Topics of Crypto World

QNT is the native utility token of the Quant Network ecosystem. Transparency filings and product descriptions commonly link it to Overledger licensing and platform use, and state that it grants no corporate governance voting rights. Reading QNT well means separating platform access and fee mechanics from token-price investment assumptions.

A timestamp on a blockchain is data in each block that records when that block was created, usually as Unix time. It helps nodes agree on transaction order, supports consensus, and makes later changes to the ledger easier to detect.

Overledger is Quant Network’s multi-DLT interoperability platform. Through a unified gateway, connectors, and workflow orchestration, apps and institution systems can reach public chains, permissioned networks, and traditional payment infrastructure. It is middleware above ledgers—not a new Layer-1 blockchain.

Phala Network (PHA) provides infrastructure for privacy-preserving computation and Confidential AI, leveraging TEE, GPU TEE, dstack, and remote attestation to protect AI models, user data, and Agent runtime environments.

Confidential AI is a new computing architecture that uses TEEs, GPU TEEs, and remote attestation to protect AI models and data. This article explores how Phala safeguards AI Agents’ prompts, memory, API keys, and tool calls.

Native XRP cannot currently be frozen on the XRP Ledger. Unlike stablecoins and other issued tokens, XRP has no issuer with the authority to freeze individual balances, blacklist an address or claw XRP back from an account.

Starting with SAGA’s recent price performance and Marché interest, this article explores the relationship among Saga Chainlet, validators, Stake, and Token economics, while establishing a framework for evaluating SAGA’s long-term value.

Using Meta’s Ray-Ban line and Muse as a starting point, explore how AI glasses integrate vision, voice, sensors, and intelligent agents, and analyze the product and industry dynamics reshaping eyewear as AI enters the sector.

Starting with Meta’s AI glasses product strategy, this analysis examines chips, optics, displays, sensors, software, and capital expenditures to build a framework for analyzing the AI glasses supply chain and the transmission of value across U.S. equities.

Layered crypto storage splits balances by how often they move and how large they are—keeping frequent small amounts on a hot trading layer, isolating medium-term savings, and minimizing online exposure and signatures for long-term cold holdings.

In 2026, on-chain perpetual DEXs show softer aggregate volume and denser share at the top. Using a DefiLlama snapshot around 2026-09-28, Hyperliquid still leads 30-day volume by a wide margin, while Aster, Lighter, Variational, and edgeX form a competitive second tier. The eight venues are sorted by volume and labeled by architecture for comparison—not as investment advice.

Open interest vs. volume isn't really a contest between two interchangeable indicators. Volume is usually more useful for short-term order flow and execution because it measures how much trading is happening now, while open interest is more useful for judging whether positions are building or being closed. Traders analysing futures or options generally get a clearer picture by using both.

For order flow trading, Volume Delta is generally better than Net Volume because it separates aggressive buying from aggressive selling based on trades executed at the ask and bid. That makes it more useful for identifying buying or selling pressure, absorption, exhaustion, and divergence. Net Volume is simpler and better suited to confirming whether overall directional participation supports a price move.

When trading spot markets on Gate, the most common basic order types are limit orders, market orders, and stop orders. The main differences come down to when the order enters the market, how the execution price is determined, and whether the trader prioritizes price control or execution speed.

PCE, Nonfarm Payrolls (NFP), and FOMC events are among the most closely watched U.S. macroeconomic releases. They focus on different parts of the economy—inflation, employment, and monetary policy—but markets often interpret them through the same core question: