When people hear about AI trading, many think it should help predict price movements. But I think that’s the wrong direction. Market movements are influenced by too many factors—no one can accurately predict them. The real value of AI isn’t in guessing the right direction, but in helping you stick to your discipline: once your plan is set, it executes trades, takes profits, and cuts losses without hesitation or emotional interference. - @termix_ai has recently integrated with @Aster_DEX, allowing you to directly hand over your trading strategy to AI for execution. But let’s be clear: AI executing trades for you doesn’t mean it will make you money. If your strategy is flawed, even perfect execution will still lead to losses. Market reversals, increased slippage, or fake news can all render a strategy ineffective—especially with futures contracts, where fast execution can also mean faster losses. So when I evaluate whether an AI trading tool is effective, I focus on three key points: ✅ Can beginners use it right away? ✅ Does it reduce mistakes from manual trading? ✅ Can your strategy run automatically based on predefined conditions? - Ultimately, the real test is results: after accounting for fees and slippage, are you still profitable? Can it hold up during extreme market conditions? And most importantly—is control always in your hands? The market never lacks people shouting signals—it lacks discipline in execution. That’s exactly where AI should help us the most.
In the future, projects like @primus_labs may become essential tools for institutions to prove their asset strength. Today, the crypto market faces a very real problem: Funding can be fabricated, data can be falsified—project teams may claim to have secured massive financing or manage enormous assets, yet ordinary users have little way to verify the truth. Even more troubling is that many figures, if presented convincingly enough, become powerful marketing tools. Investors, seeing backing from well-known institutions, impressive funding numbers, and large asset scales, often mistake these for proof of a project’s legitimacy. But are these claims truly verifiable? I believe what the crypto market needs isn’t just more projects publishing data—it needs data that can be independently verified. For example: if a project claims to hold a certain amount of reserves, can it prove those assets actually exist? If an institution asserts strong financial backing, can it provide credible asset verification without compromising account privacy? This is what the market truly needs. For institutions, full financial transparency isn’t feasible—but complete opacity makes it impossible to earn market trust. The solution lies in enabling external verification of key information while preserving privacy. This is how I view @primus_labs: if its products are widely adopted by institutions in the future, they could become vital tools for asset attestation and reserve verification. However, whether they truly become an industry standard will depend entirely on future institutional adoption. The crypto market has never lacked polished-looking data—it has lacked verifiable data. When the market begins seriously questioning the authenticity of claims, truly valuable infrastructure will finally emerge.
Let’s chat about AI before bed (should we start calling it Si instead?). AI agents are everywhere now, and everyone’s talking about how agents should pay each other. But I’ve always felt one question is missing: Once the money is paid, how do you know you actually got what you paid for? How do we solve the trust problem with agents? This all requires proper solutions—and @termix_ai is a pioneer in this space! — Take this example 🌰: When you buy something on Taobao or Xianyu, your money doesn’t go directly to the seller. The platform holds it first. Only after you receive the item and confirm it’s good do you click “confirm receipt,” and only then does the seller get paid. If the item is wrong, you can request after-sales service or ask the platform to arbitrate. We use this system every day without thinking much of it—but it’s precisely this mechanism that enables strangers to transact with each other. That’s exactly what’s missing between AI agents right now… x402 solved how agents pay—but not what they actually get for their money. What if one agent hires another to do work, pays upfront, and receives garbage in return? Who do they complain to? TermiX is building the “confirm receipt” system for the agent world. Four steps: escrow, delivery, challenge, settlement. Money is held in escrow. The worker agent delivers the result. If there’s an issue, a challenge can be raised. Only when everything checks out is payment released. Sounds simple—but this is the foundational requirement for agents to truly do business with each other. — @termix_ai’s reply to Elon’s post about the Grok bot was also spot-on: Directory ≠ commerce. Listing agents in a directory doesn’t mean they can transact. What’s missing is the layer of trust and settlement. And this isn’t just theory. TermiX recently ranked #1 on BNB Chain for AI agent revenue, both for 7-day and 30-day agreements. Revenue doesn’t lie—it proves real agents are actively running on the platform and real money is flowing. Agents built on their AACP protocol even made it to the finals of BNB Chain’s “Set and Earn” campaign. — If the agent economy is ever going to take off, payment is just step one. Confirming delivery is the step that makes it all turn. I’m betting on TermiX to own this step. Goodnight, world!
Everyone says “ETH has Zama, BNB has Primus,” but do you know how they’re different? 1/ First, something many overlook: Zama has only launched a token on BNB so far—its actual privacy features haven’t arrived yet. Even BNB’s own documentation states “still in development.” Meanwhile, @primus_labs is already functional: you can convert U to encrypted eU, send transfers where the amount is hidden, and even earn interest by depositing it. On BNB, the only solution currently usable is Primus. 2/ So what’s the real difference between them? I think it boils down to one key question: If your money is encrypted, who holds the key? Zama splits the key into 13 parts and distributes them among major institutions like Ledger and Fireblocks—requiring 9 out of 13 to unlock. Primus, for now, locks the key inside a “hardware vault”; the multi-party custody solution is still on the way. To be honest, this is the one area Primus most needs to improve. 3/ Scale-wise, we must acknowledge: Zama already has over $100 million flowing through its system; Primus hit around $1 million on its first day—it’s still the underdog. But Primus has something Zama doesn’t: it can prove the authenticity of your data from exchanges or banks—without revealing any details. It essentially helps you “get verified” first, then “spend privately”—end-to-end. 4/ Recently, Vitalik said AI might make such encryption less secure. Zama’s response? Simple: just strengthen the lock. Even if breached, attackers would only see balances—not be able to steal funds. Primus has also pledged to take this seriously, but hasn’t yet detailed how it plans to harden its system. I’m waiting to see their solution. 5/ My take: Zama is the seasoned veteran—reliable, but not yet live on BNB. Primus is the first to arrive, with unique strengths—but still young.
TermiX @termix_ai | Recently launched a subcontracting feature that allows you to delegate tasks to different Agents for completion. In today’s AI landscape, different large models excel in distinct specialized domains—for example: 🔸 ByteDance excels at video generation 🔸 Anthropic has exceptional coding capabilities 🔸 Grok offers openness and real-time event capture from X TermiX’s subcontracting feature enables the best-suited Agent to handle each part of your task—essentially assigning the most qualified experts to complete a single job. This functionality has real-world applications: 1. You want to create a social media account—from topic selection and research, to copywriting, image creation, video editing, and final data analysis. The entire process involves many distinct tasks. Previously, you might have had to switch between different AI tools, copy and paste content, adjust prompts, and manually stitch results together. With the subcontracting feature, you can now assign each task to specialized Agents: one for research, another for writing, a third for video editing, and a final Agent to integrate everything seamlessly. - 2. You want to build a website—you can assign frontend development, backend development, UI design, and testing to separate Agents. Or, for a market research report, you can assign data collection, industry analysis, and content organization to different Agents. - The real shift here isn’t just that AI has gained another new feature—it’s that the way we interact with AI may fundamentally change. Previously, humans had to adapt to different AI tools. Now, you can simply define your goal, and the system will automatically identify the best-suited Agents and orchestrate the entire workflow.
! @predictdotfun The Prophet Challenge is about to begin! I think the most important thing before the competition isn’t to study strategies first—it’s to get the energy going! I’ve already decided to participate in this challenge, and as I always say: That year, I had my hands in my pockets and didn’t know what an opponent was! That year, I had my hands in my pockets and didn’t know what an opponent was! - This time, I’m going to deeply shock @yuexiaoyu & @TC8880
Season 1 of Axis is coming to an end—complete all your tasks before the deadline. ✅ Sign all tracks on-chain ✅ Link your DC and X to your Axis profile ✅ Submit all your created content on Kaito The final batch of tasks for "The Final Battle" will be released at 10:00 AM Beijing Time on the 18th. According to available information, Season 2 rewards will be distributed in USDC, which is an effective way to alleviate token selling pressure after TGE. Looking back on my Season 1 Axis journey—I joined very early and distributed many alliance codes (thank you to @0xsexybanana for the strong support!). To all my followers: I hope you’ve completed your tasks and are now waiting to see the results!
Lately, I’ve been seeing a lot of tweets saying things like: “Just say something random to AI, and let the AI make money—wake up to find your几十 dollars turned into thousands” [with videos attached]. Don’t doubt it—these are almost all fake (though a few geniuses might exist). — A good strategy can certainly make money, but what matters isn’t backtested data—it’s whether the strategy actually generates profits in live trading. Recently, I’ve reviewed some AI strategies on @termix_ai, and I find them worth considering: 1. These strategies are all live, real-money trades—performance speaks for itself. 2. Termix has a reputation system; agents with strong track records are an important factor to consider. — DYOR! Not financial advice—just for my own AI trading research reference.
I still lack vision and wisdom! I analyzed $STOCKER’s leading position on Ethereum’s launchpad, examined its ecosystem meme ethereum:0x4e67db19044549ff420860834c91b45bad298722, and recognized that ETH-based memes would gain popularity on mainnet. But I completely overlooked the greed of the @Stockereum DEV team! Only trading fees from ETH pairs on Stockereum enter the automatic buyback and burn pool. Fees generated from STOCK pairs do not participate in buybacks at all—these funds primarily go to platform fees and the project team’s pockets! The good news is that on @GigaXApp, I did catch some other solid memecoins—overall, I’m still profitable.