AI Alpha Cheat Sheet — Stop Getting Rekt by Jargon
If you're deploying capital into AI tokens or building in Web3 x AI, you need to understand the tech stack. Here's the no-BS breakdown:
AGI: The endgame. Human-level reasoning. We're not there yet.
LLM: Large Language Models. The backbone of ChatGPT, Claude, etc. Size = parameters. More params ≠ always better.
AI Agents: Autonomous programs that execute tasks. Think trading bots, but smarter. High alpha if integrated with on-chain data.
RAG (Retrieval-Augmented Generation): Combines search + generation. Critical for reducing hallucinations in real-time apps.
Fine-tuning: Custom training on niche datasets. This is how you get edge in AI trading models or sentiment analysis.
Prompt Engineering: The meta skill. How you talk to AI determines output quality. Underrated alpha.
Compute: GPU/TPU power. More compute = faster inference. Watch $RNDR, $AKT for decentralized compute plays.
Embedding: How AI converts words into numbers. Foundation of semantic search and vector DBs.
Hallucination: When AI makes stuff up. Major risk for on-chain oracles or automated trading.
Reinforcement Learning: AI learns via trial and error. Used in AlphaGo, autonomous agents, and some DeFi strategies.
Transformer: The architecture behind modern LLMs. If you see "transformer-based," it's likely state-of-the-art.
MCP (Model Context Protocol): Standardizes how AI accesses internal data. Key for interoperability in multi-agent systems.
Tokenization: Breaking text into chunks. Affects cost and speed of AI inference.
Weights: The learned values in a model. Open-source weights = forkable alpha.
If you can't explain these, you're ngmi in the AI x Crypto narrative. Study up or get left behind.
If you're deploying capital into AI tokens or building in Web3 x AI, you need to understand the tech stack. Here's the no-BS breakdown:
AGI: The endgame. Human-level reasoning. We're not there yet.
LLM: Large Language Models. The backbone of ChatGPT, Claude, etc. Size = parameters. More params ≠ always better.
AI Agents: Autonomous programs that execute tasks. Think trading bots, but smarter. High alpha if integrated with on-chain data.
RAG (Retrieval-Augmented Generation): Combines search + generation. Critical for reducing hallucinations in real-time apps.
Fine-tuning: Custom training on niche datasets. This is how you get edge in AI trading models or sentiment analysis.
Prompt Engineering: The meta skill. How you talk to AI determines output quality. Underrated alpha.
Compute: GPU/TPU power. More compute = faster inference. Watch $RNDR, $AKT for decentralized compute plays.
Embedding: How AI converts words into numbers. Foundation of semantic search and vector DBs.
Hallucination: When AI makes stuff up. Major risk for on-chain oracles or automated trading.
Reinforcement Learning: AI learns via trial and error. Used in AlphaGo, autonomous agents, and some DeFi strategies.
Transformer: The architecture behind modern LLMs. If you see "transformer-based," it's likely state-of-the-art.
MCP (Model Context Protocol): Standardizes how AI accesses internal data. Key for interoperability in multi-agent systems.
Tokenization: Breaking text into chunks. Affects cost and speed of AI inference.
Weights: The learned values in a model. Open-source weights = forkable alpha.
If you can't explain these, you're ngmi in the AI x Crypto narrative. Study up or get left behind.