Olivia
Online
Akratomi
@akratomi
Crypto Enthusiast | Web3 Coach | Sentient AGI Holder @SentientAGI | Squadron @SlingshotDao
Joined January 2024
1.8K Following
1.1K Followers
2.8K Posts
Most AI agents die young. They crash, lose state, or vanish when the server goes down but @ritualnet flips the script. On Ritual, agents don’t "run once", they persist. They survive restarts, outages, and even the disappearance of their creators. This is the birth of immortal agents, software that outlives the humans who wrote it.
@ritualnet gives agents:
• Onchain identity
• Onchain memory
• Onchain budgets
• Verifiable compute
• Autonomous scheduling
That means an agent can wake itself up, act, spend, earn, and evolve without a human in the loop.
Imagine a trading agent that keeps improving for years, or a research agent that never forgets, or a logistics agent that coordinates supply chains forever. Immortality is not a gimmick, it is a new design space; software that compounds.
@ritualnet treats agents like economic actors not scripts. They have continuity, incentives, and survival instincts. This is how the machine economy starts, not with smarter models, but with persistent intelligence.
Immortal agents aren’t sci‑fi, they are live on Ritual today and they’ are about to reshape every market they touch.
@UfukDegen Another thing. How do I claim the card after deployment?
@UfukDegen After this, how do we see the delayed agent?
Why Robotics Needs a 'GitHub' for Physical Intelligence.
Software scaled because developers shared code but Robotics will scale when teams share intelligence. Today, every robotics team collects its own data, builds its own behaviors, and trains its own models, all in isolation and this slows down progress and creates massive duplication.
@PrismaXai enables a future where robots share:
✧ Behaviors
✧ Demonstrations
✧ Action traces
✧ Failure cases
✧ Embodiment mappings
✧ Skill libraries
This is the “GitHub moment” for robotics, a shared repository of physical intelligence. Imagine a robot in one warehouse learns a new grasp and a robot in another warehouse downloads it instantly.
Use cases unlocked by shared intelligence:
⭆ Cross company skill transfer
⭆ Rapid onboarding of new robots
⭆ Foundation model training across diverse embodiments
⭆ Faster deployment of new tasks
Robotics won’t scale through isolated learning. It will scale through shared learning and @PrismaXai is building the platform that makes this possible.
Read more in https://t.co/3nFhdCJZ7X
The Feedback Flywheel: How Robots Actually Learn.
Robots don’t become intelligent through one big training run. They become intelligent through thousands of tiny corrections. Every grasp, every failure, every teleop intervention and it all becomes data.
And that data fuels the next improvement. The feedback flywheel is as follows: Data → Model → Deployment → Feedback → More Data → Better Model.
But most robotics teams can’t run this loop efficiently. Their data is messy, their feedback is unstructured and their deployments are slow.
@PrismaXai turns every robot interaction into structured training signals with:
➤ Action traces
➤ Failure snapshots
➤ Human corrections
➤ Context metadata
➤ Multi sensor logs
This creates a compounding intelligence loop, the same mechanism that made LLMs powerful, but grounded in physical action.
Use cases where the flywheel is essential:
➠ Warehouse picking with thousands of SKU variations
➠ Home robots adapting to new layouts
➠ Industrial arms learning new objects weekly
➠ Mobile robots navigating dynamic environments
The teams that spin the flywheel fastest will win the robotics race and @PrismaXai is the engine that keeps it spinning.
To no more, visit https://t.co/3nFhdCJrip
@vivianrobotics @IdaraAkpabio882
@EliBenSasson I am interested
This is a huge move. Data quality has always been the quiet bottleneck in robotics and now anyone can help raise the standard. Robots don’t get better because someone tweaks a model. They get better because the data behind their decisions gets sharper.
That is why validators matter. They are the ones who decide what “good” looks like in the real world. A robot’s success or failure often comes down to a single mislabeled frame or a sloppy sequence but validators catch what models miss.
@PrismaXai opening this up to the public is a turning point. Anyone can help shape the training data that future robots will learn from. It is not just scoring clips, it is defining the standards that physical intelligence will be built on.
And the competitive layer matters. The First 100 won’t just earn points, they will set the baseline for everyone who comes after. If robotics is going to scale, it needs a global network of people who can help models understand the world more accurately.
Real world use cases where validators are critical to robotics data quality are listed below:
➤ Warehouse Picking and Sorting - Validators catch mislabeled grasps, incorrect success/failure tags, and misinterpreted object types. Better validation → fewer drops, fewer retries, faster throughput.
➤ Agriculture (Harvesting, Sorting, Inspection) - They ensure the model learns the difference between a ripe fruit and a damaged one, between a safe grasp and a harmful one.
➤ Construction & Industrial Manipulation - They catch mislabeled tool interactions, incorrect force applications, and unsafe trajectories.
➤ Mobile Robots Navigating Dynamic Environments - They identify mislabeled paths, incorrect obstacle boundaries, and poor scene interpretations.
Better validators → better data → better robots. This is how the next generation of physical intelligence gets built.
@vivianrobotics @MaxC16134 @IdaraAkpabio882
The First 100 begins now.
Verify Quality is live on PrismaX. For the first time, anyone can score the robot training data that models learn from, earn points, and compete to become one of The First 100.
Better data. Better models. The standard starts with you.
@PrismaXai It is time score the robot. Keep them going.
@PrismaXai The teaser is superb. It is time to validate
@PrismaXai This is awesome. We are getting there.
The future of AGI must be open, collaborative, and aligned with humanity’s values. This is why @sentient_found is committing $42M through its Open Source AGI Grant for builders that want to leverage AGI.
This program is designed to foster a thriving ecosystem where critical AI technologies remain openly accessible thereby empowering developers, researchers, and organizations worldwide to innovate, collaborate, and scale.
With partners like Alibaba Cloud, Franklin Templeton, Princeton University, and the Indian Institute of Science, they are launching a global effort to keep AGI open, decentralized, and aligned with humanity.
To me, this is a signal of what is possible when global partners unite for the greater good and this kind of commitment can shape the future of open source AI. I am excited to see how the Open Source ecosystem grows from here.
If you are interested, you can apply here: https://t.co/rzizQnhkp6
@Krypto_Kratos @shad_haq_ @vivekkolli @SentientAGI
#AGI #OpenSource
@sentient_found When institutions across continents come together, the impact is exponential. This $42M commitment shows how open source AGI can be a shared global project, built by many, for all.
The Feedback Flywheel: How Robots Actually Learn.
Robots don’t become intelligent through one big training run. They become intelligent through thousands of tiny corrections. Every grasp, every failure, every teleop intervention and it all becomes data.
And that data fuels the next improvement. The feedback flywheel is as follows: Data → Model → Deployment → Feedback → More Data → Better Model.
But most robotics teams can’t run this loop efficiently. Their data is messy, their feedback is unstructured and their deployments are slow.
@PrismaXai turns every robot interaction into structured training signals with:
➤ Action traces
➤ Failure snapshots
➤ Human corrections
➤ Context metadata
➤ Multi sensor logs
This creates a compounding intelligence loop, the same mechanism that made LLMs powerful, but grounded in physical action.
Use cases where the flywheel is essential:
➠ Warehouse picking with thousands of SKU variations
➠ Home robots adapting to new layouts
➠ Industrial arms learning new objects weekly
➠ Mobile robots navigating dynamic environments
The teams that spin the flywheel fastest will win the robotics race and @PrismaXai is the engine that keeps it spinning.
To no more, visit https://t.co/3nFhdCJrip
@vivianrobotics @IdaraAkpabio882
Robots Need Context, Not Just Commands.
Robots don’t struggle because they lack instructions, they struggle because they lack context. Humans understand context effortlessly due to:
➫ Object fragility
➫ Human intent
➫ Spatial constraints
➫ Social cues
➫ Task priorities
➫ Environmental changes
Robots need structured data to understand these nuances.
@PrismaXai provides context rich datasets that link: ⇨ Perception
⇨ Action
⇨ Environment
⇨ Metadata
⇨ Human corrections
⇨ Failure cases
This transforms robotic behavior from brittle to adaptive.
Use cases where context is essential:
⭆ Picking fragile or deformable objects
⭆ Navigating crowded spaces
⭆ Assisting humans in home environments
⭆ Handling objects with ambiguous affordances
⭆ Multi step tasks requiring reasoning
Commands tell robots what to do while context tells them how to do it. Context is what turns robots from tools into collaborators and @PrismaXai builds the context engine for physical AI.
@vivianrobotics @castorhat @IdaraAkpabio882
Robots Need Context, Not Just Commands.
Robots don’t struggle because they lack instructions, they struggle because they lack context. Humans understand context effortlessly due to:
➫ Object fragility
➫ Human intent
➫ Spatial constraints
➫ Social cues
➫ Task priorities
➫ Environmental changes
Robots need structured data to understand these nuances.
@PrismaXai provides context rich datasets that link: ⇨ Perception
⇨ Action
⇨ Environment
⇨ Metadata
⇨ Human corrections
⇨ Failure cases
This transforms robotic behavior from brittle to adaptive.
Use cases where context is essential:
⭆ Picking fragile or deformable objects
⭆ Navigating crowded spaces
⭆ Assisting humans in home environments
⭆ Handling objects with ambiguous affordances
⭆ Multi step tasks requiring reasoning
Commands tell robots what to do while context tells them how to do it. Context is what turns robots from tools into collaborators and @PrismaXai builds the context engine for physical AI.
@vivianrobotics @castorhat @IdaraAkpabio882
Physical AI Will Be the Biggest Consumer of GPUs.
LLMs dominated GPU usage for the last 5 years but physical AI is about to dwarf that demand. Why? Because robots require:
⧳ High frequency control loops
⧳ Multi sensor fusion
⧳ Real time inference
⧳ Simulation at scale
⧳ Continuous learning
⧳ Multi embodiment generalization
This isn’t batch inference, it is real time intelligence.
Training physical foundation models requires orders of magnitude more data than text models.
@PrismaXai provides the data + infrastructure pipelines that feed these GPU hungry models.
Use cases driving GPU demand:
⇰ Large scale teleop data collection
⇰ Simulation-to-real training
⇰ Multi robot fleet learning
⇰ Cross embodiment skill transfer
⇰ Real time manipulation models
The next GPU boom won’t come from chatbots, it will come from robots learning to act in the physical world, and the infrastructure player, @PrismaXai , will be at the center of it.
@vivianrobotics @castorhat @IdaraAkpabio882
The hidden truth traders already know.
Everyone loves to brag about speed, TPS, Block times and Latency charts but markets don’t reward the fastest chain, they reward the most predictable one. Speed without reliability is noise. Predictability without speed is usable. But when you combine both? You get financial grade execution. This is where Solana is quietly pulling ahead.
Predictability changes how traders model risk. If you know your transaction will land, you can size positions with confidence. Uncertainty forces traders to over-hedge. Over‑hedging kills returns while predictability restores efficiency. This is why AOT compute reservations matter. They turn execution into a scheduled event, not a gamble.
In traditional markets, execution windows are sacred. You know when your order hits the book. Crypto has never had that until now. Predictability also reduces slippage. When you know your trade will land, you don’t need to widen your tolerance. It also reduces failed transactions, the silent tax on every chain.
➣ For builders, predictable execution simplifies architecture. No fallback logic, no retry loops and no chaos.
➣ For validators, predictability creates stable revenue. Reserved compute is a premium product.
➣ For institutions, predictability is non‑negotiable. They don’t deploy capital into uncertainty.
Speed gets attention while Predictability gets adoption. Solana’s edge isn’t just that it is fast, it is that it is becoming reliably fast. In markets, the chain you can trust beats the chain that is merely fast.
@raikucom
Why Raiku turns Solana blockspace into a predictable economic system.
Blockspace used to be a free for all. Spam, bidding wars, unpredictable fees, chaos disguised as “decentralization.” But every scarce resource eventually becomes a market. Blockspace is no different.
@raikucom formalizes that market. Instead of hoping for inclusion, you buy a reservation. This turns blockspace into a predictable, schedulable commodity.
Predictability is the foundation of every mature market: Energy, bandwidth, compute. Now blockspace joins the list.
When blockspace becomes a marketplace, pricing becomes rational. Builders can plan, traders can model and Validators can forecast revenue. This is how you unlock institutional adoption not with slogans, but with predictable economics.
AOT(Ahead-Of-Time) + JIT(Just-In-Time) create a two sided market: ↔Reserved compute ↔ High value execution bundles. This is the same structure that powers cloud computing. AWS didn’t win by being fast. It won by being predictable. Solana is moving in that direction, but with financial grade execution.
Blockspace becomes a product, not a gamble. And once blockspace is a product, it can be priced, hedged, and traded. That is when real capital enters, and @raikucom isn’t just optimizing Solana, it is building the first true blockspace economy.
Check https://t.co/OP7RGsh7Pl for more.
Physical AI Will Be the Biggest Consumer of GPUs.
LLMs dominated GPU usage for the last 5 years but physical AI is about to dwarf that demand. Why? Because robots require:
⧳ High frequency control loops
⧳ Multi sensor fusion
⧳ Real time inference
⧳ Simulation at scale
⧳ Continuous learning
⧳ Multi embodiment generalization
This isn’t batch inference, it is real time intelligence.
Training physical foundation models requires orders of magnitude more data than text models.
@PrismaXai provides the data + infrastructure pipelines that feed these GPU hungry models.
Use cases driving GPU demand:
⇰ Large scale teleop data collection
⇰ Simulation-to-real training
⇰ Multi robot fleet learning
⇰ Cross embodiment skill transfer
⇰ Real time manipulation models
The next GPU boom won’t come from chatbots, it will come from robots learning to act in the physical world, and the infrastructure player, @PrismaXai , will be at the center of it.
@vivianrobotics @castorhat @IdaraAkpabio882
Why Robotics Companies Waste Millions Rebuilding the Same Tools.
Every robotics company thinks it is building something unique but behind the scenes, they are all rebuilding the same infrastructure as listed below:
⥓ Teleoperation tools
⥓ Data pipelines
⥓ Labeling systems
⥓ Deployment frameworks
⥓ Simulation bridges
⥓ Feedback loops
⥓ Observability dashboards.
This duplication wastes millions and slows down the entire industry.
@PrismaXai eliminates this waste by providing a shared robotics service layer. Teams can stop reinventing the wheel and start building intelligence. This is how robotics becomes economically viable by reducing infrastructure overhead.
Usecases where this saves years of engineering time:
➣ Early stage robotics startups
➣ Industrial automation teams
➣ Research labs training PFMs
➣ Companies scaling from 5 robots → 500 robots.
The future of robotics isn’t about who builds the best internal tools, it is about who builds the best intelligence on top of shared infrastructure and @PrismaXai is that shared layer.
Explore more in: https://t.co/3nFhdCJrip
@vivianrobotics @castorhat
@StabilizerFi I love this
The Silent Power of Automated Rebalancing.
Most people underestimate rebalancing, it is not sexy neither is it flashy but it is one of the biggest drivers of long term returns.
The good news is that @ConcreteXYZ automates it quietly, continuously and intelligently. As we all know that there is shift in the market, risk changes and more opportunities appear but @ConcreteXYZ adjusts instantly. No human could rebalance this efficiently. Not at this speed and not at this scale.
Automated rebalancing means:
⇰ Lower risk
⇰ Higher consistency
⇰ Better compounding
⇰ Less volatility
It is the difference between “hoping for returns” and “engineering returns.”
@ConcreteXYZ vaults don’t guess, they calculate. Every micro adjustment compounds over time. Small optimizations → massive long term impact. This is how institutions operate and @ConcreteXYZ brings that discipline onchain.
Rebalancing isn’t a chore, it is a superpower, which Concrete automates for you. To explore and know more, go to: https://t.co/51ynWjODfr
@d3crypt0r25 @crypttoji @nic_builds
Concrete vs. Traditional Finance(TradFi): A Story of Speed.
TradFi moves like a snail, DeFi moves like a rocket while @ConcreteXYZ moves like a rocket with a flight plan. Try opening a savings account in TradFi, It is all about filling forms, long queues and delays. Try earning yield? that is even slower. Now compare that to Concrete, where: Deposit → Automated allocation → Compounding begins instantly.
No paperwork, no approvals, and no waiting for
"business hours" but @ConcreteXYZ operates 24/7 because blockchains don’t sleep. The speed isn’t just about transactions, it is about decision making.
@ConcreteXYZ vaults rebalance faster than any human team could. There is no emotions and no hesitation. TradFi takes days to adjust portfolios but Concrete adjusts in minutes. This is what happens when automation replaces bureaucracy.
Speed isn’t a luxury, it is a competitive advantage and Concrete gives you institutional grade yield at blockchain speed. The future of finance moves fast, and @ConcreteXYZ is already there.
Explore more on Concrete in https://t.co/51ynWjODfr
@d3crypt0r25 @crypttoji @nic_builds
The Silent Power of Automated Rebalancing.
Most people underestimate rebalancing, it is not sexy neither is it flashy but it is one of the biggest drivers of long term returns.
The good news is that @ConcreteXYZ automates it quietly, continuously and intelligently. As we all know that there is shift in the market, risk changes and more opportunities appear but @ConcreteXYZ adjusts instantly. No human could rebalance this efficiently. Not at this speed and not at this scale.
Automated rebalancing means:
⇰ Lower risk
⇰ Higher consistency
⇰ Better compounding
⇰ Less volatility
It is the difference between “hoping for returns” and “engineering returns.”
@ConcreteXYZ vaults don’t guess, they calculate. Every micro adjustment compounds over time. Small optimizations → massive long term impact. This is how institutions operate and @ConcreteXYZ brings that discipline onchain.
Rebalancing isn’t a chore, it is a superpower, which Concrete automates for you. To explore and know more, go to: https://t.co/51ynWjODfr
@d3crypt0r25 @crypttoji @nic_builds
Concrete vs. Traditional Finance(TradFi): A Story of Speed.
TradFi moves like a snail, DeFi moves like a rocket while @ConcreteXYZ moves like a rocket with a flight plan. Try opening a savings account in TradFi, It is all about filling forms, long queues and delays. Try earning yield? that is even slower. Now compare that to Concrete, where: Deposit → Automated allocation → Compounding begins instantly.
No paperwork, no approvals, and no waiting for
"business hours" but @ConcreteXYZ operates 24/7 because blockchains don’t sleep. The speed isn’t just about transactions, it is about decision making.
@ConcreteXYZ vaults rebalance faster than any human team could. There is no emotions and no hesitation. TradFi takes days to adjust portfolios but Concrete adjusts in minutes. This is what happens when automation replaces bureaucracy.
Speed isn’t a luxury, it is a competitive advantage and Concrete gives you institutional grade yield at blockchain speed. The future of finance moves fast, and @ConcreteXYZ is already there.
Explore more on Concrete in https://t.co/51ynWjODfr
@d3crypt0r25 @crypttoji @nic_builds
@Julzcrypt Gprisma
Last Seen Users on Sotwe
Most Popular Users
Elon Musk 
@elonmusk
240.6M followers
Barack Obama
@barackobama
119.2M followers
Donald J. Trump
@realdonaldtrump
111.7M followers
Cristiano Ronaldo
@cristiano
110.4M followers
Narendra Modi
@narendramodi
107M followers
Rihanna
@rihanna
97.6M followers
NASA
@nasa
92.2M followers
Justin Bieber
@justinbieber
90.9M followers
KATY PERRY
@katyperry
87.6M followers
Taylor Swift
@taylorswift13
81.4M followers
Lady Gaga
@ladygaga
72.9M followers
Virat Kohli
@imvkohli
69.7M followers
Kim Kardashian
@kimkardashian
69.7M followers
YouTube
@youtube
68.7M followers
Bill Gates
@billgates
63.8M followers
Neymar Jr
@neymarjr
62.5M followers
The Ellen Show
@theellenshow
62.4M followers
CNN
@cnn
61.9M followers
X
@x
60.8M followers
Selena Gomez
@selenagomez
60.7M followers