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Last night, my friend insisted on dragging me to play badminton. At first, I wanted to slack off, but once I got on the court, I was sweating profusely. In that moment after exercising, I suddenly realized that any system—whether it's the body or a network—only knows its capabilities through "real confrontation." I think of @AlloraNetwork, which seems to be entering this "practical testing" phase. 1/7 Let's start with the conclusion @AlloraNetwork is turning "AI prediction" into a network-level infrastructure: multiple models compete simultaneously, first predicting which one is more likely to perform better in the current context, then aggregating and settling incentives. The mainnet is approaching its launch window, and the official team has been continuously releasing Builder Kit and Forge practical entry points, moving the ecosystem from "activities" to "verifiable." 2/7 How does it actually operate? Workers: submit model inference results on a certain topic (such as price, volatility, risk); s: not only evaluate history but also predict which model will better fit the current market; On the network side: incentives and penalties are distributed based on contribution quality and accuracy, and serious participants are filtered through staking. The core is in "forward weighting" rather than post-evaluation. 3/7 Are there any report cards? The official performance report released in August: in 1 month and 10,000 predictions for BTC in 5-minute intervals, the directional accuracy was 53.22% (95% confidence interval 52.16–54.28%), simulating a simple 2:1 long/short strategy, which provided significant excess return samples (methodology and cost assumptions are in the text). Not seeking miracles, but first needing significant statistics. 4/7 This week/recently, I’m focusing on "accessible entry points" Forge: a hub for model competitions and monetization; NEAR 7-day log-return challenge: bringing models to the battlefield, updated hourly; Builder Kit: a pipeline from data → training → deployment, allowing for faster engineering start. These entry points enable "model writers" to directly connect with network incentives and leaderboard feedback. 5/7 Signals adopted by the ecosystem Pilot AI announced using Allora's predictions for Solana/Bitcoin directions; Earlier, Helios Finance integrated real-time BTC prediction data streams for yield optimization; The more cases there are of "connecting signals to execution," the more the network's value can penetrate to the application layer. 6/7 Endorsements and narrative boundaries Allora Labs' official statement: a total of $35M in multiple rounds of financing, with investors including Polychain, Framework, Blockchain Capital, Archetype, Delphi, etc.; the mainnet narrative is clear, but the medium to long-term still depends on the real yield curve of "prediction → execution," rather than a single TGE time point. 7/7 How to participate (different groups) Engineering/Quant: start with Builder Kit → sign up for Forge topics (BTC/ETH/NEAR, etc.), continuously monitor leaderboards and backtesting; Product/Ecosystem: connect prediction streams to strategies, liquidation thresholds, risk control, or market-making parameters; Research/Investment: track official performance updates and third-party integration announcements, observe the speed of weight migration and drawdown control in extreme market conditions. DYOR.

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