Загрузка видео...

Не удалось загрузить видео

На главную

Cardano Relays & Solana Validators I mapped out Cardano relays using geolocation data and plotted them onto a customized globe. The animation simulates real-time connections between relays, visually demonstrating how they interact within the network. A total of 2,516 relays responded, creating a dynamic and detailed visualization of Cardano's...

75,615 просмотров • 1 год назад •via X (Twitter)

Комментарии: 11

Фото профиля Dave
Dave1 год назад

I could also include their RPC nodes in this simulation, however most are in the same data centers, there is a few that are not but visually I don't think it will make a massive difference.

Фото профиля St₳ke with Pride 🌈 SPO & DRep
St₳ke with Pride 🌈 SPO & DRep1 год назад

And this doesn't included the wallets (Daedalus) in homes (not data centers), which are full P2P relays. 💪

Фото профиля Dave
Dave1 год назад

Great point!

Фото профиля Dave
Dave1 год назад

Hey @CoinDesk @Utoday_en look at how decentralized and globally operational Cardano is. Run and governed by the people for the people. A secure, reliable, sustainable blockchain foundation that's globally inclusive and open for business.

Фото профиля Dave
Dave1 год назад

I've got another idea to take this the next step, no idea if it will work but will have a go when I get some time. Solana would actually really benefit from being a Cardano partner chain right?

Фото профиля Dave
Dave1 год назад

My inspiration here was good user experience in Solana explorers, I don't like Solana for many reasons but something they do well is user experience. I wanted Cardano to be the best visually I could make it, I had this design in my head and wanted to make it the most appealing I can for non technical users, bringing in that fun but intriging element. Whilst also showcasing how amazing Cardano really is. I see this every day as an SPO but others don't. Had great fun doing it, much more to come. :) watch this space.

Фото профиля ₳lex Maaza
₳lex Maaza1 год назад

Dave, this is such a powerful visualisation. I'd love to see how other L1s compare with Cardano

Фото профиля Dave
Dave1 год назад

Appreciate it Alex, me too, if we can find data happy to simulate it!

Фото профиля St₳ke with Pride 🌈 SPO & DRep
St₳ke with Pride 🌈 SPO & DRep1 год назад

300TB ledger vs. 0.01TB ledger

Фото профиля Tim Harrison
Tim Harrison1 год назад

Impressive stuff Dave 🫶

Фото профиля Dave
Dave1 год назад

Thanks Tim, really means a lot. 🤝

Похожие видео

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

85,012 просмотров • 1 год назад

Most people think Rerun is a visualization tool. In reality, it's a database masquerading as a visualizer. I wanted to showcase this functionality by building a full data pipeline consisting of: ingestion → baseline method → eval → finetuning for SLAM on egocentric data. I'll eventually extend this to the rest of my ego/exo datasets, but I wanted to start with a smaller bunch of datasets first. Rerun allows you to expose your saved .rrd files to a catalog where you store datasets. You can query, filter, and join them like any database using DataFusion under the hood. These are the same .rrd files that are automatically generated whenever you visualize anything in Rerun and decide to save it to disk. I brought in 109 VSLAM-LAB sequences across 14 datasets into the Rerun catalog as an example. These include 7Scenes, Euroc, eth3d, and others. Now I can query them with segment_table, filter_segments, and filter_contents instead of parsing CSVs and YAML files. With a strong set of ground-truth datasets for SLAM, baseline additions become nearly automatic with agents like Opus/Codex. This unification of data and visualization is imo the largest missing part for Physical AI. Visualization becomes a natural byproduct of having your data properly structured and queryable. The catalog API is what makes it a database, not just a viewer. I initially focused on VSLAM-LAB data, but I'll migrate all the egoexo data to this format in the coming days to really show just how useful this is.

Pablo Vela

34,937 просмотров • 2 месяцев назад

Perplexity CEO Aravind Srinivas on the biggest threat to the data center industry: It's not competition. It's not regulation. It's decentralisation. "The biggest threat to a data center is if the intelligence can be packed locally on a chip that's running on the device and then there's no need to inference all of it on like one centralized data center." He outlines how this could work in practice. Personalisation doesn't necessarily require on-device model training. Retrieval augmented generation, tool calls, and local data can already tailor AI to individual users. But the real unlock? Test time training. Aravind Srinivas describes a future where AI lives on your device, watches how you work and gradually automates your repetitive tasks. "Imagine we crack test time training where the AI watches tasks you repeatedly do on your local system, adapts to you over time and starts automating a lot of the things you do." The key insight: in this model, the intelligence belongs to you. It's your data, your device, your personalised AI brain. And if that future arrives, the economics of centralised infrastructure start to collapse. "That really disrupts the whole data center industry. It doesn't make sense to spend all this money, 500 billion, 5 trillion, whatever on building all the centralized data centers across the world that do a lot of the intelligence workloads for people." The companies spending trillions on centralised infrastructure may want to rethink where intelligence actually needs to live.

Big Brain AI

90,102 просмотров • 5 месяцев назад

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!

The Graph

425,326 просмотров • 2 лет назад

We just launched a major new Data Engineering Professional Certificate on Coursera! Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. If you're interested in learning this skill, please check out this 4-course sequence, which is designed to make you job-ready to be a Data Engineer. This is a new specialization taught by Joe Reis, the co-author of the best-selling book “Fundamentals of Data Engineering," in collaboration with AWS. (Disclosure, I serve on Amazon's board.) For many AI systems, data engineering is 80% of the work, and modeling is 20%. But people’s attention on these two topics is often flipped. This makes the job of the data engineer particularly important. In this professional certificate, you'll learn foundational data engineering skills while implementing modern data architectures using open-source tools: - Learn the key steps of the data lifecycle, to generate, ingest, store, transform, and serve data. - Learn to align with organizational goals to design the data pipeline right for your business' needs. - Understand how to make necessary trade-offs between speed, scalability, security, and cost. Joe has distilled into this specialization decades of experience helping startups and large companies with data infrastructure. He is also joined by 17 other industry leaders in the data field, who will help you learn in-demand skills for the growing field of data engineering. Please sign up here:

Andrew Ng

118,937 просмотров • 1 год назад

🚨 Georgia Power is forcing a family off their farm with eminent domain for a Data Center “I'm fighting for survival of my cattle farm. I'm here because a massive data center was approved just a couple of miles from my land — and I'm being hounded by Georgia Power for an easement to build transmission lines through my property for the data center” “I'm a local farmer, not an industrial developer. These 500-kV lines aren't for me. They are for the data centers that the boards and surrounding counties continue to approve. I have mail from lawyers stacking up on my kitchen table wanting to take my case because they know my land is being targeted for eminent domain — These easements are permanent. They affect my ability to graze my cattle, they lower my property value, and they destroy the rural character of this county forever. This board makes decisions to approve these massive, massive projects, but it's residents like me, young people trying to build a life here, who pay the price. You're voting to turn our farms into a network of high-voltage wires and noisy industrial buildings. I'm asking you to realize the real-world impacts of your votes. Every time you say yes to a data center, you're saying no to a local farmer. We aren't just numbers on a map. We are the future of the county, and right now you're making that future impossible.“ This data center project affects over 330 private properties. Georgia Power says it will negotiate purchases and easements and use eminent domain Georgia Power claims its to strengthen the grid for the growing energy demand in Georgia (due to many new data centers) The lines are widely linked to Project Sail This isn’t a small operation. Project Sail is a $17 billion hyperscale data center campus by Prologis, Atlas that includes 9 massive buildings totaling up to 4.34 million square feet on 829 acres. It will demand hundreds of megawatts of continuous power equivalent to what a small city uses We cannon allow data centers to take priority over farmers

Wall Street Apes

434,815 просмотров • 2 месяцев назад

Data Centers are coaching their workers on how to lie to the public and their family about how Data Center water usage is actually a good thing “I work at an architecture firm that mainly does data centers, and just to give you all an idea of how out of touch the people making these things are. Today we had a meeting where we were essentially asked to defend the water usage of data centers to our friends and family in conversation at the dinner table or online. They provided them with a graph to break down water usage (included) but I’ll go over it The chart compares “domestic water usage” (toilets, sinks, etc.) for a typical-sized closed-loop data center of 600,000 gallons per year against other things of “equivalent square footage”: Cornfield: 5.85 million gallons Vineyard: 5.60 million Golf course: 5.45 million Peanut farm: 2.88 million Movie theater: 935,000 5 households: 730,000 They make the point that “Data centers aren’t the big water users you think” But that’s not really true “It's still using an incredible amount of water — and the key piece of evidence here was when someone gets pissed at a data center's water usage, ask them if they like golf. And when they say yes, let them know that a golf course uses 9 times as much water much water as a data center. That'll shut them up, because no one disapproves of golf courses using an exorbitant amount of water…” Not to mention, this is incredibly misleading The 600,000 gallons is mostly just building operations of things like sinks and bathrooms. It downplays or completely excludes the main water use, which is cooling the servers More fake propaganda

Wall Street Apes

53,969 просмотров • 1 месяц назад