My old account, @skilllANAS, was suspended for no reason.π
I didn't break any rules or do anything that could have put my account in danger,
Yet my account was gone.
For months, I worked day and night to grow my account.
@nikitabier, do your job properly. Remove bots, not real people who work for months to grow their accounts. At least give a warning before suspending accounts.
I'm Back
start again,
rebuild stronger.
Got the Assistive role in @PrismaXai Discord ππ
Thank you to the team and everyone who supported me.
Really appreciate it. Letβs keep learning and growing together.
gPrisma
Grateful to have a Reactive on the @PrismaXai Discord.
Thank you so much to everyone in the team and community for their support
Really thankful for this recognition!π€
Still learning, still creating, and excited to contribute even more.
People think @PrismaXai is just another Oracle⦠but that's completely wrong.
- > What Oracles do ?
Oracles bring external data into the blockchain.
Example:
Price feeds, weather data, sports results.
- > What PrismaXai does ?
PrismaXai goes much further than that.
It collects and uses real-world data to train AI and robots.
- > Difference
Oracles = Deliver Data
PrismaXai = Creates Data + Learns from It
This is a big difference.
- >Example
An oracle tells you the temperature.
PrismaXai teaches a robot how to react to that temperature.
- > Common Mistake
Big mistake: Thinking the two are the same.
Oracles are data pipelines.
PrismaXai is a learning system for real-world AI.
AI agents seem powerful... but without real-world data and feedback, they're limited.
@PrismaXai as a system that helps AI agents learn, improve, and function in the real world.
- > Provides AI agents with real-world data
Most AI agents are trained on:
- Text
- Internet data
> Problem:
They struggle in real-life situations
> What PrismaXai does:
- Provides image, video, and sensor data
- Uses data from real environments
> Result:
- AI agents understand the real world better
- > Adds a human feedback loop.
Guidance helps AI improve faster.
> Problem:
It's difficult to train an agent with real feedback.
> What PrismaXai does:
- Allows humans to control robots (teleoperation).
- AI learns from human actions.
> Result:
Smarter and more accurate agents.
- > Helps agents learn by doing.
Learning isn't just about thinking, it's about acting.
> Problem:
AI agents don't get enough "practice."
> What PrismaXai does:
Enables interaction with real-world tasks.
> Result:
Agents improve with experience.
- > Creates a continuously learning system.
AI requires constant updates.
> Problem:
Static models become outdated
> What PrismaXai does:
Creates a loop: data β learning β improvement
> Result:
Agents get better over time
- > Supports scalable AI development
Developers need infrastructure.
> Problem:
Managing data and training systems is difficult
> What PrismaXai does:
Provides tools and systems for building AI agents
Result:
Making powerful applications easier
@PrismaXai helps AI agents:
- Learn from real-world data
- Improve with human feedback
- Become better through continuous learning.
Original content is very important now.
Just saw @PrismaXai latest update, and it's a great step forward for the community.
As @vivianrobotics mentioned, the team takes originality seriously.
- > What's changing?
You can't copy or repost someone else's content as your own.
The rules are simple:
β First time: Warning
β Again: Risk of blacklisting
β Continue: Downgrade or ban
- > Why it matters?
A strong community is built on trust and effort.
People spend time:
- Creating content
- Sharing ideas
- Adding value
That effort should be respected.
This is a good decision.
It supports original creators and keeps things fair for everyone.
Create your own content. Don't copy
AI seems powerful
But in real life, it struggles.
Why? Because it lacks real world experience.
- > Problem
Most AI is trained on:
- Text
- Images
- Simulations
But the real world is messy and unpredictable.
This is where AI fails.
-> Solution
@PrismaXai solves this using real world data.
Used in:
- Robots controlled by humans
- Real environments
- Continuous learning
So AI can learn by doing.
The future of AI isn't just smart,
it's real world smart.