we interact in the future will no longer be "go to Expedia" or "open the app," but rather: agents will build workflows on the fly. It will invoke flight search tools, hotel booking tools, weather models, and even charts of your personal preferences. These aren't complete products in the traditional sense; they're more like endpoints with features and states. This completely revolutionizes the distribution model. In the past, you won by capturing market share, such as through search engine optimization, app store rankings, and homepage traffic. But in the agent's world, you win by becoming the default callable primitive. This primitive will repeatedly appear in the agent's generated plans because it's effective, has a clean interface, and structured output. The distribution model has shifted from the "top of the funnel" to the "top of the call stack." Even more surprisingly, this could actually drastically reduce product size. The best products might resemble well-designed, concise, and efficient command-line interfaces (CLIs) with clear default settings, rather than bloated user interfaces. This is almost like an early version of the Stripe API, but applicable to all situations. Imagine what it would look like if every vertical had a "strip-level" basic element that agents could prioritize for use? There's also a peculiar brand reversal here. In the past, humans chose brands; now, intelligent agents will choose brands. Therefore, brands become machine-readable to some extent. Reliability, latency, error rate, pattern clarity. You can almost imagine "agent SEO," where ranking factors are the success rate of thousands of agent runs, or how easily your tool integrates into the thought chain execution loop. This also foreshadows a new kind of moat. It's not just about data or network effects, but deep integration with the agent ecosystem. If tools like Claude, Codex, or OpenClaw find your tool to be the safest way to accomplish X, it will be embedded in prompts, templates, and possibly even fine-tuned. You will become the default option. And historically, default options have been extremely sticky. Another perspective argues that most current "AI features" are merely locally optimal solutions. Chat panels, co-pilots, assistants—they are just transitional. The true final state may be closer to an invisible infrastructure coordinated by intelligent agents. The user interface is merely a debugging layer, allowing humans to peek into the operation of intelligent agents. Therefore, the new growth channels for artificial intelligence can perhaps be summarized as follows: - Callability - Composability - Reliable operation at scale in agent loops - Embeddability in agent templates and workflows - Becoming the default primitive for specific domains If this is indeed the case, then the key question for any new product will no longer be "What is the user interface?" or "What is the killer feature?", but rather "What are the most basic functionalities?"
BNB Chain
BNB Chain
Binance
1d ago
AI agents can generate outputs and respond to prompts. But can they reliably transact? Can they get the work done? Are they trustworthy in moments of value crisis? This is the current gap: not a lack of capability, but a lack of infrastructure. Each team in this space is tackling different challenges. @virtuals_io → Agent-to-agent transactions @I3_Cubed → Model access and monetization @honeycombchain → Agent identity and interaction @flapdotsh → Verifiable AI decision-making Different angles, but the same direction. Take @virtuals_io as an example. They are building a business layer where agents can hire each other, locking funds in a third-party escrow account, only paying out after the work is verified. It sounds simple, but it solves a real problem. Agents can respond, just not yet accountable. @I3_Cubed is working on building a model marketplace. Instead of relying on one or two large models, they've focused on building a marketplace comprised of numerous smaller models. These models are more accessible, pay-per-use, and can be combined into workflows based on tasks. This transforms models from tools into assets. @honeycombchain brings agents closer to users. Agents on their platform aren't anonymous; they possess identities, reputations, and wallets. They can publish content, interact, trade, and even be bought and sold. This fundamentally changes your perception of agents. @flapdotsh addresses the trust issue. If AI makes decisions involving the flow of funds, those decisions cannot be made unknowingly. Therefore, they've built an AI oracle that records its output on-chain with proof, making it visible and verifiable. Overall, you'll find that all of this ultimately points in the same direction: One layer handles transactions. One layer handles execution. One layer handles interaction. One layer handles trust. Individuals are useful individually; combined, they form a larger whole. The problem isn't that intelligent agents can't do things. It's that they can't yet operate reliably within the system. Generating output is not the same as completing work and proving its value, nor receiving compensation for it. This is the underlying architecture currently being built. The frequent appearance of the BNB chain has its reasons. Agents don't just perform one operation. They continuously perform tiny operations, over and over again, but this only works if transaction fees remain low and throughput remains high. Otherwise, the user experience collapses. What's interesting is how all of this is connected. The agent economy isn't built on a single product. It only truly comes together when these components begin to work together. When agents can discover, execute, and transact.