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Fantom Foundation Launches Testnet for Fantom Sonic
October 24, 2023
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We are thrilled to announce Fantom Sonic, the latest breakthrough upgrade to Fantom that will scale the network to new heights.

With a brand-new virtual machine, improved database storage, and optimized consensus, Sonic is anticipated to achieve 2,000+ transactions per second (TPS) at an average finality of one second while consuming a fraction of the storage used by its predecessor, Opera. The upgrade is the latest step in Fantom’s mission to improve its underlying platform without resorting to sharding or additional layers.

Today, we are releasing access to the Fantom Sonic testnet environment to give users and developers a first-hand experience of the groundbreaking speed offered by the upgrade before its mainnet release, which is scheduled for spring 2024. Scroll further to learn more about Sonic and instructions on how to use the testnets.

What is Fantom Sonic?

Fantom Sonic is the name that covers the new Fantom technology stack, replacing the previous Opera. The new technology stack is included in the new Fantom Sonic Client that validators and other nodes will run to power the network, which comprises mainly the Fantom Virtual Machine, Carmen database storage, and an optimized Lachesis consensus mechanism. In other words, Sonic is the next iteration of the Fantom network, with no hard fork required for the upgrade. Existing smart contracts, services, and tools on Fantom Opera should be fully compatible with mainnet Fantom Sonic.

In unison, these three upgraded components elevate Fantom to unprecedented levels and allow the network to achieve an anticipated 2,000 TPS at a finality of around one second with up to a 90% reduction in storage, putting Fantom far ahead of its peers. Learn more about these components in technical detail further below.

As users continue to embrace blockchain-powered applications, a single popular application can slow an entire network. Sluggish performance of the network prevents the overall adoption of emerging decentralized applications. With its innovative technology, Fantom will allow new markets to adopt blockchain technology previously hindered by limited transaction throughput and slow finality.

We envision a new era of DeFi platforms, blockchain games, high-frequency oracles for perpetual trading, and many other applications that can leverage the speed and scalability of Sonic. Additionally, due to the significantly reduced storage requirements, it will be far more affordable and accessible to run a node on Fantom to partake in network consensus or provide data to dApps.

Sonic testnet environment

The Fantom Sonic testnet environment consists of two separate testnets to demonstrate the upgrade before its mainnet release. The closed testnet aims to showcase the maximum theoretical limits of Sonic, whereas the open testnet is interactive, allowing any user to experience Sonic directly.

Closed testnet

The Sonic closed testnet is observable to the public but does not allow users to submit transactions. A web dashboard shows the maximum performance of Sonic, such as transactions per second, time to finality, average block time, and more.

The dashboard shows that the closed testnet Sonic can process around 2,048 TPS with end-to-end transaction confirmation times (finality) of around 1.1 seconds. At the time of writing, the testnet is processing over 175 million transactions per day, showing the stability of the network even when driven to its limits.

In the closed testnet, a transaction feeder submits synthetic transactions and drives the network to its maximum performance. The transaction feeder is throttled when transaction finality rises beyond roughly 1.1 seconds, which occurs when transactions per second surpass roughly 2,048.

There is no room for public interaction in this testnet setup as the transaction feeder drives the network to its maximum performance. The synthetic transactions resemble transactions on the current Fantom mainnet with an average of 210,000 gas. The workload is distributed roughly as follows:

 

The closed testnet uses an evenly spread stake of 10 million FTM per validator. With 21 validators, the total stake is 210 million FTM. The required consensus quorum is two-thirds of the validators plus one, with a stake of 140,000,001 FTM. The consensus mechanism requires at least 15 validators to confirm blocks for this setup.

We will compare this to the Fantom mainnet, on which the stake is non-uniformly distributed. The total stake on September 25, 2023, at 2:00 PM UTC was 1,379,985,181 FTM. With these numbers, a quorum is reached by a minimum of 919,990,122 FTM. The combined stake of the top 14 validators is 926,970,795 FTM, which was sufficient to confirm blocks with one less validator than the Sonic closed testnet. As such, the closed testnet mimics the consensus of mainnet closely to demonstrate a realistic performance.

With this setup, the closed testnet achieves above 2,000 TPS with a finality of around 1.1 seconds and over 400 million gas per second. This is far beyond the achievable performance of the current Fantom mainnet, which sits at around 30 TPS. There will be a significant reduction in disk space requirements for validators and archive nodes. Currently, for approximately 518 million transactions, an offline pruned validator requires 1,194 GB (i.e. offline pruning removes historical states by stopping the validator), whereas Sonic with online pruning requires 351 GB only. Similarly, archival nodes require 10,893 GB on Opera but only 1,000 GB on Sonic.

We will upgrade and maintain the closed testnet with Fantom’s latest technology regularly. Hence, the closed testnet will be reset every two weeks.

Open testnet

The Sonic open testnet allows anyone to interact with Sonic by submitting transactions and experiencing the true speed the new Fantom upgrade offers.

Similarly to the closed testnet, the open testnet has a transaction feeder that submits synthetic transactions. However, it submits 130 TPS at an end-to-end finality of around 0.6 seconds, which leaves ample throughput for user interactions.

The open testnet dashboard allows users to search for addresses, transactions, and blocks. Note that the open testnet’s history will be retained for longer than the closed testnet’s history. Furthermore, it is possible to deploy dApps on this testnet with a few limitations: the client source code currently is unavailable, and there is no full explorer similar to FTMScan and no transaction tracing support on our public RPCs. 

The tutorial below covers the instructions to interact with the open testnet.

How to use Sonic testnet

Follow this tutorial to unveil the capabilities of Sonic and experience the next generation of blockchain technology. We use MetaMask in this tutorial, but any wallet that can mimic MetaMask will work.

  1. Connect wallet
    1. Go to the Sonic open testnet dashboard
    2. In the top-right corner, click on Connect
    3. Choose your desired wallet account and connect
    4. Go to the account page, unless automatically redirected
  1. Configure network
    1. In the Network section, click on Add to MetaMask
    2. Ensure the network details in your wallet match those on the web page
    3. Approve the action in your wallet and click Switch network when prompted
  1. Get testnet tokens
    1. In the Faucet section, choose a token to request
    2. Click Request and sign the transaction in your wallet
    3. We recommend requesting Fantom for gas and various other tokens to test the swapping feature
  1. Use Sonic Trade
    1. In the Sonic Trade section, choose a token to swap for another token
    2. Click on Swap and confirm the transaction in your wallet
    3. Witness the incredible speed of Sonic!

Behind the scenes

A range of technological innovations have been introduced to enable Sonic to scale Fantom beyond its current potential. This section will provide a more in-depth technical overview of these innovations.

Fantom Virtual Machine

Sonic uses a new virtual machine that achieves superior execution performance compared to the previous Ethereum Virtual Machine implementation.

The Fantom Virtual Machine (FVM) converts EVM bytecode of smart contracts seamlessly into a new virtual machine format on the fly (while executing transactions). Deployed smart contracts that are available only in EVM bytecode remain executable without retranslating the high-level source code (e.g. Solidity) into the new virtual machine format.

The new virtual machine format accelerates the execution of single operations and permits super instructions, optimized bundles of commonly occurring instruction patterns. Super instructions comprise multiple instructions that are consolidated and executed as one instruction, reducing the instruction dispatch time of the virtual machine. The conversion from EVM bytecode to the new format of the FVM is cached, such that subsequent executions of the same code reuse the previously converted EVM bytecode, saving execution time.

The FVM supports caching of cryptographic hashing for the EVM instruction SHA3. Cryptographic operations, especially hashing, are computationally expensive. Repetitive calculations of the identical hashes can occur due to contract operations, state changes, or transaction verifications. By caching previously computed hashes, the FVM can bypass the need to recalculate the same values, saving time and resources. 

Additionally, the FVM supports the caching of JUMPDEST analysis results. In the virtual machine, there are special instructions called JUMP and JUMPDEST. The JUMP instruction allows the code to leap to different locations, while JUMPDEST marks safe places for these jumps to land. The JUMPDEST analysis pre-scans the bytecode to map out all these safe landing spots. By doing so, the FVM ensures that during execution, any jumps are directed only to legitimate and safe points in the code, optimizing performance and increasing security against potential malicious manipulations.

Block processing intense operations, such as synchronizing a new validator with up to 65 million blocks from the first block, can take up to four weeks with Opera. Sonic’s advanced block processing, which includes the FVM and new database storage, can synchronize a new validator in less than two days entirely for the full range from scratch.

Carmen database storage

Sonic uses a new database storage, called Carmen, which reduces node storage requirements and improves performance. Carmen is a new StateDB that stores the world state of Fantom’s blockchain. The world state contains account information, such as balance, nonce, EVM bytecode, and persistent storage of smart contracts.

Carmen features implicit live pruning. Pruning refers to discarding historical data that is no longer needed, which is essential due to a growing blockchain with increasing storage demands. Previously, pruning required nodes to be offline, which burdened validators with financial and operational risks due to their temporary lack of network rewards and the pressure to restart the client software successfully after offline pruning. However, unlike on Opera, validators now can leverage live pruning to remain operational around the clock, preventing disruptions. Consequently, validators will require smaller disks that can yield savings of up to 65% using Sonic’s new database storage.

We achieve live pruning by specializing the database into two types: the LiveDB and the ArchiveDB. The LiveDB contains the world state of the current block only, whereas the ArchiveDB contains the world states of historical blocks of the blockchain. The diagram below shows the interaction of LiveDB and ArchiveDB with the block processing.

The FVM interacts with the LiveDB and the ArchiveDB. As mentioned, the LiveDB contains only the current world state and is optimized for progressing the world state from one block to the next. Validators only have a LiveDB but no ArchiveDB. The FVM reads and writes the data in the LiveDB. In contrast, archive nodes have the LiveDB and ArchiveDB to stay synced. They process requests of historical states via the RPC interface. Its data is read only by the RPC server, and the FVM adds the world state of new blocks.

This specialization of LiveDB and ArchiveDB has been a performance-critical insight for an efficient StateDB design and implementation. We discovered that the access patterns of validators and archive nodes require different implementation techniques. So far, we have developed five evolutionary steps, so called schemas, for LiveDB and ArchiveDB, which differ in how the world state is structured and stored on disk for LiveDB and ArchiveDB, respectively. The version deployed for the Sonic testnet, Schema 3, offers superior storage performance compared to Opera’s MPT data structure.

Schema 3 uses flat storage, which stores data sequentially instead of tree-like or hierarchical structures like the ones used in the MPT. Its flat storage approach simplifies data retrieval. Importantly, Schema 3 still provides cryptographic signatures for a world state and archive capabilities using an incremental version of a prefix algorithm. All schemas utilize a native disk format rather than storing the world state indirectly via key-value stores (e.g. LevelDB/PebbleDB).

Lachesis consensus mechanism

Lachesis is Fantom’s aBFT consensus mechanism. A consensus mechanism is the engine that receives user transactions and serializes them to form blocks. Lachesis has a peer-to-peer module that exchanges events and a transaction pool module for collecting transactions from users and queuing them for validators.

Sonic continues to use the Lachesis technology of Opera, but it has vastly improved the transaction pool for collecting transactions of users. Optimizing and fine-tuning the peer-to-peer network was essential for sustaining such high transaction throughput with a very low time to finality.

Sonic and Opera comparison

A summary of the key differences between Fantom Sonic and the Opera mainnet is shown below.

Summary of Sonic

As shown by the performance of the Sonic closed testnet, the mainnet release will bring a groundbreaking blockchain experience, which we have summarized below for a quick overview.

Sonic is anticipated to achieve beyond 2,000 TPS at a finality of around one second. However, as this is the upper limit, the network will offer a far quicker sub-second finality under real-world circumstances. Storage requirements are reduced by up to 90%, which reduces validator node size from around 2,000 GB to 300 GB and non-pruned archival node size from above 11 TB to below 1 TB.

The storage reductions will allow anyone to launch validator nodes at far lower costs with vastly improved synchronization times and live-pruning support. The Fantom Foundation, and other relevant parties, can deploy archival nodes in approximately 36 hours, which previously would take weeks. If a genesis file for a certain block height is available, or an actual copy of an archive StateDB, the synchronization will be even shorter.

The Sonic mainnet is scheduled for spring 2024, which will transport Fantom into a new era of blockchain technology.

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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.
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This means anyone can contribute compute, contribute models, validate outputs and can participate.
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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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