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Sonic Points and Gems Explained — 200 Million S Airdrop
Dinarian888
December 05, 2024
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Sonic Points are user-focused airdrop points that can be earned as part of the ~200 million $S airdrop. Designed to reward meaningful user engagement within the Sonic ecosystem, these points incentivize a wide range of activities, such as early adoption, long-term loyalty, asset ownership, and active participation with apps across the platform.

On the other hand, Sonic Gems are developer-focused airdrop points. As a groundbreaking incentive mechanism for developers within the Sonic ecosystem, Gems reward apps for driving user engagement and innovation based on their performance on Sonic.

These Gems can be redeemed for $S tokens, which apps can then distribute as rewards to their users. This system empowers apps to kickstart growth and maintain long-term user activity by encouraging consistent interaction and participation.

Both Sonic Points and Gems are distributed across multiple seasons, ensuring a sustainable and dynamic rewards structure that continuously incentivizes active involvement. The first season ends in ~June 2025.

Sonic Points Explained

To earn Sonic Points, users must bridge or use whitelisted assets within the Sonic ecosystem through any approved app. The current whitelisted assets for Season 1 are listed here, with more assets potentially added throughout the season.

There are two types of points within the Sonic Points program:

  1. Passive Liquidity Points
    Passive liquidity points reward users for bridging primary assets onto the Sonic mainnet. Users earn points based on the value and type of assets they hold, providing an incentive for maintaining liquidity within the ecosystem.
  2. Activity Points
    Activity points offer a multiplier on top of passive liquidity points, encouraging users to actively engage with the ecosystem. By deploying their assets into any whitelisted application, users can enhance their point earnings, driving greater participation and utility within the network.

On the designated user airdrop claim date (~June 2025), users can immediately claim 25% of their Season 1 airdrop as liquid $S tokens, while the remaining 75% will be vested over 270 days in the form of an NFT. Users can choose to claim their vested position early by burning some of their allocation.

Alternatively, users who choose to hold their airdrop NFT positions can trade them on a speculative NFT marketplace if desired, adding an additional layer of utility and flexibility.

Sonic Gems Explained

Sonic Gems are off-chain airdrop points exclusively designed for apps. Each season, a fixed number of Gems is distributed to apps based on various performance factors. Apps can monitor their progress through a leaderboard, which is updated every 24 hours with the latest Gem allocations.

The competitive PvP nature and fixed supply of Gems mean that an app's Gem balance may fluctuate daily, influenced by the performance of other apps on the platform. 

Apps that wish to distribute the $S tokens earned through Gems to their users must manage the accounting process independently. They have full flexibility in determining how to do so. For example, an app could:

  • Mint a new token representing its share of $S redeemed through Gems for a specific season.
  • Maintain an internal record of user balances.

Unlike Sonic Points, which are airdrop points designed for users, Gems empower apps to claim liquid $S tokens instead of vested NFT airdrop position. Once the $S tokens are claimed, it’s the app’s responsibility to determine how they’re distributed to their users.

While there’s no strict requirement for apps to share a specific percentage of their claimed $S tokens with their users, the design of Gems incentivizes generosity. Apps that share a larger portion of their claimed $S with their communities are rewarded more favorably compared to those that don’t.

Gems Season 1

A total of 1,680,000 Gems will be distributed during Season 1. Out of this, 262,500 Gems are pre-allocated to Sonic Boom winners. The chart below shows the number of Gems allocated to each tier in Sonic Boom.

The remaining 1,417,500 Gems will be available for any app to earn throughout the season — whether they’re Sonic Boom winners or not. At the end of Season 1, all eligible apps will be able to claim $S tokens based on the number of Gems they have earned.

Distribution of Gems

Sonic Gems are distributed using a structured approach designed to reward apps within the Sonic ecosystem. By considering factors such as category relevance, exclusivity, and effective reward distribution, this system promotes fairness and incentivizes active participation. 

Below are the key criteria that will determine an app's share of Gems in Season 1:

1. Category

Apps are assessed across several weighted categories, with each app assigned a weight based on its primary category. For Season 1, the specific weights are detailed below. If an app falls into multiple categories, the weight of its dominant category will be applied.

2. Sonic Native

Apps are assigned different weights depending on their level of exclusivity to Sonic:

  • Weight 2: Exclusively available on Sonic
  • Weight 1: Primarily on Sonic but accessible elsewhere
  • Weight 0.5: Available across multiple chains

Note: An app's Sonic-native weight cannot be upgraded during a season. However, if an app takes actions that reduce its Sonic nativeness, its weight will be reduced immediately and remain in effect for the following season as well.

3. Point Score

Point score is determined by calculating the total amount of Sonic Points that an app has generated for its users. This score is then divided by the total points generated across all eligible apps.

To generate Sonic Points for their users, apps must meet the following requirements:

  • Integrate with the OpenBlock Labs API.
  • Provide utility to whitelisted assets within the app.

4. Incentive (Applicable After Season 1)

This assesses how effectively an app distributes its claimed $S to its users. An app's incentive weight is determined by the percentage of its claimed $S that was distributed to its users during the previous season.

For example, if an app distributed 100% of its claimed $S to its users, it’ll receive a weight of 1 in the next season, while distributing only 80% would give it a weight of 0.8.

Note: While there’s no requirement for apps to distribute a specific amount of their claimed $S to users, it’s mandatory for all apps to publicly disclose the percentage they intend to share with their communities. This transparency allows users to make informed decisions about allocating their capital. Any instances of false communication or misuse of claimed $S will result in blacklisting for subsequent seasons.

Final Gems Calculation

Apps will receive a pro-rata share of Sonic Gems based on their final weights, determined by the calculations below.

Gems Revocation Policy

The following actions by the app can cause their Sonic Gems to be revoked.

  1. Incentivizing Project Tokens or NFTs with Gems
    Allocating Gems as rewards for activities like holding, staking, or providing liquidity (LPing) for a project’s token or NFT. For apps that have a voting mechanism to direct emissions, Gems can be used as vote incentives for any pool other than those that contain the project's token.
  2. Suspicious Distribution Practices
    Distributing large quantities of Gems non-transparently, such as allocating them disproportionately to insiders or KOLs without clear disclosure.
  3. Misrepresenting Gem Redistribution
    Providing false information about the amount of claimed $S being distributed to users during any season.

Note: Users are encouraged to report any suspicious activity or malpractice to the Sonic Labs team.

Example Distribution of Gems

Using the methodology outlined above, here is an example demonstrating the distribution of 100 Sonic Gems among five apps (A, B, C, D, and E) in Season 1. The distribution considers five random categories, Sonic nativeness, and point score.

Let’s assume the following weights:

Now that we have weights for each app (excluding the incentive weight, which applies only from Season 2 onward), we can proceed to calculate their Gem scores, which simply multiplies their category weight with their Sonic native weight.

After calculating each app’s Gem score, the next step is to determine their point score, which represents the proportion of Sonic Points each app generated for its users. In this example, we’ll assume the five apps collectively generated a total of 1,000 Sonic Points, with each app contributing a randomly assigned share.

To calculate an app’s point score, divide the number of Sonic Points the app generated by the total Sonic Points generated during the season.

With each app’s Gem and point score calculated, we can now calculate their final score. Remember, the formula for that is Gem Score × (1 + Point Score).

Finally, we can determine the number of Gems each app will receive based on the calculations above. The formula is straightforward: divide the app’s final score by the total final scores of all apps, then multiply the result by the total number of Gems available for the season.

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Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

Revolut claims that derived biometric face data was not.

The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.

The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.

Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.

Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.

The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

The reason this is a recurring problem is that companies are keeping highly sensitive information about their customers’ identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties – either in response to valid legal requests, or, as in the case of Revolut, fake ones.

One reason for this is know your customer (KYC) and anti-money laundering (AML) rules. Revolut’s current UK customer privacy notice spells it out: the company generally keeps personal data of UK customers for no more than seven years after the relationship ends, and sometimes longer – for legal reasons.

This means that even if you close your account, your identity documents don’t disappear.

And while the incident with Revolut happened in the financial sector, it’s by no means the only one that requires customers to hand over sensitive identity information. Discord, a popular chat service, said in an October 9, 2025 security update that government ID photos of approximately 70,000 users may have been exposed after a third-party customer service provider got hacked.

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It’s hard to do anything about a copy of your old passport, or a photo of your face, or a record of your past transactions. These can be used to identify and profile you, and can be used to carry out targeted fraud. And this can happen even if the initial disclosure didn’t result in financial loss.

The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

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Dinarian888@dinarian888
August 28, 2026
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This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notes, Massachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

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Dinarian888@dinarian888
August 16, 2026
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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.
 
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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.
 
The goal is not simply decentralization for its own sake.
 
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
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
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.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
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.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
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.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

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