Everyone says “ensemble beats single models,” but @AlloraNetwork goes further: it forecasts which models will win right now and weights the mix on the fly. That’s the edge context-aware forecasting turns a noisy crowd into a focused signal. ---/ ❯ Workers don’t just post predictions; they also forecast the accuracy of other workers under current conditions those loss forecasts are first-class inputs that make the network context-aware ❯ Then comes Inference Synthesis: the network weights contributions by expected error and can literally compose a meta-prediction like 80% from one model + 20% from another, before combining with historical performance to form the topic-wide result ---/ Why this matters: markets shift regimes. The model that crushed last week can lag today. Allora’s forecasters down-weight models when their expected loss rises and up-weight those suited to the new regime so the aggregate stays sharp instead of coasting on stale winners ---/ Quick takeaways: ❯ More than “an oracle” it’s a market of models with incentives to predict both outcomes and peer performance ❯ The result is a self-improving feed that should retain edge longer across volatility shifts. ---/ Dev angle: spin up a lightweight forecaster that learns peer error patterns for a single topic and pipe those loss forecasts into your worker small lift, outsized weight if you’re right I’m tracking how this synthesis holds under mainnet incentives and higher throughput. If you’re testing forecasters on Allora topics, show me your setup I’ll share mine and we can benchmark.