#SKHynixTopsKOSPIByMarketCap told me the surroundings are all office buildings, and white-collar workers would definitely come after work. What happened? The first three months were packed thanks to opening discounts, but once the promotions stopped, even delivery drivers outnumbered the customers. I advised her to first squat at the spot for two months to count foot traffic before signing the contract, but she wouldn't listen, saying the window of opportunity waits for no one. Later, she held on for half a year, and didn't even recover the transfer fee.

@OpenGradient主网上线那会儿也是这路子. The testnet data surged like a roller coaster, but once the incentives were removed, the actual call volume plummeted right back to square one. The team wasn't idle—they kept slowly refining the HACA architecture and Model Hub—but the market's initial excitement had long faded. The token $OPG just swayed with the broader market, neither crashing nor soaring, becoming one of those painfully slow-burn projects. The friends who jumped in early gradually had their patience drained, while those who didn't board secretly felt relieved to have saved the tuition fee.
I didn't act back then, not because I was particularly smart, but because there were several hard-to-ignore flaws in the documentation. For ZKML, the performance overhead—according to the official data itself—is thousands to tens of thousands of times slower than normal inference. A verification that takes tens of milliseconds for inference would take minutes. This isn’t something optimization can fix; it’s a mathematical cost. More troublesome is model compatibility: the operator set supported by EZKL is still stuck at standards from years ago, and by 2026, mainstream models will have long iterated past that.