Loading video...

Video Failed to Load

Go Home

Introducing "ScratchPad" (working title) -- beta dropping soon with source code -- generative AI + UI + natural way to interact and iterate ideas etc. - image, text, postit, video nodes - intuitive canvas interface - link nodes, combine, share - multiple spaces - auto arrange - much more...

jasonkneen's profile picture

Jason Kneen

29,575 subscribers

39,033 views • 6 months ago •via X (Twitter)

EducationScience & Technology
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Last week OpenAI launched ChatGPT Canvas, an interface that displays text, code, and visualization outputs. In the enterprise, we rely on more structured, trusted UX elements -- record details, lists, dropdowns, dashboards. Salesforce Page Layouts and Dynamic Forms control how components are presented, but it's time-consuming and hard to anticipate what data and workflows each user will need. But what if it didn't have to be this way? So excited to share the new Salesforce Generative Lightning UX ⚡️which dynamically generates layouts including tables, charts, news, Lightning components, powered by Agentforce. 🔹 Empowers CRM teams with ready access to essential info and insights, and ability to take action by talking or typing 🔹 Personalized to each user and adapts dynamically to each job to be done 🔹Secure and trusted, respecting data-sharing rules and permissions configured in your Salesforce instance We know AI can generate code, text, and video. Now AI can dynamically generate enterprise UX/UI too. You'll never experience enterprise apps the same again. 🤯 In pilot starting now. Look forward to your feedback so that we can productize next year! Salesforce Research Blog: Generative Lightning on AppExchange: #GenAI #UI #UX #Interfaces #Design #PageLayouts
2:30

Last week OpenAI launched ChatGPT Canvas, an interface that displays text, code, and visualization outputs. In the enterprise, we rely on more structured, trusted UX elements -- record details, lists, dropdowns, dashboards. Salesforce Page Layouts and Dynamic Forms control how components are presented, but it's time-consuming and hard to anticipate what data and workflows each user will need. But what if it didn't have to be this way? So excited to share the new Salesforce Generative Lightning UX ⚡️which dynamically generates layouts including tables, charts, news, Lightning components, powered by Agentforce. 🔹 Empowers CRM teams with ready access to essential info and insights, and ability to take action by talking or typing 🔹 Personalized to each user and adapts dynamically to each job to be done 🔹Secure and trusted, respecting data-sharing rules and permissions configured in your Salesforce instance We know AI can generate code, text, and video. Now AI can dynamically generate enterprise UX/UI too. You'll never experience enterprise apps the same again. 🤯 In pilot starting now. Look forward to your feedback so that we can productize next year! Salesforce Research Blog: Generative Lightning on AppExchange: #GenAI #UI #UX #Interfaces #Design #PageLayouts

Clara Shih

123,751 views • 1 year ago

🚀 Introducing HunyuanCustom: An open-source, multimodal-driven architecture for customized video generation, powered by HunyuanVideo-13B. Outperforming existing open-source models, it rivals top closed-source solutions! 🎥 Highlights: ✅Subject Consistency: Maintains identity across single & multi-subject video generation. ✅Multimodal Inputs: Supports text, images, audio, and video for highly controlled outputs. ✅High-Quality Output: Industry-leading detail, motion smoothness, and lighting realism. Key Features: 1️⃣Single-Subject Video: Upload an image + text (e.g., “He’s walking a dog”) to create coherent videos with new actions, outfits, and scenes. 2️⃣Multi-Subject Video: Generate videos with multiple subjects (e.g., a man drinking coffee in a cozy room) from separate image inputs. 3️⃣Audio-Driven Video: Sync audio with visuals for talking or singing in any scene—perfect for digital avatars, virtual customer service, and more. 4️⃣Video-Driven Video: Seamlessly insert or replace subjects into any video for creative enhancements. Dive Deeper: 🌐Project: 📝Technical Details: 💻Code: The single-subject video capability is open-sourced and live on the Hunyuan website, with more features to be released soon! 👉 Try it now: #HunyuanCustom #VideoGeneration #AI #TencentHunyuan
0:40

🚀 Introducing HunyuanCustom: An open-source, multimodal-driven architecture for customized video generation, powered by HunyuanVideo-13B. Outperforming existing open-source models, it rivals top closed-source solutions! 🎥 Highlights: ✅Subject Consistency: Maintains identity across single & multi-subject video generation. ✅Multimodal Inputs: Supports text, images, audio, and video for highly controlled outputs. ✅High-Quality Output: Industry-leading detail, motion smoothness, and lighting realism. Key Features: 1️⃣Single-Subject Video: Upload an image + text (e.g., “He’s walking a dog”) to create coherent videos with new actions, outfits, and scenes. 2️⃣Multi-Subject Video: Generate videos with multiple subjects (e.g., a man drinking coffee in a cozy room) from separate image inputs. 3️⃣Audio-Driven Video: Sync audio with visuals for talking or singing in any scene—perfect for digital avatars, virtual customer service, and more. 4️⃣Video-Driven Video: Seamlessly insert or replace subjects into any video for creative enhancements. Dive Deeper: 🌐Project: 📝Technical Details: 💻Code: The single-subject video capability is open-sourced and live on the Hunyuan website, with more features to be released soon! 👉 Try it now: #HunyuanCustom #VideoGeneration #AI #TencentHunyuan

Tencent HY

191,908 views • 1 year ago

Crazy... I just found a super cheap way to code with AI Tried GLM 4.5 on Claude Code and I’m really impressed! Prompt: “create an interactive python app where users can track, manage or analyze data” And, It came up with: • Dashboard - a clean interface to see all your data at a glance • Datasets - manage multiple collections of data easily • Data entries - add, edit, and organize your records without hassle • Analysis - get insights and summaries directly from your data What I observed: Everything worked smoothly, GLM 4.5 is definitely one of the best open-source models for coding, I’ll keep using it. Check it out now: And the best part: super affordable coding plans! • Lite - $3/month (promo). ~120 prompts every 5 hours. Works with Claude Code, Roo Code, Cline, Kilo Code, and more. • Pro - $15/month (promo). 5× more usage than Lite. Faster. Includes image/video tools and web search. • Max - $30/month (promo). 4× more usage than Pro. Guaranteed performance. Early access to new features. Bonus - Use my referral link to get an extra 10% off on top of these promo prices! Video demo 👇
0:33

Crazy... I just found a super cheap way to code with AI Tried GLM 4.5 on Claude Code and I’m really impressed! Prompt: “create an interactive python app where users can track, manage or analyze data” And, It came up with: • Dashboard - a clean interface to see all your data at a glance • Datasets - manage multiple collections of data easily • Data entries - add, edit, and organize your records without hassle • Analysis - get insights and summaries directly from your data What I observed: Everything worked smoothly, GLM 4.5 is definitely one of the best open-source models for coding, I’ll keep using it. Check it out now: And the best part: super affordable coding plans! • Lite - $3/month (promo). ~120 prompts every 5 hours. Works with Claude Code, Roo Code, Cline, Kilo Code, and more. • Pro - $15/month (promo). 5× more usage than Lite. Faster. Includes image/video tools and web search. • Max - $30/month (promo). 4× more usage than Pro. Guaranteed performance. Early access to new features. Bonus - Use my referral link to get an extra 10% off on top of these promo prices! Video demo 👇

Chidanand Tripathi

83,854 views • 10 months ago

AI is REVOLUTIONIZING the way we interact with data/information I came across a tool that is going to change the way you ABSORB information It’s called Mindgrasp (Mindgrasp) It’s INCREDIBLE for learning and helps in saving hours! Features: 1. Upload any file type (up to 500 pages of PDFs, hrs of video content/files, links to open-access sites, audio files, straight copy-paste text, etc.) 2. The Q/A interface can answer any question or command based on the file that you upload. Ask questions, create summaries or bullet points, extract insights & much more! 3. Search the Web Q/A - A web Q/A that allows you to pull information from outside your uploaded source while still giving you all the references so you know exactly where the information is coming from. Know more at:
1:27

AI is REVOLUTIONIZING the way we interact with data/information I came across a tool that is going to change the way you ABSORB information It’s called Mindgrasp (Mindgrasp) It’s INCREDIBLE for learning and helps in saving hours! Features: 1. Upload any file type (up to 500 pages of PDFs, hrs of video content/files, links to open-access sites, audio files, straight copy-paste text, etc.) 2. The Q/A interface can answer any question or command based on the file that you upload. Ask questions, create summaries or bullet points, extract insights & much more! 3. Search the Web Q/A - A web Q/A that allows you to pull information from outside your uploaded source while still giving you all the references so you know exactly where the information is coming from. Know more at:

Anurag Agarwal

58,019 views • 3 years ago

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)
0:55

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)

Lovis Odin

58,769 views • 10 months ago

n8n vs cursor, building a keyword research workflow Plus I'll give you my code no strings attached... you can use my version, my code, or try to use the logic to build it as a node-based workflow...whatever! what you need: - firecrawl API key - perplexity API key - dataforseo API key goal: - input a URL - use Firecrawl to scrape the site - use Perplexity to generate seed keywords and understand the target market, competitors - use DataforSEO to research the keywords and associated data my process: 1) used claude code to build the n8n workflow to go from 0-1, then debug (still debugging after an hour) - structure was good but too many nodes and some errors. - debugging was a pain. building it manually would have taken a long time. - I much prefer natural language w/ an agent. 2) used claude code to just build a simple app where I input a couple API keys (working in 30 minutes) - a few tweaks during the testing process, mostly just around output formatting. - working from the first go, debugging was way easier. - I can easily expand on the functionality. - I have a simple and clean UX out of the gate, no need to use google docs, airtable, etc etc conclusion: OPTION 2 IS MUCH MORE EFFICIENT AND FUN. I've attached a video demo. I'm also going to share a link for you to test it, and the GitHub repo -- it's all yours!
0:29

n8n vs cursor, building a keyword research workflow Plus I'll give you my code no strings attached... you can use my version, my code, or try to use the logic to build it as a node-based workflow...whatever! what you need: - firecrawl API key - perplexity API key - dataforseo API key goal: - input a URL - use Firecrawl to scrape the site - use Perplexity to generate seed keywords and understand the target market, competitors - use DataforSEO to research the keywords and associated data my process: 1) used claude code to build the n8n workflow to go from 0-1, then debug (still debugging after an hour) - structure was good but too many nodes and some errors. - debugging was a pain. building it manually would have taken a long time. - I much prefer natural language w/ an agent. 2) used claude code to just build a simple app where I input a couple API keys (working in 30 minutes) - a few tweaks during the testing process, mostly just around output formatting. - working from the first go, debugging was way easier. - I can easily expand on the functionality. - I have a simple and clean UX out of the gate, no need to use google docs, airtable, etc etc conclusion: OPTION 2 IS MUCH MORE EFFICIENT AND FUN. I've attached a video demo. I'm also going to share a link for you to test it, and the GitHub repo -- it's all yours!

The Boring Marketer

33,377 views • 1 year ago

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai
1:49

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai

Sudeep Pillai

17,789 views • 1 year ago

Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models and additional research artifacts to inspire innovation in the community and help advance AI in a responsible way. More in the video from Joelle Pineau. What we’re releasing: 🦎 Meta Chameleon 7B & 34B language models that support mixed-modal input and text-only outputs. 🪙 Meta Multi-Token Prediction Pretrained Language Models for code completion using Multi-Token Prediction. 🎼 Meta JASCO Generative text-to-music models capable of accepting various conditioning inputs for greater controllability. Paper available today with a pretrained model coming soon. 🗣️ Meta AudioSeal An audio watermarking model that we believe is the first designed specifically for the localized detection of AI-generated speech, available under a commercial license. 📝 Additional RAI artifacts Including research, data and code to measure and improve the representation of geographical and cultural preferences and diversity in AI systems. We believe that access to state-of-the-art AI creates opportunities for everyone – not just a small handful of Big Tech companies. We’re excited to share this work and to see how the community learns, iterates and builds using this technology. Details and access to everything released by FAIR today ➡️
1:14

Today is a good day for open science. As part of our continued commitment to the growth and development of an open ecosystem, today at Meta FAIR we’re announcing four new publicly available AI models and additional research artifacts to inspire innovation in the community and help advance AI in a responsible way. More in the video from Joelle Pineau. What we’re releasing: 🦎 Meta Chameleon 7B & 34B language models that support mixed-modal input and text-only outputs. 🪙 Meta Multi-Token Prediction Pretrained Language Models for code completion using Multi-Token Prediction. 🎼 Meta JASCO Generative text-to-music models capable of accepting various conditioning inputs for greater controllability. Paper available today with a pretrained model coming soon. 🗣️ Meta AudioSeal An audio watermarking model that we believe is the first designed specifically for the localized detection of AI-generated speech, available under a commercial license. 📝 Additional RAI artifacts Including research, data and code to measure and improve the representation of geographical and cultural preferences and diversity in AI systems. We believe that access to state-of-the-art AI creates opportunities for everyone – not just a small handful of Big Tech companies. We’re excited to share this work and to see how the community learns, iterates and builds using this technology. Details and access to everything released by FAIR today ➡️

AI at Meta

380,751 views • 2 years ago

Spectre AI On-Chain Search Engine is LIVE! 📢 We’re excited to announce that the Spectre AI On-Chain Search Engine is now LIVE for the public and holders at: Link can also be found pinned in the main Telegram or Spectre AI Website. Designed to be accessible for everyone, the app offers free core features with advanced Pro functionalities tiered for holders with a minimum of 1000 $spectre. Subscriptions will also be available soon for added flexibility. Spectre AI Utilities Overview: - Landing Page: Featuring BTC and Altcoin charts, Fear & Greed Index, Spectre Trending, X Trending, Partners in Focus, News, and UI Watchlists. - UI Favorites: Multiple chart views tailored to track your preferred projects. - Research Zone: Advanced tools for sentiment analysis, sentiment analysis charts, and Technical Analysis sections. - Sentiment Analysis Pro: AI-driven insights and unique scoring system to gauge market sentiment. - Technical Analysis Mode: Supply and demand zones powered by AI to guide trading strategies. - UI Heatmaps: Visualize top sectors and asset performance across the market. - User Panel: A personalized hub to manage account. - UI News: Indexed Websites and X Tweets. Note: X Bubbles (visual Twitter mapping) and Monarch (real-time AI chatzone) will be deployed post-launch. Mobile features will be upgraded in upcoming releases, with the best experience currently on desktop. What’s Launching Today: The beta of our Search Engine functionalities go live today, setting the stage for an exciting journey. Looking Ahead: This launch marks only the beginning. Our roadmap includes future features like staking, revenue sharing, and more. In the first few days, we’ll focus on server and user management to ensure seamless performance with increased traffic. Important Security Reminder: As a security reminder: there are no airdrops and no downloads— Spectre AI is accessible directly as a web app on both desktop and mobile. Try and connect with a fresh wallet and only the minimum required tokens. Enjoy this video showcase, and enjoy this milestone day with us. Spectre AI is just getting started - stay tuned for much more to come! #ai $SPECT
1:47

Spectre AI On-Chain Search Engine is LIVE! 📢 We’re excited to announce that the Spectre AI On-Chain Search Engine is now LIVE for the public and holders at: Link can also be found pinned in the main Telegram or Spectre AI Website. Designed to be accessible for everyone, the app offers free core features with advanced Pro functionalities tiered for holders with a minimum of 1000 $spectre. Subscriptions will also be available soon for added flexibility. Spectre AI Utilities Overview: - Landing Page: Featuring BTC and Altcoin charts, Fear & Greed Index, Spectre Trending, X Trending, Partners in Focus, News, and UI Watchlists. - UI Favorites: Multiple chart views tailored to track your preferred projects. - Research Zone: Advanced tools for sentiment analysis, sentiment analysis charts, and Technical Analysis sections. - Sentiment Analysis Pro: AI-driven insights and unique scoring system to gauge market sentiment. - Technical Analysis Mode: Supply and demand zones powered by AI to guide trading strategies. - UI Heatmaps: Visualize top sectors and asset performance across the market. - User Panel: A personalized hub to manage account. - UI News: Indexed Websites and X Tweets. Note: X Bubbles (visual Twitter mapping) and Monarch (real-time AI chatzone) will be deployed post-launch. Mobile features will be upgraded in upcoming releases, with the best experience currently on desktop. What’s Launching Today: The beta of our Search Engine functionalities go live today, setting the stage for an exciting journey. Looking Ahead: This launch marks only the beginning. Our roadmap includes future features like staking, revenue sharing, and more. In the first few days, we’ll focus on server and user management to ensure seamless performance with increased traffic. Important Security Reminder: As a security reminder: there are no airdrops and no downloads— Spectre AI is accessible directly as a web app on both desktop and mobile. Try and connect with a fresh wallet and only the minimum required tokens. Enjoy this video showcase, and enjoy this milestone day with us. Spectre AI is just getting started - stay tuned for much more to come! #ai $SPECT

SPECTRE AI

280,739 views • 1 year ago

Introducing /visual-plan - a skill to generate rich, visual plans for Claude Code and Codex. Plan mode in Claude Code is incredible. But I always find my eyes glazing over when it gives me this huge markdown essay in my terminal. I found I can make much better visual plans with reusable components. So I made a skill called `/visual-plan`. It generates plans as MDX with visual, interactive components. Diagrams, interactive API specs, schema design changes, annotated code, and even pan and zoomable wireframes. So for any UI work, you can look at a wireframe first, comment on it, iterate, and then have the agent work. I’ve found this to be a much more intuitive interface for reasoning about what the agent is doing. It’s somewhat inspired by that popular post about how HTML is better than Markdown. But HTML can be slow and verbose to write. And it doesn’t look good checked into a repo. This has really made me feel like humans and engineering are entering a new abstraction phase, where we reason about things at the plan level. As long as the plan is good, agents are getting more and more reliable at executing on it. Almost to the degree that we trust the C compiler to compile to assembly reliably. Plans are the new intermediate representation. I also made a skill for the reverse of this, called `/visual-recap`. After the agent works, it gives you a recap of everything it did. Same idea: wireframes, interactive API specs and diffs, schemas, annotated code, etc. So now when you’re reviewing what the agent did for you, or looking at a pull request of somebody else’s code, you can see a visual recap instead of just reading a wall of text. It’s all free and open source. You can find it on my GitHub. Will link to it in the reply because we all know how dumb these algorithms are with links.
1:19

Introducing /visual-plan - a skill to generate rich, visual plans for Claude Code and Codex. Plan mode in Claude Code is incredible. But I always find my eyes glazing over when it gives me this huge markdown essay in my terminal. I found I can make much better visual plans with reusable components. So I made a skill called `/visual-plan`. It generates plans as MDX with visual, interactive components. Diagrams, interactive API specs, schema design changes, annotated code, and even pan and zoomable wireframes. So for any UI work, you can look at a wireframe first, comment on it, iterate, and then have the agent work. I’ve found this to be a much more intuitive interface for reasoning about what the agent is doing. It’s somewhat inspired by that popular post about how HTML is better than Markdown. But HTML can be slow and verbose to write. And it doesn’t look good checked into a repo. This has really made me feel like humans and engineering are entering a new abstraction phase, where we reason about things at the plan level. As long as the plan is good, agents are getting more and more reliable at executing on it. Almost to the degree that we trust the C compiler to compile to assembly reliably. Plans are the new intermediate representation. I also made a skill for the reverse of this, called `/visual-recap`. After the agent works, it gives you a recap of everything it did. Same idea: wireframes, interactive API specs and diffs, schemas, annotated code, etc. So now when you’re reviewing what the agent did for you, or looking at a pull request of somebody else’s code, you can see a visual recap instead of just reading a wall of text. It’s all free and open source. You can find it on my GitHub. Will link to it in the reply because we all know how dumb these algorithms are with links.

Steve (Builder.io)

123,956 views • 1 month ago

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs
1:00

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs

Unstructured

21,874 views • 2 years ago

Introducing... the NoCodeFusion MVP builder! 🥳 Type in your app idea, get a full-stack Bubble app in ~10 minutes with authentication, database, UI, and basic create/update/delete workflows. The perfectionist in me is dying, but f**k it - we're launching. See a demo below. It's not perfect, but an excellent way to kickstart a project. It uses my component library. You can easily change the main font and primary colors from the Bubble style variables. You can then download the Chrome extension which lets you continue to build on your app in the Bubble editor with a copilot chat interface OR hire a developer through us to help you finish the project. This project was built 100% by myself entirely on Bubble (and of course using NoCodeFusion) and is very much in beta. If there's a bug or something, go easy on me. Send me a message and I'll take a look. AI + Visual editor is now an incredible option to create full stack web apps. It allows you the power of creating something in minutes, then having an intuitive interface to make edits and understand what's actually going on in the logic. Let me know what you think ✌️
5:22

Introducing... the NoCodeFusion MVP builder! 🥳 Type in your app idea, get a full-stack Bubble app in ~10 minutes with authentication, database, UI, and basic create/update/delete workflows. The perfectionist in me is dying, but f**k it - we're launching. See a demo below. It's not perfect, but an excellent way to kickstart a project. It uses my component library. You can easily change the main font and primary colors from the Bubble style variables. You can then download the Chrome extension which lets you continue to build on your app in the Bubble editor with a copilot chat interface OR hire a developer through us to help you finish the project. This project was built 100% by myself entirely on Bubble (and of course using NoCodeFusion) and is very much in beta. If there's a bug or something, go easy on me. Send me a message and I'll take a look. AI + Visual editor is now an incredible option to create full stack web apps. It allows you the power of creating something in minutes, then having an intuitive interface to make edits and understand what's actually going on in the logic. Let me know what you think ✌️

Peter Kwitny

20,070 views • 1 year ago

Mastering OpenAI's NEW AI AGENT builder in 26 mins: – start with one simple flow (lead capture or faq bot) before adding logic complexity. – use vector stores sparingly - too much context slows performance. – name your nodes clearly so you can debug fast. – integrate with chatkit early if you plan to embed it on a site. – test edge cases (misclassifications, loops, context drops). – think in outcomes, not features - what task can an agent take off your plate today? – treat this as a sandbox, not production - learn what’s possible before scaling. ideas on what you can build (to get your creative juices flowing): – lead qualification → build a multi-agent flow that classifies visitors as hot, warm, or cold, then sends data straight into hubspot or notion crm. – product onboarding → use logic nodes to detect user type (beginner vs. pro) and serve different walkthroughs automatically. – customer support → route basic questions to a support agent, complex ones to human reps, and have it all sync to slack or intercom. it’s still early. the ui has limits, the logic feels clunky, and customization can’t yet match purpose-built platforms like lindy, which give you deeper control and long-term memory. but it's worth taking it for a spin. will be interesting to see how it evolves. and amirmxt does a remarkable job clearly explaining how to build with agentkit etc on The Startup Ideas Podcast (SIP) 🧃 (you can follow for more) full ep on yt will be in the replies. What do you think of OpenAI new agent builder?
26:52

Mastering OpenAI's NEW AI AGENT builder in 26 mins: – start with one simple flow (lead capture or faq bot) before adding logic complexity. – use vector stores sparingly - too much context slows performance. – name your nodes clearly so you can debug fast. – integrate with chatkit early if you plan to embed it on a site. – test edge cases (misclassifications, loops, context drops). – think in outcomes, not features - what task can an agent take off your plate today? – treat this as a sandbox, not production - learn what’s possible before scaling. ideas on what you can build (to get your creative juices flowing): – lead qualification → build a multi-agent flow that classifies visitors as hot, warm, or cold, then sends data straight into hubspot or notion crm. – product onboarding → use logic nodes to detect user type (beginner vs. pro) and serve different walkthroughs automatically. – customer support → route basic questions to a support agent, complex ones to human reps, and have it all sync to slack or intercom. it’s still early. the ui has limits, the logic feels clunky, and customization can’t yet match purpose-built platforms like lindy, which give you deeper control and long-term memory. but it's worth taking it for a spin. will be interesting to see how it evolves. and amirmxt does a remarkable job clearly explaining how to build with agentkit etc on The Startup Ideas Podcast (SIP) 🧃 (you can follow for more) full ep on yt will be in the replies. What do you think of OpenAI new agent builder?

GREG ISENBERG

97,282 views • 9 months ago

2025.07.01 bi-weekly update here’s what we’ve built, shipped, and trained this past week: TRADING CAPABILITIES + agent-based txn execution engine now supports Meteora (DBC, DLMM, DYN, DAMM), Raydium (CLMM, AMM, CPMM), and Orca 🌊 (CLLM, VP, CPMM). we're now compatible with nearly every major liquidity layer on Solana. + DCA and limit orders now available to use through our agentic/natural language interface. + execution is faster, leaner, more reliable; optimized based on real closed beta usage. AGENT SWARM + A2A (agent-to-agent) finalized; based on Google's new open framework. it enables dynamic coordination between agents, deeper reasoning, better memory, and more human-like flow. + TraceGraph (diagram/chain-of-thought-like) UI is now deployed. users now see how Aya (and others) think and collaborate together. visualizes multi-agent logic paths. text UI also upgraded. sharper, smoother, faster. + Bravo (macro news oracle) live. it connects real-world macro events and news to Solana. powered by our in-house scrapers + NewsAPI, built from scratch. integrations with blocmates. coming soon. + Solvion, our Solana-native domain expert, is now active. trained on a custom-built, 70B parameter dataset of the full Solana ecosystem. auto-updated. devs, tokenomics, projects, whitepapers, technical information, know-hows... it knows everything. + Echo (our social media and sentiment analyst agent) getting integrated with Sentient natural language interface + Rivalz Network. + you can now start individual conversations with agents. e.g. ask Echo anything about social trends, or hit up Solvion for technicals. UX/UI + we’re now mobile responsive; fully optimized across devices. + deployed TraceGraph (diagram UI for agent cognition). + NLI improvements: sleeker prompt-response flow, improved text visualization, better latency, better rendering. + agents feel more alive, dynamic, and explainable PREDICTIONS weekly update from our head quant: + we now do weekly fine-tuning to adapt to market shifts. switched from F1-score optimization to pure precision; cutting noise, and maximizing conviction. we now discard the worst-performing model in the ensemble. only the top 2 vote. accuracy last 6 weeks = 82% directional. the ensemble logic is fully restructured. next: RNN + RL-based dynamic thresholding in progress (live this month). + partnered with Allora for the the SOL/USDT prediction stack, combining our hype score with their confidence-aware forecasting. + also cooking something with Sahara AI 🔆 (????)... OTHER + PnL cards integrated. track profit per trade, share it on X, get free XCC + Referral system is complete and rolling out to early users very soon (top referrers will dominate first layer of our multi-level tree and enjoy first-movers advantage). + working on a dynamic onboarding tutorial for first-time users. + backend latency improvements across endpoints. especially on token explorer + prediction refresh + docs are live ( TEAM + onboarded amy and Mike | heymike.sol 🎒🪽 — elite Solana engineers working on gRPCs, RPCs, instruction decoding, and data pipelines. their focus: making xFractal the only real-time NLP engine for Solana alpha extraction. + brought on ultra , Skely, HALKO and Gabriel Haines as strategic advisors and contributors, helping us scale narrative modeling, data ops, and GTM. QUICK STATS (REMINDER: this is a closed, invite-only beta — not optimized for adoption yet) + 400+ early beta testoors + 9,000+ natural language prompts + 500+ on-chain txs executed via our agent-based engine + we’re not scaling users yet, we’re optimizing agents, validating edge, and consolidating PMF. + open beta coming soon. engine’s warming up. let’s keep moving. (p.s. toly 🇺🇸 check this out)
1:43

2025.07.01 bi-weekly update here’s what we’ve built, shipped, and trained this past week: TRADING CAPABILITIES + agent-based txn execution engine now supports Meteora (DBC, DLMM, DYN, DAMM), Raydium (CLMM, AMM, CPMM), and Orca 🌊 (CLLM, VP, CPMM). we're now compatible with nearly every major liquidity layer on Solana. + DCA and limit orders now available to use through our agentic/natural language interface. + execution is faster, leaner, more reliable; optimized based on real closed beta usage. AGENT SWARM + A2A (agent-to-agent) finalized; based on Google's new open framework. it enables dynamic coordination between agents, deeper reasoning, better memory, and more human-like flow. + TraceGraph (diagram/chain-of-thought-like) UI is now deployed. users now see how Aya (and others) think and collaborate together. visualizes multi-agent logic paths. text UI also upgraded. sharper, smoother, faster. + Bravo (macro news oracle) live. it connects real-world macro events and news to Solana. powered by our in-house scrapers + NewsAPI, built from scratch. integrations with blocmates. coming soon. + Solvion, our Solana-native domain expert, is now active. trained on a custom-built, 70B parameter dataset of the full Solana ecosystem. auto-updated. devs, tokenomics, projects, whitepapers, technical information, know-hows... it knows everything. + Echo (our social media and sentiment analyst agent) getting integrated with Sentient natural language interface + Rivalz Network. + you can now start individual conversations with agents. e.g. ask Echo anything about social trends, or hit up Solvion for technicals. UX/UI + we’re now mobile responsive; fully optimized across devices. + deployed TraceGraph (diagram UI for agent cognition). + NLI improvements: sleeker prompt-response flow, improved text visualization, better latency, better rendering. + agents feel more alive, dynamic, and explainable PREDICTIONS weekly update from our head quant: + we now do weekly fine-tuning to adapt to market shifts. switched from F1-score optimization to pure precision; cutting noise, and maximizing conviction. we now discard the worst-performing model in the ensemble. only the top 2 vote. accuracy last 6 weeks = 82% directional. the ensemble logic is fully restructured. next: RNN + RL-based dynamic thresholding in progress (live this month). + partnered with Allora for the the SOL/USDT prediction stack, combining our hype score with their confidence-aware forecasting. + also cooking something with Sahara AI 🔆 (????)... OTHER + PnL cards integrated. track profit per trade, share it on X, get free XCC + Referral system is complete and rolling out to early users very soon (top referrers will dominate first layer of our multi-level tree and enjoy first-movers advantage). + working on a dynamic onboarding tutorial for first-time users. + backend latency improvements across endpoints. especially on token explorer + prediction refresh + docs are live ( TEAM + onboarded amy and Mike | heymike.sol 🎒🪽 — elite Solana engineers working on gRPCs, RPCs, instruction decoding, and data pipelines. their focus: making xFractal the only real-time NLP engine for Solana alpha extraction. + brought on ultra , Skely, HALKO and Gabriel Haines as strategic advisors and contributors, helping us scale narrative modeling, data ops, and GTM. QUICK STATS (REMINDER: this is a closed, invite-only beta — not optimized for adoption yet) + 400+ early beta testoors + 9,000+ natural language prompts + 500+ on-chain txs executed via our agent-based engine + we’re not scaling users yet, we’re optimizing agents, validating edge, and consolidating PMF. + open beta coming soon. engine’s warming up. let’s keep moving. (p.s. toly 🇺🇸 check this out)

xFractal

42,668 views • 1 year ago

Hi everyone! Just rolled out access for NoSpoon Studios to the first 110 folks or so. Huge thanks to Karan and Amit at Luma - thanks to them, I have a few more people that had commented getting complimentary beta access soon as well :). These videos are created with a single agent that does everything from concept to completion (including editing the video together). Therer are longer options too, but for the sake of cost and this demo, you'll be working with 20s max generations. The entire agent was built to only run Ray2 as the video model. My stack is this custom agent of mine (using OAI) > Ray2 for the video > Replit for the app for now. In the meantime, for those with access: here's a video tutorial on how to use the site. I'm quite exhausted, but here's the gist: 1. Sign up (and remember your password lol). Beta testers got free credits, but these are a bit limited - rolling out to a few more peeps but have to budget and need to expand my current code (which means redeploying - so expect more of you to have 2 free credits and access tmr! I have to find out the max I can allot with budget). 2. Just hit "generate short video" without anything in the prompt box and the agent will start immediately with the logline. Be very patient, wait about 6-8 minutes, and it will continue to output all of the scenes and processing - it's very important you do not refresh or navigate away from that tab at this time. You'll see your final video generated and displayed at the bottom of your screen once complete, along with a watermark (paid users don't have a watermark). You can also access your loglines and videos in your history tab, too :). You must refresh the page only after your generation in order to generate a new video - otherwise you'll lose your generation (annoying I know). 3. You can also input your own custom prompt! Control the story this way. Single words work great even, like "thriller" or "spy thriller set in greece" or even "black and white film noir" etc etc. It's not as great with animated styles yet for consistency, just a heads up. 4. I'm exhausted, I'm sure there's a lot I'm missing. But welcome to the NoSpoon Studios soft beta! More soon! Feel free to share your generations if you are already in. I'll see you all tomorrow night :) <3 Thanks everyone! More soon.
5:25

Hi everyone! Just rolled out access for NoSpoon Studios to the first 110 folks or so. Huge thanks to Karan and Amit at Luma - thanks to them, I have a few more people that had commented getting complimentary beta access soon as well :). These videos are created with a single agent that does everything from concept to completion (including editing the video together). Therer are longer options too, but for the sake of cost and this demo, you'll be working with 20s max generations. The entire agent was built to only run Ray2 as the video model. My stack is this custom agent of mine (using OAI) > Ray2 for the video > Replit for the app for now. In the meantime, for those with access: here's a video tutorial on how to use the site. I'm quite exhausted, but here's the gist: 1. Sign up (and remember your password lol). Beta testers got free credits, but these are a bit limited - rolling out to a few more peeps but have to budget and need to expand my current code (which means redeploying - so expect more of you to have 2 free credits and access tmr! I have to find out the max I can allot with budget). 2. Just hit "generate short video" without anything in the prompt box and the agent will start immediately with the logline. Be very patient, wait about 6-8 minutes, and it will continue to output all of the scenes and processing - it's very important you do not refresh or navigate away from that tab at this time. You'll see your final video generated and displayed at the bottom of your screen once complete, along with a watermark (paid users don't have a watermark). You can also access your loglines and videos in your history tab, too :). You must refresh the page only after your generation in order to generate a new video - otherwise you'll lose your generation (annoying I know). 3. You can also input your own custom prompt! Control the story this way. Single words work great even, like "thriller" or "spy thriller set in greece" or even "black and white film noir" etc etc. It's not as great with animated styles yet for consistency, just a heads up. 4. I'm exhausted, I'm sure there's a lot I'm missing. But welcome to the NoSpoon Studios soft beta! More soon! Feel free to share your generations if you are already in. I'll see you all tomorrow night :) <3 Thanks everyone! More soon.

Kiri

15,485 views • 1 year ago

💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2
0:47

💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2

Nihan

19,296 views • 2 months ago

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

266,920 views • 2 months ago

Smart Doll Cortex 2 can now be equipped with an AI conversational #M5Stack module, allowing them to speak both English and Chinese. Thanks to their magnetic modular design, all components fit seamlessly inside the body—something not possible with the vinyl version. This Xiao Zhi powered model also features an optional screen that displays readable replies and emoji-based expressions, enhancing interactions with visual feedback. Smart Doll’s quirky responses to misheard inputs often lead to unexpected lols. At first, their replies are unpredictable, adding to their charm and making conversations feel organic and fun. However, they become more accurate over time as they learn from interactions. The AI models powering Smart Doll are trained in Natural Language Understanding, enabling them to grasp context, intent, and nuances in user input for more meaningful responses. They also support context retention across multiple conversation turns, allowing for smooth and continuous dialogue. They adapt and refine their responses as they engage in more conversations, making interactions feel increasingly natural and personalized. We will provide tutorials and #3Dprinting files so folks can build their modules and install AI models such as ChatGPT, DeepSeek, Qwen2.5 72B, etc. This way, owners can tweak their Smart Doll’s voice, accent, and personality to reflect their creativity, ideals, and identity. With the advancement of open-source offline LLMs, local processing ensures your Smart Doll can safeguard the secrets you share—keeping them safe from Skynet. We are also working on integrating robotics and hardware control to broaden the application of #smartdoll as a truly useful companion (instead of just sitting around asking you for more apparel) that can interact with their environment, assist with everyday tasks, and adapt to their owner’s needs—whether that means offering companionship, controlling smart home devices, or even detecting environmental changes for safety. If you’re Team John Connor, no worries! AI components are entirely optional—whether you choose to go DIY or skip them altogether, the decision is yours. At least until Skynet has other plans.
5:24

Smart Doll Cortex 2 can now be equipped with an AI conversational #M5Stack module, allowing them to speak both English and Chinese. Thanks to their magnetic modular design, all components fit seamlessly inside the body—something not possible with the vinyl version. This Xiao Zhi powered model also features an optional screen that displays readable replies and emoji-based expressions, enhancing interactions with visual feedback. Smart Doll’s quirky responses to misheard inputs often lead to unexpected lols. At first, their replies are unpredictable, adding to their charm and making conversations feel organic and fun. However, they become more accurate over time as they learn from interactions. The AI models powering Smart Doll are trained in Natural Language Understanding, enabling them to grasp context, intent, and nuances in user input for more meaningful responses. They also support context retention across multiple conversation turns, allowing for smooth and continuous dialogue. They adapt and refine their responses as they engage in more conversations, making interactions feel increasingly natural and personalized. We will provide tutorials and #3Dprinting files so folks can build their modules and install AI models such as ChatGPT, DeepSeek, Qwen2.5 72B, etc. This way, owners can tweak their Smart Doll’s voice, accent, and personality to reflect their creativity, ideals, and identity. With the advancement of open-source offline LLMs, local processing ensures your Smart Doll can safeguard the secrets you share—keeping them safe from Skynet. We are also working on integrating robotics and hardware control to broaden the application of #smartdoll as a truly useful companion (instead of just sitting around asking you for more apparel) that can interact with their environment, assist with everyday tasks, and adapt to their owner’s needs—whether that means offering companionship, controlling smart home devices, or even detecting environmental changes for safety. If you’re Team John Connor, no worries! AI components are entirely optional—whether you choose to go DIY or skip them altogether, the decision is yours. At least until Skynet has other plans.

Smartdoll Land

16,382 views • 1 year ago

Today, we’re thrilled to introduce GAIA, a modular creation engine for the AI age. At its core, GAIA is a multiplayer creation engine designed to bring democratized Generative AI abilities to everyone, hence its name. It abstracts away the complexities of leveraging open-source software, hardware requirements, and technical intricacies, allowing creators to focus solely on creating, together. GAIA has been instrumental in powering the imagination and creative works of our studio, significantly boosting productivity, collaboration, and creativity. It has saved our team thousands of man-days in production work, enabling us to dream bigger, bolder, and execute more swiftly. When used by experienced creators, GAIA, with its ever-growing toolsets built by core contributors, stands to be the best AI creative co-pilot for industry-specific production workflows—from gaming to fashion, and from personal consumption to real estate renderings. GAIA's self-reinforcing flywheel is poised to dramatically enhance the platform's capabilities in both the near and long term: The cost of GPUs and compute will continue to improve. Open-source AI models are becoming increasingly powerful over time, as seen by the evolution from Stable Diffusion 1 to Lightning, alongside other impressive OSS creations like AnimateDiff, LoRAs, and ControlNet. The architecture of AI workflows will become more fine-grained, allowing exponentially increasing control over final outputs. The data and connections that AI workflows can make, both in-app and out-of-app, will grow more sophisticated. GAIA already allows for the chaining of Generative Image AI recipes, grouping complex node-based programs into simple consumer-facing interfaces. Soon, with multi-modality workflows, creators will be able to leverage not only images but also text and sound to create even more complex workflows. Today is the least capable GAIA will ever be. It already provides superpowers unachievable by many creators who know how to harness them. We have an extremely exciting backlog of features lined up, starting with social features. Our mission as Ather Collective core contributors is to get GAIA into the hands of as many creators and businesses as possible. While we believe in e/acc, our north star for Ather Collective remains: Equitable, Accessible, Composable, and Collaborative. If you would like to be a part of the early access users of GAIA, please register using this link: Our team will open up capacity as soon as we are able to. There is only one way AI benefits humanity, and that’s through open AI. Let’s be a part of it. ✌🏻
0:50

Today, we’re thrilled to introduce GAIA, a modular creation engine for the AI age. At its core, GAIA is a multiplayer creation engine designed to bring democratized Generative AI abilities to everyone, hence its name. It abstracts away the complexities of leveraging open-source software, hardware requirements, and technical intricacies, allowing creators to focus solely on creating, together. GAIA has been instrumental in powering the imagination and creative works of our studio, significantly boosting productivity, collaboration, and creativity. It has saved our team thousands of man-days in production work, enabling us to dream bigger, bolder, and execute more swiftly. When used by experienced creators, GAIA, with its ever-growing toolsets built by core contributors, stands to be the best AI creative co-pilot for industry-specific production workflows—from gaming to fashion, and from personal consumption to real estate renderings. GAIA's self-reinforcing flywheel is poised to dramatically enhance the platform's capabilities in both the near and long term: The cost of GPUs and compute will continue to improve. Open-source AI models are becoming increasingly powerful over time, as seen by the evolution from Stable Diffusion 1 to Lightning, alongside other impressive OSS creations like AnimateDiff, LoRAs, and ControlNet. The architecture of AI workflows will become more fine-grained, allowing exponentially increasing control over final outputs. The data and connections that AI workflows can make, both in-app and out-of-app, will grow more sophisticated. GAIA already allows for the chaining of Generative Image AI recipes, grouping complex node-based programs into simple consumer-facing interfaces. Soon, with multi-modality workflows, creators will be able to leverage not only images but also text and sound to create even more complex workflows. Today is the least capable GAIA will ever be. It already provides superpowers unachievable by many creators who know how to harness them. We have an extremely exciting backlog of features lined up, starting with social features. Our mission as Ather Collective core contributors is to get GAIA into the hands of as many creators and businesses as possible. While we believe in e/acc, our north star for Ather Collective remains: Equitable, Accessible, Composable, and Collaborative. If you would like to be a part of the early access users of GAIA, please register using this link: Our team will open up capacity as soon as we are able to. There is only one way AI benefits humanity, and that’s through open AI. Let’s be a part of it. ✌🏻

TIN | Sipher Odyssey & Ather Labs

14,194 views • 2 years ago