Sarma Sitanggang ./ (✱,✱)

God is King
Equalty is my golden role
Web3 enthusiasts
@ama_protocol

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

Just joined the wander 🌲
Into the forest with @elderwoodWiz - see you beyond the trees. https://t.co/5QobZZrIIg

[Elderwood wizards @elderwoodwiz]

A little magic is stirring in the woods…
Elderwood awaits.

Join the Elderwood Waitlist ���‍♂️
► https://t.co/fwniPXRZER

[Elderwood wizards @elderwoodwiz]

A little magic is stirring in the woods…
Elderwood awaits.

Join the Elderwood Waitlist 🧙‍♂️
► https://t.co/fwniPXRZER

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

@elderwoodwiz Already joined the waitlist!

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

Just joined @cookhousememe
Let's cook 🧑‍🍳 https://t.co/Ow0UFtOFMi

[Cookhouse @cookhousememe]

https://t.co/tVwkEyWxxV

[Cookhouse @cookhousememe]

The kitchen is heating up👨‍🍳🔥

Launch. Trade. Cook.

Join the CookHouse waitlist🍳

➔ https://t.co/X61zM3ITNN

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

@cookhousememe COOK

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

The next launchpad advantage may not be the launch. It may be what happens to the fees afterward.

A token can attract buyers in minutes. Building an incentive structure that keeps creators and holders aligned is a much harder problem.

That’s the design question behind Hood Launcher. Its approach combines programmable trading rules through Uniswap V4 hooks with a fee-sharing model intended to reward participation. Anti-snipe controls, decaying launch fees and whitelist mechanisms address different risks, while keeping those rules outside the token itself changes where the trading logic lives.

The more interesting layer is the planned connection to $MINR. If launching a token creates demand for MINR and associated fees contribute to the HoodMiners reward ecosystem, launch activity could become part of a broader economic loop rather than an isolated event.

But the distinction matters: fee flow is not the same as sustainable value. The model still depends on genuine trading demand, transparent distribution, sufficient liquidity and rules that work as intended under real market conditions.

That’s what I want to see tested on Robinhood Chain: not just how many tokens can launch, but whether the infrastructure can turn activity into incentives that participants can actually verify.

A launch is a moment. The economics that follow determine whether there’s a reason to keep participating.
https://t.co/WeiZq4hXTb

@hoodminers_rh #HoodMiners #MINR #RobinhoodChain #DeFi #TokenLaunch

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

Failure Is Not Always Bad Data.

A robot that never makes mistakes in training might still be unprepared for the real world.

Think about learning to ride a bicycle. You don't become skilled simply by watching perfect rides. You learn to maintain balance, recognize when something is going wrong, and adjust before you fall.

Robots face a similar challenge, although the way they learn from mistakes requires much more careful engineering.

A failed grasp can reveal something useful about an object's position or the limits of a gripper. A small error followed by an effective recovery can provide valuable information about how to handle uncertainty.

But not every failure deserves to become a training example. Repeated struggling, meaningless corrections, or inconsistent actions can introduce noise instead of useful knowledge.

This is where robotics data curation becomes more than a quality-control exercise.

PrismaX's published data standards distinguish between acceptable, recoverable mistakes and demonstrations whose behavior or execution makes them unsuitable for training.

The goal isn't to pretend that the physical world is perfect. It's to ensure that the model receives demonstrations that help it learn the right lessons.

That distinction could become increasingly important as robots move beyond controlled demonstrations and into environments where objects shift, surfaces vary, and unexpected situations are unavoidable.

The real objective isn't to build a robot that has only seen success. It's to build a robot that can perform reliably when reality becomes complicated and that begins with being deliberate about what we teach it.
@PrismaXai
#PrismaX #RoboticsData #PhysicalAI #Robotics

[Bond Super App @BondSuperApp_]

Your savings have more potential.

When savings earn no yield, inflation can reduce what that money buys over time. Bond is building toward offering savings yield within one platform that brings Web2 and Web3 access together, so users can access more financial products from one place.

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

@BondSuperApp_ Bringing savings yield, traditional finance, and onchain opportunities into one connected platform could make managing money a much more seamless experience.

That’s the kind of financial flexibility worth building toward.

[Amadeus Protocol @ama_protocol]

Private execution is useless if the result path leaks the strategy.

The owner should get the output.
The host should be able to show the run was the one that was signed.
Those are not the same document.

Compose on AMA

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

@ama_protocol A sealed strategy isn't truly sealed if the output reveals the logic behind it.

That's where confidential systems are tested.

[Amadeus Protocol @ama_protocol]

A sealed run still has to land somewhere.
Who should see the result: the owner, the host, or anyone who can open the log?

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

@ama_protocol The strongest systems separate verification from disclosure, allowing outcomes to be trusted without exposing sensitive details.

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

Verification and disclosure don't have to be the same thing.
The host should prove the computation was authentic.
The owner should receive the result.
The strategy should remain protected.
Blending all three into a single log defeats the purpose of confidential execution. @ama_protocol

#AMA #ConfidentialExecution #AIAgents

[Amadeus Protocol @ama_protocol]

Private execution is useless if the result path leaks the strategy.

The owner should get the output.
The host should be able to show the run was the one that was signed.
Those are not the same document.

Compose on AMA

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

The next useful application on Beldex could start with a few lines of JavaScript.

With the Beldex Web3.js SDK, developers can integrate wallet connections, BDX transfers, message signing and verification, plus BNS name resolution into their projects. That matters because infrastructure becomes more valuable when builders can actually use it without reinventing every basic function.

@BeldexCoin ����

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

@LeoOliemans91 That’s a good point. Some bad data can easily slip through if it looks reasonable at first glance. Keeping the original demo and its review history linked makes it much easier to spot those mistakes before they affect the model’s behavior.

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

The Hidden Cost of Bad Robotics Data.

The most expensive robotics data might be the data that teaches a robot the wrong thing.

Imagine training a robot to pick up a cup. It completes the task thousands of times, but many demonstrations contain awkward movements, inconsistent grips, or unnecessary corrections.

The dataset grows. The training pipeline keeps running. Yet the model may still struggle to perform a simple task reliably.

This is the hidden cost of poor robotics data: the problem isn't always a lack of information. Sometimes, it's the information we're asking the model to learn from.

That's why I find PrismaX's approach interesting. Its focus goes beyond collecting demonstrations to establishing standards for the quality of the trajectories, the sensor information, and the actual task performance. Human validators then assess the quality of the demonstrations themselves.

This distinction matters because physical AI has to connect what it sees with what it does. If the training examples contain misleading relationships between perception and action, simply increasing the dataset size won't necessarily solve the problem.

For robotics teams, the more useful question may not be how many hours of data they can collect, but how much useful learning each hour produces.

More data can increase coverage. Better data can make that coverage meaningful.

And as robotics scales, the ability to distinguish between the two could become a serious competitive advantage. That's the part of the robotics data problem PrismaX is working to address.@PrismaXai

[KiiChain @KiiChainio]

The KiiChain Mainnet Leaderboard has been live for two months already!

Let us know where you’re sitting right now, and how you’re planning to step up your game for the next month.🏆

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

@KiiChainio it’s a signal of who keeps showing up and contributing consistently

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

4/4

Amadeus uses a matrix-multiplication-based approach to Useful Proof of Work, connecting its mining design with AI-oriented computation.

The long-term test is whether that design can support real workloads while maintaining robust network security.

If it succeeds, the conversation around mining changes from how much computation was spent? to what did that computation help make possible?

That is the question worth following. @ama_protocol

[Sarma Sitanggang ./ (✱,✱) @SarmaSSP]

3/4

That is where AI agents enter the discussion.

Agents create potential demand for inference and other computational tasks. Miners can supply compute, while applications create the workloads that give it purpose.

The difficult part is making this connection reliable, verifiable, and economically sustainable — rather than assuming that every unit of compute automatically becomes useful intelligence.