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Crypto ETFs Attract $3.04B Weekly as Bitcoin Inflows Hit $2.4B; Ethena Expands USDe Into Equities

By
Paul Ugbede Godwin
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0

The crypto market is entering a period in which institutional capital and increasingly sophisticated on-chain financial products are moving in parallel. Spot crypto exchange-traded funds recorded about $3.04 billion in weekly inflows.

While Bitcoin ETFs alone attracted approximately $2.4 billion, marking their strongest weekly inflow since October 2025. Ethena is expanding the strategy behind its USDe synthetic dollar into tokenized equities and equity perpetuals on Binance.

Signaling a broader convergence between crypto infrastructure and traditional financial markets. The ETF figures are significant because they show that demand for digital assets is not confined to speculative activity on centralized or decentralized exchanges.

Spot ETFs provide regulated market access through conventional investment structures, allowing institutional investors, wealth managers and other market participants to gain exposure without directly managing wallets or private keys.

A $2.4 billion weekly inflow into Bitcoin ETFs therefore represents a substantial movement of capital through an increasingly established investment channel. The strength of the Bitcoin flows also changes the market conversation.

Rather than simply measuring short-term price momentum, ETF flows provide a window into investor positioning. Persistent inflows can increase the amount of capital competing for available Bitcoin exposure, while withdrawals can have the opposite effect.

The latest weekly figure, being the strongest since October 2025, suggests that institutional demand has regained considerable momentum. Yet Bitcoin is no longer the only asset benefiting from the expansion of regulated crypto investment products.

The broader $3.04 billion inflow indicates that investors are allocating across multiple parts of the digital-asset ecosystem. Ethereum and other crypto ETFs are increasingly becoming part of the same institutional allocation framework, potentially widening the market beyond Bitcoin’s traditional dominance.

Parallel to this institutionalization, Ethena is pushing USDe into a more complex financial environment. USDe was designed as a synthetic dollar, using a combination of crypto assets and derivatives-based strategies rather than relying solely on conventional cash reserves.

Its expansion into bStocks and equity perpetuals on Binance extends that architecture toward tokenized equity exposure and leveraged derivatives.

The development is important because it illustrates how stable-value crypto instruments are increasingly being connected to financial products traditionally associated with banks, brokerages and derivatives markets.

Tokenized equities can bring representations of traditional stocks onto blockchain infrastructure, while perpetual contracts allow traders to maintain leveraged exposure without holding the underlying asset in the conventional manner.

For Ethena, the opportunity is potentially larger than simply adding another trading product. Expanding USDe’s backing strategy into equity-related markets could diversify the sources of yield and market exposure supporting its ecosystem.

However, greater complexity also introduces additional risks, including derivatives losses, funding-rate changes, liquidity constraints, counterparty exposure and the possibility of sharp market dislocations.

The ETF inflows and Ethena’s expansion point toward the same structural trend from different directions. Traditional capital is moving deeper into crypto through regulated investment vehicles, while crypto-native protocols are moving outward into equities and sophisticated derivatives.

The boundary between digital assets and traditional finance is therefore becoming increasingly difficult to define. ETFs are bringing institutional money into crypto, while products such as USDe are attempting to bring traditional market exposure into blockchain-based financial infrastructure.

The next phase of the market may be shaped less by the separation of these systems and more by how successfully they become connected.

Fake GIWA Bridge Scams Users Out of $2M as Circle and Tether Freeze Bitget Hack Funds

By
Paul Ugbede Godwin
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0

The latest wave of crypto security incidents highlights two different vulnerabilities in the digital-asset economy: the ability to manufacture convincing blockchain infrastructure and the difficulty of recovering assets once a major exchange breach has occurred.

A fake GIWA bridge reportedly drained about $2 million in Ether, while Circle and Tether moved to freeze a small portion of assets connected to the much larger Bitget hack. The GIWA incident is particularly revealing because the attackers did not simply create a fraudulent website.

They constructed a counterfeit Layer-2 environment that appeared to represent GIWA, an Ethereum-based network developed by Dunamu, the operator of South Korean crypto exchange Upbit. GIWA’s actual mainnet had not launched, yet the fraudulent network presented users with infrastructure that looked sufficiently legitimate to attract deposits.

According to DYORSWAP’s reconstruction, approximately 1,335 addresses deposited around 767.65 ETH into the fake bridge. About 766.25 ETH was subsequently drained, representing roughly $2 million at the time.

The attackers reportedly used GIWA-associated chain information, including Chain ID 9134, alongside an RPC endpoint and bridge infrastructure. The combination created a convincing imitation of a blockchain that users expected to become operational.

The episode demonstrates why blockchain verification cannot depend on a single identifier. A chain ID can help wallets distinguish networks, but it does not establish who controls the network, bridge contracts or RPC infrastructure.

DYORSWAP said its own contracts were not compromised and has begun compensating affected users, reportedly distributing more than 200 ETH from its own funds while continuing to investigate the attackers. The Bitget incident presents a different security problem.

The exchange initially reported that approximately $351.6 million in assets had been affected by unauthorized transfers from some hot wallets. Bitget later revised the estimated amount to approximately $387.5 million after accounting for additional assets on Zcash and TRON.

The company said its cold wallets and separate self-custodial Bitget Wallet were not affected. In response, stablecoin issuers Circle and Tether froze a wallet associated with the attack. The address contained approximately 218,023 USDT and 99,990 USDC.

Meaning only about $318,000 in stablecoins was immobilized. That amount is small compared with the overall losses, but the intervention illustrates an important characteristic of centralized stablecoins: issuers can sometimes blacklist specific addresses and prevent their tokens from being transferred.

The limitations are equally important. Freezing USDC and USDT does not freeze Ether, XRP or other native blockchain assets. Reports indicated that substantial amounts of stolen XRP were moved after the breach, demonstrating how quickly assets can travel beyond the reach of issuer-level controls.

The incidents underline a broader lesson for crypto markets. Security is no longer simply about auditing smart contracts. Users must also verify bridge addresses, RPC endpoints, chain identifiers, deployment status and official announcements.

Meanwhile, exchanges and stablecoin issuers must balance rapid intervention against the decentralized architecture of the networks they serve. As blockchain adoption expands.

Trust increasingly depends on infrastructure verification as much as cryptographic security. The GIWA scam showed how easily legitimacy can be manufactured, while the Bitget response showed how difficult it can be to contain stolen assets once they cross multiple networks.

How Tech Firms Are Fighting the Backlash Against Pervert Glasses and Smart Wearable Privacy Concerns

By
Paul Ugbede Godwin
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0

The next battle in wearable technology may not be about battery life, artificial intelligence or processing power. It may be about trust. Smart glasses have become one of the most visible attempts by technology companies to move computing from the smartphone into the physical world.

But as cameras, microphones and artificial intelligence become embedded in eyewear, the industry is confronting an uncomfortable problem: people may not want to be recorded, analyzed or identified without knowing it.

That anxiety has produced an unflattering nickname for some camera-equipped devices — “pervert glasses” — reflecting fears about covert recording and privacy. For technology companies, overcoming that backlash is becoming almost as important as improving the hardware.

The attraction of smart glasses is easy to understand. Unlike smartphones, glasses can remain in front of the user’s eyes while leaving both hands free. They can provide navigation, translate conversations, identify objects, summarize meetings, answer questions and capture photographs from a first-person perspective.

Generative AI makes the concept even more powerful because the glasses can potentially understand what the wearer is seeing and hearing. But those same capabilities create an unusual social problem. A smartphone camera is normally visible and deliberately pointed at someone.

Glasses can be much more discreet. A person standing nearby may not know whether the wearer is simply looking at them or capturing an image, recording a conversation or using AI to interpret what is happening.

The industry’s response is increasingly focused on visible signals and clearer controls. Camera-equipped glasses can use indicator lights or other physical cues to signal when recording is taking place. Manufacturers can also make it easier for users to disable microphones, cameras and connected services.

These measures do not eliminate privacy concerns, but they can make the technology less ambiguous. Software safeguards are another important part of the strategy. Companies developing AI-powered wearables have incentives to limit what their systems can recognize, store or share.

Restrictions around facial recognition, sensitive locations and private conversations can reduce some of the most controversial applications. Data minimization — collecting less information and retaining it for shorter periods — can become a central selling point.

Yet technology alone cannot solve the problem. Social norms will matter. Every new form of recording technology has forced society to negotiate new boundaries. Camera phones initially generated concerns about privacy in public spaces.

Drones raised similar questions about surveillance from above. Smart glasses bring those questions closer to the human face because the recording device is worn rather than held.

That means companies may need to communicate not only what their products can do, but what they deliberately prevent them from doing. Privacy settings buried inside an application may not be enough.

Consumers, businesses and regulators will increasingly expect straightforward explanations about where data goes, how long it is retained and whether information is used to train AI systems.

The backlash could ultimately become a test of whether wearable computing can achieve mainstream acceptance. The industry’s challenge is not simply persuading consumers to buy smarter glasses. It is persuading everyone around the wearer that the glasses can exist in public without turning ordinary interactions into invisible data collection.

If companies succeed, smart glasses could become a natural extension of personal computing. If they fail, the technology may remain impressive but socially uncomfortable.

The future of smart eyewear therefore depends on more than better lenses and better AI. It depends on making privacy visible, understandable and credible. In wearable technology, trust may prove to be the most important feature that companies have to build.

Why LinkedIn Visibility Could Be More Valuable Than Sending Hundreds of Applications

By
Paul Ugbede Godwin
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0

The modern job market has quietly changed the meaning of a job application. For decades, applying for a position was treated as the central act of job hunting: find an opening, prepare a résumé, write a cover letter, submit the application, and wait.

Today, that process still matters, but it is increasingly only one part of the equation. Recruiters often search proactively for people who appear capable of solving a specific problem before those candidates ever apply.

That changes where job seekers should spend their most valuable hours. A recruiter looking for a software engineer, marketing strategist, financial analyst, journalist, or product manager may begin by searching LinkedIn for particular skills, industries, experiences, locations, or accomplishments.

They are not necessarily waiting for hundreds of applications to arrive before deciding whom to contact. In many cases, they are trying to identify the right person first. This means visibility has become an important form of career capital.

Consider two professionals with similar qualifications. One spends every morning submitting applications but has an almost invisible online presence.

The other applies selectively while consistently demonstrating expertise online, sharing thoughtful industry observations, publishing useful work, and maintaining a profile that clearly explains what problems they can solve.

The second professional gives recruiters something to discover before a formal application is ever submitted. This does not mean applications are useless. They remain essential for many organizations, particularly where applicant-tracking systems, compliance requirements, or standardized recruitment processes determine who moves forward.

The lesson is not to stop applying. It is to stop treating applications as the entire job search. A more effective approach is to divide the job hunt into two parallel activities: application and discovery. Application means responding to opportunities that already exist.

Discovery means making it easier for opportunities to find you. LinkedIn can function as the bridge between the two. A profile should not simply list previous employers and responsibilities. It should communicate a professional identity.

Instead of saying only what you have done, it should make clear what you understand, what you can build, what problems you solve, and where you create value. Content can reinforce that positioning.

A person interested in technology might analyze emerging technologies. A finance professional might explain market developments. A designer might document projects and decisions. A journalist might publish original reporting or thoughtful analysis.

The objective is not to become an influencer. It is to create evidence of competence that someone can encounter without being asked. Networking also becomes more strategic under this model.

Rather than sending generic requests to hundreds of strangers, job seekers can engage with people working in their target industries, contribute to relevant conversations, attend professional events, and develop relationships before asking for employment.

The deeper shift is psychological. Job hunting is no longer simply about convincing an employer that you deserve a position. It is also about making your capabilities visible enough that employers can recognize a potential fit.

Monday morning energy is valuable because it is usually when concentration is highest. Spending all of it repeatedly filling forms can produce activity without necessarily producing visibility.

The modern job search therefore requires a broader strategy: apply where there is a genuine fit, but build a professional presence that allows opportunity to find you too. In a competitive labor market, being qualified may open a door. Being discoverable can help someone notice the door exists.

Anthropic Launches Cheaper Sonnet 5.5 Days After CEO Called for AI Industry to Slow Development Pace

By
Samuel Nwite
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0

Anthropic is pushing its AI business deeper into the cost-sensitive enterprise market with the release of Sonnet 5.5, a faster and cheaper model designed for routine coding and business tasks, as the company continues to balance rapid commercial expansion with growing scrutiny over the risks of increasingly capable AI systems.

The launch on Monday is Anthropic’s second model release since CEO Dario Amodei called for the industry to slow the pace at which frontier AI systems are developed. It follows the introduction of Opus 5.5 less than a week ago, while the company said its lower-priced Haiku 5.5 model is also coming soon.

The three-model lineup points to a clearer segmentation of Anthropic’s products. Opus is positioned for tasks requiring greater reasoning and judgment, while Sonnet is intended for customers who prioritize speed and cost and do not require the highest level of model intelligence.

“Sonnet is really for the cost-conscious customer where they might not need as much intelligence,” Theo Chu, a research product manager at Anthropic, told CNBC. “It might be routine tasks that just need execution, but don’t need that judgment that Opus can bring.”

Sonnet 5.5 costs $2 per million input tokens and $10 per million output tokens, half the price of Opus 5.5. Anthropic also said the new model requires fewer tokens to complete tasks than its predecessor, potentially reducing the effective cost further for customers.

That pricing strategy matters as competition in AI increasingly moves beyond headline model performance. Businesses deploying AI at scale are becoming more focused on the cost of running models across millions of interactions, particularly for coding, customer service, document generation and other repetitive workloads.

Sonnet 5.5 is designed to improve coding, scoped task completion, and the creation of polished documents, presentations, and spreadsheets. These are commercially important applications because they can be embedded into existing corporate workflows rather than requiring users to experiment with AI as a general-purpose chatbot.

The model is available across Anthropic’s platforms as well as Amazon Web Services, Google Cloud and Microsoft Azure.

By making Sonnet cheaper to run while maintaining capabilities suited to common enterprise tasks, Anthropic is effectively targeting a larger portion of the AI workload market. Companies do not necessarily need their most expensive model for every task, and using a lower-cost model for routine work can materially change the economics of large-scale deployment.

Anthropic’s decision to launch multiple models in quick succession also gives customers more flexibility over the trade-off between intelligence, speed, and price.

The approach has been touted as AI agents take on longer sequences of tasks. A company running an agent continuously may incur substantially higher costs than one using AI for isolated queries. Reducing the number of tokens required to complete each task can therefore improve margins for both Anthropic and its customers.

The Safety Question Has Not Disappeared

The timing of Sonnet 5.5 is especially notable because it comes shortly after Amodei called for a slower pace of frontier AI development.

Anthropic and its competitors have faced growing scrutiny over whether more capable models could create significant cybersecurity and other risks.

Anthropic said Sonnet 5.5 does not advance the frontier of its models’ capabilities. As a result, the company said its alignment testing focused primarily on a “targeted set of risks that apply to models of any capability level.”

This makes a difference because Anthropic is not presenting the launch as another step in the race to build the most powerful possible model. However, the company acknowledged that Sonnet 5.5 has significantly stronger cybersecurity capabilities than its predecessor.

Anthropic described the improvement as a “large improvement,” making Sonnet 5.5 the first Sonnet model to receive fallbacks and cybersecurity safeguards similar to those developed for the company’s most capable models.

“Focusing on alignment and safety has been a key part of our mission from the very beginning,” Chu said. “This is something that we’ve always prioritized across our models.”

The combination of stronger cybersecurity capabilities and additional safeguards underpins one of the central complications of AI development: capabilities that make models more useful for legitimate customers can also increase their potential usefulness in harmful activities.

Anthropic is now treating cybersecurity safeguards as necessary even for a model positioned below its frontier systems.

A Different Interpretation of Amodei’s Slowdown Proposal

The launch also provides some context for Amodei’s call to “slow down” AI development. His proposal did not mean Anthropic would stop releasing models or withdraw from commercial competition. Instead, it focused on slowing the development of advanced frontier systems while continuing to build and deploy AI products.

Sonnet 5.5 fits that distinction.

Anthropic can continue expanding its commercial footprint through cheaper, specialized models while taking a more cautious approach to the development of its most powerful systems. That may become a relevant business model as the industry shifts from the initial race to demonstrate benchmark-leading intelligence toward the more difficult question of whether companies can generate sustainable returns from massive AI infrastructure investments.

For customers, the economics are becoming as important as raw capability. A model that is slightly less capable but significantly cheaper to operate can be preferable for high-volume workloads.

Anthropic’s product strategy now resembles a portfolio rather than a single-model race. Opus 5.5 is positioned for demanding tasks where higher intelligence and judgment justify greater expense. Sonnet 5.5 targets routine and cost-sensitive workloads. Haiku 5.5 will complete the lower-cost end of the range when it launches.

Analysts expect that structure to allow Anthropic to capture different levels of enterprise demand while giving customers an incentive to use its models more extensively.

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