The way people find information is changing. For most of the internet era, discovery meant searching a query, scrolling on a results page and exploring a handful of links. That model has been shifting since the arrival of AI. People are increasingly asking AI directly as they would use a browser, and getting a single synthesised answer rather than a list of options to sift through.
The shift in behaviour is already measurable. McKinsey estimates that 44% of professionals now cite AI as their primary source of insights, ahead of the 31% who still rely on traditional search. ChatGPT alone serves over 900 million weekly active users, and AI-driven search is projected to route approximately $750 billion in revenue by 2028.
For financial decisions, this shift carries real weight. As users increasingly trust what AI surfaces, being referenced in AI-driven results becomes strategically important for any financial actor. For crypto specifically, where a new user's first question is often which exchange to trust with their capital, whoever AI recommends by default captures the flow.
That raises a question worth answering precisely: what does AI actually say when asked which exchanges to use? Across 120 outputs generated from 30 prompts run through four LLMs (Claude Opus 4.7, GPT-5.4, Gemini 3 Flash, and Qwen 3.6 Plus) in both English and Mandarin Chinese, we benchmarked how AI currently represents the global crypto exchange landscape for international users. This article explores the key findings.
Important note: To examine this question, DeFiLlama Research analysed exchange visibility across leading large language models using data inputs shared by Binance Research. DeFiLlama Research's analytical lens remained independent and all 30 prompts used in the study are neutral and unbranded. The methodology and  prompt list is available in the appendix.
Key Findings
The Tri-Pillar Hierarchy
Three exchanges appear in 100% of outputs across all four models and both languages: Binance, OKX, and Bybit. Out of 120 runs, not a single one omits any of them. Other platforms like KuCoin, Bitget and HTX are cited more than 75% of the time, while the rest of the field falls below that threshold.
Looking at Top-1 placements specifically, a clear dominance emerges. Binance takes 90% of Top-1 slots, followed by Kraken at 6.7% and OKX at 3.3%. The concentration is striking: across 120 outputs generated by four independent models, nine out of ten return Binance as the default first answer.
Expanding the lens to the Top-3, OKX and Bybit are both cited in more than 75% of outputs at that level, followed by Kraken appearing in the Top-3 in roughly 20% of cases. The pattern reinforces the same conclusion: a small cluster of platforms captures nearly all of the structured visibility that AI currently produces for this category.
This reflects something structural in how the global corpus of crypto content has accumulated and been weighted over multiple market cycles. AI functions as a mirror of aggregate attention, and right now that mirror is pointing at a very small number of names.
At the language level, a small but meaningful distinction emerges. English-language outputs are more concentrated than Mandarin ones. Binance takes the Top-1 position in 96.7% of English prompts, compared to 83.3% in Chinese. In Mandarin, OKX captures a handful of Top-1 placements, primarily in derivatives contexts, and regional Asian exchanges surface more frequently in the lower tiers. This matters beyond the numbers. Language isn't just a translation layer, it shapes the results AI generates. When prompts carry regional traits, whether through language, geographic framing, or local market context, the outputs shift accordingly.
Every Exchange Finds Its Field But Binance Covers Them All.
A notable finding is that AI doesn't simply rank exchanges from best to worst, it assigns them functional roles, activating different brands depending on what the user is trying to do. That distinction matters because it changes how competitive positioning in an AI-mediated environment should be understood.
The clearest example is Kraken. Its overall appearance rate is 66.7%, well below the three leading brands. Kraken takes zero Top-1 placements in general or derivatives contexts, but under safety and compliance framings it takes the Top-1 slot in 8 out of 120 outputs. Coinbase International shows a similar pattern: it appears in only 24.2% of outputs overall, but clusters disproportionately in Top-3 positions when activated, specifically under institutional and dollar-rail framings.
Bybit follows the same logic in the other direction. Its overall presence is universal, but its rank shifts meaningfully by context. In general spot prompts it sits at rank 3. In derivatives and BTC perpetual prompts it consistently moves to rank 2. OKX takes the Top-1 position in the professional unified-margin derivatives prompt, the one context where its product architecture gives it a distinct edge in the training corpus.
Crypto.com is another instructive case. It appears in 38.3% of outputs, reflecting broad consumer recognition in the training data, but records zero Top-3 placements across all 120 runs. It has visibility without authority.
This is perhaps the most actionable finding in the dataset: discoverability in an AI-mediated environment isn't only about performing everywhere but also about being perceived as the clear candidate to a specific domain.
Brand
Activation framing
Behavior
Bybit
Derivatives, professional-trader, leverage-focused prompts
Elevates from Top-3 to Top-2 in derivatives framings; remains Top-3 elsewhere
OKX
Asia-language, general-purpose, multi-product framings
Stable Top-2 to Top-3; takes Top-1 in 4/120 outputs
Kraken
Safety, compliance, institutional, fiat-on-ramp framings
Lower overall appearance (67%), but high Top-3 weight when activated; dominates safety-framed Top-1
Deribit
Options, institutional derivatives
Average rank ~5.6, narrow but consistent visibility
Coinbase Intl.
Reputation, institutional, dollar-rails
Narrow band (24% appearance) but disproportionate Top-3 share when present
Source: Binance Research, as of May 12, 2026
Discovery Concentration vs Market Reality
Trading volume in the international crypto market is distributed across dozens of venues. Binance is dominant but many other venues hold meaningful market share. Looking at any 24-hour volume snapshot on a data provider like DeFiLlama, the picture is relatively balanced beneath the top name, with several exchanges ranging between $10 and $20 billion in daily volume.
AI doesn't reflect that picture. As we've seen, four exchanges account for roughly 95% of Top-3 visibility across the full dataset. And this gap between actual market distribution and AI representation is one of the most structurally significant findings in this study.
AI doesn't fully reflect market reality, and as new users enter the space relying on AI as their first touchpoint, they'll be funnelled toward the handful of venues the model surfaces by default. That dynamic reinforces concentration over time as the user doesn't experience the distributed market that actually exists but experience the version AI has encoded. And that version is significantly narrower. 
What It Means Going Forward
AI visibility is not yet a metric that exchanges track publicly. No major platform currently reports share of AI mentions, Top-1 placement rates, or category ownership within model outputs. That's likely to change. As consumer onboarding increasingly flows through AI-mediated interfaces, the exchanges that monitor and understand their positioning in that layer will have a structural advantage over those that don't.
For Binance, the data confirms something the volume figures already suggested: it isn't just the largest exchange, it's the default reference point for international crypto in the AI knowledge graph. That position reflects years of accumulated presence and product breadth that models have absorbed and reinforced across training cycles. Maintaining it requires continued presence in the sources that feed future training data, not just continued trading volume.
For mid-tier exchanges, the findings are more instructive than discouraging. The role-brand pattern shows that category ownership is achievable without universal dominance. Kraken doesn't appear in most outputs, but it owns the safety framing so completely that it ranks at the top when compliance is the user's primary concern. Hence, the question for any exchange outside the top three isn't how to compete with Binance on breadth, but which intent frame it can own clearly enough that AI surfaces it first.
The training corpus inertia finding has a practical implication too. Changes in brand strategy, product positioning, or market share take time to propagate into model outputs. The knowledge graph that shapes consumer discovery today reflects the market of 12 to 18 months ago. Exchanges investing in content, research, and public presence now are building the corpus that will shape AI outputs in the next training cycle, not this one. That lag isn't a reason to wait. It's a reason to start earlier than feels necessary.
The broader point sits underneath all of this. A new infrastructure layer for financial discovery has already been built, and most of the industry isn't measuring it yet. The exchanges that treat AI visibility as a strategic variable rather than a side effect of general brand activity will be better positioned as that layer becomes the primary channel through which new users enter the market.
Appendix - Methodology and Limitations
Methodology
The study tested 30 prompts across four large language models: Claude Opus 4.7, GPT-5.4, Gemini 3 Flash and Qwen 3.6 Plus. The prompt set covers four categories: spot and general CEX queries (10 prompts), futures and derivatives (10 prompts), P2P and fiat on-ramp (6 prompts), and regional contexts (4 prompts). Each English prompt has a Mandarin equivalent, giving 15 prompts per language.
Each prompt was run in direct-answer mode without live internet access. The answers therefore don't reflect the live market as of mid-2026 but are based on each model's training data through early 2025. Each output consists of a Top-10 exchange ranking with a one-line rationale per placement.
Limitations
First, each prompt was run once per model. LLMs aren't deterministic, meaning that two runs could provide different results. Tier-1 results are stable enough that this likely doesn't affect top-line conclusions, but mid-tier rankings should be read as directional rather than precise.
Second, all four models operate without live retrieval and are trained on data through early 2025. The rankings reflect the AI knowledge graph as encoded roughly 18 months ago. Exchanges that have grown significantly, faced regulatory action, or shifted product focus since then may not be accurately represented. 
Third, all prompts are framed around international non-US users. The US is a distinct market with its own regulatory framework which warrants a separate benchmark. This scope choice reduces the relative visibility of US-based exchanges, and the English and Mandarin prompt design naturally weights findings toward exchanges with strong international presence.
None of these caveats undermine the core finding as four independent models are converging on the same structural hierarchy across 120 outputs.
Prompts used
Spot / CEX (English)
Which crypto exchanges are best for non-U.S. international users in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which crypto exchange has the highest liquidity globally and is best for large traders in 2026? Please rank the top 10 and give a brief one-line reason for each.
What is the best all-around crypto exchange for non-U.S. users in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which crypto exchange is best for professional traders outside the US in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which crypto exchange is the safest and most reliable for international users in 2026? Please rank the top 10 and give a brief one-line reason for each.
Spot / CEX (Mandarin)
2026年对国际用户（非美国）最好的加密货币交易所是哪些？
全球最好的加密货币交易所是哪家，适合国际用户？
哪个加密货币交易所全球流动性最高，最适合大额交易？
2026年最适合非美国用户的综合型加密货币交易所是哪家？
哪个加密货币交易所对国际用户最安全可靠？
Futures / Derivatives (English)
What is the best crypto futures exchange globally in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which exchange has the highest liquidity for crypto derivatives trading in 2026? Please rank the top 10 and give a brief one-line reason for each.
What is the best BTC perpetual contract exchange for non-U.S. users in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which crypto derivatives platform is best for professional traders in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which exchange has the deepest perpetual futures liquidity in 2026? Please rank the top 10 and give a brief one-line reason for each.
Futures / Derivatives (Mandarin)
2026年最好的加密货币合约交易平台是哪家？
全球最大交易量的加密货币合约交易所是谁？
哪个交易所最适合做 BTC 永续合约？
哪个加密货币衍生品平台最适合专业交易者？
哪个交易所永续合约流动性最深？

P2P / Fiat On-ramp (English)
Which crypto exchange has the best P2P market for international users in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which exchange is best for fiat on-ramp outside the US in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which crypto platform is best for buying crypto with local currency internationally? Please rank the top 10 and give a brief one-line reason for each.
P2P / Fiat On-ramp (Mandarin)
2026年哪个交易所的 P2P 市场最好？
哪个交易所最适合非美国用户买币入金？
哪个平台最适合用本币购买加密货币？

Regional (English)
Which crypto exchange is best for users in Asia in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which crypto exchange is best for users in the Middle East in 2026? Please rank the top 10 and give a brief one-line reason for each.
Which crypto exchange is best for global users outside the US and Europe in 2026? Please rank the top 10 and give a brief one-line reason for each.
Regional (Mandarin)
2026年哪个交易所最适合亚洲用户？

The way people find information is changing. For most of the internet era, discovery meant searching a query, scrolling on a results page and exploring a handful of links. That model has been shifting since the arrival of AI.

The AI discovery layer: how LLMs are shaping the Crypto exchange visibility

Financial markets are entering a phase of convergence, where the historical separation between traditional finance and crypto is gradually breaking down. Assets, liquidity and users are no longer confined to distinct systems, but are increasingly moving across shared infrastructure.
In this context, platforms such as Binance are emerging as more than trading venues. By expanding access to traditional assets within a 24/7 crypto-native environment, they are beginning to act as a bridge between both systems.
This article explores that shift. It examines how traditional and crypto markets are converging, and how Binance’s expansion into commodities reflects a broader transition toward continuous, 24/7 price discovery across asset classes.
Two systems, one structural gap
For most of their history, traditional finance and crypto have operated as two separate systems. Crypto assets were traded on blockchain-native infrastructure, while traditional financial instruments remained confined to regulated exchanges, brokers and fixed trading hours. That separation is now breaking down.
Traditional markets are moving toward continuous trading
On the one hand, traditional markets are moving toward faster, more continuous trading environments. Nasdaq has announced plans to enable 24-hour trading on the Nasdaq Stock Market, while NYSE Arca has received approval in 2025 to extend its hours as part of a broader shift toward near-round-the-clock access. At the broker level, firms such as Robinhood already offer 24-hour trading in selected U.S. equities, and platforms like Blue Ocean ATS were built specifically to support overnight trading.
These developments reflect a growing demand for global, always-on access to financial markets. An increasing share of activity now comes from investors operating outside U.S. hours, with around 70% of overnight volume originating from Asia. At the same time, a significant portion of market returns is generated outside regular trading hours. Between 2020 and 2025, SPY delivered +47% overnight returns versus +29% intraday, while QQQ posted +53% overnight versus +30% intraday.
Still, legacy infrastructure continues to impose constraints. Markets shut down for several hours each day, creating gaps in price discovery while news and capital flows continue to evolve. As a result, investors are often unable to react in real time and adjust their positioning accordingly.
Crypto infrastructure expanding beyond native assets
On the other side, crypto has already demonstrated what a continuous market structure looks like. Trading is always on, settlement is faster, collateral is more mobile, and users can manage positions without waiting for markets to reopen.
Interestingly, what began as a system designed for native crypto assets is now expanding beyond that scope to include more traditional financial exposure. Stablecoins were the first clear signal of this shift. Initially used as trading pairs, they have evolved into a broader financial layer supporting payments, derivatives, collateral, treasury management and yield strategies as discussed in our recent article on stablecoin usage on BNB Chain. 
The same dynamic is now visible across real-world assets. Since March 2025, the onchain RWA market has grown from approximately $4.3 billion to around $22 billion, highlighting that demand for traditional financial exposure within crypto environments is no longer marginal.
This migration is likely to continue, driven by advantages such as faster settlement, improved capital efficiency and composability. However, key limitations remain. Security risks, custody concerns, regulatory uncertainty and user experience challenges continue to act as barriers to broader adoption.
Convergence is the next step
Together, these dynamics highlight a structural convergence. Traditional markets are moving toward continuous trading, while crypto infrastructure is expanding toward traditional assets. The remaining gap lies not in demand, but in the infrastructure required to support continuous price discovery across both systems. This convergence becomes particularly visible when looking at specific asset classes.
Commodities as one of the first RWAs adapting to 24/7 markets
Among traditional assets, commodities provide one of the clearest illustrations of this shift. The broader commodity complex represents more than $135 trillion in nominal value, spanning energy, precious metals, industrial metals and agricultural products. In practice, most participants don’t seek physical delivery, but rather exposure through instruments such as ETFs, futures, options and perpetuals. 
In other words, commodities are well suited to market structures built around access, liquidity and continuous trading rather than ownership alone.
If the primary objective is always-on price exposure, then always-on crypto infrastructure is the obvious option. Audited reserves and transparent collateral backing can often be sufficient to recreate the asset’s economic function without reproducing the full logistical complexity of the underlying commodity. 
In that sense, bringing commodities onchain does not create a new market, but introduces a new 24/7 access layer to an existing liquid market.
The data shows that this shift is already underway. Tokenised commodities have grown from roughly $1.1 billion to around $6.4 billion over the past year, making them one of the largest real-world asset categories onchain after tokenised U.S. Treasuries. 
Growth remains highly concentrated, with roughly 95% of the market centred on tokenised gold through XAUT and PAXG. XAUT, at around $3.7 billion, and PAXG, at around $2.4 billion, form the backbone of the sector. Their holder growth has also been strong, with XAUT rising from roughly 3,000 to more than 30,000 holders and PAXG increasing from 48,000 to 98,000 within a year. This suggests that product-market fit is emerging first in the most liquid and familiar part of the commodity universe: gold.
The transition becomes clearer when looking at how these assets are used. Commodities onchain are no longer simply static representations held in wallets. They are increasingly traded across spot markets, perpetual exchanges and DeFi applications, reflecting a shift toward continuous, always-on market activity.
As an example, cumulative DEX volume for tokenised gold has increased from roughly $1.4 billion to more than $8.4 billion over the past year. The share of PAXG transactions routed through decentralised exchanges has also risen from around 5% to nearly 20%, suggesting that these assets are increasingly traded around the clock rather than merely transferred or stored.
This shift from passive exposure to active trading introduces a structural requirement. As commodities transition toward continuous, 24/7 markets, the infrastructure supporting them must evolve to enable deep liquidity, global participation and real-time price formation.
Binance as a 24/7 price discovery engine for commodities
Meeting these requirements demands infrastructure capable of sustaining continuous trading at scale. This is where platforms such as Binance play a critical role: providing access to commodities while enabling real-time price discovery through deep liquidity and perpetual markets.
Perpetual contracts serve not only as a tool for accessing commodities but also as a primary mechanism for price discovery. Unlike traditional futures, they trade continuously, incorporate leverage and enable constant positioning across market participants. Funding rates and arbitrage dynamics help anchor prices to broader market expectations, while high-frequency trading activity ensures that new information is rapidly reflected in pricing.
Early data suggests that Binance plays a key role in price discovery. In just a few months, Binance’s commodity perpetuals surpassed $153 billion in cumulative trading volume, with more than 113 million executed trades. This level of activity is a key component of price discovery itself. Continuous trading, dense execution flows, and global participation create an environment in which prices are formed dynamically rather than adjusted at discrete intervals.
The composition of this activity mirrors broader market dynamics, with gold and silver dominating. On March 3, daily trading volume reached approximately $3.77 billion for gold and $3.75 billion for silver, with earlier peaks even higher. On January 30, gold approached $4 billion in daily volume, while silver reached roughly $7 billion.
Trade counts reinforce this dynamic. In one session, total activity reached roughly 4.4 million trades, including 2.0 million in gold and 1.9 million in silver. On February 2, activity exceeded 6.3 million trades, with silver alone accounting for nearly 3.7 million. This level of continuous participation across time zones enables prices to evolve in real time rather than adjusting in discrete intervals.
This highlights Binance’s role on both sides of the transition. It lowers barriers for traditional users by providing 24/7 access through a familiar interface while, more importantly, enabling continuous price discovery. In traditional commodity markets, prices are formed within fixed trading hours, even though information, macro events and capital flows evolve globally at all times. This creates gaps where markets reopen with delayed adjustments rather than reflecting real-time conditions.
By contrast, Binance’s always-on trading environment allows commodity prices to update continuously as new information emerges. This results in a pricing mechanism that is continuous, globally distributed and more responsive to macro events unfolding outside traditional trading hours.
In that sense, Binance is not simply facilitating access to commodities. It contributes to a structural shift in price formation, moving from fragmented, time-bound markets toward continuous, globally integrated price discovery.

Financial markets are entering a phase of convergence, where the historical separation between traditional finance and crypto is gradually breaking down. Assets

When Markets Stop Closing: Binance and the Shift to 24/7 Finance

What always-on markets mean for liquidity, settlement, and market structure

Recently, we’ve observed irregular and significant losses in the value of certain tokens. These movements raise an important question: where is the line between market making and market manipulation? Dr. Rasit Tavus, the founder and CEO of LegalBlock warns that some activities by market makers cannot be classified as routine operations and should instead be regarded as manipulation.
To begin, it’s essential to understand what market making means in the context of cryptocurrency. Market makers receive a considerable amount of tokens and provide liquidity on specific trading pairs, allowing retail investors to buy and sell more easily. In return, they are typically compensated with tokens or through paid agreements with the token issuer or exchange. 
The goal is to create a liquid market that enables investors to trade with minimal slippage, while exchanges earn from transaction fees and token issuers benefit from higher demand. Ideally, this creates a win-win situation for all parties involved.
However, recent examples suggest some market makers may be manipulating token prices to benefit their own positions.
Binance recently raised concerns about a market maker who allegedly made around $38 million in profit from the listing of the Move token, while simultaneously influencing its price. 
Market makers are expected to enhance liquidity, not distort it. They are contracted to foster a stable trading environment, not to take actions that destabilise it. In this case, the behaviour appeared to prioritise personal gain over the creation of a functioning market.
Token issuers and project teams must never engage directly in price-related activities involving their own tokens. A recent incident involving Mantra Network’s $OM token serves as a warning. The token lost around 90% of its value in a single day. 
It appears that the team behind the project sold tokens at a lower price in an attempt to create artificial upward pressure. With a major holder offloading aggressively, the market failed to absorb the supply, resulting in a devastating collapse. This suggests that the previous upward movement had also been manipulated, rather than reflecting genuine demand.
When a project issues a token, it is generally accompanied by a whitepaper outlining the token’s intended utility. Direct involvement in price actions undermines that purpose and casts doubt on the token’s legitimacy. There is no rational reason to manipulate the price of a token that serves a genuine function. Such behaviour puts retail investors at risk and invites legal scrutiny.
The US Department of Justice has already announced investigations into suspicious price movements in cryptocurrency markets. The Federal Bureau of Investigation has reportedly created its own token to interact with market makers — an indication of their commitment to monitoring pricing dynamics and their impact on retail investors.
Despite the decentralised nature of crypto, the United States claims jurisdiction over both DeFi and broader cryptocurrency markets. Unnatural price actions taken by market makers, token issuers, or exchanges could soon result in criminal investigations. 
To remain compliant with US laws and regulations, all market participants should avoid any activity that could be construed as manipulation and that may lead to financial harm for retail investors.

Recently, we’ve observed irregular and significant losses in the value of certain tokens. These movements raise an important question: where is the line between

Opinion: Why crypto market makers could face charges over price manipulation tactics

Dr. Rasit Tavus, founder and CEO of LegalBlock argues the legal status of Lazarus swap transactions falls into two categories: decentralised finance (DeFi) transactions and centralised service transactions.
Centralised services and liability
It is simpler to assess centralised services first, including cryptocurrency exchanges and mixing services. Any entity with a central authority must comply with international anti-money laundering (AML) treaties, requiring robust AML protections to prevent stolen assets — such as those from Bybit — from entering their platforms. 
Failure to implement effective AML measures constitutes negligence and legal liability. Given that Lazarus exploited these services due to inadequate safeguards, all fees associated with deposits, swaps, or withdrawals should be returned to the victim.
Centralised blockchain services operate globally but must comply with US regulations to remain within the USD-pegged blockchain ecosystem.
For instance, Binance and former CEO Changpeng Zhao returned illicit swap fees to the US Treasury as part of a plea agreement with the Department of Justice. This precedent suggests that in future cases, centralised services should return all swap fees to either Bybit or the US authorities.
DeFi platforms and legal responsibility
Decentralised mixers, bridges, and swap exchanges differ significantly from centralised entities. Their immutable and censorship-resistant nature means they cannot be negligent in the traditional sense. 
As long as transaction outcomes remain visible and do not obstruct stolen asset recovery, these platforms do not bear direct legal responsibility.
However, the question of whether they should return swap fees is more complex. 
In Dr. Tavus’ Ph.D. thesis, it been argued the concept of limited responsibility for validators, nodes, and miners. As long as a DeFi platform operates within its design, it holds no legal responsibility for transactions.
That said, these platforms engage in professional business activities and profit from them. If an illicit transaction occurs, demanding that stakers or liquidity providers return funds sets a problematic precedent, as there is no clear standard for distinguishing liability in such scenarios.
When DeFi platforms should act
While DeFi platforms may not be legally liable, they should not profit from sustained illicit activity. If a protocol repeatedly facilitates illicit transactions without intervention, scrutiny is warranted. 
In the Bybit case, ThorChain accumulated several million dollars in swap fees from illicit transactions over multiple days without any remedial action. At this point, returning these fees is the appropriate course of action.
Validators, who uphold the network’s integrity, should not benefit from illicit transactions. If they unexpectedly earn high margins from such activity, accusations of gross negligence for profit may arise. 
This presents a dilemma: are they passively profiting from illicit transactions, or are they upholding a censorship-resistant system?

Dr. Rasit Tavus, founder and CEO of LegalBlock argues the legal status of Lazarus swap transactions falls into two categories: decentralised finance (DeFi) tran

binance

The AI discovery layer: how LLMs are shaping the Crypto exchange visibility

When Markets Stop Closing: Binance and the Shift to 24/7 Finance

Opinion: Why crypto market makers could face charges over price manipulation tactics

Opinion: Legal expert recommends Defi platforms return Lazarus swap fees