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Cross chain swaps on Matcha!
December 05, 2023
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Seize any opportunity across 7 networks with cross chain swaps on Matcha! Trade tokens directly for a different asset on another blockchain in one go, with aggregated liquidity from 100s of DEXs.

If you have ever tried to move assets to another blockchain, you’ve probably been stuck wondering which bridge is safe to use, or which tokens are compatible with the other chain. Maybe you want to keep assets on one chain while still having access to tokens on another, but there's no way of knowing how long the process will take - or if it will work at all.

Now, there’s a better way! Rather than navigating shallow liquidity and unverified platforms, switch to Matcha for simple, cost-effective cross chain swaps which get the job done, first time round.

                     Cross chain is available as a new tab in the Matcha trade module.

We've made it easy to swap directly from one token to another so you can use web3 without limits, as it's meant to be. With smooth cross-chain capabilities, you can be ready for NFT mints in minutes and take your trading multichain for a seamless experience that crosses the EVM ecosystem.

How cross chain works on Matcha

Combining bridge aggregation with DEX aggregation.

You can find the new Cross Chain feature in your trade module, right next to the Limit tab, keeping the familiar look and feel you’re used to. To set up a new cross chain trade, you simply choose the chain and token you want to sell, and the chain and token you want to receive. Fill out the amount to receive an instant quote and hit Review order to see a summary of the trade. All good? Then select Place order and confirm the transaction in your wallet. 

Cross chain swaps step-by-step:

  1. Go to matcha.xyz and select Start Trading.
  2. Open the Cross Chain tab in the trade module.
  3. Select a token to Buy on any supported network using the menu.
  4. Click on Select token and use the pop-up module to choose a token to Sell.
  5. Enter an amount to Sell to receive a quote.
  6. Select Review order.
  7. Select Place order and confirm the transaction in your wallet.

Cross chain swaps have been integrated in Matcha using Socket API, allowing us to enhance the DEX aggregation you already know and love with industry-leading bridge aggregation. With over 3.5 million transactions processed to date, and an all-star list of integrators including Coinbase, MetaMask, Rainbow and Zerion, Socket was the obvious choice for security and ease of use. Read more about the integration in our blog what is a cross chain swap.

More than just a bridge

Most bridges are clunky and slow, leaving you with a forest of browser tabs and the hope that your crypto will come through eventually. Why can’t you just bridge from A to B without jumping through multiple hoops to get there? Well, now you can! 

Cross chain swaps are better than regular bridges because you can swap one token for another directly, across networks, without multiple steps in-between. Matcha aggregates available bridges to find the best route for you. Instead of searching for the right bridge, moving your WETH across, and then swapping for the token you wanted, you can do it all in a single trade. Matcha will first swap to a compatible token and then bridge to the chain you want to get to. 

Get confident about cross chain with simple, fast swaps and transparent routing, so you always know how many tokens will land in your wallet. Just choose the token you have, the token you want from those available, and the network to bridge to - Matcha will choose an optimal route that gives you the best value for your crypto, settled in minutes on your chain of choice.

Cross chain liquidity

The beauty of a DEX aggregator like Matcha is that you aren’t limited to one liquidity source. From blue-chip to long-tail tokens, you’ll tap into deep liquidity whatever you’re trading - and prevent unwanted price impact from eating your profits! 

Finding healthy liquidity on low market cap tokens can be a challenge. Even relatively small trades can quickly drain the supply, leaving you overpaying or receiving fewer tokens than you expect. Matcha overcomes these limitations by drawing together all available liquidity, so you can execute your trades across multiple sources and networks with minimal impact on any individual source, keeping prices low.  

Supercharge your trading with cross chain swaps

Whether you're trading large volumes or just speculating with spare crypto, you need an efficient path across blockchains. Cross chain on Matcha is for traders at any level, with everything you need in one place. From fund managers to small time investors, you can use cross chain to supercharge your trading strategy. 

A growing number of DeFi traders have crypto on multiple networks, with over 3.7 million wallets active on multiple chains each month. You’ll want to use cross chain if you:

  • Manage a web3 portfolio
  • Diversify your risk
  • Need flexibility of low-cost chains
  • Want to find the perfect NFT
  • Look for low-cap tokens
  • Chase large yields
  • Like to be first on new dApps

Every trader benefits from direct cross chain swaps.

As the bull market excitement ramps up, the number of users bridging their assets has risen as much as four times from where it was just weeks ago. This coming bull cycle will likely see all-time highs in cross chain volume, which has been around 5% of total DEX volume in 2023, with $1.3B in volume over the last 7 days, compared to $28B on DEXs.  

Diversify your portfolio across chains 

In today’s multichain landscape, it’s hard to tell where the real opportunity is. With cross chain swaps you can spread your investments across established coins as well as up-and-coming moonshots on layer 2 chains without wasting gas. And if an investment turns out to be a dud, you can rely on Matcha to help salvage the remainder quickly and hassle-free!

As bear turns to bull, it becomes even more important to stay agile. Avoid network congestion on the mainnet by moving your holdings to a layer 2, or redistribute your profits into assets that generate passive income. Whatever your strategy is, cross chain gives it a whole new dimension! 

Hunting airdrops 

Ever since the early Bitcoin forks, airdrops have provided some of the highest returns in crypto, just for holding the right tokens or signing up to the right newsletter. Nowadays, airdrop farming has become a trading strategy in its own right, with new opportunities popping up every day. 

But it’s not enough to sit around and wait for tokens to show up in your wallet anymore. You need to be flexible and fast to position your funds in the right place at the right time for that all-important snapshot. 

While most bridges will leave you waiting up to an hour for your funds before you can swap to the token you really wanted, Matcha makes it easy to establish your position in one go, faster than most bridges will process your gas!

Token farming

Anyone who’s locked their tokens for rewards knows that all good things come to an end. Farms rarely sustain their rates for more than a few weeks. Cross chain lets you uproot your tokens when the season’s over and move directly to new opportunities, where the grass is greener.

Follow the tech

New apps and projects spring up all the time, from onchain games to tradeable social tokens. If you like to be seen as an early adopter, you need to be everywhere at once. Keep up with all the latest experiments and access to the tokens you need on the chains you need them. 

Save time and money

Swap across chains and seize trading opportunities in minutes.

Bridging between networks is also time-consuming and can lead to missed opportunities and wasted gas. Save time and money by switching to Matcha, where you know in advance the route your tokens will take to reach the destination, and exactly how much it will cost.

 

                               Quick cross chain swaps come with zero fees at launch!

Fast on-chain settlement means that you can deploy capital to a new network in minutes and be ideally positioned to take advantage of any opportunity before the market catches up. Don’t get sidelined waiting for your wrapped Ether to confirm when you can load your bags in a single trade.

Better than CEXs

Avoid the risk of giving up custody of your crypto.

Decentralized bridges can be off-putting to users looking for a simple and smooth way to move assets from one chain to another. That leaves many people turning to centralized exchanges (CEXs) where trading across networks is made easier due to the custodial nature of the platforms. 

Since these exchanges retain control of all deposited assets, no onchain transactions need to take place to complete the transfer, but you also risk that your assets will not be returned to you, as you do not control their keys. 

Cross chain bridging on Matcha ensures that your assets remain under your control at all times, while the efficiency of aggregated liquidity keeps costs low without compromising on decentralization. Now, you can swap your tokens for a different token on another chain without extra steps, while retaining full control of your crypto.  

Move seamlessly across chains  

Cross chain swaps let you position yourself wherever opportunity beckons, by swapping your tokens directly for another across networks without any extra steps. No more hopping between bridges and DEXs - get where you need to be in just one trade! That’s the Matcha way - simple, efficient, and great value for money. 

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
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The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
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Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
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The challenge isn't just decentralizing intelligence.
 
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That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
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The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
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It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

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Navigating the world of blockchain 🧭
Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.

Whether you are exploring digital assets, building on-chain, or simply trying to understand decentralized technology, here is your foundational glossary of essential blockchain terms every beginner should know.

🏛️ 1. Core Architecture: The Base Layer

  • Blockchain: A distributed, immutable digital ledger that records transactions across a peer-to-peer network of computers. Once data is written to a block and added to the chain, it cannot be altered without altering all subsequent blocks.
  • Block: A collection of verified transactions grouped together. Once filled, the block is cryptographically linked to the previous one, forming a chronological "chain."
  • Node: An individual computer connected to a blockchain network that helps validate transactions, store ledger data, and maintain network consensus.
  • Consensus Mechanism: The set of rules and algorithms that network nodes use to agree on the validity of transactions.

    • Proof of Work (PoW): Requires miners to solve complex mathematical puzzles using computational power (e.g., Bitcoin).
    • Proof of Stake (PoS): Requires validators to lock up ("stake") native tokens as collateral to participate in block validation (e.g., Ethereum).

🔑 2. Ownership & Security: Wallets and Keys

  • Public Key (Address): An alphanumeric string that acts like your bank account number or email address. It is safe to share publicly so others can send you digital assets.
  • Private Key: A secret cryptographic passphrase or key that grants full access and control over your wallet assets. Never share your private key or seed phrase with anyone.
  • Seed Phrase (Recovery Phrase): A sequence of 12 to 24 random words generated when you set up a wallet. It acts as the master backup key to restore your wallet and access your funds on any device.
  • Hot Wallet vs. Cold Wallet:

    • Hot Wallet: A software-based crypto wallet connected to the internet (e.g., browser extensions, mobile apps), making it convenient for frequent transactions but higher risk.
    • Cold Wallet: An offline hardware device (e.g., Ledger, Coldcard) designed to isolate private keys from internet-connected threats.

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

  • Smart Contract: Self-executing code stored on a blockchain that automatically enforces agreement terms once predetermined conditions are met—eliminating the need for intermediaries.
  • dApp (Decentralized Application): Applications built on top of a blockchain network that run via smart contracts rather than centralized cloud servers.
  • Gas Fees: Network transaction fees paid to validators or miners to cover the computational energy required to process actions on a blockchain.
  • Layer 1 vs. Layer 2:

    • Layer 1 (L1): The underlying primary blockchain network (e.g., Bitcoin, Ethereum, Solana) that handles base security and finality.
    • Layer 2 (L2): Secondary frameworks or companion networks built on top of an L1 to increase transaction speeds and lower gas fees (e.g., Arbitrum, Optimism, Base).

💰 4. Financial & Market Concepts

  • Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
  • DeFi (Decentralized Finance): Financial services—such as lending, borrowing, trading, and earning interest—built on smart contracts without traditional banks or financial intermediaries.
  • Liquidity: The ease with which an asset can be bought or sold in a market without significantly impacting its price.
  • DYOR (Do Your Own Research): A foundational golden rule in the Web3 space reminding users to independently verify technical code, whitepapers, and team backgrounds before making any capital commitments.

💡 Quick Cheat Sheet

"Not your keys, not your coins."

If you do not hold the private keys or seed phrase to your digital wallet, you do not truly own the assets inside it—a centralized entity or exchange does. Always prioritize security first as you explore the space.

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AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

Source

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