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We just launched our biggest update ever 🚀 Introducing Fiscal Enterprise - Unparalleled Data Speed (all financials are updated within minutes of earnings) - Data Exporting - Click-Through Auditability (click directly into the source) - As Reported & Standardized View

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Fiscal.ai

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198,574 просмотров • 1 год назад •via X (Twitter)

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1/ We are speed running industrial robotics. It took us just 19 days from the first day of data collection to filming a 2.5-hour continuous run of our model autonomously breaking down industrial containers — zero human intervention. The data efficiency of our DVA model is fundamentally changing how fast we bring robots out of the lab and into the factory. Autonomous operation with 3 hours of data collection at a customer factory.
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1/ We are speed running industrial robotics. It took us just 19 days from the first day of data collection to filming a 2.5-hour continuous run of our model autonomously breaking down industrial containers — zero human intervention. The data efficiency of our DVA model is fundamentally changing how fast we bring robots out of the lab and into the factory. Autonomous operation with 3 hours of data collection at a customer factory.

Rhoda AI

26,315 просмотров • 6 месяцев назад

1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n
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1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n

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.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."
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.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

Jawwwn

1,105,305 просмотров • 24 дней назад

Beautiful new raster data layers are now available on Google Earth’s intuitive, no-code canvas. 🗺️👉 With the analytical power of Google Earth Engine directly integrated into Google Earth, geospatial analysis is as easy as point and click. Rolling out over the coming days, you will have access to ten new raster layers from Earth Engine's iconic data catalog, including: • Slope • Aspect • Land cover • and more It's never been easier to quickly combine raster data, vector data, imagery, and your own data to save time and make better decisions. Explore these new data layers, now.
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Beautiful new raster data layers are now available on Google Earth’s intuitive, no-code canvas. 🗺️👉 With the analytical power of Google Earth Engine directly integrated into Google Earth, geospatial analysis is as easy as point and click. Rolling out over the coming days, you will have access to ten new raster layers from Earth Engine's iconic data catalog, including: • Slope • Aspect • Land cover • and more It's never been easier to quickly combine raster data, vector data, imagery, and your own data to save time and make better decisions. Explore these new data layers, now.

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Introducing Docker 🐳 AI Models for local + private LLMs in VSCode 🤩 You can now use Docker as a local model provider in VSCode, JetBrains, Cursor, and soon Visual Studio Enterprise. With Docker Model Runner (v4.40+), run AI models locally on your machine... no data sharing, no cloud dependency 👏 Just update Docker, open CodeGPT, select the model, click "Download", and enjoy 👉 Here’s how to update Docker:
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Introducing Docker 🐳 AI Models for local + private LLMs in VSCode 🤩 You can now use Docker as a local model provider in VSCode, JetBrains, Cursor, and soon Visual Studio Enterprise. With Docker Model Runner (v4.40+), run AI models locally on your machine... no data sharing, no cloud dependency 👏 Just update Docker, open CodeGPT, select the model, click "Download", and enjoy 👉 Here’s how to update Docker:

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Introducing Ollama fully Integrated into VSCode with CodeGPT Now you can download open-source models from ollama with a single click and install them as code assistants in VSCode. In this case, I download the qwen2:0.5b (Qwen) model in under a minute, and it becomes fully functional with just one click. The era of locally running code assistants has begun! 🚀
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Introducing Ollama fully Integrated into VSCode with CodeGPT Now you can download open-source models from ollama with a single click and install them as code assistants in VSCode. In this case, I download the qwen2:0.5b (Qwen) model in under a minute, and it becomes fully functional with just one click. The era of locally running code assistants has begun! 🚀

Daniel San

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Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.
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Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

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There are over 90 robot gyms launching in China right now. 100+ robots per center. We are in the robot data gold rush. China's humanoid makers now ship 97% of all humanoids globally. The central government just put $138 billion into a robotics venture fund. And this is only the tip of the iceberg. Robots need an ungodly amount of data to work in the real world. The biggest bottleneck isn't hardware, it's data. So a whole industry appeared overnight to feed them. Welcome to our generation's gold rush: the robot data gold rush. We made a full video on it. We cover why it's such an insane business, how it actually works, the shady stuff (yes, sweatshops for robot data are a thing), and why this might be the completely wrong approach.
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There are over 90 robot gyms launching in China right now. 100+ robots per center. We are in the robot data gold rush. China's humanoid makers now ship 97% of all humanoids globally. The central government just put $138 billion into a robotics venture fund. And this is only the tip of the iceberg. Robots need an ungodly amount of data to work in the real world. The biggest bottleneck isn't hardware, it's data. So a whole industry appeared overnight to feed them. Welcome to our generation's gold rush: the robot data gold rush. We made a full video on it. We cover why it's such an insane business, how it actually works, the shady stuff (yes, sweatshops for robot data are a thing), and why this might be the completely wrong approach.

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13,810 просмотров • 1 месяц назад

Today we are launching SEC Agent v0 . A specialized AI agent purpose built to dig through SEC filings, 10-Ks, and earnings transcripts, to find you an edge. As a part of the larger Xynth agent suite, it can be paired directly with other data like options flow, darkpool feeds, politician trades, and of course, regular live market data. We are on a mission to equip every trader with the most powerful alpha generation tools. RT + Comment “Alpha” and we’ll send it to you
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Today we are launching SEC Agent v0 . A specialized AI agent purpose built to dig through SEC filings, 10-Ks, and earnings transcripts, to find you an edge. As a part of the larger Xynth agent suite, it can be paired directly with other data like options flow, darkpool feeds, politician trades, and of course, regular live market data. We are on a mission to equip every trader with the most powerful alpha generation tools. RT + Comment “Alpha” and we’ll send it to you

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We've seen our fair share of uphill battles while building on Bittensor. But through it all, we've had 0 days of downtime. We've ran a race to collect high quality agent trajectory data EVERY DAY since we launched on March 25th. How did we manage that? Join Seth Schilbe as he walks us through the details of ORO's subnet architecture.

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Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇
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Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇

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“There are no Ethernet switches in the picture. All the data is moved through the galaxies themselves and through simple Ethernet cables which is how we achieve our effective cost as well as scale. And it’s really really critical for performance because that’s what gives us our low latency and high throughput. ” Jasmina Vasiljevic Learn how Tenstorrent Galaxy scales into superclusters.
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“There are no Ethernet switches in the picture. All the data is moved through the galaxies themselves and through simple Ethernet cables which is how we achieve our effective cost as well as scale. And it’s really really critical for performance because that’s what gives us our low latency and high throughput. ” Jasmina Vasiljevic Learn how Tenstorrent Galaxy scales into superclusters.

Tenstorrent

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We’re excited to finally introduce Kled Special Tasks, the final major feature included in the V2 app update. Users will now have access to a fully interactive terminal where they can view and complete domain specific upload tasks directly from enterprise buyers. These tasks can be region locked and person specific. For example, PhD students at Stanford might be prompted to upload their coursework or research materials and get paid for it. Our first domain specific task will focus on homework collection from high school and college students across Europe and the United States. Students will verify their emails and academic credentials directly within the app. We’ve built labeling workflows to ensure all uploaded content meets our criteria, and participants will receive weighted payouts based on the value of their submissions. We’ve already built a network of over 3,800 students from Stanford, MIT, UIUC, Rutgers, and Duke who will be actively onboarded to contribute content. Kled will work hand in hand with our research division, HADES, to justify the large scale purchase of this homework content. Several enterprise buyers have already expressed interest, each confirming that academic data from students represents a growing multi year industry requiring a continuous flow of fresh material. Kled Special Tasks also gives us the ability to internally identify valuable content types, issue calls for specific datasets, and collect 1,000-2,000 unique samples per task. We can then package these datasets into specialized data packs that our sales team will use to pitch directly to AI labs and enterprise clients with matching data needs. This will be one of our most powerful tools for expanding Kled’s buyer network. All of this will be fully available in the V2 update. We’re excited to show just how advanced our segmentation and data validation software has become as we bring this release to market.
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We’re excited to finally introduce Kled Special Tasks, the final major feature included in the V2 app update. Users will now have access to a fully interactive terminal where they can view and complete domain specific upload tasks directly from enterprise buyers. These tasks can be region locked and person specific. For example, PhD students at Stanford might be prompted to upload their coursework or research materials and get paid for it. Our first domain specific task will focus on homework collection from high school and college students across Europe and the United States. Students will verify their emails and academic credentials directly within the app. We’ve built labeling workflows to ensure all uploaded content meets our criteria, and participants will receive weighted payouts based on the value of their submissions. We’ve already built a network of over 3,800 students from Stanford, MIT, UIUC, Rutgers, and Duke who will be actively onboarded to contribute content. Kled will work hand in hand with our research division, HADES, to justify the large scale purchase of this homework content. Several enterprise buyers have already expressed interest, each confirming that academic data from students represents a growing multi year industry requiring a continuous flow of fresh material. Kled Special Tasks also gives us the ability to internally identify valuable content types, issue calls for specific datasets, and collect 1,000-2,000 unique samples per task. We can then package these datasets into specialized data packs that our sales team will use to pitch directly to AI labs and enterprise clients with matching data needs. This will be one of our most powerful tools for expanding Kled’s buyer network. All of this will be fully available in the V2 update. We’re excited to show just how advanced our segmentation and data validation software has become as we bring this release to market.

Kled AI

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Your AI Agents can only analyze 1GB of data Enterprises have 1,000,000x as much data We fixed that... It took 3 years and building an entirely new execution environment to pull this off. TextQL lets AI agents analyze your ENTIRE enterprise data - not just the 0.0001% they can handle today. Here's what makes it so different: 1. ALL your data, not samples. AI today: 1GB demos. Your reality: 350TB across 47 systems. TextQL: Everything. At once. 2. Real enterprise questions.ChatGPT writes poems. We answer why renewal rates dropped 3% after ERP upgrades. 3. No more sandbox limitations.Everyone else: Jupyter notebook with 10GB RAM. Us: A data center. ------------------------------ In 2021, I watched Fortune 500s spend millions on AI that could only analyze data that fits on a USB stick. Today: - We process 10+ petabytes daily - Our biggest customer analyzes 100TB in real-time - Average query spans 50+ data sources And we just proved what everyone said was impossible - AI that can actually build a digital twin of your enterprise ------------------------------ To celebrate, we're giving away free data architecture assessments. We'll show you exactly how much of your data is invisible to AI right now (spoiler: it's probably 99.9%). Comment "TextQL" and we'll send you the assessment link.
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Your AI Agents can only analyze 1GB of data Enterprises have 1,000,000x as much data We fixed that... It took 3 years and building an entirely new execution environment to pull this off. TextQL lets AI agents analyze your ENTIRE enterprise data - not just the 0.0001% they can handle today. Here's what makes it so different: 1. ALL your data, not samples. AI today: 1GB demos. Your reality: 350TB across 47 systems. TextQL: Everything. At once. 2. Real enterprise questions.ChatGPT writes poems. We answer why renewal rates dropped 3% after ERP upgrades. 3. No more sandbox limitations.Everyone else: Jupyter notebook with 10GB RAM. Us: A data center. ------------------------------ In 2021, I watched Fortune 500s spend millions on AI that could only analyze data that fits on a USB stick. Today: - We process 10+ petabytes daily - Our biggest customer analyzes 100TB in real-time - Average query spans 50+ data sources And we just proved what everyone said was impossible - AI that can actually build a digital twin of your enterprise ------------------------------ To celebrate, we're giving away free data architecture assessments. We'll show you exactly how much of your data is invisible to AI right now (spoiler: it's probably 99.9%). Comment "TextQL" and we'll send you the assessment link.

ethan ding 📊

162,238 просмотров • 1 год назад

I don't think people understand that one of the biggest real-world use cases for Crypto it is happening - right now. FedEx, just sat down with Mance Harmon of $HBAR and explained why they chose Hedera to track millions of packages a day, to 220 countries. Dale Christie, Blockchain Strategist at FedEx said in 2018, FedEx tried building its own blockchain... But it wouldn’t scale so they chose Hedera. Why? Every package generates hundreds - sometimes thousands - of data points, and Hedera enables the key facts to move through customs at the speed of data, not paper. “When you are a trusted shipper and I am a trusted receiver… that data can move through customs at the speed of data and keep it moving.” One of the largest logistics companies in the world just publicly said Hedera is the foundation they’re building on. Hedera is the most undervalued token imo. and this is one of the biggest use cases Crypto's ever seen.
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I don't think people understand that one of the biggest real-world use cases for Crypto it is happening - right now. FedEx, just sat down with Mance Harmon of $HBAR and explained why they chose Hedera to track millions of packages a day, to 220 countries. Dale Christie, Blockchain Strategist at FedEx said in 2018, FedEx tried building its own blockchain... But it wouldn’t scale so they chose Hedera. Why? Every package generates hundreds - sometimes thousands - of data points, and Hedera enables the key facts to move through customs at the speed of data, not paper. “When you are a trusted shipper and I am a trusted receiver… that data can move through customs at the speed of data and keep it moving.” One of the largest logistics companies in the world just publicly said Hedera is the foundation they’re building on. Hedera is the most undervalued token imo. and this is one of the biggest use cases Crypto's ever seen.

Mark

44,717 просмотров • 7 месяцев назад

We all know about Jihadists. But there's a second group of Islamists that are just as dangerous. Think of them like progressives. They infiltrate and destroy from within. And they're already in our schools, the halls of Congress, and now the Mayor's office of our biggest city.
3:46

We all know about Jihadists. But there's a second group of Islamists that are just as dangerous. Think of them like progressives. They infiltrate and destroy from within. And they're already in our schools, the halls of Congress, and now the Mayor's office of our biggest city.

Glenn Beck

99,345 просмотров • 10 месяцев назад

Data centers in space are a "no brainer." SpaceX's 1st employee Tom Mueller (Tom Mueller) on Elon Musk's plan to move compute into space: "It makes the most sense of anything to move to space. All you need as an input is power, and all you have as an output is data." "You move it to space, you have all the power you would ever need, and you transmit that data back down on a terawatt laser beam—it's solved. It's just so simple."
1:42

Data centers in space are a "no brainer." SpaceX's 1st employee Tom Mueller (Tom Mueller) on Elon Musk's plan to move compute into space: "It makes the most sense of anything to move to space. All you need as an input is power, and all you have as an output is data." "You move it to space, you have all the power you would ever need, and you transmit that data back down on a terawatt laser beam—it's solved. It's just so simple."

Molly O’Shea

358,064 просмотров • 4 месяцев назад

When choosing a blockchain solution for our game deployment, we thoroughly assessed all the options in the web3 space 🌏 Immutable emerged as the unparalleled choice to achieve our milestone of reaching one million players with Ponchiqs due to its scalable and gasless ecosystem with the best onboarding through Passport🚀 So we are proud to announce our partnership with Immutable 🧸🥰🥂
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When choosing a blockchain solution for our game deployment, we thoroughly assessed all the options in the web3 space 🌏 Immutable emerged as the unparalleled choice to achieve our milestone of reaching one million players with Ponchiqs due to its scalable and gasless ecosystem with the best onboarding through Passport🚀 So we are proud to announce our partnership with Immutable 🧸🥰🥂

Ponchiqs

17,579 просмотров • 2 лет назад