Do you have what it takes to beat me in a PvP match on BlinkMarkets? Lemme know in the comments 👇

There’s a difference between using a product and realizing you can build something entirely new on top of it. Two people can look at the same platform and see completely different things. One sees buttons, charts and features to interact with. The other sees data, systems and opportunities to build something beyond the original experience. The difference isn’t necessarily what the product offers. It’s how deeply you understand what sits beneath it. The interface shows you how to use a product. Understanding its infrastructure reveals what else you can create with it. This is a side of @seeryx_com worth exploring. Beyond its prediction markets, Seeryx provides developer tools that let software interact with markets programmatically. You can build on existing infrastructure instead of starting from scratch. First, the data. Seeryx’s public API provides access to current and historical rounds, order books, recent trades, trading volume, fees and event markets. Developers can access this public data without connecting a wallet. That means you could build a custom dashboard, analyse historical activity or monitor liquidity without being restricted to how information appears on Seeryx’s website. Then, real-time updates. Seeryx supports WebSocket connections that stream updates to order books, trades and volume. Your application can react as new information arrives instead of repeatedly requesting updates. This opens the door to live dashboards, market alerts and monitoring tools that respond to changing conditions. But what if you want your software to do more than observe? That’s where trading APIs and SDKs come in. Seeryx provides official SDKs for Python and Rust, alongside a trading API supporting market orders, limit orders, post-only orders, batch placements and cancellations. With the required authentication and signing, developers can build custom trading interfaces or software that manages orders according to predefined rules. These aren’t just technical features. They’re building blocks for new applications. Seeryx even provides a market-making bot guide. Market making involves placing buy and sell orders to provide liquidity and potentially earn the spread between them. Imagine a bot buying at $0.47 and selling at $0.51 for the same outcome. That’s a potential gross spread of $0.04 per share if both orders fill at those prices. But it isn’t guaranteed profit. One order might fill while the market moves against the position. Fees, liquidity and execution also affect the outcome. A bot must monitor the order book, adjust quotes, track fills and manage its inventory. The infrastructure can automate parts of this process, but it cannot eliminate trading risk. There are boundaries, too. Public market-data access doesn’t mean every operation is permissionless. Trading and account operations require appropriate authentication and signing, while trading requires the necessary funds, including USDC. So, what’s the bigger opportunity? You could build analytics tools without placing a trade, create your own interface for exploring markets, or experiment with automated strategies using existing infrastructure. You don’t have to rebuild an entire platform. You can focus on solving one specific problem with the tools already available. That’s what makes Seeryx’s developer infrastructure interesting. It gives builders ways to move beyond using the product and start exploring what they can create with it. Explore the documentation: seeryx.com/developers

I can prove I’m telling the truth without showing you what I know. Imagine a friend asks you to prove you know the password to a locked account. You type it in front of them. Now they know it too. But what if you could prove you know the password without revealing it? Your friend verifies your claim, but the secret stays yours. That’s the idea behind zero-knowledge proofs: proving something is true without revealing the secret behind it. Now imagine applying that to Bitcoin payments. Can you prove a transaction is valid without exposing your financial activity to everyone? That’s the problem @lxdev_ is exploring with Zerosats. Here’s why it matters. Bitcoin’s public ledger is one of its biggest strengths. Anyone can inspect transactions and independently verify activity on the network. But that transparency comes with a trade-off. Imagine receiving BTC from a known exchange, sending it across several addresses, then moving it into another wallet. Someone analysing the blockchain may be able to connect those transactions and piece together parts of your financial activity. This is called transaction graph analysis. Your address might not reveal your identity immediately, but the relationships between your transactions can reveal more than you expect. This is where zero-knowledge proofs become interesting. Instead of exposing all the underlying information, a system can use cryptographic proofs to demonstrate that specific rules have been satisfied. The goal isn’t to stop verification. It’s to make verification possible while revealing less information. So, what is Zerosats? @lxdev_ describes Zerosats as a privacy-preserving payment system that uses zero-knowledge proofs to obscure transaction graphs. In simple terms, it explores how Bitcoin-denominated payments can work without making transaction relationships so easy for outside observers to analyse. Zerosats is written in Noir, a language designed for building zero-knowledge applications. It helps developers express the rules a system needs to prove. But there’s another detail worth paying attention to. Privacy and control over your money are two different things. A system can hide transaction information while still requiring you to trust an operator with your funds. Zerosats’ documentation describes a design where the operator does not custody the funds backing its Bitcoin-denominated tokens. That’s worth noting, but it doesn’t automatically answer every question about security, backing or redemption. And zero-knowledge proofs aren’t a magic privacy switch. Depending on the design, network activity, user behaviour and interactions with external services can still expose information. So, when evaluating a system like Zerosats, I’d want to know: What information is actually hidden? How does it prevent double spending? What do users need to trust? Has the implementation undergone independent security review? Because using zero-knowledge proofs doesn’t automatically make an entire system private or secure. The real question is whether the complete design delivers the guarantees it promises. Bitcoin gives anyone the ability to verify transactions. Research like LXDEV’s Zerosats explores whether we can preserve that verification while exposing less of our financial activity. Prove what needs to be proved. Keep what doesn’t need to be revealed private. That’s the idea worth paying attention to. Explore LXDEV: lx.dev/ Zerosats documentation: ciphera.gitbook.io/zerosats-…

A hardware wallet is supposed to protect your crypto. But what happens when the device itself becomes part of the problem? On October 9, 2026, wallets belonging to people who bought Ledger devices through reseller CryptoBilis were drained. Bitquery’s investigation eventually traced $102 million in stolen crypto across 335 wallets and six blockchains. The investigation also found that the thief had spent two weeks testing small transactions before the drain. But here’s the part I want you to pay attention to. The money didn’t simply disappear after the wallets were emptied. Investigators followed the funds across different chains, tracked swaps between assets, identified wallets holding the stolen funds, and monitored movements through services used to exchange or obscure transactions. Some of the funds were frozen. Others continued moving. You can read the investigation here: bitquery.io/investigations/l… Now, why does this matter beyond the incident itself? Because finding out that money has left a wallet is only the beginning of an investigation. The next questions are harder. Where did the funds go? Which addresses received them? What happened to those funds afterward? And how do you separate what the blockchain actually shows from what you merely suspect? Answering these questions requires more than looking at isolated transactions. You need a way to follow wallet activity, investigate transaction connections, and examine market information in context. This is where @tickingdev comes in. Ticking is building an on-chain research platform that brings wallet activity, transaction tracking, market intelligence, and charting into one research experience. One way to understand what the platform offers is to look at how its features help you investigate what’s happening on-chain, starting with the movement of funds between wallets. With WALLETS MAPS & TRANSACTION TRACKING, you can explore connections between addresses, follow transaction paths, and inspect the records behind those connections. Every transfer leaves a trail, but following that trail across different addresses can become complicated, especially when funds move repeatedly or cross between networks. For example, if Wallet A sends funds to Wallet B, you can inspect that transfer and investigate what happens next. Does Wallet B send the funds elsewhere? Are the assets swapped for another token? Do subsequent transactions lead to other addresses? Each transaction provides another piece of evidence to investigate. This is the idea behind wallet tracking: turning a collection of individual transactions into a trail you can follow. But there’s an important distinction. A connection between two wallets doesn’t automatically mean they belong to the same person. A transaction alone doesn’t tell you why it happened either. Good on-chain research means following the evidence without pretending it proves more than it does. Ticking’s wallet explorer lets users search for wallets, tokens, protocols, and transactions, then explore recorded activity and transaction connections. However, tracing transactions is only part of understanding the bigger picture. If those transactions involve a particular token, you may also want to know what’s happening with its price and the market around it. That’s where MARKET INTELLIGENCE & TOKEN RESEARCH becomes relevant. Knowing where a transaction went doesn’t necessarily tell you what’s happening in the market. Suppose you discover a token while investigating on-chain activity. You want to understand its price, how that price has changed over time, and what market information is available to help you investigate it. Looking at one price figure won’t answer every question. You need context. What market does the price refer to? When was the observation recorded? How has the price moved over the period you’re examining? What does the available historical data show?⤵️

Your MEXC Membership Score Is More Than Just a Number Imagine getting a score of 750 out of 1,000. You know where you stand, but do you know what contributed to that score? Which parts of your activity are working in your favour? Which areas could use some attention? Without understanding what’s behind the number, it’s difficult to answer those questions. That’s what makes @MEXC VVIP Score Insights update interesting. It gives users a clearer view of the different dimensions behind their membership score. Let’s break it down. First, what exactly is MEXC VVIP? MEXC VVIP is a membership system built around something called M-Score. Your M-Score determines your membership tier, which is linked to the benefits available to you. There are three tiers: → Standard: 350–599
 → Premier: 600–799
 → Elite: 800+ One thing worth noting is that you don’t need to meet a predefined asset-balance or trading-volume threshold just to enter at Standard. The idea is to make entry into the membership system accessible without requiring users to reach a particular trading volume or asset balance first. But the score itself is more interesting than a single number. Your M-Score has five dimensions. Think of them as different parts of the bigger picture. 1. Trading Performance This reflects your recent trading activity and volume. One important distinction: this doesn’t measure whether your trades are profitable. The focus is on trading activity and volume, not whether you made money. So, a trading-related score shouldn’t automatically be interpreted as a measure of trading skill or profitability. 2. Asset Level This looks at your account assets and participation in eligible Earn services. In other words, your membership score considers more than how frequently you trade. Your assets and participation in eligible services are also part of the picture. 3. Security Verification Your account security matters, too. This dimension covers identity verification and security settings, including passkeys. While exploring the VVIP page, I came across an Advanced KYC Verification task that referenced an estimated increase of 100 points in security weight. That caught my attention because it illustrates how verification fits into the broader membership system. It also highlights something easy to overlook when using an exchange: protecting your account is important alongside trading and managing assets. The estimated points shown for a task shouldn’t be treated as a guarantee of a specific final M-Score. 4. Platform Activity How you interact with the platform is another part of the picture. This dimension reflects things such as regular platform use, exploring services and participating in campaigns. While exploring the VVIP page, I noticed tasks related to applying for the MEXC Card, activating Futures Earn and engaging with other platform features. These examples helped put the platform activity dimension into context. Your relationship with an exchange can extend beyond placing trades. There may be other services and activities that contribute to your overall membership progress. That doesn’t mean you need to use every available service. It’s more important to understand what is available and decide what actually suits your needs. 5. Account Tenure This dimension considers your ongoing relationship with MEXC, including continued use and engagement over time. It adds another factor to the bigger picture beyond your current assets and recent trading activity. Together, these five dimensions give you a broader view of the factors behind your M-Score. But understanding your score is only half the story. The other part is understanding what your membership tier means. MEXC VVIP connects its membership tiers with different privileges, including benefits such as vouchers, card cashback and enhanced support services, subject to the applicable tier and terms⤵️

You can correctly predict Bitcoin’s next move and still make a bad trade. Because in prediction markets, getting the outcome right is only part of the equation. The price you pay and what you stand to gain matter just as much. This is what makes @seeryx_com worth looking into. Built on Solana, Seeryx lets you trade predictions on BTC, ETH and BNB across different timeframes, from 5-minute rounds to daily markets. It also offers markets tied to real-world events. But to understand what Seeryx does, you first need to understand how prediction markets work. Imagine Bitcoin is trading around $82,000. You expect BTC to close above its opening price in a particular round, so you buy an UP share. Let’s say that share costs $0.60. If UP wins, the share pays $1, giving you $0.40 profit before fees. If DOWN wins, your share pays nothing, and you lose the $0.60 you paid. The $82,000 is our example’s reference price. The $0.60 share price is hypothetical, not a live Seeryx quote. Here’s the important part. A share priced at $0.60 can reflect a market-implied probability of roughly 60%, but that doesn’t guarantee the outcome has a 60% chance of happening. Even if your prediction is correct, your entry price determines how much you stand to make. Being confident in a prediction doesn’t automatically make it a good trade. This is where Seeryx’s order book comes in. It supports market orders, which execute against available offers, and limit orders, which let you specify the price you’re willing to pay. A market order prioritizes execution, while a limit order gives you more control over your entry price, although it may never fill. You can also sell your position before a round closes, subject to the applicable trading cutoff and available liquidity. So you’re not just choosing UP or DOWN. You’re making decisions about entry price, execution and potentially when to exit. But what happens when the market closes? For Seeryx’s crypto markets, outcomes are determined using Binance spot-market pricing at the specified settlement time. For real-world event markets, Seeryx uses UMA’s Optimistic Oracle, with Wormhole relaying settled outcomes to Solana. These different mechanisms determine how markets resolve, which is why understanding the question and its settlement rules matters just as much as choosing your side. And none of this removes the risk. You can lose your entire stake if your position resolves against you. Fees and limited liquidity can also affect your returns. Before entering a market, understand what you’re predicting, what you’re paying, what you could receive and what you stand to lose. That’s what makes Seeryx worth exploring beyond its interface. It brings short-term crypto predictions and real-world event markets together on Solana, with order-book trading and defined resolution mechanisms. Because being right about the market and making a good trade are two different things. Explore Seeryx: seeryx.com/

In Crypto, Everyone Has a Prediction. Few Have a Track Record You Can Follow. Every market cycle brings people calling the next breakout, naming the tokens they believe will outperform, or explaining why a particular narrative is about to take off. Sometimes they’re right. Sometimes they’re wrong. And sometimes, by the time you realise what happened, the original prediction is buried several weeks down your timeline. But here’s the thing: making a prediction is easy. Building a track record people can actually evaluate is much harder. Anyone can make a bold call when the market is moving. The more interesting question is what happens after that call, and whether you can look back at someone’s predictions to understand how they’ve performed over time. Confidence isn’t the same as credibility A convincing argument doesn’t automatically make a prediction accurate. Someone can sound certain about a token’s next move and still get it wrong. Another person might explain their reasoning carefully, acknowledge the risks, and reach a different conclusion. That’s why judging someone’s market opinions from a single post can be misleading. A clearer picture comes from seeing their predictions over time, understanding the reasoning behind them, and being able to revisit those calls after the outcomes are known. Even then, a track record isn’t a guarantee of future success. Market conditions change, and a few correct predictions don’t necessarily prove that someone has a reliable strategy. But having that history available gives people more context than confidence alone ever could. Where @tryimoo fits in This is one reason I’m interested in what IMO is building. IMO is an early-stage, mobile-first social network for prediction markets on Solana. Part of its planned experience centres on trader profiles and prediction histories, alongside a social feed and market discussions. The idea is to connect people with the predictions they make, rather than leaving those predictions scattered across a fast-moving social timeline. Imagine being able to explore a trader’s previous calls, revisit the reasoning behind them, and follow the discussions around the markets they’re interested in. That could make it easier to evaluate someone’s opinions in context instead of judging them by their latest post. The important distinction is that IMO is building towards this experience. Its planned features still need to prove their usefulness as the product develops. What I’d be watching For me, the interesting part isn’t simply whether a platform lets people post predictions. It’s whether it makes those predictions easier to revisit and evaluate. Can users understand what someone predicted and why? Can they look back after an event has resolved? Can they distinguish a consistent record from a few lucky calls? Those are the kinds of details that could determine whether prediction histories become genuinely useful or just another profile feature. IMO’s approach creates room to explore that possibility by bringing trader profiles, prediction histories and market conversations into one experience. How well it delivers on that idea is what I’ll be watching as development progresses. My takeaway Crypto doesn’t have a shortage of people telling us what they think will happen next. What it often lacks is an easy way to put those predictions into context over time. IMO is exploring how social prediction markets could make that history more accessible, connecting predictions with the people behind them. Its first launch is planned for Solana Mobile, with iOS intended to follow, so the product still has to demonstrate how useful this approach can be in practice. But the question is worth asking: When someone makes a bold prediction in crypto, do you judge the call itself, or do you look at their track record first? Follow @tryimoo or visit tryimo.xyz to follow what the team is building.