Ritual İstanbul’dan güzel anılar ve güzel merch’lerle ayrıldık 🇹🇷 Uzun süredir ekrandan gördüğümüz insanlarla yüz yüze tanışmak, Ritual ve ürettiklerimiz üzerine sohbet etmek gerçekten çok keyifliydi. Emeği geçen herkese teşekkürler.

A robot doing more things at once does not necessarily produce better training data. Imagine a dual-arm robot working at a table. One arm is arranging objects on a plate while the other is preparing a completely different plate. From an operational perspective, that might look efficient. Both arms are occupied, and multiple actions are progressing simultaneously. But from a robotics training perspective, this introduces a different problem. Can the resulting demonstration be separated into clear, meaningful actions? This is an interesting detail in @PrismaXai's published Standards for Data. One of its requirements is that trajectories should be separable. Individual actions should not combine multiple independent activities, and both arms should operate on one task at a time. The reasoning is closely connected to how robotics models learn from demonstrations. Training pipelines often divide longer demonstrations into smaller segments and associate those segments with detailed descriptions of what the robot is doing. Those descriptions help connect observations and actions with the intended task. But when several unrelated actions overlap, identifying exactly what a segment represents becomes more difficult. Is the robot picking up an object? Placing another object? Completing one task while simultaneously beginning another? If several independent goals are mixed into the same sequence, creating unambiguous training labels can become difficult. That creates an important distinction between operational efficiency and training-data clarity. A demonstration optimized to complete as much work as possible is not necessarily optimized to teach a model how individual actions relate to particular objectives. And this does not mean that two robot arms should never coordinate. Two arms working together on one clearly defined task are different from two arms independently pursuing unrelated objectives inside the same demonstration. The goal is not to eliminate complex behavior. It is to make the structure of that behavior understandable. This is why PrismaX treats trajectory separability as a data-quality requirement rather than simply rewarding demonstrations for containing more activity. For Physical AI, a useful dataset needs more than successful task outcomes. It needs demonstrations whose actions can be interpreted, segmented, and connected to meaningful instructions. More motion does not always mean more useful information. Sometimes the most valuable demonstration is the one that makes every action easier to understand. A robot can do several things at once. But its training data still needs to make sense.

Buying access to robotics data is not the same as owning the dataset. And that distinction matters more than it might seem. As Physical AI develops, robotics companies need enormous amounts of recorded experience to train, fine-tune, and evaluate their models. But does every company need exclusive ownership of every demonstration it learns from? Not necessarily. An interesting detail in @PrismaXai's current customer subscription terms illustrates how these two layers can be separated. PrismaX licenses robotics demonstration data rather than selling ownership of the recordings themselves. Customers receive defined rights to use that data for internal model development, including training, fine-tuning, evaluation, and benchmarking. But the agreement draws a different boundary around the resulting models. Under its terms, customers own the models and model weights they train using the licensed data, as between the contracting parties. They can also deploy and commercialize those models, provided the underlying data is not extractable from or redistributed with them. That creates two distinct assets. The recorded experience: a licensed training input, subject to restrictions on redistribution and resale. The trained intelligence: a model developed using that experience, which the customer can own and commercialize under the agreement. Why is that distinction important? Because the same underlying demonstration does not necessarily produce the same intelligence in every training pipeline. Two robotics teams could work with similar recorded experiences but use different architectures, training objectives, fine-tuning strategies, and evaluation procedures. What they ultimately build can be very different. This suggests a business model where access to physical experience can be commercialized separately from the intelligent systems developed using it. Data providers build the infrastructure to capture, validate, organize, and license demonstrations. Model developers use those demonstrations to build capabilities suited to their own robots and applications. The difficult boundary is ensuring that commercializing a trained model does not become a way to redistribute the licensed data itself. That is why licensing terms are not merely administrative details. They help define how value can move between data contributors, infrastructure providers, and model builders. For Physical AI, the economic value of a robotics dataset does not have to come exclusively from restricting who can learn from it. The experience can be licensed. The intelligence built from it can become a separate asset.

A robot can handle an unfamiliar environment and still be extremely sensitive to a centimeter of hardware misalignment. That sounds contradictory. But it reveals something important about how Physical AI systems generalize. At IROS 2026, @PrismaXai demonstrated a π0.5 VLA checkpoint running on two YAM robot arms, picking up fruit and placing it into containers. The model had been trained using data from a white-background work cell. At the conference, it was deployed zero-shot on a different setup, in a venue it had never encountered before. People were moving behind the robot. The environment was unfamiliar. The hardware arrangement was different. Yet the system was able to operate. What makes this interesting is the experimentation behind the demonstration. Before the event, PrismaX reports running more than 10,000 simulation ablations across lighting, backgrounds, camera placement, robot mounting, and training configurations. The results suggested that not every difference between training and deployment mattered equally. Some variations were surprisingly manageable. Others were extremely consequential. One particularly striking finding was mounting height. According to PrismaX, a mounting-height error of just 1 cm reduced task success rates by approximately 70% in its tests. That is not a universal sensitivity threshold for robotics models. It is a result from PrismaX's reported experimental setup. But the broader lesson is significant. A VLA model does not interact with the world through images alone. Its predicted actions must ultimately translate into physical movements on a real robot. Even when visual conditions are manageable, small differences in hardware geometry can affect how an action is executed. PrismaX identified several important choices in its experiments: fine-tuning the complete model rather than only the action expert, using relative actions to tolerate small differences between rigs, maintaining the correct mounting height, and reducing latency. Together, these observations highlight a distinction that often gets lost in conversations about generalization. A model that tolerates an unfamiliar background is not necessarily tolerant of every change in its physical setup. And solving that problem is not always about collecting more demonstrations. Sometimes it is about understanding which deployment variables the policy is sensitive to and controlling them carefully. For Physical AI, robustness is not the absence of constraints. It is knowing which constraints matter.

Not every mistake is bad robotics training data. Imagine a robot trying to insert a connector into a socket. On the first attempt, the alignment is slightly off. The robot makes a small correction, adjusts its position, and completes the insertion. Now imagine another demonstration where the robot repeatedly misses the socket, hesitates, and makes several unsuccessful attempts before finally completing the same task. Both demonstrations end in success. But do they teach the same thing? This distinction appears in @PrismaXai's published Standards for Data, particularly in its criteria around struggling and error recovery. The standards recognize that small mistakes can be useful because they show how an operator recovers from unintended errors. Repeated or prolonged mistakes, however, risk teaching the model to reproduce those same inefficient behaviors. That creates an important distinction between learning to recover and learning to struggle. A short correction can demonstrate how to return to a valid trajectory after a minor deviation. A long sequence of failed attempts may instead reinforce unnecessary movements, hesitation, and repeated approaches that the model should ideally avoid. PrismaX also distinguishes between recoverable mistakes and errors that undo previously completed work. For example, accidentally spilling an already packed container while handling the next item is different from making a small alignment correction during an ongoing manipulation. The first destroys earlier progress. The second may provide useful information about recovering within the task. This matters because Physical AI models learn from trajectories, not just final outcomes. A completed task does not automatically mean every action leading to that result is worth reproducing. And filtering out every imperfect demonstration could remove examples of useful corrective behavior. The challenge is deciding which deviations teach resilience and which ones introduce undesirable habits. That is what makes robotics data quality more complicated than simply labeling demonstrations as successful or unsuccessful. The goal is not to teach robots that mistakes never happen. It is to teach them how to recover without learning to repeat the mistake.

stablecoin adoption is often measured by transfers, payment volume, or how many wallets hold the asset. but there is another stage of adoption that matters more: when traditional financial instruments begin depending on stablecoins for their actual cash flows. @StartaleGroup’s new Startale Bond is an interesting example. Startale Japan is issuing a one-year digital corporate bond where both interest payments and principal redemption are designed to be paid in JPYSC, the yen-denominated trust-type stablecoin developed with SBI Group. that changes the role of the stablecoin. JPYSC isn’t simply being used to move money after the financial transaction is finished. it becomes part of the lifecycle of the instrument itself. investors subscribe to a corporate bond, receive interest twice during the term, and receive the principal back at maturity. in this structure, those outgoing cash flows are delivered in JPYSC through Startale App. the bond itself is relatively small: roughly ¥99.9 million in total issuance, with a one-year maturity and purchases starting from ¥100,000. that makes this more useful as a real-world test case than as evidence that Japan’s bond market has suddenly moved onchain. but the model is the interesting part. a regulated yen stablecoin can sit between a traditional financial instrument and an onchain financial environment, carrying scheduled interest and principal from the issuer to the investor in programmable digital money. and once the investor receives that money inside Startale App, the stablecoin doesn’t necessarily have to stop at settlement. it can become part of another onchain financial action. this is where stablecoins start looking less like crypto payment tokens and more like financial infrastructure. tokenizing an asset changes how ownership is represented. using stablecoins for its cash flows starts changing how the financial instrument actually operates.

A software agent can make a mistake and try again. A robot may not get that luxury. If an AI agent generates the wrong output, the system can often reject it, retry the task, restore a previous state, or isolate the failure inside a sandbox. Physical AI operates under a different constraint. When a robot acts, it changes the real world. A bad trajectory can drop an object. A wrong movement can damage equipment. An unsafe action can put a person at risk. And once that physical state has changed, there may be nothing to roll back. This is one of the central arguments in @PrismaXai’s recent No Robot Is Safe essay. PrismaX draws a parallel between incorrect outputs from language models and physically inappropriate actions from robotics models, but the acceptable tolerance becomes much stricter once actions leave the digital world. That changes what “AI safety” means for robotics. The solution cannot live entirely inside the model. Physical systems need layers around intelligence: deterministic safety controllers, motion limits, safe operating boundaries, human supervision where necessary, and deliberate decisions about which behaviors should never enter the training distribution in the first place. That last point is especially important. A robot does not need to “decide” to do something dangerous for dangerous behavior to emerge. A bug can produce the wrong motion. A learned behavior can surface in the wrong context. Or an adversarial instruction can push an autonomous system toward an action it should never execute. The physical world does not care why the mistake happened. It only sees the consequence. That is why reliability in Physical AI cannot simply mean: “the model is usually correct.” It also has to mean: “when the model is wrong, the system remains physically safe.” Language models taught us to think about alignment in terms of outputs. Robotics forces us to think about consequences. Physical AI has no undo button.

Most trading apps are built around charts. Fomo is being built around people. Markets have always been social. Traders share ideas, watch each other's positions, discuss conviction and chase the same narratives. The problem is that all of that usually happens outside the place where you actually trade. Fomo is pulling the social layer and the execution layer together. See the trade. Read the thesis. Check the trader. Track the PnL. Take the position. And now that the same experience is expanding beyond crypto into stocks, pre-IPO markets, indices and commodities, this starts to look much bigger than another crypto trading app. The interesting bet behind @fomo: your portfolio becomes your financial identity.

More compute cannot automatically rescue the wrong dataset. When a robotics model struggles, the instinct is often to scale something: more demonstrations, a larger backbone, more training steps, more compute. But there is another variable upstream of all of them: what experience the dataset actually contains. @PrismaXai makes this point explicitly in its recent Robotics Data release. The team argues that data selection does more than influence what behavior a model learns. It can also shape how much data is required, how large a backbone needs to be to absorb it, and how much training compute is consumed. That changes how we should think about dataset quality. Imagine two collections for the same manipulation task. One contains thousands of nearly identical demonstrations from similar scenes. The other is deliberately constructed around the variations the model actually needs to handle: relevant objects, viewpoints, environments, embodiments, and failure boundaries. Both datasets contain robot experience. But they may not place the same learning burden on the model. If the useful signal is buried beneath redundancy or irrelevant variation, increasing scale may mean asking a larger model to discover structure that better data design could have exposed more directly. This does not mean good data makes model scale or compute irrelevant. It means those resources interact with the dataset rather than operating independently from it. PrismaX’s approach reflects this by connecting collection with training and evaluation. The team collects demonstrations, trains on them, observes where models succeed or fail, and feeds those findings back into subsequent collection decisions. That creates a different optimization problem. Not simply: How much data can we collect? Or: How large can we make the model? But: What data would make the next unit of training compute more useful? For Physical AI, dataset design is not only a data problem. It is part of the compute strategy.

5 tane kod var isteyen yazsın

crypto spent years optimizing individual layers: faster chains, better wallets, better stablecoins, better apps. but improving each component doesn’t automatically improve the experience of connecting them. users still deal with bridges, different gas systems, fragmented liquidity, separate wallets, and interfaces built around individual pieces of the stack. that’s what makes @StartaleGroup’s vertical integration strategy interesting. instead of treating blockchain infrastructure, financial rails, and consumer applications as isolated products, Startale is building across multiple layers of the same system. Soneium targets consumer and entertainment use cases, while Strium is being developed around tokenized securities and RWA markets. above that, JPYSC and USDSC provide programmable money, while account abstraction and developer infrastructure can reduce the complexity between users, applications, and chains. Startale App then sits closer to the user, bringing assets, Mini Apps, and earning into one interface, while expanding toward payments and broader financial products. the interesting part isn’t simply that Startale has more products. it’s what becomes possible when those products are designed to work as parts of the same stack. the wallet doesn’t need to constantly expose the chain underneath. the stablecoin doesn’t need to feel like a separate product. infrastructure can move into the background while the user interacts with a more coherent financial experience. that’s the real promise of vertical integration: less friction between layers and more control over how the full experience fits together. crypto spent years proving that modular infrastructure could work. the next competition may be about who can make the stack feel like one product.

%32 @ritualfnd

A robot can learn the right task for the wrong reason. Imagine training a robot to pick up a specific object. Every demonstration uses the same color, similar lighting, and an identical workspace. The model performs well during training. But what happens when the object's color changes or the robot encounters a different environment? It may have learned a visual shortcut instead of the underlying skill. This is an important distinction in Physical AI. A successful demonstration shows that a task was completed. It does not necessarily reveal which features the model will rely on when learning from it. @PrismaXai's approach to robotics data collection highlights a fundamental question: what should vary, and what should remain consistent, to help a model learn the intended behavior? These decisions matter because object properties, lighting conditions, camera placement, and task design all influence what the resulting dataset can teach. For example, if the goal is to recognize an object's geometry rather than its color, deliberately varying color during data collection could reduce the risk of the model relying on the wrong visual feature. But simply introducing more variation is not always the answer. Changes need to reflect the intended learning objective, and controlled experiments help identify which factors actually influence performance. This is why dataset design involves more than maximizing sample count or diversity. PrismaX also trains on the data it collects, using findings from training and evaluation to inform subsequent collection decisions. That creates a feedback relationship between data production and model development. Instead of repeatedly collecting successful demonstrations, the collection process can evolve around what the model actually needs to learn. The deeper lesson is that a dataset does not simply teach a robot what happened. It influences which patterns the robot learns to depend on. Don't just collect successful actions. Control what the model learns from them.

A robot can successfully grasp an object and still fail to place it. The problem might not be the robot's ability to grasp. It could be what the model can actually see. In Physical AI, camera placement is not simply a hardware setup decision. It shapes the information available to the learning system. Consider a pick-and-place task. A camera might clearly capture the initial grasp but provide a poor view of the target location, the gripper's approach, or the moment the object is released. The demonstration may look successful to a human observer while leaving important information out of the model's observations. This is an interesting finding from @PrismaXai's recent work. In its September Robotics Data announcement, PrismaX describes a fine-tuning experiment in which changing camera placement during simulated evaluation affected a model's ability to place an object accurately. A successful grasp did not necessarily translate into successful task completion. That highlights a broader problem. Collecting more demonstrations cannot automatically compensate for an observation setup that fails to capture the information a model needs. Camera placement, sensor configuration, object arrangement, and task design all influence what the resulting data can teach. PrismaX approaches these decisions as part of the collection process rather than treating data quality as something that can only be determined after recording. The team also trains on the data it collects, allowing findings from training and evaluation to inform subsequent collection standards. This creates an important feedback relationship between data production and model development. Instead of simply collecting more examples of the same task, the collection process can evolve around what evaluation reveals. The deeper point is that a robotics dataset is not just a record of successful actions. It is a record of what the learning system was able to observe while those actions happened. Before asking whether a demonstration is useful, ask whether the model was given the right view of the task.