Platforms that provide turnkey tooling and infrastructure to deploy and operate custom rollups, similar to cloud for L2s.

Technique where a model retrieves relevant external documents at query time and conditions generation on them to improve accuracy and reduce hallucinations.

On‑chain randomness mechanism where participants commit to and reveal random values, combined to produce an unpredictable beacon for consensus or leader selection.

Controlling the number of requests a client can make in a period, protecting RPCs or APIs from abuse and helping ensure fair access.

Bitcoin mempool policy allowing an unconfirmed transaction to be replaced with a version that pays a higher fee, improving confirmation reliability under congestion.

Token that programmatically expands or contracts user balances to target a price or supply, keeping proportional ownership the same.

Systematic probing of an AI model with adversarial inputs to discover safety, security, and alignment weaknesses before release.

Smart contract vulnerability where an external call re‑enters the calling contract before state updates finalize, enabling double‑spend style exploits without proper guards.

Movement using web3 rails to fund public goods and ecological projects with transparent on‑chain incentives and impact tracking.

Practice where custodians or lenders reuse client assets as collateral for their own borrowing, a risk factor in centralized crypto finance if not transparently disclosed.

Learning paradigm where an agent interacts with an environment and optimizes actions to maximize cumulative reward over time.

In multi‑chain designs like Polkadot, the central chain that coordinates consensus and communication among connected parachains.

Service that submits transactions or messages on behalf of users or protocols, often paying gas and being reimbursed via meta‑transactions or fees.

Property of a network or application to tolerate short‑range chain reorganizations without loss or double‑spend, often via confirmations or finality gadgets.

When a node switches to a longer or heavier chain tip, replacing recent blocks. Causes temporary rollbacks of unfinalized transactions.

Attack where a valid transaction is captured and resent on the same or another chain to repeat effects, mitigated by chain IDs, nonces, and domain separation.

Mechanisms like chain IDs, EIP‑155 signing domains, and unique nonces that prevent transactions from being valid across different networks or contexts.

Methods that learn useful latent features from raw data, enabling downstream tasks with minimal task‑specific engineering.

Principles and practices for building and deploying AI systems that are safe, fair, transparent, privacy‑preserving, and accountable.

Reusing staked assets or their yield to secure additional services or networks, extending economic security across multiple protocols (e.g., via middleware frameworks).

Selecting the most relevant documents, chunks, or vectors from a corpus given a user query, typically via embeddings and similarity search.

Data structure that accelerates similarity search, for example HNSW graphs or IVF lists over embedding vectors.

Specification that maps states or actions to scalar rewards in reinforcement learning, defining the agent’s objective, often the hardest part to get right.

Model trained to score outputs according to preferences or policies, used to guide reinforcement learning or re‑ranking of generations.

Network rewards are split among Block Keepers, Mobile Verifiers, and Block Managers. Rewards are precomputed per epoch and paid at epoch end.

Privacy‑preserving signature scheme where a signer is indistinguishable within a group, used in some cryptocurrencies to hide the true signer.

Privacy technique combining ring signatures with confidential amounts to hide sender, receiver, and value in transactions (popularized by Monero).

Training approach where feedback labels are generated by AI systems instead of humans, often used to scale preference data for alignment.

Alignment method where a reward model is trained on human preference comparisons, then the base model is fine‑tuned with reinforcement learning to follow desired behavior.

Reverting a chain’s state to a prior block height. Generally avoided in public chains except during reorgs or critical incidents with community consensus.

A Layer 2 that executes transactions off‑chain and posts data or proofs to Layer 1 for security.

A Layer 2 that executes transactions off‑chain and posts data or proofs to a Layer 1 for security, commonly optimistic or zero‑knowledge designs.

Contract and off‑chain infra that allows deposits to and withdrawals from a rollup, often using canonical bridges with exit delays or validity proofs.

Component that generates validity proofs (e.g., zk‑SNARKs) attesting to correct state transitions in a rollup, verified on Layer 1.

Actor that orders transactions within a rollup and submits batches to Layer 1; can be centralized, decentralized, or shared among rollups.

How a rollup finalizes on its base chain, including data availability, proof verification, and the economic guarantees for bridging assets.

Top hash of a Merkle tree summarizing all leaves; enables compact proofs of inclusion for transactions or state.

Minimal set of entities or keys implicitly trusted by a system’s security model, such as genesis validators or hardware secure modules.

Endpoint that lets applications read chain data and send transactions to nodes, usually via JSON‑RPC over HTTP or WebSockets.

Public‑key cryptosystem based on the difficulty of factoring large integers; used for signatures, encryption, and randomness beacons in some protocols.

Systems programming language with strong safety guarantees and performance, widely used for blockchain clients, provers, and smart contracts on some chains.