Little Octopus @OpenledgerHQ $OPEN will go live on Binance spot at 9 PM tonight.

After digging deep into its listing strategy and project details, I can only say that the ambition of this project far exceeds the surface.

1/ Listing Strategy

First, let's talk about the listing strategy. $OPEN adopts a "global layout + upward compatibility" dual strategy.

The "fully blooming" listing strategy

Global market: Binance

European and American market: Kraken, Uphold

Asia-Pacific market: Kucoin, Bitget, Coinone

and so on

The "upward compatibility" listing potential

By establishing user bases and trading volumes in different regions, $OPEN paves the way for subsequent listings on larger exchanges. For example, having a user base in Europe and America may lead to a listing on Coinbase; having trading volume in Korea may provide the opportunity to enter Upbit.

The core of this strategy is: to validate product-market fit with small and medium exchanges, accumulate real users, and then use data to apply for larger exchanges. Compared to projects that want to go directly to large exchanges without any user base, $OPEN's path is more pragmatic and has a higher probability of success.

2/ Openledger - Restructuring the AI Value Distribution System

Returning to the project itself, after carefully reading OpenLedger's white paper, to be honest, the ambition of this project is much larger than I expected.

Technical Architecture

A blockchain specially designed for AI, not a modified general-purpose chain

Supports data ownership, model version control, and refined rewards

Drives economic incentives through the $OPEN token

Market Positioning

Aiming at specialized AI models, rather than general large models. This direction is correct; there is indeed a need for more finely-tuned models in vertical fields now.

Traditional model: Data → Large Model → General Application

OpenLedger model: Specialized Data → Specialized Model → Vertical Application

The logic behind this shift is that while general large models have strong capabilities, they often lack precision and usefulness in specific scenarios compared to specialized models. Training specialized models requires high-quality vertical data, which is precisely the supply-demand matching problem that OpenLedger aims to solve.

Let's see what happens next.