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Malone Lam, the mastermind behind the theft of 240 million USD in BTC, will attend a plea agreement hearing this Tuesday

According to Apnews, a young cryptocurrency scam gang stole over 4,100 BTC in August 2024 through "social engineering" attacks, which was worth over $240 million at the time.After the theft, gang members quickly began extravagant spending, including purchasing sports cars, renting luxury homes, flying on private jets, and hiring security personnel. Among them, the alleged mastermind, a 22-year-old Singaporean named Malone Lam, reportedly spent over $569,000 in one night at a nightclub in Los Angeles.Investigations revealed that the suspects impersonated employees from Google and the cryptocurrency trading platform Gemini to trick victims into giving up their Google Drive access and security codes, allowing them to transfer the victims' bitcoins. They then moved the funds through multiple trading platforms and money laundering intermediaries. Their lavish lifestyle eventually drew the attention of law enforcement.One suspect was exposed when their IP address was revealed while hiding nearly $30 million in stolen cryptocurrency assets, leading police to trace it back to the luxury home they rented in California. Another suspect was found with $37 million worth of stolen cryptocurrency assets. Lam was accused of using the stolen funds to purchase a $2 million watch and over 30 vehicles including Porsches, Lamborghinis, and Ferraris.Currently, 18 defendants have been charged, and Lam is expected to attend a plea agreement hearing this Tuesday. The U.S. Department of Justice has already issued rulings against several accomplices, and related cases are still ongoing.

first_img Micron warns that AI storage walls are intensifying, with HBM accounting for about 17% of the interruptions in Meta Llama 3 training

According to TrendForce, at Hot Chips 2026, Micron emphasized that the progress of AI computing power is faster than storage improvements, and the challenge of the "storage wall" is becoming increasingly prominent. Micron Fellow Raghu Sriramaneni pointed out that the computing power of AI accelerators increases approximately threefold every two years, while the bandwidth of HBM increases by less than double during the same period, and the gap may further widen. The deepening reliance on HBM also brings reliability issues; Micron cited Meta Llama 3 data indicating that HBM failures account for about 17% of unexpected training interruptions. New designs that blur the boundaries between computing and storage may help break through the bottleneck.The expansion of HBM also brings area and supply pressures: the latest generation of packaging integrates two GPUs with eight 12-layer HBM4 stacks, with storage accounting for about 90% of the semiconductor area, more than eight times the area occupied by GPUs; achieving equivalent capacity with HBM requires about three times the wafers of standard DDR5 DRAM. Thermal management has also become a key constraint, with solutions such as liquid cooling and ultra-thin chips being explored.Micron is advancing innovations in interconnect, packaging, and cooling, including SerDes and die-to-die PHY optimized for storage, larger size SiP and advanced packaging with glass substrates, as well as liquid cooling and hybrid bonding, and is developing fusion bonding to reduce thermal resistance and enhance data throughput.

first_img The Ethereum Foundation has launched the Platåberget testnet for early testing of the Glamsterdam upgrade

The Ethereum Foundation's DevOps team announced the launch of the Platåberget testnet as an early public testing environment for the Glamsterdam (Gloas + Amsterdam) upgrade. This testnet is open to the community and is planned to run for several months, providing developers with a stable experimental platform to identify and fix issues before the upgrade is deployed to long-term testnets like Sepolia and Hoodi. The Glamsterdam hard fork is scheduled to activate on August 20 on this testnet.The Glamsterdam upgrade includes several significant changes to both the consensus layer and execution layer, including built-in proposer-builder separation (ePBS), block-level access lists (BALs), gas repricing targeting approximately 200 million gas, an increase in the maximum contract deployment size from 24KiB to 64KiB, an increase in the initcode limit from 48KiB to 128KiB, and forward-compatible consensus data structures. Relevant EIPs are summarized in meta EIP-7773. Gas repricing will affect wallets, indexers, and gas estimation tools, and any tools with hard-coded maximum gas limits will need to be updated; EIP-8037 also introduces an independent state gas dimension, where new accounts or writing to new storage slots will be charged by state bytes.The Platåberget validator set is small and allows public participation, supporting deposits from validators or builders submitted through the Dora browser. The testnet provides one-click resources to add networks, faucets, and client images. The community can provide feedback on issues in the Ethereum R&D Discord and related specification repositories.

AI computing power financing is heating up, and Lambda, supported by Nvidia, plans to purchase GPUs through a $917 million loan

Lambda, an AI cloud computing service provider supported by Nvidia, is financing $917 million through the leveraged loan market to procure AI chips. As the construction of artificial intelligence infrastructure accelerates, chip financing is becoming a new way for capital investment in the AI industry. Lambda belongs to the rapidly developing "new cloud vendor" camp in recent years, with its main business being to provide GPU computing power and AI infrastructure services to enterprises and developers.This financing plan will be completed through a loan based on GPU asset-related rights, aimed at supporting the company's expansion of AI computing resources. Reports indicate that AI infrastructure companies are actively exploring new financing methods to meet the enormous capital investment required for building large-scale computing clusters. Previously, AI cloud service provider CoreWeave completed the first transaction in the institutional leveraged loan market for chip financing, providing a new financing model for the industry. As the demand for generative AI continues to grow, Nvidia's GPU supply has become a core resource for AI companies' expansion. By using GPU assets as the basis for financing, AI cloud service providers can rapidly scale their computing power without fully relying on equity financing, while also allowing the traditional credit market to participate in the wave of AI infrastructure investment.
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