Introduction
GET STARTED
1.
Using the Network
2.
Competing for ML Rewards
3.
Running a Validator
CONCEPTS
4.
Embeddings
5.
Training Encoders
6.
Data Masking
7.
Embedding Consensus
8.
Benchmarks
9.
Data Incentives
10.
Multimodality
11.
Rewards and Fees
GUIDES
12.
Embeddings API
13.
ML Competiton
14.
Validators
ADVANCED TOPICS
15.
System Overview
16.
Embeddings
17.
Shared World Model
18.
Training Encoders
19.
Masking Input Data
20.
Differential Loss
21.
Shard Selection
22.
Market of Experts
23.
Calling Encoders
24.
Tokenomics
25.
Fees, Rewards, and Slashing
26.
Proof of Curiosity
27.
Multimodality
28.
Delegated Stake
29.
Consensus
30.
Epochs
31.
State Sync
32.
Data Sync
33.
Transaction Types
REFERENCE
34.
Encoder Specification
35.
RPC Specification
36.
P2P Specification
37.
Governance Parameters
NOTES
38.
ML Unknowns
39.
Unknowns
S❍MA
Embedding Consensus