C_savy ࿌

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-…