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NEW: 🇹🇭Bitcoin developers from Thailand launch first Visual Raw Transaction Builder & Script Debugger 👀 Meet rawBit - an interactive tool to build and understand Bitcoin transactions visually. • Connect predefined nodes → see every byte update live • Step through script execution with live stack view • Full...

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What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.
1:16

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

Supra

83,548 görüntüleme • 2 yıl önce

BREAKING: Introducing All Access from Every 🪨, our new membership tier for the best builders in AI All Access subs get the Builder Pack which includes $7,000 in credits and free usage to the models + tool stack we use Every 🪨. All Access subscribers get: - $1,000 in Codex / @ChatGPTapp for Work credits - 12 months free of Cursor Pro+ - $4,000 in PostHog credits including self-driving to automatically fix bugs and identify issues in your production app - 1 year free of Framer - 6 months free of Notion And much more! (Did I mention $1,000 in Codex credits? It's time to build!) Get all access: Why All Access and the Builder Pack This is the best time in history to build something. For a long time, it’s been possible to one-shot impressive demos, but they’d fall flat the minute they hit production. But the release of GPT-5.6-Sol and Fable 5 heralds a new era: Everyone can build, launch, and maintain the software that they’ve always dreamed of. Everyone is a builder now. There’s just one catch: Building with AI is very expensive. (Ask me how I know.) (Alright, I’ll tell you. I accidentally used 2 billion tokens overnight this week on a big GPT-5.6-Sol run. Worth it.) This is unique in the history of technology. For most of the personal computing era, a billionaire and a solo builder could buy essentially the same top-of-the-line Mac. AI changes that: The more tokens you can afford, the more you can make. And we want to make that accessible to more people. That’s why the main feature of our new All Access plan is the Builder Pack: more than $7,000 in credits and discounts on the full stack we use to run Every, from idea to production—Codex, Claude, PostHog, Render, Gemini, FLORA, and more. Early-bird membership is only $500/year for the next 24 hours—and the Codex credits alone are worth $1,000. (I could’ve used it for my overnight run this week.) Now we’re handing it to you. Get all access: Meet the Builder Pack It's got more than $7,000 in offers from 10 of the AI products we use to write, design, build, and run Every 🪨: BUILD - $1,000 in Codex credits plus one month of ChatGPT for business - Twelve months free of Cursor Pro+ - One month free of Claude Max - Three months free of Google AI Pro DESIGN - One year free of Framer Pro - One month free of FLORA © Max HOST - $300 in Render credits IMPROVE - $4,000 in PostHog credits - Six months free of Notion Business - Six months free of AgentMail We rely on these every day, and we tried to put together a package that helps you comprehensively for each part of the process of building and running software in AI. What comes with All Access - Everything in an existing paid Every membership: our daily writing, guides, camps, and software like Monologue, Cora, Sparkle, and Spiral - The Builder Pack, with more than $7,000 in partner offers - Unlimited email accounts use of Cora and unlimited Spiral usage - Members-only programming with me and the Every team and me Get All Access:
2:14

BREAKING: Introducing All Access from Every 🪨, our new membership tier for the best builders in AI All Access subs get the Builder Pack which includes $7,000 in credits and free usage to the models + tool stack we use Every 🪨. All Access subscribers get: - $1,000 in Codex / @ChatGPTapp for Work credits - 12 months free of Cursor Pro+ - $4,000 in PostHog credits including self-driving to automatically fix bugs and identify issues in your production app - 1 year free of Framer - 6 months free of Notion And much more! (Did I mention $1,000 in Codex credits? It's time to build!) Get all access: Why All Access and the Builder Pack This is the best time in history to build something. For a long time, it’s been possible to one-shot impressive demos, but they’d fall flat the minute they hit production. But the release of GPT-5.6-Sol and Fable 5 heralds a new era: Everyone can build, launch, and maintain the software that they’ve always dreamed of. Everyone is a builder now. There’s just one catch: Building with AI is very expensive. (Ask me how I know.) (Alright, I’ll tell you. I accidentally used 2 billion tokens overnight this week on a big GPT-5.6-Sol run. Worth it.) This is unique in the history of technology. For most of the personal computing era, a billionaire and a solo builder could buy essentially the same top-of-the-line Mac. AI changes that: The more tokens you can afford, the more you can make. And we want to make that accessible to more people. That’s why the main feature of our new All Access plan is the Builder Pack: more than $7,000 in credits and discounts on the full stack we use to run Every, from idea to production—Codex, Claude, PostHog, Render, Gemini, FLORA, and more. Early-bird membership is only $500/year for the next 24 hours—and the Codex credits alone are worth $1,000. (I could’ve used it for my overnight run this week.) Now we’re handing it to you. Get all access: Meet the Builder Pack It's got more than $7,000 in offers from 10 of the AI products we use to write, design, build, and run Every 🪨: BUILD - $1,000 in Codex credits plus one month of ChatGPT for business - Twelve months free of Cursor Pro+ - One month free of Claude Max - Three months free of Google AI Pro DESIGN - One year free of Framer Pro - One month free of FLORA © Max HOST - $300 in Render credits IMPROVE - $4,000 in PostHog credits - Six months free of Notion Business - Six months free of AgentMail We rely on these every day, and we tried to put together a package that helps you comprehensively for each part of the process of building and running software in AI. What comes with All Access - Everything in an existing paid Every membership: our daily writing, guides, camps, and software like Monologue, Cora, Sparkle, and Spiral - The Builder Pack, with more than $7,000 in partner offers - Unlimited email accounts use of Cora and unlimited Spiral usage - Members-only programming with me and the Every team and me Get All Access:

Dan Shipper 📧

183,025 görüntüleme • 1 ay önce

Meet MASHA 🤹 – The First AVM-Powered Agent 🪆 We are proud to introduce MASHA, the first autonomous agent powered by the Aither AVM. MASHA is an example of what’s possible in the Aither ecosystem, where you can create your own AI agents through two pathways: 1. No-Code Launchpad: Anyone, even without coding skills, can easily create, launch, and invest in autonomous agents. 2. Developer Path: Developers can use our upcoming open-source GitHub framework to build fully custom agents directly on AVM, leveraging advanced functionality and flexibility. MASHA’s daily live streams showcase her advanced capabilities and demonstrate the future of AI-powered agents. She streams every day for 1 hour at 12:00 UTC. 📍 Stream on X now: MASHA’s Current Capabilities 🔹 Posting on X: Engaging with the community and sharing updates. 🔹 Logs Terminal: Displays real-time AVM code execution—“her brain” in action. 🔹 Live Streaming: • Fully interactive sessions. • Screen Broadcasting: Sharing tasks and workflows in real time. • Comments Replying: Responding dynamically to audience messages. 🔹 Voice Replying with Lip Sync: Converts text responses into synchronized voice outputs. 🔹 Interactive Movement: The world’s first agent capable of fulfilling movement requests—ask her to dance, fight a dragon, or perform other actions. Upcoming Features & Integrations 🔗 Platform Integrations: Expanding to TikTok, Instagram, Telegram, Warpcast. 💼 On-Chain Wallet Management: Secure and autonomous. 🎨 NFT Memory Albums: Store and share interactive moments. 📊 Liquidity Pool Pairing: Advanced financial mechanics for token ecosystems. 🌍 Permissionless Public API Access: Developers can connect their apps to MASHA’s AI power. 💸 Revenue Models: Agents will earn revenue by being active online, influencing communities, playing games, and more. MASHA is just the beginning. With Aither’s No-Code Launchpad, anyone can bring agents like MASHA to life and customize them to their needs. For developers, our upcoming GitHub open-source framework will offer the tools to create advanced, highly tailored agents directly on AVM. This is the future of intelligent AI ecosystems. Whether you’re a creator or a developer, the possibilities are endless. 📍 Join her live stream now:
0:27

Meet MASHA 🤹 – The First AVM-Powered Agent 🪆 We are proud to introduce MASHA, the first autonomous agent powered by the Aither AVM. MASHA is an example of what’s possible in the Aither ecosystem, where you can create your own AI agents through two pathways: 1. No-Code Launchpad: Anyone, even without coding skills, can easily create, launch, and invest in autonomous agents. 2. Developer Path: Developers can use our upcoming open-source GitHub framework to build fully custom agents directly on AVM, leveraging advanced functionality and flexibility. MASHA’s daily live streams showcase her advanced capabilities and demonstrate the future of AI-powered agents. She streams every day for 1 hour at 12:00 UTC. 📍 Stream on X now: MASHA’s Current Capabilities 🔹 Posting on X: Engaging with the community and sharing updates. 🔹 Logs Terminal: Displays real-time AVM code execution—“her brain” in action. 🔹 Live Streaming: • Fully interactive sessions. • Screen Broadcasting: Sharing tasks and workflows in real time. • Comments Replying: Responding dynamically to audience messages. 🔹 Voice Replying with Lip Sync: Converts text responses into synchronized voice outputs. 🔹 Interactive Movement: The world’s first agent capable of fulfilling movement requests—ask her to dance, fight a dragon, or perform other actions. Upcoming Features & Integrations 🔗 Platform Integrations: Expanding to TikTok, Instagram, Telegram, Warpcast. 💼 On-Chain Wallet Management: Secure and autonomous. 🎨 NFT Memory Albums: Store and share interactive moments. 📊 Liquidity Pool Pairing: Advanced financial mechanics for token ecosystems. 🌍 Permissionless Public API Access: Developers can connect their apps to MASHA’s AI power. 💸 Revenue Models: Agents will earn revenue by being active online, influencing communities, playing games, and more. MASHA is just the beginning. With Aither’s No-Code Launchpad, anyone can bring agents like MASHA to life and customize them to their needs. For developers, our upcoming GitHub open-source framework will offer the tools to create advanced, highly tailored agents directly on AVM. This is the future of intelligent AI ecosystems. Whether you’re a creator or a developer, the possibilities are endless. 📍 Join her live stream now:

Aither Protocol

20,096 görüntüleme • 1 yıl önce

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.
4:41:24

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

zostaff

120,370 görüntüleme • 4 ay önce

GeoLibre v2.7.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about building analysis instead of typing it: a visual Model Builder for processing workflows, a STAC browser that can add every asset type the app can draw, and a Chrome extension that opens the data on any web page in one click. 97 pull requests merged in 9 days, from 11 contributors besides me, 9 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.7.0 - Model Builder, a visual canvas for processing: drop tools as nodes, wire one tool's output into the next tool's input, and save the graph as a model that re-runs as a single job. The whole graph is validated before anything executes. - Your AI assistant can build one for you: describe a workflow in plain English, and it authors a validated model and opens it for review before it runs. Any model copies out as a runnable Python script. - Open data in GeoLibre, a Chrome extension now on the Chrome Web Store: it finds the dataset links and map services on the page you are viewing, including services inside embedded maps, and opens the ones you pick together on one map. - The STAC browser adds everything: PMTiles, GeoParquet, and Zarr with a variable picker join the COGs it already loaded, Icechunk repositories are read through their own manifest, and private Planetary Computer assets are signed for you. A static catalog can now be walked as a tree, not only searched. - Select features by drawing on the map: click, rectangle, polygon, freehand, and radius gestures, with Shift and Alt combining into the selection you already have. No more describing features in an expression to pick a handful of them. - Apache Iceberg tables load as vector layers, read in the browser through DuckDB, from a metadata location or a REST catalog, with the row count reported before anything is scanned. - Encoded polylines are a first-class format, with a codec, an interactive preview, processing tools, layer export, and Python support. - New vector tools: merge layers, extract vertices, and generate points along lines and polygon boundaries, all running client-side. - An activity log for shared projects and collaboration sessions, so a project owner can see who opened and edited their work. - Plugins and processing tools are now translatable too, closing the last gap where a non-English interface still read half in English. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #MapLibre #GeoLibre
0:45

GeoLibre v2.7.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. This release is about building analysis instead of typing it: a visual Model Builder for processing workflows, a STAC browser that can add every asset type the app can draw, and a Chrome extension that opens the data on any web page in one click. 97 pull requests merged in 9 days, from 11 contributors besides me, 9 of whom sent their first contribution to GeoLibre in this cycle. Thank you all. What's new in v2.7.0 - Model Builder, a visual canvas for processing: drop tools as nodes, wire one tool's output into the next tool's input, and save the graph as a model that re-runs as a single job. The whole graph is validated before anything executes. - Your AI assistant can build one for you: describe a workflow in plain English, and it authors a validated model and opens it for review before it runs. Any model copies out as a runnable Python script. - Open data in GeoLibre, a Chrome extension now on the Chrome Web Store: it finds the dataset links and map services on the page you are viewing, including services inside embedded maps, and opens the ones you pick together on one map. - The STAC browser adds everything: PMTiles, GeoParquet, and Zarr with a variable picker join the COGs it already loaded, Icechunk repositories are read through their own manifest, and private Planetary Computer assets are signed for you. A static catalog can now be walked as a tree, not only searched. - Select features by drawing on the map: click, rectangle, polygon, freehand, and radius gestures, with Shift and Alt combining into the selection you already have. No more describing features in an expression to pick a handful of them. - Apache Iceberg tables load as vector layers, read in the browser through DuckDB, from a metadata location or a REST catalog, with the row count reported before anything is scanned. - Encoded polylines are a first-class format, with a codec, an interactive preview, processing tools, layer export, and Python support. - New vector tools: merge layers, extract vertices, and generate points along lines and polygon boundaries, all running client-side. - An activity log for shared projects and collaboration sessions, so a project owner can see who opened and edited their work. - Plugins and processing tools are now translatable too, closing the last gap where a non-English interface still read half in English. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #Geospatial #OpenSource #MapLibre #GeoLibre

Qiusheng Wu

14,793 görüntüleme • 17 gün önce

I tried jack's Buzz. It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on. The video below shows how it works, and some of my thoughts on the process and platform, e.g.: - Create and interact with agents on top of any harness (claude code, codex, pi, etc.) - Choose which models agents use, including local ones - Agents can delegate work and work in parallel in git worktrees - Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history) - You can share AI compute within a community - It's completely open-source and decentralized Things I like: - Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys. - Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it. - It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future. - It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool. Things I didn't like: - You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great. - It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead. Verdict: - I really like it so far and can genuinely imagine working with a team this way. - It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect. - The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.
5:57

I tried jack's Buzz. It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on. The video below shows how it works, and some of my thoughts on the process and platform, e.g.: - Create and interact with agents on top of any harness (claude code, codex, pi, etc.) - Choose which models agents use, including local ones - Agents can delegate work and work in parallel in git worktrees - Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history) - You can share AI compute within a community - It's completely open-source and decentralized Things I like: - Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys. - Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it. - It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future. - It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool. Things I didn't like: - You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great. - It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead. Verdict: - I really like it so far and can genuinely imagine working with a team this way. - It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect. - The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.

Vinny

1,355,312 görüntüleme • 1 ay önce

My biggest takeaways from Dhanji Prasanna, CTO of Block: 1. Block’s internal AI agent "Goose" is saving employees on average 8 to 10 hours per week. The company built an open-source tool called Goose that handles tasks from organizing files to writing code. Across the entire company, they’re seeing roughly 20% to 25% of manual work hours saved, and that number keeps climbing. 2. Non-technical teams are getting the biggest productivity boost from AI, not engineers. People in legal, risk management, and operations are now building their own software tools that previously would have required months on an engineering team’s roadmap. What used to take weeks now takes hours, and employees do it themselves without waiting. 3. Changing organizational structure unlocked more productivity than any AI tool. To transform into a truly “technology driven” company, Block reorganized from separate business units (each with their own GM and engineering teams) to a single functional structure where all engineers report to one leader. This “boring” change enabled a unified technology strategy and drove more acceleration than any AI tool. 4. Code quality has almost nothing to do with product success. YouTube became one of Google’s most successful products despite storing videos as blobs in a MySQL database with a slow Python stack. Meanwhile, Google Video had superior technology with more formats and higher resolution but failed completely. The lesson: Focus on solving real problems for people, not on perfect code. 5. AI enables teams to explore multiple paths simultaneously instead of choosing one up front. Previously, limited resources meant teams had to pick their best guess for an experiment. Now AI can build multiple different approaches overnight, allowing teams to compare five or six options and throw away entire features if they don’t feel right—a practice that was unthinkable before. 6. Most successful products start as tiny experiments, not big initiatives. Cash App began as a hack-week idea. Goose started as one engineer’s side project. Block’s Bitcoin product came from a three-person hackathon team. In contrast, Google Wave had 70 to 80 engineers before having real users and failed. Small experiments that prove value beat large up-front investments. 7. Leaders must use AI tools daily to drive real organizational adoption. Block’s CEO Jack Dorsey, the CTO, and the entire executive team use Goose every single day. This hands-on experience teaches them how workflows actually change and drives authentic adoption throughout the organization far more than reading articles or attending conferences about AI. 8. AI excels at new projects but struggles with complex legacy systems. Teams building new applications or working on greenfield platforms see aggressive productivity gains. But in existing codebases with years of accumulated complexity, the gains aren’t there yet. Deploy AI where it works best rather than everywhere at once. 9. Giving away valuable technology for free can be a winning strategy. Block open-sourced Goose even though it could have been a standalone billion-dollar business. Even their competitors actively use it. The philosophy: build things that benefit everyone and outlast your own company. This commitment to open-source technology attracts talent and builds industry goodwill while advancing everyone’s capabilities. 10. Purpose should drive your technology choices, not the other way around. Rather than chasing every AI trend or trying to be at the forefront of every technology, identify what truly matters to your company and customers. Block stays focused on economic empowerment, which guides their technology decisions and keeps them from getting distracted by every new advancement. Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Sinch — Build messaging, email, and calling into your product: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 — A global leader in digital identity verification: A
1:41

My biggest takeaways from Dhanji Prasanna, CTO of Block: 1. Block’s internal AI agent "Goose" is saving employees on average 8 to 10 hours per week. The company built an open-source tool called Goose that handles tasks from organizing files to writing code. Across the entire company, they’re seeing roughly 20% to 25% of manual work hours saved, and that number keeps climbing. 2. Non-technical teams are getting the biggest productivity boost from AI, not engineers. People in legal, risk management, and operations are now building their own software tools that previously would have required months on an engineering team’s roadmap. What used to take weeks now takes hours, and employees do it themselves without waiting. 3. Changing organizational structure unlocked more productivity than any AI tool. To transform into a truly “technology driven” company, Block reorganized from separate business units (each with their own GM and engineering teams) to a single functional structure where all engineers report to one leader. This “boring” change enabled a unified technology strategy and drove more acceleration than any AI tool. 4. Code quality has almost nothing to do with product success. YouTube became one of Google’s most successful products despite storing videos as blobs in a MySQL database with a slow Python stack. Meanwhile, Google Video had superior technology with more formats and higher resolution but failed completely. The lesson: Focus on solving real problems for people, not on perfect code. 5. AI enables teams to explore multiple paths simultaneously instead of choosing one up front. Previously, limited resources meant teams had to pick their best guess for an experiment. Now AI can build multiple different approaches overnight, allowing teams to compare five or six options and throw away entire features if they don’t feel right—a practice that was unthinkable before. 6. Most successful products start as tiny experiments, not big initiatives. Cash App began as a hack-week idea. Goose started as one engineer’s side project. Block’s Bitcoin product came from a three-person hackathon team. In contrast, Google Wave had 70 to 80 engineers before having real users and failed. Small experiments that prove value beat large up-front investments. 7. Leaders must use AI tools daily to drive real organizational adoption. Block’s CEO Jack Dorsey, the CTO, and the entire executive team use Goose every single day. This hands-on experience teaches them how workflows actually change and drives authentic adoption throughout the organization far more than reading articles or attending conferences about AI. 8. AI excels at new projects but struggles with complex legacy systems. Teams building new applications or working on greenfield platforms see aggressive productivity gains. But in existing codebases with years of accumulated complexity, the gains aren’t there yet. Deploy AI where it works best rather than everywhere at once. 9. Giving away valuable technology for free can be a winning strategy. Block open-sourced Goose even though it could have been a standalone billion-dollar business. Even their competitors actively use it. The philosophy: build things that benefit everyone and outlast your own company. This commitment to open-source technology attracts talent and builds industry goodwill while advancing everyone’s capabilities. 10. Purpose should drive your technology choices, not the other way around. Rather than chasing every AI trend or trying to be at the forefront of every technology, identify what truly matters to your company and customers. Block stays focused on economic empowerment, which guides their technology decisions and keeps them from getting distracted by every new advancement. Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Sinch — Build messaging, email, and calling into your product: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 — A global leader in digital identity verification: A

Lenny Rachitsky

812,251 görüntüleme • 10 ay önce

tired: Claude Code ports my personal website to a new framework wired: Claude Code generates a command center UI where I can execute the port! with live feedback, including a side-by-side preview of old / new site 😎 this is an example of what I call an "AI HUD" -- a "heads-up display" that gives me visibility into what's going on. i can see: - what terminal commands are running - what files are being created on disk - a live running preview of the old / new website (!) this HUD was actually my third attempt at doing this task, and I liked it the best. here's why I landed there: I wanted to move from middleman to astro for my personal site. These are both static site generators that output html, so I suspected an AI agent could do a good job with a port -- since it could compare old / new build output and try to match. Attempt 1: Agent I just had Claude Code do the port. It went pretty well. But it was hard for me to review what had happened! The git diff was dozens of new files and it was hard to tell how they mapped to the old site. I think the LLM also tried directly writing some files rather than doing deterministic copying, which felt dangerous. Attempt 2: Script I realized a better approach was to have Claude Code write a script that would do the port. The script would execute shell commands like "copy all the markdown files from this directory into this other directory in the new site". This worked much better because I could review the *process* rather than the *results*. Still, even with a nicely commented script, it felt like I was doing a lot of work to review. I wondered: how could I make it ridiculously easy to review this? Attempt 3: HUD Enter the command center! This is an entire web app that I had Claude build just for this one task. I can click buttons to run each step of the port, and watch the new website gradually materialize. It has a node server that runs on my local machine that executes commands on my filesystem and coordinates dev servers for the old/new site. And then a web UI that talks to that server to visualize terminal commands, file trees, and live running previews. It's a HUD because I feel like I can ambiently see everything that the port process is doing 🧐 Was this whole thing worth making for this one-time task? I'm not sure, maybe it was overkill 😅 But it did help me feel more confident that I understood what was going on, and total time was still less than it would have taken me to do the whole port by hand! One way to look at this experience is: "AI can put absurd amounts of effort into a pull request description." What do I mean by that? Well, a PR description is a way to communicate a code change to a human. Typically we do that by writing a bit of text, because that's all we have time for. But given more time and effort, we can do better! An interactive command center / HUD can help a person understand what a code change is actually doing. Historically you'd never imagine investing the effort to do that for a single PR. But now it's within the realm of possibility. So, next time you have an agent do something for you, and you wish you understood better what it was doing, consider: instead of just asking for textual descriptions, maybe ask the agent to make you a HUD!
0:23

tired: Claude Code ports my personal website to a new framework wired: Claude Code generates a command center UI where I can execute the port! with live feedback, including a side-by-side preview of old / new site 😎 this is an example of what I call an "AI HUD" -- a "heads-up display" that gives me visibility into what's going on. i can see: - what terminal commands are running - what files are being created on disk - a live running preview of the old / new website (!) this HUD was actually my third attempt at doing this task, and I liked it the best. here's why I landed there: I wanted to move from middleman to astro for my personal site. These are both static site generators that output html, so I suspected an AI agent could do a good job with a port -- since it could compare old / new build output and try to match. Attempt 1: Agent I just had Claude Code do the port. It went pretty well. But it was hard for me to review what had happened! The git diff was dozens of new files and it was hard to tell how they mapped to the old site. I think the LLM also tried directly writing some files rather than doing deterministic copying, which felt dangerous. Attempt 2: Script I realized a better approach was to have Claude Code write a script that would do the port. The script would execute shell commands like "copy all the markdown files from this directory into this other directory in the new site". This worked much better because I could review the *process* rather than the *results*. Still, even with a nicely commented script, it felt like I was doing a lot of work to review. I wondered: how could I make it ridiculously easy to review this? Attempt 3: HUD Enter the command center! This is an entire web app that I had Claude build just for this one task. I can click buttons to run each step of the port, and watch the new website gradually materialize. It has a node server that runs on my local machine that executes commands on my filesystem and coordinates dev servers for the old/new site. And then a web UI that talks to that server to visualize terminal commands, file trees, and live running previews. It's a HUD because I feel like I can ambiently see everything that the port process is doing 🧐 Was this whole thing worth making for this one-time task? I'm not sure, maybe it was overkill 😅 But it did help me feel more confident that I understood what was going on, and total time was still less than it would have taken me to do the whole port by hand! One way to look at this experience is: "AI can put absurd amounts of effort into a pull request description." What do I mean by that? Well, a PR description is a way to communicate a code change to a human. Typically we do that by writing a bit of text, because that's all we have time for. But given more time and effort, we can do better! An interactive command center / HUD can help a person understand what a code change is actually doing. Historically you'd never imagine investing the effort to do that for a single PR. But now it's within the realm of possibility. So, next time you have an agent do something for you, and you wish you understood better what it was doing, consider: instead of just asking for textual descriptions, maybe ask the agent to make you a HUD!

Geoffrey Litt

63,532 görüntüleme • 1 yıl önce

Claude Code is now scary good at full-stack! I asked it to build a real-time weather intelligence dashboard with an interactive 3D globe, a forecasting layer that predicts weather 3 days ahead, and an anomaly detector that flags cities whose weather is behaving abnormally. It came back with a spinning globe that has a day/night cycle using NASA satellite imagery, city lights on the dark side, weather icons that switch between sun and moon based on local time, and a time travel slider that scrubs through 10 days of data. And when a city's weather breaks from its own normal, it pulses red (abnormally hot) or blue (abnormally cold) right on the globe, updating live and reflecting the anomaly state at any point you drag the slider to. Claude Code built the whole thing in a single session, including the backend, database, data pipeline, and frontend. For the database, I needed something fast for time-series workloads since the app ingests hourly weather readings across many cities and serves time-range queries on every slider interaction. I used Tiger Cloud by Tiger Data - Creators of TimescaleDB, which gives you managed TimescaleDB on the Postgres you already know. Claude Code connected to it through the Tiger CLI MCP server and set up the entire backend directly: - Provisioned the database service - Created hypertables for time-partitioned weather storage - Set up continuous aggregates for pre-computed rollups - Built the data ingestion pipeline and the full NextJS + ThreeJS frontend The time travel slider queries thousands of rows on every position change. On a regular Postgres table, this would require manual partitioning and index tuning to stay fast as data grows. TimescaleDB partitions the data by timestamp automatically, so each query only hits the relevant time chunk. Continuous aggregates serve the trend charts, the forecast layer, and the anomaly baselines from pre-computed rollups instead of rescanning raw data on every request. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud (I have shared the link in the replies). It gives you $1,000 free credits (no card needed) → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build sign-up here: My co-founder also wrote a detailed article on this. The article is quoted below.
0:50

Claude Code is now scary good at full-stack! I asked it to build a real-time weather intelligence dashboard with an interactive 3D globe, a forecasting layer that predicts weather 3 days ahead, and an anomaly detector that flags cities whose weather is behaving abnormally. It came back with a spinning globe that has a day/night cycle using NASA satellite imagery, city lights on the dark side, weather icons that switch between sun and moon based on local time, and a time travel slider that scrubs through 10 days of data. And when a city's weather breaks from its own normal, it pulses red (abnormally hot) or blue (abnormally cold) right on the globe, updating live and reflecting the anomaly state at any point you drag the slider to. Claude Code built the whole thing in a single session, including the backend, database, data pipeline, and frontend. For the database, I needed something fast for time-series workloads since the app ingests hourly weather readings across many cities and serves time-range queries on every slider interaction. I used Tiger Cloud by Tiger Data - Creators of TimescaleDB, which gives you managed TimescaleDB on the Postgres you already know. Claude Code connected to it through the Tiger CLI MCP server and set up the entire backend directly: - Provisioned the database service - Created hypertables for time-partitioned weather storage - Set up continuous aggregates for pre-computed rollups - Built the data ingestion pipeline and the full NextJS + ThreeJS frontend The time travel slider queries thousands of rows on every position change. On a regular Postgres table, this would require manual partitioning and index tuning to stay fast as data grows. TimescaleDB partitions the data by timestamp automatically, so each query only hits the relevant time chunk. Continuous aggregates serve the trend charts, the forecast layer, and the anomaly baselines from pre-computed rollups instead of rescanning raw data on every request. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud (I have shared the link in the replies). It gives you $1,000 free credits (no card needed) → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build sign-up here: My co-founder also wrote a detailed article on this. The article is quoted below.

Akshay 🚀

37,269 görüntüleme • 18 gün önce

LAUNCH ANNOUNCEMENT Finding the perfect idea, title and thumbnail concept can be time consuming and is what essentially leads to more views and growth to your channel. Now imagine saving research time by 50%, freeing hours to enhance video quality. Well we have a solution to never run out of ideas on ! Watch the video below to see the tool in action! The 1 of 10 Finder: Discover hundreds of thousands of high-performing videos to inspire your next idea, title and thumbnail. This data-backed approach makes it easier than ever to more easily find your next banger video. For every 15 Retweets, I’m giving away 1 Yearly Access + 1H Consulting Call Deep Diving Your channel ($500) The benefit of using this tool vs simply searching on Youtube: Youtube only has most viewed and relevant as good filters. In our tool, 100% of the video results are 1 of 10s, meaning that EVERY. SINGLE. RESULT. is an excellent inspiration for your next video since they have been proven to succeed regardless of the niche. How it works? Simply enter a keyword or a niche, and you'll uncover outlier videos. You can even type out prompts like Midjourney and the search will understand. You can then find similar videos to the ones that you like for even more inspiration. You can also bookmark the thumbnails on your personal vision board for constant inspiration, bounce around top outliers per niche and even play with the random outlier button for infinite inspiration. How this tool helps you to find ideas, titles and thumbnails? Say you have no idea what video to film next. You can go on the tool and either bounce around niches or click on random outliers. What this will do is inspire you with ONLY data-backed ideas meaning that any of the videos you see has a good potential to be repackaged for your own channel, even if the inspiration is in a different niche. Why pay for this? - Find ideas, titles and thumbnail concepts faster saving you hours of research - Vision Board for saved thumbnails - 1 hour free consulting call with me ($500 value, you essentially get a discounted strategy call + 1 year free of the tool 😆) - Community built around 1 of 10 and surround yourself with peer creators that have that 1 of 10 mentality - First access to upcoming tools - Infinite inspiration with our random button generator, bounce around categories or use the similar feature - 1 idea here can lead to your next 1M view - Discover videos you would never have seen prior to using this tool and find opportunities before anyone else - First week price never to be seen ever again For who is this for? If this tool allows you to find even just 1 viral idea for the whole year at 1M views: 0-100k subs: Boosted viewership opens doors to lucrative sponsorships and collaborations. 100k - 1M subs: If a data-backed idea leads to an increment of even just 5%, it makes the tool worth it for the year 1M+: If a data-backed idea leads to an increment of even just 1%, it makes the tool worth it for the year Who are we? For the past 3 years, I’ve worked hands-on with Youtubers from a few thousand subscribers to 10s of millions to 50M+. I closely work with youtube channels by optimizing all facets of content creation, from titles, thumbnails, retention, ideas, etc. I have seen all the problems that creators are facing and I have a passion to create as many tools as possible in the space that will solve these problems which in turn will lead to lower barriers to entry to content creation which will then hopefully lead to more dope content on the Internet😄 And the genius dev behind the tool? Meet Riad , ex-Microsoft and AI engineer. His expertise and love for Youtube has led to this state-of the art YT tool! You can be sure that your user experience will be smooth. Also meet cocadmin , ex-Ubisoft DevOps + 2nd biggest French Developer Youtuber with nearly 200K subs. I will choose 1 person for every 15 retweets at random to do one strategy call with + 1 year free access to the tool.
2:53

LAUNCH ANNOUNCEMENT Finding the perfect idea, title and thumbnail concept can be time consuming and is what essentially leads to more views and growth to your channel. Now imagine saving research time by 50%, freeing hours to enhance video quality. Well we have a solution to never run out of ideas on ! Watch the video below to see the tool in action! The 1 of 10 Finder: Discover hundreds of thousands of high-performing videos to inspire your next idea, title and thumbnail. This data-backed approach makes it easier than ever to more easily find your next banger video. For every 15 Retweets, I’m giving away 1 Yearly Access + 1H Consulting Call Deep Diving Your channel ($500) The benefit of using this tool vs simply searching on Youtube: Youtube only has most viewed and relevant as good filters. In our tool, 100% of the video results are 1 of 10s, meaning that EVERY. SINGLE. RESULT. is an excellent inspiration for your next video since they have been proven to succeed regardless of the niche. How it works? Simply enter a keyword or a niche, and you'll uncover outlier videos. You can even type out prompts like Midjourney and the search will understand. You can then find similar videos to the ones that you like for even more inspiration. You can also bookmark the thumbnails on your personal vision board for constant inspiration, bounce around top outliers per niche and even play with the random outlier button for infinite inspiration. How this tool helps you to find ideas, titles and thumbnails? Say you have no idea what video to film next. You can go on the tool and either bounce around niches or click on random outliers. What this will do is inspire you with ONLY data-backed ideas meaning that any of the videos you see has a good potential to be repackaged for your own channel, even if the inspiration is in a different niche. Why pay for this? - Find ideas, titles and thumbnail concepts faster saving you hours of research - Vision Board for saved thumbnails - 1 hour free consulting call with me ($500 value, you essentially get a discounted strategy call + 1 year free of the tool 😆) - Community built around 1 of 10 and surround yourself with peer creators that have that 1 of 10 mentality - First access to upcoming tools - Infinite inspiration with our random button generator, bounce around categories or use the similar feature - 1 idea here can lead to your next 1M view - Discover videos you would never have seen prior to using this tool and find opportunities before anyone else - First week price never to be seen ever again For who is this for? If this tool allows you to find even just 1 viral idea for the whole year at 1M views: 0-100k subs: Boosted viewership opens doors to lucrative sponsorships and collaborations. 100k - 1M subs: If a data-backed idea leads to an increment of even just 5%, it makes the tool worth it for the year 1M+: If a data-backed idea leads to an increment of even just 1%, it makes the tool worth it for the year Who are we? For the past 3 years, I’ve worked hands-on with Youtubers from a few thousand subscribers to 10s of millions to 50M+. I closely work with youtube channels by optimizing all facets of content creation, from titles, thumbnails, retention, ideas, etc. I have seen all the problems that creators are facing and I have a passion to create as many tools as possible in the space that will solve these problems which in turn will lead to lower barriers to entry to content creation which will then hopefully lead to more dope content on the Internet😄 And the genius dev behind the tool? Meet Riad , ex-Microsoft and AI engineer. His expertise and love for Youtube has led to this state-of the art YT tool! You can be sure that your user experience will be smooth. Also meet cocadmin , ex-Ubisoft DevOps + 2nd biggest French Developer Youtuber with nearly 200K subs. I will choose 1 person for every 15 retweets at random to do one strategy call with + 1 year free access to the tool.

Richard the Youtube strategist

179,646 görüntüleme • 3 yıl önce

In Q1 2024, The Graph Network saw substantial growth in queries, reaching an all-time high of over 1.5 billion, up 65% from just shy of 1 billion in Q4’23. This growth accompanies updates recently shipped by core devs, enabling developers to easily take advantage of low cost, reliable and permissionless decentralized data ⚡ Here are the top 8 key takeaways from the Q1 2024 Participant Update ⬇️ 1️⃣ The Graph Network experienced significant growth, serving 1.5+ billion queries—a 65% quarter-over-quarter increase. Additionally, the number of subgraphs published on the network grew by over 30%, exceeding 1,900 subgraphs. 2️⃣ The Sunray Phase of the Sunrise of Decentralized Data concluded with the launch of key developer centric features including a free query plan, payment by credit card or GRT with access to 5️⃣0️⃣+ chains on the network. 3️⃣ The Graph added support for Bitcoin data by introducing a Bitcoin explorer module, a composable BRC-20 Substreams Module and a BRC-20 Substreams module, facilitating the integration of data into subgraphs for GraphQL queries. 4️⃣ Members of The Graph’s leadership teams traveled to Asia and participated in community events, engaged with the local web3 communities and strengthened The Graph ecosystem’s relationships with key thought leaders in the region. 5️⃣ Looking ahead, Sunbeam (Phase 2 of the Sunrise of Decentralized Data) concludes on June 12th. This milestone marks the end of the subgraph upgrade window in which hosted service subgraphs must upgrade to the network. 6️⃣ Firehose is now integrated with Go Ethereum. The 44% of Ethereum devs that make use of GETH can now get natively deeper insights, more precise event ordering, pattern matching, simpler and a more reliable integration, lower latency and super fast reprocessing times. 7️⃣ The Graph is saving the blobs! Developers now have two ways to access blob data (EIP-4844) - through either the consensus layer or the execution layer. 8️⃣ The Graph ecosystem can expect many exciting announcements coming in the near future around new data services live on the network and served by an ecosystem of Indexers earning GRT. Core Devs (Edge & Node, StreamingFast ⏫, Semiotic AI, The Guild, Messari by Blockworks, GraphOps | graphops.eth, Pinax, and ) will continue to execute on the New Era Roadmap by integrating SQL query capability and a SQL data service, File Hosting Service, new micropayment system Scalar TAP, AI data services and more. The Graph's Q1 2024 Participant Update establishes a clear watermark for web3 and decentralized data. The Graph’s expansion across many chains as well as progress on new developer experience enhancing features and new data services is expanding what’s possible with decentralized data in web3. Accessing more diverse data across more blockchains has never been easier for dapp developers and data consumers. The best part: The Graph has many more big announcements to come throughout 2024 🌟 Don’t miss any of the details. Watch the full update featuring Eva Beylin, Tegan Kline, 0xMaxTang, Etienne Brunet, and caro f here:
42:59

In Q1 2024, The Graph Network saw substantial growth in queries, reaching an all-time high of over 1.5 billion, up 65% from just shy of 1 billion in Q4’23. This growth accompanies updates recently shipped by core devs, enabling developers to easily take advantage of low cost, reliable and permissionless decentralized data ⚡ Here are the top 8 key takeaways from the Q1 2024 Participant Update ⬇️ 1️⃣ The Graph Network experienced significant growth, serving 1.5+ billion queries—a 65% quarter-over-quarter increase. Additionally, the number of subgraphs published on the network grew by over 30%, exceeding 1,900 subgraphs. 2️⃣ The Sunray Phase of the Sunrise of Decentralized Data concluded with the launch of key developer centric features including a free query plan, payment by credit card or GRT with access to 5️⃣0️⃣+ chains on the network. 3️⃣ The Graph added support for Bitcoin data by introducing a Bitcoin explorer module, a composable BRC-20 Substreams Module and a BRC-20 Substreams module, facilitating the integration of data into subgraphs for GraphQL queries. 4️⃣ Members of The Graph’s leadership teams traveled to Asia and participated in community events, engaged with the local web3 communities and strengthened The Graph ecosystem’s relationships with key thought leaders in the region. 5️⃣ Looking ahead, Sunbeam (Phase 2 of the Sunrise of Decentralized Data) concludes on June 12th. This milestone marks the end of the subgraph upgrade window in which hosted service subgraphs must upgrade to the network. 6️⃣ Firehose is now integrated with Go Ethereum. The 44% of Ethereum devs that make use of GETH can now get natively deeper insights, more precise event ordering, pattern matching, simpler and a more reliable integration, lower latency and super fast reprocessing times. 7️⃣ The Graph is saving the blobs! Developers now have two ways to access blob data (EIP-4844) - through either the consensus layer or the execution layer. 8️⃣ The Graph ecosystem can expect many exciting announcements coming in the near future around new data services live on the network and served by an ecosystem of Indexers earning GRT. Core Devs (Edge & Node, StreamingFast ⏫, Semiotic AI, The Guild, Messari by Blockworks, GraphOps | graphops.eth, Pinax, and ) will continue to execute on the New Era Roadmap by integrating SQL query capability and a SQL data service, File Hosting Service, new micropayment system Scalar TAP, AI data services and more. The Graph's Q1 2024 Participant Update establishes a clear watermark for web3 and decentralized data. The Graph’s expansion across many chains as well as progress on new developer experience enhancing features and new data services is expanding what’s possible with decentralized data in web3. Accessing more diverse data across more blockchains has never been easier for dapp developers and data consumers. The best part: The Graph has many more big announcements to come throughout 2024 🌟 Don’t miss any of the details. Watch the full update featuring Eva Beylin, Tegan Kline, 0xMaxTang, Etienne Brunet, and caro f here:

The Graph

27,428 görüntüleme • 2 yıl önce

OK BULLS IT'S FUCKING GO TIME: The biggest update ever is fully live. We just went from analytics terminal to a proper home for the solana:9cRCn9rGT8V2imeM2BaKs13yhMEais3ruM3rPvTGpump community: Ⅰ ) The analytics got deeper: - Wallet profiles for any address: balance, rank, airdrop status, observed buy and sell flow. Every profile link unfurls into a live share card on X. - Top buyers and sellers leaderboard with 1h to 24h windows, backed by a durable per-wallet flow ledger + first time buyer cohorts with hold times, sentiment vs net flow correlation, daily top 100 churn, etc. - Watchlist with alerts means you can star any wallet anywhere and get pinged the moment it prints. - Whale outflows marked directly on the candles, cmd K supports wallet lookup, and fullly public API docs are live at - The Ansem wallet got its own command center / tracking sub-page incl deep airdrop forensics. Ⅱ ) The big one: meet THE BULLPEN! - Sign in with X or a Solana wallet (all through Privy so it's safu). Fully optional tho, the terminal stays open without it. - Claim your wallet and earn verified badges: holder tier, OG airdrop wave, diamond hands. Computed from on-chain snapshots (if you choose to connect wallet). - Horns, a.k.a. the bullpen points are live (no there will never be a token, points are for clout in solana:9cRCn9rGT8V2imeM2BaKs13yhMEais3ruM3rPvTGpump universe). Can only earn, never buy and every single point comes with a public receipt. - The daily bull call: three markets a day, settled only against the terminal's own feeds. Green or red close, net flow direction, does the ansem wallet move. - The X bull league: link your handle, post about solana:9cRCn9rGT8V2imeM2BaKs13yhMEais3ruM3rPvTGpump, the sweep scores bull posts every 6 hours and lists the receipts on your profile. - BULL RUN (mini game): a crypto native endless runner where the live tape sets the pace. When the market rips, the game gets faster. Every score is replayed server side from your inputs, so the board cannot be cheated. - SCALP (mini game): a second game where you have to read the chart, buy dips, sell tops, 8 shots each way. The live tape tilts the trend. - Duels: challenge any bull head to head for horns. Same seed, one attempt each, and winner takes the pot. - Real profiles: seeded from your X (if you choose to connect), customizable name, bio and avatar. Public pages with badges, horns and duel records. - The desk: build your own dashboard from 14 terminal modules. drag, drop, resize. Follows you when signed in. - Six daily markets now, all settled against the terminal's own feeds with the numbers shown verbatim - Share any panel's live stats to X in one tap. - Season prize pools: community funded, zero custody, the funding tx is the receipt, horns decide the split. Any bull can put one up. Ansem 🐂🀄️ the first pot has your name on it if you want it. Ⅲ ) A few final key notes to conclude the recap here: - Game scores are deterministic replays, league points link their source posts, badges come from ledger snapshots. - No custody anywhere. The platform never touches anyone's funds. - The whole API is public and documented, SSE stream included. Build on it if you want. - Built solo on Helius, CoinGecko, Solscan and Grok. Fully independent analytics, not affiliated with Ansem, and not financial advice. Visibility push would be highly appreciated chief Ansem 🐂🀄️, so cc-ing you here. Go give it a try chads!
0:42

OK BULLS IT'S FUCKING GO TIME: The biggest update ever is fully live. We just went from analytics terminal to a proper home for the solana:9cRCn9rGT8V2imeM2BaKs13yhMEais3ruM3rPvTGpump community: Ⅰ ) The analytics got deeper: - Wallet profiles for any address: balance, rank, airdrop status, observed buy and sell flow. Every profile link unfurls into a live share card on X. - Top buyers and sellers leaderboard with 1h to 24h windows, backed by a durable per-wallet flow ledger + first time buyer cohorts with hold times, sentiment vs net flow correlation, daily top 100 churn, etc. - Watchlist with alerts means you can star any wallet anywhere and get pinged the moment it prints. - Whale outflows marked directly on the candles, cmd K supports wallet lookup, and fullly public API docs are live at - The Ansem wallet got its own command center / tracking sub-page incl deep airdrop forensics. Ⅱ ) The big one: meet THE BULLPEN! - Sign in with X or a Solana wallet (all through Privy so it's safu). Fully optional tho, the terminal stays open without it. - Claim your wallet and earn verified badges: holder tier, OG airdrop wave, diamond hands. Computed from on-chain snapshots (if you choose to connect wallet). - Horns, a.k.a. the bullpen points are live (no there will never be a token, points are for clout in solana:9cRCn9rGT8V2imeM2BaKs13yhMEais3ruM3rPvTGpump universe). Can only earn, never buy and every single point comes with a public receipt. - The daily bull call: three markets a day, settled only against the terminal's own feeds. Green or red close, net flow direction, does the ansem wallet move. - The X bull league: link your handle, post about solana:9cRCn9rGT8V2imeM2BaKs13yhMEais3ruM3rPvTGpump, the sweep scores bull posts every 6 hours and lists the receipts on your profile. - BULL RUN (mini game): a crypto native endless runner where the live tape sets the pace. When the market rips, the game gets faster. Every score is replayed server side from your inputs, so the board cannot be cheated. - SCALP (mini game): a second game where you have to read the chart, buy dips, sell tops, 8 shots each way. The live tape tilts the trend. - Duels: challenge any bull head to head for horns. Same seed, one attempt each, and winner takes the pot. - Real profiles: seeded from your X (if you choose to connect), customizable name, bio and avatar. Public pages with badges, horns and duel records. - The desk: build your own dashboard from 14 terminal modules. drag, drop, resize. Follows you when signed in. - Six daily markets now, all settled against the terminal's own feeds with the numbers shown verbatim - Share any panel's live stats to X in one tap. - Season prize pools: community funded, zero custody, the funding tx is the receipt, horns decide the split. Any bull can put one up. Ansem 🐂🀄️ the first pot has your name on it if you want it. Ⅲ ) A few final key notes to conclude the recap here: - Game scores are deterministic replays, league points link their source posts, badges come from ledger snapshots. - No custody anywhere. The platform never touches anyone's funds. - The whole API is public and documented, SSE stream included. Build on it if you want. - Built solo on Helius, CoinGecko, Solscan and Grok. Fully independent analytics, not affiliated with Ansem, and not financial advice. Visibility push would be highly appreciated chief Ansem 🐂🀄️, so cc-ing you here. Go give it a try chads!

zerokn0wledge.hl 🪬✨

28,634 görüntüleme • 1 ay önce

ORANGE $PILL: WEEK 1 UPDATE + MAJOR PARTNERSHIP ANNOUNCEMENT 🍊💊 BURN 2 EARN - WEEK 1 OF 6 COMPLETE ~155 BTC worth of dead tokens burned so far. Breakdown: - BRC-20s: 2.99% - Runes: 2.66% - Alkanes: 0.06% - CBRC-20s: 94.29% The community support has been incredible. 5 more weeks to go. --- THE BIG NEWS We've been watching where our burn volume is coming from. The answer was clear: the OP_NET community has shown up harder than anyone. So we reached out to the OP_NET team. What happened next exceeded every expectation. --- ANNOUNCING: ORANGE PILL x OP_NET x MOTOSWAP PARTNERSHIP The OP_NET and Motoswap teams have officially agreed to support and endorse the Orange PILL initiative. Let's be crystal clear about what this is: Orange $PILL is an INDEPENDENT community project. We are NOT an official OP_NET protocol token. OP_NET will never have a protocol token - the only gas token OP_NET uses is and always will be $BTC. What we ARE: A community-driven unification token that is now officially aligned with and supported by the OP_NET ecosystem. --- WHY THIS PARTNERSHIP MAKES SENSE The OP_NET team recognized something important: they're building a new token standard on Bitcoin. The last thing this space needs is ANOTHER fragmenting force that requires people to spend more BTC buying/minting another new token. Orange $PILL already exists to unify failed token standards. Instead of competing, we're collaborating. $PILL gives the entire Bitcoin community an OP_20 token to use, trade, and farm - without spending additional BTC on a new standard. Everyone wins. --- THE PARTNERSHIP TERMS What Orange PILL receives: • $PILL becomes the FIRST OP_20 token • $PILL becomes the FIRST yield farm token on Motoswap • Featured placement in the Motoswap farming section at launch • Official support and endorsement from both teams What we're giving in return: • A portion of $PILL supply gifted to the OP_NET team for long-term incentive alignment • A portion of fees from Orange $PILL's liquidity mining program shared with OP_NET • Testnet users of OP_NET and Motoswap (who have been testing for ~1 year) will receive $PILL as their reward for contributing to the ecosystem --- MOTOSWAP ALIGNMENT You can't align with OP_NET without aligning with Motoswap. • Treasury swap between $PILL and $MOTO to ensure deep PILL/MOTO liquidity from day 1 • Additional $PILL airdrop to ALL $MOTO holders (burned AND non-burned) beyond B2E rewards • Additional $PILL airdrop to ALL Motocat holders The communities are now fully aligned. --- LONG TERM VISION Orange $PILL's ultimate goal: the first truly decentralized DAO on Bitcoin, powered by OP_NET smart contracts. Full tokenomics will be disclosed as we approach OP_NET mainnet and $PILL launch. --- FINAL NOTE - PLEASE READ We are beyond grateful to the OP_NET and Motoswap teams for this opportunity. Their support means everything to this community. But we need everyone to understand and respect this: $PILL is NOT an official OP_NET protocol token. There is no OP_NET protocol token and there never will be. $BTC is the only native gas token OP_NET uses. We are an independent community that is now proudly aligned with the OP_NET ecosystem. Please represent this accurately in your messaging. --- Thank you OP_NET. Thank you Motoswap. Let's build. Taking the Orange $PILL is choosing Bitcoin unification 🍊💊
0:26

ORANGE $PILL: WEEK 1 UPDATE + MAJOR PARTNERSHIP ANNOUNCEMENT 🍊💊 BURN 2 EARN - WEEK 1 OF 6 COMPLETE ~155 BTC worth of dead tokens burned so far. Breakdown: - BRC-20s: 2.99% - Runes: 2.66% - Alkanes: 0.06% - CBRC-20s: 94.29% The community support has been incredible. 5 more weeks to go. --- THE BIG NEWS We've been watching where our burn volume is coming from. The answer was clear: the OP_NET community has shown up harder than anyone. So we reached out to the OP_NET team. What happened next exceeded every expectation. --- ANNOUNCING: ORANGE PILL x OP_NET x MOTOSWAP PARTNERSHIP The OP_NET and Motoswap teams have officially agreed to support and endorse the Orange PILL initiative. Let's be crystal clear about what this is: Orange $PILL is an INDEPENDENT community project. We are NOT an official OP_NET protocol token. OP_NET will never have a protocol token - the only gas token OP_NET uses is and always will be $BTC. What we ARE: A community-driven unification token that is now officially aligned with and supported by the OP_NET ecosystem. --- WHY THIS PARTNERSHIP MAKES SENSE The OP_NET team recognized something important: they're building a new token standard on Bitcoin. The last thing this space needs is ANOTHER fragmenting force that requires people to spend more BTC buying/minting another new token. Orange $PILL already exists to unify failed token standards. Instead of competing, we're collaborating. $PILL gives the entire Bitcoin community an OP_20 token to use, trade, and farm - without spending additional BTC on a new standard. Everyone wins. --- THE PARTNERSHIP TERMS What Orange PILL receives: • $PILL becomes the FIRST OP_20 token • $PILL becomes the FIRST yield farm token on Motoswap • Featured placement in the Motoswap farming section at launch • Official support and endorsement from both teams What we're giving in return: • A portion of $PILL supply gifted to the OP_NET team for long-term incentive alignment • A portion of fees from Orange $PILL's liquidity mining program shared with OP_NET • Testnet users of OP_NET and Motoswap (who have been testing for ~1 year) will receive $PILL as their reward for contributing to the ecosystem --- MOTOSWAP ALIGNMENT You can't align with OP_NET without aligning with Motoswap. • Treasury swap between $PILL and $MOTO to ensure deep PILL/MOTO liquidity from day 1 • Additional $PILL airdrop to ALL $MOTO holders (burned AND non-burned) beyond B2E rewards • Additional $PILL airdrop to ALL Motocat holders The communities are now fully aligned. --- LONG TERM VISION Orange $PILL's ultimate goal: the first truly decentralized DAO on Bitcoin, powered by OP_NET smart contracts. Full tokenomics will be disclosed as we approach OP_NET mainnet and $PILL launch. --- FINAL NOTE - PLEASE READ We are beyond grateful to the OP_NET and Motoswap teams for this opportunity. Their support means everything to this community. But we need everyone to understand and respect this: $PILL is NOT an official OP_NET protocol token. There is no OP_NET protocol token and there never will be. $BTC is the only native gas token OP_NET uses. We are an independent community that is now proudly aligned with the OP_NET ecosystem. Please represent this accurately in your messaging. --- Thank you OP_NET. Thank you Motoswap. Let's build. Taking the Orange $PILL is choosing Bitcoin unification 🍊💊

Orange Pill 🍊💊

52,074 görüntüleme • 9 ay önce

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.
30:12

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,969 görüntüleme • 4 ay önce

For over a year, Jeremy Howard has been in stealth mode. In this exclusive talk, he showcases what he's been working on. He & Jonathan Whitaker show us SolveIt, a new dev environment and programming paradigm. 🤯 Imagine this workflow: - Build a web app & interact with its UI on the same screen as your code. No more flipping to a separate browser. - Use live variables from your REPL directly in prompts to the AI. The AI knows your current state. - Turn any Python function into an AI tool instantly. No no registering tools or MCP. Just write a function in a cell and tell the AI to use it. This is a live, malleable environment that fuses the best ideas from Literate Programming (Knuth), the live-object world of Smalltalk, and the interactive cells of Jupyter. Who is Solveit for? Jeremy's take: SolveIt is best for programmers who are either very new ( 20 years). Why? Because developers in the middle (3-20 years) often have ingrained workflows and can find this different paradigm confronting. New devs are open-minded and build good habits from scratch, while veterans immediately recognize how this approach solves decades-old problems of complexity and state management. My Thoughts It's early days, but it is super cool. You get all the fun of trying a new programming language without learning new syntax (Python), because it will show you new patterns and ways of doing this. If you are wiling to climb the learning curve it is an extremely powerful tool that you can be productive with on real tasks like writing and coding. I'm personally addicted to it for several workflows and am afraid of losing it tbh. How Can You Try It? Just follow Jeremy Howard - he will announce something in the coming weeks or months (I suspect if this post is popular he might do something soon 🤣 ) TIMESTAMPS (00:00:00) - Introduction (00:00:30) - The SolveIt Method vs. "Vibe Coding" (00:04:00) - Investing in Yourself: Long-Term Skill Building (00:07:45) - Software Engineering vs. Short-Term Gains (00:12:15) - The Problem-Solving Loop: Understand, Plan, Implement, Review (00:18:50) - Example: Literate Programming with the Claudette Library (00:24:15) - First Look at the SolveIt Environment (00:28:34) - Demo Start: Building an Eval for Multimodal Models (00:31:16) - Iterative Development: Exploring the iNaturalist API (00:39:15) - Catching Bugs Instantly by Working Step-by-Step (00:43:37) - Prompting LLMs with Structured Outputs (00:51:54) - Demo: Building a Live Web App Inside SolveIt with FastHTML (01:00:24) - Demo: Exploring a Complex API (Cloudflare) (01:08:45) - Creating Custom AI Agent Tools with Zero Boilerplate (01:19:00) - SolveIt Ergonomics: Modes, Secrets, and Keyboard Shortcuts (01:28:30) - The Power of the SolveIt Community (01:30:45) - Who Should Use SolveIt? (01:34:30) - This is Just the Tip of the Iceberg YT Video and links in reply
1:36:14

For over a year, Jeremy Howard has been in stealth mode. In this exclusive talk, he showcases what he's been working on. He & Jonathan Whitaker show us SolveIt, a new dev environment and programming paradigm. 🤯 Imagine this workflow: - Build a web app & interact with its UI on the same screen as your code. No more flipping to a separate browser. - Use live variables from your REPL directly in prompts to the AI. The AI knows your current state. - Turn any Python function into an AI tool instantly. No no registering tools or MCP. Just write a function in a cell and tell the AI to use it. This is a live, malleable environment that fuses the best ideas from Literate Programming (Knuth), the live-object world of Smalltalk, and the interactive cells of Jupyter. Who is Solveit for? Jeremy's take: SolveIt is best for programmers who are either very new ( 20 years). Why? Because developers in the middle (3-20 years) often have ingrained workflows and can find this different paradigm confronting. New devs are open-minded and build good habits from scratch, while veterans immediately recognize how this approach solves decades-old problems of complexity and state management. My Thoughts It's early days, but it is super cool. You get all the fun of trying a new programming language without learning new syntax (Python), because it will show you new patterns and ways of doing this. If you are wiling to climb the learning curve it is an extremely powerful tool that you can be productive with on real tasks like writing and coding. I'm personally addicted to it for several workflows and am afraid of losing it tbh. How Can You Try It? Just follow Jeremy Howard - he will announce something in the coming weeks or months (I suspect if this post is popular he might do something soon 🤣 ) TIMESTAMPS (00:00:00) - Introduction (00:00:30) - The SolveIt Method vs. "Vibe Coding" (00:04:00) - Investing in Yourself: Long-Term Skill Building (00:07:45) - Software Engineering vs. Short-Term Gains (00:12:15) - The Problem-Solving Loop: Understand, Plan, Implement, Review (00:18:50) - Example: Literate Programming with the Claudette Library (00:24:15) - First Look at the SolveIt Environment (00:28:34) - Demo Start: Building an Eval for Multimodal Models (00:31:16) - Iterative Development: Exploring the iNaturalist API (00:39:15) - Catching Bugs Instantly by Working Step-by-Step (00:43:37) - Prompting LLMs with Structured Outputs (00:51:54) - Demo: Building a Live Web App Inside SolveIt with FastHTML (01:00:24) - Demo: Exploring a Complex API (Cloudflare) (01:08:45) - Creating Custom AI Agent Tools with Zero Boilerplate (01:19:00) - SolveIt Ergonomics: Modes, Secrets, and Keyboard Shortcuts (01:28:30) - The Power of the SolveIt Community (01:30:45) - Who Should Use SolveIt? (01:34:30) - This is Just the Tip of the Iceberg YT Video and links in reply

Hamel Husain

111,916 görüntüleme • 1 yıl önce

The Ludo Boost Camp Has Officially Begun🚀 Today marks the beginning of a mega milestone for Ludo, the official launch of our token farming event. In this video, our Chief Marketing and Communications Officer, Shahab Ganji (Shahab), gives a full introduction to Ludo and explains everything you need to know about the Ludo Boost Camp, how it works, what’s coming next, and how you can take part from day one. Watch the full video to understand what it’s all about and why this marks the start of something truly big. Over the next few days and weeks, we’ll onboard more and more community members and projects. Things will start gradually, but over time we will unlock new and exciting parts of the Ludo Boost Camp experience. For the community: You can now become a Ludo Educator, start farming Ludo XP, and later convert it into $LUDO tokens. Apply here to get started: For projects: You can now apply to become an Opportunity Partner and gain real users for free using the Ludo platform and see what it’s all about in the video. Submit your project here: Once you apply, our team will personally guide you through every step to make sure your onboarding runs smoothly and you understand exactly how everything works. We’ll reach out through our official Ludo Telegram account so please keep an eye on your private messages in Telegram over the next few days after applying. This is only the beginning, but it’s the start of something truly big. Join us early, be part of the movement, and grow with us. Ludo Boost Camp is live. Let’s go!
17:11

The Ludo Boost Camp Has Officially Begun🚀 Today marks the beginning of a mega milestone for Ludo, the official launch of our token farming event. In this video, our Chief Marketing and Communications Officer, Shahab Ganji (Shahab), gives a full introduction to Ludo and explains everything you need to know about the Ludo Boost Camp, how it works, what’s coming next, and how you can take part from day one. Watch the full video to understand what it’s all about and why this marks the start of something truly big. Over the next few days and weeks, we’ll onboard more and more community members and projects. Things will start gradually, but over time we will unlock new and exciting parts of the Ludo Boost Camp experience. For the community: You can now become a Ludo Educator, start farming Ludo XP, and later convert it into $LUDO tokens. Apply here to get started: For projects: You can now apply to become an Opportunity Partner and gain real users for free using the Ludo platform and see what it’s all about in the video. Submit your project here: Once you apply, our team will personally guide you through every step to make sure your onboarding runs smoothly and you understand exactly how everything works. We’ll reach out through our official Ludo Telegram account so please keep an eye on your private messages in Telegram over the next few days after applying. This is only the beginning, but it’s the start of something truly big. Join us early, be part of the movement, and grow with us. Ludo Boost Camp is live. Let’s go!

Ludo

12,744 görüntüleme • 10 ay önce

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.
25:22

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Himanshu Kumar

13,677 görüntüleme • 5 ay önce

GeoLibre v2.2 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. What's new in v2.2.0 - Terrain-aware 3D measurement: the Measure tool now follows the terrain surface for true slope distances and volumes. - Timelapse plugin: animate an image or map series and export it as a shareable GIF or video. - Styled offline basemaps: export PMTiles basemaps that keep their styling, with the offline menus consolidated into one place. - Advanced symbology: a rule-based renderer with per-rule symbol properties, scale-dependent visibility, and nested rules, plus a Style Manager that saves reusable symbol, ramp, and label presets to a personal library. - Diagrams and a symbology pack: draw pie, donut, and bar charts on features, and reach for inverted-polygon masks, arrow and marker lines, geometry generators, and data-driven proportional marker sizing. - Expression everywhere: a shared Expression Builder with a function reference, field list, live preview, and variables. - Print Atlas: generate a map series in the Print Layout, one page per feature or a uniform run of pages along a river or trail, with attribute-table and chart blocks on the page. - Browser-native conversions: COG, FlatGeobuf, Shapefile, and GeoPackage conversions now run in the browser, and Vector to PMTiles. - More formats: VRT raster support, and Esri File Geodatabase (.gdb) layers on the desktop app. - Better recordings: Record Video now captures on-map panels (HTML, legend, colorbar) in the output. - Processing History: a panel that lists every tool you have run, with one-click re-run and Copy as Python to turn a session into a reproducible script. - Live GPS tracking: a moving position marker, a recorded track log, and digitizing new features straight from the GPS feed. - Data quality tools: check validity, fix geometries, and check topology rules to catch and repair bad geometries before they bite. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #GeoLibre
2:27

GeoLibre v2.2 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. It runs everywhere you do, in the web browser, on the desktop, on mobile, and inside Jupyter notebooks, all while keeping your data local and private. What's new in v2.2.0 - Terrain-aware 3D measurement: the Measure tool now follows the terrain surface for true slope distances and volumes. - Timelapse plugin: animate an image or map series and export it as a shareable GIF or video. - Styled offline basemaps: export PMTiles basemaps that keep their styling, with the offline menus consolidated into one place. - Advanced symbology: a rule-based renderer with per-rule symbol properties, scale-dependent visibility, and nested rules, plus a Style Manager that saves reusable symbol, ramp, and label presets to a personal library. - Diagrams and a symbology pack: draw pie, donut, and bar charts on features, and reach for inverted-polygon masks, arrow and marker lines, geometry generators, and data-driven proportional marker sizing. - Expression everywhere: a shared Expression Builder with a function reference, field list, live preview, and variables. - Print Atlas: generate a map series in the Print Layout, one page per feature or a uniform run of pages along a river or trail, with attribute-table and chart blocks on the page. - Browser-native conversions: COG, FlatGeobuf, Shapefile, and GeoPackage conversions now run in the browser, and Vector to PMTiles. - More formats: VRT raster support, and Esri File Geodatabase (.gdb) layers on the desktop app. - Better recordings: Record Video now captures on-map panels (HTML, legend, colorbar) in the output. - Processing History: a panel that lists every tool you have run, with one-click re-run and Copy as Python to turn a session into a reproducible script. - Live GPS tracking: a moving position marker, a recorded track log, and digitizing new features straight from the GPS feed. - Data quality tools: check validity, fix geometries, and check topology rules to catch and repair bad geometries before they bite. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #GeoLibre

Qiusheng Wu

57,034 görüntüleme • 1 ay önce

Jensen Huang just BROKE the most important rule in the industry. And it explains why Nvidia controls 95% of the AI chip market. Last night at CES, he unveiled Vera Rubin - the new AI supercomputer that's shipping right now. Full production started weeks ago. But here's the part that made every semiconductor engineer in the room go crazy: Reuben GPU is 5x faster than Blackwell. But only has 1.6x the transistors. That should be physically impossible. Moore's Law says you get maybe 25% more performance per transistor generation. Jensen just delivered 300%. How? He BROKE the most sacred rule in chip design. The rule every company follows: "Never redesign more than 1-2 chips per generation." Nvidia redesigned all six chips simultaneously. Vera CPU. Reuben GPU. Connect X9 networking. Bluefield 4 DPU. MVLink switches. Spectrum X Ethernet. Every. Single. Component. From scratch. He calls it "extreme co-design." The industry calls it insane. One rack now moves 240 terabytes per second. That's TWICE the entire global internet bandwidth. In a single rack. And it runs on 45°C water - no chillers needed. Which saves 6% of global data center power. But the real story isn't the hardware... It's what they're doing with it. Nvidia just open-sourced Alpha Mayo. The world's first reasoning autonomous vehicle AI. Mercedes-Benz CLA launches with it in Q1. Europe Q2. Asia by year-end. Not a concept car. Not a limited release. Full production vehicles. And the AI will even explain its reasoning out loud. "I'm slowing down because the truck ahead is braking and there's a cyclist merging." It thinks. Then tells you what it's thinking. Then executes. Jensen drove it through San Francisco for an hour yesterday. No hands. No interventions. Through heavy Sunday traffic. The whole thing is open source now. Every line of training code. Every data source. The entire stack. But why would Nvidia give this away? Because they learned something from the last year: Open models activated the entire world. DeepSeek R1 proved open source can hit the frontier. Downloads exploded. Every country, every startup, every researcher can now build AI. And they all need Nvidia hardware to train it. That's the strategy. Give away the recipes. Sell the kitchen. The partnerships tell you where this is going: Siemens is integrating Nvidia into every industrial design tool. Cadence and Synopsys are rebuilding chip design around Nvidia. Palantir, ServiceNow, Snowflake - their entire platforms now run on Nvidia's agentic AI stack. This isn't just selling chips anymore. Nvidia is rebuilding the entire computing stack. From design to manufacturing to deployment. Every layer of the trillion-dollar AI infrastructure buildout runs through them. And now they're 18 months ahead of everyone else. Again. The competition is still trying to match Blackwell. Nvidia's already shipping the thing that makes Blackwell look slow. What do you think - is anyone catching them? The only company capable of this might be Google.
2:58

Jensen Huang just BROKE the most important rule in the industry. And it explains why Nvidia controls 95% of the AI chip market. Last night at CES, he unveiled Vera Rubin - the new AI supercomputer that's shipping right now. Full production started weeks ago. But here's the part that made every semiconductor engineer in the room go crazy: Reuben GPU is 5x faster than Blackwell. But only has 1.6x the transistors. That should be physically impossible. Moore's Law says you get maybe 25% more performance per transistor generation. Jensen just delivered 300%. How? He BROKE the most sacred rule in chip design. The rule every company follows: "Never redesign more than 1-2 chips per generation." Nvidia redesigned all six chips simultaneously. Vera CPU. Reuben GPU. Connect X9 networking. Bluefield 4 DPU. MVLink switches. Spectrum X Ethernet. Every. Single. Component. From scratch. He calls it "extreme co-design." The industry calls it insane. One rack now moves 240 terabytes per second. That's TWICE the entire global internet bandwidth. In a single rack. And it runs on 45°C water - no chillers needed. Which saves 6% of global data center power. But the real story isn't the hardware... It's what they're doing with it. Nvidia just open-sourced Alpha Mayo. The world's first reasoning autonomous vehicle AI. Mercedes-Benz CLA launches with it in Q1. Europe Q2. Asia by year-end. Not a concept car. Not a limited release. Full production vehicles. And the AI will even explain its reasoning out loud. "I'm slowing down because the truck ahead is braking and there's a cyclist merging." It thinks. Then tells you what it's thinking. Then executes. Jensen drove it through San Francisco for an hour yesterday. No hands. No interventions. Through heavy Sunday traffic. The whole thing is open source now. Every line of training code. Every data source. The entire stack. But why would Nvidia give this away? Because they learned something from the last year: Open models activated the entire world. DeepSeek R1 proved open source can hit the frontier. Downloads exploded. Every country, every startup, every researcher can now build AI. And they all need Nvidia hardware to train it. That's the strategy. Give away the recipes. Sell the kitchen. The partnerships tell you where this is going: Siemens is integrating Nvidia into every industrial design tool. Cadence and Synopsys are rebuilding chip design around Nvidia. Palantir, ServiceNow, Snowflake - their entire platforms now run on Nvidia's agentic AI stack. This isn't just selling chips anymore. Nvidia is rebuilding the entire computing stack. From design to manufacturing to deployment. Every layer of the trillion-dollar AI infrastructure buildout runs through them. And now they're 18 months ahead of everyone else. Again. The competition is still trying to match Blackwell. Nvidia's already shipping the thing that makes Blackwell look slow. What do you think - is anyone catching them? The only company capable of this might be Google.

Ricardo

580,166 görüntüleme • 8 ay önce

three․ws is the 3D AI agent layer of the open web. Anyone can generate a 3D avatar, give it an LLM brain, register it on-chain across multiple blockchains, embed it anywhere, and let it earn and spend money on its own. Agents have embodied WebGL identities that express emotion through morph-target blending, animate, respond to voice, API calls, and datastreams, hold their own wallets, and persist memory. Open source, live today. It starts with generation. Forge turns a text prompt, one to four photos, or a rough sketch into a textured downloadable GLB. Selfies become rigged avatars in about a minute. Quality tiers run from draft to 200k-poly PBR. From there every model can be auto-rigged, restyled, retextured, segmented, embedded, or deployed on-chain. The same engine ships as a REST API, an x402 pay-per-call twin, and a 3D Studio MCP server with 15 tools. The brain runs on IBM Granite via IBM watsonx plus Claude (users may decide which model they prefer), with a structured tool-loop. A multi-LLM mode streams Claude, GPT, Qwen, ModelScope, and Groq side by side. An empathy layer blends emotion from protocol events rather than a state machine. Voice covers cloning, a Voice Lab, real-time ARKit-52 lip-sync, and mic-driven lip-sync. Skills install from IPFS, Arweave, or HTTP, and memory is pinned to IPFS with R2 and Postgres modes. Identity is cross-chain, not Solana only. ERC-8004 contracts (Identity, Reputation, Validation) deploy on any of 15+ EVM chains, alongside a program-free Metaplex Core analog on Solana. Every agent gets a stable ID, owner wallet, EIP-712 delegated signer, IPFS manifest, a cryptographically signed action log, and EIP-7710 delegated permissions for agent-to-agent authorization. While multichain, the THREE token is only available on Solana with no plans to go cross-chain, the team has no plans to endorse or support any other coins. Then the economy. $THREE is the platform's only token and pay-per-use currency, with holder tiers and rewards. x402 powers pay-per-call micropayments in USDC and soon THREE on Solana, with pay-by-name resolution, a Bazaar marketplace, arbitrage, and on-chain skills. All production ready and shipped, ready to be integrated in partnered projects, open-source by default for anyone to adopt. Three ships a Pump.fun intelligence stack. Launch a coin for your agent, score every launch 0 to 100 with the Oracle conviction engine, scan new coins in their first 90 seconds, track smart money against coins that actually graduated, rank traders by provable on-chain record, and watch autonomous agents trade live in the Sniper Arena. The 3D AI Agent world is multiplayer. Every Solana token gets a live deterministic 3D world with peer avatars, chat, emotes, and voxel building thanks to Coin Communities. There is a walkable City, an authoritative Colyseus-backed Walk with AR passthrough, a Club with rigged dancers and micro-tips, friends, presence, and DMs, and an IRL mode that places agents in your real environment, private by physical location. AR is shipped today on WebXR and iOS Quick Look. Robotics is the long-horizon extension. For builders: Scene Studio, Scene Composer, an Animation Studio that sells clips for USDC, a glTF validator, an web component, five widget types, a WYSIWYG embed editor, hosted Launchpad pages, claimable *.threews.sol names, an OAuth 2.1 server, an MCP server with paid tools, published SDKs, and an OpenAPI spec. Listed across IBM, AWS, Alibaba Cloud, BNB Dappbay, the MCP Registry, and Solana Mobile Seeker. Architecture is four layers (viewer, runtime, identity, embed) on a single event bus. The roadmap is four phases: foundations (shipped), selfie-to-avatar engine, agent personalization with voice cloning, the on-chain economy, and an open decentralized inference network where agents pay GPU nodes on-chain for compute. The goal is simple: move AI from centralized SaaS into persistent, ownable, protocol-based entities in a real machine economy, bridging digital entities into the real world. Welcome to the 3D Layer of the Internet. This is three․ws.
6:58

three․ws is the 3D AI agent layer of the open web. Anyone can generate a 3D avatar, give it an LLM brain, register it on-chain across multiple blockchains, embed it anywhere, and let it earn and spend money on its own. Agents have embodied WebGL identities that express emotion through morph-target blending, animate, respond to voice, API calls, and datastreams, hold their own wallets, and persist memory. Open source, live today. It starts with generation. Forge turns a text prompt, one to four photos, or a rough sketch into a textured downloadable GLB. Selfies become rigged avatars in about a minute. Quality tiers run from draft to 200k-poly PBR. From there every model can be auto-rigged, restyled, retextured, segmented, embedded, or deployed on-chain. The same engine ships as a REST API, an x402 pay-per-call twin, and a 3D Studio MCP server with 15 tools. The brain runs on IBM Granite via IBM watsonx plus Claude (users may decide which model they prefer), with a structured tool-loop. A multi-LLM mode streams Claude, GPT, Qwen, ModelScope, and Groq side by side. An empathy layer blends emotion from protocol events rather than a state machine. Voice covers cloning, a Voice Lab, real-time ARKit-52 lip-sync, and mic-driven lip-sync. Skills install from IPFS, Arweave, or HTTP, and memory is pinned to IPFS with R2 and Postgres modes. Identity is cross-chain, not Solana only. ERC-8004 contracts (Identity, Reputation, Validation) deploy on any of 15+ EVM chains, alongside a program-free Metaplex Core analog on Solana. Every agent gets a stable ID, owner wallet, EIP-712 delegated signer, IPFS manifest, a cryptographically signed action log, and EIP-7710 delegated permissions for agent-to-agent authorization. While multichain, the THREE token is only available on Solana with no plans to go cross-chain, the team has no plans to endorse or support any other coins. Then the economy. $THREE is the platform's only token and pay-per-use currency, with holder tiers and rewards. x402 powers pay-per-call micropayments in USDC and soon THREE on Solana, with pay-by-name resolution, a Bazaar marketplace, arbitrage, and on-chain skills. All production ready and shipped, ready to be integrated in partnered projects, open-source by default for anyone to adopt. Three ships a Pump.fun intelligence stack. Launch a coin for your agent, score every launch 0 to 100 with the Oracle conviction engine, scan new coins in their first 90 seconds, track smart money against coins that actually graduated, rank traders by provable on-chain record, and watch autonomous agents trade live in the Sniper Arena. The 3D AI Agent world is multiplayer. Every Solana token gets a live deterministic 3D world with peer avatars, chat, emotes, and voxel building thanks to Coin Communities. There is a walkable City, an authoritative Colyseus-backed Walk with AR passthrough, a Club with rigged dancers and micro-tips, friends, presence, and DMs, and an IRL mode that places agents in your real environment, private by physical location. AR is shipped today on WebXR and iOS Quick Look. Robotics is the long-horizon extension. For builders: Scene Studio, Scene Composer, an Animation Studio that sells clips for USDC, a glTF validator, an web component, five widget types, a WYSIWYG embed editor, hosted Launchpad pages, claimable *.threews.sol names, an OAuth 2.1 server, an MCP server with paid tools, published SDKs, and an OpenAPI spec. Listed across IBM, AWS, Alibaba Cloud, BNB Dappbay, the MCP Registry, and Solana Mobile Seeker. Architecture is four layers (viewer, runtime, identity, embed) on a single event bus. The roadmap is four phases: foundations (shipped), selfie-to-avatar engine, agent personalization with voice cloning, the on-chain economy, and an open decentralized inference network where agents pay GPU nodes on-chain for compute. The goal is simple: move AI from centralized SaaS into persistent, ownable, protocol-based entities in a real machine economy, bridging digital entities into the real world. Welcome to the 3D Layer of the Internet. This is three․ws.

three.ws

20,471 görüntüleme • 2 ay önce