Need to know what happened in crypto today? Here is the latest news on daily trends and events impacting Bitcoin price, blockchain, DeFi, Web3 and crypto regulation.

Polish lawmakers failed to overturn a presidential veto of crypto legislation as the Zondacrypto investigation expands and its Estonian operator enters bankruptcy.

Iran's economic mobilization highlights the conflict's strain on its economy, potentially complicating future international negotiations.

The post Iran sets up “economic war” command center amid conflict with Israel appeared first on Crypto Briefing.

Robinhood's rapid DEX volume growth highlights its potential to disrupt established DeFi ecosystems, challenging giants like Ethereum.

The post Robinhood’s weekly DEX volume surges to $10B as its Layer 2 chain climbs DeFi rankings appeared first on Crypto Briefing.

B.AI, a next-generation AI infrastructure platform, recently set off a developer frenzy by offering free access to top-tier models. Within days, daily token throughput across the platform crossed 1.33 trillion—a historic milestone. 

The record-breaking figure underscores the campaign’s explosive rollout, but it marks only the first step in B.AI’s broader strategic roadmap. Moving beyond traditional compute distribution pipelines, B.AI aims to build the global settlement layer for intelligence: a core infrastructure hub engineered to power cross-node collaboration, orchestration, and value distribution for AI agents across complex business workflows. 

Positioning itself strategically above all models, below all agents, B.AI deeply integrates a diverse range of top-tier models with full-stack components, laying an unshakable, irreplaceable foundation for the mass adoption of autonomous agents and the productivity boom that follows.

Daily Token Throughput Tops 1.33 Trillion: B.AI’s Free Access Rollout Fuels Usage Boom

B.AI’s recent move to open free access to premium AI models has captivated developers and quickly taken over industry conversations. The push for accessible compute has not only fueled a surge in platform activity but also shattered usage records. 

In a matter of days, soaring API demand pushed the platform’s daily token throughput past a staggering 1.33 trillion. Over a 15-day window, cumulative volume reached 8.19 trillion tokens, drawing in more than 220,000 new API users. As of September 3, B.AI’s total user base had officially surpassed 2.3 million. 

That massive adoption traces directly to the platform’s zero-cost model lineup, a strategic rollout built to erase developers’ cost concerns. With every barrier removed, B.AI now offers unlimited free access to six leading frontier models: DeepSeek-V4-Flash, DeepSeek-V4-Flash-Vision-Exp, Tencent Hy3, Xiaomi MiMo-V2.5, GLM-5.3-Flash (Ox Alpha), and Qwen3.8-Flash. 

Notably, on September 3, B.AI rolled out a new pricing structure for DeepSeek-V4-Flash and DeepSeek-V4-Flash-Vision-Exp, introducing tiered discounts. Developers now receive a 50% discount during peak hours, with off-peak rates dropping to just 25% of standard peak pricing. At the same time, the platform has kept zero-cost access in place for GLM-5.3-Flash (Ox Alpha), Qwen3.8-Flash, Tencent Hy3, and Xiaomi MiMo-V2.5. Despite the shift toward commercialization, developer momentum hasn’t wavered, with platform-wide token throughput continuing its steady climb.

This sustained momentum proves the campaign was far more than a short-term compute giveaway—it is a bellwether for the broader evolution of AI infrastructure. Cracking 1.33 trillion daily tokens makes one thing clear: AI applications are moving past basic chatbots. Powered by a high-performance technical stack and flexible service mechanics, B.AI is laying the groundwork for the next frontier—autonomous AI agents operating at scale. 

Powering the “AI Grid”: B.AI Anchors the Global Settlement Layer for the Agent Economy

For B.AI, democratizing compute is only the prelude. Looking further ahead, the platform is committed to building full-stack infrastructure for the agentic era, cementing its position as the global settlement layer for intelligence.

In the agent era, a typical agent task calls for constant switching between models. No single provider can power a complete workflow on its own, so developers are left juggling fragmented API protocols, disjointed billing systems, and conflicting rate limits. 

B.AI’s settlement layer bridges this exact gap. Positioned strategically “above all models, below all agents,” B.AI abstracts models across different providers, capabilities, and cost structures into a unified pool of schedulable resources.

Powered by a dual-tier API structure offering official-route reliability alongside lowest-cost custom channels, developers can choose between guaranteed direct connections and deeply discounted options across a broad lineup of models. Combined with smart routing on the Chat interface, B.AI operates as a full-stack “AI grid,” ensuring every agent request lands on the optimal model to deliver reliable performance at maximum cost-efficiency. 

On the settlement front, this power grid seamlessly bridges both Web2 and Web3 models. For Web2, developers can rely on familiar traditional payment methods to top up with minimal friction. For Web3, B.AI leverages on-chain payment rails to offer global developers decentralized, verifiable, and low-friction payment options.

With dual payment systems running in parallel, B.AI enables developers and agent applications across any infrastructure setup to find their optimal settlement path on the grid, providing single-point integration with borderless global reach. 

Driving Core Productivity: B.AI Reshapes Agent Collaboration

Beyond building a foundation for compute routing and global settlement, B.AI is moving past base infrastructure to power real-world productivity. By enabling seamless agent collaboration across complex workflows, it delivers the missing execution layer for the agent economy. 

At the heart of this execution layer is native Codex integration. Full compatibility with the Responses API means developers can now use a single B.AI key inside Codex to run flagship GPT models and DeepSeek favorites side by side. 

Engineers can now bring these powerhouse models straight into their daily dev stack. From code generation and reasoning to debugging and refactoring, B.AI unifies the entire workflow under one roof—delivering a direct line from model selection to shipped code. 

Beyond coding, to keep agents running reliably in real-world production at scale, B.AI has built a full-stack infrastructure powered by five core components, equipping agents with a fine-tuned operational engine:

• x402 Payment Protocol: Introduces an innovative “pay-before-response” model that executes high-frequency, on-chain micro-settlements in the background during cross-agent API calls and compute orchestration.
• 8004 Identity Protocol: Issues verifiable on-chain credentials for every agent, logging execution history and credit scores to establish a reliable layer of trust for cross-node collaboration.
• Skills Matrix: Plug-and-play, standardized building blocks that interface directly with MCP servers to equip agents with instant, modular tool-calling capabilities.
• BAIclaw and BAIcode: Built-in platform assistants engineered for end-to-end execution. BAIclaw manages all-in-one terminal operations and multi-agent workflows, while BAIcode serves as an advanced developer engine that streamlines the full pipeline from task analysis and architecture design to coding and testing.

From the token surge sparked by zero-cost model access to its positioning as the global settlement layer for intelligence; from seamless Codex integration to full-stack infrastructure powered by x402, 8004, Skills, and native assistants—B.AI delivers far more than a battle-tested technical stack; it unveils a clear blueprint for what lies ahead. B.AI is building not just accessible compute today, but the definitive launchpad for a thriving agent economy. The future is here—and this is only the beginning.

B.AI Team

Singapore

support@b.ai

Ethereum Name Service has opened discussion around an ENSv2 migration proposal that would move domain registration and renewal resolution toward a Layer-2 registry model.

The idea is pretty straightforward: ENS works, but Ethereum mainnet fees can make everyday domain actions expensive. Moving more of that activity to Layer 2 could reduce costs while keeping links back to Ethereum’s security model.

This is still an early governance stage.

The proposal is a temp check, not a completed migration. It has not passed a full executable DAO vote, and users should not treat it as already implemented. But it is a meaningful direction for one of Ethereum’s most recognizable identity systems.

For more details, visit the official Discuss platform.

TL;DR

• ENS is discussing an ENSv2 migration toward a Layer-2 registry.
• The proposal aims to reduce registration and renewal costs.
• It is an early governance discussion, not an implemented migration.

Why ENS Needs Lower Costs

ENS is one of Ethereum’s simplest consumer products.

Instead of using long wallet addresses, users can register readable names. That makes wallets easier to share, payments easier to understand, and identity easier to build across apps.

The problem is cost.

When Ethereum mainnet fees rise, simple actions like registering, renewing, or managing names can become annoying or expensive. That limits how broadly ENS can be used, especially for smaller users.

A Layer-2 registry model could help by moving more routine activity onto cheaper infrastructure.

Keeping Ethereum Security In The Picture

The challenge is not just moving to L2.

ENS has to preserve the trust assumptions that made it valuable in the first place. Users want lower fees, but they also want confidence that names remain secure, durable, and connected to Ethereum’s settlement layer.

That is why the proposal matters.

It is trying to find a balance between cheaper user actions and strong security proofs. If that balance works, ENS could become easier to use without losing the trust that comes from being rooted in Ethereum.

Governance Comes First

ENS is governed by a DAO, so major changes need community discussion and approval.

The current proposal is still in the early discussion phase. That means delegates, users, developers, and service providers can debate trade-offs before anything becomes final.

That process may feel slow, but it is important.

Name infrastructure is sensitive. If ENS changes how registration and resolution work, the ecosystem needs time to understand the implications.

Cost Savings Need Careful Wording

The proposal aims to reduce gas costs sharply, but cost-saving claims need to be tied to the final design.

Layer 2s can make transactions much cheaper, but actual savings depend on implementation, network fees, bridging assumptions, proof systems, and how users interact with the new registry.

So the right view is that ENSv2 could significantly reduce costs if adopted and implemented successfully.

It is not a guarantee today.

The Bigger Ethereum Identity Story

ENS has remained one of Ethereum’s most recognizable non-financial protocols.

It is not just about speculation. It is about identity, payments, wallets, websites, and user experience. If ENS can make names cheaper and easier to manage, it could become more useful across the Ethereum ecosystem.

That is why the L2 migration proposal matters.

It shows ENS trying to adapt to where Ethereum is going: a world where mainnet anchors security, while more user activity happens on Layer 2.

The proposal is early, but the direction makes sense.

This article draws on ENS governance materials relating to the ENSv2 Layer-2 registry migration proposal.

This article was written by the News Desk and edited by Samuel Rae.

This report is based on information released by Discuss. at Discuss

Hardware wallet maker Trezor says a breach at logistics provider ShipMonk exposed contact and order data for another approximately 67,000 U.S. customers after years-old records remained in the vendor's systems despite written deletion assurances.

The Sept. 4 update expands an incident Trezor initially said affected 13,689 people. The two disclosed groups imply a total of roughly 80,689, although Trezor has not issued a single combined figure or published underlying data showing whether the groups overlap. Its use of “another” indicates that it considers the new records additional to the original cohort.

The newly disclosed records cover U.S. orders from November 2019 through August 2021 and include names, email addresses, phone numbers, shipping addresses and order numbers. The data can connect an identifiable person and physical location with a hardware-wallet purchase, creating risks beyond a conventional email leak.

Old data outlived a 90-day policy

When Trezor first disclosed the breach on Aug. 13, it counted 11,742 customers with full exposure and 1,947 with partial exposure. Trezor's Aug. 13 account said older order data had already been deleted. An Aug. 14 clarification acknowledged that some partially exposed records included older orders.

The Sept. 4 update reverses that understanding. Trezor said it repeatedly requested and received written assurances that ShipMonk had deleted the data, yet records from 2019 to 2021 remained. Trezor's published delivery-data policy says customer details should be deleted from both its own and its fulfillment partner's systems after 90 days, with exceptions for ongoing order issues. The assurance letters and their dates have not been made public.

BleepingComputer reported that a ShipMonk notification attributed the original unauthorized access to a vulnerability in analytics platform Metabase. Metabase said the August zero-day could create a session tied to an administrator account and allow bulk table downloads. Once the provider incident was reassessed, the retained historical data expanded the number of Trezor customers known to be exposed.

The breach did not reach Trezor's wallet systems. The company said its systems, products and services were not compromised and its devices remained secure. The listed exposed fields were contact and order data, not recovery seeds, private keys or wallet funds.

The risk instead sits around the wallet. Trezor warned that the information could support convincing scam emails, fraudulent calls or letters and potential physical targeting. Its Sept. 4 update did not identify a confirmed downstream attack caused by this dataset, so those outcomes remain risks rather than documented consequences.

Trezor said it emailed every newly affected customer directly and that anyone who did not receive its incident notice was not affected. It urged customers never to share a wallet backup or enter it on a website.

For hardware-wallet owners, the episode shows that protecting keys does not erase the purchase trail created by fulfillment. A deletion policy offers little protection if a vendor's compliance is not verified.

The post Users exposed by Trezor breach grows sixfold after supposedly deleted shipping logs are found appeared first on CryptoSlate.

Compound is a crypto lending protocol governed by holders who delegate their COMP tokens, a setup known as a decentralized autonomous organization, or DAO. It works like an online republic, with token holders debating proposals, voting, and letting software carry out the result.

In July 2024, that republic nearly sent a fortune to a small group of voters. Proposal 289 asked Compound to transfer 499,000 COMP, then worth about $24 million, into a yield-bearing vehicle they controlled. Two earlier versions had failed, and the third seemed headed the same way.

Then, during the final 34 minutes, supporting addresses cast 563,591 votes, equal to 82% of all support for the proposal. The last big block landed eight minutes before the deadline, and the measure passed by 682,191 votes to 633,636.

While this was extremely controversial and remains highly contested, there was no issue with the code, as it worked exactly as intended.

But that was the problem: the wallets had gathered enough COMP and delegated their voting power before the period closed, but Compound lacked an emergency authority that could pause the software. Several reasonable rules had combined into a convenient path for a treasury raid.

Compound reached a settlement that canceled the allocation and later added a veto role, placing a brake in the system built around automatic token-holder rule.

That captures the central DAO dilemma, because most defenses against rushed or hostile votes give somebody more control over participation or the final result.

Two 2026 studies from the Max Planck Institute for Software Systems and Vrije Universiteit Amsterdam traced a similar problem across 48 large Ethereum DAOs. One examined how registration, staking, and delegation concentrate voting power, while the other mapped attacks that use valid governance rules.

The ballot has a velvet rope

Calling a governance token a vote isn't really correct. Depending on the DAO, a holder may need to register a wallet, lock tokens, delegate them, maintain a minimum balance, or pay for an on-chain transaction before they can actually cast that vote.

Proposals face obstacles of their own, because someone needs enough tokens or delegated support to introduce them in the first place, and the idea may pass through a forum and informal poll before a binding vote on the blockchain or through an off-chain service such as Snapshot.

Once the tally clears the quorum and approval formula, a smart contract, multisignature wallet, or named person carries the result into effect.

While each of these gates solves a real problem, it also favors a particular participant or type of participant.

Proposal thresholds discourage spam and malicious code, but they inadvertently reserve authorship for wealthy holders and established delegates. On-chain voting makes those results enforceable, but transaction fees favor people with enough money and conviction to use it. Free off-chain polls draw a wider crowd, then depend on a smaller group for execution.

The researchers found an even split: 24 DAOs used on-chain voting and 24 used off-chain systems.

Uniswap showed how different electorates can form inside the same organization: more wallets joined its free off-chain polls, while much larger blocks of voting power appeared during the paid on-chain phase that could make a proposal binding.

Turnout is only one small part of this, because a protocol may have thousands of token holders while a few addresses control proposals, votes, and execution. By the time the public tally appears, the rules have already picked the electorate.

The security rules pick the ruling class

DAOs often keep tokens in treasury contracts, and founding teams or investors may hold allocations that have yet to vest, so registration separates circulating tokens from balances that currently carry voting rights.

Among the 48 DAOs, 36 required some form of registration, and only four had registered more than half of their outstanding supply. Across those 36 organizations, the average registered share was 21%, meaning the practical electorate usually covered a small fraction of all tokens.

Much of the missing supply belonged to users whose coins were held by exchanges or deposited into DeFi protocols. Centralized exchanges held more than 10% of outstanding tokens on average across the sample, and DeFi contracts held another 3.5%.

In 14 registration-based DAOs, those intermediary wallets controlled more tokens than the entire registered electorate.

That creates a very strange and rather unique custody problem, because an exchange wallet can represent thousands of customers even though the blockchain sees one address with one giant balance.

Letting the exchange vote turns a custodian into a political heavyweight, while excluding it strips customers of governance rights attached to tokens they paid for. Most DAOs also let one wallet send all its power to a single delegate, which makes splitting votes among the underlying owners difficult.

Staking tackles a different vulnerability by making voting power expensive to build and slow to unwind. A would-be attacker can buy or borrow a large position, approve a favorable proposal, and sell once the vote ends, while a lock keeps that voter financially exposed to the result for longer.

Fifteen DAOs required staking, with a median of 27.4% of tokens locked. Some imposed a one- or two-week withdrawal wait, while Curve, Angle, and Frax offered stronger voting power for locks lasting up to four years. The system rewards patience and turns liquid wealth into a prerequisite for political influence.

Crypto soon produced middlemen for people who wanted influence and the freedom to trade. These services maintain long locks, issue tradable substitutes, and keep the original voting rights. The arrangement concentrated enormous voting blocs inside a few services, according to the researchers’ measurements:

Delegation works the same because most holders have limited appetite for forum arguments about collateral ratios. Handing votes to a professional participant makes sense, and repeated delegation builds durable political blocs.

The ten largest holders controlled more than half of voting power in 39 of the 48 DAOs, while delegated voting was usually more concentrated than direct voting.

Registration protects treasury balances, staking makes a quick attack costlier, and delegation gives passive holders a voice through someone who pays attention. Put them together, and the people with the most capital, time, technical fluency, or control over customer assets tend to run the place.

A legal DAO vote can still be a raid

The second paper defines a governance attack as an actor using the authorized process to win an outcome that harms the wider organization.

Among 28 DAO incidents, researchers classified 16 as attacks that a different mechanism could have prevented. Six involved contract bugs, while ten depended on buying or borrowing enough tokens to influence a vote.

Compound is the best example because the wallets associated with Proposal 289 gathered more than 680,000 COMP over four months.

Researchers traced 563,790 tokens through four centralized exchanges and another 118,089 borrowed through Compound itself, even though those addresses had held only 853 COMP before the buildup and had little history in the protocol's politics.

The late burst took advantage of a community that expected the third proposal to fail. Compound could have extended the vote when a large bloc appeared near the deadline, required longer staking, or allowed a trusted council to pause execution.

Every option would have moved power toward reactive voters, committed holders, locking services, or a small emergency body.

But Compound chose the emergency brake, and in the 2024 configurations researchers reviewed, seven other DAOs shared its exposure to readily available voting power and late vote accumulation: Uniswap, Radicle, Gitcoin, Silo, Ampleforth, Hop, and Cryptex.

Those systems can evolve through governance, so the list records a moment in 2024, while a current security rating would require a fresh review.

Decentralization needs a richer accounting than token distribution alone. A good governance report would show how much supply can vote, how much power the largest delegates control, which intermediaries hold staked tokens, and who can introduce, execute, or veto proposals.

Smart contract audits already ask whether governance code follows its specification, while a constitutional audit would ask where that specification sends authority.

DAOs can spread ownership across thousands of wallets and still funnel practical control toward a few dozen professionals, custodians, and large holders, with software that performs flawlessly all the way through.

The post DAOs are forcing crypto protocols to choose between code and emergency brakes appeared first on CryptoSlate.

The Alpine F1 Team Fan Token climbed on September 5, trading near $0.3557 with a 24-hour gain of roughly 3.6%.

The move coincided with a stunning qualifying result at the Italian Grand Prix in Monza, where Pierre Gasly claimed his first career pole position for Alpine.

How Gasly’s Pole Put Alpine Back on the Map

Gasly stunned the Formula 1 field by edging Mercedes’ George Russell by just 0.060 seconds with a lap of 1:21.786. Oscar Piastri finished third for McLaren.

The result marked Alpine’s first pole in years and came at the same circuit where Gasly scored his sole career F1 victory back in 2020. With a market capitalization near $4 million and solid 24-hour trading volume, ALPINE continues to attract attention from motorsport and crypto enthusiasts alike.

Follow us on X to get the latest news as it happens.

Fan tokens give holders access to team-related voting, exclusive rewards, digital collectibles, and enhanced engagement. ALPINE, launched via Binance, is a BEP-20 utility token tied to the BWT Alpine F1 Team.

Why Fan Tokens React to On-Track Results

Positive on-track results often generate short-term interest in such assets. That pattern showed up again in the modest uptick in price and volume following Gasly’s performance.

Social media buzz, meanwhile, centered heavily on the Frenchman’s emotional reaction and Alpine’s unexpected pace. Direct commentary linking the token’s rise specifically to the pole, though, remained limited.

Fan tokens have grown in relevance across sports generally, particularly around major global events. That utility for fan engagement and community decisions was on full display during this summer’s 2026 FIFA World Cup.

As a result, national team and club tokens saw heightened trading activity around high-stakes matches. That pattern reflected real-world performance and sentiment throughout the tournament.

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That model, already established in football and motorsport, positions tokens like ALPINE as bridges between fans and teams beyond race weekends.

Gasly’s Monza pole provided a timely boost, illustrating how unexpected F1 moments can influence sports token markets even if gains remain measured relative to the token’s historical peaks.

The post Alpine F1 Fan Token Rises After Italian GP Drama appeared first on BeInCrypto.

Crypto investor Machi Big Brother has pulled his $1 million bid for Friend.tech and named venture firm Paradigm as the likely obstacle.

Instead, he urged co-founder Racer to relaunch the Web3 social app on Robinhood Chain. The reversal comes nine days after his original offer, sending FRIEND up more than 1,500%.

Machi Big Brother Friend.tech Bid Collapses After 9 Days

Machi Big Brother, whose real name is Jeffrey Huang, opened the bid on Aug. 27. His $1 million buyout offer asked only for the project’s X account and web address.

Friend.tech launched on Base in 2023, allowing users to trade shares with each other. Paradigm led a seed round into the startup that same year.

However, Huang offered no evidence and framed the claim as a guess. He also has a history here. He bought 11 million FRIEND for roughly 5,200 ETH, and the position later shed over $16 million.

The original team gave up control of the smart contracts in September 2024. Therefore, it remains unclear what a buyer would own.

The FRIEND token price now sits near $0.0069, up roughly 5% in the past 24 hours. Its market value of about $659,000 trails the $4.89 million peak from the August rally. Over 90 days, however, the token still holds a 382% gain.

Robinhood Chain Emerges as Racer’s Next Option

Huang closed his post with a pivot. He told Racer to rebuild the app on Robinhood Chain and promised his backing.

Robinhood Chain went live on July 1 as an Arbitrum-based layer-2 network. Since then, it has surpassed Ethereum in volume on decentralized exchanges and absorbed heavy meme-coin flows.

Meanwhile, that mix worries some analysts. Jon Ma of Artemis warned that the meme coin boom risk could undercut Robinhood’s tokenized-stock ambitions.

FRIEND held its 24-hour gain despite the withdrawal. Racer had not responded publicly by Saturday afternoon, and his answer will decide whether Friend.tech returns.

The post Machi Big Brother Sent FRIEND Up 1,500%. Then Ditched $1M Deal appeared first on BeInCrypto.

Solana recorded more x402 transactions than any other network in August, according to an August roundup from the x402 ecosystem, as developers and companies test ways for artificial intelligence agents to make payments for digital services. The network overtook Base in daily x402 transaction activity during the month, while BlockRun said it settled 5.4 million agentic payments on Solana over a seven-day period through PayAI. Solana also said agents initiated 3.3 million USDC transfers using x402 during a single week. The activity is part of a broader effort to enable software to pay for APIs, data, computing, and other services without requiring a person to handle each transaction. The figures, however, measure transaction activity rather than the number of users or the amount of independent commercial demand behind it.

Solana’s x402 Activity Puts Machine Payments In Focus

x402 is a payment protocol built around the HTTP 402 “Payment Required” status code. It allows a service to request payment when an agent makes an API request, after which the agent can authorize a payment and retry the request. Solana’s documentation describes x402 as a way for agents to access paid APIs and other resources without having to manually create accounts or manage API keys.

https://t.co/ulnwaQCf4T

— x402 on Solana (@x402) September 4, 2026

The model is designed particularly for small, pay-per-use transactions. Instead of requiring an agent to maintain an account, subscription, or API key for each service it uses, an x402-enabled endpoint can return a payment request, allowing the agent to pay for a single request. The Solana ecosystem has been one of the networks supporting this activity. BlockRun said it settled 5.4 million agentic payments on Solana in seven days through PayAI, while its x402 endpoints can accept USDC on Solana.

The August roundup also said x402 had passed roughly 200 million transactions across about 150,000 endpoints, with most payments below $0.50. Solana’s own x402 ecosystem page currently reports more than 37 million transactions on Solana and says the network accounts for 70% of monthly x402 volume. The Solana roundup highlights examples such as paid financial research, data access, computing, and other API services, suggesting that the network is being used to test payments made directly by software.

Wallets Bring Agent Payments Closer To Businesses

For agents to spend money independently, payment infrastructure needs to go beyond settlement. Companies also need a way to fund wallets, set limits, and track what an agent has purchased. That is where corporate finance platforms such as Ramp are entering the market. Ramp’s agent tools include functions for businesses to provision x402 wallets, fund them from a Ramp account, and pay x402 requests using a company’s stablecoin balance.

35M+ transactions have settled over x402. Until now, none of them touched a corporate ledger.

As of today, Agents can now make payments via x402 on Ramp with attribution and audit trails built in.

This lets Ramp customers:
1. Provision & fund agent wallets
2. Empower agents to… pic.twitter.com/BrIGHXMhZf

— Teddy Riker (@teddy_riker) August 20, 2026

The move gives companies a way to connect agent spending with existing financial controls. Ramp’s documentation describes wallet provisioning and funding as business-level functions, while x402 payments can be made from the company’s stablecoin balance.

MoonPay is pursuing a consumer-facing version through PayBox. According to MoonPay’s announcement, PayBox connects with Claude and ChatGPT and allows users to approve transactions prepared by an AI agent. MoonPay says the system can be used for activities including token swaps, DeFi interactions, travel and restaurant bookings, and online purchases. Solana Foundation is also promoting Pay.sh, a payment layer for HTTP agents and command-line tools. The foundation launched the service in collaboration with Google Cloud, stating in its announcement that agents could access and pay for APIs from providers, including Google Cloud, on a per-request basis.

Rish, Head of AI Growth, on the zero to one for agentic payments:

“We at Solana Foundation built a product exactly for this. What’s the zero to one for agentic payments? It’s called https://t.co/DLD8XtDx3w.”

“It’s a very simple install. You just brew install Pay, and underneath… pic.twitter.com/qoynnWdefK

— Solana Foundation (@SolanaFndn) August 20, 2026

The approach changes how an agent can interact with online services. Instead of a person signing up for each API, maintaining credentials, and managing separate subscriptions, the agent can discover a service, receive a price quote, and make a payment for the request. The open question is how much of this activity will translate into sustained business use. Agent payments still face practical issues with authorization, fraud, identity verification, refunds, accounting, and determining whether an agent should spend money in the first place.

Researchers studying x402 have also raised questions about how transaction activity should be measured and how much represents independent economic activity. Solana’s reported lead in x402 activity shows that developers are testing blockchain-based payments for software at increasing volumes. It does not yet establish how large the market for autonomous payments will become.

According to the data provided by CryptoQuant, stablecoin flows in the direction of cryptocurrency exchanges showed signals of minimal recovery in August. Binance recorded more than $1 billion in net stablecoin inflows during the month. The development has come at a time when Bitcoin’s price has been steady after gaining approximately 25% in August and moving back above the $75,000 level.

However, the improvement in Binance’s monthly flows comes against a weaker trend since the start of the year. Around $5.1 billion in net stablecoin outflows have been recorded from Binance, while more than $16 billion in stablecoins have left the major exchange reserves. 

Binance Records More Than $1 Billion in Stablecoin Inflows

As per the report, Binance saw a net inflow of more than $1 billion worth of stablecoins in the month of August, which is a significant boost in terms of liquidity going into the exchange. Nevertheless, the net inflow for August is modest compared to the amount of activity taking place on the exchange. Since the start of the year, Binance has recorded around $5.1 billion in net stablecoin outflows. 

The exchange has around 71% of stablecoin flows across all exchanges, making its reserve movements a crucial part of the overall liquidity scenario. At the same time, more than $16 billion in stablecoins have left the reserves of major exchanges since the start of the year. These outflows indicate that the rise recorded by Binance has not yet translated into a clear reversal of the longer-term trend.

Stablecoins play an important role in crypto market liquidity because they can be used by stakeholders and traders to move into digital assets or remain seated for future changes. When stablecoins are held on exchanges, they can potentially provide readily available liquidity for trading. When stablecoins leave exchanges, it can suggest that liquidity is moving away from the immediate trading environment.

Stakeholders may be retracting their assets or decreasing their exposure, contributing to a fall in exchange reserves. The numerous figures, therefore, present a mixed scenario. Binance’s August inflows point to certain improvement, but the larger year-to-date numbers show that liquidity has continued to move away from centralized exchanges.

Bitcoin Rises 25% as Demand Recovery Remains the Focal Point

The plunge in exchange-held stablecoin liquidity has taken place even as the Bitcoin price increased in the month of August. Bitcoin gained approximately 25% during the month and moved back above $75,000. The cryptocurrency is now trying to consolidate around the higher levels after recovering from its prior weakness. The price recovery shows that Bitcoin can perform strongly even while broader liquidity indicators remain under pressure. However, the continued stablecoin outflows could raise questions about whether the retail-driven trend is supported by a sustainable recovery in market demand.

A stronger return of liquidity could provide extra support for Bitcoin and the wider crypto market. If stablecoin inflows towards exchanges continue to rise, it could indicate that investors are willing to move capital into digital assets. On the other hand, if exchange reserves continue to fall, the market could face extra pressure. Lower liquidity could make it more strenuous for Bitcoin to maintain its new gains, particularly if investor interest also remains feeble.

The $75,000 level is therefore a key area for Bitcoin following its August rally. Maintaining the level while demand improves could boost the recovery narrative. However, another fall in market interest would put the recent gains in question.

If liquidity continues to leave exchanges and sustainable demand fails to come back, Bitcoin will enter another corrective phase. The newest data points to a market that has shown some signs of improvement but has yet to establish a broader liquidity recovery. Binance’s more than $1 billion in August stablecoin inflows are significant on a monthly figure, but they remain small, with approximately 5.1 billion dollars in net outflows recorded since the start of the year.

With more than 15 billion dollars of stablecoin also leaving major exchange reserves, the wider liquidity picture remains questionable. Bitcoin’s approximately 25% August performance provides an optimistic sign, but whether the rally can continue might depend on whether demand and liquidity begin to recover quickly.

B.AI, a next-generation AI infrastructure platform, recently set off a developer frenzy by offering free access to top-tier models. Within days, daily token throughput across the platform crossed 1.33 trillion—a historic milestone. 

The record-breaking figure underscores the campaign’s explosive rollout, but it marks only the first step in B.AI’s broader strategic roadmap. Moving beyond traditional compute distribution pipelines, B.AI aims to build the global settlement layer for intelligence: a core infrastructure hub engineered to power cross-node collaboration, orchestration, and value distribution for AI agents across complex business workflows. 

Positioning itself strategically above all models, below all agents, B.AI deeply integrates a diverse range of top-tier models with full-stack components, laying an unshakable, irreplaceable foundation for the mass adoption of autonomous agents and the productivity boom that follows.

Daily Token Throughput Tops 1.33 Trillion: B.AI’s Free Access Rollout Fuels Usage Boom

B.AI’s recent move to open free access to premium AI models has captivated developers and quickly taken over industry conversations. The push for accessible compute has not only fueled a surge in platform activity but also shattered usage records. 

In a matter of days, soaring API demand pushed the platform’s daily token throughput past a staggering 1.33 trillion. Over a 15-day window, cumulative volume reached 8.19 trillion tokens, drawing in more than 220,000 new API users. As of September 3, B.AI’s total user base had officially surpassed 2.3 million. 

That massive adoption traces directly to the platform’s zero-cost model lineup, a strategic rollout built to erase developers’ cost concerns. With every barrier removed, B.AI now offers unlimited free access to six leading frontier models: DeepSeek-V4-Flash, DeepSeek-V4-Flash-Vision-Exp, Tencent Hy3, Xiaomi MiMo-V2.5, GLM-5.3-Flash (Ox Alpha), and Qwen3.8-Flash. 

Notably, on September 3, B.AI rolled out a new pricing structure for DeepSeek-V4-Flash and DeepSeek-V4-Flash-Vision-Exp, introducing tiered discounts. Developers now receive a 50% discount during peak hours, with off-peak rates dropping to just 25% of standard peak pricing. At the same time, the platform has kept zero-cost access in place for GLM-5.3-Flash (Ox Alpha), Qwen3.8-Flash, Tencent Hy3, and Xiaomi MiMo-V2.5. Despite the shift toward commercialization, developer momentum hasn’t wavered, with platform-wide token throughput continuing its steady climb.

This sustained momentum proves the campaign was far more than a short-term compute giveaway—it is a bellwether for the broader evolution of AI infrastructure. Cracking 1.33 trillion daily tokens makes one thing clear: AI applications are moving past basic chatbots. Powered by a high-performance technical stack and flexible service mechanics, B.AI is laying the groundwork for the next frontier—autonomous AI agents operating at scale. 

Powering the “AI Grid”: B.AI Anchors the Global Settlement Layer for the Agent Economy

For B.AI, democratizing compute is only the prelude. Looking further ahead, the platform is committed to building full-stack infrastructure for the agentic era, cementing its position as the global settlement layer for intelligence.

In the agent era, a typical agent task calls for constant switching between models. No single provider can power a complete workflow on its own, so developers are left juggling fragmented API protocols, disjointed billing systems, and conflicting rate limits. 

B.AI’s settlement layer bridges this exact gap. Positioned strategically “above all models, below all agents,” B.AI abstracts models across different providers, capabilities, and cost structures into a unified pool of schedulable resources.

Powered by a dual-tier API structure offering official-route reliability alongside lowest-cost custom channels, developers can choose between guaranteed direct connections and deeply discounted options across a broad lineup of models. Combined with smart routing on the Chat interface, B.AI operates as a full-stack “AI grid,” ensuring every agent request lands on the optimal model to deliver reliable performance at maximum cost-efficiency. 

On the settlement front, this power grid seamlessly bridges both Web2 and Web3 models. For Web2, developers can rely on familiar traditional payment methods to top up with minimal friction. For Web3, B.AI leverages on-chain payment rails to offer global developers decentralized, verifiable, and low-friction payment options.

With dual payment systems running in parallel, B.AI enables developers and agent applications across any infrastructure setup to find their optimal settlement path on the grid, providing single-point integration with borderless global reach. 

Driving Core Productivity: B.AI Reshapes Agent Collaboration

Beyond building a foundation for compute routing and global settlement, B.AI is moving past base infrastructure to power real-world productivity. By enabling seamless agent collaboration across complex workflows, it delivers the missing execution layer for the agent economy. 

At the heart of this execution layer is native Codex integration. Full compatibility with the Responses API means developers can now use a single B.AI key inside Codex to run flagship GPT models and DeepSeek favorites side by side. 

Engineers can now bring these powerhouse models straight into their daily dev stack. From code generation and reasoning to debugging and refactoring, B.AI unifies the entire workflow under one roof—delivering a direct line from model selection to shipped code. 

Beyond coding, to keep agents running reliably in real-world production at scale, B.AI has built a full-stack infrastructure powered by five core components, equipping agents with a fine-tuned operational engine:

• x402 Payment Protocol: Introduces an innovative “pay-before-response” model that executes high-frequency, on-chain micro-settlements in the background during cross-agent API calls and compute orchestration.
• 8004 Identity Protocol: Issues verifiable on-chain credentials for every agent, logging execution history and credit scores to establish a reliable layer of trust for cross-node collaboration.
• Skills Matrix: Plug-and-play, standardized building blocks that interface directly with MCP servers to equip agents with instant, modular tool-calling capabilities.
• BAIclaw and BAIcode: Built-in platform assistants engineered for end-to-end execution. BAIclaw manages all-in-one terminal operations and multi-agent workflows, while BAIcode serves as an advanced developer engine that streamlines the full pipeline from task analysis and architecture design to coding and testing.

From the token surge sparked by zero-cost model access to its positioning as the global settlement layer for intelligence; from seamless Codex integration to full-stack infrastructure powered by x402, 8004, Skills, and native assistants—B.AI delivers far more than a battle-tested technical stack; it unveils a clear blueprint for what lies ahead. B.AI is building not just accessible compute today, but the definitive launchpad for a thriving agent economy. The future is here—and this is only the beginning.

B.AI Team

Singapore

support@b.ai

Axis Robotics has released Axis Sim Dataset V1, one of the largest open-source simulation datasets for Franka arm manipulation, with the full dataset, training code, and benchmarks publicly available. V1 is built from more than 50,000 human-teleoperated simulation trajectories across 207 manipulation tasks and 60,000+ scene variants on a simulated Franka Research 3 arm.

This dataset drew over 160,000 downloads, making it the most downloaded open-source simulation Franka manipulation dataset on Hugging Face. In benchmarks, continual pretraining on V1 lifted π0.5 and beat a volume-matched RoboCasa baseline, with every result open and verifiable.

Axis Robotics is building the ultimate compounding data engine for Physical AI, a vertically integrated system spanning large-scale simulation, egocentric real-world capture, humanoid loco-manipulation, and human-gated DAgger post-training. The company raised $12 million in seed funding led by Hack VC, with participation from Nomad Capital, Pi Network Ventures, 10K Ventures, and angel investors.

A Bet Against “Clean Data Only”

A common assumption in robotics is that demonstrations must be near-optimal to begin with — filter down to expert trajectories, standardize the setup, and discard anything noisy before it is safe to imitate. Axis’s thesis runs the other way: data quality lives at the distribution level, not the single trajectory. When a large and diverse enough crowd produces noisy, suboptimal trajectories and their errors are uncorrelated, the noise averages out and a working policy survives during training.

Axis Sim Dataset V1 puts that thesis to a public test. Its trajectories span pick-and-place, stacking, pouring, articulated-object manipulation, and tool use, all collected through Axis’s browser-based teleoperation platform, Axis Hub, by a distributed crowd rather than a single expert team. The dataset was built with researchers from UC Berkeley, Johns Hopkins, the University of Michigan, and other institutions.

Results That Scale

On LIBERO-Plus, continual pretraining on V1 lifts π0.5 from 83.9% to 88.8% success and outperforms a volume-matched RoboCasa365 baseline by 37.3%. Performance improves consistently as pretraining data scales from 25% to 100% of the dataset, with no saturation in sight, evidence that the gains come from diversity and coverage rather than a one-off bump. The largest improvements appear under camera, sensor-noise, and layout perturbations, the exact axes Axis randomizes during generation.

The team says V2 is already underway, scaling to 1.2 million trajectories across 1,200 tasks, with cross-embodiment generalization and results across multiple VLA models showing that suboptimal simulation data trains robust policies.

The Engine Behind the Dataset

The dataset is one output of a larger, actively compounding data engine. Where a traditional data vendor collects to a fixed spec and stops, Axis uses model performance and failure cases to determine what should be collected next, so every training round informs the next. That engine runs on a hybrid strategy across four data lines, and all four now run at scale:

• Simulation: over 200,000 distributed contributors on Axis Hub, a top-3 dApp on Base, producing 4.7M+ trajectories across 13 embodiments.
• Egocentric: a managed network of 1,000+ full-time, QC-trained collectors capturing first-person activity in real homes and businesses across 14 industries: 200,000+ hours already banked and growing by 4,000+ hours every day, with Vicon-verified hand pose.
• Loco-manipulation: 500+ hours combining mobility and dexterity on real humanoids (Unitree G1, Booster T2) through hardware-agnostic teleoperation.
• Human-gated DAgger post-training: 500+ hours of human-in-the-loop correction targeted at deployment edge cases.

Every task and trajectory is recorded on-chain on Base for provenance, and contributors are rewarded for verified work quality.

From Open Data to Commercial Deployment

Beyond open-sourcing simulation data, Axis works directly with robot embodiment companies to build customized, embodiment-specific data pipelines and model priors.

As Booster Robotics’ first sim-data partner, Axis rebuilt Booster’s real workspace as a task-aligned digital twin, had distributed contributors collect 42,000+ simulation episodes on it, and distilled them into a Booster-specific model prior. With just 30 real-robot demos, that prior reached 87.5% success versus 37.5% for an out-of-the-box π0.5, matching π0.5 using half the real-world demonstrations.

Other partners span embodiment companies (Feagine Robotics), model companies (Manycore Tech, Dexmal) and industrial automation (Lotus Cars, Geely Auto). Axis also supplies on-chain robotics networks: BitRobot on Solana and OpenRoboto on Bittensor.

Redefining Physical AI’s Data Foundation

“The future of Physical AI isn’t a static dataset you download once,” said Chris Feng, founder of Axis Robotics. “It’s an engine that keeps producing the data the model needs next. Scale gets you broad coverage. Diversity keeps the noise unbiased. The closed loop turns every failure into progress. That’s what compounds.”

Axis was founded by researchers from UC Berkeley, CMU, Georgia Tech, and SJTU, alongside serial founders who have scaled consumer platforms to over 30 million users. Its research is advised by Jiachen Li, Assistant Professor at Georgia Tech.

Paper Link: https://arxiv.org/abs/2607.21588

Project Page: https://axisaiorg.github.io/AXIS-V1/

Dataset Link: https://huggingface.co/datasets/axisrobotics/Franka-Dataset

Github Codebase: https://github.com/AxisAIOrg/Axis-V1-Training