Artist ๐จ || Content creator Believer of web3/crypto
Joined September 2023
- Tweets8.5K
- Following2.1K
- Followers1.5K
- Likes11.8K
Pinned Tweet
Some people build technology. Others help build the community around it.
Thatโs what makes @blockchainjeff an interesting part of the @get_optimum ecosystem.
In Web3, strong communities need more than updates.
They need people who keep contributors engaged, help ideas reach more people, and make participation feel meaningful.
Optimum is working on a technical challenge that often goes unnoticed making blockchain data move faster and more efficiently.
And helping a community understand, support, and contribute to that mission matters too.
Good infrastructure moves data forward.
A strong community moves the vision forward.
Respect to everyone building both.
the team touched down in Singapore for TOKEN2049
Muriel Mรฉdard spoke on the @gammaprime panel about investing in disruptive tech, and dropped some sharp points worth remembering
her point was simple a model is only as smart as the data feeding it, and where that data actually comes from matters just as much as the model itself
even after training, a model's real value depends on how fresh and reliable the data reaching it stays over time
and none of that happens in isolation, that data still has to travel across a network fast enough to be useful in the moment it's needed
her bigger takeaway everyone's racing to build better AI models, but almost nobody is paying attention to the infrastructure that actually moves data fast across decentralized systems, even though both AI and blockchains depend on exactly that
basically, compute gets all the attention, but decentralized memory and fast data movement are the quiet bottleneck behind all of it
today 7 Oct and team is at DAS Asia
tomorrow, Oct 8 team will attend Brews by the Bay with @TheSpartanGroup , @Auros_global , and @CantonNetwork
Singapore week is just getting started ๐ธ๐ฌ
@get_optimum
@blockchainjeff
Areeba retweeted
What Makes PrismaX a Service Layer for Physical AI?
Building a capable robot is not just about better hardware or smarter AI models. It also requires the right kind of real world experience.
This is where things get difficult.
Robots need demonstrations of how humans interact with objects, complete tasks, and respond to different environments.
Collecting this data consistently, while maintaining its quality, is a challenge for robotics teams.
@PrismaXai is approaching this problem by connecting the entire data process.
It brings together three important parts:
โ Teleoperation: Skilled operators control real robot arms and humanoids, creating demonstrations that AI models can learn from.
โ Egocentric Data: People record everyday tasks from a first person perspective, giving models another way to learn how humans interact with the physical world.
โ Custom Data Collection: Teams can request specific tasks, robot types, and environments when existing datasets don't meet their requirements.
There is also a searchable data bank where developers and researchers can access existing robotics demonstrations instead of building every dataset from scratch.
What makes this approach interesting is that @PrismaXai isn't simply offering a place to download data.
It connects the people collecting demonstrations, the processes used to review them, and the teams that need those demonstrations to train physical AI.
That is what makes the service layer concept important.
As robotics models evolve, their data requirements will change too. Having infrastructure that can support both existing and customized data needs could make that process easier.
The real question is how well this system can scale as demand for physical AI training data grows.
The next generation of AI won't improve only by learning from more information.
It will improve by learning from the consequences of its own actions.
โ
The Robots โ Data โ Intelligence cycle is what makes this interesting.
A robot interacts with the physical world, whether it's picking up an object, sorting items, or completing a household task.
Those interactions produce valuable training data, including visual observations, movements, and sensor information.
AI models learn from these experiences and become better at deciding what to do in similar situations.
โ
But the interesting part is what happens next.
An improved model can handle more complex tasks and unfamiliar environments.
Those new interactions generate different experiences, which become another source of learning.
It's a continuous feedback loop rather than a one time training process.
โ
I think this changes how we should look at robotics development.
The challenge isn't simply building a smarter model.
It's creating a reliable system where real world experience continuously improves the intelligence behind the machine.
โ
That's the direction @PrismaXai is working toward.
Its approach connects robot operations, structured data collection, human operators, quality reviews, and model training into one process.
The real advantage of this cycle could be its ability to turn every deployment into an opportunity to learn something new.
What Makes PrismaX a Service Layer for Physical AI?
Building a capable robot is not just about better hardware or smarter AI models. It also requires the right kind of real world experience.
This is where things get difficult.
Robots need demonstrations of how humans interact with objects, complete tasks, and respond to different environments.
Collecting this data consistently, while maintaining its quality, is a challenge for robotics teams.
@PrismaXai is approaching this problem by connecting the entire data process.
It brings together three important parts:
โ Teleoperation: Skilled operators control real robot arms and humanoids, creating demonstrations that AI models can learn from.
โ Egocentric Data: People record everyday tasks from a first person perspective, giving models another way to learn how humans interact with the physical world.
โ Custom Data Collection: Teams can request specific tasks, robot types, and environments when existing datasets don't meet their requirements.
There is also a searchable data bank where developers and researchers can access existing robotics demonstrations instead of building every dataset from scratch.
What makes this approach interesting is that @PrismaXai isn't simply offering a place to download data.
It connects the people collecting demonstrations, the processes used to review them, and the teams that need those demonstrations to train physical AI.
That is what makes the service layer concept important.
As robotics models evolve, their data requirements will change too. Having infrastructure that can support both existing and customized data needs could make that process easier.
The real question is how well this system can scale as demand for physical AI training data grows.
The next generation of AI won't improve only by learning from more information.
It will improve by learning from the consequences of its own actions.
โ
The Robots โ Data โ Intelligence cycle is what makes this interesting.
A robot interacts with the physical world, whether it's picking up an object, sorting items, or completing a household task.
Those interactions produce valuable training data, including visual observations, movements, and sensor information.
AI models learn from these experiences and become better at deciding what to do in similar situations.
โ
But the interesting part is what happens next.
An improved model can handle more complex tasks and unfamiliar environments.
Those new interactions generate different experiences, which become another source of learning.
It's a continuous feedback loop rather than a one time training process.
โ
I think this changes how we should look at robotics development.
The challenge isn't simply building a smarter model.
It's creating a reliable system where real world experience continuously improves the intelligence behind the machine.
โ
That's the direction @PrismaXai is working toward.
Its approach connects robot operations, structured data collection, human operators, quality reviews, and model training into one process.
The real advantage of this cycle could be its ability to turn every deployment into an opportunity to learn something new.
Areeba retweeted
๐๐ฉ๐๐๐ ๐๐ฌ ๐๐จ๐ง๐๐ฒ.
As a validator, every millisecond matters.
Slow message propagation can mean missed opportunities, delayed attestations, and less competitive performance.
That's why @get_optimum's Mump2P deserves attention.
If you're still relying on traditional P2P networking, it might be time to rethink your setup.
๐๐๐ฎ ๐๐ค๐๐จ ๐จ๐ฅ๐๐๐ ๐ข๐๐ฉ๐ฉ๐๐ง?
Ethereum validators need to receive and propagate block data quickly.
The faster your node communicates with the network, the better positioned you are to participate efficiently.
In validation, speed isn't just a technical metric.
It's a competitive advantage.
๐๐๐๐ฉ ๐๐ช๐ข๐ฅ2๐
Mump2P is @get_optimum networking solution built to improve Ethereum's peer to peer communication.
It aims to reduce propagation delays and make message delivery more efficient.
The goal is simple: help validators communicate faster without compromising Ethereum's decentralized foundation.
๐๐๐ ๐ฃ๐ช๐ข๐๐๐ง๐จ ๐ข๐๐ฉ๐ฉ๐๐ง.
Optimum has reported that Mump2P can deliver:
Up to 3.3x faster propagation than libp2p in tested conditions
Lower message arrival time variance
More consistent communication across validators
These improvements can make a real difference when timing matters.
๐ฆ๐ฝ๐ฒ๐ฒ๐ฑ ๐๐ ๐ ๐ผ๐ป๐ฒ๐.
When block propagation gets faster, validators can improve their network responsiveness and compete more effectively.
No networking upgrade guarantees higher rewards, but better infrastructure can help reduce avoidable performance disadvantages.
Every millisecond counts when the network keeps moving.
๐ง๐ต๐ฒ ๐ฏ๐ถ๐ด๐ด๐ฒ๐ฟ ๐ฝ๐ถ๐ฐ๐๐๐ฟ๐ฒ
Ethereum needs infrastructure that scales without sacrificing decentralization.
Mump2P is an interesting step toward that future, giving validators another reason to rethink how their nodes communicate.
Better networking. Faster propagation. More efficient infrastructure.
๐๐ถ๐ป๐ฎ๐น ๐๐ต๐ผ๐๐ด๐ต๐๐
If you're an Ethereum validator, don't overlook your networking layer.
Explore @get_optimum Mump2P, review the benchmarks, and evaluate whether it fits your setup
console.getoptimum.xyz/login
Because in Ethereum validation, SPEED IS MONEY.
@discordflash
@tgogayi
Blockchain validators don't just need to process information quickly.
They need to receive it quickly too.
Even a small delay in block delivery can affect validator performance and rewards.
This is the problem @get_optimum is tackling with its data acceleration network.
Its solution is mump2p, powered by Random Linear Network Coding (RLNC).
In simple terms, instead of repeatedly sending the same data between nodes, RLNC allows them to exchange coded pieces and rebuild the original block faster.
The goal is faster and more reliable data propagation without changing Ethereum's consensus.
And this is already running on Ethereum mainnet.
The early results are interesting:
โ 2โ4x faster block propagation.
โ 7.88x lower propagation variance.
โ Blocks arrived before Ethereum's default networking layer in 9 out of 10 measurements.
Adoption is growing too.
According to Optimum's reported milestones:
โ 3.36M ETH connected to mump2p.
โ Around $7.6B in Total Value Boosted.
โ Nearly 7.8% of all staked ETH.
โ 2,185 Blockdaemon validators examined in its operator case study.
What I find interesting is that Optimum isn't asking Ethereum to change its consensus or validators to replace their existing clients.
It's improving something underneath them: how quickly information travels across the network.
Because better blockchain performance isn't only about processing more transactions.
It's also about getting the right data to the right nodes at the right time.
That's an important part of scaling worth watching.
@blockchainjeff
๐๐ฉ๐๐๐ ๐๐ฌ ๐๐จ๐ง๐๐ฒ.
As a validator, every millisecond matters.
Slow message propagation can mean missed opportunities, delayed attestations, and less competitive performance.
That's why @get_optimum's Mump2P deserves attention.
If you're still relying on traditional P2P networking, it might be time to rethink your setup.
๐๐๐ฎ ๐๐ค๐๐จ ๐จ๐ฅ๐๐๐ ๐ข๐๐ฉ๐ฉ๐๐ง?
Ethereum validators need to receive and propagate block data quickly.
The faster your node communicates with the network, the better positioned you are to participate efficiently.
In validation, speed isn't just a technical metric.
It's a competitive advantage.
๐๐๐๐ฉ ๐๐ช๐ข๐ฅ2๐
Mump2P is @get_optimum networking solution built to improve Ethereum's peer to peer communication.
It aims to reduce propagation delays and make message delivery more efficient.
The goal is simple: help validators communicate faster without compromising Ethereum's decentralized foundation.
๐๐๐ ๐ฃ๐ช๐ข๐๐๐ง๐จ ๐ข๐๐ฉ๐ฉ๐๐ง.
Optimum has reported that Mump2P can deliver:
Up to 3.3x faster propagation than libp2p in tested conditions
Lower message arrival time variance
More consistent communication across validators
These improvements can make a real difference when timing matters.
๐ฆ๐ฝ๐ฒ๐ฒ๐ฑ ๐๐ ๐ ๐ผ๐ป๐ฒ๐.
When block propagation gets faster, validators can improve their network responsiveness and compete more effectively.
No networking upgrade guarantees higher rewards, but better infrastructure can help reduce avoidable performance disadvantages.
Every millisecond counts when the network keeps moving.
๐ง๐ต๐ฒ ๐ฏ๐ถ๐ด๐ด๐ฒ๐ฟ ๐ฝ๐ถ๐ฐ๐๐๐ฟ๐ฒ
Ethereum needs infrastructure that scales without sacrificing decentralization.
Mump2P is an interesting step toward that future, giving validators another reason to rethink how their nodes communicate.
Better networking. Faster propagation. More efficient infrastructure.
๐๐ถ๐ป๐ฎ๐น ๐๐ต๐ผ๐๐ด๐ต๐๐
If you're an Ethereum validator, don't overlook your networking layer.
Explore @get_optimum Mump2P, review the benchmarks, and evaluate whether it fits your setup
console.getoptimum.xyz/login
Because in Ethereum validation, SPEED IS MONEY.
@discordflash
@tgogayi
Blockchain validators don't just need to process information quickly.
They need to receive it quickly too.
Even a small delay in block delivery can affect validator performance and rewards.
This is the problem @get_optimum is tackling with its data acceleration network.
Its solution is mump2p, powered by Random Linear Network Coding (RLNC).
In simple terms, instead of repeatedly sending the same data between nodes, RLNC allows them to exchange coded pieces and rebuild the original block faster.
The goal is faster and more reliable data propagation without changing Ethereum's consensus.
And this is already running on Ethereum mainnet.
The early results are interesting:
โ 2โ4x faster block propagation.
โ 7.88x lower propagation variance.
โ Blocks arrived before Ethereum's default networking layer in 9 out of 10 measurements.
Adoption is growing too.
According to Optimum's reported milestones:
โ 3.36M ETH connected to mump2p.
โ Around $7.6B in Total Value Boosted.
โ Nearly 7.8% of all staked ETH.
โ 2,185 Blockdaemon validators examined in its operator case study.
What I find interesting is that Optimum isn't asking Ethereum to change its consensus or validators to replace their existing clients.
It's improving something underneath them: how quickly information travels across the network.
Because better blockchain performance isn't only about processing more transactions.
It's also about getting the right data to the right nodes at the right time.
That's an important part of scaling worth watching.
@blockchainjeff
Areeba retweeted
The next generation of AI won't improve only by learning from more information.
It will improve by learning from the consequences of its own actions.
โ
The Robots โ Data โ Intelligence cycle is what makes this interesting.
A robot interacts with the physical world, whether it's picking up an object, sorting items, or completing a household task.
Those interactions produce valuable training data, including visual observations, movements, and sensor information.
AI models learn from these experiences and become better at deciding what to do in similar situations.
โ
But the interesting part is what happens next.
An improved model can handle more complex tasks and unfamiliar environments.
Those new interactions generate different experiences, which become another source of learning.
It's a continuous feedback loop rather than a one time training process.
โ
I think this changes how we should look at robotics development.
The challenge isn't simply building a smarter model.
It's creating a reliable system where real world experience continuously improves the intelligence behind the machine.
โ
That's the direction @PrismaXai is working toward.
Its approach connects robot operations, structured data collection, human operators, quality reviews, and model training into one process.
The real advantage of this cycle could be its ability to turn every deployment into an opportunity to learn something new.
Robots performing well in a controlled environment is one thing.
Making them work reliably in the real world is a completely different challenge.
That's what makes @PrismaXai interesting.
A robot must deal with unfamiliar surroundings, changing conditions, unexpected movements and real world obstacles.
A successful simulation is only the beginning.
Real progress happens when those same capabilities work outside the lab.
This is why high quality robotics data, better training and real world testing matter so much.
Physical AI isn't just about building smarter robots.
It's about making intelligence useful in the physical world.
And PrismaX is working toward that future.
The future of AI is physical.
The next generation of AI won't improve only by learning from more information.
It will improve by learning from the consequences of its own actions.
โ
The Robots โ Data โ Intelligence cycle is what makes this interesting.
A robot interacts with the physical world, whether it's picking up an object, sorting items, or completing a household task.
Those interactions produce valuable training data, including visual observations, movements, and sensor information.
AI models learn from these experiences and become better at deciding what to do in similar situations.
โ
But the interesting part is what happens next.
An improved model can handle more complex tasks and unfamiliar environments.
Those new interactions generate different experiences, which become another source of learning.
It's a continuous feedback loop rather than a one time training process.
โ
I think this changes how we should look at robotics development.
The challenge isn't simply building a smarter model.
It's creating a reliable system where real world experience continuously improves the intelligence behind the machine.
โ
That's the direction @PrismaXai is working toward.
Its approach connects robot operations, structured data collection, human operators, quality reviews, and model training into one process.
The real advantage of this cycle could be its ability to turn every deployment into an opportunity to learn something new.
Robots performing well in a controlled environment is one thing.
Making them work reliably in the real world is a completely different challenge.
That's what makes @PrismaXai interesting.
A robot must deal with unfamiliar surroundings, changing conditions, unexpected movements and real world obstacles.
A successful simulation is only the beginning.
Real progress happens when those same capabilities work outside the lab.
This is why high quality robotics data, better training and real world testing matter so much.
Physical AI isn't just about building smarter robots.
It's about making intelligence useful in the physical world.
And PrismaX is working toward that future.
The future of AI is physical.
Areeba retweeted
Will crypto see another $100M+ hack between today & February?
After last weekโs record $6.3B Binance heist, traders on Ultramint say 72% yes.
Creator Market Spotlight ๐ minted by @the_smart_ape
ultramint.xyz/m/will-there-bโฆ
Areeba retweeted
Blockchain validators don't just need to process information quickly.
They need to receive it quickly too.
Even a small delay in block delivery can affect validator performance and rewards.
This is the problem @get_optimum is tackling with its data acceleration network.
Its solution is mump2p, powered by Random Linear Network Coding (RLNC).
In simple terms, instead of repeatedly sending the same data between nodes, RLNC allows them to exchange coded pieces and rebuild the original block faster.
The goal is faster and more reliable data propagation without changing Ethereum's consensus.
And this is already running on Ethereum mainnet.
The early results are interesting:
โ 2โ4x faster block propagation.
โ 7.88x lower propagation variance.
โ Blocks arrived before Ethereum's default networking layer in 9 out of 10 measurements.
Adoption is growing too.
According to Optimum's reported milestones:
โ 3.36M ETH connected to mump2p.
โ Around $7.6B in Total Value Boosted.
โ Nearly 7.8% of all staked ETH.
โ 2,185 Blockdaemon validators examined in its operator case study.
What I find interesting is that Optimum isn't asking Ethereum to change its consensus or validators to replace their existing clients.
It's improving something underneath them: how quickly information travels across the network.
Because better blockchain performance isn't only about processing more transactions.
It's also about getting the right data to the right nodes at the right time.
That's an important part of scaling worth watching.
@blockchainjeff
Even zombies are asking for a TurboCard now
My entry for contest
@get_optimum
#BoostedMeme
@blockchainjeff
Blockchain validators don't just need to process information quickly.
They need to receive it quickly too.
Even a small delay in block delivery can affect validator performance and rewards.
This is the problem @get_optimum is tackling with its data acceleration network.
Its solution is mump2p, powered by Random Linear Network Coding (RLNC).
In simple terms, instead of repeatedly sending the same data between nodes, RLNC allows them to exchange coded pieces and rebuild the original block faster.
The goal is faster and more reliable data propagation without changing Ethereum's consensus.
And this is already running on Ethereum mainnet.
The early results are interesting:
โ 2โ4x faster block propagation.
โ 7.88x lower propagation variance.
โ Blocks arrived before Ethereum's default networking layer in 9 out of 10 measurements.
Adoption is growing too.
According to Optimum's reported milestones:
โ 3.36M ETH connected to mump2p.
โ Around $7.6B in Total Value Boosted.
โ Nearly 7.8% of all staked ETH.
โ 2,185 Blockdaemon validators examined in its operator case study.
What I find interesting is that Optimum isn't asking Ethereum to change its consensus or validators to replace their existing clients.
It's improving something underneath them: how quickly information travels across the network.
Because better blockchain performance isn't only about processing more transactions.
It's also about getting the right data to the right nodes at the right time.
That's an important part of scaling worth watching.
@blockchainjeff
Even zombies are asking for a TurboCard now
My entry for contest
@get_optimum
#BoostedMeme
@blockchainjeff
Robots performing well in a controlled environment is one thing.
Making them work reliably in the real world is a completely different challenge.
That's what makes @PrismaXai interesting.
A robot must deal with unfamiliar surroundings, changing conditions, unexpected movements and real world obstacles.
A successful simulation is only the beginning.
Real progress happens when those same capabilities work outside the lab.
This is why high quality robotics data, better training and real world testing matter so much.
Physical AI isn't just about building smarter robots.
It's about making intelligence useful in the physical world.
And PrismaX is working toward that future.
The future of AI is physical.
There's a bigger question in robotics that deserves attention:
How do we help robots learn from the real world consistently?
A robot improves through physical interaction.
It needs to perform tasks, learn from outcomes, and use those experiences to improve future behavior.
But collecting useful demonstrations, maintaining data quality, coordinating human operators, and making that data available to developers are separate challenges.
This is where the service layer becomes important.
@PrismaXai is approaching physical AI from this infrastructure angle.
Rather than focusing only on the robot or the model, it's working on the systems connecting real world operation, human input, and training data.
The interesting part is the feedback loop.
Better organized demonstrations can support better training, while the needs of model developers can guide what data gets collected next.
To me, this is an important shift in robotics.
Building intelligent machines is one challenge.
Building the infrastructure that helps them keep learning is another.
Areeba retweeted
Speed is more than just a performance metric in blockchain.
It can directly impact profits, efficiency, and network rewards.
As @blockchainjeff explained, even milliseconds matter.
Faster data propagation can give traders an advantage in competitive markets, while validators benefit from blocks and attestations reaching the network faster.
That's one reason @get_optimum deserves attention.
Using RLNC (Random Linear Network Coding), Optimum aims to improve how data moves across networks at the protocol level.
I believe blockchain scalability isn't just about processing more transactions.
How quickly and efficiently information travels across the network matters just as much.
Better data propagation can help build faster and more efficient blockchain infrastructure.
In blockchain, speed isn't just an advantage.
It can make a real difference.
What do validators and traders have in common?
The need for speed. ๐๏ธ
@blockchainjeff explains how Optimum's speed boost is already giving validators an edge, and why the trading sector is our next target.
Speed is more than just a performance metric in blockchain.
It can directly impact profits, efficiency, and network rewards.
As @blockchainjeff explained, even milliseconds matter.
Faster data propagation can give traders an advantage in competitive markets, while validators benefit from blocks and attestations reaching the network faster.
That's one reason @get_optimum deserves attention.
Using RLNC (Random Linear Network Coding), Optimum aims to improve how data moves across networks at the protocol level.
I believe blockchain scalability isn't just about processing more transactions.
How quickly and efficiently information travels across the network matters just as much.
Better data propagation can help build faster and more efficient blockchain infrastructure.
In blockchain, speed isn't just an advantage.
It can make a real difference.
What do validators and traders have in common?
The need for speed. ๐๏ธ
@blockchainjeff explains how Optimum's speed boost is already giving validators an edge, and why the trading sector is our next target.
There's a bigger question in robotics that deserves attention:
How do we help robots learn from the real world consistently?
A robot improves through physical interaction.
It needs to perform tasks, learn from outcomes, and use those experiences to improve future behavior.
But collecting useful demonstrations, maintaining data quality, coordinating human operators, and making that data available to developers are separate challenges.
This is where the service layer becomes important.
@PrismaXai is approaching physical AI from this infrastructure angle.
Rather than focusing only on the robot or the model, it's working on the systems connecting real world operation, human input, and training data.
The interesting part is the feedback loop.
Better organized demonstrations can support better training, while the needs of model developers can guide what data gets collected next.
To me, this is an important shift in robotics.
Building intelligent machines is one challenge.
Building the infrastructure that helps them keep learning is another.
Robotics datasets need a common quality standard.
One dataset may have smooth movements but repetitive tasks. Another may offer variety but contain unclear footage.
Without shared criteria, comparing them becomes difficult.
@PrismaXai focuses on factors like task accuracy, movement quality, and data diversity.
Better standards make it easier to identify useful data before training a robot.
Areeba retweeted
As I approach the last ~1000 or so RebelKin to create, I am thinking about how to approach WLs and collabs.
I am aware some of the early hype has died off because some of the swarm of interested people realized I was building slowly and deliberately, and that I wasn't anywhere close to release ๐ .
In the meantime I received about 3000 DMs asking for spots.
My tentative plan is to mint some myself for giveaways, a handful of spots to a small group of friends I have had in the space for a few years now, and then do a GTD allocation (2 mints per wallet) that includes some of my earliest supporters. If i followed your account here you probably figured out already you are included in GTD (congratulations ๐). I will be reaching out in about a week or two to get your wallet addresses.
I am also reconsidering my decision on having a small website because I want to do one of those follow me on X type forms for sign up verifications, and I will award the majority of the rest from that.
My outreach for collabs will be to choose high signal accounts to get the word out as far as possible but also making sure to find and invite real collectors even if their socials game is small (I was a small account too so I want to be as fair as possible).
I hope when it launches it will be what you need it to be. Whether you end up a long time holder or an immediate flipper ๐ I welcome it all. In the end, I made this for you to enjoy and how you decide to interact with this project is everyone's shared destiny.
There is no roadmap for what comes after launch,I will take what I build one step at a time.
More details to follow as I wrap up the build phase.
Long live the RebelKin โ๐ฝ
Areeba retweeted
Post #75
@get_optimum weekly 11
Congrats to all the newly refined, well deserved
There was no community call this week.
But guess what on the first week of optimumโs mainnet the TVB has crossed over $3.4B
Here is the recap
AOTW by @ElsaMadre
nitter.gq/elsamadre/status/21163โฆ
A๐งต๐
Post #72
@get_optimum Weekly with JT #10
This week came with a lot of surprises, first MumP2P went live, and also the 999 NFTs collection
Congrats to the new refined, you all deserved it
@blockchainjeff hosted the community call as promised by him last two weeks
A๐งต๐๐พ
