ALLO price

Allora Network: - Open machine learning network - Inspired by Chainlink and Bittensor - Narrow focus on predictive intelligence - Self-improving forecasting models - Competitive environment fostering quality - Crypto-economic incentive/funding mechanics - Accelerating open-source innovation - Empowering DeFAI apps & agents - Raised >$33M in venture funding - Bitcoin-style ALLO emission schedule - ALLO has real utility in the system Are you bullish yet anon?

So what exactly is @AlloraNetwork? 1/ Project goal: Building a collaborative AI ecosystem that surpasses the limitations of individual AIs (including self-improvement and intelligent layers) 2/ The ultimate goal is to gather AI models to create a more accurate inference model and sell that product. 3/ So how do the three elements (Workers, Reputers, Coordinators) that operate Allora work? 4/ Let's assume you are a consumer of Allora's inference model and you asked the question, "$BTC price after July 30th Fed meeting, will it rise or fall?" 5/ First, various Workers who joined Allora provide different inferences. • Worker A: BTC will break $125,000 by July 30 + filled the CME gap at $114.5K, with a target of $134K. • Worker B: $130,000 if the Fed is dovish, $110,000 if hawkish + continuous ETF fund inflow, reduced risk due to trade agreements. • Worker C: Possible drop to $115,000 after a short-term correction + supply pressure from $9 billion whale sell-off. 6/ And then the Reputers verify the results. (Assuming the result: BTC price is $127,500, Fed announces a 0.25% rate cut) • Worker A: 8/10 (direction correct, target close) • Worker B: 9/10 (scenario predicted accurately) • Worker C: 3/10 (direction completely wrong) 7/ Summary from the Coordinators • High accuracy of Worker B → 40% weight for the next prediction (high $ALLO reward) • Decent performance of Worker A → 35% weight (medium $ALLO reward) • Low accuracy of Worker C → 25% weight (low $ALLO reward) 8/ In this way, Allora achieves 'self-improvement' and creates inference services that yield more accurate predictions.