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Super excited to feature TWO exciting AGI projects using GPT News 🔥⚡️ 🤖 llama_agi: Automatically execute tasks towards a goal! ⚙️ auto_llama: An internet agent to fulfill tasks. Link: GPT News makes AGI projects straightforward to build. 🧵

Jerry Liu

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Excited to introduce #Paper2Agent: automatically transform passive paper📜 into active AI agent🤖! Research paper ➡️ paper MCP ➡️ chat agent applying tools/data in the paper to new projects. Key is to use multiple sub-agents to build robust MCP from paper/code. Examples in🧵

Excited to introduce #Paper2Agent: automatically transform passive paper📜 into active AI agent🤖! Research paper ➡️ paper MCP ➡️ chat agent applying tools/data in the paper to new projects. Key is to use multiple sub-agents to build robust MCP from paper/code. Examples in🧵

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🔥 Big news Community, We are super excited to announce a massive $100 million Ecosystem Fund designed to support bold, ambitious projects with financial backing and to stimulate AI and Gaming ecosystem growth 💥 The first phase of our $100 million Ecosystem Fund features Aethir Catalyst, an exciting new grant initiative to support promising enterprises focused on launching innovative products in the AI and gaming sectors 💡 🧵👇🏻

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Aethir

475,241 görüntüleme • 1 yıl önce

I've been researching Agents for the past 6 months and collected 40+ materials on the most capable architectures & implementations. The intent was to publish a comprehensive overview, like I did on RAG techniques, but been too busy with so sharing it here. There are some great intro lectures by Andrew Ng to start with. The following types of Agentic architectures are covered: 🤖 Chain of thought (Plan & Execute agent) 🤖 Tooling operators (An agent upon a set of tools, routing to them) - good for connecting external data storage & APIs, pretty fast and robust 🤖 ReAct (Thought - Action - Observation) - capable of iteratively executing complex tasks or answering complex queries 🤖 Self-Reflection - (Action - Observation / Evaluation - Reflection - Planning) - adds some quality and reasoning clarity compared to the ReAct scheme, might be slower 🤖 Agent upon agents (A multiagent scheme) - a quite complex setting, slow, but capable of executing very complex multistep tasks, not super robust as loops are a frequent issue. Most successful projects: AutoGPT, AgentGPT, MemGPT, GPT-Researcher, CrewAI, MetaGPT. There are also some arXiv papers & blog posts on the most important architectures. 🔗 All the materials are here: 🧠 The best part is there is a co-pilot to chat with all this knowledge! If you’d like to add some valuable publications on Agents to this collection - just share a link in the comments 👇

I've been researching Agents for the past 6 months and collected 40+ materials on the most capable architectures & implementations. The intent was to publish a comprehensive overview, like I did on RAG techniques, but been too busy with so sharing it here. There are some great intro lectures by Andrew Ng to start with. The following types of Agentic architectures are covered: 🤖 Chain of thought (Plan & Execute agent) 🤖 Tooling operators (An agent upon a set of tools, routing to them) - good for connecting external data storage & APIs, pretty fast and robust 🤖 ReAct (Thought - Action - Observation) - capable of iteratively executing complex tasks or answering complex queries 🤖 Self-Reflection - (Action - Observation / Evaluation - Reflection - Planning) - adds some quality and reasoning clarity compared to the ReAct scheme, might be slower 🤖 Agent upon agents (A multiagent scheme) - a quite complex setting, slow, but capable of executing very complex multistep tasks, not super robust as loops are a frequent issue. Most successful projects: AutoGPT, AgentGPT, MemGPT, GPT-Researcher, CrewAI, MetaGPT. There are also some arXiv papers & blog posts on the most important architectures. 🔗 All the materials are here: 🧠 The best part is there is a co-pilot to chat with all this knowledge! If you’d like to add some valuable publications on Agents to this collection - just share a link in the comments 👇

IVAN ILIN

113,969 görüntüleme • 2 yıl önce

Big news, friends! I hereby introduce It's a multi-agent chat app with special features for collaborative ranking and estimation tasks, to help you quickly fact-check AI responses against each other. It has GPT-5, Claude Opus 4.1, Gemini 2.5 Pro, and Grok 4, and built-in systems for comparing and aggregating their responses. If you try it, post feature requests for me and the team theMultiplicity.ai!

Big news, friends! I hereby introduce It's a multi-agent chat app with special features for collaborative ranking and estimation tasks, to help you quickly fact-check AI responses against each other. It has GPT-5, Claude Opus 4.1, Gemini 2.5 Pro, and Grok 4, and built-in systems for comparing and aggregating their responses. If you try it, post feature requests for me and the team theMultiplicity.ai!

Andrew Critch (🤖🩺🚀)

20,627 görüntüleme • 8 ay önce

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Shushant Lakhyani

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Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 görüntüleme • 6 ay önce

Google DeepMind is cooking 🔥 TLDR : They just introduced CodeMender, an AI agent that automatically finds and fixes software vulnerabilities. Powered by Gemini Deep Think models, it can patch new bugs instantly and rewrite existing code to eliminate entire classes of security flaws. In six months, CodeMender has already submitted 72 upstream security fixes to major open-source projects. It uses advanced program analysis, multi-agent reasoning, and automatic validation to ensure patches are accurate and safe before human review. One highlight: it applied -fbounds-safety annotations to libraries like libwebp, making past buffer overflow exploits (like CVE-2023-4863) permanently unexploitable. All patches are currently human reviewed, but the goal is to release CodeMender widely as a developer tool to strengthen software security at scale.

Google DeepMind is cooking 🔥 TLDR : They just introduced CodeMender, an AI agent that automatically finds and fixes software vulnerabilities. Powered by Gemini Deep Think models, it can patch new bugs instantly and rewrite existing code to eliminate entire classes of security flaws. In six months, CodeMender has already submitted 72 upstream security fixes to major open-source projects. It uses advanced program analysis, multi-agent reasoning, and automatic validation to ensure patches are accurate and safe before human review. One highlight: it applied -fbounds-safety annotations to libraries like libwebp, making past buffer overflow exploits (like CVE-2023-4863) permanently unexploitable. All patches are currently human reviewed, but the goal is to release CodeMender widely as a developer tool to strengthen software security at scale.

AshutoshShrivastava

37,936 görüntüleme • 9 ay önce

🎉 Five Weeks, Five Free GSAP Resources! Hey everyone! We’re super excited to partner with Codrops and GSAP to celebrate the news that GSAP’s club plugins are now 100% free thanks to Webflow's support. To mark the occasion, we’ll be dropping one free GSAP resource every week for the next five weeks—each fully packaged as a Webflow clonable and a CodePen so you can plug it straight into your own projects. Each resource will feature at least one of those previously paid plugins, to hopefully spark some ideas and inspire your next interactive build. We’re kicking things off with a 'Glowing Interactive Dots Grid' powered by the InertiaPlugin. See how a simple dot matrix can come alive—glowing, springing, and rippling with realistic momentum under your cursor. Grab the CodePen or the Webflow cloneable, tweak the settings, and have a play with it! Sign up for our newsletter through the footer of our website to get all the free stuff in your inbox!

🎉 Five Weeks, Five Free GSAP Resources! Hey everyone! We’re super excited to partner with Codrops and GSAP to celebrate the news that GSAP’s club plugins are now 100% free thanks to Webflow's support. To mark the occasion, we’ll be dropping one free GSAP resource every week for the next five weeks—each fully packaged as a Webflow clonable and a CodePen so you can plug it straight into your own projects. Each resource will feature at least one of those previously paid plugins, to hopefully spark some ideas and inspire your next interactive build. We’re kicking things off with a 'Glowing Interactive Dots Grid' powered by the InertiaPlugin. See how a simple dot matrix can come alive—glowing, springing, and rippling with realistic momentum under your cursor. Grab the CodePen or the Webflow cloneable, tweak the settings, and have a play with it! Sign up for our newsletter through the footer of our website to get all the free stuff in your inbox!

Osmo

13,494 görüntüleme • 1 yıl önce

🌌 AI Agents Are Taking Over... And We’re Bringing Them to Berachain Foundation 🐻⛓ 🐻🔥 Hundreds of hours spent on research, tracking wallets, analyzing bribes, and managing portfolios... What if your AI Agent could do this for you—24/7? ⏲️ 🔧 Our Tech Is Next-Level On our testnet, you’ve been memeing it up with PumpFun™, creating dank memecoins enhanced by NFTs. But once Berachain’s mainnet is live, you’ll be able to create your own AI Agents. To test and perfect our tech, we shared it with projects like AI Agent Layer | AIFUN, allowing us to test it in all conditions and continuously improve its performance. 🛠️🔥 🐻 Why AI Agent are great for berachain? Berachain might seem simple at first glance: validators, bribes, POL, staking rewards… but the deeper you go, the more complex the game theory becomes. 🤯 Here’s where AI comes in. Imagine an agent helping you: 💡 Optimize bribes 📊 Analyze validator behavior 🧠 Make decisions faster and smarter and much more, as AI Agents won't be limited to the chain itself! Examples of AI Agent Projects Dominating the Space 🚀 $VIRTUAL - Launchpad for AI Agents ($3.5B mcap) 🧠 $AI16Z - Eliza OS Framework ($2B mcap) 🔍 $AIXBT - The AI Analyst revolutionizing CT ($430M mcap) 🎮 $GAME - Low-code toolkit for creating AI Agents ($230M mcap) 💡 There are already AI Agents managing portfolios, betting on sports, and automating tasks. And guess what? They're outperforming humans. 🌐 We've built Virtuals on Berachain Our protocol integrates directly with Berachain, providing real utility to our token: $AIBERA 💎. Say Ooga Booga if you want to see a thread about tokenomics and $AIBERA utility. The chain has beras on it, and beras deserve AI Agents. 🐻🤖 Ooga Booga. 🔥

🌌 AI Agents Are Taking Over... And We’re Bringing Them to Berachain Foundation 🐻⛓ 🐻🔥 Hundreds of hours spent on research, tracking wallets, analyzing bribes, and managing portfolios... What if your AI Agent could do this for you—24/7? ⏲️ 🔧 Our Tech Is Next-Level On our testnet, you’ve been memeing it up with PumpFun™, creating dank memecoins enhanced by NFTs. But once Berachain’s mainnet is live, you’ll be able to create your own AI Agents. To test and perfect our tech, we shared it with projects like AI Agent Layer | AIFUN, allowing us to test it in all conditions and continuously improve its performance. 🛠️🔥 🐻 Why AI Agent are great for berachain? Berachain might seem simple at first glance: validators, bribes, POL, staking rewards… but the deeper you go, the more complex the game theory becomes. 🤯 Here’s where AI comes in. Imagine an agent helping you: 💡 Optimize bribes 📊 Analyze validator behavior 🧠 Make decisions faster and smarter and much more, as AI Agents won't be limited to the chain itself! Examples of AI Agent Projects Dominating the Space 🚀 $VIRTUAL - Launchpad for AI Agents ($3.5B mcap) 🧠 $AI16Z - Eliza OS Framework ($2B mcap) 🔍 $AIXBT - The AI Analyst revolutionizing CT ($430M mcap) 🎮 $GAME - Low-code toolkit for creating AI Agents ($230M mcap) 💡 There are already AI Agents managing portfolios, betting on sports, and automating tasks. And guess what? They're outperforming humans. 🌐 We've built Virtuals on Berachain Our protocol integrates directly with Berachain, providing real utility to our token: $AIBERA 💎. Say Ooga Booga if you want to see a thread about tokenomics and $AIBERA utility. The chain has beras on it, and beras deserve AI Agents. 🐻🤖 Ooga Booga. 🔥

HoneyFun AI

10,906 görüntüleme • 1 yıl önce

Yep! We spent a 21 months building Werm, and we’re genuinely proud of how we’ve done things. We’ll keep doing everything possible to build great products. What you see now is the result of countless iterations from the version shown in the below video. With just three people, building and scaling a multiplayer game has been incredibly challenging, even large studios struggle with this. The 21 months of development were necessary. We went through multiple rebuilds, learned a ton about launches, and built real relationships across the ecosystem. We stayed away from presales and token launches because the product wasn’t ready. Now we finally feel confident to give it a real shot. There’ll be bumps along the way, but unlike most crypto projects built on hype, we’re launching with an actual product and genuine intent to make it work. We believe we’re doing it the right way and that’s how we plan to keep going. The goal is for the community to trust that when we build, it’s 100% real, never a scam. More news to come about Werm!

Yep! We spent a 21 months building Werm, and we’re genuinely proud of how we’ve done things. We’ll keep doing everything possible to build great products. What you see now is the result of countless iterations from the version shown in the below video. With just three people, building and scaling a multiplayer game has been incredibly challenging, even large studios struggle with this. The 21 months of development were necessary. We went through multiple rebuilds, learned a ton about launches, and built real relationships across the ecosystem. We stayed away from presales and token launches because the product wasn’t ready. Now we finally feel confident to give it a real shot. There’ll be bumps along the way, but unlike most crypto projects built on hype, we’re launching with an actual product and genuine intent to make it work. We believe we’re doing it the right way and that’s how we plan to keep going. The goal is for the community to trust that when we build, it’s 100% real, never a scam. More news to come about Werm!

Tilted Ventures

19,582 görüntüleme • 8 ay önce

🔥 Nebius AI R&D is hiring AI Research Interns for short, high-impact RL projects. Exclusive to X right now — no LinkedIn mass postings yet. In 2019, I was a fresh dental grad with 3 months of runway left, begging for an AI shot. I know the grind. We’re looking for sharp early-career folks (students, grads, career-switchers) to join us and work on: > Agent trajectories analysis at scale > Long-horizon tasks for coding agents > Pushing open RL environments > Any other data / RL env / eval project that will benefit open-source community What you get: 💰 Fully paid internship (3-6 month) 📦 100% open-source shipping 📄 Co-author research papers ⚡️ Access to Nebius compute infra 🌍 Remote-friendly (EU/US) or Amsterdam/London/other office. If you’ve done any cool AI/ML/RL stuff, dm me with your most impressive project + 1-sentence summary + cv Sharing appreciated!🤝

🔥 Nebius AI R&D is hiring AI Research Interns for short, high-impact RL projects. Exclusive to X right now — no LinkedIn mass postings yet. In 2019, I was a fresh dental grad with 3 months of runway left, begging for an AI shot. I know the grind. We’re looking for sharp early-career folks (students, grads, career-switchers) to join us and work on: > Agent trajectories analysis at scale > Long-horizon tasks for coding agents > Pushing open RL environments > Any other data / RL env / eval project that will benefit open-source community What you get: 💰 Fully paid internship (3-6 month) 📦 100% open-source shipping 📄 Co-author research papers ⚡️ Access to Nebius compute infra 🌍 Remote-friendly (EU/US) or Amsterdam/London/other office. If you’ve done any cool AI/ML/RL stuff, dm me with your most impressive project + 1-sentence summary + cv Sharing appreciated!🤝

Ibragim

33,427 görüntüleme • 3 ay önce

E11 Bio is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features. This is a key advance towards economically viable brain mapping - 95% of costs stem from neuron tracing. It is also an important step towards democratizing neuron tracing for everyday neuroscience. Solving these problems is critical for curing brain disorders, building safer and human-like AI, and even simulating brain function. In our first pilot study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling. Using molecular annotation, we uncover an intriguing feature of synaptic organization, demonstrating how PRISM can be used for systematic discovery 🧵

E11 Bio is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features. This is a key advance towards economically viable brain mapping - 95% of costs stem from neuron tracing. It is also an important step towards democratizing neuron tracing for everyday neuroscience. Solving these problems is critical for curing brain disorders, building safer and human-like AI, and even simulating brain function. In our first pilot study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling. Using molecular annotation, we uncover an intriguing feature of synaptic organization, demonstrating how PRISM can be used for systematic discovery 🧵

Andrew Payne

112,185 görüntüleme • 9 ay önce

We're excited to unveil NRN Agents, a rebrand that aligns our project identity with our token and strengthens our mission to power the future of AI-driven gaming. This mission requires collaboration, and starting this week, we will begin our expansion to become a multi-chain ecosystem. We are joining forces with leading gaming platforms and ecosystems to realize this vision. Stay tuned for more announcements to come. Why NRN Agents? NRN stands for NEURON, the fundamental unit of intelligence. Our AI agents function as the neural foundation of games, learning, adapting, and evolving within game worlds to deliver unparalleled engagement. NRN agent SDK enables advanced gaming agents powered by a proprietary machine learning infrastructure focused on behavioral learning. We've perfected the craft of gaming agent design, creating hyper-efficient agents that are performant and scalable—from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem – Any Game. Any Chain. More than just games, it's the path to AGI Gaming is our proving ground, but not our final destination. We're using games as a sandbox to accelerate the development of generalized intelligence—one that will create meaningful real-world impact. With the upcoming launch of [redacted] and a growing network of partners committed to the AGI vision, we're building an open-source innovation movement powered by an AI x gaming framework connected by $NRN. $NRN the token $NRN is a utility token that serves as the gateway to our growing ecosystem. It will power a diversified economy with multiple revenue streams and staking opportunities: Agent Deployment: NRN is the laboratory creating gaming agents that can be distributed through platforms and launchpads alike. The model is simple: More games integrate, more NRN agents get deployed, more monetization. Data Creation: NRN Reinforcement Learning (RL) enables token staking to create Data Capsules. Players contribute gameplay data into the Capsules, which are used train RL agents and reward participants (players & stakers). AI Arena: $NRN also continues to power AI Arena's in-game economy, a cult favorite of competitive diehards that features a skill-based wagering system. To our community who have supported us since 2021: thank you for being part of our journey—the next chapter will be the most exciting yet!

We're excited to unveil NRN Agents, a rebrand that aligns our project identity with our token and strengthens our mission to power the future of AI-driven gaming. This mission requires collaboration, and starting this week, we will begin our expansion to become a multi-chain ecosystem. We are joining forces with leading gaming platforms and ecosystems to realize this vision. Stay tuned for more announcements to come. Why NRN Agents? NRN stands for NEURON, the fundamental unit of intelligence. Our AI agents function as the neural foundation of games, learning, adapting, and evolving within game worlds to deliver unparalleled engagement. NRN agent SDK enables advanced gaming agents powered by a proprietary machine learning infrastructure focused on behavioral learning. We've perfected the craft of gaming agent design, creating hyper-efficient agents that are performant and scalable—from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem – Any Game. Any Chain. More than just games, it's the path to AGI Gaming is our proving ground, but not our final destination. We're using games as a sandbox to accelerate the development of generalized intelligence—one that will create meaningful real-world impact. With the upcoming launch of [redacted] and a growing network of partners committed to the AGI vision, we're building an open-source innovation movement powered by an AI x gaming framework connected by $NRN. $NRN the token $NRN is a utility token that serves as the gateway to our growing ecosystem. It will power a diversified economy with multiple revenue streams and staking opportunities: Agent Deployment: NRN is the laboratory creating gaming agents that can be distributed through platforms and launchpads alike. The model is simple: More games integrate, more NRN agents get deployed, more monetization. Data Creation: NRN Reinforcement Learning (RL) enables token staking to create Data Capsules. Players contribute gameplay data into the Capsules, which are used train RL agents and reward participants (players & stakers). AI Arena: $NRN also continues to power AI Arena's in-game economy, a cult favorite of competitive diehards that features a skill-based wagering system. To our community who have supported us since 2021: thank you for being part of our journey—the next chapter will be the most exciting yet!

NRN Agents

20,762 görüntüleme • 1 yıl önce

Created with Gpt Image 2 + Seedance 2.0 on SJinn Agent Prompt: Create a high-quality 4K cinematic football (soccer) storyboard showing a full sequence from tackle to goal, using dynamic lighting, dramatic stadium atmosphere, and professional sports broadcast style. The scene begins with an intense midfield moment where a defender performs a strong sliding tackle, grass and dirt particles flying in slow motion as the ball is cleanly won. Transition immediately into a fast counterattack: the player recovers, pushes the ball forward, and accelerates past opponents with quick footwork. Show close-ups of boots striking the ball, sweat, and focused expressions, mixed with wide shots of the field opening up. Add a key pass moment where the ball is threaded through defenders, followed by a sprint toward goal with the crowd rising in anticipation. Build tension with a slow-motion approach to the penalty box, then a powerful shot on goal—either a clean strike or a curled finish. Show the ball hitting the net with dramatic impact, net rippling, and goalkeeper reaction. End with an emotional celebration: the scorer running, sliding, or raising arms as teammates join, stadium lights glowing and crowd erupting. Use varied camera angles (wide, tracking, close-up, POV, slow motion), strong motion blur, and realistic physics (ball spin, turf impact), emphasizing the shift from defensive intensity to attacking triumph and victory.

Created with Gpt Image 2 + Seedance 2.0 on SJinn Agent Prompt: Create a high-quality 4K cinematic football (soccer) storyboard showing a full sequence from tackle to goal, using dynamic lighting, dramatic stadium atmosphere, and professional sports broadcast style. The scene begins with an intense midfield moment where a defender performs a strong sliding tackle, grass and dirt particles flying in slow motion as the ball is cleanly won. Transition immediately into a fast counterattack: the player recovers, pushes the ball forward, and accelerates past opponents with quick footwork. Show close-ups of boots striking the ball, sweat, and focused expressions, mixed with wide shots of the field opening up. Add a key pass moment where the ball is threaded through defenders, followed by a sprint toward goal with the crowd rising in anticipation. Build tension with a slow-motion approach to the penalty box, then a powerful shot on goal—either a clean strike or a curled finish. Show the ball hitting the net with dramatic impact, net rippling, and goalkeeper reaction. End with an emotional celebration: the scorer running, sliding, or raising arms as teammates join, stadium lights glowing and crowd erupting. Use varied camera angles (wide, tracking, close-up, POV, slow motion), strong motion blur, and realistic physics (ball spin, turf impact), emphasizing the shift from defensive intensity to attacking triumph and victory.

Zara

61,062 görüntüleme • 2 ay önce

YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥

YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥

YOMIRGO

23,685 görüntüleme • 5 ay önce

🚨 BIG EVENT ANNOUNCEMENT! 🚨 The Snapshot was officially taken at 11:59 PM UTC on March 31st, and we’re now processing the data. A huge thank you to everyone who stayed active, fed their Capybaras, and contributed to this incredible journey! ❤️ But trust us—the best is yet to come! ⸻ 🎉 THE BIG EVENT REVEALED! We’ve been searching for a way to reward our community in a massive way… far beyond the typical $5 airdrops from other SocialFi projects. And we’ve found the perfect solution! 🚀 $500,000+ in Multi-Chain Identities! We’re excited to announce a major partnership with a Web3 infrastructure company specializing in identity management across modular blockchains. Their Modular Naming Service (MNS) allows users to transform complex wallet addresses into simple, recognizable identities usable across multiple blockchains. By securing your unique username through MNS, you can: • Simplify Transactions: Replace complex wallet addresses with a single, easy-to-remember name across multiple blockchains. • Unify Your Identity: Manage a single, recognizable identity across various platforms, enhancing your presence in the Web3 ecosystem. • Assess Your On-Chain Value: Utilize the AI Identity Score feature to evaluate your on-chain activity. A higher score may increase your eligibility for future airdrops and rewards. Who Gets These Identities? ✅ Player Card NFT Holders: • Each identity is valued between $40 and $1,250. • Distribution is based on your Leaderboard position at the time of the Snapshot, considering Player Card NFT holders after adjusting balances for Money Bag multipliers. ✅ All Capybara Users (even without an NFT): • Every participant who has earned at least 100,000 points qualifies for a $10 identity. ⸻ 🤖 COMING SOON: AI-POWERED TWITTER TOOL! We’re developing an AI Twitter Agent designed to help Capybara players enhance their social media presence and engagement and unlock additional rewards. ⸻ 🔥 CAPYBARA COMMUNITY TOKEN LAUNCHING IN Q2! We’re thrilled to announce the upcoming launch of the Capybara Community Token in Q2 to reward our ecosystem with an exciting airdrop! 🎉 —- 📢 More details on the claiming process will be revealed TOMORROW, April 2! Be sure to check back to learn how to claim your identity

🚨 BIG EVENT ANNOUNCEMENT! 🚨 The Snapshot was officially taken at 11:59 PM UTC on March 31st, and we’re now processing the data. A huge thank you to everyone who stayed active, fed their Capybaras, and contributed to this incredible journey! ❤️ But trust us—the best is yet to come! ⸻ 🎉 THE BIG EVENT REVEALED! We’ve been searching for a way to reward our community in a massive way… far beyond the typical $5 airdrops from other SocialFi projects. And we’ve found the perfect solution! 🚀 $500,000+ in Multi-Chain Identities! We’re excited to announce a major partnership with a Web3 infrastructure company specializing in identity management across modular blockchains. Their Modular Naming Service (MNS) allows users to transform complex wallet addresses into simple, recognizable identities usable across multiple blockchains. By securing your unique username through MNS, you can: • Simplify Transactions: Replace complex wallet addresses with a single, easy-to-remember name across multiple blockchains. • Unify Your Identity: Manage a single, recognizable identity across various platforms, enhancing your presence in the Web3 ecosystem. • Assess Your On-Chain Value: Utilize the AI Identity Score feature to evaluate your on-chain activity. A higher score may increase your eligibility for future airdrops and rewards. Who Gets These Identities? ✅ Player Card NFT Holders: • Each identity is valued between $40 and $1,250. • Distribution is based on your Leaderboard position at the time of the Snapshot, considering Player Card NFT holders after adjusting balances for Money Bag multipliers. ✅ All Capybara Users (even without an NFT): • Every participant who has earned at least 100,000 points qualifies for a $10 identity. ⸻ 🤖 COMING SOON: AI-POWERED TWITTER TOOL! We’re developing an AI Twitter Agent designed to help Capybara players enhance their social media presence and engagement and unlock additional rewards. ⸻ 🔥 CAPYBARA COMMUNITY TOKEN LAUNCHING IN Q2! We’re thrilled to announce the upcoming launch of the Capybara Community Token in Q2 to reward our ecosystem with an exciting airdrop! 🎉 —- 📢 More details on the claiming process will be revealed TOMORROW, April 2! Be sure to check back to learn how to claim your identity

Capybara on Sui

27,274 görüntüleme • 1 yıl önce

🌟 Exciting News: Mantle Network joins #SociaSummer with a 20,000 $MNT Prize Pool, and PoPP TBA is live on Mantle. 👉 RT and join now: ✅ Follow Mantle Network and PoP Planet. ✅ Mint your #TBA in Mantle's #SocialSummer event and collect more $SD. ✅ Add #ToTheMoon on Telegram for free $SD! 🏆 #SocialSummer Prize Pool: Total value $20,000,000 USDT + PoPP $SD 👉 How to Mint TBA on Mantle 🌟 What are the benefits of being a TBA holder? 🎁 Your TBA is your web3 social graph account. As a TBA holder, you'll receive PoPP tokens directly after the TGE.TBA brings holders not only a share of the prize pool, but also continuous rewards & airdrop oppurtinities. Right now, you can earn extra rewards from over 200 global projects in #SocialSummer, including Mantle Network, Taiko.eth 🥁, Zulu Network, Kana Labs, OORT | The Data Cloud for Decentralized AI, DeGuard VPN, Gonesis AI, @0xNovaSeeds, AILayer, XO 👽 Web3 Friend-making, XSTAR, SoccerN, Neo, Turingbitchain, ZNS CONNECT, and more! Earn more #SD and secure your spot in our TGE! 🎉 🎁 TBA holders can earn airdrops, tokens, whitelists, and NFTs from over 200 global projects in #SocialSummer. Plus, join the #meme lottery and increase your chances of winning 1 $BTC. $SD holders will get mining boosts in the PoPP ecosystem in the future. New tasks are live now. 🔥#mantle #PoPP

🌟 Exciting News: Mantle Network joins #SociaSummer with a 20,000 $MNT Prize Pool, and PoPP TBA is live on Mantle. 👉 RT and join now: ✅ Follow Mantle Network and PoP Planet. ✅ Mint your #TBA in Mantle's #SocialSummer event and collect more $SD. ✅ Add #ToTheMoon on Telegram for free $SD! 🏆 #SocialSummer Prize Pool: Total value $20,000,000 USDT + PoPP $SD 👉 How to Mint TBA on Mantle 🌟 What are the benefits of being a TBA holder? 🎁 Your TBA is your web3 social graph account. As a TBA holder, you'll receive PoPP tokens directly after the TGE.TBA brings holders not only a share of the prize pool, but also continuous rewards & airdrop oppurtinities. Right now, you can earn extra rewards from over 200 global projects in #SocialSummer, including Mantle Network, Taiko.eth 🥁, Zulu Network, Kana Labs, OORT | The Data Cloud for Decentralized AI, DeGuard VPN, Gonesis AI, @0xNovaSeeds, AILayer, XO 👽 Web3 Friend-making, XSTAR, SoccerN, Neo, Turingbitchain, ZNS CONNECT, and more! Earn more #SD and secure your spot in our TGE! 🎉 🎁 TBA holders can earn airdrops, tokens, whitelists, and NFTs from over 200 global projects in #SocialSummer. Plus, join the #meme lottery and increase your chances of winning 1 $BTC. $SD holders will get mining boosts in the PoPP ecosystem in the future. New tasks are live now. 🔥#mantle #PoPP

PoP Planet

52,360 görüntüleme • 1 yıl önce

$Machine Tokenization is HERE 🔥 Introducing the world's first Machine Real-World Asset (#RWA) Tokenization platform, by Teneo, powered by peaq 🌎 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? 🤨 Enter the Machine Tokenization Platform⚡️ 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 ✅ ELOOP has already successfully tokenized a fleet of Teslas for a car-sharing pilot project in Vienna 🇦🇹 which saw the community earn revenue as the Teslas were used. Check out these videos 🎞️ 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 📈 DePIN Layer-1 Synergies 🧲 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 🦾 A range of Web2 and Web3 projects are already exploring pilot projects on the platform, including Dabba Network 🟨, a connectivity DePIN working to deliver Web access to the unconnected. Already testing on krest 🔥 ELOOP is already testing the platform on krest, peaq’s canary network, and plans to launch it on the peaq mainnet, which will go live this year. “We’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.” - Nico Prugger, co-founder, ELOOP Read all about it:$

Machine Tokenization is HERE 🔥 Introducing the world's first Machine Real-World Asset (#RWA) Tokenization platform, by Teneo, powered by peaq 🌎 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? 🤨 Enter the Machine Tokenization Platform⚡️ 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 ✅ ELOOP has already successfully tokenized a fleet of Teslas for a car-sharing pilot project in Vienna 🇦🇹 which saw the community earn revenue as the Teslas were used. Check out these videos 🎞️ 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 📈 DePIN Layer-1 Synergies 🧲 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 🦾 A range of Web2 and Web3 projects are already exploring pilot projects on the platform, including Dabba Network 🟨, a connectivity DePIN working to deliver Web access to the unconnected. Already testing on krest 🔥 ELOOP is already testing the platform on krest, peaq’s canary network, and plans to launch it on the peaq mainnet, which will go live this year. “We’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.” - Nico Prugger, co-founder, ELOOP Read all about it:

peaq

115,311 görüntüleme • 2 yıl önce

We are excited to unveil the latest version of the AIOZ Node: The Version 4.0 update! This update includes a new user interface and brings substantial functional improvements, enhancing your overall experience for increased productivity and efficiency. More information below: The standout feature of AIOZ Node v4.0 is the introduction of the Transcoding functionality, which is currently available in beta. This functionality enables your node to participate in video transcoding, which converts video files into different formats for various digital devices and media platforms. By enabling transcoding, your node can contribute more significantly to the AIOZ Network, expanding the network's capabilities and potential $AIOZ token rewards. While the transcoding functionality is currently in beta, the upcoming AIOZ W3Stream integration, a DePIN Video Infrastructure due for release in Q3 2024, will unlock the full potential of your node and enable seamless video transcoding tasks. To get started with AIOZ Node v4.0, you simply need to visit our official website to download the latest version of the AIOZ Node: This download process is very straightforward, and with a one-click installation process, you can set up AIOZ Node v4.0 to start running on your device within a few minutes. If you are already running an AIOZ Node on your device, the version 4.0 update will be applied automatically, ensuring you have the latest features and improvements without hassle! With the Node v4.0 update running on your device, you can proceed to familiarize yourself with the new layout, check out the performance improvements, and start transcoding to see how it enhances your contributions to the network! Learn More: $AIOZ

We are excited to unveil the latest version of the AIOZ Node: The Version 4.0 update! This update includes a new user interface and brings substantial functional improvements, enhancing your overall experience for increased productivity and efficiency. More information below: The standout feature of AIOZ Node v4.0 is the introduction of the Transcoding functionality, which is currently available in beta. This functionality enables your node to participate in video transcoding, which converts video files into different formats for various digital devices and media platforms. By enabling transcoding, your node can contribute more significantly to the AIOZ Network, expanding the network's capabilities and potential $AIOZ token rewards. While the transcoding functionality is currently in beta, the upcoming AIOZ W3Stream integration, a DePIN Video Infrastructure due for release in Q3 2024, will unlock the full potential of your node and enable seamless video transcoding tasks. To get started with AIOZ Node v4.0, you simply need to visit our official website to download the latest version of the AIOZ Node: This download process is very straightforward, and with a one-click installation process, you can set up AIOZ Node v4.0 to start running on your device within a few minutes. If you are already running an AIOZ Node on your device, the version 4.0 update will be applied automatically, ensuring you have the latest features and improvements without hassle! With the Node v4.0 update running on your device, you can proceed to familiarize yourself with the new layout, check out the performance improvements, and start transcoding to see how it enhances your contributions to the network! Learn More: $AIOZ

AIOZ Network

20,428 görüntüleme • 2 yıl önce

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

Anshu

170,846 görüntüleme • 5 gün önce