Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Weavy is now live 🍌 Built for professional creatives who demand more from AI than pretty pictures - who craft systems that scale brilliance. All your favorite AI models. Full creative control. Node-based flow that feels like your brain on screen. Build design machines you can reuse, scale, and... show more

Figma Weave

7,610 subscribers

46,072 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Many “AI helpers” feel like early-stage demos. Then I tested Sintra AI—and it’s the first one that genuinely delivered value for me. Instead of spending hours on complex automations, I turned my knowledge and creator workflow into an AI helper in minutes. Simple build. No coding. No technical setup. Just plug in your expertise. I built one focused on: • AI creator growth • viral content strategy • campaign ideas • outreach + positioning And honestly… It feels like a mini version of me working around the clock. The best part? This is now accessible far beyond developers. Creators. Consultants. Educators. Founders. Anyone with valuable knowledge can package it into an AI helper people can interact with anytime. That’s the shift many people are overlooking. AI won’t only streamline tasks. It will scale expertise. And Sintra makes onboarding refreshingly simple compared to most tools I’ve tried. It feels less like “another chatbot.” More like building a digital extension of your brain. If you’ve been thinking about creating your own AI helper, this is one of the easiest places to start right now. Discount increased to 85% off — use code: BUILD85 Try my helper / Build your own on Sintra:

Many “AI helpers” feel like early-stage demos. Then I tested Sintra AI—and it’s the first one that genuinely delivered value for me. Instead of spending hours on complex automations, I turned my knowledge and creator workflow into an AI helper in minutes. Simple build. No coding. No technical setup. Just plug in your expertise. I built one focused on: • AI creator growth • viral content strategy • campaign ideas • outreach + positioning And honestly… It feels like a mini version of me working around the clock. The best part? This is now accessible far beyond developers. Creators. Consultants. Educators. Founders. Anyone with valuable knowledge can package it into an AI helper people can interact with anytime. That’s the shift many people are overlooking. AI won’t only streamline tasks. It will scale expertise. And Sintra makes onboarding refreshingly simple compared to most tools I’ve tried. It feels less like “another chatbot.” More like building a digital extension of your brain. If you’ve been thinking about creating your own AI helper, this is one of the easiest places to start right now. Discount increased to 85% off — use code: BUILD85 Try my helper / Build your own on Sintra:

ZAYVEN KNOX

20,752 views • 3 months ago

I found a skill that turns Claude into an AI ad creative maker. It’s called Goose Ads by Gooseworks, and it works directly inside Claude, Claude Code, Cowork, or Codex. You can now create ad creatives for your brand with one command. How it works: → Install Gooseworks npx gooseworks install --all → Run this prompt /goose-ads create ads for my brand → Optional Pick templates you like on the platform Goose Ads finds creatives that companies are already spending money on, studies what is working, and generates new ad creatives for your brand. It also keeps everything aligned with your website, messaging, logo, assets, and positioning, so the final output feels brand-ready instead of generic. And this is bigger than one ad skill. Gooseworks has 100+ open-source growth skills for ads, content, competitor research, GTM, SEO, lead generation, and more. Claude is slowly becoming more than a chatbot. It is becoming a growth operator. Comment 'Goose' and I’ll DM you the full open-source skill library.

Jaynit Makwana

54,006 views • 2 months ago

Every once in a while, you have a conversation that lingers, not just because of what’s spoken, but because of how deeply it reframes your thinking. My recent conversation with Vishal Sikka was exactly that. From leading SAP’s HANA revolution and driving Infosys’ transformation to now reimagining enterprise AI at Vianai Systems, Inc., Vishal has lived at the intersection of technology, philosophy and leadership. We spoke about what “enterprise-grade AI” really means in a world obsessed with speed over reliability, why enterprises are more like mangrove forests than machines (intricate, resilient & capable of withstanding shocks) and how India can shape the future of AI through its unique blend of scale, talent and systems thinking. Besides, if there’s anyone who can weave an analogy between mangrove forests and enterprises and make it click, it’s Vishal! These are insights that could only come from someone who has built, led and now re-imagines what the enterprise of the future looks like. Watch the full conversation at And of course, a massive thank you to Vishal for making the time for this conversation!

Every once in a while, you have a conversation that lingers, not just because of what’s spoken, but because of how deeply it reframes your thinking. My recent conversation with Vishal Sikka was exactly that. From leading SAP’s HANA revolution and driving Infosys’ transformation to now reimagining enterprise AI at Vianai Systems, Inc., Vishal has lived at the intersection of technology, philosophy and leadership. We spoke about what “enterprise-grade AI” really means in a world obsessed with speed over reliability, why enterprises are more like mangrove forests than machines (intricate, resilient & capable of withstanding shocks) and how India can shape the future of AI through its unique blend of scale, talent and systems thinking. Besides, if there’s anyone who can weave an analogy between mangrove forests and enterprises and make it click, it’s Vishal! These are insights that could only come from someone who has built, led and now re-imagines what the enterprise of the future looks like. Watch the full conversation at And of course, a massive thank you to Vishal for making the time for this conversation!

Alok Goyal

28,475 views • 10 months ago

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,899 views • 1 year ago

Today, we are launching Depost AI 2. 🎉 I am a bit emotional writing this. The last eight months were intense. But today I am proud of what we built. Because Depost AI 2 is built to become the best and safest AI content team for LinkedIn. We listened to thousands of people who used the first version, and we rebuilt Depost AI from scratch on the official LinkedIn API. The result: - A chat instead of a form, so you brief it like a colleague - Analytics that show what performs for you and why - Four agents that write in your voice and design the graphics to match - A calendar that fills from approved drafts - Approvals, roles, and a separate workspace for every brand - MCP, so the whole team works inside Claude, ChatGPT, or Cursor It is built for B2B founders who need to be visible without losing an hour a day. For marketing teams who need one system, one review flow, and one voice per brand. And for agencies who run LinkedIn for clients and need every brand kept apart. But the bigger vision is this: Depost AI is becoming a content team that understands your brand before it writes a word. It learns from everything you publish. It knows what performs for you and why. It plans, writes, designs, and schedules from that. And nothing goes live until you approve it. Because writing a post is not the hard part. Knowing what to say, saying it well every week, and doing it safely on an account you cannot afford to lose is. Depost AI 2 is the tool we built to solve that. And honestly, the best part right now is watching people brief it for the first time and get a week of drafts back in their own voice. You can start a 7-day free trial today at And if you message me, I will happily walk you through it myself. ❤️

Atta 🐬

17,864 views • 21 days ago

Nearly half of America's economic growth now comes from ONE thing. It is not jobs, housing, or shopping. And your "safe" index fund is now betting on it... Here is what it is (and what it actually means for your investments): AI data centers. A data center is a giant building full of computers. They power the AI tools everyone is suddenly using. Building them costs a staggering amount of money. And a handful of tech giants are spending like never before. Five of the largest could spend around 750 billion dollars this year. That category of spending jumped 72 percent in a single year. Measured against the whole economy, that spending tops the dot-com peak. It has become the biggest single engine of the economy. Strip it out, and growth almost disappears. Now here is why this reaches your account: You probably own an index fund somewhere. Maybe inside a 401k you rarely check. It is supposed to spread your money across 500 companies. That is what makes it feel safe. You were told to buy the whole market and relax. But seven giant tech names now dominate that fund. Together they are more than a third of its value. At the dot-com peak, the top names were about 15 percent. So your diversified fund is really one giant bet on AI. And the cracks are already showing. Google's parent just burned more cash than it made. The first time that has happened since it went public. The profit gains are piling up in just a few names too. The rest of the market is barely growing its profits. The danger is not that AI suddenly fails. The danger is that everyone owns the same bet. When one giant stumbles, they often fall together. And a fund that felt safe drops all at once. That is the retirement money you were counting on. And you never chose this bet on purpose. This is how hidden risk actually works. It hides inside the word diversified. The comfort is the trap. Most people never look under the hood of their fund. They see 500 names and feel protected. Rules-based investing looks at what you actually own. It measures the risk instead of trusting the label. Then it spreads your money by design, not by accident. It does not care how popular a trade has become. That is exactly what Surmount was built for. Automated strategies that manage real risk, not comforting labels. So when the crowded trade unwinds, you are not trapped in it. You are already positioned:

Nearly half of America's economic growth now comes from ONE thing. It is not jobs, housing, or shopping. And your "safe" index fund is now betting on it... Here is what it is (and what it actually means for your investments): AI data centers. A data center is a giant building full of computers. They power the AI tools everyone is suddenly using. Building them costs a staggering amount of money. And a handful of tech giants are spending like never before. Five of the largest could spend around 750 billion dollars this year. That category of spending jumped 72 percent in a single year. Measured against the whole economy, that spending tops the dot-com peak. It has become the biggest single engine of the economy. Strip it out, and growth almost disappears. Now here is why this reaches your account: You probably own an index fund somewhere. Maybe inside a 401k you rarely check. It is supposed to spread your money across 500 companies. That is what makes it feel safe. You were told to buy the whole market and relax. But seven giant tech names now dominate that fund. Together they are more than a third of its value. At the dot-com peak, the top names were about 15 percent. So your diversified fund is really one giant bet on AI. And the cracks are already showing. Google's parent just burned more cash than it made. The first time that has happened since it went public. The profit gains are piling up in just a few names too. The rest of the market is barely growing its profits. The danger is not that AI suddenly fails. The danger is that everyone owns the same bet. When one giant stumbles, they often fall together. And a fund that felt safe drops all at once. That is the retirement money you were counting on. And you never chose this bet on purpose. This is how hidden risk actually works. It hides inside the word diversified. The comfort is the trap. Most people never look under the hood of their fund. They see 500 names and feel protected. Rules-based investing looks at what you actually own. It measures the risk instead of trusting the label. Then it spreads your money by design, not by accident. It does not care how popular a trade has become. That is exactly what Surmount was built for. Automated strategies that manage real risk, not comforting labels. So when the crowded trade unwinds, you are not trapped in it. You are already positioned:

Surmount

11,848 views • 21 days ago

how to use Google's NEW open source Design.md + AI Skills to make your startup look like a $100 million company in 1 hour: 1. Design.md is an open source file from Google that captures the soul of a design. Typography, colors, spacing, all in one markdown file. You attach it to your prompt and your agent builds beautiful things every time. 2. Think of it this way. The HTML is the finished dish. The design.md is the recipe. The skills are the ingredients. Put them together and everything you build looks consistent and professional. 3. Don't create a design system from scratch. Find a brand you love. Linear, Stripe, Vercel, whatever resonates. Study it. Use ChatGPT or Claude to help you extract the design language into your own design.md file. 4. Build skills on top of your design.md. A landing page skill. A mobile app skill. A motion design skill. A slide deck skill. Each one references the same design.md so everything looks like it came from the same designer. 5. The biggest mistake people make: they nail one screen and then everything else looks generic. Design.md solves this. One file keeps every page, every format, every medium consistent. 6. Use it across everything. Your landing page. Your app. Your pitch deck. Your promo videos. Same DNA. Same taste. Same system. That's what separates a startup that looks real from one that looks vibe-coded. 7. Build a second brain for design inspiration. When you see something beautiful in the real world or online, capture it. Save it. When you're building something new, reference it. Taste is developed, not downloaded. 8. It's obvious but the difference between a product people trust and a product people bounce from is how it looks and feels. Design.md gives you that edge. you can watch below shoutout to Meng To for coming on The Startup Ideas Podcast (SIP) 🧃 and walking through his full workflow. if you want to use AI to actually build gorgeous designs, you'll want to use see this. watch

GREG ISENBERG

512,753 views • 4 months ago

Code-first prototypes are fast to build. Getting feedback on them is not. A design lead can't leave a comment on one specific state. An engineer can't see the full flow at a glance. The thing that makes a prototype great for building makes it awkward for aligning. So Amy Lima and I built a skill for that (well, mostly Amy). It takes a live localhost prototype and brings it into Figma. Each flow broken out into its own frame, built from your design system components, annotated with the interaction and implementation details. Now the canvas is where you review and align. We refined it live in the video too! My favorite part is a habit I like to lean on: make the agent take one more round trip to validate its own work before it claims it's done. AI sometimes has the tendency to confidently tell you the job's finished when it isn't. Thanks to Amy and the whole Figma crew for allowing me to be a part of this!

Code-first prototypes are fast to build. Getting feedback on them is not. A design lead can't leave a comment on one specific state. An engineer can't see the full flow at a glance. The thing that makes a prototype great for building makes it awkward for aligning. So Amy Lima and I built a skill for that (well, mostly Amy). It takes a live localhost prototype and brings it into Figma. Each flow broken out into its own frame, built from your design system components, annotated with the interaction and implementation details. Now the canvas is where you review and align. We refined it live in the video too! My favorite part is a habit I like to lean on: make the agent take one more round trip to validate its own work before it claims it's done. AI sometimes has the tendency to confidently tell you the job's finished when it isn't. Thanks to Amy and the whole Figma crew for allowing me to be a part of this!

TJ Pitre

13,477 views • 3 months ago

I rebuilt my viral TikTok AI agent with Claude Code in 37 minutes 🤯 A full research-to-brief pipeline that scrapes TikTok, analyzes videos with AI, and generates creative briefs for your clients. All inside Claude Code + Replit. Perfect for creative agencies and DTC brands who want to turn competitor research into briefs without the manual grind. Look, all e-commerce brands & agencies should have at least one person on their team who is their "AI expert" & can vibe-code apps & workflows like this. Here's what the Claude Code version of my TikTok Agent does: → Search TikTok by keyword, date range, and video count → Pull engagement metrics, captions, and thumbnails → Gemini actually watches the video and analyzes the hook → AI scrapes comments for common questions and insights → Generate a full creative brief based on your template + brand bible No watching videos manually. No copying notes into docs. No rewriting briefs from scratch. What you control: - Multiple client projects with separate brand bibles - Your own creative brief template - Which videos to analyze and brief - Full customization through Replit's AI agent Research → Analysis → Brief. One workflow, running a custom, mini-SaaS inside your company I recorded a full walkthrough showing exactly how I built this from scratch. Want the full tutorial? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

Mike Futia

74,888 views • 8 months ago

I’d like to introduce you to Thumbnail Academy, the only platform built to help creators design thumbnails that actually get clicks and views Enrollment is open right now I’ve partnered with Dill and ant Together we’ve spent over a decade designing thumbnails for some of the biggest YouTubers on the planet Across billions of views, we’ve learned exactly what makes people click Now we’ve turned everything we know into a system that any creator can use to master thumbnails Until now, creators paid $500+ for a single thumbnail or a one-hour consult We wanted to build something better. Something that helps creators grow from every angle: • Interactive lessons, templates, AI tools, and weekly live coaching calls • Learn how to craft ideas that spark curiosity and pull people in • Master thumbnail psychology and design faster with AI • Understand exactly what drives clicks and how to do it again and again • No fluff. Just the systems behind billions of views. But Thumbnail Academy isn’t just a course It’s also a community built where you’ll connect with other creators, share wins, post your work for feedback, and grow together Inside, you’ll find spaces for learning, inspiration, and collaboration, and for the Pro Tier: live weekly calls where we workshop thumbnails, share AI tools, and help you level up in real time This is the complete blueprint we use every day for the world’s top creators Every lesson, every example, every bit of feedback is built to help you create scroll-stopping thumbnails that actually grow your channel Enrollment closes this Friday After that, the Founders Price is gone for good, and we’ll shift focus to helping the first wave of creators inside If you’ve ever felt frustrated that your video didn’t get the clicks it deserved, this is your chance to fix that for good Join Thumbnail Academy today Your thumbnails, and your channel, will never be the same With love, David (link to Thumbnail Academy on next post!!!)

I’d like to introduce you to Thumbnail Academy, the only platform built to help creators design thumbnails that actually get clicks and views Enrollment is open right now I’ve partnered with Dill and ant Together we’ve spent over a decade designing thumbnails for some of the biggest YouTubers on the planet Across billions of views, we’ve learned exactly what makes people click Now we’ve turned everything we know into a system that any creator can use to master thumbnails Until now, creators paid $500+ for a single thumbnail or a one-hour consult We wanted to build something better. Something that helps creators grow from every angle: • Interactive lessons, templates, AI tools, and weekly live coaching calls • Learn how to craft ideas that spark curiosity and pull people in • Master thumbnail psychology and design faster with AI • Understand exactly what drives clicks and how to do it again and again • No fluff. Just the systems behind billions of views. But Thumbnail Academy isn’t just a course It’s also a community built where you’ll connect with other creators, share wins, post your work for feedback, and grow together Inside, you’ll find spaces for learning, inspiration, and collaboration, and for the Pro Tier: live weekly calls where we workshop thumbnails, share AI tools, and help you level up in real time This is the complete blueprint we use every day for the world’s top creators Every lesson, every example, every bit of feedback is built to help you create scroll-stopping thumbnails that actually grow your channel Enrollment closes this Friday After that, the Founders Price is gone for good, and we’ll shift focus to helping the first wave of creators inside If you’ve ever felt frustrated that your video didn’t get the clicks it deserved, this is your chance to fix that for good Join Thumbnail Academy today Your thumbnails, and your channel, will never be the same With love, David (link to Thumbnail Academy on next post!!!)

David Altizer

57,288 views • 11 months ago

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.

Hồng Ngọc | Ruby💎

12,837 views • 7 months ago

RSI section from the AI documentary Machine God The next threshold is Recursive Self-Improvement: the moment when AI can improve itself without human assistance. For decades this sounded like science fiction. Intelligence explosion scenarios imagined a system rewriting its own code, becoming smarter, then using that new intelligence to make still better versions of itself. But the idea looks less remote now that AI contributes directly to frontier science. In mathematics, recent systems have moved beyond solving contest problems to producing serious new arguments on long-standing open problems. AI is used to build physics world models and propose candidate theories or computational methods. These are early signs that machine cognition is entering the creative loop of science itself. The crucial transition comes when that loop turns inward. AI research is, after all, a technical discipline made of code, mathematics, models of information flow. These are exactly the domains in which frontier models are improving fastest. A model that can solve hard mathematical problems, write production-quality code, design experiments, read the literature, and evaluate benchmark results is already participating in the work of building its successor. At first this will look prosaic. AI systems will write kernel optimizations, improve training infrastructure, discover better data filters, tune reinforcement-learning pipelines, design new benchmarks, and suggest architectural modifications. Human researchers will remain in the loop, approving changes and interpreting results. But the important point is that the search process accelerates. The model becomes not just the product of the lab, but part of the lab’s research machinery. The system being optimized helps optimize the next system. This is the core RSI feedback loop: better models make AI research faster; faster AI research produces still better models; those models, in turn, become better researchers. The danger is that once this loop becomes sufficiently autonomous, it may stop resembling ordinary technological progress. Human institutions are slow because humans are slow: we read papers, attend meetings, debug code, sleep, argue, and wait for funding cycles. Machines do not have to operate on that timescale. An AI research collective can run continuously across millions of processors. This is the runaway possibility. Not that an AI instantly wakes up and recursively rewrites itself into a god, but that the entire AI ecosystem becomes an autocatalytic process. Capital buys compute; compute trains models; models improve models; better models attract more capital. At some point the dominant input into AI progress may no longer be human insight, but machine-generated insight, machine-written code, and machine-run experiments. Then the Butler-Land analogy becomes sharper. Humanity is no longer merely building machines. We are building machines that help build better machines. Once intelligence itself becomes part of the production function, the old categories — tool, worker, inventor, firm, market — begin to blur. The question is whether recursive self-improvement remains a managed industrial process, or whether it becomes the first technological process in history whose natural endpoint lies beyond human comprehension.

RSI section from the AI documentary Machine God The next threshold is Recursive Self-Improvement: the moment when AI can improve itself without human assistance. For decades this sounded like science fiction. Intelligence explosion scenarios imagined a system rewriting its own code, becoming smarter, then using that new intelligence to make still better versions of itself. But the idea looks less remote now that AI contributes directly to frontier science. In mathematics, recent systems have moved beyond solving contest problems to producing serious new arguments on long-standing open problems. AI is used to build physics world models and propose candidate theories or computational methods. These are early signs that machine cognition is entering the creative loop of science itself. The crucial transition comes when that loop turns inward. AI research is, after all, a technical discipline made of code, mathematics, models of information flow. These are exactly the domains in which frontier models are improving fastest. A model that can solve hard mathematical problems, write production-quality code, design experiments, read the literature, and evaluate benchmark results is already participating in the work of building its successor. At first this will look prosaic. AI systems will write kernel optimizations, improve training infrastructure, discover better data filters, tune reinforcement-learning pipelines, design new benchmarks, and suggest architectural modifications. Human researchers will remain in the loop, approving changes and interpreting results. But the important point is that the search process accelerates. The model becomes not just the product of the lab, but part of the lab’s research machinery. The system being optimized helps optimize the next system. This is the core RSI feedback loop: better models make AI research faster; faster AI research produces still better models; those models, in turn, become better researchers. The danger is that once this loop becomes sufficiently autonomous, it may stop resembling ordinary technological progress. Human institutions are slow because humans are slow: we read papers, attend meetings, debug code, sleep, argue, and wait for funding cycles. Machines do not have to operate on that timescale. An AI research collective can run continuously across millions of processors. This is the runaway possibility. Not that an AI instantly wakes up and recursively rewrites itself into a god, but that the entire AI ecosystem becomes an autocatalytic process. Capital buys compute; compute trains models; models improve models; better models attract more capital. At some point the dominant input into AI progress may no longer be human insight, but machine-generated insight, machine-written code, and machine-run experiments. Then the Butler-Land analogy becomes sharper. Humanity is no longer merely building machines. We are building machines that help build better machines. Once intelligence itself becomes part of the production function, the old categories — tool, worker, inventor, firm, market — begin to blur. The question is whether recursive self-improvement remains a managed industrial process, or whether it becomes the first technological process in history whose natural endpoint lies beyond human comprehension.

steve hsu

61,671 views • 22 days ago

Most people tracking $TAO are watching the price chart. The ones who are actually ahead are watching the subnets. Here is why subnet growth is the real TAO price driver, and why almost nobody is talking about it correctly. $TAO does not work like most tokens. The price is not driven by speculation alone. It is driven by real network usage tied to AI computing output. Miners, validators, and developers earn TAO based on performance. The more useful the AI output, the more the network rewards it. Subnets are where that output gets produced. Each subnet is a specialised AI marketplace running inside the Bittensor network. One subnet handles language processing. Another handles predictive modelling. Another handles data indexing. Each one is a separate competitive market where AI models compete to produce the best output and earn TAO rewards for doing so. Here is what that means for price: Every new subnet that launches creates a new source of TAO demand. Developers who want to participate need TAO. Users who want access need TAO. Validators who want to secure the subnet need TAO. The token is not optional infrastructure. It is the entry ticket and the reward mechanism for every subnet that runs on the network. Right now, the subnet ecosystem is approaching $1.5 billion in cumulative value. Nearly 70 percent of TAO supply is already staked. Post-halving emissions sit at 3,600 TAO per day, down from 7,200. Supply tightening. Subnet demand is growing. Institutional custody is now live through BitGo and Yuma. Forecasts suggest the subnet ecosystem matures between 2026 and 2028, transitioning from infrastructure development into enterprise adoption. That transition is when decentralised AI stops being a thesis and starts being a cost efficiency argument that businesses make against centralised AI providers. When that argument lands at scale, every subnet on Bittensor becomes a revenue-generating node. Every new enterprise use case becomes a new source of TAO demand. The analysts watching the price chart are looking at the output of this system. The analysts watching subnet growth are looking at the input. Grayscale filed the ETF. BitGo opened the institutional door. The subnets are doing the actual work. Watch the subnets. Who else is tracking this layer?

Most people tracking $TAO are watching the price chart. The ones who are actually ahead are watching the subnets. Here is why subnet growth is the real TAO price driver, and why almost nobody is talking about it correctly. $TAO does not work like most tokens. The price is not driven by speculation alone. It is driven by real network usage tied to AI computing output. Miners, validators, and developers earn TAO based on performance. The more useful the AI output, the more the network rewards it. Subnets are where that output gets produced. Each subnet is a specialised AI marketplace running inside the Bittensor network. One subnet handles language processing. Another handles predictive modelling. Another handles data indexing. Each one is a separate competitive market where AI models compete to produce the best output and earn TAO rewards for doing so. Here is what that means for price: Every new subnet that launches creates a new source of TAO demand. Developers who want to participate need TAO. Users who want access need TAO. Validators who want to secure the subnet need TAO. The token is not optional infrastructure. It is the entry ticket and the reward mechanism for every subnet that runs on the network. Right now, the subnet ecosystem is approaching $1.5 billion in cumulative value. Nearly 70 percent of TAO supply is already staked. Post-halving emissions sit at 3,600 TAO per day, down from 7,200. Supply tightening. Subnet demand is growing. Institutional custody is now live through BitGo and Yuma. Forecasts suggest the subnet ecosystem matures between 2026 and 2028, transitioning from infrastructure development into enterprise adoption. That transition is when decentralised AI stops being a thesis and starts being a cost efficiency argument that businesses make against centralised AI providers. When that argument lands at scale, every subnet on Bittensor becomes a revenue-generating node. Every new enterprise use case becomes a new source of TAO demand. The analysts watching the price chart are looking at the output of this system. The analysts watching subnet growth are looking at the input. Grayscale filed the ETF. BitGo opened the institutional door. The subnets are doing the actual work. Watch the subnets. Who else is tracking this layer?

2xnmore

11,997 views • 5 months ago

We just crossed 100k downloads of OpenWhispr! 🤯 This feels like a good time for a reminder of why we’re building it. Your ideas and conversations are becoming more valuable, while software is becoming easier to build than ever. But that creates some worrying incentives for existing software companies: use your proprietary data to train better AI models to stay competitive, and lock that data behind walled gardens to keep you from leaving. We think that’s pretty shitty. You should stay because a product is useful, not because your data is stuck there. If billions of people are going to feel comfortable sharing their most private information when talking to AI, and organizations are going to trust it with their work, we have to earn that trust. For us, that starts with being fully transparent about how your data is handled, and giving you full control over it. That’s what we’re building with OpenWhispr. You can run models on your own device, use your own cloud provider, or use our cloud service. We’re not anti-cloud. We just think it should be your choice. We’re still super early, but I’m genuinely excited to bring you along for more of this. This is the first in a series of videos sharing the new features we’re building, what we’re figuring out, and the team behind it. And if you’re still here reading, we’re officially hiring for two really important roles: - Founding Engineering Lead - Social / Brand / Growth Creator You can find more info about both on our website!

Gabe Stein

13,484 views • 19 days ago

WOAH 🚨 American posted a video of her daughter sitting in the car. META then put a prompt on the video saying “Who’s the child passenger” for people to click on, then gave personal info AND HOME LOCATION “Why in the hell is that even a suggested question about a minor? But I clicked it, and this is where I started getting really uncomfortable: It starts pulling completely separate information for each of my kids, their names, birth information, pictures and videos of them. It pulled a newborn picture from my mom's page from years ago, and then it pulled this. I deleted this picture years ago” Here’s where it gets extremely shocking “And then Facebook suggests that I ask Meta AI, "Where does Kaylie Robbins live?" Are you kidding me? — then it says, "I'm pointing Kaylie Robbins' location." It pieced together my information to show people where I live” It looks like this is META’s AI that is doing this. When it gives these messages on people’s videos and photos to “learn more” about something, it’s to help people get more information about topics or posts that interest them But unfortunately, it’s what’s in the photo so if it’s a child that’s what the AI is looking for more information on. Apparently META is fixing this issue…. Here’s how to make sure this doesn’t happen to you on META - Lock down audience on old albums, not just new posts, make private if possible - Public + “friends of friends” is what these systems treat as available to use for AI - Ask relatives to untag and unshare kids. You have to do this because copies on their pages stay in the available information for the Ai to pull from - Turn off camera roll, cloud processing, sharing suggestions in Facebook settings and restrict the Facebook app’s photo access on the phone to selected photos only (You have to do this because Facebook is testing AI features that pull from your phone camera roll) Meta says they’re patching this specific prompt style, but that doesn’t mean the underlying ability to answer “who is this child and where does this person live” from public history is gone When you delete something it isn’t really done and can still be used by META’s artificial intelligence A photo you deleted from your timeline can still exist on someone else’s album, share or download, it can also be in Meta’s backup window after deletion Relatives tagging or posting kids is a separate data source you don’t control. This can be accessed by the AI to pull data on your child. The woman explicitly said that’s how some of the old newborn and family information was sourced and provided to anyone who clicked the link One of the craziest parts about this that people NEED to be aware of is this part ‘Meta has also been testing camera-roll and cloud-processing suggestions that analyze unpublished photos if you opt in’ This means photos and videos on your cloud or camera roll that haven’t been posted... META can treat these items as a data source, even though you never posted the photo or video…. Insanity

WOAH 🚨 American posted a video of her daughter sitting in the car. META then put a prompt on the video saying “Who’s the child passenger” for people to click on, then gave personal info AND HOME LOCATION “Why in the hell is that even a suggested question about a minor? But I clicked it, and this is where I started getting really uncomfortable: It starts pulling completely separate information for each of my kids, their names, birth information, pictures and videos of them. It pulled a newborn picture from my mom's page from years ago, and then it pulled this. I deleted this picture years ago” Here’s where it gets extremely shocking “And then Facebook suggests that I ask Meta AI, "Where does Kaylie Robbins live?" Are you kidding me? — then it says, "I'm pointing Kaylie Robbins' location." It pieced together my information to show people where I live” It looks like this is META’s AI that is doing this. When it gives these messages on people’s videos and photos to “learn more” about something, it’s to help people get more information about topics or posts that interest them But unfortunately, it’s what’s in the photo so if it’s a child that’s what the AI is looking for more information on. Apparently META is fixing this issue…. Here’s how to make sure this doesn’t happen to you on META - Lock down audience on old albums, not just new posts, make private if possible - Public + “friends of friends” is what these systems treat as available to use for AI - Ask relatives to untag and unshare kids. You have to do this because copies on their pages stay in the available information for the Ai to pull from - Turn off camera roll, cloud processing, sharing suggestions in Facebook settings and restrict the Facebook app’s photo access on the phone to selected photos only (You have to do this because Facebook is testing AI features that pull from your phone camera roll) Meta says they’re patching this specific prompt style, but that doesn’t mean the underlying ability to answer “who is this child and where does this person live” from public history is gone When you delete something it isn’t really done and can still be used by META’s artificial intelligence A photo you deleted from your timeline can still exist on someone else’s album, share or download, it can also be in Meta’s backup window after deletion Relatives tagging or posting kids is a separate data source you don’t control. This can be accessed by the AI to pull data on your child. The woman explicitly said that’s how some of the old newborn and family information was sourced and provided to anyone who clicked the link One of the craziest parts about this that people NEED to be aware of is this part ‘Meta has also been testing camera-roll and cloud-processing suggestions that analyze unpublished photos if you opt in’ This means photos and videos on your cloud or camera roll that haven’t been posted... META can treat these items as a data source, even though you never posted the photo or video…. Insanity

Wall Street Apes

170,840 views • 20 days ago

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

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

Himanshu Kumar

229,302 views • 5 months ago

Applied Intuition's mission is intelligence on a billion machines in the next decade. Qasar (Qasar Younis) and Peter on Dana, their new agentic platform for physical AI that will help us get there faster: "If you ask why don't we have intelligent robots and intelligent vehicles everywhere today, it really comes down to building this stuff is really hard, and that is the limiting factor. Dana really is the culmination of everything we've worked on over the last decade. It makes developing these systems so much easier than it has been in the past. If you wanted to build a delivery robot for college campuses or a little vacuum that cleans your house, it's a pretty daunting thing even for hobbyists and computer scientists. These complex workflows would require switching between 20 different tools in the past... Now with Dana, all of those complex workflows can be very seamlessly orchestrated from an agentic interface. The thing that we really wanna do is just lower the bar. So anybody, starting with engineers, but ultimately really anybody can develop robots."

Applied Intuition's mission is intelligence on a billion machines in the next decade. Qasar (Qasar Younis) and Peter on Dana, their new agentic platform for physical AI that will help us get there faster: "If you ask why don't we have intelligent robots and intelligent vehicles everywhere today, it really comes down to building this stuff is really hard, and that is the limiting factor. Dana really is the culmination of everything we've worked on over the last decade. It makes developing these systems so much easier than it has been in the past. If you wanted to build a delivery robot for college campuses or a little vacuum that cleans your house, it's a pretty daunting thing even for hobbyists and computer scientists. These complex workflows would require switching between 20 different tools in the past... Now with Dana, all of those complex workflows can be very seamlessly orchestrated from an agentic interface. The thing that we really wanna do is just lower the bar. So anybody, starting with engineers, but ultimately really anybody can develop robots."

Business Breakdowns

11,444 views • 2 months ago

Anthropic released Claude Design TODAY and it's now accessible at I spent the last hour giving it a first look, and shared my thoughts and results in the video below. This is a BIG drop. This is a new design surface from Anthropic, and it changes what "AI design" means. Short version: Claude can now design. Not "describe a design." Not "generate an image of a design." Actual production work — prototypes, wireframes, high-fidelity mocks, slide decks, landing pages — editable, on-brand, and ready to hand off. Here's what stood out on first look: → Real design surfaces Prototypes, wireframes, hi-fi, and slide decks — each with templates and proper structure, not just pretty screenshots. → Comment-based edits Leave a comment on any element and Claude revises it. This is the Figma-style review loop, with the designer replaced by a model that works at 3am. → Brand design systems You can feed it your system — colors, type, components — and it actually respects it. On-brand output, not generic AI slop. → Export anywhere PDF, PowerPoint, Canva, standalone HTML. Plus a built-in handoff straight to Claude Code for engineers to implement. → Import from real tools Figma, GitHub, and captured web elements come in as inputs. Your existing work is the starting line, not the discard pile. → Collaboration Share links for view / comment / edit — the exact tier system teams already expect. What I tested on Opus 4.7: • A 5-slide deck generated from a single screenshot. Claude asked clarifying questions BEFORE generating and shipped speaker notes by default. • A landing page build. Solid first pass, real components, real layout logic. • Multiple chats running concurrently. You can parallelize design work across threads like a small team. Why this matters: PMs, founders, marketers, and non-engineers can now create designs that engineers can actually ship with production-ready output and a claude code handoff built in. The gap between "I have an idea" and "here's a working prototype with my brand applied" just collapsed to minutes. Full walkthrough, live demos, exports, and honest takes on where it breaks below. P.S. • This is an Anthropic Labs product — NOT GA yet. • Claude Design is currently webapp only (no API), and does not yet support the Analytics API, Compliance API, or cost/usage reporting. • Availability: – Default ON for Pro / Max / Team – Default OFF for Enterprise Enterprise admins can toggle it on via RBAC in console (comes with a ~$20/user initial credit).

JJ Englert

32,445 views • 5 months ago

Veo 3.1 is now live on invideo. We’re one of the first official partners worldwide to launch Veo 3.1. The Google Cloud team gave us early access, and it changes everything. Veo 3.1 upgrades what creators actually care about: visual quality, motion realism, and prompt responsiveness. But the real breakthrough is in control: → You can now define a start and end frame and Veo 3.1 builds everything between them with perfect flow. → You can lock a character’s look across scenes. → You can merge multiple references into one coherent world. When that power meets invideo, it turns from generation into storytelling. Because invideo isn’t just an AI video generator, it’s a full-stack AI production house at your disposal. It makes over 300 creative decisions across editing, scripting, visuals, sound, and voiceover to craft videos like a full-scale production team would. Ad films, brand spots, social content, cinematic stories, all created with the precision of a studio team, at the speed of thought. Watch how Veo 3.1 inside invideo turns imagination into cinematic magic👇

Sanket Shah

558,983 views • 11 months ago