Powerpei

I will share some of my experiences here, welcome to follow me, I will also reply to build the OKX planet together

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Yesterday I renewed a subscription for an overseas data tool I usually use. It was just a bill for a few dozen dollars, but I kept getting a payment failure. In the end, my domestic bank sent me a risk control message and froze my credit card. I called and argued with customer service for almost half an hour. I had to exhaust my words to prove "I am me, I am spending my own money and I haven't been scammed," before they reluctantly unblocked it. After hanging up, I looked at the card on my desk and really felt that this old financial system is completely rotten. You think the money is yours, but in their eyes, you are just a tenant with the right to use it. > This is also why I kept staring at the announcement of Bybit's 10 million WLFI reward today, reading it several times. ———— Many people look at such activities, their eyes glued to daily check-ins and playing TradeGPT to collect fragments for a lottery. To be honest, I can't even be bothered to click on those time-consuming data games that act as free labor. ➤ What really caught my attention was the most inconspicuous line in the announcement: permanent zero fees between USDC and USD1, and full access to mainstream coin pairs. Think about it, what keeps an exchange alive? It's the transaction fees. Bybit is now willing to cut this fat piece off and even subsidize a few million dollars worth of tokens for the event, what are they after? If you look at the on-chain data, you'll understand. ➤ USD1 has recently quietly increased its issuance by over 20 million coins. Moreover, institutions like MovaLab have directly become the first batch of super nodes. They are starting to slice into the cake of foreign exchange remittances and AI payments. This is not just a simple spring promotion for the platform; it’s political capital behind it using real money for subsidies. They are forcibly laying down a parallel dollar track in the market. ———— In this circle, don’t go against the trend. Now major exchanges are competing for the pricing power and liquidity of this new track. For us retail investors, arguing about whether this thing is truly decentralized is meaningless. My operations are extremely boring, but also the most stable. ➤ Move the idle USDC sitting on the chain over; after all, the exchange is zero-cost, and I can easily hit a $500 spot trading volume threshold. Since the giants are willing to spend money to seize the ecological base, then this certain liquidity subsidy is something to take advantage of. As for the 1 million prize pool's Mantle chain interaction, if you have the energy, just click a couple of times to grab it. In this market full of landmines, earning subsidies that you can understand is much more reassuring than blindly guessing the market. Alright, enough said. I still need to renew that damn tool; after the bank's hassle, I even forgot my login password.

Do you read a company's financial report from start to finish before buying its stock? I don't. At least, not often. Recently, something from @dappOS_com exposed this bad habit of mine. It's embarrassing to admit. Last week, I wanted to add to a position, so I opened its 10-K report—several hundred pages. I flipped through just a few pages, got annoyed, swiped it away, and turned to see what others were saying. News headlines, a few influencers' buy calls, plus a gut feeling like "it seems pretty strong lately," and that was it—I placed the order. I guess that's probably how you buy stocks too. But real buy-side analysts don't do that. For one company, they can quietly dig in for fifteen to twenty hours. They go through the 10-K page by page, extract every number to plug into models, and finally produce a memo. I used to think this was about information asymmetry. Later I realized it’s not. Anyone can get the 10-K, all the earnings calls are replayed online, the data is out there. The difference is never "can you get it"—it's whether you're willing to sit down and spend those twenty hours to thoroughly read it. Let's call this kind of hard work "depth." This is exactly what retail investors lack the most. The other day, I tried something. On xBubble, there's a work mode with a very official name: "Buy-Side Equity Research SOP." The process is ridiculously simple: enter a stock ticker. What it gives back isn’t some patchwork news summary, but a... how to say, a structure that only a real analyst could produce. First, it shows where the company sits in the industry chain, maps out the competitive landscape and moat, points out key risks; then it dives into financial data at the report level, with some interpretation of management; valuation is done via SOTP segment summation, showing bull, base, and bear case per-share prices; finally, it lists several catalysts to watch over the next three to six months. Arguments come first. The conclusion hits you upfront, then data stacks up one by one. One ticker in, a ready-to-use deep research report comes out. But what really made me say "hmm" was something else. Every number it provides is followed by the original source and the date it was taken. 10-K, 10-Q, 8-K, official IR materials, earnings call transcripts—you can trace each item back. Every sentence it writes, you can verify yourself. This doesn’t sound sexy at all, I know. But precisely this sets it apart from the flashy stuff. I've seen too many AI-generated "research reports" that read like templates; if you dig into the data, it’s all fuzzy and inconsistent. Anyone in this field knows that "traceability" is worth a hundred times more than "pretty writing." A blunt truth upfront: research reports are a starting point, not the end. Always rely on original documents for data, don’t trust secondhand summaries—including this one. There’s another layer, which might be its real power: what it gives you isn’t a fixed template at all. Behind it is the Bubble Engine, which builds SOPs, tests them, and keeps producing stronger versions. Want a version tailored to a specific industry, market, or even your own investment logic? It can do that too. So what it hands you is less a template and more a research engine that can grow smarter on its own. One reason I care so much about this is the people behind it—they’re interesting. DAPPOS used to work on-chain with intent execution—say something, and a long chain of on-chain operations runs automatically. Now they’ve flipped that whole approach toward US stock research. From on-chain operating systems to buy-side research engines, it’s actually the same thing: compressing a big chunk of complex work into a single sentence. Buy-side research used to take fifteen to twenty hours to thoroughly understand a company. Now? One sentence. Information was leveled out ten years ago; financial reports are there for anyone to check. But depth hasn’t been leveled. It’s always belonged to those willing to sit down and spend the time. What they want to do this time is put that depth into the hands of ordinary people too. —— More updates 👉 @dappOS_com ｜ @xBubble_ai Text / Powerpei

No need to spend dozens of hours poring over financial reports, @dappOS_com helps you handle professional US stock investment research Before you buy a US stock, what are the chances you actually read its financial report from start to finish? Honestly, I often don’t manage to do it myself. Most of us place orders based on news headlines, a few influencers’ calls, plus a feeling like "it seems pretty strong lately." >>> What real buy-side analysts do A company, they quietly spend 15 to 20 hours on it. They flip through the 10-K page by page, extract numbers one by one, build models, and write memos. → → → Here’s something many haven’t fully realized: The information gap actually no longer exists. Anyone can download the 10-K, and earnings calls are replayed all over the internet. What truly creates the gap is never about access, but whether you’re willing to spend those 20 hours to thoroughly read it. It’s about depth. And depth happens to be exactly what retail investors lack the most. —— I tried something these past couple of days. On xBubble, there’s a work mode called "Buy-Side Equity Research SOP" The process is ridiculously simple: you throw it a stock ticker What it spits back isn’t a news summary, but a structure only a real analyst would write ◇ Where this company stands in the industry chain ◇ Competitive landscape, its anchor points, key risks ◇ Financial data down to the report level, plus a segment interpreting management ◇ SOTP segment valuation, with bull/neutral/bear scenarios, giving three price targets per share ◇ The catalysts to watch for in the next 3 to 6 months Arguments come first, conclusions up front, then data backs them up one by one. One request in, a complete deep research report out. But what really stopped me was something else. Every number it provides is tagged with the original source and the date it was retrieved. 10-K, 10-Q, 8-K, official IR materials, earnings call transcripts — each item can be traced back. Every sentence it says, you can personally verify. That’s the key. I’ve seen too many AI-generated reports that sound convincing but have fuzzy data when checked. For those serious about this field, traceability is a hundred times more important than polished writing. Of course, research reports are a starting point, not the end — I say this upfront: always rely on original documents for data, don’t fully trust secondhand summaries, including this one. ————➤ There’s another layer, which might be its truly powerful aspect. What it gives you isn’t a fixed template. Behind it, the Bubble Engine builds its own SOPs, tests them, and continuously generates stronger versions. Want a version tailored to a specific industry, market, or even aligned with your own investment logic? It can do that too. So what you get isn’t a template. It’s a research engine. // I’ve been watching this for a while, partly because the people behind it are interesting. DAPPOS originally worked on-chain to execute intents. You say a sentence, and a long chain of on-chain operations runs itself. Now they’ve turned the same logic toward US stock research. From on-chain operating systems to buy-side research engines, the line connecting them is actually continuous. It’s the same thing: compressing a complex mess of work into you saying just one sentence. —— Buy-side research used to take 15 to 20 hours per company. Now, it’s a one-sentence matter. Information equality happened ten years ago. Depth equality hasn’t. This time, it wants to make depth something you can reach out and grab —— More updates 👉 @dappOS_com ｜ @xBubble_ai Note: The above content is purely a personal project breakdown and does not constitute any investment advice, DORY

Recently, in the group chat about AI, everyone is more and more obsessed. Several group friends who usually don't touch code now casually say things like "I had AI write a script" or "I put together a little tool." They've practically become half engineers. Looking at these messages, a question suddenly popped up: What about those brothers who really rely on coding for a living? Those who have been doing it for ten or eight years—what are they doing now? Even outsiders can now command AI to type a few lines; for professionals, are they becoming more valuable, or is it the opposite—being pushed out first? This kind of question is useless just spinning in the mind; you have to see a living example. @CreaoAI, this company, lives quite extremely. 25 people, 10 engineers, 99% of production code is written by AI. To what extent? A new feature goes live in the morning. At noon, A/B testing shows the data isn't good. In the afternoon, they cut it directly. By evening, they replace it with a better version and relaunch. The same thing three months ago would have taken them six weeks. Seeing this, I thought: Oh no, those brothers who used to code might really be getting pushed out. But the deeper I dug, the more I felt something was off. Panic is warranted, but the panic is in the wrong direction. → → → What exactly did they do right? First, distinguish two terms; this is the watershed of everything. Most companies use AI like this: ■ Engineers open Cursor to write code. ■ Product managers use ChatGPT to write documents. ■ Testers use AI to patch test cases. The process doesn't change at all; efficiency rises by 10% or 20%, and that's the limit. This is called AI-assisted. Hanging an AI on the old process. CREAO does something else. They dismantle the entire process and reimagine it: AI is the main builder, and humans only provide direction and make judgments. It's not about "how AI helps engineers." It's about "how to rearrange all steps so AI builds and humans oversee." ➤ Here's the harshest example: AI can build a feature in two hours, but product managers still take weeks to write requirements. At that moment, the bottleneck isn't development but the product manager. So they removed that position entirely. Reconstructing the process, when done ruthlessly, even changes the organization. This is completely different from just giving engineers an AI tool. One is addition; the other is multiplication. These two are not the same. → → → So what exactly is the product they make? Here's a particularly counterintuitive point. The truly valuable thing is never the Agent itself. Agents make mistakes, hallucinate, and take paths you don't understand. The more you try to make it smarter, the less controllable it becomes. The real product is the harness outside the Agent. OpenAI gave it a name: harness engineering. >> Meaning: the core work of an engineering team is no longer writing code but building a framework that AI can understand, verify, and constrain to work within. When problems arise, the solution is never to make AI try harder. >> It's about asking: what capability is missing? How to supplement it with rules that AI can read and follow. This idea sounds abstract. On the ground, it’s this thing that impresses me. --- Who backs up the AI? This is the most asked question to CREAO. The answer is not "hire more QA." They don't have a QA team at all. Every morning at 9, an AI automatically scans all service logs and generates a health report. No one needs to ask for it. An hour later, another AI clusters online errors, scores them across nine dimensions, automatically opens tickets, and even directly submits a fix PR. Over 50% of bugs are fixed by AI itself. No QA, no testing environment with a bunch of people clicking through before launch. Instead, there is an "AI jury." Three large models from different vendors score every online response. If the score drops, new code won't go live. AI tests it, AI approves it, and if something goes wrong, AI recalls it. This system can fix itself. —— ➤ At this point, the most interesting part of CREAO emerges. It itself is a platform for making Agents. In other words: they use Agents to build a platform that builds Agents. This sounds like a tongue twister but is the strongest self-proof of the entire methodology. If this harness can truly let AI independently do the work well, then the first product it should deliver is this company itself. Being able to build itself—that’s when it counts. All these efficiency numbers are disclosed by themselves. Whether it can be replicated and how much is exaggerated, you can judge for yourself. But one thing I keep thinking about: Their CTO, a physics PhD and former Meta LLaMA team member, said he didn’t write a single line of code in 2026. He said the most valuable thing for engineers going forward isn’t coding speed but finding loopholes in AI’s plans and judging what truly has value. This is worth every person still staying up late grinding new frameworks to pause and think for three seconds. Note: The above content is purely project analysis and does not constitute any investment advice, ha, DORY