The economic design of a cryptocurrency token: supply schedule, distribution, utility, incentive mechanisms, and value accrual. Key parameters: total/circulating supply, inflation/deflation, vesting schedules, staking rewards, fee burning, and governance rights. Good tokenomics aligns incentives between users, developers, and token holders. AI tools increasingly help analyze tokenomics models.
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The economic design of a cryptocurrency token: supply schedule, distribution, utility, incentive mechanisms, and value accrual. Key parameters: total/circulating supply, inflation/deflation, vesting schedules, staking rewards, fee burning, and governance rights. Good tokenomics aligns incentives between users, developers, and token holders. AI tools increasingly help analyze tokenomics models.
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Tokenomics (tokenomics) Category: AI / ML Definition: The economic design of a cryptocurrency token: supply schedule, distribution, utility, incentive mechanisms, and value accrual. Key parameters: total/circulating supply, inflation/deflation, vesting schedules, staking rewards, fee burning, and governance rights. Good tokenomics aligns incentives between users, developers, and token holders. AI tools increasingly help analyze tokenomics models. Related: Governance Token, Staking
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A token that grants holders voting power over protocol decisions. Governance tokens enable decentralized control of DeFi protocols, DAOs, and blockchain parameters. Examples on Solana: JUP (Jupiter), MNDE (Marinade), RAY (Raydium). Voting power is typically proportional to token holdings, though some systems use quadratic voting or delegation.
The process of locking cryptocurrency as collateral to participate in network consensus (validation) or to earn rewards. Stakers either run validators directly or delegate to existing validators. Staking provides economic security—validators risk losing staked tokens (slashing) for misbehavior. Annual staking yields typically range from 3-15% depending on the network.
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The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
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A token that grants holders voting power over protocol decisions. Governance tokens enable decentralized control of DeFi protocols, DAOs, and blockchain parameters. Examples on Solana: JUP (Jupiter), MNDE (Marinade), RAY (Raydium). Voting power is typically proportional to token holdings, though some systems use quadratic voting or delegation.
The process of locking cryptocurrency as collateral to participate in network consensus (validation) or to earn rewards. Stakers either run validators directly or delegate to existing validators. Staking provides economic security—validators risk losing staked tokens (slashing) for misbehavior. Annual staking yields typically range from 3-15% depending on the network.
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A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.