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Rynus Ecosystem - The Power of Accessibility ⚡ What if accessing top-notch computing resources was as easy as a few clicks? With Rynus, it is! Rynus Cloud simplifies job submission, while Rynus Transfer ensures rapid data uploads. Turn idle GPUs into income as a Rynus Worker, and gain insights... show more

Rynus

19,773 subscribers

21,352 次观看 • 1 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

9 条评论

Jisoyung CLONE 的头像

Jisoyung CLONE1 年前

Lfg

Avijit Shit 的头像

Avijit Shit1 年前

Rynus to the top

PACHAN "(✸,✸)" 的头像

PACHAN "(✸,✸)"1 年前

wow

New games today X 的头像

New games today X1 年前

thank for sharing bro

M-Lyn 的头像

M-Lyn1 年前

Moveee

New games today X 的头像

New games today X1 年前

keep it up

M-Lyn 的头像

M-Lyn1 年前

😚😚😚

kong vs drekula Fardog 🦙🔥 的头像

kong vs drekula Fardog 🦙🔥1 年前

Nice project

Fanani | CLONE 的头像

Fanani | CLONE1 年前

nice project

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𝐖𝐓𝐅 𝐢𝐬 𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠?! 🤔 An indexer's job is to process and organize the raw data created on-chain, into a format that's easily accessible and understandable, and then create structured databases that can be queried efficiently by developers so dApps and services can be built and maintained. 🗄️ For example, just think about your favourite web3 marketplace, exchange, game, analytics dashboard etc….the data on those sites is provided by indexers.👌 HOWEVER, on Bitcoin, 99.9% of indexers are centralised, so as it stands, we all have to just trust their version(s) of ‘the truth’! 👀 ....."𝘵𝘳𝘶𝘴𝘵 𝘶𝘴 𝘣𝘳𝘰, 𝘸𝘦 𝘸𝘰𝘯'𝘵 𝘤𝘩𝘢𝘯𝘨𝘦 𝘰𝘳 𝘮𝘢𝘯𝘪𝘱𝘶𝘭𝘢𝘵𝘦 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢, 𝘸𝘦 𝘱𝘳𝘰𝘮𝘪𝘴𝘦!" 😬 At this point, you should pause for a second and ask yourself this one question: 🔽 What was the point of Satoshi creating such a perfectly decentralised data creator like Bitcoin, if we then have to trust one company’s interpretation of that data?! 🙈 This is why 𝐓𝐫𝐚𝐜 𝐂𝐨𝐫𝐞 from @trac_btc is SO vital! 🙏 It is #Bitcoin’s fully decentralised indexer, and it will make on-chain data reliably accessible through its thousands of distributed validator nodes, that MUST first reach consensus, similarly to Bitcoin when it creates the raw data. 🤝 Importantly, those who run validator nodes on their device, and help to support the decentralisation of the Trac network, will be rewarded with token APY! 🪙 Just like for those who run Bitcoin nodes, if you contribute to the decentralisation of the Trac network, you should be rewarded! 🙌 Decentralised indexers are one of the most fundamentally important pieces of infrastructure within a blockchain ecosystem, and they play a crucial role in fostering a scalable and secure ecosystem for blockchain-based economies to thrive and grow! 📈 𝐑𝐞𝐦𝐢𝐧𝐝𝐞𝐫: If you'd like to own/run your own Trac Validator Node, we are currently running regular rounds, with 300 Validator Licences purchasable per round, and with increasing price points. 📈 Follow @trac_btc to stay up to date with the sales rounds in the coming weeks and months 🔔

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Kled AI

52,199 次观看 • 11 个月前

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs

Unstructured

21,913 次观看 • 2 年前

$People in Utah have started asking us an important question, when you build these power facilities for AI data centers, can some of that energy be sold back into the local grid? The answer is yes. That completely changes the conversation. Instead of being seen as “the evil data center guys,” we can help power Utah’s grid while also building critical AI infrastructure for the future. And despite the headlines, we’re not building the world’s largest data center overnight. We’re starting with just 1.5 gigawatts with 1 gigawatt of net IT load a small fraction of the long-term proposal and proving the model first. And it’s not a 40,000 acre data center. Our project a full build out will span 10-13k acres 7,500 of which will be preserved. We want the people of Box Elder County to come see it themselves, the EPA air quality standards, the water permits, the noise compliance, and how the project actually operates before anything scales further.$

People in Utah have started asking us an important question, when you build these power facilities for AI data centers, can some of that energy be sold back into the local grid? The answer is yes. That completely changes the conversation. Instead of being seen as “the evil data center guys,” we can help power Utah’s grid while also building critical AI infrastructure for the future. And despite the headlines, we’re not building the world’s largest data center overnight. We’re starting with just 1.5 gigawatts with 1 gigawatt of net IT load a small fraction of the long-term proposal and proving the model first. And it’s not a 40,000 acre data center. Our project a full build out will span 10-13k acres 7,500 of which will be preserved. We want the people of Box Elder County to come see it themselves, the EPA air quality standards, the water permits, the noise compliance, and how the project actually operates before anything scales further.

Kevin O'Leary aka Mr. Wonderful

71,642 次观看 • 4 个月前

I am very excited to release GeoLibre v1.0 🌍 GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. In this video, I walk through GeoLibre 1.0 step by step: how to install and run it, load vector and raster data, stream gigabyte-scale cloud-native datasets without downloading them, edit attributes and geometry, work with PMTiles, 3D tiles, LiDAR point clouds, and DuckDB, and share your projects as static links. GeoLibre is built on MapLibre GL JS, React + TypeScript, Tauri (Rust), DuckDB, and Try it: GitHub: Full video tutorial: If you have feature requests or run into bugs, leave a comment or open an issue on GitHub. And if you find GeoLibre useful, please give it a star. #GIS #OpenSource #Geospatial #MapLibre #Python #DataVisualization #GeoLibre

I am very excited to release GeoLibre v1.0 🌍 GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. In this video, I walk through GeoLibre 1.0 step by step: how to install and run it, load vector and raster data, stream gigabyte-scale cloud-native datasets without downloading them, edit attributes and geometry, work with PMTiles, 3D tiles, LiDAR point clouds, and DuckDB, and share your projects as static links. GeoLibre is built on MapLibre GL JS, React + TypeScript, Tauri (Rust), DuckDB, and Try it: GitHub: Full video tutorial: If you have feature requests or run into bugs, leave a comment or open an issue on GitHub. And if you find GeoLibre useful, please give it a star. #GIS #OpenSource #Geospatial #MapLibre #Python #DataVisualization #GeoLibre

Qiusheng Wu

39,593 次观看 • 3 个月前

Spark is starting to become real. A self-evolving intelligence, beyond a persistent memory, with multiple ways it learns and evolves Calibrate the intelligence while building, talking to it, sharing the things you love as content, UI, art, etc., While it learns constantly learns about the tools you use: Their strengths, shortcomings, and how to better use everything in its arsenal, as well as doing the same with the specialized agents and teams it orchestrates. In a few days, it gathered 30000 memories and 500+ insights, and continued to calibrate itself with me, which will speed up how we can vibe code the next projects by Spark doing at evolved understandings. Super excited to see how it evolves and can't wait to bring it to everyone. Already integrated it with many IDEs and LLMs. It also works on your local machine. As this conversation was recorded with Ollama qwen 2.5 running on my PC.

Spark is starting to become real. A self-evolving intelligence, beyond a persistent memory, with multiple ways it learns and evolves Calibrate the intelligence while building, talking to it, sharing the things you love as content, UI, art, etc., While it learns constantly learns about the tools you use: Their strengths, shortcomings, and how to better use everything in its arsenal, as well as doing the same with the specialized agents and teams it orchestrates. In a few days, it gathered 30000 memories and 500+ insights, and continued to calibrate itself with me, which will speed up how we can vibe code the next projects by Spark doing at evolved understandings. Super excited to see how it evolves and can't wait to bring it to everyone. Already integrated it with many IDEs and LLMs. It also works on your local machine. As this conversation was recorded with Ollama qwen 2.5 running on my PC.

Meta Alchemist

244,721 次观看 • 7 个月前

We just made the Warp API and MCP completely free. Most HR platforms make you sit through a sales call and a questionnaire just to reach your own data. We think that's backwards. You can pull your entire company and employee data, time off, compensation, payroll, IT, basically anything in Warp. Build your own dashboards. Connect Warp to your internal systems. Run your own automations on top of your data. We built this because our customers are technical and AI-native. They don't want to stare at a dashboard. They want to build on top of their data. Legacy platforms silo it on purpose, and the API, when it exists at all, is a pricey add-on buried behind a sales process. We shipped the API eight weeks ago, and some of our top customers have already built real tools on top of Warp. Today we're removing the last barrier to doing that. If you're an AI-native founder, exec, CIO, or Chief People Officer who's been duct taping this together, the API and MCP are now included free on most plans. On Enterprise, a small fee unlocks the more powerful version with uptime SLAs, webhooks, and audit logs. Your data should be yours. Excited to see what you build with it.

We just made the Warp API and MCP completely free. Most HR platforms make you sit through a sales call and a questionnaire just to reach your own data. We think that's backwards. You can pull your entire company and employee data, time off, compensation, payroll, IT, basically anything in Warp. Build your own dashboards. Connect Warp to your internal systems. Run your own automations on top of your data. We built this because our customers are technical and AI-native. They don't want to stare at a dashboard. They want to build on top of their data. Legacy platforms silo it on purpose, and the API, when it exists at all, is a pricey add-on buried behind a sales process. We shipped the API eight weeks ago, and some of our top customers have already built real tools on top of Warp. Today we're removing the last barrier to doing that. If you're an AI-native founder, exec, CIO, or Chief People Officer who's been duct taping this together, the API and MCP are now included free on most plans. On Enterprise, a small fee unlocks the more powerful version with uptime SLAs, webhooks, and audit logs. Your data should be yours. Excited to see what you build with it.

Ayush S

730,614 次观看 • 3 个月前

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:

Jim Fan

332,238 次观看 • 2 年前