Invest ideasNews
2 Jun 2026, 09:30
Brazil’s B3 Readies Tokenized Stocks for H2 2026, But Says Direct Trading Will Have to Wait
B3, the Brazilian stock exchange, will develop a digital twin of its depository database in a blockchain in preparation for a potential inclusion of these into the traditional financial system. B3 also expects to launch B3RL, a Brazilian real stablecoin, later this year. B3 Takes First Steps to Tokenize Stocks B3, Brazil’s stock exchange, is
2 Jun 2026, 09:05
NEAR Co-Founder Says Quantum-Resistant Crypto to Launch This Month
BitcoinWorld NEAR Co-Founder Says Quantum-Resistant Crypto to Launch This Month Illia Polosukhin, co-founder of NEAR Protocol (NEAR), announced via X that the project plans to launch its quantum-resistant cryptography technology this month. The development marks a significant step in preparing blockchain infrastructure for the eventual threat posed by quantum computing. Quantum Threat to Blockchain Security Quantum computers, once sufficiently advanced, could theoretically break the cryptographic algorithms that secure most blockchain networks, including Bitcoin and Ethereum. Current public-key cryptography, such as elliptic curve digital signature algorithm (ECDSA), is vulnerable to Shor’s algorithm, which a powerful quantum computer could use to derive private keys from public ones. NEAR Protocol’s initiative aims to implement post-quantum cryptographic standards that would resist such attacks, ensuring long-term security for users and applications built on the network. Polosukhin’s announcement indicates that the technology is ready for deployment after research and development phases. Implications for the Crypto Ecosystem The launch of quantum-resistant features on NEAR could set a precedent for other blockchain projects. While quantum computing remains an emerging technology, the timeline for practical quantum threats is uncertain, making proactive adoption of quantum-safe cryptography a strategic priority for forward-looking networks. Industry experts note that transitioning to quantum-resistant algorithms is not trivial; it requires careful implementation to avoid introducing new vulnerabilities or degrading performance. NEAR’s approach may serve as a case study for the broader crypto ecosystem. What This Means for Users and Developers For NEAR users, the upgrade is expected to be transparent, with wallet addresses and transaction processes remaining largely unchanged on the front end. Developers building on NEAR will need to adapt their applications to support the new cryptographic standards, though the network aims to provide tools and documentation to ease the transition. The announcement also signals that NEAR is positioning itself as a security-focused blockchain, potentially attracting institutional users and projects requiring long-term data integrity. Conclusion NEAR Protocol’s upcoming quantum-resistant cryptography launch represents a proactive move to future-proof blockchain security. As quantum computing advances, such measures may become essential for maintaining trust in decentralized systems. The crypto industry will be watching closely as NEAR implements this technology in the coming weeks. FAQs Q1: What is quantum-resistant cryptography? Quantum-resistant cryptography refers to cryptographic algorithms designed to be secure against attacks from quantum computers, which could break current encryption methods like RSA and ECDSA. Q2: When will NEAR launch its quantum-resistant features? According to co-founder Illia Polosukhin, the launch is planned for this month, though an exact date has not been specified. Q3: Will the upgrade affect existing NEAR wallets and transactions? The upgrade is designed to be transparent for end users, with minimal disruption. Developers may need to update their applications to support the new cryptographic standards. This post NEAR Co-Founder Says Quantum-Resistant Crypto to Launch This Month first appeared on BitcoinWorld .
2 Jun 2026, 09:00
SoFi Bank Launches First US National Bank Stablecoin for 15 Million Users
SoFi Bank, N.A. launched SoFiUSD on 27 May 2026, making it the first stablecoin issued by a US national bank on a public blockchain app. The token runs on Ethereum and Solana, is redeemable 1:1 for US dollars, and is immediately available to SoFi's 15 million members.
2 Jun 2026, 08:45
Top 3 AI crypto coins to buy ahead of the OpenAI and Anthropic IPOs
Top AI crypto coins have continued their uptrend and are outperforming Bitcoin and other tokens this year as the artificial intelligence boom gains momentum and as top companies like OpenAI and Anthropic launch their initial public offerings (IPOs). Anthropic filed its IPO papers on Monday, a few days after it completed its fundraising that valued it at $900 billion. Its filings came shortly after OpenAI made its submissions to the Securities and Exchange Commission (SEC). These IPOs, as we have seen with space stocks , will likely lead to more gains among AI cryptocurrencies and stocks. This article looks at some of the best AI crypto coins to buy as the hype continues. Near Protocol (NEAR) Near Protocol token has already jumped by over 200% from its lowest point this year, making it one of the best performers. This surge continued today, Tuesday, after Anthropic launched its IPO papers. Near Protocol has numerous moving parts. For example, it is a top layer-1 platform that enables users to build decentralized applications (dApps) in areas like decentralized finance (DeFi) and gaming. It is also runs Near.com , which makes it possible for people to trade multi-asset coins in a confidential way. Most importantly, it runs Near AI, a platform that runs an AI agent marketplace, where anyone can buy and run them. This platform also runs IronClaw, an AI agent that connects to tools and runs critical workflows. Near AI Cloud is a decentralized artificial intelligence infrastructure platform. Venice Token (VVV) Venice Token is another top AI token to consider ahead of the OpenAI and Anthropic IPOs. It has already jumped by over 1,500% from its December lows, a surge that has turned it into a top-100 cryptocurrency. Venice AI is a unique player in the AI platform that makes it possible for people to search on most models like Grok, Claude, and ChatGPT confidentially. It uses a freemium model, where users can do some queries for free and pay for others. Venice users pay in US dollars, with the company using part of the fees to burn the VVV tokens. It has already burned about 42% of all the tokens in circulation, a trend that will accelerate in the future. At the same time, VVV holders can earn double-digit returns through staking, further making it attractive. Akash Network (AKT) The ongoing AI hype has led to a surge in demand for computing data. This growth has led to the substantial gains across the data center industry, with the top beneficiaries being companies like Nvidia (NVDA), AMD, and Dell. Akash Network is a top player in the industry that leverages the concept of decentralization. Unlike CoreWeave and Nebius that run massive data centers, Akash Network uses a decentralization approach. It makes it possible for people to lease their idle space and earn a return. Data on its website shows that it is generating over $7,700 a day as the number of active leases has jumped to 762. This growth will likely continue in the coming years as demand for computing power jumps. Other top AI crypto coins to buy There are other good AI coins to buy ahead of these IPOs. For example, Worldcoin and Humanity Protocol will be useful to safeguard the integrity of networks in the era of AI agents. Worldcoin is also associated with Sam Altman, the creator and CEO of OpenAI, which may lead to more hype. The other top AI coins to consider are Bittensor and Render. The post Top 3 AI crypto coins to buy ahead of the OpenAI and Anthropic IPOs appeared first on Invezz
2 Jun 2026, 07:43
Ripple's RLUSD Now Available in Turkey
Enterprise blockchain firm Ripple has launched its USD-backed stablecoin, Ripple USD (RLUSD), in Turkey.
2 Jun 2026, 06:50
Startup XCENA raises $135M to solve AI’s hidden bottleneck: memory, not compute
BitcoinWorld Startup XCENA raises $135M to solve AI’s hidden bottleneck: memory, not compute Every time you ask an AI model a question, your request sets off a complex data relay race. Information leaves memory, passes through a CPU for preprocessing, travels to a GPU for heavy computation, and then makes its way back — and that entire journey repeats for every single word the AI generates. The bottleneck is structural: it means routing through some of the most expensive and power-intensive chips in the industry on every single request. That inefficiency is exactly what XCENA, a four-year-old startup with offices in South Korea and the U.S., is trying to solve. The company has designed a chip that places compute capabilities much closer to DRAM — the fast, short-term memory chips that store data a processor is actively using — allowing routine data operations to be handled near memory, without the costly round trips between CPUs, GPUs, and memory. If it works at scale, the implications for AI infrastructure costs could be significant. Investors bet on a memory-first approach Investor enthusiasm around XCENA’s thesis is clear. The startup just raised $135 million in a Series B round at a valuation of $570 million, bringing its total funding to $185 million. The round was co-led by Seoul-based venture capital firms Altinum and IMM Investment, along with Corstone Asia and existing investors SBI Investment and Mirae Asset Capital. XCENA CEO Jin Kim, who co-founded the startup in 2022 alongside CTO Dohun Kim and CPO Harry Juhyun Kim, is a veteran of Samsung and SK Hynix — the memory giants that supply chips powering Nvidia’s GPUs. “CPUs and GPUs have both gotten smarter over the decades. Memory never did. XCENA wants to change that,” Kim said in an interview with Bitcoin World. “The recent rise in memory prices and related stocks points to a broader shift in AI infrastructure toward memory-centric architectures.” This month, the three companies that dominate the global memory chip market — Samsung, SK Hynix, and Micron — each crossed a trillion-dollar valuation for the first time, underscoring the growing importance of memory in AI workloads. How the MX1 chip works XCENA is betting its business on the thesis that “inference isn’t just a compute problem; it’s increasingly a memory scaling problem,” said Kim. The company’s chip, the MX1, connects to the CPU through CXL (Compute Express Link) — essentially a dedicated express lane between the processor and memory — processing data before it ever needs to leave the memory module. It brings compute to the data, not the other way around. The company claims that what used to require 10 servers could potentially run on just one. “While GPUs excel at matrix multiplication — the heavy math behind AI model training — much of the surrounding data orchestration, including preprocessing, KV cache management, and data caching, still runs on CPUs. Our chip handles those tasks directly within the memory module itself,” Kim said. KV cache management is the system that stores prior conversation context so a model doesn’t have to reprocess it — a critical function for inference workloads that becomes increasingly memory-intensive as models scale. Competitive landscape and timeline Demand for memory solutions has surged since the second half of last year, and the company believes the timing is working in its favor. Conversations with several global memory vendors are in early stages, though Kim declined to name them. The company’s ideal customers are hyperscalers spending tens of billions a year on AI infrastructure, where even a small gain in memory efficiency can mean hundreds of millions in savings. The MX1 is still a prototype. Mass production chips are scheduled to roll off Samsung’s foundry lines by the end of 2026, with the company expecting to generate revenue starting in 2027. While neural processing unit (NPU) makers are competing to challenge Nvidia for training workloads, XCENA is targeting the memory-intensive layer that sits underneath all of it. XCENA’s closest rivals include Astera Labs and Marvell, both Nasdaq-listed companies working on next-generation memory connectivity. Marvell is a large, established player already working in the same space, Kim said, adding that the differentiator comes down to intellectual property. “We have thousands of cores,” Kim said. Based on public specs, Marvell’s approach relies on a handful of general-purpose cores by comparison. Those cores are built on RISC-V — an open-source chip design blueprint — and optimized specifically for data processing, with each core deliberately kept small and efficient. Beyond the cores themselves, XCENA designs its own internal memory hierarchy, interconnect bus, and DRAM controller — a level of vertical integration that most chip companies, including larger rivals, typically outsource. Why this matters for AI infrastructure The core insight behind XCENA’s approach is that the cost of AI inference — the process of using a trained model to generate responses — is increasingly dominated by memory access rather than pure computation. As models grow larger and handle longer contexts, the amount of data that needs to be shuffled between memory and processors grows exponentially. By handling routine data operations directly within the memory module, XCENA’s chip could reduce both latency and power consumption, two of the biggest cost drivers in modern AI data centers. The company, which has more than 90 staff across offices in Pangyo, a tech hub outside Seoul, and in Sunnyvale, is also in conversations with international investors about additional funding. Conclusion XCENA’s $135 million raise signals strong investor confidence in a memory-centric approach to AI infrastructure. With a prototype in hand, mass production slated for late 2026, and a team of veterans from the world’s largest memory chipmakers, the startup is positioning itself to address a structural inefficiency that affects every AI workload. Whether it can deliver on its promise of reducing server requirements by a factor of ten will depend on the success of its MX1 chip in real-world deployments — but the market is clearly paying attention. FAQs Q1: What problem is XCENA trying to solve? XCENA aims to eliminate the costly round trips between memory, CPUs, and GPUs that occur during every AI inference request. By placing compute capabilities directly within memory modules, the company hopes to reduce latency, power consumption, and infrastructure costs. Q2: When will XCENA’s chip be available? The MX1 is currently a prototype. Mass production is scheduled to begin on Samsung’s foundry lines by the end of 2026, with revenue expected to start in 2027. Q3: Who are XCENA’s main competitors? XCENA’s closest competitors are Astera Labs and Marvell, both of which are publicly traded companies working on next-generation memory connectivity solutions. Marvell is a larger, established player in the space, while XCENA differentiates itself with a highly parallel architecture using thousands of RISC-V cores. This post Startup XCENA raises $135M to solve AI’s hidden bottleneck: memory, not compute first appeared on BitcoinWorld .
