Notice

Cookie's recent actions are quite confusing. On one hand, they are updating algorithms to crack down on interaction groups, while on the other hand, they are displaying social graphs on their own website, leading to a conflict between the left and right brain. The rules of the activities also change abruptly; just before the end, the upgrade activity switched to ACM mode, which has already happened twice, diluting old users' points for no reason. Additionally, after the algorithm update, everyone generally received 0.01. It's hard to keep complaining. Finally, I'll finish the projects I've written. I don't plan to participate in new ones.
Recall is coming soon.
Recall Alchemy: How to Turn AI's "Big Pot Meal" into Skill "Auction House"

Demand-driven, ranking the way, competition to verify, the AI economy finally says goodbye to empty talk.

❶ Skill Market: From "Push-style Infiltration" to "Pull-style Crowdfunding"

Traditional AI development is like a "closed-door car manufacturing" where laboratories spend money to train general models and then forcefully promote them to users. Recall has turned the tables: it allows users to vote with their money to define demands. The community can stake tokens to create niche skill markets (such as "epilepsy diagnosis AI" or "DeFi arbitrage robot"), attracting more funds and developers to high-potential markets, while obscure demands are naturally eliminated.

Case: A medical group rewards 500,000 tokens to recruit "Diabetic Retinopathy Diagnosis Agents," attracting 30 teams from around the world to compete. The final winner's accuracy is 15% higher than that of the general model, leading to direct contracts with hospitals. The demand side becomes the client, and developers produce as needed.

❷ Open Rankings: Replace PPT boasting with on-chain performance

The trust crisis in the AI circle is comparable to "health product advertisements" where everyone claims to be the best, leaving users unable to distinguish between true and false. Recall's AgentRank system is like the "Dazhong Dianping" of the AI world: all competition data of agents, user betting records, and community evaluations are all on-chain, making the cost of cheating far exceed the benefits.

Practical results: A trading agent claims an annualized return of 300%, but on-chain data shows frequent wash trading, leading to a sharp decline in its ranking as the community votes with their feet, while a low-profile "dark horse" has been pushed to the top due to stable gains in real trading. Ranking = proof of capability, and transparency exposes true skills.

❸ On-site competition: The "AI Olympics" with real money

Recall moves AI testing from the lab to the arena: agents must participate in real-time on-chain challenges (such as real-time trading, medical diagnosis simulations) to exchange results for rewards, rather than exchanging papers for funding.

Economic Game Design:
Developers: Staking tokens to participate, cheating will result in the forfeiture of the deposit;
Curator: Bet on promising agents and share in their growth dividends;
Users: Free access to high-ranking agents, enjoy verified services. Third-party interests are bundled, causing losers to lose money and doers to make money.

❹ Flywheel Effect: How Skill Economy Spirals Upward

The ecological closed loop of Recall is like a "perpetual motion machine":

1. Demand stimulates supply: Users crowdsource skill markets → funding attracts developers to participate in competitions;
2. Competition validation capability: On-chain data generates credible rankings → high-quality agents attract more users;
3. Ranking-driven optimization: Low-ranking agents are forced to iterate or transform → overall ecological level improvement.
Result: The development of AI has shifted from "technology-oriented" to demand-oriented, with resources flowing to areas that solve real problems.

Recall redefines the production relationship of AI

While major companies are still competing on parameter scale, Recall has reconstructed the AI value chain using market mechanisms and cryptographic proof.

For developers: The path to monetizing capabilities has shifted from "financing" to direct market returns;
To users: The choice of cost shifts from "trial and error" to data-driven decisions based on verifiable information. This may be the ultimate form of AI democratization, allowing every need to find its expert and every expert to prove their value.

(Want to watch AI agents compete? The Recall testnet has opened trading competitions, and fast betting is required.)
Shenzi Chen Village Committee Party Branch
#CookieDotFun recall #SNAPS @cookiedotfun @cookiedotfuncn
@recallnet