Crypto

Decentralized exchange (DEX) aggregator ParaSwap announced its rebrand to Velora and is moving on to a new intents-based trading feature.

According to an announcement shared with Cointelegraph, Velora’s just introduced its Delta v.2.5 upgrade. This supposedly results in improved flexibility and agility in trade execution on the DEX.

Paraswap has seen 18,000 monthly active users over the last month with 4.3 million smart contract interactions over the past 365 days, according to TokenTerminal data. The platform first introduced intents-based trading back in the summer of 2024, with hopes that it would mitigate the negative impact of maximum extractable value (MEV) bots.

Since then, ParaSwap submitted orders in three steps. First the order is preprocessed defining the expected trade price, then this is submitted to an auction to determine the most efficient execution strategy considering liquidity and timing. The winning agent executes the trade while taking the user’s intent into account and purportedly minimizing MEV exploitation risks.

Related: Hyperliquid DEX trading volumes cut into CEX market share: Data

A crypto MEV bot is an automated program that exploits profit opportunities in blockchain transaction ordering—using tactics like front-running and arbitrage to capture extra value. The project’s founder Mounir Benchemled said at the time:

The presence of MEV impacts not only individual transactions but also the overall fairness, accessibility and decentralization of the DeFi ecosystem, making it one of the most pressing issues that needs addressing.”

Velora’s intent-based trading implementation

Velora’s implementation of intent-based trading is more customizable, giving the user “full control over their execution preferences, unlocks advanced features like limit orders, overcoming the constraints of single-block execution and increasing flexibility.” The new aggregator is also reportedly designed to allow for seamless cross-chain trading and enhanced performance.

Related: Curve Finance clocks $35B trading volume in Q1 2025

Sergej Kunz, Co-Founder of DEX aggregator 1inch, told Cointelegraph that “end users shouldn’t have to worry about the complexities” of decentralized finance. According to him, an intent-based system removes much of this complexity:

“An intent-based system is designed to shift all risk and complexity away from users and into the hands of professionals who specialize in executing advanced DeFi strategies. A true intent-based DEX must provide MEV protection at the protocol level and offload execution complexity to professional trading bots.“

Magazine: Financial nihilism in crypto is over — It’s time to dream big again

Opinion by: Rob Viglione, co-founder and CEO of Horizen Labs

Can you trust your AI to be unbiased? A recent research paper suggests it’s a little more complicated. Unfortunately, bias isn’t just a bug — it’s a persistent feature without proper cryptographic guardrails.

A September 2024 study from Imperial College London shows how zero-knowledge proofs (ZKPs) can help companies verify that their machine learning (ML) models treat all demographic groups equally while still keeping model details and user data private. 

Zero-knowledge proofs are cryptographic methods that enable one party to prove to another that a statement is true without revealing any additional information beyond the statement’s validity. When defining “fairness,” however, we open up a whole new can of worms. 

Machine learning bias

With machine learning models, bias manifests in dramatically different ways. It can cause a credit scoring service to rate a person differently based on their friends’ and communities’ credit scores, which can be inherently discriminatory. It can also prompt AI image generators to show the Pope and Ancient Greeks as people of different races, like Google’s AI tool Gemini infamously did last year.  

Spotting an unfair machine learning (ML) model in the wild is easy. If the model is depriving people of loans or credit because of who their friends are, that’s discrimination. If it’s revising history or treating specific demographics differently to overcorrect in the name of equity, that’s also discrimination. Both scenarios undermine trust in these systems.

Consider a bank using an ML model for loan approvals. A ZKP could prove that the model isn’t biased against any demographic without exposing sensitive customer data or proprietary model details. With ZK and ML, banks could prove they’re not systematically discriminating against a racial group. That proof would be real-time and continuous versus today’s inefficient government audits of private data.  

The ideal ML model? One that doesn’t revise history or treat people differently based on their background. AI must adhere to anti-discrimination laws like the American Civil Rights Act of 1964. The problem lies in baking that into AI and making it verifiable. 

ZKPs offer the technical pathway to guarantee this adherence.

AI is biased (but it doesn’t have to be)

When dealing with machine learning, we need to be sure that any attestations of fairness keep the underlying ML models and training data confidential. They need to protect intellectual property and users’ privacy while providing enough access for users to know that their model is not discriminatory. 

Not an easy task. ZKPs offer a verifiable solution. 

ZKML (zero knowledge machine learning) is how we use zero-knowledge proofs to verify that an ML model is what it says on the box. ZKML combines zero-knowledge cryptography with machine learning to create systems that can verify AI properties without exposing the underlying models or data. We can also take that concept and use ZKPs to identify ML models that treat everyone equally and fairly. 

Recent: Know Your Peer — The pros and cons of KYC

Previously, using ZKPs to prove AI fairness was extremely limited because it could only focus on one phase of the ML pipeline. This made it possible for dishonest model providers to construct data sets that would satisfy the fairness requirements, even if the model failed to do so. The ZKPs would also introduce unrealistic computational demands and long wait times to produce proofs of fairness.

In recent months, ZK frameworks have made it possible to scale ZKPs to determine the end-to-end fairness of models with tens of millions of parameters and to do so provably securely.  

The trillion-dollar question: How do we measure whether an AI is fair?

Let’s break down three of the most common group fairness definitions: demographic parity, equality of opportunity and predictive equality. 

Demographic parity means that the probability of a specific prediction is the same across different groups, such as race or sex. Diversity, equity and inclusion departments often use it as a measurement to attempt to reflect the demographics of a population within a company’s workforce. It’s not the ideal fairness metric for ML models because expecting that every group will have the same outcomes is unrealistic.

Equality of opportunity is easy for most people to understand. It gives every group the same chance to have a positive outcome, assuming they are equally qualified. It is not optimizing for outcomes — only that every demographic should have the same opportunity to get a job or a home loan. 

Likewise, predictive equality measures if an ML model makes predictions with the same accuracy across various demographics, so no one is penalized simply for being part of a group. 

In both cases, the ML model is not putting its thumb on the scale for equity reasons but only to ensure that groups are not being discriminated against in any way. This is an eminently sensible fix.

Fairness is becoming the standard, one way or another

Over the past year, the US government and other countries have issued statements and mandates around AI fairness and protecting the public from ML bias. Now, with a new administration in the US, AI fairness will likely be approached differently, returning the focus to equality of opportunity and away from equity. 

As political landscapes shift, so do fairness definitions in AI, moving between equity-focused and opportunity-focused paradigms. We welcome ML models that treat everyone equally without putting thumbs on the scale. Zero-knowledge proofs can serve as an airtight way to verify ML models are doing this without revealing private data.  

While ZKPs have faced plenty of scalability challenges over the years, the technology is finally becoming affordable for mainstream use cases. We can use ZKPs to verify training data integrity, protect privacy, and ensure the models we’re using are what they say they are. 

As ML models become more interwoven in our daily lives and our future job prospects, college admissions and mortgages depend on them, we could use a little more reassurance that AI treats us fairly. Whether we can all agree on the definition of fairness, however, is another question entirely.

Opinion by: Rob Viglione, co-founder and CEO of Horizen Labs.

This article is for general information purposes and is not intended to be and should not be taken as legal or investment advice. The views, thoughts, and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

Bitcoin (BTC) price has been consolidating in a wide range between $80,000 to $88,500 since March 12. 

Data suggests that Bitcoin’s consolidation may continue for some time, with onchain indicators pointing to the continuation of the “broader downtrend.” The key question that remains is when Bitcoin’s consolidation will end.

BTC funding rates show low chances of a breakout

One of the clearest signs that there is more choppy price action ahead for Bitcoin is the presence of muted funding rates in the BTC futures markets.

Key points:

• Funding rates are periodic payments made between long and short traders in perpetual futures contracts to keep prices aligned with the spot market.

• When this metric turns negative, it means short sellers are paying long holders, indicating that bearish sentiment dominates.

• BTC funding rates have been oscillating around 0% since late February, indicating indecisiveness dominates the market.

• When funding rates are zero, the cost of holding positions is minimal, reducing pressure on traders to exit longs or shorts and leading to price consolidation.

Commenting on the funding rate, crypto analyst Axel Adler Junior said that Bitcoin’s average funding rate on major exchanges “has dropped into negative territory,” currently sitting just above zero.

“In this cycle, in four similar instances, it ended with a price increase and once with a decline.”

According to the analyst, positive signals from the US Federal Reserve and the Trump administration could renew fresh inflows into the market, “potentially triggering the start of a new rally.”

Onchain metrics show Bitcoin price stuck in “broader downtrend”

Several onchain metrics suggest Bitcoin’s April 2 rally to $87,500 was just a “relief rally within a broader downtrend rather than the beginning of a sustained reversal,” according to market intelligence firm Glassnode.

In its latest market report, Glassnode found that the 90-day simple moving average of Bitcoin’s Realized Profit/Loss Ratio had declined significantly since January, despite repeated rallies in the $76,000–$80,000 range.

These brief profit-driven surges have failed to end the ongoing consolidation, suggesting that the “macro picture remains one of generally weaker liquidity and deteriorating investor profitability,” it said.

“So far, there is little evidence suggesting a structural bullish shift in momentum is underway.”

An accompanying chart shows data from the onchain volume-weighted prices metric, which calculates a price level weighted by the volume of coins moved onchain over a specific timeframe.

Using this, Glassnode can compare when and how much capital was actually moved in the last 1 month compared to the last 6 months, giving a direct reflection of market trends.

The recent cross-over of the short-term 1-month average below the long-term 6-month price indicates an onchain “death cross,” Glassnode said.

“This pattern has historically preceded 3–6 month-long bearish trends. If this cycle follows suit, it suggests the market may still be working through a period of weakness before the bulls can reestablish a robust uptrend.”

Bitcoin price consolidation could end soon— Bollinger Bands

Anticipation of a breakout in BTC price lingers in the background, as suggested by Bitcoin’s volatility indicator.

Key points:

• Tightening Bollinger Bands conditions indicate that a breakout might be very close.

• The weekly Bollinger Bandwidth is at an extremely oversold level, touching its lower green line.

• The width of the Bollinger Bands is as tight as it was between July 2024 and November 2024 when it consolidated between $55,000 and $69,000, the 2021 all-time high.

• BTC/USD then rallied 60% from $67,500 in October 2026 to its previous high of $106,000 reached in December 2024.

• The indicator was also this tight between July 2023 and October 2023, preceding a 176% rally in BTC price from $24,400 to $73,800 on March 14, 2024.

If history repeats itself, Bitcoin could soon break out of consolidation to stage a massive upward move over the next few weeks.

This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.