把复杂的事，讲成听得懂的话

Hi, I’m Xing Ge. This site is my personal science blog, dedicated to making complex things simple: Blockchain — consensus, wallets, smart contracts, explained in plain language Web3 × Real Economy — RWA, NFT ecosystems, the bridge between on-chain and the physical world AI — large models, agents, and how AI truly lands in enterprises Essays — occasional industry observations and reflections My writing principle: give the conclusion first, then the principles, then a one-line summary. No jargon piles, no mystification. ...

The bottom line A blockchain is essentially a public, decentralized, extremely hard-to-tamper ledger. Think of it as a ledger book placed in a town square: everyone holds a copy; if someone wants to record an entry, most people must approve it, and once recorded, secretly changing it is nearly impossible. Understanding it through everyday life Imagine a village’s bookkeeping: Traditionally the village chief kept the books alone — whatever he wrote stood. That’s centralization. If the chief erred, or was bribed to alter records, no one could challenge him. ...

The bottom line RWA (Real World Assets — tokenization of real-world assets) uses blockchain to turn real-world assets like property, gold, bonds, even receivables into on-chain, fractional, tradable, programmable digital certificates (tokens). In one line: it lets real-world assets flow like stocks, at lower cost and across wider borders. Understanding it through everyday life Imagine “digitizing” a ¥5M apartment into 5 million tokens: You don’t have to sell it whole: need ¥100k? Sell 100k tokens — no need to transfer the whole property. Global investors can all buy: buying a Shanghai apartment used to mean cross-border wires, title transfers, hefty agent fees; now a few clicks and you hold it. Everything recorded on-chain: who holds it, how many times it traded, how dividends split — public, transparent, tamper-proof. That’s the core imagination of RWA: turning assets that were “illiquid, high-barrier, opaque” in traditional finance into “liquid, low-barrier, fully transparent” assets. ...

The bottom line A large model (like the GPT behind ChatGPT) is essentially a machine extremely good at playing “word chain”: give it an opening, it predicts the most likely next word based on “experience,” then the next… until a full answer forms. Its “experience” comes from the massive human text it has read. Understanding it through everyday life Imagine a child who read a hundred thousand books. You give them an opener: “The weather is nice today, let’s go to the park,” and they’ll most likely continue with “for a walk” or “to play” — because that’s the most common pairing in the books they’ve read. ...