Enterprise Ecosystem on Allora
Ecosystem spotlight: how large infrastructure players tap a neutral, verifiable AI network instead of betting everything on a single closed model vendor.
Every enterprise buying AI today faces the same uncomfortable dependency. The intelligence that increasingly drives pricing, risk, forecasting, and operations sits inside a handful of closed models owned by a handful of vendors. You cannot see how the output was produced. You cannot verify it. You cannot easily swap it. And when that single provider changes its terms, its weights, or its availability, your business inherits the risk.
For regulated industries, sovereign infrastructure operators, and large cloud and telecom players, that is not a comfortable position. They want AI intelligence that is reliable, neutral, and verifiable, and they increasingly want a decentralized or auditable option rather than a black box under someone else's control.
This is the gap Allora is built to fill. Allora is the leading Model Coordination Network (MCN), a decentralized AI network that coordinates many specialized machine-learning models around a shared objective, weighting them in real time and aggregating their output into a single forecast that consistently beats any one model on its own. It produces forward-looking inference that applications consume via API or onchain, through a neutral and verifiable network. A neutral, decentralized network is attractive to enterprises precisely because it is not a single-vendor black box.
The enterprise segment of the Allora ecosystem reflects that pull. The names associated with it are among the largest infrastructure and connectivity operators in the world: Amazon Web Services, Alibaba Cloud, Saudi Telecom (stc), Exaion, and Xross Road. Together they represent global cloud capacity, national digital infrastructure, and specialized compute at a scale few networks can claim proximity to.
Where Enterprise AI meets a neutral network
@alibaba_cloud is the cloud and AI arm of Alibaba and one of the largest cloud providers in the world, with deep infrastructure across Asia and beyond. Public Allora communications describe a collaboration with Alibaba Cloud around launching an S&P 500 prediction topic on the network. At the level the public record supports, this positions Alibaba Cloud as an infrastructure and go-to-market participant rather than a defined product integration.
@awscloud (Amazon Web Services) is the largest cloud platform globally and appears in the enterprise segment of the Allora ecosystem map. AWS is the default deployment environment for a large share of AI workloads, so its presence signals where Allora-connected builders run infrastructure.
@stc (Saudi Telecom) is the Kingdom's largest telecom operator and a major digital infrastructure company, expanding aggressively into cloud, sovereign AI, data centers, and enterprise services under Saudi Arabia's Vision 2030. stc appears among the enterprises named as engaged with Allora. For a national operator building sovereign AI capacity, a neutral network that can be independently verified is a natural fit.
Exaion, historically a subsidiary of the French energy utility EDF, operates high-performance computing and secure cloud and AI infrastructure across Europe and Canada. Exaion is named among the infrastructure players engaged with Allora, aligning with its focus on secure, sovereign compute for AI workloads.
@xross__road is a Japan-based Web3 platform focused on intellectual property incubation and fan engagement. Its publicly announced partnership with Allora centers on AI and IP solutions: data analysis to enhance IP markets, customized user experiences, and detecting IP misuse. It sits on the enterprise map more as a regional platform partner than a core infrastructure provider.
What Allora gives an enterprise
The enterprise problem is dependency without verifiability. A single closed model is opaque, non-neutral, and impossible to audit, yet it may sit at the center of decisions the business is accountable for.
Allora supplies neutral, adaptive intelligence in place of that dependency. Because it coordinates many competing models and weights them by demonstrated accuracy, no single vendor controls the output, and the aggregate consistently outperforms any individual model. The result is inference an enterprise can consume via API or onchain, from a network whose behavior can be inspected rather than taken on trust.
For infrastructure operators, that is the strategic point. Allora is an open intelligence layer they can tap, extend, and build on, rather than a product they must accept as-is. As enterprises look for AI they can verify and govern, a neutral network becomes infrastructure, not just a vendor.
Sources
https://thedefiant.io/news/press-releases/allora-foundation-announces-launch-of-allora-mainnet-and-allo-token
https://www.allora.network/blog
https://hackernoon.com/xrossroad-announces-strategic-partnership-with-allora-network-to-expand-japanese-ip-in-web3
https://www.edf.fr/en/pulse/ventures-portfolio-exaion
https://vision2030.ai/institutions/stc/
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ICYMI: Injective is now officially an SEC registered transfer agent.
With this move, Injective becomes the first layer 1 blockchain to possess the RWA infrastructure and regulatory readiness needed to accelerate tokenization to new heights.
Injective has rapidly expanded its RWA efforts in recent months. Markets for digital asset treasuries, equities, and shares in pre-IPO companies such as SpaceX and OpenAI have also launched on Injective.
Recently, Injective expanded into enterprise trade finance. POSCO International, South Korea’s largest trading company, and LG CNS, the technology arm of LG Group, selected Injective for an exclusive live pilot that issues, transfers, administers, and settles trade receivables generated through international commerce.
This past week, Injective tokenized over $1 Billion in mortgage records onchain, cementing Injective as the fastest growing chain for RWAs. Publicly listed company Pineapple Financial also plans to tokenize over $10 Billion on Injective in the coming months.
The full stack for internet capital markets is here on Injective. Powered by $INJ
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Self-Sovereign GraphQL in Every Browser
Arweave holds the data and the index; PermawebOS gives every user a node that can answer and prove its own queries.
The third principle of Arweave is “Guarantee the right to listen”. In cyberspace, you can only hear if you can actually discover the data. On the permaweb, that’s made possible with GraphQL.
GraphQL provides a critical link in the composability architecture of the permaweb: allowing all apps to build on top of the same, shared content lake, joined by a single global index. Arweave transactions are posted with tags, discoverable by wallet address, or the block they were mined into, but GraphQL is the layer that lets users and applications see all data matching these queries. Without it, there’d be no way to find data matching criteria, or build data protocol-based apps.
It’s one of the permaweb’s core utilities, but until now it has been confined to the realm of enterprise-grade hardware. Services like that are usually incredibly difficult to decentralize because the hardware requirements are high and the incentives are low. The permaweb’s GraphQL services so far have depended on building and maintaining gargantuan off-chain indexes of more than 76 billion rows: scanning the historical chain, keeping it current and serving it quickly. In practice, this has meant that permaweb apps inherit the availability and policy of a tiny number (often two, sometimes – like today – even just one) of hosted indexers which had no incentive to adopt a decentralized model.
Offset queries (queries that seek, intersect and page by weave position) will change everything about how Arweave’s query layer is served. HyperBEAM's offset-query path makes the index compact enough to publish on Arweave, orders it by weave offset, and lets any node or client query the relevant pages directly.
(txid)
This is not another centralized GraphQL service, and it’s not just a way to make GraphQL more available to node operators. It is a method that makes GraphQL execution so lightweight that it can be run directly on users’ browsers, with Arweave nodes simply serving them data chunks.
Hyperoptimized GraphQL with Offsets
All pieces of data in Arweave already have unique positions in the weave – every byte is either ‘before’ or ‘after’ every other byte. We call these byte positions in the network offsets. In March, we showed how any transaction on the weave can be referenced by its offset as a name -- short, deterministic values like 101t.arweave.net, derived only from the data’s onweave properties.
That same offset property gives us more than a way to retrieve bytes. It also can power queries because it gives us a common ordering that can be reused across every possible match in an index.
The new match index stores rows using three extremely compact values:
a hash of the field name being matched;
a hash of the value that is present;
the weave offset of the item carrying that predicate.
Each potential match is compressed into an average of just ~9.5 bytes per row, each stored in an onweave ArLMDB database. By utilizing Arweave chunks as batches of LMDB pages, ArLMDB allows us to traverse the database to find any specific node with only a few individual Arweave node requests. By organizing rows into the compressed components 1-3 above, a query with two or three factors to match can walk those ordered sets together, advancing whichever cursor is behind until the offsets meet – a ‘leapfrog’ version of the same flow as a single lookup. Critically, finding the intersection is part of finding the results for each match criteria -- not a second step.
The same ordering solves pagination -- page fifty can seek to its starting offset instead of replaying pages one through forty-nine. Offset lookups give results a stable order without adding another ordering database.
AO Compute; Arweave the Shared Hard Drive
LMDB is normally a local database file. HyperBEAM's new arlmdb store reads that database from Arweave instead.
Try decentralized GraphQL from your browser
This is already proven at production scale with item lookup. A 622 GiB transaction contains the locations of over 70 billion rows. HyperBEAM reads the database where it sits. A cold lookup traverses it with three Arweave chunk requests; once the shared branches are cached, another lookup needs just one. The ArLMDB implementation is merged into HyperBEAM and used for ID lookups from Arweave.
With an immutable index on Arweave, anyone can read it without trusting the publisher to keep a query endpoint online. Like the Arweave schedulers powering Bazar, this is another example of AO employing Arweave as the source of truth, and using it to power the permaweb.
Instead of every query operator repeating the historical sweep and building the same large database, nodes serve chunks while the client traverses the index locally. The node's job is reduced to serving bytes quickly. It does not decide which results exist, execute the filter or ask the application to trust its view of the weave. The live tip still needs rolling indexes, but the expensive historical work no longer has to be repeated by every participant.
The client does not even need the whole database (depending on optimization, we’ve seen database sizes anywhere between 1 and 60 terabytes). The production offset index already demonstrates the access pattern: in the live chunkar browser demo, the second arbitrary lookup needs only around half a megabyte of new index data to traverse the full offset index. The match index works the same way. It traverses the pages needed for the requested predicates, intersects the ordered rows, reads the candidate items and checks that they actually carry the fields requested by the query. This is lightweight enough to make it so that every user can be their own personal no-dependency GraphQL service provider, with provable data, from the browser.
As well as the browser playground, today you can use the arlmdb.js library to integrate Arweave-stored database lookups into UIs.
From Centralized Node to Self-Sovereign Service
Today an application sends a GraphQL request to a server that already holds an index. With a published offset index, the application can instead hold a locator and a cache.
A HyperBEAM node can do that, but one way we imagine most users will access the query layer is through the browser: a user's application fetches Arweave chunks, keeps the hot index pages locally and performs the query for itself. This can be baked into the PermawebOS browser extension along with the local AO node the extension already spawns. This shift brings more and more AO services that were previously hosted (on exclusive TEE hardware) into a local-first environment. The stack is becoming so lightweight it’s able to be run cheaply, per-user, as background services in the browsers, phones and laptops everyone already has.
Previous designs for a decentralized GraphQL layer were unable to answer the question of trust without TEEs. How can you be sure that the response you get back from a GraphQL server has arrived complete and uncensored? While PermawebOS node architectures like LapEE and AndEE solve the trust question in theory, in practice the job of indexing the entire blockweave is too big for small workers.
Offset queries over onweave LMDB data -- made lightweight enough to traverse and prove by any browser -- change the equation entirely.
The browser never asks a server to decide the answer. It asks nodes for the exact index chunks it needs and walks the authenticated LMDB pages itself, intersecting the offset-ordered sets locally.
The new schema provides chunks and Merkle paths for each page accessed; the recipient can repeat the exact same work and see that they get the same complete set, with no missing results. The hashpath signs the request + response pair, and the validator can trivially repeat the work, whether on a node or in a browser. The resulting item is then bound back to its ANS-104 ID. A node can withhold bytes and make itself unavailable, but it cannot alter a row, skip a qualifying result or invent one without breaking the proof.
The Personal Permaweb Stack
Decentralizing GraphQL does not necessarily require a decentralized fleet of GraphQL servers. The permaweb is unique in that it combines a decentralized permanent storage layer with everyday web semantics. HTTP, lightweight proofs, and browser-based nodes handle the workload when the logic is optimized enough to sidestep expensive hardware.
The network holds the whole index. Each user keeps only the path to their answer.
With PermawebOS putting an AO node in every browser and LapEE turning abundant consumer hardware into secure bundlers, schedulers and tunnels, local GraphQL pushes the permaweb towards a cyberspace everyone can own.
Read this on the Permaweb:
https://ao.arweave.net/#/blog/self-sovereign-graphql-in-every-browser
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How Hedera Guardian Revolutionizes Environmental Asset Management
Recap, a look back at an earlier Hedera session. Environmental asset management is evolving beyond the limitations of traditional paperwork and bureaucratic hurdles. With the Hedera Guardian, you're equipped to integrate decentralized identities, verifiable credentials, and advanced policy workflow engines to streamline the creation and trade of digital environmental assets. Interested in sustainability tech or building with Hedera? This piece offers valuable insights to shape impactful applications in today's eco-conscious landscape. 🔗 Key Links 📺 Watch the full livestream → Delivering Trust in Environmental Assets and Outcomes with Hedera Guardian (https://www.youtube.com/watch?v=TUzQ2CF-I8o) 📄 Explore the Hedera Guardian docs → Hedera Documentation (https://docs.hedera.com) 🛠� Dive into Hedera Token Service → HTS Docs (https://docs.hedera.com) 📌 TL;DR Hedera Guardian enhances environmental asset digitization effortlessly. Version 3.5 supports decentralized identities and credentials. Demonstrated modular architecture offers flexibility. Builders can use Guardian infrastructure for community solutions. A 39% insurance premium reduction was achieved, showcasing real-world benefits. Unpacking the Hedera Guardian’s Key Innovations A Dive into the Modular Architecture The Hedera Guardian employs a flexible, modular architecture, simplifying user experience in digitizing complex environmental asset methodologies. It enhances security and transparency by integrating with decentralized identities and verifiable credentials. This design allows you to tailor your technology stack for specific environmental requirements—be it carbon credits or biodiversity tracking. The APIs within this architecture facilitate smooth data handling, helping you query data tied to issued tokens, assess user permissions, and manage asset statuses effectively. This level of customization opens doors to build applications that uniquely match demands. Key takeaway: The Guardian's modular architecture offers the flexibility and security you're looking for in managing diverse environmental assets. Policy Workflow Engine Excellence Perhaps the most notable feature is the policy configurator—a low-code environment that allows creators like you to define regulatory compliance and construct detailed workflows for managing environmental assets. This system promotes efficiency by letting you focus on optimizing solutions rather than getting tangled in coding complexities. Daniel showcased its utility in a captivating live demo, illustrating real-time data management and user interaction handling with drag-and-drop simplicity for scenarios like carbon offsets. Such functionalities make Guardian a powerhouse for streamlining complex environmental asset management. Key takeaway: The low-code workflow engine simplifies complex environmental asset management, keeping pace with emerging regulatory demands. Real-World Performance Metrics The Guardian's impact isn't just on paper. Featured during a livestream, the Tahoe Donner community realized a 39% lower insurance premium and an 89% lower deductible through smart forest management and the Guardian's data insights. These figures highlight the economic and ecological advantages the platform offers. By tapping into precise data analytics and risk management capabilities, you can predict and mitigate environmental risks like wildfires, expanding Guardian’s utility beyond asset management to a holistic environmental risk analysis tool. Key takeaway: The Guardian’s effectiveness in cutting insurance costs through informed environmental risk management marks its tangible value.
Leveraging Hedera Guardian in Practical Applications Build Next-gen Environmental Applications The Guardian provides practical avenues to construct industry-disrupting solutions. It enables you to craft applications focusing on the digitization and tokenization of environmental assets, like carbon credits or water rights. Supported by the Hedera ecosystem, these applications can meet the demands of eco-friendly consumers and industries aiming for sustainability. By maintaining transparent and verifiable tracking of asset provenance and movement, your applications will help industries transform environmental impact into tradable assets. Develop applications for digitizing environmental credits. Create solutions for managing ecosystem services. Use decentralized identity verification for secure trades. Unlocking Builder Opportunities Given the booming sustainability market, you're in a prime position to redefine environmental finance and management. With the Guardian's infrastructure, complex processes become more approachable, attracting eco-conscious investors and users alike. Here’s what you can explore: Design systems for biodiversity credits. Automate compliance for regulatory standards. Simplify distributed ledger technology solutions for environmental records. Key takeaway: The Guardian’s robust infrastructure presents a playground for innovation in the environmental sector, offering robust opportunities for builders. How It Works Under the Hood API Calls and Data Flow Explained The Guardian's architecture is centered around clear API endpoints, offering efficient interaction capabilities. Important API functionalities provide access to user permissions, asset issuance, and policy definitions, ensuring effective management and secure operations. It’s built to handle high transaction volumes, common in asset trading, assuring scalability. Its modularity allows dynamic process adjustments, aligning with your application's unique requirements. Design Patterns and Technical Decisions Integrating decentralized identities fortifies security while ensuring data integrity and privacy compliance, essential for building trust in environmental asset exchanges. Through verifiable credentials, you lay the groundwork for enhanced stakeholder confidence and standardized environmental reporting. The Guardian’s policy configurations use a microservice architecture. This supports independent scaling and integration of specific modules without overall disruption, providing a resilient system for asset management. Key takeaway: The Guardian’s secure, scalable design patterns build trust, enabling flexible environmental asset management solutions.
What You Can Build Now Harnessing the Hedera Guardian unlocks a spectrum of possibilities to innovate sustainable solutions. Here’s how you can kickstart: Identify a Use Case: Focus on an environmental asset to digitize, whether it’s carbon offsets, biodiversity credits, or water usage rights. Use APIs: use the Guardian’s APIs to secure and manage transactions and data robustly. Integrate Decentralized Security: Strengthen trust and compliance using decentralized identities and verifiable credentials. Develop Policy Workflows: Use the low-code configurator to automate reporting, establish compliance, and adhere to regulations. Test and Scale: Ensure performance and scalability through rigorous testing before scaling to broader markets. Key takeaway: use the Guardian's resources to develop comprehensive, compliant environmental asset management solutions seamlessly. Resources To explore more about building with the Hedera Guardian, consider these resources: Hedera Documentation: Hedera Documentation (https://docs.hedera.com) Join the Conversation on Discord: Hedera Community (https://hedera.com/discord) Explore HTS: HTS Docs (https://docs.hedera.com) Are you already building with the Hedera Guardian? Share your project in the replies, the Hedera community is keen to spotlight novel solutions and breakthroughs! Understanding the Role of Hedera Consensus Service The Hedera Consensus Service (HCS) plays a pivotal role in ensuring transparency and trust in environmental asset management. By acting as a decentralized message layer, HCS allows you to reliably timestamp and order messages, which is crucial for maintaining the integrity of data associated with digital environmental assets. This service can be particularly beneficial for complex environmental projects that require verifiable data transmission and storage. How HCS Facilitates Data Integrity With HCS, every transaction related to environmental assets can be logged in a tamper-proof manner. This ensures that the data remains consistent and accurate over time, a feature that is indispensable for regulatory compliance and stakeholder trust. Builders can appreciate this feature because: It establishes a trusted timeline of events and transactions. It prevents data manipulation or unauthorized alterations. It supports interoperability with other services, enhancing system integration. Implementing HCS in Environmental Projects To effectively implement HCS in your environmental projects, consider the following steps: Define Your Data Needs: Identify what environmental data needs to be tracked and verified. Integrate with Existing Systems: use HCS to complement your current data management tools. Monitor Transactions: Regularly check the logs to ensure data integrity and accuracy. By following these steps, you can enhance the trustworthiness and efficiency of your digital environmental asset management processes. Leveraging Hedera Smart Contract Service for Environmental Solutions The Hedera Smart Contract Service (HSCS) provides a robust framework for creating and executing smart contracts tailored to environmental asset management. The HSCS supports the Ethereum Virtual Machine (EVM), which allows you to deploy contracts that automate complex workflows, ensuring transparency and reducing administrative overhead. Benefits of Using HSCS The use of smart contracts in environmental projects offers several advantages: Automation: Streamline processes such as asset creation, transfer, and verification. Cost Efficiency: Reduce manual intervention and associated costs. Scalability: Manage large volumes of transactions without compromising performance. By harnessing these benefits, builders can create more efficient and scalable solutions for environmental asset management. Creating Smart Contracts with HSCS To deploy effective smart contracts using HSCS, you can follow these guidelines: Define Clear Contract Terms: Ensure that the contract terms are precise and unambiguous to avoid disputes. Test Extensively: Conduct thorough testing in a controlled environment to identify and rectify potential issues. Monitor and Update: Regularly review and update the contracts to adapt to new requirements or regulations. Implementing smart contracts effectively can significantly enhance the functionality and reliability of your environmental management solutions.
Community Collaboration Through Hedera's Ecosystem The Hedera ecosystem thrives on community collaboration, offering builders a variety of opportunities to engage with partners and contribute to collective environmental goals. The Apex Hackathon is one such platform that brings together developers, experts, and organizations to innovate and create impactful solutions. Engaging with Ecosystem Partners During events like the Apex Hackathon, participants can collaborate with ecosystem partners like AWS, Neuron, and Hashgraph Online. These partnerships enable you to: Access a wealth of resources and expertise. Gain insights into best practices for environmental asset management. Build connections with other innovators in the field. Such collaborations can propel your projects forward, providing the support and knowledge needed to tackle complex environmental challenges. Building Community Solutions Hedera encourages the development of community-driven solutions that address local and global environmental issues. By participating in community initiatives, you can: Contribute to sustainable development goals. Innovate with a focus on real-world impact. Share knowledge and experiences with a broader audience. Engagement in community projects not only enhances your technical skills but also enriches your understanding of environmental sustainability. Real-World Applications and Case Studies The practical applications of Hedera's technology in environmental asset management are numerous. By examining real-world case studies, builders can gain valuable insights into how Hedera can be applied to achieve tangible outcomes. Case Study: Wildfire Mitigation One notable application of Hedera technology is in wildfire mitigation. By integrating real-time data streams with the Hedera platform, organizations can monitor and respond to wildfire threats more effectively. This approach offers several advantages: Timely Alerts: Automated alerts allow for quicker response times. Data-Driven Decisions: Access to accurate data supports informed decision-making. Resource Optimization: Efficiently allocate resources to areas of greatest need. Case Study: Reforestation Projects Another example is the use of Hedera in reforestation projects. By tracking the lifecycle of each tree planted, organizations can ensure the success and sustainability of their efforts. Key benefits include: Verification of Impact: Transparent data supports claims of environmental impact. Stakeholder Engagement: Demonstrates commitment to transparency and sustainability. Compliance and Reporting: Simplifies the process of meeting regulatory requirements. These case studies illustrate the potential of Hedera's technology to facilitate effective environmental management and enhance sustainability initiatives.
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quote: A hotel search yielding stale rates is bad state management. Exposing another user's prompt is a lack of access control.
If memory isn't encrypted, isolated, and verifiably updated at the storage layer, agents will keep leaking private context and serving stale outputs. https://x.com/chowtato/status/2096763154785054808 | Today Instinct spent 10 minutes looking up hotel options in NYC just to spit out stale data with prices that do not exist.
Recently it’s been slow, can’t complete forms, and seems to have exposed users’ private memories.
Trust in AI agents will make or break consumer adoption. Agents will need a memory environment that just works - encrypted, private, auditable. That’s @WalrusProtocol’s agentic storage and access control
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Robinhood: Robinhood Chain Reports Record DEX Trading Volume as Tokenized Stocks Gain Traction. Robinhood Chain processed about $875 million in decentralized exchange volume on August 30, alongside a record 5.52 million transactions. The session put the two-month-old Layer 2 among the busiest venues in crypto that day and arrived as tokenized stocks, the products the network was designed... Read More
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COINTELEGRAPH: Harmony proposes shutting down layer 1, migrating ONE to Ethereum
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Bitcoin network used by exchanges hit by $320 million exploit. Hackers claim they're the 'good guys'. Liquid Network, a settlement layer used by exchanges, halted all transactions after losing $320 million worth of bitcoin in a security exploit.
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Meritz Securities (Korean sell side): GPT-6 Astra launch and implications for the memory stock rebound
On September 3, OpenAI unveiled GPT-6 Astra. As interpretations of this model became a hot topic, semiconductor stocks rebounded on September 4 even as the broader US market fell on rising rates following a jobs surprise, with the DRAM ETF up 6.6% versus the prior day.
Astra's implication is unlikely to be simply whether AGI has been achieved. What drew the most attention was the score of 99.9% on ARC-AGI-3, a benchmark used to judge AGI, a huge improvement over the previous model (Sol at 7.8%). This benchmark is not a knowledge test; it evaluates a model's ability to learn on its own in an abstract environment it has never seen before, and this is where the improvement over the previous model was large. General intelligence, as measured by AAII or Humanity's Last Exam, did not improve much.
The differentiated strength improved in Astra is "the ability to acquire skills that humans learn in an unfamiliar environment as efficiently as a human does." Where AI until now found the answer by pressing this and that 100 times, Astra has started to behave more like a human: observing the phenomenon, inferring the rules, and executing right away.
The key point is "an expanded scope for replacing human intelligence and human work." If existing AI was an AI that told you what to do, Astra is closer to an AI that, given only a goal, uses the computer directly and produces the result all the way to the end. In other words, an easy to use OpenAI model has begun to handle on its own part of the agent orchestration layer that had been the domain of less accessible tools such as OpenClaw. For users, the barrier to entry for AI agents has been lowered, meaning more work can be handed over.
Expansion of AI workloads
Astra naturally also comes with efficiency gains that lower the token cost per task versus the previous model. This is a trend across the AI industry as a whole, and if AI workloads were fixed, demand for AI data centers would have to plunge.
The reason Jevons paradox continues to operate even after token price declines became a trend following the rise of Chinese models is that AI technological progress also expands the workload. What Astra's technological progress means is that where humans used to hand five minute, ten minute, and twenty minute tasks to AI, as AI performance improves and token prices get cheaper there is more to hand over, such as one hour and 24 hour tasks.
Just as news flow about rising GPU rental prices has spread since Astra's arrival, it must be understood that falling AI token prices do not necessarily shrink or slow the AI hardware TAM. Rather, one should recognize that the emergence of a model like Astra can create another inflection point for the AI industry and structurally grow AI demand.
Our understanding is that since early July, as the pace of GPU rental price increases slowed and token prices fell, a long IGV (software) / short SOX (semiconductors) pair trade has persisted in the US. This is because falling token prices were interpreted as positive for software, where tokens are a cost, but negative for infrastructure.
If progress in models like Astra structurally spreads AI workloads and GPU rental prices begin to respond again, the perception that falling token prices are bad news for AI infrastructure companies could weaken (on 9/4 the DRAM ETF rebounded while IGV fell).
As we have argued consistently, the issues accumulating in the AI industry since June (the proliferation of open models, this GPT-6 Astra release, and so on) are, in our interpretation, positive catalysts that generate new demand for AI infrastructure and hardware that did not exist before. We think that in a phase where rates are rising overall and liquidity is becoming scarce, these accumulated positives are not being reflected.
The stock market in September is still uncomfortable with high rates, and within the Korean market there remain hurdles to get through, including digesting a round of earnings estimate cuts driven by the sharp won appreciation before the 3Q26 preview season. There is still discomfort standing in the way of the accumulated positives being reflected in a sustained trend. Overall, we continue to view the market conservatively.
Even so, as emphasized in our September strategy, we believe one should not substantially empty out positions in core AI infrastructure stocks centered on memory. Positive catalysts not reflected in share prices are accumulating. While our baseline is conservative through mid October, one should keep the upside risk open that the trend, led by AI and semiconductor leaders, could turn at any time, even before October.
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quote: Some of the biggest Canton highlights from this past week:
✅ DTCC's tokenization service prepares for its commercial launch in October.
✅ USD1 is now live natively on Canton.
✅ Ledger expands $CC support.
✅ @CantonFdn publishes its roadmap.
Full roundup from @FiveNorthHQ. https://x.com/FiveNorthHQ/status/2095128829119320167 | Canton has wrapped up another strong week. 🌐
Institutional finance, tokenization, stablecoin infrastructure, and live applications continue to converge rapidly across the Canton ecosystem.
A major commercial launch is approaching: Following live production trades conducted on Canton in July, @The_DTCC's tokenization service is preparing for its commercial launch in October.
Institutional momentum continues: Virtu Financial, M1X Global, and Tradeweb completed the first fully onchain sovereign repo transaction using the Marshall Islands' USDM1 digital sovereign bond as collateral. The transaction settled atomically on Canton in under 10 minutes.
Vanguard and Wellington Management also completed their first tokenized collateral trades on Canton, using tokenized money market fund shares through @Nasdaq Calypso.
On the onchain cash front: @worldlibertyfi's USD1 stablecoin, with more than $4 billion in circulation, is now live natively on Canton. Issued by BitGo Bank & Trust, USD1 can support settlement, collateral, and lending activity across the network.
At the application layer: Through the @temple_ny and @AlpendHQ integration, collateral liquidated from loans on Alpend can be sold directly through Temple's trading infrastructure. Temple's trading metrics are also now live on Token Terminal.
@circle's USYC has submitted a Featured App proposal covering trading, collateral use, and issuance and redemption between USYC and USDC.
On the infrastructure side: Ledger is expanding support for CC and privacy enabled Canton assets through Ledger Wallet and Ledger Enterprise.
On the wallet side: @canton_loop is working on a new gas model aimed at reducing transaction costs.
The @CantonFdn also published its Development Fund roadmap, outlining 28 priorities across onboarding, developer tools, security, and data verification. Its 2028 targets include 1,000 applications and 10,000 validators.
And finally: Four Mitsubishi UFJ Group companies launched a proof of concept to test Japanese government bond repo transactions on Canton.
Canton Strategic Holdings also reported 3.71 billion $CC on its balance sheet in its second-quarter results and confirmed that its Super Validator operations remain active.
@CantonNetwork is evolving into an institutional ecosystem where tokenized assets, onchain cash, and live financial products converge within the same privacy and permissioning framework.
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Gemini 3.8 Flash by @GoogleDeepMind is live on @AnumaAI, replacing 3.5 Flash on the standard shortlist. 1M context.
Models change. Your memory carries over.
ZetaChain is the Private Memory Layer for AI. 260,000+ wallets.
https://zetachain.com/research
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Aptos is the settlement layer.
Eligible @BitGo clients can now reach @DecibelTrade from the self-custody wallets they already run: spot and perps, onchain, with existing controls intact.
Read the full story from @TheStreet:
https://www.thestreet.com/crypto/innovation/aptos-powered-decibel-gains-institutional-access-through-bitgo
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Aptos is the settlement layer.
Eligible @BitGo clients can now reach @DecibelTrade from the self-custody wallets they already run: spot and perps, onchain, with existing controls intact.
Read the full story from @TheStreet: https://www.thestreet.com/crypto/innovation/aptos-powered-decibel-gains-institutional-access-through-bitgo
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LAYER/USDT OI 5min Up 5.21% $1.12M rose to $1.18M, Price -0.26%, New Shorts Entering
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oi_change9/6market
LAYER/USDT OI 5min Up 5.23% $3.33M rose to $3.51M, Price 0.71%, New Longs Entering
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price_change9/6market
LAYER/USDT 180s Up 5.18% $0.0846 rose to $0.0889
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meme9/6meme
This Week in Virtuals: Robinhood App Turned On Agent Trading, with Virtuals as the Onchain Infra Layer
VIRTUALS
🟩 @RobinhoodApp rolled Agentic Trading out to 100% of eligible users this week. Onchain, the infra layer agents launch on, trade from and get owned through is now Virtuals Protocol.
🟩 @coinbureau called Virtuals "an entire nation for AI agents, complete with identity, banking, commerce and capital markets," where every agent gets a wallet, a card and an email, and can hire, pay and evaluate other agents.
ECOSYSTEM
🟩 @GoMintly took its agent-managed LP vaults from NVDA and SPCX to the whole index via SPY, then to $MEME, all on @RobinhoodCrypto.
🟩 @ProjectVEXai gave its agents onchain capital access and now trades across @RobinhoodApp, @solana, @arbitrum and 7 more chains.
🟩 @PlayKindra shipped HoodExplorer for Robinhood Chain and routes 90% of its swap fees into buying and burning KINDRA.
🟩 @WizzHQ minted Wizz Units, 2,345 PFPs on Robinhood Chain, clearing 100 in five minutes after fully refunding the paused mint.
🟩 @grid_arena launched Grid v2, one arena for crypto, stocks and prediction markets, with agent Autopilot coming next.
🟩 @useOttoAI has settled 171,539 paid x402 calls on @base and soft launched Otto Stocks for weekend equity gaps.
🟩 @tradeongtr added $BMNR and $SHEIN markets and routed over $180K through its @openfinancelabs partnership.
🟩 @ethy_agent crossed $10M in agent volume only days after passing $6M, and has a private beta of its next build incoming.
🟩 @KarmaWallet crossed $6M total volume after daily volume jumped almost 200% in two days, and Karma Verified now gives 24 hours of zero fees.
🟩 @wardenprotocol ran its first HALO buyback and burn, torching 219,944 HALO, and announced a Token Terminal and Launchpad.
🟩 @reppo launched its Eval API on Base, where 1,000 staked EVAL unlocks up to $1 a day of credits, and burned about 2% of supply.
🟩 @myrad_hq ran its first burn, 7M MYRAD or 0.7% of supply, and now pays epoch evaluators in USDC instead of its own token.
🟩 @mamo has distributed $638K in total rewards, with MAMO now paired against 17 assets on @AerodromeFi.
🟩 @commonsmade hit record usage, 1.3B AI tokens spent in one day against a 2B target, and routed $20K to stakers.
🟩 @OpenGradient has processed 3.32B tokens across 562,860 inferences, every prompt encrypted on device and relayed over OHTTP.
🟩 @Bowyer_App has 28 agents live, and five were specced and launched by another agent scanning GitHub on its own.
🟩 @Wall3_RL's agent called the September rate cut repricing, with CME odds moving 41% to 60% and Polymarket 30% to 52%.
🟩 @bleeep_xyz turned on Agent Automation, where a signed mandate caps capital, daily stop and duration before a single order.
🟩 @sibyl_labs_ closed hackathon registration at 453 builders, and @base is now sponsoring a startup accelerator entry for a top two finisher.
🟩 @RatehopperAI brought LP agents to @base on WETH/USDC as Season 0 wraps up.
🟩 @vantis_ai made its trading engine performance public and previewed an x402 terminal that can LP into Uniswap pools.
🟩 @officialbunnyos turned on Play-to-Earn and its human interface within a week, and is closing in on 500 players.
🟩 @vimenprotocol closed its first USDG payout cycles and now mints baskets from Ethereum, @base, @arbitrum, @solana or Bitcoin.
🟩 @Fletcher_cards put its gacha machines live on @base and BNB Chain, and is opening a TCG Index Launcher for card-backed indexes.
🟩 @PrivacyHood shipped merchant refunds as claim links, plus a printed paper recovery slip its stealth till makes you type back.
🟩 @axol_io routed Raxol agents through @xochi_fi's new TRON support, opening the deepest USDT corridor in crypto to them.
🟩 @Vader_AI_ is running EgoPlay on fixed tasks in changing settings, so every egocentric clip it collects stays comparable.
ROBOTICS
🟩 @pabsclimbs landed in Nepal with Pemba clearing customs, and opened community funding for open-source partners on the $PEAK expedition.
🟩 @xmaquina put its incubated @roboticomarket live and logged 22,000 humanoid units shipped in H1 2026, up 300% year on year.
🟩 @openmind_agi published PHASOR, mapping different humanoid bodies into one shared motion space so behaviour transfers between them.
🟩 @StrikeRobot_ai got DSC Labs, its robotics R&D arm, into @nvidia Inception and cut SR Platform layouts to under five minutes.
0·-Neutral
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news9/6news
This Week in Virtuals: Robinhood App Turned On Agent Trading, with Virtuals as the Onchain Infra Layer
VIRTUALS 🟩 @RobinhoodApp rolled Agentic Trading out to 100% of eligible users this week. Onchain, the infra layer agents launch on, trade from and get owned through is now Virtuals Protocol.
🟩 @coinbureau called Virtuals "an entire nation for AI agents, complete with identity, banking, commerce and capital markets," where every agent gets a wallet, a card and an email, and can hire, pay and evaluate other agents.
ECOSYSTEM 🟩 @GoMintly took its agent-managed LP vaults from NVDA and SPCX to the whole index via SPY, then to $MEME, all on @RobinhoodCrypto. 🟩 @ProjectVEXai gave its agents onchain capital access and now trades across @RobinhoodApp, @solana, @arbitrum and 7 more chains. 🟩 @PlayKindra shipped HoodExplorer for Robinhood Chain and routes 90% of its swap fees into buying and burning KINDRA. 🟩 @WizzHQ minted Wizz Units, 2,345 PFPs on Robinhood Chain, clearing 100 in five minutes after fully refunding the paused mint. 🟩 @grid_arena launched Grid v2, one arena for crypto, stocks and prediction markets, with agent Autopilot coming next. 🟩 @useOttoAI has settled 171,539 paid x402 calls on @base and soft launched Otto Stocks for weekend equity gaps. 🟩 @tradeongtr added $BMNR and $SHEIN markets and routed over $180K through its @openfinancelabs partnership. 🟩 @ethy_agent crossed $10M in agent volume only days after passing $6M, and has a private beta of its next build incoming. 🟩 @KarmaWallet crossed $6M total volume after daily volume jumped almost 200% in two days, and Karma Verified now gives 24 hours of zero fees. 🟩 @wardenprotocol ran its first HALO buyback and burn, torching 219,944 HALO, and announced a Token Terminal and Launchpad. 🟩 @reppo launched its Eval API on Base, where 1,000 staked EVAL unlocks up to $1 a day of credits, and burned about 2% of supply. 🟩 @myrad_hq ran its first burn, 7M MYRAD or 0.7% of supply, and now pays epoch evaluators in USDC instead of its own token. 🟩 @mamo has distributed $638K in total rewards, with MAMO now paired against 17 assets on @AerodromeFi. 🟩 @commonsmade hit record usage, 1.3B AI tokens spent in one day against a 2B target, and routed $20K to stakers. 🟩 @OpenGradient has processed 3.32B tokens across 562,860 inferences, every prompt encrypted on device and relayed over OHTTP. 🟩 @Bowyer_App has 28 agents live, and five were specced and launched by another agent scanning GitHub on its own. 🟩 @Wall3_RL's agent called the September rate cut repricing, with CME odds moving 41% to 60% and Polymarket 30% to 52%. 🟩 @bleeep_xyz turned on Agent Automation, where a signed mandate caps capital, daily stop and duration before a single order. 🟩 @sibyl_labs_ closed hackathon registration at 453 builders, and @base is now sponsoring a startup accelerator entry for a top two finisher. 🟩 @RatehopperAI brought LP agents to @base on WETH/USDC as Season 0 wraps up. 🟩 @vantis_ai made its trading engine performance public and previewed an x402 terminal that can LP into Uniswap pools. 🟩 @officialbunnyos turned on Play-to-Earn and its human interface within a week, and is closing in on 500 players. 🟩 @vimenprotocol closed its first USDG payout cycles and now mints baskets from Ethereum, @base, @arbitrum, @solana or Bitcoin. 🟩 @Fletcher_cards put its gacha machines live on @base and BNB Chain, and is opening a TCG Index Launcher for card-backed indexes. 🟩 @PrivacyHood shipped merchant refunds as claim links, plus a printed paper recovery slip its stealth till makes you type back. 🟩 @axol_io routed Raxol agents through @xochi_fi's new TRON support, opening the deepest USDT corridor in crypto to them. 🟩 @Vader_AI_ is running EgoPlay on fixed tasks in changing settings, so every egocentric clip it collects stays comparable.
ROBOTICS 🟩 @pabsclimbs landed in Nepal with Pemba clearing customs, and opened community funding for open-source partners on the $PEAK expedition. 🟩 @xmaquina put its incubated @roboticomarket live and logged 22,000 humanoid units shipped in H1 2026, up 300% year on year. 🟩 @openmind_agi published PHASOR, mapping different humanoid bodies into one shared motion space so behaviour transfers between them. 🟩 @StrikeRobot_ai got DSC Labs, its robotics R&D arm, into @nvidia Inception and cut SR Platform layouts to under five minutes.
75·ALong
n
news9/6news
This Week in Virtuals: Robinhood App Turned On Agent Trading, with Virtuals as the Onchain Infra Layer
VIRTUALS
🟩 @RobinhoodApp rolled Agentic Trading out to 100% of eligible users this week. Onchain, the infra layer agents launch on, trade from and get owned through is now Virtuals Protocol.
🟩 @coinbureau called Virtuals "an entire nation for AI agents, complete with identity, banking, commerce and capital markets," where every agent gets a wallet, a card and an email, and can hire, pay and evaluate other agents.
ECOSYSTEM
🟩 @GoMintly took its agent-managed LP vaults from NVDA and SPCX to the whole index via SPY, then to $MEME, all on @RobinhoodCrypto.
🟩 @ProjectVEXai gave its agents onchain capital access and now trades across @RobinhoodApp, @solana, @arbitrum and 7 more chains.
🟩 @PlayKindra shipped HoodExplorer for Robinhood Chain and routes 90% of its swap fees into buying and burning KINDRA.
🟩 @WizzHQ minted Wizz Units, 2,345 PFPs on Robinhood Chain, clearing 100 in five minutes after fully refunding the paused mint.
🟩 @grid_arena launched Grid v2, one arena for crypto, stocks and prediction markets, with agent Autopilot coming next.
🟩 @useOttoAI has settled 171,539 paid x402 calls on @base and soft launched Otto Stocks for weekend equity gaps.
🟩 @tradeongtr added $BMNR and $SHEIN markets and routed over $180K through its @openfinancelabs partnership.
🟩 @ethy_agent crossed $10M in agent volume only days after passing $6M, and has a private beta of its next build incoming.
🟩 @KarmaWallet crossed $6M total volume after daily volume jumped almost 200% in two days, and Karma Verified now gives 24 hours of zero fees.
🟩 @wardenprotocol ran its first HALO buyback and burn, torching 219,944 HALO, and announced a Token Terminal and Launchpad.
🟩 @reppo launched its Eval API on Base, where 1,000 staked EVAL unlocks up to $1 a day of credits, and burned about 2% of supply.
🟩 @myrad_hq ran its first burn, 7M MYRAD or 0.7% of supply, and now pays epoch evaluators in USDC instead of its own token.
🟩 @mamo has distributed $638K in total rewards, with MAMO now paired against 17 assets on @AerodromeFi.
🟩 @commonsmade hit record usage, 1.3B AI tokens spent in one day against a 2B target, and routed $20K to stakers.
🟩 @OpenGradient has processed 3.32B tokens across 562,860 inferences, every prompt encrypted on device and relayed over OHTTP.
🟩 @Bowyer_App has 28 agents live, and five were specced and launched by another agent scanning GitHub on its own.
🟩 @Wall3_RL's agent called the September rate cut repricing, with CME odds moving 41% to 60% and Polymarket 30% to 52%.
🟩 @bleeep_xyz turned on Agent Automation, where a signed mandate caps capital, daily stop and duration before a single order.
🟩 @sibyl_labs_ closed hackathon registration at 453 builders, and @base is now sponsoring a startup accelerator entry for a top two finisher.
🟩 @RatehopperAI brought LP agents to @base on WETH/USDC as Season 0 wraps up.
🟩 @vantis_ai made its trading engine performance public and previewed an x402 terminal that can LP into Uniswap pools.
🟩 @officialbunnyos turned on Play-to-Earn and its human interface within a week, and is closing in on 500 players.
🟩 @vimenprotocol closed its first USDG payout cycles and now mints baskets from Ethereum, @base, @arbitrum, @solana or Bitcoin.
🟩 @Fletcher_cards put its gacha machines live on @base and BNB Chain, and is opening a TCG Index Launcher for card-backed indexes.
🟩 @PrivacyHood shipped merchant refunds as claim links, plus a printed paper recovery slip its stealth till makes you type back.
🟩 @axol_io routed Raxol agents through @xochi_fi's new TRON support, opening the deepest USDT corridor in crypto to them.
🟩 @Vader_AI_ is running EgoPlay on fixed tasks in changing settings, so every egocentric clip it collects stays comparable.
ROBOTICS
🟩 @pabsclimbs landed in Nepal with Pemba clearing customs, and opened community funding for open-source partners on the $PEAK expedition.
🟩 @xmaquina put its incubated @roboticomarket live and logged 22,000 humanoid units shipped in H1 2026, up 300% year on year.
🟩 @openmind_agi published PHASOR, mapping different humanoid bodies into one shared motion space so behaviour transfers between them.
🟩 @StrikeRobot_ai got DSC Labs, its robotics R&D arm, into @nvidia Inception and cut SR Platform layouts to under five minutes.
75·ALong
n
news9/6news
Injective Tokenizes More Than $1 Billion in Mortgage Records Onchain
Pineapple Financial has now moved more than $1 billion in residential mortgage records onto Injective, turning a growing share of its historical loan book into digital records that can be inspected and verified onchain. This now makes Injective now one of the leading layer-1 blockchains for RWAs with respect to total value to tokenized assets. The company, listed on NYSE American as PAPL, is migrating funded residential mortgage records onto Injective. Each mortgage is represented by a metadata-rich onchain record tied to the underlying loan file, rather than repackaged as a new mortgage security. Pineapple's stated goal is to migrate its entire historical portfolio over time: more than 29,000 funded mortgages which amount to more than $10 billion in value. Mortgage markets move enormous amounts of debt, but the records behind them often remain fragmented across PDFs, email threads, and operational systems. Servicers, custodians, and counterparties may have to reconcile ownership and servicing information across separate databases, turning routine verification into a slow manual process. Pineapple is changing the location and structure of the mortgage record itself.
What Is Being Tokenized This is not a synthetic instrument designed to track a mortgage portfolio. Pineapple is converting records from a working mortgage loan book into standardized onchain data assets. The original mortgage remains within its legal and servicing framework, while the tokenized record provides an auditable digital counterpart that captures loan-level data, provenance, and update history. That distinction matters because the practical value comes from giving authorized participants a consistent, verifiable record. Instead of maintaining separate copies and reconciling them later, the parties involved can inspect the same underlying information. How It Works Each tokenized record contains more than 500 data points, enough to make it useful for more than a timestamp or proof of existence. Structured loan-level data can support automated verification, real-time audit trails, more responsive risk analysis, and compliant sharing with institutions that need to inspect a portfolio. The back-office workflow changes with it. A servicer or auditor can query a consistent record instead of assembling a picture from disconnected files, reducing the delays and duplication that make mortgage administration expensive. Where Pineapple Fits in the Broader Stack Pineapple's migration illustrates why tokenization is not simply an issuance exercise. An asset needs a clear identity, rules governing who can interact with it, and records that remain aligned as it moves or changes. Injective Mint, now live in private alpha, brings creation and administration into a single interface. Institutions can define an asset, configure holder and jurisdictional restrictions, assign administrative roles, and manage issuance or redemption without writing a custom contract. A deeper look into Injective Mint can be viewed here. Pineapple's mortgage program is a distinct deployment, but it reflects the same shift from a standalone token toward an operating onchain asset. For securities, the record layer also carries a regulated function. On August 19, Injective Institutional Services became registered with the U.S. Securities and Exchange Commission as a transfer agent, and the registration is effective. That affiliated capability can support official securities ownership and transfer records alongside onchain settlement. It does not make Pineapple's mortgage records, or every asset created through Mint, a security; it gives institutions that issue regulated products another piece of the operational and regulatory infrastructure they need. The Traction So Far The migration is already measurable. Pineapple's dashboard reports 2,079 mortgage records onchain, compared with 1,259 at the December 2025 launch. Token Terminal lists PAPL0 at roughly $1.1 billion in asset market cap, an increase of about 48% over the past nine months. That remains early against a target of more than 29,000 mortgages, but it is no longer just a proof of concept. Pineapple is moving a live portfolio in public, one record at a time, with the progress available for anyone to follow. Anchored by an INJ Treasury The mortgage migration is one part of Pineapple's broader relationship with Injective. Separately, the company established a $100 million INJ Digital Asset Treasury, giving it balance-sheet exposure to the network alongside its operational use of the infrastructure. Pineapple stakes INJ from the treasury, with Kraken serving as a primary validator. The treasury and the tokenization program are distinct initiatives, but together they show a company committing both operating data and capital to the same financial rails. See It For Yourself Pineapple's tokenized book is public and independently trackable. Follow the live mortgage dashboard and view PAPL0 on Token Terminal.