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Liquidity Pool Mining In Osmosis Zone For Cosmos
February 12, 2023
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(Dinarian Note: I do hope this helps some of you realize the potential that lays within Osmosis Zone.. any questions let me know, I do this regularly to not only make a few 100% APY, but to help support XPRT liquidity..)

 

Getting Started

Before opening the Osmosis AMM App, make sure to install the Keplr Wallet.

Open the App

Go to https://app.osmosis.zone/

 

Connect Wallet

Click Approve. This confirms that you are connecting to the app.osmosis.zone and the chain osmosis-1.

⚠️ Always make sure you are connected "app.osmosis.zone" and network "osmosis-1"

 

Deposit Funds

Click "Assets", Then click on the deposit link next to the asset name. For this example we are            clicking the ATOM deposit link.

Approve connection to cosmoshub-4

Once connected, select how much you would like to deposit, then click the deposit button

 

Approve the transaction

Once the transaction is completed a series if confirmations notifications will be displayed                    including the IBC confirmation.

 

Swapping Tokens

Swapping tokens is as easy as selecting the tokens you wish to swap and selecting the swap button at the bottom of the order window.  Be sure to read the glossary at the bottom of this tutorial to learn about terms such as slippage and impermanent losses.

Adding Liquidity to a Pool

Select a pool from the Pools page. 

Then click Add/Remove Liquidity

 Input a quantity of one of the assets. The quantity of the other asset(s) will auto-complete. (Pools          require assets to be deposited in pre-determined weights.)

⚠️ In order to get your rewards you must bond the liquidity pool tokens.

To remove liquidity, input the percentage amount to withdraw.

Incentivized pools receive OSMO liquidity mining rewards. Rewards are distributed to                    bonded LP tokens in the pools that meet the bonding time requirements. The longer you                provide liquidity to the pool, the more rewards you get.

Bonded Liquidity Pool (LP)Tokens

You can choose to bond your LP tokens after depositing liquidity. LP tokens remain bonded for any amount of time you choose. Remember, bonded LP tokens are eligible for liquidity mining rewards ONLY if they meet the minimum bonding length requirement for that incentive.

Click "Bond Shares" and approve the transaction in your Keplr wallet.

When you want to un-bond an LP token, you need to submit a transaction that begins the                unbonding period. After the end of the pre-determined period (usually 14-21 days), you then will        submit another transaction to withdraw the tokens.

Liquidity Bootstrapping Pools

Osmosis offers a convenient design for Liquidity Bootstrapping Pools (LBPs), a special type of automated market maker designed for token sales. New protocols can use Osmosis’ LBP feature to distribute tokens and achieve initial price discovery.

LBPs differ from other liquidity pools in terms of the ratio of assets within each pool. In LBPs,the ratio changes over time. LBPs involve an initial ratio, a target ratio, and an interval of time over which the ratio adjusts. These weights are customizable before launch. One can create an LBP with an initial ratio of 90-10, with the goal of reaching 50-50 over a month. The ratio continues to gradually adjust until the weights are equal within the pool. Like traditional LPs, the prices of assets within the pool is based on the ratio at the time of trade.

After the LBP period ends or the final ratio is reached, the pool converts into a traditional LP pool.

LBPs facilitate price discovery by demonstrating the acceptable market price of an asset. Ideally, LBPs will have very few buyers at the time of launch. The price slowly declines until traders are willing to step in and buy the asset. Arbitrage maintains this price for the remainder of the LBP. The process is shown by the blue line below

Choosing the correct parameters is very important to price discovery for an LBP. If the initial price is too low, the asset will get bought up as soon as the pool is launched. It is also possible that the targeted final price is too high, creating little demand for the asset. The green line above shows this scenario. 

Osmosis' goal is to provide an easy-to-use LBP design to give protocols the best chances at a  successful pool. The LBP feature facilitates initial price discovery for tokens and allows protocols to fairly distribute token rewards to the pool liquidity providers.

Bonded Liquidity Gauges

Bonded Liquidity Gauges are mechanisms for distributing liquidity incentives to LP token holders that have been bonded for a pre-determined and disclosed amount of time. Close to half of the daily issuance of OSMO gets distributed as liquidity incentives.

For instance, in a Pool 1 LP share, 1-week gauge would distribute rewards to users who have bonded Pool1 LP tokens for one week or longer. The quantity of OSMO that each liquidity provider receives is directly in proportion to the number of their personally bonded tokens.

A single bonded LP position may be eligible for multiple gauges. The only thing that effects the guages is the minimum amount of bonding time required for each gauge.

Tux, the Linux mascot

Another incentive is the fact that, the rewards earned from liquidity mining are not subject to unbonding. Rewards can be transferred immediately to use how you see fit. Only the principal bonded shares are subject to the unbonding period.

Allocation Points

Not all of the liquidity pools have incentivization gauges. Staked OSMO hodlers choose which pools to incentivize via on-chain proposals. To incentivize a pool, governance can assign “allocation points” to specific gauges. At the end of every daily epoch, around half of the newly released OSMO (the tokens meant for liquidity incentives) are distributed proportionally to the points that each gauge has. The percent of the OSMO liquidity rewards that each gauge receives is calculated as its number of points divided by the total number of allocation points.

Take, for example, a scenario in which three gauges are incentivized:

  • Gauge #3 – 10 allocation points
  • Gauge #4 – 5 allocation points
  • Gauge #7 – 5 allocation points

20 total allocation points are assigned in this scenario. At the end of the daily epochs, Gauge #3 will receive 50% (10 out of 20) of the liquidity incentives minted. Gauges #4 and #7 will receive 25% each.

Governance can pass an Update Pool Incentives proposal to edit the existing allocation points of any gauge. By setting a gauge’s allocation to zero, it can remove it from the list of incentivized gauges entirely. Proposals can also set the allocation points of new gauges. When a new gauge is added, the total number of allocation points increases, thus diluting all the existing incentivized gauges. Gauge #0 is a special gauge that sends its incentives directly to the chain community pool. Assigning allocation points to gauge #0 allows governance to save some of the current liquidity mining incentives to be spent at a later time.

At genesis, the only gauge that will be incentivized is Gauge #0, (the community pool gauge). However, a governance proposal can come immediately after launch to choose which gauges/pools to incentivize. Governance voting period at launch is only 3 days at launch, so liquidity incentives may be activated as soon as 3 days after genesis.

External Incentives

Osmosis not only allows the community to add incentives to gauges. Anyone is eligable to deposit tokens into a gauge to be distributed. This feature allows outside parties to augment Osmosis’ own liquidity incentive program.

For example, there may be an ATOM/XPRT pool that has a one-day gauge incentivized by governance OSMO rewards. However, PersistenceOne(XPRT), may also choose to add additional incentives to the one-day gauge or even add incentives to a new gauge (such as one-week gauge).

External incentive providers can also set up long-lasting programs that distribute rewards over an extended time period. For example, PersistenceOne can deposit 30,000 XPRT to be distributed over a one-month liquidity program. The program will automatically distribute 1000 XPRT per day to the gauge.

Fees

Osmosis also provides three sources of revenue: transaction fees, swap fees, and exit fees.

TX Fees

Transaction fees are paid by any user to post a transaction on the chain. The fee amount is determined by the computation and storage costs of the transaction. Minimum gas costs are determined by the proposer of a block in which the transaction is included. This transaction fee is distributed to OSMO stakers on the network. Validators can choose which assets to accept for fees in the blocks that they propose. This optionality is a unique feature of Osmosis.

Swap Fees

Swap fees are fees charged for making a swap in an LP pool. The fee is paid by the trader in the form of the input asset. Pool creators specify the swap fee when establishing the pool. The total fee for a particular trade is calculated as percentage of swap size. Fees are added to the pool, effectively resulting in pro-rata distribution to LPs proportional to their share of the total pool.

Exit Fees

Osmosis liquidity providers pay a small fee when withdrawing from any pool. Similar to swap fees, exit fees per pool are set by the pool creator. Exit fees are paid in LP tokens. Users withdraw their tokens, minus a percent for the exit fee. These LP shares are burned, resulting in pro-rata distribution to remaining LPs.

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This could mark a massive turning point for innovation and compliance in the U.S. crypto industry! 🚀📊

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#Crypto #SEC #PaulAtkins #CryptoNews #Bitcoin #Ethereum #Web3 #Regulation

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Sign → perceive → understand.

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September 13, 2026
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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.

This was not a financial service, nor the same type of attack. But the result was similar – because the underlying business process was the same: requiring and storing sensitive identity documents. In the case of Discord, these were used to review age-related appeals.

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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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 notesMassachusetts 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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🤖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.
 
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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