Payments giant Stripe acquires team from crypto wallet app Valora

cointelegraphPublicado em 2025-12-11Última atualização em 2025-12-11

Resumo

Payments giant Stripe has acquired the team behind crypto wallet app Valora, a day after launching the testnet for its stablecoin-focused blockchain project, Tempo. Valora CEO Jackie Bona stated the team will join Stripe to contribute expertise in web3 and user-first experiences. Valora, which spun out of Celo development group cLabs in 2021, offers a mobile wallet supporting stablecoins across multiple blockchains. The Valora app will continue operating under cLabs. This move accelerates Stripe's growing blockchain initiatives, particularly Tempo network, which has a $5 billion pre-launch valuation and emphasizes easy stablecoin creation.

Payments giant Stripe has acquired the team from crypto wallet firm Valora, just a day after launching its testnet for its stablecoin-focused blockchain project Tempo.

According to Valora CEO Jackie Bona, the acquisition will see the Valora team join Stripe and work on the firm’s blockchain initiatives.

Valora was founded in mid-2021 after spinning out of Celo development group cLabs and raising $20 million in Series A funding in the process.

The Valora app is a mobile wallet that supports stablecoins and other assets across Celo, Ethereum, Base, Optimism and Arbitrum. Apart from the wallet, the team also developed an open protocol launchpad for Web3 apps geared toward a mobile-native experience.

“Stripe shares our conviction that stablecoins and crypto can dramatically expand who gets to participate in the global economy,” said Bona, adding that “by bringing Valora’s team to Stripe, we’ll be able to contribute our expertise in web3 and user-first experiences to a platform with unparalleled reach.”

It is not explicitly stated what the Valora team will work on; however, Stripe will be tapping a team that has had a strong focus on global payments, digital wallets and user-friendly smartphone-based Web3 apps.

“Through this work, we’ve seen firsthand how access to stablecoins and crypto rails can expand economic opportunity. In the past few months, it became clear that we could accelerate this mission by joining Stripe, one of the world’s leading financial infrastructure platforms,” Bona said.

Source: Jackie Bona

Related: The easiest and safest methods for gifting crypto at Christmas in 2025

The Valora app will continue to function, but its operations and future development will be handed over to cLabs.

Momentum building for Stripe and Tempo

After an on-again-off-again relationship with crypto, Stripe has been making strides in the blockchain space over the past couple of years. Momentum has been building particularly since first unveiling Tempo four months ago in partnership with crypto VC firm Paradigm, with the network already having a $5 billion pre-launch.

The latest move from Stripe comes just a day after Stripe and Paradigm’s layer-1 blockchain project Tempo launched its open testnet.

One of the key features highlighted during the testnet launch was the ease and simplicity of creating stablecoins directly in the browser, among other benefits.

Magazine: 11 critical moments in Ethereum’s history that made it the No.2 blockchain

Leituras Relacionadas

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

NVIDIA's Rubin Ultra, the top-tier variant of the newly announced Rubin AI accelerators, has reportedly seen significant specification downgrades, according to an industry report from SemiAnalysis. Initially designed with four compute dies (4-die), the Rubin Ultra is now said to be reduced to a 2-die design. Key changes highlighted in the report include: * **No increase in peak theoretical compute performance**, remaining at 35 PFLOPs like the standard Rubin. * **Severe reduction in memory capacity** to 192GB using 8-Hi HBM stacks, which is less than the standard Rubin's 288GB using 12-Hi stacks. * **Negligible memory bandwidth improvement** of only 1 TB/s. * **Slightly higher chip-level power consumption**. * The **primary upgrade is a massive increase in scale-up interconnect capacity**, supporting connections for up to 576 GPUs via NVLink, compared to 72 for the standard Rubin. The report suggests the redesign is primarily a cost-optimization move driven by the sharp rise in HBM (High-Bandwidth Memory) prices. By reducing the expensive HBM content and shifting investment towards enhanced system-scale networking, NVIDIA aims to maintain the platform's value for large-scale AI training clusters while managing soaring material costs. The news reportedly triggered a sell-off in South Korean memory stocks, with SK Hynix and Samsung shares falling around 8%, as markets grew concerned that NVIDIA—a major HBM buyer—might be reducing its reliance on high-capacity memory, potentially capping future pricing power for memory makers.

Odaily星球日报Há 11m

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

Odaily星球日报Há 11m

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinistHá 1h

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinistHá 1h

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbitHá 1h

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbitHá 1h

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbitHá 1h

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbitHá 1h

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbitHá 1h

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbitHá 1h

Trading

Spot
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow