Little Octopus @OpenledgerHQ $OPEN will be listed on Binance spot at 9 PM tonight.
After digging 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 dual strategy of "global layout + upward compatibility."
"Fully Blooming" Listing Strategy
Global Market: Binance
European and American Market: Kraken, Uphold
Asia-Pacific Market: Kucoin, Bitget, Coinone
And so on.
"Upward Compatibility" Listing Potential
By establishing a user base and trading volume in different regions, $OPEN paves the way for subsequent listings on larger exchanges. For example, having a user base in Europe and America could lead to a listing on Coinbase; having trading volume in South Korea could create an opportunity to enter Upbit.
The core of this strategy is: to validate product-market fit through 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 - Reconstructing the AI Value Distribution System
Returning to the project itself, after carefully reading the OpenLedger white paper, to be honest, the ambition of this project is much greater than I expected.
Technical Architecture
A blockchain specifically designed for AI, not a repurposed general 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.
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 are often not as precise and useful as specialized models in specific scenarios. 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.
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