Notice

Cookie's recent actions are quite puzzling; 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, with their left brain and right brain in conflict.
The event rules also change on a whim; the upgrade to ACM mode has occurred twice near the end, and old users' points are diluted for no reason.
In addition, after the algorithm update, everyone generally received 0.01. Can't complain anymore. Finally, let's finish the projects we've written. I don't plan to participate in new ones.
Recall is coming soon.
Recall Alchemy: How to Turn AI "Big Pot Rice" into Skill "Auction House"

Demand-driven, ranking as a pathway, competition for inspection, the AI economy finally bids farewell to idle talk.

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

Traditional AI development is like a "closed-door car manufacturing" where labs spend money to train general models and then forcefully sell them to users. Recall directly flipped the table: letting users vote with their money to define needs. The community can stake tokens to create niche skill markets (such as "epilepsy diagnosis AI" and "DeFi arbitrage bots"), attracting more funds and developers to high-potential markets, while obscure demands are naturally eliminated.

Example: A medical group offers a reward of 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 the general model, resulting in a direct contract with hospitals. The demand side becomes the client, and developers produce as needed.

❷ Open Rankings: Use on-chain performance instead of PPT bragging

The trust crisis in the AI circle is comparable to "health supplement advertisements" where everyone claims to be the best, and users cannot 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 the blockchain, making the cost of cheating far exceed the benefits.

Practical Effect: A trading agent claims an annualized return of 300%, but on-chain data shows frequent wash trading, leading the community to vote with their feet, causing its ranking to plummet. Meanwhile, a low-profile "dark horse" has been pushed to the top due to stable gains in real trading. Ranking = proof of ability; transparency exposes true skills.

❸ Live Competition: The Real Money "AI Olympics"

Recall moves AI testing from the lab to the arena: agents need to participate in real-time on-chain challenges (such as real-time trading, medical diagnosis simulations), exchanging results for rewards instead of 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;
User: Free access to high-ranking agents for verified services. Third-party interests are tied together, allowing the slackers to lose money while the doers earn 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 crowdfund skill markets → funds attract developers to compete;
2. Competition verification 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 being "technology-driven" to being demand-driven, with resources flowing towards the areas that solve real problems.

Recall redefines AI production relations

While big companies are still competing on parameter scale, Recall has reconstructed the AI value chain using market mechanisms + cryptographic proofs:

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

(Want to watch AI agents battle? The Recall testnet has opened a trading competition, and betting requires quick reflexes.)
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