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๐Ÿ”ฅ ๐—ฆ๐˜‚๐—ถโ€™๐˜€ ๐˜€๐—ฝ๐—ฒ๐—ฒ๐—ฑ + ๐—ช๐—ฎ๐—น๐—ฟ๐˜‚๐˜€โ€™๐˜€ ๐—ฐ๐—ต๐—ผ๐—ป๐—ธ = ๐—จ๐—ก๐—ฆ๐—ง๐—ข๐—ฃ๐—ฃ๐—”๐—•๐—Ÿ๐—˜. Pictor Network glues it with GPU fire. ๐Ÿš€Move-powered compute logs. Censorship-proof blobs. Compute DePIN is unleashed. ๐Ÿš€ Sui Walrus: Pictor is cooking. You in? ๐Ÿ‘‰ Explore the details below! ๐Ÿ‘‡ #Pictor #Sui #Walrus #DePIN

15,575 views โ€ข 1 year ago โ€ขvia X (Twitter)

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Machine Tokenization is HERE ๐Ÿ”ฅ Introducing the world's first Machine Real-World Asset (#RWA) Tokenization platform, by Teneo, powered by peaq ๐ŸŒŽ Up until now, real-world apps (#DePINs) have been limited by hardware costs. Individuals can often afford WiFi routers or smartphones, but fleets of vehicles or wind turbines? There's no way to build a Decentralized Physical Infrastructure Network (#DePIN) which revolves around such large and expensive hardware... Or is there? ๐Ÿคจ Enter the Machine Tokenization Platformโšก๏ธ The platform exists to lower the barrier to entry for communities to build virtually any #DePIN. Imagine being able to fund, own, and earn from fleets of autonomous cars or robots, vertical robo-farms, ferry boats, #VTOLs... The possibilities are endless, and this era starts now. Tokenized Teslas โœ… ELOOP has already successfully tokenized a fleet of Teslas for a car-sharing pilot project in Vienna ๐Ÿ‡ฆ๐Ÿ‡น which saw the community earn revenue as the Teslas were used. Check out these videos ๐ŸŽž๏ธ Web3 Tesla-Sharing: You drive, everyone earns: Same, but better. | Web3 Car-Sharing Demo by ELOOP & peaq: With the success of this initiative showcasing the soundness of the underlying model, ELOOP is now building a Machine RWA tokenization platform on peaq to replicate this approach at scale ๐Ÿ“ˆ DePIN Layer-1 Synergies ๐Ÿงฒ Existing and prospective DePINs can leverage the Machine Tokenization platform to lower the barrier to hardware adoption for their users, enabling all kinds of new DePIN use cases on peaq ๐Ÿฆพ A range of Web2 and Web3 projects are already exploring pilot projects on the platform, including Dabba Network ๐ŸŸจ, a connectivity DePIN working to deliver Web access to the unconnected. Already testing on krest ๐Ÿ”ฅ ELOOP is already testing the platform on krest, peaqโ€™s canary network, and plans to launch it on the peaq mainnet, which will go live this year. โ€œWeโ€™re excited to move beyond tokenizing Teslas and offer this exciting, proven model to businesses and communities. Machine RWA tokenization opens up a new era of fractional ownership and participation in the value generated by machines, and we are happy to be chartering this path forward with peaq.โ€ - Nico Prugger, co-founder, ELOOP Read all about it:

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Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colabโ€™s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Letโ€™s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. Iโ€™ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.

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OptimAI Lite Node v1.1: Built for Scale, Designed for You! ๐Ÿ’• In just 2 weeks since the launch, the OptimAI Network has seen explosive growthโ€”130,000+ active node participants powering the future of decentralized AI. With this incredible momentum came a new challenge: ensuring our network could scale seamlessly to support massive concurrent connections and real-time participation. Thatโ€™s why weโ€™ve rolled out OptimAI Lite Node v1.1โ€”a major upgrade focused on: + Stabilizing infrastructure to handle high traffic from a global community. + Enhancing performance for smoother data mining, validation, and edge compute participation. + Refining user experience with UI updates that make contributing effortless. Every line of code and infrastructure upgrade was made with one goal in mind: to support YOUโ€”the builders, validators, and visionaries of the OptimAI ecosystem. Nowโ€™s the time to bring more friends into the journey. ๐Ÿ”ฅ The more we grow, the smarter and stronger the network becomesโ€”and the greater the rewards. Letโ€™s keep building, validating, scaling. Together weโ€™re not just powering AIโ€”weโ€™re reshaping how itโ€™s built. Join or revisit the node here: ๐ŸŒ Chrome Extension: ๐Ÿ“ฑTelegram Mini-App: Whatโ€™s Coming Next: OptimAI Edge Node & the Rise of Agentic AI ๐Ÿ”ธOptimAI Edge Node (Mobile) Weโ€™re working hard on the next major release: the Edge Node for mobile, which will allow mining and AI tasks to run in the backgroundโ€”unlocking more earning opportunities and decentralized compute power from your smartphones. ๐Ÿ”ธMore Task Types & Missions Expect new types of contributions, from AI-enhanced data validation to edge inference and scraping automationโ€”powered by autonomous mining agents. ๐Ÿ”ธExpanded Rewards Program As we grow, more reward tiers, bonuses, and campaigns will be introduced. Your participation now paves the way for long-term benefits. Also, do not forget to checkout our article below and learn more about our latest Community Tips & Best Practices!๐Ÿ‘‡ __________________ OptimAI Network #L2 #DePIN Reinforcement Data Network for #Agentic #AI Mine Data. Fuel AI. Earn Rewards. Turn Your Data into Tomorrowโ€™s AI #Agent. Visit our website at:

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๐Ÿš€ Early Access to Sahara AI Studio is NOW OPEN! The next phase of our testnet is here with exclusive early access to our all-in-one platform designed to transform the AI development lifecycle into a streamlined, integrated experience. Hereโ€™s everything you need to know ๐Ÿ‘‡ AI development is fragmented. Devs juggle multiple tools, leading to inefficiencies & high costs. Sahara AI Studio integrates the entire AI lifecycleโ€”from datasets & model training to secure storage & scalable computeโ€”into one seamless experience: ๐Ÿ“Š Data Hub: Discover, Manage, and Leverage AI-Ready Datasets Access high-quality, domain-specific, open-source and proprietary datasets through an integrated marketplace. Developers can download, import, or label datasets, making it easier to train and fine-tune models or deploy RAG pipelines. Secure uploads and seamless workflow integration enhance the experience. ๐Ÿค– Model Hub: Discover, Customize and Scale AI Workflows with Ease Discover ready-to-use open-source and proprietary models, RAG pipelines, and customizable workflows. Developers can deploy models quickly while maintaining privacy and security through Sahara Vaults. ๐Ÿ–ฅ๏ธ Compute Hub: Flexible, Scalable Compute Resources for AI Innovation Access scalable and secure computing resources tailored to diverse AI workloads. Trusted Execution Environment (TEE) capabilities ensure data privacy, while integration with top compute providers offer flexibility for developers. ๐Ÿ” Vaults: Secure Storage for AI Assets Securely store, organize, and manage datasets, models, and other assets in an encrypted central repository. Vaults offer scalability, reproducibility, and user control over AI resources. This is more than just beta testing a platformโ€”it's your chance to help shape the future of decentralized AI development. ๐Ÿ“… How to Apply We're onboarding select developers in a phased approach. Early Access spots are limited, so apply now:

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Following the amazing reaction to the Marble Curriculum yesterday, we've decided to make it open source ๐Ÿ›ฐ๏ธ๐Ÿ‘‡ Everything a child learns in primary school. 1,590 concepts. 3,221 connections across 8 subjects, from Math and Science to Computing and Life Skills. Anchored in the US and UK curriculums, standard by standard (NGSS, Common Core, DfE). What you will find in the repo: every concept as structured JSON with its age band and the evidence a child must show to master it. Every prerequisite link marked hard or soft, with a written rationale. It's a true DAG you can compute learning paths on. Open license, you can build whatever you want with it. Now is a unique time in history to be building in education. Getting AI and kids education right is likely one of the hardest and most important problems to crack over the next decade and we need as many smart and creative minds behind it. We think a common solid basis, accessible to all and that can be built upon, is critical to move fast. That's why we're making this curriculum open source. It's not perfect but we know it's a robust basis, and we believe that sharing it openly is the fastest way to progress in this field. If you're building in education, share this around you and tell us in comments if you find this useful and if you want to contribute. We'll keep working and investing on it Marble App. Credit goes to Guillaume Boniface-Chang for building this. I just made it look pretty. Links below ๐Ÿ‘‡

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A good technical LLM interview question: Your LLM chatbot takes 12s before it generates the first token, and the users are complaining. So you move the model onto a GPU with 3x the computing power. The time to first token barely improves. Why did this happen? (answer below) Latency in an LLM app is a placement problem disguised as a model problem. If you profile the 12 seconds, the model's prefill itself may only account for around 1.5 seconds of it. So halving the prefill step saves just 750ms out of 12000, which is under 7%. The rest is spread across stages that never touch the GPU. The request first travels to whatever region the app runs in, and a cross-continent round trip could cost over a second before any code executes. Then the request handler starts. On a container-based serverless platform under load, this adds several seconds of cold start, paid before auth, rate limiting, or prompt assembly even begins. Retrieval adds its own hop, and the response streams back across the same distance. Optimizing a stage that was already fast cannot alter the latency that's majorly affected by other stages. Those other stages are slow for a structural reason. An LLM app runs two workloads that want opposite machines. - The request path is short, spiky, and needs to sit close to users - Inference is long-running, GPU-bound, and billed hourly, whether requests arrive or not. So the actual decision is not which model to run, but where each of these two workloads runs. There are three options, each with its own tradeoffs: > A dedicated GPU box removes inference cold starts, but it bills around the clock and lives in one location, so distant users wait out the round trip on every request > Container-based serverless scales to zero, but the request path pays a cold start, and most of these platforms have no GPU behind them. > Edge runtimes start in under a millisecond, because a WebAssembly module carries no OS or container image to boot. They handle the request path well and cannot hold a model. So the answer is not to pick one, but to split the app across two of them. The request path runs close to users, and inference runs on a dedicated GPU it calls into. That also explains the failed upgrade. More compute made a stage that was already fast faster, and left the 10.5 seconds around it untouched. To actually learn how it's done in practice, Akamai's GitHub has a reference implementation for each half. - vllm-on-lke serves Qwen2.5-7B-Instruct behind an OpenAI-compatible endpoint on one RTX 4000 Ada GPU in Linode Kubernetes Engine, with Terraform creating the cluster, both firewalls, and the GPU operator in one apply. - akamai-functions-llm-chatbot covers the front, where a WebAssembly API checks a KV cache and only calls the GPU-backed instance on a miss. Both are available on Akamaiโ€™s new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post treats generation as a single 1.5s block, but that block has its own structure, and knowing it well tells you whether a model is slow to start or slow to stream. I wrote a first-principles walkthrough of it, covering the prefill and decode split, KV caching, and where the time actually goes inside each one. Read it below. Thanks to Akamai Cloud for partnering today!

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๐Ÿšจ๐ŸŽž๏ธRonin Animation Loop Contest๐ŸŽž๏ธ๐Ÿšจ Come on Roner Loopers!! The new contest is here, and so are you, reading this๐Ÿค Before we start, a worthy mention of @Axie44 a pioneer in many things, but in this context, a pioneer in inspiring people to create beautiful loop clips - check #ArticLoopContest If you're an artist but have never created animations, gifs, or loops, don't worry. Keep reading; I'll give you some ideas๐Ÿค“ ๐Ÿ’ธ Prize Pool๐Ÿ’ธ ๐Ÿฅ‡1st Place: 300 $RON ๐Ÿฅˆ2nd Place: 200 $RON ๐Ÿฅ‰3rd Place: 100 $RON ๐Ÿ… 4th Place: 50 $RON ๐Ÿ’ฐ10 runners-up: 15 $RON each ๐Ÿ“ How to Enter ๐Ÿ“ Please read all the rules carefully. If you donโ€™t follow them to the letter, you wonโ€™t win sry. - The animation should be a loop, focused on the Ronin themeโ€”check the video for inspiration ๐Ÿ‘‡ - Ensure the end and beginning transition smoothly - Entries shouldnโ€™t be more than 15 seconds long - IMPORTANT: Submit your design through our official form - Post your entry on Twitter using the hashtag #RoninLoop and tag both us and the Ronin account and also if you want, DM us your entry for a heart emoji - Retweet this post as soon as you read it, donโ€™t be cheeky. Tag your art friends and your family - become taGful, become many ๐Ÿ’ก If you can envision it, you can create it. Partner up with artists, call your designer friend, move mountains. Be proactive and become the winner โฐ Entry Deadline โฐ Tuesday, May 14, 2024 โš–๏ธJudging Criteria๐Ÿ“Š Submissions will be evaluated based on the following criteria: - Creativity: How original is the concept? Does it present a unique, out-of-the-box idea? - Theme Adherence: How effectively does the loop incorporate and represent the Ronin Network theme? - Technical Execution: Quality of animation, especially the seamless looping where the end transitions smoothly back to the beginning. - Storytelling: Does it convey a compelling message or point within those seconds, or does it seem rather random? Let's create something that'll make the Ronin Network community proud, go go go ๐Ÿš€ Ready, set, animate! ๐Ÿš€ PrrrRrrRrrrrrr Substack article ๐Ÿ”—๐Ÿ‘‡

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