Terra Turns To AVAX Reserves To Stay Ahead Of Competitors

BitcoinistPubblicato 2022-04-09Pubblicato ultima volta 2022-04-09

Introduzione

Terra has been making waves in the stablecoin and staking space for its features and returns. However, with the success...

Terra has been making waves in the stablecoin and staking space for its features and returns. However, with the success of the network, there have been other blockchains that have sprung up with similar offerings to try to get a piece of the pie from Terra. This has led the network to take steps to guarantee its spot in the market. First was the bitcoin reserves, and now, it has been revealed that Terra is looking to add AVAX reserves to ward off competitors.
Terra Using AVAX Reserves
Terra has taken to purchasing leading cryptocurrencies in the market to act as reserves for its growing stablecoin UST. After purchasing hundreds of millions worth of bitcoin, it has now turned its focus to another cryptocurrency to serve as a reserve. This time around, Terraform Labs (TFL) in conjunction with the Luna Foundation Guard (LFG) has purchased $200 million worth of AVAX. This trade was made directly from the Avalanche Foundation to serve as a crypto reserve asset.
There are solid reasons why the Foundation has chosen certain cryptocurrencies to serve as its reserve asset. For bitcoin, it was the fact that it was the leading cryptocurrency and its value to investors. For AVAX, founder Do Kwon explained that it chose AVAX due to the growth and loyalty from users of the AVAX ecosystem.
“Avalanche is still a growing ecosystem — a lot of it is fueled by loyalty to the AVAX token and users feel a lot of affinity with an asset that aligns itself with AVAX,” Kwon explained. “Whereas for the average Ethereum user, aligning yourself with Ether doesn’t really mean that much.”

Bitcoin price chart from TradingView.com

LUNA price drops to $98 | Source: LUNAUSD on TradingView.com
The announcement comes on the heels of competitor NEAR Protocol announcing that it was planning to launch its own stablecoin called USN that would offer APR as high as 20%. This IS no doubt a play to poach users from the Terra platform which has attracted a large number of crypto investors due to its high APR.
NEAR Protocol currently only has about $300 million in TVL compared to Terra’s $29 billion but Crypto Insiders founder Zoran Kole believes that by launching the USN stablecoin with such high APR, combined with the fact that it is one of the protocols with the most active development, then NEAR will be able to rival, if not surpass, the TVL and market cap of Terra.
Terra’s partnership with Avalanche promises a unique cross-chain experience for its users while strengthening the position of the UST in the market. “The partnership will also provide distinct benefits to the Avalanche ecosystem at large, by connecting the #LUNAtics & Terra builders with the vibrant Avalanche ecosystem, ushering in an immersive cross-chain experience,” Terra said in a Twitter post.

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U.S. Government Bans Foreign Nationals from Using Fable 5, Anthropic Issues Rebuttal

U.S. Government Bans Foreign Access to Fable 5, Anthropic Issues Rebuttal On June 12th, the U.S. government ordered AI company Anthropic to immediately suspend all foreign access—including foreign nationals within the U.S. and Anthropic's own foreign employees—to its newly released Fable 5 and Mythos 5 AI models, citing national security concerns. This forced Anthropic to temporarily disable access to both models for all users globally, as it cannot technically differentiate user nationality at scale. The models, released just three days prior, represent Anthropic's highest public capability tier. Fable 5 is the first publicly available model from the advanced "Mythos" family, while Mythos 5 is a less-restricted version for approved cybersecurity and critical infrastructure partners. The government's directive was reportedly triggered by claims from another company that it could "jailbreak" Mythos 5, raising alarm within the Trump administration. Anthropic, in a detailed public statement, strongly challenged this rationale. The company argues the demonstrated "jailbreak" is a narrow, non-generalized technique that merely involves identifying minor, known software vulnerabilities—a capability common to other publicly available models like OpenAI's GPT-5.5 and routinely used by cybersecurity defenders. Anthropic stated it has complied with the order but disagrees with the government's standard, warning that applying it industry-wide would halt all new frontier model deployments. The company criticized the lack of a transparent, fact-based legal process and expressed confidence the situation stems from a misunderstanding. It is working to restore access and will release more technical details within 24 hours. Other Anthropic models remain unaffected.

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The Revelation from the Raydium Theft Incident: New DeFi Vulnerabilities Lurking in Forgotten Old Contracts

**Raydium Exploit Reveals DeFi's Hidden Risk: Forgotten "Zombie" Contracts** A recent attack on Raydium's deprecated V3 AMM pools resulted in a loss of approximately $1.34 million. The hacker exploited pools that were no longer supported by Raydium's current UI or SDK but remained fully functional and accessible on-chain. This incident highlights a critical, often overlooked category of risk in DeFi: inactive or legacy smart contracts that projects fail to properly decommission. Since March 2025, there have been at least 8 publicly reported attacks targeting such abandoned contracts, with total losses around $10.8 million. Including older pools and deprecated features, the count rises to 10 incidents with roughly $22.5 million in losses. These "zombie contracts" represent a lifecycle management failure rather than a code vulnerability, yet they are typically misclassified under general "code bug" categories in security reports, masking the true scale of the problem. The root cause is that projects often merely document a contract as "deprecated" without taking essential technical steps to secure it: withdrawing remaining assets, disabling external call functions, and implementing ongoing monitoring. These forgotten, under-monitored components become prime targets for attackers. To address this, the industry needs to recognize "zombie contracts" as a distinct risk category and establish standardized decommissioning protocols. Essential steps should include: 1) a formal retirement announcement, 2) removal of all front-end integrations, 3) withdrawal of locked assets, 4) disabling key contract functions, 5) ongoing security monitoring, 6) clear user communication, and 7) a post-mortem analysis. The value of a DeFi project lies not only in its current TVL but also in the security of its historical codebase, which has now become a new attack surface.

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Robots Begin to 'Consume Data': The Hidden Production Chain from Indian Data Factories to Billion-Dollar Humanoid Robots

Robots have started to 'consume data,' driving the formation of a new industrial supply chain focused on producing training data for embodied AI. Unlike large language models, which are trained on vast internet text corpora, embodied AI models face a 'data desert' in the physical world. This has created a massive demand for first-person perspective video data (Ego Data), captured by workers wearing cameras in places like Indian garment factories. Companies like Neocambrian AI are establishing 'data factories' where workers perform standardized tasks (e.g., sorting clothes, kitchen organization) to generate thousands of hours of video. Research, such as NVIDIA's EgoScale, demonstrates that scaling this human demonstration data predictably improves robot performance, particularly for dexterous manipulation. This has validated a training path combining large-scale human data for pre-training with smaller amounts of robot-specific data for fine-tuning. The value of different data types varies significantly, forming a 'data pyramid.' The base consists of low-cost, large-scale internet and Ego Data. Higher layers include more expensive motion-capture data (e.g., from data gloves), simulation/synthetic data, and the most costly and scarce layer: real robot teleoperation data. This demand has spawned a layered ecosystem of data suppliers: low-cost data factories, motion capture and alignment specialists, robot-native teleoperation service providers, simulation data companies, and platforms aiming for data standardization. Robot companies themselves are adopting a 'layered procurement' strategy: outsourcing generic Ego Data while building in-house capabilities for robot-specific adaptation data and the critical deployment/failure data generated in real-world applications. The industry is shifting focus from hardware and basic mobility to the data pipelines required for general-purpose capability. While parallels exist to data labeling companies like Scale AI in the LLM boom, the physical complexity of robot data—involving action success ambiguity and sim-to-real gaps—requires more integrated solutions for data collection, annotation, and a continuous feedback loop. The race is on to build the data engines that will teach robots to operate reliably in the unstructured real world.

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