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How Internet Computer contracts fetch web data without an oracle Smart contracts on DFINITY Foundation's Internet Computer can call ordinary web APIs directly, with no oracle service in between. The chain builds the trust step into its own consensus instead. Every node running the contract sends the same request,...

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Smart Contracts. ✓ Live. Oracle Machines. ✓ Live. Outsourced Computing. ▲ Design starts this week. Qubic's third and final pillar is in motion. Smart contracts execute on-chain. Oracle Machines let them read the outside world. Outsourced Computing lets them request work from the outside world, verified by quorum, with no central operator. A smart contract that can observe, decide, and trigger real-world consequences is no longer just a smart contract. Two pillars live. Third in design. Four years in the making.
0:12

Smart Contracts. ✓ Live. Oracle Machines. ✓ Live. Outsourced Computing. ▲ Design starts this week. Qubic's third and final pillar is in motion. Smart contracts execute on-chain. Oracle Machines let them read the outside world. Outsourced Computing lets them request work from the outside world, verified by quorum, with no central operator. A smart contract that can observe, decide, and trigger real-world consequences is no longer just a smart contract. Two pillars live. Third in design. Four years in the making.

Qubic

18,143 görüntüleme • 3 ay önce

a contractor in Shenzhen priced a ¥12,470,900 hospital contract, about $1.7m, in one afternoon and beat firms carrying forty people he explained how he did it: the bid consultancy he used to pay took three days and ¥46,000 for the same envelope. he did this one alone, off one screen, at 11.4% margin, uploaded before the 17:00 cutoff 214 pages of tender documents read, 68 binding clauses pulled out, 9,485 building parts loaded, 14 places found where a duct and a beam sit in the same cubic metre, deepest one 38mm, all of them fixed, 3,318 lines of quantities priced and the package encrypted and uploaded before the 17:00 cutoff this is Graph Engineering: the job gets cut into small nodes, one narrow task each, wired so that one node's output is the next node's input, and any node is allowed to stop the whole run. it turns a model that answers you into a machine that finishes the job: - give every node one job and one output. a node doing two things fails at both and you cannot tell which one broke - put the cheapest rejection first. his qualification node reads clause 7.4, foreign-owned firms barred, and ends the run four seconds in, before anything expensive touches the model - what moves between nodes is a file. the model travels as a model, the quantities as a table, the price as a number - build exactly one loop: the checker finds 14 collisions, the fixer drops the duct 550mm, the checker runs again, and nothing moves on until the count is zero - cap that loop, or a graph will grind on three impossible clashes until the deadline passes - keep one node whose only job is to say no, and give it authority over everything above it - log each node's output on its own, because when the price comes out wrong you need to know which node believed the wrong thing - run the expensive nodes last, always the catch is that a graph is an extremely confident machine: point it at an outdated rate book and it prices an entire hospital off it without a single node noticing, because no node is asked to doubt the input, only to process it so the nodes that earn their keep are the ones that reject, and almost nobody builds those first bookmark this, the full build with all nine nodes and what each one hands to the next is written out in the article ↓
0:16

a contractor in Shenzhen priced a ¥12,470,900 hospital contract, about $1.7m, in one afternoon and beat firms carrying forty people he explained how he did it: the bid consultancy he used to pay took three days and ¥46,000 for the same envelope. he did this one alone, off one screen, at 11.4% margin, uploaded before the 17:00 cutoff 214 pages of tender documents read, 68 binding clauses pulled out, 9,485 building parts loaded, 14 places found where a duct and a beam sit in the same cubic metre, deepest one 38mm, all of them fixed, 3,318 lines of quantities priced and the package encrypted and uploaded before the 17:00 cutoff this is Graph Engineering: the job gets cut into small nodes, one narrow task each, wired so that one node's output is the next node's input, and any node is allowed to stop the whole run. it turns a model that answers you into a machine that finishes the job: - give every node one job and one output. a node doing two things fails at both and you cannot tell which one broke - put the cheapest rejection first. his qualification node reads clause 7.4, foreign-owned firms barred, and ends the run four seconds in, before anything expensive touches the model - what moves between nodes is a file. the model travels as a model, the quantities as a table, the price as a number - build exactly one loop: the checker finds 14 collisions, the fixer drops the duct 550mm, the checker runs again, and nothing moves on until the count is zero - cap that loop, or a graph will grind on three impossible clashes until the deadline passes - keep one node whose only job is to say no, and give it authority over everything above it - log each node's output on its own, because when the price comes out wrong you need to know which node believed the wrong thing - run the expensive nodes last, always the catch is that a graph is an extremely confident machine: point it at an outdated rate book and it prices an entire hospital off it without a single node noticing, because no node is asked to doubt the input, only to process it so the nodes that earn their keep are the ones that reject, and almost nobody builds those first bookmark this, the full build with all nine nodes and what each one hands to the next is written out in the article ↓

Argona

38,189 görüntüleme • 27 gün önce

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.
0:14

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

Alex Prompter

7,105,546 görüntüleme • 1 ay önce

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)
0:14

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)

Tyler

24,994 görüntüleme • 1 yıl önce

The Future of Decentralized Social Media CAW | TestNet opens on July 9th! We worked hard, every day, with our huge team of 22 people, we worked hard for the construction of social media and the manifesto. In this context, on July 9, we will launch the test version of CAW, which is decentralized and completely systematized smart contracts with blockchain technology. Progress of the Process - On July 6, with a 3-day countdown, the testnet of our social media will meet with you. We will upload all contracts and lines of code related to social media to GitHub on the date of TestNet launch. After uploading it, CAW sent the message to its founder via etherscan "cawmnty.eth" we will send a message with the wallet address for the community and the CAW founder to review everything. Everything's going to be great. TestNet's Development and Progress CAWmunity prepares smart contracts, which are the most important point of decentralized social media, with great care. We aim to make every feature available by integrating these agreements into the system at specified times and intervals. Smart Contracts and Duties - Contract that provides features for NFT mint and user logins. - An NFT market where usernames created for CAW can be traded and the contract of this market (like opensea) - Agreement on liking, CAW (posting), reCAW, commenting and all other means of interaction and sharing. - Contract providing follow and follower options. - Contract providing links to Explore and other categories. - The contract that ensures the flow and security of payment systems. - Agreement on Free Messaging. (This is one of the most important contracts.) and other conventions in the background that will allow all these to communicate with each other... CAW | TestNet; We have completed the NFT market transactions for NFT printing and listing and will make them available. All other contracts will be shared with the community at the specified time through the principle of transparency and used on the blockchain testnet. Thank you very much to everyone who supported! CAW is a hope. CAW is life. The #CAWmunity spirit will take us to the pinnacle of the future. The strength of our community will spread everywhere. 🌙 - tehppl. Don't forget to like and support! 💛 #CAW Ryoshi
0:10

The Future of Decentralized Social Media CAW | TestNet opens on July 9th! We worked hard, every day, with our huge team of 22 people, we worked hard for the construction of social media and the manifesto. In this context, on July 9, we will launch the test version of CAW, which is decentralized and completely systematized smart contracts with blockchain technology. Progress of the Process - On July 6, with a 3-day countdown, the testnet of our social media will meet with you. We will upload all contracts and lines of code related to social media to GitHub on the date of TestNet launch. After uploading it, CAW sent the message to its founder via etherscan "cawmnty.eth" we will send a message with the wallet address for the community and the CAW founder to review everything. Everything's going to be great. TestNet's Development and Progress CAWmunity prepares smart contracts, which are the most important point of decentralized social media, with great care. We aim to make every feature available by integrating these agreements into the system at specified times and intervals. Smart Contracts and Duties - Contract that provides features for NFT mint and user logins. - An NFT market where usernames created for CAW can be traded and the contract of this market (like opensea) - Agreement on liking, CAW (posting), reCAW, commenting and all other means of interaction and sharing. - Contract providing follow and follower options. - Contract providing links to Explore and other categories. - The contract that ensures the flow and security of payment systems. - Agreement on Free Messaging. (This is one of the most important contracts.) and other conventions in the background that will allow all these to communicate with each other... CAW | TestNet; We have completed the NFT market transactions for NFT printing and listing and will make them available. All other contracts will be shared with the community at the specified time through the principle of transparency and used on the blockchain testnet. Thank you very much to everyone who supported! CAW is a hope. CAW is life. The #CAWmunity spirit will take us to the pinnacle of the future. The strength of our community will spread everywhere. 🌙 - tehppl. Don't forget to like and support! 💛 #CAW Ryoshi

dgn 🌙

77,778 görüntüleme • 3 yıl önce

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
0:10

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 görüntüleme • 10 ay önce

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.
0:12

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

Akshay 🚀

63,725 görüntüleme • 1 ay önce

In 2025, demand for blockchain applications with genuine real-world utility has collided with a technical barrier that leaves developers questioning what they can realistically build. Anyone building things like tokenized assets, supply chains, AI agents, or prediction markets still juggle a mess of middleware, and somehow end up spending more time stitching than innovating. How so? Every: - Bridges to move assets, - oracles to fetch data, - indexers to make that data searchable, - relayers and bots to keep everything on schedule— is necessary, but each layer also adds cost, latency, and new risks. The end result is an application that’s expensive to run, fragile under stress, and slower than the Web2 software it’s trying to replace. This is the problem Rialo says it wants to solve. Built by Subzero Labs and backed by $20 million from investors like Pantera Capital and Coinbase Ventures 🛡️, Rialo’s pitch is simple: instead of accepting the middleware tower as an unavoidable cost of doing business, compress it into the base chain itself. But Rialo doesn’t describe itself as another Layer 1, its very name, Rialo Isn’t a Layer One, makes that clear. The team frames it instead as a unified real-world network: a protocol rebuilt from the ground up with the assumption that external connectivity is not an afterthought but a core design principle. To understand what this means, consider how today’s dApps are typically assembled. A typical RWA dApp stack involves: - Oracle providers (Chainlink, Pyth, Band) for asset pricing and event settlement - Bridges (Wormhole, Multichain, custodians) for cross-chain asset movement - Indexers (The Graph, Aleph, Stacks API) for querying and preprocessing chain data - Schedulers/relayers for automated tasks and monitoring - Web2 integrations via cloud services, centralized APIs, and off-chain pipelines Each of these steps adds another vendor, another trust boundary, and another operational layer to monitor. By the time the application is live, it resembles a patchwork of loosely coupled services, each carrying its own risks. You don’t have to look far for proof: - Base went dark for 29-43 minutes in August 2025 when its sequencer misfired, freezing every DeFi app on it. - A few months earlier, an AWS outage rippled through Binance and KuCoin, stalling withdrawals because even “decentralized” systems leaned on centralized middleware. - When Infura has faltered, Ethereum dApps have gone offline in sync, not because Ethereum broke, but because the middleware holding it together did. What should feel like building an application instead feels like maintaining a fragile machine. Rialo architecture embeds the primitives that normally live in middleware directly into the protocol. Smart contracts on Rialo can: - be event-driven, able to respond not just to blockchain state changes but also to external events through built-in webhook and API triggers. - fetch data from the web natively, without relying on external oracles or relayers. - include privacy and identity management—KYC hooks and two-factor authentication, at the protocol level rather than as add-ons. - handle cross-chain communication without wrapped assets or third-party bridges. - run on a virtual machine that is compatible with ecosystems like Solana but extended with RISC-V to support modern programming concepts such as async/await and event loops. If these features work as intended, the implications are significant. Today, much of a team’s energy goes into building and maintaining infrastructure: fullnodes, indexers, monitoring scripts, oracle integrations, relayer logic, bridge infrastructure. Each requires engineering headcount and ongoing maintenance. With Rialo, much of this is absorbed by the protocol, freeing developers to concentrate on business logic. Projects can deliver production-grade dApps with smaller, leaner groups focused directly on product design and execution. Operational costs also shrink: indexing and oracle services can run into thousands of dollars a month; collapsing those into built-in functions reduces recurring expenses while simplifying onboarding for new developers. But folding middleware into the chain doesn’t erase complexity, it reshapes it. Some of the problems to be encountered include: - Scale and complexity: Rialo’s validators won’t just be securing transactions; they’ll also be securing APIs, cross-chain data, and scheduled triggers. Any failure in one subsystem could ripple across the entire network. - Performance vs. decentralization: Richer indexing, scheduling, and data ingress could make nodes heavier to run, narrowing who can realistically participate as a validator. That risks reducing the decentralization blockchains depend on for resilience. - Governance pressures: Disputes or failures involving real-world data feeds, external APIs, or cross-chain actions will arise more often, requiring not just technical fixes but robust social infrastructure, clear rules for voting, transparent arbitration, and mechanisms for community trust. Without them, Rialo risks re-centralizing decision-making around a handful of operators. Where, then, does this model make the most sense? That would be in sectors where external connectivity is indispensable and middleware bloat has consistently been a blocker: - Real-world assets: settling tokenized securities or commodities against off-chain events. - Supply chains: triggering a payment the moment a shipment clears customs, without relying on a third-party oracle. - Agent systems: AI agents interacting with real-world APIs and on-chain contracts simultaneously. - Real-time markets: prediction markets or insurance contracts that must resolve immediately against external data. For purely on-chain domains like DeFi primitives or NFTs, where composability matters more than external triggers, the advantages may be less pronounced. This shift is familiar to anyone who remembers the rise of Web2 platform services. Just as Heroku and Firebase abstracted away server maintenance so developers could focus on building products, Rialo is betting that a unified real-world network can let blockchain developers do the same. Adoption will ultimately depend on: - whether its protocol primitives mature quickly, - whether the ecosystem builds out SDKs and tooling that make them usable, - whether compliance features can adapt to changing regulations, - and whether governance proves resilient under adversarial conditions. The first applications will be the test case. If they show that Rialo can replace a fragile patchwork of middleware with a secure, auditable, and cost-effective base layer, it could set a new standard for real-world connectivity in blockchains. If not, it risks simply moving complexity from one part of the stack to another. But at a minimum, Rialo has forced the question: should real-world connectivity in blockchains continue to depend on layers of external vendors, or should it be built into the chain itself? That’s the question Rialo has put on the table — and it’s why I got interested in Rialo .
0:17

In 2025, demand for blockchain applications with genuine real-world utility has collided with a technical barrier that leaves developers questioning what they can realistically build. Anyone building things like tokenized assets, supply chains, AI agents, or prediction markets still juggle a mess of middleware, and somehow end up spending more time stitching than innovating. How so? Every: - Bridges to move assets, - oracles to fetch data, - indexers to make that data searchable, - relayers and bots to keep everything on schedule— is necessary, but each layer also adds cost, latency, and new risks. The end result is an application that’s expensive to run, fragile under stress, and slower than the Web2 software it’s trying to replace. This is the problem Rialo says it wants to solve. Built by Subzero Labs and backed by $20 million from investors like Pantera Capital and Coinbase Ventures 🛡️, Rialo’s pitch is simple: instead of accepting the middleware tower as an unavoidable cost of doing business, compress it into the base chain itself. But Rialo doesn’t describe itself as another Layer 1, its very name, Rialo Isn’t a Layer One, makes that clear. The team frames it instead as a unified real-world network: a protocol rebuilt from the ground up with the assumption that external connectivity is not an afterthought but a core design principle. To understand what this means, consider how today’s dApps are typically assembled. A typical RWA dApp stack involves: - Oracle providers (Chainlink, Pyth, Band) for asset pricing and event settlement - Bridges (Wormhole, Multichain, custodians) for cross-chain asset movement - Indexers (The Graph, Aleph, Stacks API) for querying and preprocessing chain data - Schedulers/relayers for automated tasks and monitoring - Web2 integrations via cloud services, centralized APIs, and off-chain pipelines Each of these steps adds another vendor, another trust boundary, and another operational layer to monitor. By the time the application is live, it resembles a patchwork of loosely coupled services, each carrying its own risks. You don’t have to look far for proof: - Base went dark for 29-43 minutes in August 2025 when its sequencer misfired, freezing every DeFi app on it. - A few months earlier, an AWS outage rippled through Binance and KuCoin, stalling withdrawals because even “decentralized” systems leaned on centralized middleware. - When Infura has faltered, Ethereum dApps have gone offline in sync, not because Ethereum broke, but because the middleware holding it together did. What should feel like building an application instead feels like maintaining a fragile machine. Rialo architecture embeds the primitives that normally live in middleware directly into the protocol. Smart contracts on Rialo can: - be event-driven, able to respond not just to blockchain state changes but also to external events through built-in webhook and API triggers. - fetch data from the web natively, without relying on external oracles or relayers. - include privacy and identity management—KYC hooks and two-factor authentication, at the protocol level rather than as add-ons. - handle cross-chain communication without wrapped assets or third-party bridges. - run on a virtual machine that is compatible with ecosystems like Solana but extended with RISC-V to support modern programming concepts such as async/await and event loops. If these features work as intended, the implications are significant. Today, much of a team’s energy goes into building and maintaining infrastructure: fullnodes, indexers, monitoring scripts, oracle integrations, relayer logic, bridge infrastructure. Each requires engineering headcount and ongoing maintenance. With Rialo, much of this is absorbed by the protocol, freeing developers to concentrate on business logic. Projects can deliver production-grade dApps with smaller, leaner groups focused directly on product design and execution. Operational costs also shrink: indexing and oracle services can run into thousands of dollars a month; collapsing those into built-in functions reduces recurring expenses while simplifying onboarding for new developers. But folding middleware into the chain doesn’t erase complexity, it reshapes it. Some of the problems to be encountered include: - Scale and complexity: Rialo’s validators won’t just be securing transactions; they’ll also be securing APIs, cross-chain data, and scheduled triggers. Any failure in one subsystem could ripple across the entire network. - Performance vs. decentralization: Richer indexing, scheduling, and data ingress could make nodes heavier to run, narrowing who can realistically participate as a validator. That risks reducing the decentralization blockchains depend on for resilience. - Governance pressures: Disputes or failures involving real-world data feeds, external APIs, or cross-chain actions will arise more often, requiring not just technical fixes but robust social infrastructure, clear rules for voting, transparent arbitration, and mechanisms for community trust. Without them, Rialo risks re-centralizing decision-making around a handful of operators. Where, then, does this model make the most sense? That would be in sectors where external connectivity is indispensable and middleware bloat has consistently been a blocker: - Real-world assets: settling tokenized securities or commodities against off-chain events. - Supply chains: triggering a payment the moment a shipment clears customs, without relying on a third-party oracle. - Agent systems: AI agents interacting with real-world APIs and on-chain contracts simultaneously. - Real-time markets: prediction markets or insurance contracts that must resolve immediately against external data. For purely on-chain domains like DeFi primitives or NFTs, where composability matters more than external triggers, the advantages may be less pronounced. This shift is familiar to anyone who remembers the rise of Web2 platform services. Just as Heroku and Firebase abstracted away server maintenance so developers could focus on building products, Rialo is betting that a unified real-world network can let blockchain developers do the same. Adoption will ultimately depend on: - whether its protocol primitives mature quickly, - whether the ecosystem builds out SDKs and tooling that make them usable, - whether compliance features can adapt to changing regulations, - and whether governance proves resilient under adversarial conditions. The first applications will be the test case. If they show that Rialo can replace a fragile patchwork of middleware with a secure, auditable, and cost-effective base layer, it could set a new standard for real-world connectivity in blockchains. If not, it risks simply moving complexity from one part of the stack to another. But at a minimum, Rialo has forced the question: should real-world connectivity in blockchains continue to depend on layers of external vendors, or should it be built into the chain itself? That’s the question Rialo has put on the table — and it’s why I got interested in Rialo .

Jen

12,178 görüntüleme • 10 ay önce

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable
0:18

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable

Om Patel

176,154 görüntüleme • 1 ay önce

⚙️ Attention n8n Builders: Time to Upgrade Your Crawlers. If you are running web scraping or data retrieval workflows on n8n, it is time to flip the switch. Stop relying on fragile datacenter proxies that get blocked every five minutes. Try UpRock. As a verified n8n integration, UpRock gives your workflows access to a global network of real devices for: → Live Web Crawling → Deep Research → Semantic Video Search → Real-Time Intelligence Retrieval No more building separate stacks for crawling, research, video understanding, and web intelligence. One node. One workflow. One global swarm. Want a Cheeky Credit Boost? We want to see what you are building. If you transition an active data-gathering workflow over to the UpRock node this week, reach out directly to our dev team. Drop a message or reply below with your use case, and we will hook you up with custom API testing credits to supercharge your swarm infrastructure. Get Started on n8n: Search for Scraper - UpRock Crawler in your n8n Nodes panel or grab the package directly on npm!
0:10

⚙️ Attention n8n Builders: Time to Upgrade Your Crawlers. If you are running web scraping or data retrieval workflows on n8n, it is time to flip the switch. Stop relying on fragile datacenter proxies that get blocked every five minutes. Try UpRock. As a verified n8n integration, UpRock gives your workflows access to a global network of real devices for: → Live Web Crawling → Deep Research → Semantic Video Search → Real-Time Intelligence Retrieval No more building separate stacks for crawling, research, video understanding, and web intelligence. One node. One workflow. One global swarm. Want a Cheeky Credit Boost? We want to see what you are building. If you transition an active data-gathering workflow over to the UpRock node this week, reach out directly to our dev team. Drop a message or reply below with your use case, and we will hook you up with custom API testing credits to supercharge your swarm infrastructure. Get Started on n8n: Search for Scraper - UpRock Crawler in your n8n Nodes panel or grab the package directly on npm!

UpRock

91,772 görüntüleme • 2 ay önce

grokbot it's an agent with its own identity, its own computer, and it stays on when you're not here's what "active AI employee" actually looks like in the demo: - chief of staff agent - checks in on your other agents, reads your calendar, dispatches tasks to the right one automatically - shopping agent - logged into your accounts, books tickets, buys groceries, reports back - marketing agent - signed into your actual linkedin, browses your past posts for tone, then writes and publishes a new one on its own - engineering agents - self-triage bug reports, kick off cloud coding agents, come back with a pull request, a screenshot, and a video of the fix the interface isn't a dashboard, it's a chat - same shape as texting a coworker, no tool calls to babysit the number that matters more than any of the demos: grok 4.6 scored 70.8% on cursor bench at $2.81 a task, fable 5 max scored 70.5% at $17.32 same capability, 6x the cost difference - that's the unlock that makes running a fleet of these actually affordable instead of a novelty
0:13

grokbot it's an agent with its own identity, its own computer, and it stays on when you're not here's what "active AI employee" actually looks like in the demo: - chief of staff agent - checks in on your other agents, reads your calendar, dispatches tasks to the right one automatically - shopping agent - logged into your accounts, books tickets, buys groceries, reports back - marketing agent - signed into your actual linkedin, browses your past posts for tone, then writes and publishes a new one on its own - engineering agents - self-triage bug reports, kick off cloud coding agents, come back with a pull request, a screenshot, and a video of the fix the interface isn't a dashboard, it's a chat - same shape as texting a coworker, no tool calls to babysit the number that matters more than any of the demos: grok 4.6 scored 70.8% on cursor bench at $2.81 a task, fable 5 max scored 70.5% at $17.32 same capability, 6x the cost difference - that's the unlock that makes running a fleet of these actually affordable instead of a novelty

rewind

731,770 görüntüleme • 5 gün önce

Introducing the Buy-Side Equity Research SOP on xBubble. State a ticker. Get a research memo built to the standard of an internal buy-side note. Thesis first, every number sourced to the original filing. One request in. A full equity deep dive out. What comes back isn't a news recap. It's the structure a real analyst writes: ✅ Where the company sits in its value chain ✅ Competitive map, edge, and key risks ✅ Filing-level financials + management read ✅ SOTP valuation, Bull / Base / Bear per share ✅ The catalysts to watch over the next 3 to 6 months Every claim is traceable. Each data point carries its original source and access date: SEC filings (10-K, 10-Q, 8-K), official IR decks, earnings call transcripts. You can check the work. That's the point. Here's what makes this more than one good report. The SOP isn't fixed. Bubble Engine builds it, tests it, and keeps generating stronger ones. Need a version tuned to a sector, a market, or your own thesis style? It generates that too. You don't get a template. You get a research engine. Buy-side research used to take 15 to 20 hours per company. Now it's one request. The edge was never about access. It was about depth. xBubble puts the depth on your side. Work mode → Buy-Side Equity Research SOP.
0:16

Introducing the Buy-Side Equity Research SOP on xBubble. State a ticker. Get a research memo built to the standard of an internal buy-side note. Thesis first, every number sourced to the original filing. One request in. A full equity deep dive out. What comes back isn't a news recap. It's the structure a real analyst writes: ✅ Where the company sits in its value chain ✅ Competitive map, edge, and key risks ✅ Filing-level financials + management read ✅ SOTP valuation, Bull / Base / Bear per share ✅ The catalysts to watch over the next 3 to 6 months Every claim is traceable. Each data point carries its original source and access date: SEC filings (10-K, 10-Q, 8-K), official IR decks, earnings call transcripts. You can check the work. That's the point. Here's what makes this more than one good report. The SOP isn't fixed. Bubble Engine builds it, tests it, and keeps generating stronger ones. Need a version tuned to a sector, a market, or your own thesis style? It generates that too. You don't get a template. You get a research engine. Buy-side research used to take 15 to 20 hours per company. Now it's one request. The edge was never about access. It was about depth. xBubble puts the depth on your side. Work mode → Buy-Side Equity Research SOP.

DAPPOS

427,279 görüntüleme • 2 ay önce

Very recent ... directly from your mouth MJ the Lawyer and these are only two pieces of evidence and there are more ... You’re still spreading misinformation and lies. VANZAN is related to the film. You haven’t provided any evidence of it. The WF Parties have denied it several times. VANZAN is a corporation affiliated with Blake Lively and Ryan Reynolds VANZAN a good lawyering?! The VANZAN sham action issued by manatt alleges a breach of contract claim against ten Doe defendants, who although parties to an allegedly breached contract with VANZAN, were never named or served. There’s no link to Abel and the film. And it’s very unclear how the VANZAN sham subpoena could be court-ordered given that no judge was assigned to this action, which is a requirement for Doe actions in NY. Surely you must know that. Can you explain to us how you can sign a contract, but you don't know the parties? Do you actually know that Doe actions must be assigned to a judge?
0:41

Very recent ... directly from your mouth MJ the Lawyer and these are only two pieces of evidence and there are more ... You’re still spreading misinformation and lies. VANZAN is related to the film. You haven’t provided any evidence of it. The WF Parties have denied it several times. VANZAN is a corporation affiliated with Blake Lively and Ryan Reynolds VANZAN a good lawyering?! The VANZAN sham action issued by manatt alleges a breach of contract claim against ten Doe defendants, who although parties to an allegedly breached contract with VANZAN, were never named or served. There’s no link to Abel and the film. And it’s very unclear how the VANZAN sham subpoena could be court-ordered given that no judge was assigned to this action, which is a requirement for Doe actions in NY. Surely you must know that. Can you explain to us how you can sign a contract, but you don't know the parties? Do you actually know that Doe actions must be assigned to a judge?

Roman Schatow

14,496 görüntüleme • 1 yıl önce

Hi everyone, this week I finally received the new ANyONe Relay! ANyONe Protocol is an interesting DePIN focused on building a more decentralized internet and making our privacy more secure 💯 I'm still waiting to release a video because the new Dashboard isn't active yet but for those interested the most important points that I want to highlight are these: 1) The #Miner is Plug&Play so easy to install, just follow the initial steps and you're fine, once installed you don't have to do anything else. 2) There is no limit for IP but if for example you put 2 Relays in the same #IP it isn't certain that you will have double rewards, because they basically do the same job, to get more rewards it's better to use different IPs. 3) To make the Relay operational it's necessary to open port 9001 in your Modem. Without this operation the #mining will basically not start, so make sure you have a modem or connection that can open the ports. If you have the classic public IP you should definitely be able to do it 4) Currently the rewards arrive automatically every week, usually Friday, and I received 8 $ANYONE as first earnings 👍 Since the new #Dashboard isn't yet operational the #rewards aren't yet clear (which is why I am still waiting to release the video) and personally I expect there will be more 🔥 The only negative note that I have found so far in the project is that: ANYONE #token is only on Ethereum So unfortunately the fees for the swap or even future claims and sendings will not be low, I hope that the team in the future will also consider Arbitrum or Base for the rewards to Nodes 🙏 $ANYONE: Docs to learn more: If you are interested in the project you can order the Anyone on @ampchampment and you can receive it in a few weeks 🚀 They ship Worldwide, Paypal and Crypto are available, and you can use the coupon ILCAPOFOX for a 5% discount! Shop: For Europe I also recommend FreshMiners, they also ship quickly and you can use the coupon ILCAPOFOX3 for a 10€ discount! Shop: #DePIN #Anyone #Relay #Node #Privacy #Internet
0:13

Hi everyone, this week I finally received the new ANyONe Relay! ANyONe Protocol is an interesting DePIN focused on building a more decentralized internet and making our privacy more secure 💯 I'm still waiting to release a video because the new Dashboard isn't active yet but for those interested the most important points that I want to highlight are these: 1) The #Miner is Plug&Play so easy to install, just follow the initial steps and you're fine, once installed you don't have to do anything else. 2) There is no limit for IP but if for example you put 2 Relays in the same #IP it isn't certain that you will have double rewards, because they basically do the same job, to get more rewards it's better to use different IPs. 3) To make the Relay operational it's necessary to open port 9001 in your Modem. Without this operation the #mining will basically not start, so make sure you have a modem or connection that can open the ports. If you have the classic public IP you should definitely be able to do it 4) Currently the rewards arrive automatically every week, usually Friday, and I received 8 $ANYONE as first earnings 👍 Since the new #Dashboard isn't yet operational the #rewards aren't yet clear (which is why I am still waiting to release the video) and personally I expect there will be more 🔥 The only negative note that I have found so far in the project is that: ANYONE #token is only on Ethereum So unfortunately the fees for the swap or even future claims and sendings will not be low, I hope that the team in the future will also consider Arbitrum or Base for the rewards to Nodes 🙏 $ANYONE: Docs to learn more: If you are interested in the project you can order the Anyone on @ampchampment and you can receive it in a few weeks 🚀 They ship Worldwide, Paypal and Crypto are available, and you can use the coupon ILCAPOFOX for a 5% discount! Shop: For Europe I also recommend FreshMiners, they also ship quickly and you can use the coupon ILCAPOFOX3 for a 10€ discount! Shop: #DePIN #Anyone #Relay #Node #Privacy #Internet

Osvaldo

28,170 görüntüleme • 1 yıl önce

‼️ALERT: ETHEREUM'S TOP 1,000 WALLETS COULD BE DRAINED IN NINE DAYS WITHOUT A QUANTUM UPGRADE Google's quantum research team warns that a future fast-clock quantum computer could compromise Ethereum's 1,000 highest-value accounts in less than nine days. Those accounts collectively hold approximately 20.5 million $ETH. Bitcoin users can avoid exposing their public keys. Ethereum users cannot. The moment an Ethereum user sends their first transaction, their public key is permanently exposed on the blockchain, according to the report. It stays exposed indefinitely. The paper identifies five distinct quantum attack vectors on Ethereum. Among the most alarming is the threat to smart contract admin keys. Those keys control over $200 billion in stablecoins and tokenised real-world assets. Ethereum's Proof-of-Stake consensus mechanism also relies on quantum-vulnerable BLS signatures. A sufficiently advanced quantum attacker could use this to compromise the blockchain's finality itself. There is a silver lining. The Ethereum Foundation is already researching post-quantum alternatives. The paper suggests this gives Ethereum a potential advantage over Bitcoin in executing an orderly transition to quantum-safe cryptography.
0:15

‼️ALERT: ETHEREUM'S TOP 1,000 WALLETS COULD BE DRAINED IN NINE DAYS WITHOUT A QUANTUM UPGRADE Google's quantum research team warns that a future fast-clock quantum computer could compromise Ethereum's 1,000 highest-value accounts in less than nine days. Those accounts collectively hold approximately 20.5 million $ETH. Bitcoin users can avoid exposing their public keys. Ethereum users cannot. The moment an Ethereum user sends their first transaction, their public key is permanently exposed on the blockchain, according to the report. It stays exposed indefinitely. The paper identifies five distinct quantum attack vectors on Ethereum. Among the most alarming is the threat to smart contract admin keys. Those keys control over $200 billion in stablecoins and tokenised real-world assets. Ethereum's Proof-of-Stake consensus mechanism also relies on quantum-vulnerable BLS signatures. A sufficiently advanced quantum attacker could use this to compromise the blockchain's finality itself. There is a silver lining. The Ethereum Foundation is already researching post-quantum alternatives. The paper suggests this gives Ethereum a potential advantage over Bitcoin in executing an orderly transition to quantum-safe cryptography.

BSCN

45,635 görüntüleme • 4 ay önce

Thanks to an Android app called Liquid Glass, I can finally achieve an almost limitless Liquid Glass lock screen experience on One UI. It supports depth effects, lets you tweak all kinds of glass parameters, and even allows full font customization. You can use virtually any font you download, giving you an incredible level of freedom. The results are genuinely stunning. The biggest downside is that the setup is quite complicated. Adjusting the clock, in particular, takes a lot of steps instead of being something you can edit directly, so the learning curve is fairly steep. I really hope the developer can make the customization process more intuitive in future updates, and add more language options, especially Chinese, so more users can enjoy it.
0:10

Thanks to an Android app called Liquid Glass, I can finally achieve an almost limitless Liquid Glass lock screen experience on One UI. It supports depth effects, lets you tweak all kinds of glass parameters, and even allows full font customization. You can use virtually any font you download, giving you an incredible level of freedom. The results are genuinely stunning. The biggest downside is that the setup is quite complicated. Adjusting the clock, in particular, takes a lot of steps instead of being something you can edit directly, so the learning curve is fairly steep. I really hope the developer can make the customization process more intuitive in future updates, and add more language options, especially Chinese, so more users can enjoy it.

Ice Universe

51,021 görüntüleme • 1 ay önce

Why the internet needs Grokipedia? ➤ Wikipedia isn’t the neutral source of truth it once was. It’s been taken over by far-left activists and often used as a propaganda tool, not an unbiased encyclopedia. ➤ A lot of AIs today get their info from the internet, but the web is full of unfair views, wrong facts, and tons of junk. ➤ Grokipedia will be an open-source encyclopedia focused solely on TRUTH. ➤ Grokipedia won’t be controlled by any activist or political bias. ➤ It will be open-source and free for all to use, a truly public resource without limits. ➤ xAI’s ultimate mission is to understand the universe, and that requires honest, unfiltered information. Grokipedia is a crucial step toward that goal, because you can’t build a truthful AI on biased information. ➤ The vision is for Grokipedia to become the global standard for knowledge. One day, every person and every AI system could rely on it as a trusted source of truth. ➤ Seeking truth is the greatest mission of all and that’s what Grokipedia stands for. Grokipedia is coming.
0:15

Why the internet needs Grokipedia? ➤ Wikipedia isn’t the neutral source of truth it once was. It’s been taken over by far-left activists and often used as a propaganda tool, not an unbiased encyclopedia. ➤ A lot of AIs today get their info from the internet, but the web is full of unfair views, wrong facts, and tons of junk. ➤ Grokipedia will be an open-source encyclopedia focused solely on TRUTH. ➤ Grokipedia won’t be controlled by any activist or political bias. ➤ It will be open-source and free for all to use, a truly public resource without limits. ➤ xAI’s ultimate mission is to understand the universe, and that requires honest, unfiltered information. Grokipedia is a crucial step toward that goal, because you can’t build a truthful AI on biased information. ➤ The vision is for Grokipedia to become the global standard for knowledge. One day, every person and every AI system could rely on it as a trusted source of truth. ➤ Seeking truth is the greatest mission of all and that’s what Grokipedia stands for. Grokipedia is coming.

DogeDesigner

69,846 görüntüleme • 10 ay önce

Polymorphic moulding: a manufacturing method that forms parts using a grid of computer-controlled pins: Each pin can move up or down independently, so together they shape a surface that acts like a custom mould. Instead of building a fixed mould for every product, the machine quickly repositions the pins to match a new digital model. This means a mould can be created in minutes, used, and then reshaped again for the next design. Because the hardware stays the same and only its configuration changes: > no permanent tooling is needed > very little material is wasted > setup time between products is minimal The system essentially turns mould making into a programmable process. Engineers send a design file, the pins form the mould geometry, and the material is cast or formed inside it. Especially useful for rapid prototyping and customised manufacturing, where many different shapes must be produced in small quantities without rebuilding tools each time. Credit: ---- Weekly robotics and AI insights. Subscribe free:
0:14

Polymorphic moulding: a manufacturing method that forms parts using a grid of computer-controlled pins: Each pin can move up or down independently, so together they shape a surface that acts like a custom mould. Instead of building a fixed mould for every product, the machine quickly repositions the pins to match a new digital model. This means a mould can be created in minutes, used, and then reshaped again for the next design. Because the hardware stays the same and only its configuration changes: > no permanent tooling is needed > very little material is wasted > setup time between products is minimal The system essentially turns mould making into a programmable process. Engineers send a design file, the pins form the mould geometry, and the material is cast or formed inside it. Especially useful for rapid prototyping and customised manufacturing, where many different shapes must be produced in small quantities without rebuilding tools each time. Credit: ---- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

38,234 görüntüleme • 6 ay önce

While working on a new video with solutions to the previous one, I found ChatGPT's new UI struggles even more with concurrent updates: entries lose state and stick around for too long (see video). If this was a LiveView app, we would be getting so much flak.😅 --- I believe part of the problem here is having separate mutate and fetch requests on every deletion. The first fetch is cancelled when the second one comes up, causing items to stick around for longer. Many said yesterday that you could do the mutation and fetch as a single request, but that leads to other problems, such zombie entries. For example, imagine you delete link1 and link2 within a brief period of time. There is no guarantee the deletion order in the database will match the order the client receives the response, so you may end up with this: 1. (client) request to delete link1 sent 2. (client) request to delete link2 sent 3. (server) deletes link1 and loads a new list (includes link2) 4. (server) deletes link2 and loads a new list (no link1 or link2) 5. (client) receives link2 response 6. (client) receives link1 response So if you choose to use the latest response (link1), you brought link2 back to life. If you say you will use the response from the last request, events 3-4 can be swapped, and now you bring link1 back to life. Another way to solve this is by basically not allowing concurrent requests at all but that can affect the user experience drastically in other ways. Next week I should publish a video explaining how LiveView tackles this. Stay tuned!
0:10

While working on a new video with solutions to the previous one, I found ChatGPT's new UI struggles even more with concurrent updates: entries lose state and stick around for too long (see video). If this was a LiveView app, we would be getting so much flak.😅 --- I believe part of the problem here is having separate mutate and fetch requests on every deletion. The first fetch is cancelled when the second one comes up, causing items to stick around for longer. Many said yesterday that you could do the mutation and fetch as a single request, but that leads to other problems, such zombie entries. For example, imagine you delete link1 and link2 within a brief period of time. There is no guarantee the deletion order in the database will match the order the client receives the response, so you may end up with this: 1. (client) request to delete link1 sent 2. (client) request to delete link2 sent 3. (server) deletes link1 and loads a new list (includes link2) 4. (server) deletes link2 and loads a new list (no link1 or link2) 5. (client) receives link2 response 6. (client) receives link1 response So if you choose to use the latest response (link1), you brought link2 back to life. If you say you will use the response from the last request, events 3-4 can be swapped, and now you bring link1 back to life. Another way to solve this is by basically not allowing concurrent requests at all but that can affect the user experience drastically in other ways. Next week I should publish a video explaining how LiveView tackles this. Stay tuned!

José Valim

22,976 görüntüleme • 1 yıl önce

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.
0:20

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Pablo Vela

30,070 görüntüleme • 5 ay önce