QuantSeer analyzes market behavior through pattern recognition, tracking how structure forms across crypto and equities.
Behavior builds structure.
Structure forms patterns.
These formations develop before the move becomes visible, often alongside shifts in volume.
NFA | DYOR
Raw on-chain data is hard to work with.
Modo API changes that.
Query what you need, get structured outputs, and use it directly in your app.
Here’s what that looks like in practice 👇
Public or private? It really depends on your end goal.
Public: explore the network, discover activity
Private: go deeper, monitor what matters
Different levels of access built for different workflows.
Everything starts to make more sense once you open Modo’s @CantonNetwork Explorer.
It’s a much faster way to go from raw data to something you can actually use while building.
@inference_labs Monolithic proving doesn’t scale well as demand grows. DSperse breaks proofs into smaller, composable units that can run in paralle reducing bottlenecks and significantly improving throughput.
Monolithic proving is a bottleneck.
DSperse changes the shape of the problem by breaking workloads into smaller verifiable units that can be processed independently and composed back together.
Less bottleneck. More throughput.
subnet2.inferencelabs.com
Fun Fantasy Football testing is officially underway ⚽️
Powered by Funcy Network, this is where fantasy football turns into something way more strategic.
@FNC_Network