Cryptocurrency News

Starknet Eyes L1 Status to Go Quantum-Safe by 2027 Starknet is considering becoming an L1, which it says would make it the first fully quantum-resistant network, with 2027 as the target. StarkWare CEO Eli Ben-Sasson laid out the case at Token2049. Starknet already runs on ZK-STARKs and has a migration roadmap to post-quantum signatures, but as an L2 it inherits Ethereum’s security, and Ethereum targets full quantum resistance only by the end of 2029. Its own L1 would let Starknet control security upgrades instead of waiting for Ethereum or Bitcoin. Nothing is decided yet: Starknet calls it one of several options and is asking the community for feedback.

📈 $10B Flowed Into Crypto ETFs in Q3 2026

🔥 Most Searched Tokens on CryptoRank This Week

📈 Top Large-Cap Gainers in Q3 2026

📈 Tokenized ETF Market Cap Surpasses $670M Tokenized ETF market capitalization reached $673M in Q3, up roughly 59% QoQ. BNB Chain led the growth, with market cap rising more than 4x to nearly $160M, while the top three networks now account for around 84% of the market.

📈 Tokenized Commodities Continue to Expand The market capitalization of tokenized commodities reached $8.0B in Q3 2026, up about 10% QoQ. Despite Ethereum remaining the largest network, XRP Ledger has driven much of the sector’s expansion this year, growing from $736M to $2.2B YTD.

Tether Releases Genesis III Dataset for STEM AI Tether released QVAC Genesis III, a 191.43B-token synthetic dataset built to make small AI models far better at science, technology, engineering and math, so tutors and technical assistants can run locally instead of in the cloud. Genesis III expands the earlier Genesis I and II corpora to 159.6M documents across 19 curriculum-aligned STEM areas, from high school to professional level. It relies on two methods: Failure Analysis, where a stronger teacher model turns a smaller student model's mistakes into new training material, and Option-Level Reasoning, which explains why the right answer is right and why every alternative is wrong. Against a token-matched Cosmopedia-v2 model it lifted scores by 28.57 points on ARC-Easy, 21.35 on ARC-Challenge and 15.03 on MMLU STEM, while a 1.7B-parameter model trained on its Option-Level data gave valid answers in 99.45% of benchmark responses.

📈 Tokenized ETFs Expand Across Multiple Chains in Q3