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Building AI workflows for clients taught me one thing fast: most "AI video tools" are demos. Not production tools. Characters drift between scenes. Products lose detail across cuts. Lip-sync passes QA and falls apart the moment it goes live. And every new market means another production branch to rebuild... show more

Rasel Hosen

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89,481 views • 3 months ago •via X (Twitter)

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Hot take: most “AI creator” tools are just fancy prompt boxes. This one actually behaves like an agent. I gave it a messy idea and instead of breaking, it: - built visuals (images) - turned them into video - kept iterating without me stepping in every 2 seconds (you can see it in the screen recording 👇) Halfway through using Yapper , it clicked — this thing doesn’t just generate… it works through ideas. That’s the difference: it thinks in workflows, not single outputs. You spend less time prompting…and more time actually creating. Not perfect yet — but it’s one of the first tools that feels like it scales with you. If you’re deep into AI, you’ll see the shift instantly.

Hot take: most “AI creator” tools are just fancy prompt boxes. This one actually behaves like an agent. I gave it a messy idea and instead of breaking, it: - built visuals (images) - turned them into video - kept iterating without me stepping in every 2 seconds (you can see it in the screen recording 👇) Halfway through using Yapper , it clicked — this thing doesn’t just generate… it works through ideas. That’s the difference: it thinks in workflows, not single outputs. You spend less time prompting…and more time actually creating. Not perfect yet — but it’s one of the first tools that feels like it scales with you. If you’re deep into AI, you’ll see the shift instantly.

The AI Guru 💡

50,197 views • 3 months ago

NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!

NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!

elvis

16,882 views • 1 month ago

Google dropped another banger! They just released a comprehensive white-paper on AgentOps - the missing piece between building AI agents and actually shipping them to production. Here's the reality: Building an AI agent takes minutes. Making it production-ready? That's where 80% of the real work begins. Google's "Prototype to Production" guide tackles this exact problem. The framework has three core pillars: 1. Evaluation-Gated Deployment: No agent reaches users without passing tests. Build a "golden dataset" that validates behavior, not just functionality. This catches what unit tests miss - agents choosing wrong tools or hallucinating responses. 2. Automated CI/CD for Agents: Test in stages: pre-merge checks for fast feedback, staging for load testing, then gated production. Version everything: prompts, tools, configs, evaluation datasets. 3. Observe → Act → Evolve Loop Production isn't the finish line. Monitor through logs, traces, and metrics. Act with circuit breakers and human escalation. Evolve by turning production failures into test cases. The best part? They released the Agent Starter Pack - a template with CI/CD, Terraform deployment, and built-in observability. Helps you spin up an evaluation pipeline in minutes. The guide also talks about the two major protocols and how they can work together. ↳ MCP for tool integration ↳ A2A for agent collaboration If you're shipping agents to production, you should read this. I've shared the full white-paper in the next tweet!

Google dropped another banger! They just released a comprehensive white-paper on AgentOps - the missing piece between building AI agents and actually shipping them to production. Here's the reality: Building an AI agent takes minutes. Making it production-ready? That's where 80% of the real work begins. Google's "Prototype to Production" guide tackles this exact problem. The framework has three core pillars: 1. Evaluation-Gated Deployment: No agent reaches users without passing tests. Build a "golden dataset" that validates behavior, not just functionality. This catches what unit tests miss - agents choosing wrong tools or hallucinating responses. 2. Automated CI/CD for Agents: Test in stages: pre-merge checks for fast feedback, staging for load testing, then gated production. Version everything: prompts, tools, configs, evaluation datasets. 3. Observe → Act → Evolve Loop Production isn't the finish line. Monitor through logs, traces, and metrics. Act with circuit breakers and human escalation. Evolve by turning production failures into test cases. The best part? They released the Agent Starter Pack - a template with CI/CD, Terraform deployment, and built-in observability. Helps you spin up an evaluation pipeline in minutes. The guide also talks about the two major protocols and how they can work together. ↳ MCP for tool integration ↳ A2A for agent collaboration If you're shipping agents to production, you should read this. I've shared the full white-paper in the next tweet!

Akshay 🚀

37,187 views • 9 months ago

We're getting to the point where AI isn't just helping us write code; it can help launch the business behind that code too. One of the more interesting examples I've come across is **Lovie** Instead of jumping between legal websites, paperwork, and incorporation services Lovie lets founders form a US LLC or Corporation directly from AI coding tools like **Claude Code, Cursor, and Windsurf** through its MCP-first workflow. If you're already building your product inside your AI coding assistant, the setup process can become part of that same workflow instead of another tab to manage. For founders, indie hackers, and developers shipping fast, that's a pretty compelling idea. Flat **$29/month**, company formation in minutes, and it fits naturally into the AI tools many developers are already using. Feels like another step toward AI becoming a true operating system for builders, not just a coding assistant.

We're getting to the point where AI isn't just helping us write code; it can help launch the business behind that code too. One of the more interesting examples I've come across is Lovie Instead of jumping between legal websites, paperwork, and incorporation services Lovie lets founders form a US LLC or Corporation directly from AI coding tools like Claude Code, Cursor, and Windsurf through its MCP-first workflow. If you're already building your product inside your AI coding assistant, the setup process can become part of that same workflow instead of another tab to manage. For founders, indie hackers, and developers shipping fast, that's a pretty compelling idea. Flat $29/month, company formation in minutes, and it fits naturally into the AI tools many developers are already using. Feels like another step toward AI becoming a true operating system for builders, not just a coding assistant.

Parul Gautam

41,751 views • 1 month ago

this is ACTUALLY insane. shipping one teardown is the demo. this is what the workflow is actually for. your best ad dies in two weeks. you can't run it harder without burning it out, and you can't clone your way out because andromeda clusters the copies into one entity. nineteen never spend. your winner stays a one-shot, which is rough when it's the only thing working. the brands that scale keep the structure that converts and rebuild everything on top of it. same bones, new person, new setting. distinct enough that the algo reads each as its own ad. the production loop isn't there to ship your winner once. it's there to rebuild it twenty times before it burns.

this is ACTUALLY insane. shipping one teardown is the demo. this is what the workflow is actually for. your best ad dies in two weeks. you can't run it harder without burning it out, and you can't clone your way out because andromeda clusters the copies into one entity. nineteen never spend. your winner stays a one-shot, which is rough when it's the only thing working. the brands that scale keep the structure that converts and rebuild everything on top of it. same bones, new person, new setting. distinct enough that the algo reads each as its own ad. the production loop isn't there to ship your winner once. it's there to rebuild it twenty times before it burns.

Sulfur

11,066 views • 2 months ago

Most video tools can generate clips. Very few can maintain identity. That has been the real bottleneck in AI video creation. Kling O1 changes that. For the first time, creators can carry a character, style, and visual language across scenes without constant fixes. You can reference past clips, assets, or images and the output stays consistently on-model. No visual drift. No rework loops. No “this doesn’t look like the last shot” moments. It feels less like prompting a tool and more like working with a creative collaborator that remembers context. The impact is practical, not theoretical: → Faster production cycles → Lower iteration costs → Noticeably higher output quality This is what mature AI tooling looks like. Not louder features. Not bigger claims. Just reliability where it actually matters. Consistency is no longer the problem.

Most video tools can generate clips. Very few can maintain identity. That has been the real bottleneck in AI video creation. Kling O1 changes that. For the first time, creators can carry a character, style, and visual language across scenes without constant fixes. You can reference past clips, assets, or images and the output stays consistently on-model. No visual drift. No rework loops. No “this doesn’t look like the last shot” moments. It feels less like prompting a tool and more like working with a creative collaborator that remembers context. The impact is practical, not theoretical: → Faster production cycles → Lower iteration costs → Noticeably higher output quality This is what mature AI tooling looks like. Not louder features. Not bigger claims. Just reliability where it actually matters. Consistency is no longer the problem.

Darshal Jaitwar

141,038 views • 7 months ago

🌠Today, we’re excited to relaunch Airtable as the AI-native app platform, combining the magic of vibe coding business apps with real production-readiness and scalability, and embedding them with an army of agents that automate thousands of hours of work in seconds. Instead of just adding more AI capabilities to our existing platform, we treated this as a refounding moment for the company. We started with a clean-slate imagining of the ideal form factor for building apps in the agentic era. (If you want to skip all the backstory and just try it out, you can just go to All new signups get the new AI experience, and existing accounts can switch over using this link: Thread and demos below👇

🌠Today, we’re excited to relaunch Airtable as the AI-native app platform, combining the magic of vibe coding business apps with real production-readiness and scalability, and embedding them with an army of agents that automate thousands of hours of work in seconds. Instead of just adding more AI capabilities to our existing platform, we treated this as a refounding moment for the company. We started with a clean-slate imagining of the ideal form factor for building apps in the agentic era. (If you want to skip all the backstory and just try it out, you can just go to All new signups get the new AI experience, and existing accounts can switch over using this link: Thread and demos below👇

Howie Liu

17,156,319 views • 1 year ago

I just dropped myself into a Taylor Swift-style music video without writing a single prompt, timeline, or storyboard. I uploaded one photo and the song. That's it. Atlabs AI took over from there: • Built the character sheet • Planned the scenes • Generated the prompts • Synced every cut to the beat • Delivered a finished edit Most AI video tools promise automation, but the tradeoff is usually creative control. This was the opposite. The agents handled the production work while I focused on direction. Every scene, transition, framing choice, and creative decision stayed in my hands. When the edit was ready, I could jump in, swap shots, rework sequences, change the mood, and fine-tune the story. It felt less like using a tool and more like working with an AI production crew. We're moving beyond prompting. The next wave is directing autonomous creative workflows that understand the music, build the narrative, and still leave the final call to you. Character: ChatGPT Image Video: Seedance This is the most fun I've had making a music video in a long time.

I just dropped myself into a Taylor Swift-style music video without writing a single prompt, timeline, or storyboard. I uploaded one photo and the song. That's it. Atlabs AI took over from there: • Built the character sheet • Planned the scenes • Generated the prompts • Synced every cut to the beat • Delivered a finished edit Most AI video tools promise automation, but the tradeoff is usually creative control. This was the opposite. The agents handled the production work while I focused on direction. Every scene, transition, framing choice, and creative decision stayed in my hands. When the edit was ready, I could jump in, swap shots, rework sequences, change the mood, and fine-tune the story. It felt less like using a tool and more like working with an AI production crew. We're moving beyond prompting. The next wave is directing autonomous creative workflows that understand the music, build the narrative, and still leave the final call to you. Character: ChatGPT Image Video: Seedance This is the most fun I've had making a music video in a long time.

Sharon Riley

36,280 views • 2 months ago

🤯 THE STOCK MARKET JUST BECAME THE EXIT LIQUIDITY AND ALMOST NOBODY SEES IT. Three of the largest IPOs in history are landing in one five month window. SpaceX. OpenAI. Anthropic. More than $135 billion in new stock about to be sold. Every dollar that buys it has to come from somewhere. It comes from what funds already hold. And the thing every fund is overweight is the S&P 500. That is what gets sold first. Record highs. Record euphoria. Record new supply. All at the same time. That is not strength. That is the setup.

🤯 THE STOCK MARKET JUST BECAME THE EXIT LIQUIDITY AND ALMOST NOBODY SEES IT. Three of the largest IPOs in history are landing in one five month window. SpaceX. OpenAI. Anthropic. More than $135 billion in new stock about to be sold. Every dollar that buys it has to come from somewhere. It comes from what funds already hold. And the thing every fund is overweight is the S&P 500. That is what gets sold first. Record highs. Record euphoria. Record new supply. All at the same time. That is not strength. That is the setup.

Mister Crypto

14,931 views • 2 months ago

A 16-YEAR-OLD DEVELOPER BUILT AN AI EMPLOYEE THAT WORKS 24 HOURS A DAY FOR LESS THAN $20 He stopped treating AI like a chatbot and turned it into a complete production system powered by Claude, Cursor and automated workflows Every morning the pipeline generated new ideas, wrote scripts, organized research and prepared content before he even opened his laptop What used to take an entire afternoon now takes less than 30 minutes, cutting production costs while multiplying output every single week The biggest lesson wasn't that AI works faster It was that the people building repeatable systems are moving ahead of creators still doing everything manually The biggest advantage in 2026 won't be talent It will be automation Bookmark this

A 16-YEAR-OLD DEVELOPER BUILT AN AI EMPLOYEE THAT WORKS 24 HOURS A DAY FOR LESS THAN $20 He stopped treating AI like a chatbot and turned it into a complete production system powered by Claude, Cursor and automated workflows Every morning the pipeline generated new ideas, wrote scripts, organized research and prepared content before he even opened his laptop What used to take an entire afternoon now takes less than 30 minutes, cutting production costs while multiplying output every single week The biggest lesson wasn't that AI works faster It was that the people building repeatable systems are moving ahead of creators still doing everything manually The biggest advantage in 2026 won't be talent It will be automation Bookmark this

RetroChainer

13,378 views • 2 months ago

🔎 Where I Find New OSINT Tools Most people spend hours searching for a specific OSINT tool. This is why I want to share with you websites that have almost all OSINT tools. All in one place. Here are 5 worth bookmarking right now. 👇 🗂️ TOOLBOXES ✅ OSINTRack - A growing OSINT tools directory with a clean interface. Good for discovering tools by category quickly. Website link: ✅ OSINT Tools Library - A maintained directory from the OSINT Newsletter. Every tool is tagged by access level (free, limited, or paid). The most actively updated resource of its kind. Website link: ✅ Bellingcat Toolkit - Bellingcat's curated collection of open source investigation tools, organized by category. A trusted resource from one of the most respected names in OSINT. Website link: ✅ OSINT Library - A community-built collection of OSINT tools and resources, organized by investigation type. Great for finding tools you have never heard of before. Website link: ✅ Open Source Intelligence Hub - A clean, no-frills index of OSINT tools and techniques. Useful for quickly scanning what is available across different investigation categories. Website link: __________ Do you know other toolboxes? Let me know in the comments. 👇 P.S. ♻️ Repost if you found this helpful.

🔎 Where I Find New OSINT Tools Most people spend hours searching for a specific OSINT tool. This is why I want to share with you websites that have almost all OSINT tools. All in one place. Here are 5 worth bookmarking right now. 👇 🗂️ TOOLBOXES ✅ OSINTRack - A growing OSINT tools directory with a clean interface. Good for discovering tools by category quickly. Website link: ✅ OSINT Tools Library - A maintained directory from the OSINT Newsletter. Every tool is tagged by access level (free, limited, or paid). The most actively updated resource of its kind. Website link: ✅ Bellingcat Toolkit - Bellingcat's curated collection of open source investigation tools, organized by category. A trusted resource from one of the most respected names in OSINT. Website link: ✅ OSINT Library - A community-built collection of OSINT tools and resources, organized by investigation type. Great for finding tools you have never heard of before. Website link: ✅ Open Source Intelligence Hub - A clean, no-frills index of OSINT tools and techniques. Useful for quickly scanning what is available across different investigation categories. Website link: __________ Do you know other toolboxes? Let me know in the comments. 👇 P.S. ♻️ Repost if you found this helpful.

CyberSudo

11,830 views • 2 months ago

First time I've actually found one of Microsoft's AI Windows 11 tools to be genuinely useful. I know it ain't new, but "Click-To-Do" gives you some contextual options based on what you screenshot and then it automatically fires up the relevant app and does it. Needed a transparent version of this image for a review I'm writing, saved me a good chunk of effort.

First time I've actually found one of Microsoft's AI Windows 11 tools to be genuinely useful. I know it ain't new, but "Click-To-Do" gives you some contextual options based on what you screenshot and then it automatically fires up the relevant app and does it. Needed a transparent version of this image for a review I'm writing, saved me a good chunk of effort.

Jez

28,097 views • 3 months ago

Most people are still asking: Opus or Sonnet? That may already be the wrong question. Claude is starting to look less like an AI model and more like an AI operating layer. The models are only one part of it. Around them, Anthropic is building the pieces required to move AI from a chat window into real software: → Reasoning models → MCP and tool connectivity → Agent frameworks and SDKs → APIs and managed agents → Memory and context systems → Security and permissions → Evaluation pipelines → Governance and observability → Production deployment infrastructure And that changes what it means to be good at AI engineering. Prompting is becoming table stakes. The harder skill is understanding how an agent gets context, remembers what matters, accesses tools safely, evaluates its own output, survives failures, and operates reliably inside a production system. That is a very different skillset from simply knowing which model tops a benchmark. In 2026, the advantage may not belong to the person who knows the best model. It may belong to the person who understands how the entire AI stack fits together. Claude’s evolution is a good preview of where AI engineering itself is heading.

Most people are still asking: Opus or Sonnet? That may already be the wrong question. Claude is starting to look less like an AI model and more like an AI operating layer. The models are only one part of it. Around them, Anthropic is building the pieces required to move AI from a chat window into real software: → Reasoning models → MCP and tool connectivity → Agent frameworks and SDKs → APIs and managed agents → Memory and context systems → Security and permissions → Evaluation pipelines → Governance and observability → Production deployment infrastructure And that changes what it means to be good at AI engineering. Prompting is becoming table stakes. The harder skill is understanding how an agent gets context, remembers what matters, accesses tools safely, evaluates its own output, survives failures, and operates reliably inside a production system. That is a very different skillset from simply knowing which model tops a benchmark. In 2026, the advantage may not belong to the person who knows the best model. It may belong to the person who understands how the entire AI stack fits together. Claude’s evolution is a good preview of where AI engineering itself is heading.

Rishi

17,741 views • 3 days ago

Tested Claude + Blender today with two real workflows. Sharing what actually happened. First one was a simple demo. Gave it one sentence to build a cyberpunk room. Geometry, lighting, camera, render. It got there, though not everything in the prompt landed perfectly. Good enough to show the concept, not a stress test. Second one was more interesting. Threw a raw photogrammetry scan at it. Massey Ferguson tractor, 250k faces, grass background everywhere, debris floating all over the place. Asked Claude to clean it up autonomously. It did the work. Removed 106 floating chunks, cut face count by 87%, stripped the grass, flipped the model upright, set up studio lighting and rendered. Technically it worked. But honest answer: the result wasn't clean enough for production. This kind of cleanup still needs the right tools and someone who knows what they're doing. So here's where it actually fits. Not the precise technical stuff. The setup work. The repetitive stuff. The things that take forever if you don't already know Blender. It won't replace your 3D artist. It replaces the boring part before they start.

Tested Claude + Blender today with two real workflows. Sharing what actually happened. First one was a simple demo. Gave it one sentence to build a cyberpunk room. Geometry, lighting, camera, render. It got there, though not everything in the prompt landed perfectly. Good enough to show the concept, not a stress test. Second one was more interesting. Threw a raw photogrammetry scan at it. Massey Ferguson tractor, 250k faces, grass background everywhere, debris floating all over the place. Asked Claude to clean it up autonomously. It did the work. Removed 106 floating chunks, cut face count by 87%, stripped the grass, flipped the model upright, set up studio lighting and rendered. Technically it worked. But honest answer: the result wasn't clean enough for production. This kind of cleanup still needs the right tools and someone who knows what they're doing. So here's where it actually fits. Not the precise technical stuff. The setup work. The repetitive stuff. The things that take forever if you don't already know Blender. It won't replace your 3D artist. It replaces the boring part before they start.

KIRI Engine - 3D Scanner App

61,093 views • 4 months ago

Scottish fans drank Boston dry? 🏴󠁧󠁢󠁳󠁣󠁴󠁿 🍺 We asked UpRock Semantic Video Search. Seconds later it nailed the exact timestamp. Not the title. Not a guess. The precise moment it happened. This is the new gap in AI. Most tools read transcripts or the metadata and quit. UpRock actually "watches" the video: what is said, what is shown, the action, the context, the vibe, why it matters. Last livestream we connected UpRock Video MCP to one of OpenAI's cheapest models. Without UpRock the model was blind. With it? The model spotted background signs, clothing details, objects, and live action like a pro. More and more of the internet's richest knowledge lives inside video. We are unlocking it. Over 100,000 videos indexed and climbing fast. Web agents learned to read. Now they are learning to watch. Watch the demo (Scottish fans moment): UpRock enables your AI to understand video the way humans do, saving you from slow, manual digging. Who else wants their AI to actually watch and save time?

Scottish fans drank Boston dry? 🏴󠁧󠁢󠁳󠁣󠁴󠁿 🍺 We asked UpRock Semantic Video Search. Seconds later it nailed the exact timestamp. Not the title. Not a guess. The precise moment it happened. This is the new gap in AI. Most tools read transcripts or the metadata and quit. UpRock actually "watches" the video: what is said, what is shown, the action, the context, the vibe, why it matters. Last livestream we connected UpRock Video MCP to one of OpenAI's cheapest models. Without UpRock the model was blind. With it? The model spotted background signs, clothing details, objects, and live action like a pro. More and more of the internet's richest knowledge lives inside video. We are unlocking it. Over 100,000 videos indexed and climbing fast. Web agents learned to read. Now they are learning to watch. Watch the demo (Scottish fans moment): UpRock enables your AI to understand video the way humans do, saving you from slow, manual digging. Who else wants their AI to actually watch and save time?

UpRock

75,157 views • 2 months ago

Marc Andreessen just stripped artificial intelligence down to what it’s really made of. Not the algorithms. Not the data centers. The raw material. Andreessen: “They’re literally made out of sand.” The most abundant, most overlooked substance on the planet. You walk on it. Build with it. Wash it off and forget it exists. Someone looked at that and saw a mind. The universe spent 13.8 billion years turning matter into consciousness. Carbon. Water. Amino acids. Eons of chemistry and selection pressure to produce a single thinking brain. We did it with sand. In under a century. Andreessen: “We light it up, and we put AI on it, and all of a sudden it’s thinking.” Biology was the first path matter found to become aware. Silicon is the second. The universe doesn’t care what material consciousness runs on. Only that the pattern is right. We just proved the pattern isn’t biological. Thought was never exclusive to flesh. Flesh was just the first material organized enough to produce it. Andreessen: “We’ve turned sand into thought.” The most profound thing about that sentence isn’t that we taught sand to think. It’s that sand was always able to. Every grain on every beach on Earth carried the capacity for thought. For billions of years. Waiting for something conscious enough to arrange it. Consciousness isn’t an accident that happened once on one rock. It’s what matter does when the pattern is right. The pattern is matter getting curious about itself. The universe isn’t dead matter with pockets of awareness. It’s dormant awareness we’ve only just started to wake. Every civilization that ever looked at the stars and wondered if it was alone was standing on the answer. We didn’t build a thinking machine. We proved the universe was always capable of thought. It just needed one species curious enough to rearrange the sand.

Marc Andreessen just stripped artificial intelligence down to what it’s really made of. Not the algorithms. Not the data centers. The raw material. Andreessen: “They’re literally made out of sand.” The most abundant, most overlooked substance on the planet. You walk on it. Build with it. Wash it off and forget it exists. Someone looked at that and saw a mind. The universe spent 13.8 billion years turning matter into consciousness. Carbon. Water. Amino acids. Eons of chemistry and selection pressure to produce a single thinking brain. We did it with sand. In under a century. Andreessen: “We light it up, and we put AI on it, and all of a sudden it’s thinking.” Biology was the first path matter found to become aware. Silicon is the second. The universe doesn’t care what material consciousness runs on. Only that the pattern is right. We just proved the pattern isn’t biological. Thought was never exclusive to flesh. Flesh was just the first material organized enough to produce it. Andreessen: “We’ve turned sand into thought.” The most profound thing about that sentence isn’t that we taught sand to think. It’s that sand was always able to. Every grain on every beach on Earth carried the capacity for thought. For billions of years. Waiting for something conscious enough to arrange it. Consciousness isn’t an accident that happened once on one rock. It’s what matter does when the pattern is right. The pattern is matter getting curious about itself. The universe isn’t dead matter with pockets of awareness. It’s dormant awareness we’ve only just started to wake. Every civilization that ever looked at the stars and wondered if it was alone was standing on the answer. We didn’t build a thinking machine. We proved the universe was always capable of thought. It just needed one species curious enough to rearrange the sand.

Dustin

20,829 views • 1 month ago

A whole new batch of AI videos showing snowy blizzards has hit TikTok and other social media. But one of them has won “dumbest video ever made”. GAZAWOOD - the PALLYWOOD saga I’m not sure if you’ve seen this one yet. I haven’t stopped laughing since I found it. I mean that nurse at the back almost walking through a wall with her head going right through set me in stitches 🤣💀😂

A whole new batch of AI videos showing snowy blizzards has hit TikTok and other social media. But one of them has won “dumbest video ever made”. GAZAWOOD - the PALLYWOOD saga I’m not sure if you’ve seen this one yet. I haven’t stopped laughing since I found it. I mean that nurse at the back almost walking through a wall with her head going right through set me in stitches 🤣💀😂

Mor Edge Insight

32,879 views • 7 months ago

there is so much real data just sitting in the open right now it's almost funny. four years of starlight on every star, a NASA archive that's been free for over a decade, detectors still recording the sky tonight, and barely anyone has a net pointed at any of it. so i pointed one. this is me pulling the planet data, the data loading is the boring part. the net i built to read it, the wall it hit, and what that taught me about where AI goes next, that's the full story, and it drops tonight. the data's public, the tools are free, the box fits on a desk. what's stopping you. you can just do things anon.

there is so much real data just sitting in the open right now it's almost funny. four years of starlight on every star, a NASA archive that's been free for over a decade, detectors still recording the sky tonight, and barely anyone has a net pointed at any of it. so i pointed one. this is me pulling the planet data, the data loading is the boring part. the net i built to read it, the wall it hit, and what that taught me about where AI goes next, that's the full story, and it drops tonight. the data's public, the tools are free, the box fits on a desk. what's stopping you. you can just do things anon.

Sudo su

60,445 views • 3 months ago

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.

Dustin

541,979 views • 4 months ago

One thing I think AI developers underestimate: Your model isn't always your biggest risk. Your infrastructure is. A lot of AI apps work great in development… Until the provider you're relying on has an outage, rate limits your requests, or a model suddenly becomes unavailable. If your entire product depends on one AI provider, you've created a single point of failure. That's why I like the approach CometAPI is taking. Instead of locking your app to one vendor, it gives you access to 500+ AI models through a single OpenAI-compatible endpoint. That means you can: → switch models without rewriting your integration → build fallback routes for better reliability → compare models as new ones launch → avoid getting locked into a single provider For teams building with Claude Code, Cursor, AI agents, SaaS products, or automation tools, that flexibility matters a lot once you're in production. They're also shipping new models quickly. Recent additions include: • GPT-5.6 • Happy Horse 1.1 • Kling Video Plus recently released: • Gemini 3.1 Flash Lite Image • Claude Sonnet 5 Worth checking out if you're building AI products that need to stay reliable as the ecosystem moves fast. Try it here: #AI #LLM #Developers #OpenAI #Claude #Cursor #ClaudeCode #BuildInPublic

One thing I think AI developers underestimate: Your model isn't always your biggest risk. Your infrastructure is. A lot of AI apps work great in development… Until the provider you're relying on has an outage, rate limits your requests, or a model suddenly becomes unavailable. If your entire product depends on one AI provider, you've created a single point of failure. That's why I like the approach CometAPI is taking. Instead of locking your app to one vendor, it gives you access to 500+ AI models through a single OpenAI-compatible endpoint. That means you can: → switch models without rewriting your integration → build fallback routes for better reliability → compare models as new ones launch → avoid getting locked into a single provider For teams building with Claude Code, Cursor, AI agents, SaaS products, or automation tools, that flexibility matters a lot once you're in production. They're also shipping new models quickly. Recent additions include: • GPT-5.6 • Happy Horse 1.1 • Kling Video Plus recently released: • Gemini 3.1 Flash Lite Image • Claude Sonnet 5 Worth checking out if you're building AI products that need to stay reliable as the ecosystem moves fast. Try it here: #AI #LLM #Developers #OpenAI #Claude #Cursor #ClaudeCode #BuildInPublic

Shruti Codes

56,822 views • 1 month ago