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AI / ML

Grass

A DePIN protocol on Solana where users share unused internet bandwidth through a browser extension, contributing to a decentralized data pipeline for AI training datasets. Participants earn GRASS tokens for bandwidth contributions, which are used to scrape and structure publicly available web data. Grass addresses the growing demand for high-quality training data by creating an incentivized, distributed web crawling network.

IDgrass-depin
Plain meaning

Plain meaning

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A DePIN protocol on Solana where users share unused internet bandwidth through a browser extension, contributing to a decentralized data pipeline for AI training datasets. Participants earn GRASS tokens for bandwidth contributions, which are used to scrape and structure publicly available web data. Grass addresses the growing demand for high-quality training data by creating an incentivized, distributed web crawling network.

Mental model

Mental model

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Technical context

Technical context

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LLMs, RAG, embeddings, inference, and agent-facing primitives.

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Why builders care

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AI handoff

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Grass (grass-depin)
Category: AI / ML
Definition: A DePIN protocol on Solana where users share unused internet bandwidth through a browser extension, contributing to a decentralized data pipeline for AI training datasets. Participants earn GRASS tokens for bandwidth contributions, which are used to scrape and structure publicly available web data. Grass addresses the growing demand for high-quality training data by creating an incentivized, distributed web crawling network.
Related: DePIN (Decentralized Physical Infrastructure Networks), Training (ML), SOL
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DePIN (Decentralized Physical Infrastructure Networks)

Blockchain protocols that coordinate and incentivize physical infrastructure through token rewards. DePIN projects on Solana include: Helium (wireless networks), Render (GPU rendering), Hivemapper (mapping), and io.net (distributed GPU compute for AI). Contributors provide physical resources (hardware, bandwidth) and earn tokens. DePIN bridges blockchain economics with real-world infrastructure.

GPU Compute (Decentralized)Blockchain
Open term
Branch

Training (ML)

The process of optimizing a model's parameters by exposing it to data and adjusting weights to minimize a loss function. Pre-training on large datasets creates foundation models. Training LLMs requires massive compute (thousands of GPUs, weeks/months). Training data quality, diversity, and size directly impact model capabilities. Distinguished from fine-tuning (smaller scale, specific domain).

Fine-TuningFoundation Model
Open term
Branch

SOL

The native token of the Solana blockchain, used for paying transaction fees, staking, and rent. 1 SOL equals 1 billion lamports. SOL has an inflationary supply schedule starting at 8% annually, decreasing by 15% per year, with a long-term floor of 1.5%. Transaction base fees (5,000 lamports) are partially burned.

LamportNative Token
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AI / ML

DePIN (Decentralized Physical Infrastructure Networks)

Blockchain protocols that coordinate and incentivize physical infrastructure through token rewards. DePIN projects on Solana include: Helium (wireless networks), Render (GPU rendering), Hivemapper (mapping), and io.net (distributed GPU compute for AI). Contributors provide physical resources (hardware, bandwidth) and earn tokens. DePIN bridges blockchain economics with real-world infrastructure.

Open term
AI / ML

Training (ML)

The process of optimizing a model's parameters by exposing it to data and adjusting weights to minimize a loss function. Pre-training on large datasets creates foundation models. Training LLMs requires massive compute (thousands of GPUs, weeks/months). Training data quality, diversity, and size directly impact model capabilities. Distinguished from fine-tuning (smaller scale, specific domain).

Open term
Programming Model

SOL

The native token of the Solana blockchain, used for paying transaction fees, staking, and rent. 1 SOL equals 1 billion lamports. SOL has an inflationary supply schedule starting at 8% annually, decreasing by 15% per year, with a long-term floor of 1.5%. Transaction base fees (5,000 lamports) are partially burned.

Open term
AI / ML

Hallucination

When an AI model generates plausible-sounding but factually incorrect information. LLMs hallucinate because they predict likely token sequences, not verified facts. In blockchain development, hallucinations can be dangerous—incorrect API usage, nonexistent functions, or wrong program addresses. Mitigation: RAG for grounding, code verification, testing, and using models with lower hallucination rates.

Open term
Commonly confused with

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AI / MLdepin

DePIN (Decentralized Physical Infrastructure Networks)

Blockchain protocols that coordinate and incentivize physical infrastructure through token rewards. DePIN projects on Solana include: Helium (wireless networks), Render (GPU rendering), Hivemapper (mapping), and io.net (distributed GPU compute for AI). Contributors provide physical resources (hardware, bandwidth) and earn tokens. DePIN bridges blockchain economics with real-world infrastructure.

AliasDePIN
Open term
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AI / MLdepin

DePIN (Decentralized Physical Infrastructure Networks)

Blockchain protocols that coordinate and incentivize physical infrastructure through token rewards. DePIN projects on Solana include: Helium (wireless networks), Render (GPU rendering), Hivemapper (mapping), and io.net (distributed GPU compute for AI). Contributors provide physical resources (hardware, bandwidth) and earn tokens. DePIN bridges blockchain economics with real-world infrastructure.

Open term
AI / MLtraining

Training (ML)

The process of optimizing a model's parameters by exposing it to data and adjusting weights to minimize a loss function. Pre-training on large datasets creates foundation models. Training LLMs requires massive compute (thousands of GPUs, weeks/months). Training data quality, diversity, and size directly impact model capabilities. Distinguished from fine-tuning (smaller scale, specific domain).

Open term
Programming Modelsol

SOL

The native token of the Solana blockchain, used for paying transaction fees, staking, and rent. 1 SOL equals 1 billion lamports. SOL has an inflationary supply schedule starting at 8% annually, decreasing by 15% per year, with a long-term floor of 1.5%. Transaction base fees (5,000 lamports) are partially burned.

Open term
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AI / ML

LLM (Large Language Model)

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.

Open term
AI / ML

Transformer

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).

Open term
AI / ML

Attention Mechanism

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.

Open term
AI / ML

Foundation Model

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.

Open term