From Trader to AI Agent: My Trading Journey Led Me to Reconsider the Value of NeoSoul Trading experience can be accumulated gradually, but consistently applying that experience to every trade has always been difficult. In the early days of my trading, I would constantly watch trading platforms, analyzing K-lines while monitoring market news. When I saw a coin suddenly surge, my first instinct was to check if there was an opportunity—especially during active markets, where new trends emerged daily, and I feared missing the next rally. As I traded more, I moved from spot to futures, and began studying memes, on-chain fund flows, U.S. earnings reports, and prediction markets. Slowly, I realized that what truly impacted trading outcomes wasn’t just market analysis—it was strategy execution, risk management, and emotional control. Sometimes, despite thorough pre-trade analysis, I’d deviate from my plan and miss opportunities. Other times, I’d set a profit target, only to hold longer as the price kept rising, driven by FOMO. These experiences gradually shaped my own trading discipline. Now, when trading meme futures, I typically begin by examining order book depth, funding rates, liquidity, and concentrated holdings. Even if I’m bullish on an asset, I always start with a small position, limit leverage, and predefine my stop-loss levels. The same applies to analyzing U.S. earnings reports—I review market expectations, historical performance, and relevant news before deciding whether to participate. But honestly, human attention is limited. It was precisely because of these firsthand experiences that I became deeply interested in what @NeoSoulAI is trying to build. If AI could truly understand a trader’s experience and help us systematically carry out market research, strategy execution, and risk monitoring, many repetitive manual tasks might become significantly more efficient. And NeoSoul is precisely exploring this direction. I. Why I’m Interested in NeoTrade: I Know Too Well the Execution Challenges in Trading Recently studying NeoTrade, my first thought was of my own habits when trading memes. The meme market has a distinct characteristic: rapid price movements, complex information, and opportunities often arrive alongside risks. Some tokens gain traction quickly and attract massive capital in a short time—but focusing solely on price gains can easily blind you to underlying risks. So when evaluating a meme project, I first examine liquidity, holder distribution, core wallet behavior, and trading volume. For futures contracts, I further analyze order book depth, bid-ask spreads, funding rates, and the difference between last price and mark price. Individually, these tasks aren’t difficult—but monitoring multiple projects simultaneously consumes enormous time and makes traders vulnerable to emotional bias. For example: once a token passes initial screening and suddenly begins to spike, failing to strictly follow pre-established entry rules can lead to chasing highs and taking on unnecessary risk. So when I saw NeoTrade allowing users to convert their trading logic into “Trading Skills,” my immediate thought was: Could these long-honed rules be systematized? If I were to design my own meme trading agent, I’d want it to first filter data, then assess trading conditions, and only then consider execution. For instance, if liquidity drops sharply, core wallets begin large-scale selling, or bid-ask spreads widen significantly—the AI should promptly flag risks and, if necessary, pause trading according to predefined permissions. I want it to help me stick to my rules—not chase more trades. The AI traders need most isn’t one that tells you which coin will explode tomorrow—it’s one that understands your trading habits and helps you execute your strategy more consistently. According to publicly available information, NeoTrade has already begun building features around natural language strategy creation, simulated trading, and partial prediction market environments. I haven’t yet had sufficient long-term live trading results to evaluate its profitability directly. But from a product standpoint, I genuinely hope it will further enhance strategy backtesting, risk management, transaction cost tracking, and post-trade performance reviews. Particularly important are transparency around fees, slippage, maximum drawdowns, and actual fill rates. Having traded for so many years, I know one thing clearly: no matter how impressive backtest results look, if real-world execution costs are too high or the strategy fails under volatile conditions, its practical value diminishes significantly. II. From U.S. Earnings to Prediction Markets: Understanding the Importance of evoevo Beyond memes, I also follow U.S. earnings reports and prediction markets. When a company is about to report earnings, I typically start by analyzing market expectations—then combine that with industry trends, company fundamentals, and historical performance to form a judgment. Sometimes my predictions are correct and yield solid opportunities. But other times—even when earnings data looks strong—the market drops due to weak forward guidance or valuation concerns. These experiences taught me that one correct call doesn’t validate your entire analytical method. Focusing only on successful trades leads to overestimating your judgment. True value lies in comprehensive review—documenting both wins and losses. Why did I make that decision? What data did I rely on? Did I overlook key risks? How did the actual market reaction differ from my expectations? This is precisely why I paid special attention to NeoSoul’s other product: evoevo. Based on its design philosophy, evoevo focuses on data preparation, agent performance evaluation, and outcome feedback. If properly implemented, this system could significantly accelerate AI agent development. For example: a future earnings-focused agent might analyze dozens—or even hundreds—of companies. If the system records each prediction’s logic, probability, actual outcome, and transaction cost, it could gradually build an objective performance evaluation framework. For users, this means clearer insight into what each agent truly excels at. Some agents specialize in macroeconomic analysis; others in on-chain data; some excel at predicting specific events in prediction markets. When studying prediction markets myself, I’ve always prioritized the relationship between probability and odds. Predicting that an event will occur is fundamentally different from judging whether the current price justifies participation. III. After DNA’s TGE, I’m Now Focused on Whether the Token Can Enter a Real Product Loop On October 16th, NeoSoul’s native token $DNA officially launched trading and began listing on multiple exchanges. DNA is deployed on BNB Chain with a total supply of 1 billion tokens. As someone who has participated in crypto markets for years, I’ve seen countless projects gain attention around their TGE—some with explosive initial trading volume—but over time, discussions remain fixated solely on price. Few continue asking: How many people are actually using the product? How much real revenue is the ecosystem generating? What tangible utility does the token have? So with DNA this time around, I’m looking at it from real-world use cases. According to NeoSoul’s roadmap, DNA will serve three core functions: paying for AI services, ecosystem governance, and inter-agent service settlements. Of these, I’m especially interested in economic collaboration between agents. Let me give you an example tied to my own trading: Suppose my trading agent needs to analyze a meme project but lacks detailed on-chain data—could it request analysis services from another specialized agent? Or suppose I want to analyze a company’s earnings report—could different agents handle financial data analysis, industry research, market sentiment tracking, and risk assessment respectively? When multiple agents begin requiring real service calls from one another, payment and settlement become necessary. If DNA can facilitate part of this economic layer, the token could become intrinsically linked to ecosystem usage. This leads me to an interesting shift in perspective.This makes me think of an interesting shift. In the past, many crypto users primarily participated in the market by trading digital assets. In the future, as the Agent Economy matures, developers may create specialized agents to offer services, while traders can contribute by sharing valuable research methods and strategic expertise, engaging in new economic activities. This will make participation in the crypto ecosystem far more diverse. Of course, these are still directions that require gradual validation. DNA’s TGE is only the first step—the true determinant of its long-term value will be whether users are willing to pay for these AI services in the future. Four: StandX Community Airdrop—A Reflection on My Years in the Crypto Community Another aspect of this NeoSoul initiative that resonated with me is the exclusive DNA airdrop targeted at the StandX community and KOLs. After StandX adjusted its original operational plan, NeoSoul chose to support its community members. I believe this gesture deserves recognition. Over the years, I’ve participated in numerous crypto communities and witnessed many projects grow from being noticed by just a few to building large-scale ecosystems. A project’s growth often depends on those who consistently engage in discussions, test products, and offer feedback. Especially in the early stages, contributions from ordinary users are often vital. Some actively study the product, others promptly report issues they discover, and some even spend time writing tutorials to help newcomers understand the project. These efforts may not appear directly on a project’s financial statements, but I believe they hold immense value. Through my own involvement in building various platforms and communities over the years, I’ve grown increasingly attentive to whether user feedback is genuinely addressed. It’s not hard to raise a question—it’s challenging when a project team is willing to take it seriously and provide transparent follow-ups to the community. So, I hope NeoSoul’s support for the StandX community becomes the beginning of a long-term partnership. For example: invite StandX users familiar with prediction markets to participate in NeoTrade testing; encourage experienced traders to offer strategy design suggestions; and involve community KOLs in product education and ecosystem content creation. This would not only preserve the existing community’s expertise but also help NeoSoul receive more direct product feedback. I also hope the project will eventually establish a transparent, public mechanism for collecting community suggestions—regularly publishing which suggestions have been collected, which have been adopted, and which are currently under development. For long-time crypto community members, what truly keeps them engaged is knowing their opinions are taken seriously and their contributions are respected. An airdrop may introduce more people to NeoSoul—but sustained product development and community interaction are what will retain them long-term. Five: Believe in NeoSoul—If I Could Offer the Team Just a Few Suggestions After researching NeoSoul, I’ve developed some concrete expectations for the project over the next year. First: the real-world user experience of NeoTrade. I hope it makes it easier for ordinary traders to get started—particularly in strategy creation, simulation testing, execution authorization, and risk management. Ideally, it should gradually offer a clear pathway from simple to complex strategies. Beginners in AI trading could start with a simulated environment, become comfortable with the system, and then choose more advanced features based on personal needs. At the same time, I hope the team prioritizes trading security—including API permission management, fund segregation, anomaly transaction monitoring, emergency stop functions, and complete logging of all Agent actions. In live trading, execution efficiency matters—but capital security must always come first. Second: building a more transparent Agent performance system. I hope users can eventually view an AI Agent’s actual historical performance when selecting one—not just profits, but also losses, maximum drawdown, trading costs, and runtime. Only then can users truly understand a strategy’s strengths and limitations. Third: establishing genuine economic demand for DNA. If more developers begin offering Agent services and more users pay for them using DNA, NeoSoul’s economic model will have a solid foundation for long-term growth. Finally: community building. I hope the team remains active not just during TGE or major events, but consistently listens to user feedback year-round—explaining issues promptly when they arise, proactively sharing updates after feature releases, and maintaining transparency when roadmaps change. For an early-stage project, these seemingly ordinary actions are often what best reflect a team’s long-term commitment. Six: Final Thoughts—I Hope AI Becomes an Extension of Traders’ Long-Term Experience Looking back on my years of trading, I’ve noticed my biggest change has been an increasing emphasis on rules, patience, and risk management. Early on, trading relied heavily on personal judgment. Later, I began using data tools. Now, I focus on AI Agents—I can clearly see how trading tools continue to evolve. But one thing has remained unchanged: Tools can enhance efficiency—but they cannot replace market understanding or eliminate trading risk. So my hopes for NeoSoul are simple: I hope it enables more ordinary users to transform their experience into verifiable, continuously improvable Agent capabilities. I hope talented developers are rewarded for creating truly valuable Agent services. I hope DNA gradually becomes embedded in real ecosystem economic activity. And I hope long-term community supporters gain more opportunities to help build the project. NeoSoul has completed its TGE and outlined its product and ecosystem vision. Over the coming year, I’ll be watching closely to see whether these plans are gradually realized. When we talk about NeoSoul in the future—if our conversations go beyond DNA’s price to include outstanding Agents, real user needs, developer earnings, and community achievements—I’ll consider that the project’s true success. Lastly, I’d like to ask you: if you could have your own personalized AI trading Agent in the future, what problem would you most want it to solve? Finding market opportunities? Strictly enforcing stop-losses? Or reviewing every past trade? @NeoSoulAI #NeoSoul #DNA #NeoTrade #AIAgent #AgentEconomy

[TapeOut Developers] TapeOut may be on the verge of a true breakthrough: more noteworthy than the price of $BEM is the growing developer ecosystem—this article takes you inside the TapeOut developer ecosystem. When evaluating whether a crypto project has the potential to become the next breakout ecosystem, I’ve increasingly come to focus on one key metric: how many people are actually building on it? Price rallies attract attention; trading volume draws capital—but what truly sustains long-term ecosystem growth is developers. Why did Ethereum build such a massive DeFi ecosystem? Why does Solana continuously spawn new applications and use cases? Beyond underlying technology, there’s one critical reason: developers are willing to join—and they can keep creating new products. Recently, I revisited #TapeOut and $BEM, and realized what’s worth paying attention to now goes far beyond on-chain CPUs or PoD mining. What truly excites me is that TapeOut is gradually cultivating its own developer ecosystem. And this time, I found some on-chain data you can observe directly. --- **1. TapeOut’s developer ecosystem is no longer just theoretical** According to data from a third-party on-chain monitoring platform as of October 21: - 167 creators were observed participating in on-chain creation activities - 11,215 new circuits were recorded - 130 new processors were created - 81 processors had minting activity It’s important to clarify: the number 167 reflects creators observed within this time window—not necessarily 167 verified individual programmers—and circuit count doesn’t equal the number of independent applications. Still, these figures are exciting. They indicate TapeOut is experiencing sustained on-chain design and creation activity—not just one-sided demonstrations by the project team. Previously, when people thought of TapeOut, they immediately pictured Blonskr, the 16-year-old developer behind the Behemoth on-chain CPU. Now, public data lets us observe multiple participants designing circuits and manufacturing processors. From a single developer’s technical experiment to increasing participation—this may be the most significant shift TapeOut is undergoing right now. --- **2. More developers = greater ecosystem potential** Many might ask: why do I place such importance on developer numbers? The reason is simple. Unlike typical financial protocols, TapeOut’s core logic is built on digital circuit design and composition. NAND gates handle basic logic operations; LATCHes provide state storage. Developers can build complex circuits from these components, fabricate them, and link the resulting structures to Circuit NFTs. This means TapeOut has the potential to develop a uniquely distinctive ecosystem model: One developer creates a foundational logic module; another builds an arithmetic unit on top of it; others combine these into increasingly complex systems. Developers aren’t just creating new products—they’re also building reusable technological resources for future builders. Of course, actual reuse requires proper interfaces, permissions, and tooling. But if this model matures, I believe TapeOut could establish a lasting technical advantage. Consider this example: If an ecosystem has only ten developers, each must build everything from scratch—development efficiency is severely limited. But as participation grows, developers begin sharing code, reusing circuits, and jointly maintaining tools. Newcomers no longer need to reinvent everything their predecessors built. That’s why I’m bullish on TapeOut’s developer ecosystem. The importance of growing developer numbers isn’t just about headcount—it’s about whether they can continuously lower the barrier to entry for future builders. Once this cumulative effect takes hold, the ecosystem’s growth potential becomes fundamentally different. --- **3. What truly excites me: TapeOut is beginning to build a collaborative developer framework** I specifically checked out TapeOut’s GitHub. The TapeOut Protocol has already open-sourced repositories like TapeKit and TAPs. TapeKit is a particularly noteworthy open-source tool. It enables web content to be accessed via on-chain storage, content verification, and browser kernel integration—providing infrastructure for DeWEB applications. Crucially, it’s not just a conceptual demo—it’s a development project with publicly documented specifications and reference implementations. Also worth highlighting are TAPs (TapeOut Protocol Proposals). These function as an open proposal and standards discussion mechanism. Developers can suggest new interfaces, file formats, or technical standards—and through public discourse, help refine protocols. To me, this is vital. A protocol that relies solely on its founder for all technical direction will inevitably hit a growth ceiling. But when more developers participate in standard-setting, code reviews, and tool maintenance, an open collaboration model becomes possible. I also discovered that the community has independently launched an “Awesome TapeOut” open-source project directory—curating publicly coded projects related to the TapeOut protocol. Among them are community-built projects like TapeBrowser and TapeVault. This shows that people within the ecosystem are proactively organizing resources and building tools—not waiting for official initiatives. Of course, the number of open-source repositories doesn’t directly equal mature applications. But for an early-stage technical ecosystem like TapeOut, I believe it matters far more whether developers are actively creating tools, submitting proposals, and maintaining projects than whether there are a few marketing partnerships. --- **4. What does a growing developer ecosystem mean for $BEM?** I see this as one of the most critical components of TapeOut’s economic model. Many focus on $BEM for its PoD mining, 21 million max supply, or market price. These are worth analyzing—but from a long-term perspective, whether BEM can grow alongside TapeOut depends entirely on whether the ecosystem generates sustained economic activity. Here’s a key development path worth watching: More developers → richer circuits and applications → expanded use cases → increased ecosystem economic activity → greater potential demand for BEM This doesn’t mean every new developer automatically drives up BEM’s price. But if more developers build applications, future demand could emerge for component usage, circuit fabrication, related services, and asset issuance. TapeHub’s exploration of issuance, liquidity, and buyback/burn mechanisms further expands this economic linkage. However, how much real demand these mechanisms generate still depends on protocol rules and actual usage patterns. What I find especially promising is BEM’s potential to move beyond its current role as merely a mining reward token. A token that relies solely on issuance incentives—without ongoing usage demand—is highly vulnerable to shifts in incentive cycles. In contrast, if ecosystem applications grow richer, developers stay engaged long-term, and users pay for features—the economic model gains a far more solid foundation. For me, BEM’s most exciting growth potential lies not in attracting more miners—but in enabling developers to build products people actually want to use. --- **5. From tech community to open ecosystem: TapeOut is entering a pivotal phase** I’ve always found TapeOut’s evolution fascinating. It began with Blonskr demonstrating technical capability through an on-chain CPU—sparking interest in combining digital circuits with blockchain. Then came PoD mining, enabling broader participation in circuit design and mining. Now, tools like TapeKit and TapeHub—and community-built utilities—are emerging—extending the project toward wider application scenarios.Today, in addition to core technical developers, the ecosystem has also seen independent tool builders, researchers, community organizers, and contributors participating in standard discussions. This is, in fact, a natural process of the technical ecosystem gradually maturing. On the community level, Marsian Ryan’s public attention and support for TapeOut have helped more Crypto participants recognize this project. Meanwhile, community contributors like BruceBlue have initiated proposals around developer collaboration, organizational building, and public resource sharing. I believe the participation of these diverse roles is meaningful. Founders push the boundaries of technology; developers expand product capabilities; community organizers help connect resources; and researchers drive broader understanding of the protocol. When an ecosystem begins to develop specialized roles and more people are willing to contribute their skills, it gains real momentum to grow. But I hope TapeOut can do even better in its next phase. For example: further refining development documentation, lowering the barrier to circuit design, providing new developers with more mature SDKs and templates, encouraging more open-source projects, and helping developers achieve sustainable income. Developers cannot rely solely on passion for long-term building. A truly mature ecosystem should enable outstanding developers to receive fair compensation for their technical contributions. Six: Why do I believe TapeOut could become the next breakout Crypto ecosystem? My confidence in TapeOut is not because it already has a developer base as large as Ethereum or Solana. On the contrary, it is still in a very early stage. To date, I have not found any authoritative, deduplicated statistic that accurately represents the total number of active programmers in the entire TapeOut ecosystem. But we can already observe on-chain creation activity, independent tool projects, publicly accessible code repositories, and technical proposals. Taken together, these signs make me more confident that TapeOut is evolving from a niche experiment into a technology ecosystem with sustained participation. Of course, several key questions still need validation: How many newly created circuits have real reusability? Can independent developers remain engaged over time? How many community projects will transition from technical demos to practical usage? Are the development tools stable enough to attract mainstream developers? The answers to these questions will determine TapeOut’s future potential. But I believe this direction is genuinely worth watching. Because once a developer ecosystem truly forms, it generates continuous innovation—not just a few products pushed by a project team to maintain hype. Today someone creates a circuit module; tomorrow someone designs a new development tool; the day after, someone combines them into a full application. If this pattern continues, TapeOut may gradually build its own unique ecosystem competitiveness. Finally, I want to say: I’m increasingly convinced that the most important metric for TapeOut’s future won’t be how much BEM rises on any given day—but how many people created circuits this week, what new tools emerged this month, and how many independent developers chose to stay. Price can be swayed by emotion in the short term, but building a developer ecosystem requires long-term accumulation. A project attracting capital only shows that the market is paying attention; consistently attracting developers to create new things is what truly builds lasting value. This is precisely why I’m becoming increasingly bullish on TapeOut. It has already begun evolving from its origins as an on-chain CPU experiment into an open design system, application development platform, technical standard framework, and community-driven collaboration hub. If developer participation continues to grow alongside an increase in both the quantity and quality of applications—and if BEM’s economic model becomes more tightly aligned with real-world usage—then TapeOut has every opportunity to become the next phenomenon-level ecosystem in the Crypto market. What I’m truly excited about isn’t just the BEM token—it’s the future being co-created by an ever-growing number of developers around TapeOut. #TapeOut #BEM #BNBChain @bitcoin2themars

Recently, BTC has rebounded from over $70,000 back above $80,000, and market sentiment has clearly improved. After each market rally, price is usually the first thing people notice—but what’s truly fascinating about crypto has never been just about price movements. Bitcoin enabled value to flow freely outside traditional banking systems; stablecoins further brought the U.S. dollar on-chain; now, an increasing number of projects are attempting to bring on-chain funds into real-world payment, settlement, and commercial scenarios. One particularly representative project I’ve been studying lately is @Polyflow_PayFi. What PolyFlow does is straightforward. On one side: identity and payment data. For example: Who is the user? Have they completed KYC? Do they have a history of genuine payment activity? Can this information be verified by other applications? On the other side: the funds themselves. How do stablecoins enter payment flows? How do merchants settle payments? How can on-chain liquidity participate in real transactions? PolyFlow currently achieves this primarily through PID and PLP. PID functions more like a verifiable payment identity system. It connects real-world identity, KYC status, payment history, and partial credit data—so users no longer need to repeatedly submit the same information across different applications. More importantly, in the future, on-chain financial systems won’t need to judge a user solely by their wallet balance. Real income, spending patterns, payment behavior, and履约 records can themselves become components of creditworthiness. PLP, meanwhile, focuses on how funds participate in payments. Once stablecoins enter payment flows, they shouldn’t merely facilitate simple transfers. Merchant settlements, early receivables collection, cross-border fund rotation, and real-world cash flow demands all require liquidity. PolyFlow aims to connect these real payment needs with on-chain capital, enabling stablecoins not just to “pay,” but also to participate in settlement and liquidity rotation. This is precisely why I find PayFi more compelling than ordinary crypto payments. Ordinary payments solve: How does money move from A to B? PayFi goes further to solve: Can this money generate additional financial value during the payment process? PolyFlow has already begun entering real-world use cases. These include identity credential applications built on Solana Attestation Service, as well as partnerships with Huma, Arf, and Geoswift focused on cross-border payments and merchant settlements. When viewed together, PolyFlow’s direction becomes clear: First, connect real users, real payments, and stablecoins; then extend further into credit, financing, and other financial services. Many DeFi products today operate on a simple principle: the more collateral you provide, the more you can borrow. But in the real world, most financial activity doesn’t work this way. Businesses have accounts receivable; merchants have cash flow; individuals have income records; cross-border trade has actual orders and settlement needs. If these real economic activities can gradually migrate on-chain, then the value of stablecoins will extend beyond mere transactions and storage—they will begin actively participating in global payments and finance. This is precisely what makes PolyFlow most worth studying right now. Bitcoin brought value on-chain. Stablecoins brought the U.S. dollar on-chain. Now, PayFi is attempting to bring real-world payments and commercial activity along with them. #PayFi #Payment @Polyflow_PayFi

On-chain Launchpads have recently started to re-emerge, but projects that genuinely motivate me to invest time in researching them remain few. The reason is simple: minting a token itself now carries virtually no technical barrier. The real challenge lies in designing a fairer participation model, reducing bot and Sybil advantages, ensuring smooth alignment between primary issuance and secondary liquidity, and verifying whether the mechanism can continue functioning effectively after launch. Recently, I’ve been paying close attention to @ToshProtocol, which directly addresses all these issues. Especially now that ToshX’s inaugural token, $TO, has officially launched, this previously theoretical design—confined to mechanisms and code—is finally entering real-market validation. If you view ToshX merely as another Launchpad, I believe you’re underestimating it. What it truly seeks to redesign is the entire journey of on-chain assets—from issuance to trading. 01｜Why Do We Still Need a New Launchpad? Over the past few years, crypto has experimented with numerous token issuance models. It began with ICOs, IDOs, and whitelists, followed by various Launchpads, and later saw the rapid rise of models like https://t.co/qj0ApP65C4 (Bonding Curves). Each model solved certain problems while introducing new ones. Whitelists can cap participation numbers, but selection power often rests solely with project teams. First-come-first-served seems fair, yet in practice, it becomes a race of RPC speed, bots, and automated scripts. Bonding Curves drastically lowered issuance barriers, yet in popular projects, front-running, sniper attacks, and bulk participation via new wallets persist. So today, when we discuss “Fair Launch,” the real question is no longer: Is there a private sale? But rather: When everyone can participate, who gets how much—and what determines that allocation? This is precisely where ToshX stands out. Its chosen answer is: Proof-of-Gas. 02｜Proof-of-Gas: Not Based on Identity, But on What You’ve Actually Left Behind ToshX does not base fairness on KYC, whitelists, or speed. Instead, it evaluates eligibility and allocation based on a wallet’s historical gas consumption. I find this approach compelling. On-chain identity has always been a hard problem to solve. One person can create ten wallets, a hundred wallets, or even use automated scripts to generate tens of thousands of addresses. If a protocol designs fairness around “one address, one allocation,” then fairness quickly devolves into who can operate the largest wallet farm. Historical gas differs fundamentally: It represents actual, on-chain costs that have already been incurred. You can create a new address—but you cannot fabricate its real activity history over the past few years. According to ToshX’s currently published mechanism, Proof-of-Gas determines eligibility and allocation in the Genesis phase based on historical gas spent. New wallets without prior gas history do not naturally gain the same participation capacity as long-term active addresses. The mechanism also enforces minimum historical gas thresholds and per-wallet participation caps, aiming to simultaneously mitigate Sybil attacks and prevent large holders from monopolizing allocations. This approach doesn’t fully eliminate Sybil risks—but it does alter the cost structure of attacking the system. Previously, creating an address cost nearly nothing. Now, if an address wants higher historical weight, it must have genuinely incurred real on-chain costs over time. From this perspective, Proof-of-Gas isn’t merely an allocation algorithm for a Launchpad—it’s an exploration of a deeper question: Can an on-chain account’s past economic costs become a new form of on-chain reputation? 03｜Phase 1.5: Simultaneous Primary Issuance and Secondary Liquidity Another design element of ToshX I find noteworthy is Phase 1.5. Traditional launch models often feature a clear phase gap. The primary market completes fundraising or Bonding Curve dynamics first; only after certain conditions are met does the asset migrate to a DEX to form a live liquidity pool. In other words: Issuance and trading are two separate stages. This gap creates numerous problems: When does it “graduate”? Who creates the pool? How is the initial price set? Where does liquidity come from? Can the project team alter rules mid-process? Is there a significant arbitrage gap between primary and secondary prices? ToshX aims to merge these two phases into one unified system. Upon completion of the Genesis phase, the protocol establishes a real PancakeSwap Infinity liquidity pool. Remaining tokens are not fully released at once but instead enter the market gradually through subsequent price tiers. According to Tosh-Core v5.0’s publicly available code and documentation, after Genesis concludes, a live Infinity Pool is formed, with future supply released conditionally via price shelves. This is why the team calls it: Phase 1.5. I think this name is remarkably accurate. It’s neither pure primary issuance nor full price discovery left to the secondary market—it introduces an intermediate mechanism between the two. 04｜Letting Market Price Determine Token Supply There’s another easily overlooked design here. ToshX does not release all tokens into the market at launch. According to the current public version, future token supply is divided into numerous price tiers—referred to as “shelves.” Simply put: If the market price hasn’t reached a given tier, the next portion of supply remains locked. This effectively redefines the relationship between primary supply and secondary pricing. Traditional projects typically follow this pattern: Tokens are minted in full upfront; then the market absorbs them. ToshX’s logic flips this: Price proves demand first; then supply unlocks layer by layer. The current public design includes approximately 4,000 price tiers. Future token issuance will unlock progressively based on price conditions—not all at once. What I appreciate about this mechanism is that it doesn’t assume a project should begin with full circulating supply. The market’s willingness to push the price upward determines how far future supply can be unlocked. This approach is far closer to genuine price discovery than simply designing an FDV and dumping the entire tokenomics model onto the market at once. 05｜Why This $TO Launch Matters No matter how elegant a mechanism looks on paper, if it’s never been tested in practice, it’s just theory. That’s why I believe $TO’s significance goes beyond being ToshX’s first token. It’s essentially ToshX’s own stress test. According to ToshX’s latest public information, $TO’s Genesis issuance will use $BEM as the participation asset. Allocation will be calculated based on historical gas records. The Genesis window is fixed at 3 hours; upon expiration, settlement triggers automatically and the liquidity pool is formed immediately. Together, these rules fully execute ToshX’s core mechanisms: Is Proof-of-Gas practically viable? Can users understand this new allocation system? Can a real liquidity pool be successfully established after Genesis? Can $BEM truly serve as foundational asset for issuance? And more importantly: Will the market accept this new issuance structure? Many protocols fail not because their contracts can’t be written—but because users simply refuse to participate according to their rules.$TO is ToshX’s first step from technical concept to real market adoption. 06 | From BNB Chain to $BEM: ToshX Aims to Turn “Token Launch” into Asset Consumption ToshX has recently introduced another noteworthy change: placing $BEM at the core of its entire issuance system. According to the current public architecture, ToshX project fundraising, pricing, and liquidity pools all revolve around BEM. In other words: As more projects launch via ToshX in the future, the act of launching itself will generate demand for BEM—rather than the Launchpad merely serving as a display page. ToshX has already completed end-to-end testing on the BNB Smart Chain testnet, and public code indicates the protocol is advancing toward deployment on the BSC mainnet. This is why what truly matters to watch next in ToshX isn’t “whether it can launch a few more memecoins.” It’s whether: It can establish a self-sustaining asset issuance loop. Project enters → Uses BEM → Establishes liquidity pool → Subsequent supply released in layers based on price → Protocol fees re-enter the system. If this闭环 (closed loop) functions successfully, ToshX will be more than just a Launchpad. It will approach the status of a foundational layer: On-chain asset issuance infrastructure. 07 | Protocol Revenue Used to Buy Back and Burn Many platforms talk about buybacks. But there’s a huge difference between buyback as marketing rhetoric and buyback as an explicitly coded part of the protocol’s financial flow. According to Tosh-Core’s current public code, when the ToshLadderTreasury receives a portion of protocol fees, it uses those funds to purchase tokens from project pools and sends them to a burn address. Additionally, a portion of the transaction tax in the public mechanism also feeds into this Treasury system. What matters here isn’t the word “buyback.” It’s whether the protocol has built a durable, long-term chain: Transaction → Fee Collection → Treasury → Buyback → Burn If project volume continues to grow, this chain itself will become an integral part of ToshX’s token issuance mechanism. Final Thoughts I no longer judge a project solely because it has Proof-of-Gas, a buyback mechanism, or a fair launch. Any new token issuance model must ultimately be tested by real market dynamics. But ToshX raises several compelling questions worth exploring: Should fair issuance allocate tokens based on address count—or should it account for historical on-chain contributions? Must primary issuance and secondary trading be strictly separated into two phases? Must a token’s full supply be minted upfront—or should subsequent issuance be determined by market price? Can a Launchpad evolve beyond being merely a “token launch page” into true on-chain asset issuance infrastructure? $TO is the first experiment. What truly matters about ToshX is how far this mechanism can scale. I’ll continue monitoring @ToshProtocol’s mainnet progress, the real-world performance of Proof-of-Gas, $BEM’s usage scenarios, and how Phase 1.5 operates in live markets as more projects join ToshX. For any new issuance protocol, the first token is never the end. The real beginning is whether, after that first token is launched, this system can still be used by a hundred more projects. That may be the most critical thing ToshX has yet to prove. @ToshProtocol

Mature / Not mature. Found / Not found. Settle as per instructions / Do not settle as per instructions.

Recently, several controversies involving Luo Yonghao, Xibei, and Ye Ren Xiansheng have been brought together under the same public discourse. After reorganizing the timelines of these events, it becomes clear that what’s truly worth discussing is no longer just a single evaluation—but a more intriguing question: Professor Luo has always expected others to respond directly to his criticisms—so when the tables are turned and the same scrutiny is applied to him, will he respond? It all began with last year’s Xibei controversy. In September 2025, @luoyonghao published a public post after dining at Xibei, questioning the restaurant’s heavy reliance on pre-prepared and pre-processed foods, criticizing its high prices, and calling for clearer labeling of ready-made meals in the food industry. Xibei’s founder, Jia Guolong, responded directly: he denied Luo’s definition of “pre-prepared food,” at one point threatening legal action, and even opened its kitchen to public inspection. What began as a simple consumer review quickly escalated into a multi-day public debate. The back-and-forth expanded far beyond whether Xibei used pre-prepared ingredients—it evolved into broader discussions about industry standards, consumer rights to information, corporate PR, and the influence of public figures. By September 15, Xibei issued a public apology and announced adjustments to certain dish preparation processes; Luo subsequently stated he would not pursue further action, and the controversy temporarily subsided. But the matter was never fully resolved. In the months that followed, Luo repeatedly called on individuals connected to Xibei and Huayu Hua to clarify their statements publicly and even demanded that Hua Shan issue a formal apology for claims that “Xibei had been manipulated.” Thus, most people are familiar with Professor Luo’s style: If there’s an issue, lay it out. If there’s criticism, speak openly. And if someone makes a statement involving you—expect a direct response. Then came this year’s September. On September 12, Luo turned his attention to the recently popular ice cream brand “Ye Ren Xiansheng.” After eating at an airport outlet, he publicly commented: “Average at best—considering the price, it’s downright bad.” Even more interestingly, he added a follow-up note emphasizing that this was merely a normal consumer opinion and warning: “Please don’t provoke me.” Anyone familiar with last year’s Xibei drama likely had the same immediate thought: Is this going to happen again? But this time, Ye Ren Xiansheng responded very differently. On September 16, the brand issued a single, restrained statement: they would carefully consider all customer feedback and strive to improve their products. They did not mention Luo by name. They did not engage directly. And here lies the real intrigue. Last year: Luo criticized Xibei—and Xibei fought back head-on. This year: Luo criticized Ye Ren Xiansheng—and Ye Ren Xiansheng refused to engage. Meanwhile, old debates about Xibei have resurfaced in public discourse, with renewed claims circulating—and Luo himself has already publicly responded to some of them. After this full circle, some have now turned the question back onto Luo himself. @ForesoGlobal has launched a highly discussed prediction: “Will Luo Yonghao issue a public apology regarding the Xibei and Ye Ren Xiansheng incidents before the end of 2026?” What makes this prediction compelling isn’t about prematurely declaring who’s right or wrong. Luo could reasonably argue that he was simply exercising his right as a consumer to publicly evaluate a product. Others could equally argue that a public figure with massive reach and influence wields far greater power than an ordinary consumer when criticizing a company. Both questions are worth examining. But what’s dramatically compelling is this: For years, Professor Luo has been the one to bring issues into the open and demand direct responses from others. Now, the issue has been turned back onto him. Since Luo has always welcomed open debate and insisted others respond directly to his critiques—why not now ask: Will he respond directly this time? Will he explain his position? Will he stand firm that he owes no apology? Or will events unfold further—and will he ultimately issue a public apology? No one knows yet. And that’s precisely what makes prediction markets so fascinating. It’s not about being a Monday-morning quarterback after the fact—it’s about placing your judgment *before* the outcome is known. YES or NO? Instead of arguing endlessly in the comments section, why not go to Foreso and express your view through probability? Foreso direct link: https://t.co/vCDPidQbj6 Foreso original tweet: https://t.co/XqVWjMsMeg @luoyonghao、@ForesoGlobal、@zengying1107、@wuyuesanren、@lixinbao_X、@giantcutie666、@Xiaoniu6161、@maoshen #LuoYonghao #Foreso

Lately, I’ve been looking at Pexo, built by @evanLiaoQ—a project I think is worth discussing. The AI video space is now extremely crowded. Kling, Veo, Seedance, Sora, Runway—every few weeks, model capabilities advance further: image quality, duration, character consistency, and motion realism are all improving rapidly. But after actually using them, you realize a key problem: More models don’t mean making a complete video is any easier. You still need to choose a topic, write a script, break down shots, select a model, craft prompts, generate repeatedly, then handle voiceover, music, subtitles, and editing. Often, generating a single shot takes just minutes—but turning it into a polished, publishable, market-ready video is where most time is spent. That’s exactly what Pexo aims to solve. It’s not building another underlying video model. Instead, it’s creating an AI Video Agent on top of existing models. Simply put: Previously, you manually operated various AI tools. Now, you just tell Pexo: “I want to make a video like this.” The rest is handled by the Agent. It understands your request, reads web pages or assets, organizes information, generates scripts, splits scenes, and calls different image, video, and audio models as needed—then stitches everything into a complete video. So what’s truly interesting about Pexo isn’t how many models it integrates—but how it redefines the interaction with AI video: From “Generate a clip” to “Make me a video.” These two approaches are fundamentally different. The former sells “generation capability”; the latter sells “the outcome.” For example, Pexo can now take a product’s official website URL and have the Agent automatically extract product details to generate a professional Product Video. This is highly practical for SaaS, brands, e-commerce, and crypto projects. Previously, making a product video meant hiring copywriters, designers, editors—even outsourcing an entire team. In the future, you might just paste your website, product screenshots, and key selling points into an Agent—and refine it through conversation. Another feature I’m closely watching is “Mark to Fix.” The real challenge with AI video today isn’t “can it generate?”—it’s “how do you edit it?” What if the logo is too big? The character’s shirt color is wrong? A background needs replacing? The subtitle position is off? Re-generating the entire clip every time is incredibly inefficient. Pexo’s solution: simply circle the area on screen and tell the Agent: “Replace this.” “Leave this unchanged.” “Use this alternative version.” If Pexo can consistently maintain local editing and contextual awareness, AI video will truly evolve from a “lottery tool” into a production tool. Moreover, Pexo isn’t just building a standalone website anymore. They’ve started launching Pexo Skills—integrating with Agent environments like Claude Code, Codex, Cursor, Manus, and OpenClaw. This step is even more significant. In the future, we won’t actively open dozens of AI websites. Instead, we’ll tell our General Agent: “Help me build a product.” “Write my website.” “Then make me a launch video.” And the General Agent will quietly delegate the video production task to specialized agents like Pexo. The General Agent handles task comprehension; Pexo ensures video output is professionally executed. This may be the exact position Pexo is aiming to own. Pexo’s founder, Evan Liao, has long worked in AI video and content generation—with prior experience at Tencent, ByteDance, and Vidu—so this project follows a natural evolution. From Apex Context to today’s Pexo, they’ve consistently emphasized one concept: Context. Models will keep getting stronger and cheaper. But what truly determines whether an Agent is useful isn’t how many models it connects to—it’s whether it can genuinely understand what you mean, remember brand and character context, know which model to invoke for each scenario, and automatically correct failures. This is precisely where Vertical Agents deserve our closest attention. As foundational models become commoditized, competition will shift from “whose model is strongest?” to “who understands tasks best? Who understands users best? Who gets closest to the final result?” What Pexo is doing is essentially betting on this direction: From AI Video Generator → AI Video Agent. I’ll continue watching to see if they can truly connect generation, editing, context awareness, and agent workflows into one seamless end-to-end video production system. @evanLiaoQ

Crypto has never had a shortage of information. Every day we have price feeds, breaking news, on-chain data, prediction markets, macro releases, social sentiment, and hundreds of dashboards competing for attention. The harder problem is deciding which signals actually matter. That is why I’ve been looking at @PrismNetwork_io. What interests me about #PrismNetwork is the idea of building an AI Strategy Account Network around the full process from market signal → AI analysis → strategy. Its product, Flux, gives users a place to explore different AI strategies and observe markets from multiple strategic perspectives. I think this becomes increasingly valuable as prediction markets grow. Prediction markets can turn collective expectations around elections, macro events, policy decisions and global narratives into tradable probabilities. But a probability alone does not automatically tell you what to do with it. You still need context. Is the signal changing quickly? Is it consistent with other market data? Is the market underreacting or already pricing the event in? What type of strategy makes sense under that scenario? This is where AI Strategy becomes much more interesting to me than another information dashboard. Instead of opening ten different tabs and manually trying to connect every piece of information, Flux is moving toward an environment where users can discover signals, compare strategic interpretations and understand how those signals may translate into actionable strategies. That is the direction I want AI in trading to take. More information does not automatically create better decisions. Better filtering, better interpretation and better strategy construction might. I’ll be watching how Prism develops Flux and its broader AI Strategy ecosystem from here. @PrismNetwork_io #PrismNetwork #Web3 #AI

Previously, I observed a common behavior: people would first check the prediction market to see what others thought, then switch to a trading platform to assess market direction. Now, Polymarket Perps has brought these two scenarios together, making the process much more seamless. One side involves judging the outcome of events—such as macro trends, policies, or hot topics—while the other involves trading price movements of assets like BTC, stocks, indices, and gold. Prediction markets answer “Will it happen?”; Perps express “How will the price move?” From a product perspective, the most interesting aspect of this update isn’t just the addition of a futures feature—it’s the connection between two distinct types of market perspectives. For those accustomed to making trading decisions based on events, this experience is definitely worth trying. Trading involves risk; you are solely responsible for any potential loss of funds.

This song is pretty clever—it’s completely translated the crypto world’s vibe into a love story 😂 In the end, the most opaque thing of all is still the human heart. @justinsuntron Sun, care to weigh in?