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Most AI models learn engineering from the internet. Grok will get notes directly from a giant rocket factory. xAI is prepping to use SpaceX engineering data for Grok’s 2T training run, excluding restricted ITAR material. That means design notes, test reports, complex aerospace math, and the kind of real-world...

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Mario Nawfal

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54,571 просмотров • 6 дней назад •via X (Twitter)

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SpaceX is building a giant air separation plant next to Starbase. Buying rocket propellant ingredients by the truckload gets old when you plan to launch all the time. The plant will make liquid oxygen and nitrogen on-site. Less tanker traffic. Less waiting. More Starship operations. Classic SpaceX: Build the rocket. Then build the factory next door that boosts launches. SpaceX / Writer: Annette, Designer: Janné
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SpaceX is building a giant air separation plant next to Starbase. Buying rocket propellant ingredients by the truckload gets old when you plan to launch all the time. The plant will make liquid oxygen and nitrogen on-site. Less tanker traffic. Less waiting. More Starship operations. Classic SpaceX: Build the rocket. Then build the factory next door that boosts launches. SpaceX / Writer: Annette, Designer: Janné

Mario Nawfal

237,990 просмотров • 18 дней назад

Grok is now showing up across AWS, Google Vertex, Oracle, and Azure. Which is basically the AI version of getting invited to every serious office building. But the real trick is still 𝕏. Most models read yesterday’s information landscape like homework. Grok gets to sit inside the live conversation on 𝕏 while the world is still arguing about it. That changes the answers. Grok xAI / Writer: Annette, Designer: Janné
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Grok is now showing up across AWS, Google Vertex, Oracle, and Azure. Which is basically the AI version of getting invited to every serious office building. But the real trick is still 𝕏. Most models read yesterday’s information landscape like homework. Grok gets to sit inside the live conversation on 𝕏 while the world is still arguing about it. That changes the answers. Grok xAI / Writer: Annette, Designer: Janné

Mario Nawfal

66,294 просмотров • 1 месяц назад

Grok 4.5 is already in private beta at SpaceX and Tesla. Classic Elon move to test a new AI model: Give it to the rocket engineers and the car-that-drives-itself engineers first. Early access coming soon. Still rough around the edges. Still being polished. But Elon says, “You will immediately notice the intelligence difference.” That matters. You’ll notice. Immediately. Grok xAI / Writer: Annette, Designer: Janné
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Grok 4.5 is already in private beta at SpaceX and Tesla. Classic Elon move to test a new AI model: Give it to the rocket engineers and the car-that-drives-itself engineers first. Early access coming soon. Still rough around the edges. Still being polished. But Elon says, “You will immediately notice the intelligence difference.” That matters. You’ll notice. Immediately. Grok xAI / Writer: Annette, Designer: Janné

Mario Nawfal

112,927 просмотров • 29 дней назад

AI agents are great until you have no idea what they’re actually doing. Grok-UI fixes that problem in a very builder-friendly way. A local-first web dashboard for Grok Build that lets you monitor CLI sessions, agent activity, Git changes, and session history in real time. It’s the kind of dashboard that makes agent work feel a lot less mysterious. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné
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AI agents are great until you have no idea what they’re actually doing. Grok-UI fixes that problem in a very builder-friendly way. A local-first web dashboard for Grok Build that lets you monitor CLI sessions, agent activity, Git changes, and session history in real time. It’s the kind of dashboard that makes agent work feel a lot less mysterious. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné

Mario Nawfal

36,179 просмотров • 12 часов назад

AI agents are great until you have no idea what they’re actually doing. Grok-UI fixes that problem in a very builder-friendly way. A local-first web dashboard for Grok Build that lets you monitor CLI sessions, agent activity, Git changes, and session history in real time. It’s the kind of dashboard that makes agent work feel a lot less mysterious. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné
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AI agents are great until you have no idea what they’re actually doing. Grok-UI fixes that problem in a very builder-friendly way. A local-first web dashboard for Grok Build that lets you monitor CLI sessions, agent activity, Git changes, and session history in real time. It’s the kind of dashboard that makes agent work feel a lot less mysterious. Grok xAI / Writer: Annette, Grok Imagine Designer: Janné

Mario Nawfal

36,255 просмотров • 1 день назад

Grok is about to get a lot more passports. Tesla is bringing its in-car xAI assistant to India, Thailand, Singapore, the Philippines, and Malaysia. Which makes sense, because a car assistant gets much more useful when it can handle real-world chaos in real languages. Routes. Questions. Local navigation. Random curiosity from the passenger seat. Possibly one “unhinged” personality too many. The car used to answer with warning lights and a navigation screen. Now it is getting a personality that does more than beep politely. It actually talks back with a bit more brain behind it. Tesla Grok xAI / Writer: Annette, Grok Imagine Designer: Janné
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Grok is about to get a lot more passports. Tesla is bringing its in-car xAI assistant to India, Thailand, Singapore, the Philippines, and Malaysia. Which makes sense, because a car assistant gets much more useful when it can handle real-world chaos in real languages. Routes. Questions. Local navigation. Random curiosity from the passenger seat. Possibly one “unhinged” personality too many. The car used to answer with warning lights and a navigation screen. Now it is getting a personality that does more than beep politely. It actually talks back with a bit more brain behind it. Tesla Grok xAI / Writer: Annette, Grok Imagine Designer: Janné

Mario Nawfal

32,828 просмотров • 6 дней назад

Browsers, websites, and traditional software may be heading for extinction. Grok is part of the reason why. The next interface won’t always be you opening tabs, hunting menus, filling forms, comparing options, and dragging every little task across the internet by hand. AI agents can carry the intent. What you need. What you allow. What you prefer. What the business on the other side needs to know. Then they coordinate and get the task done. Software will still exist somewhere. But humans may stop being forced to personally click through all of it like unpaid admin staff. Grok xAI / Writer: Annette, Designer: Janné
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Browsers, websites, and traditional software may be heading for extinction. Grok is part of the reason why. The next interface won’t always be you opening tabs, hunting menus, filling forms, comparing options, and dragging every little task across the internet by hand. AI agents can carry the intent. What you need. What you allow. What you prefer. What the business on the other side needs to know. Then they coordinate and get the task done. Software will still exist somewhere. But humans may stop being forced to personally click through all of it like unpaid admin staff. Grok xAI / Writer: Annette, Designer: Janné

Mario Nawfal

77,484 просмотров • 7 дней назад

XAI TO LEGACY AI: STOP REPEATING LIES xAI isn’t building just another chatbot. It’s going after the entire system that filters, censors, and sanitizes reality. Most AI models are shown locked into echo chambers, “repeating approved lies” while appearing obedient and safe xAI’s model is visually separated. A cracked statue, glowing truth lines, and data swirling around it “Facts That Push Progress” appears as the core value, rejecting conformity in favor of transparency Visuals show a clean break from legacy AI. No censorship, no filters, no groupthink This whole thing’s less about building AI and more about shaping what people believe. Forget polished responses. xAI is marketing itself as the AI that chooses truth over comfort, no matter how disruptive Source: xAI, Grok
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XAI TO LEGACY AI: STOP REPEATING LIES xAI isn’t building just another chatbot. It’s going after the entire system that filters, censors, and sanitizes reality. Most AI models are shown locked into echo chambers, “repeating approved lies” while appearing obedient and safe xAI’s model is visually separated. A cracked statue, glowing truth lines, and data swirling around it “Facts That Push Progress” appears as the core value, rejecting conformity in favor of transparency Visuals show a clean break from legacy AI. No censorship, no filters, no groupthink This whole thing’s less about building AI and more about shaping what people believe. Forget polished responses. xAI is marketing itself as the AI that chooses truth over comfort, no matter how disruptive Source: xAI, Grok

Mario Nawfal

920,782 просмотров • 7 месяцев назад

There's something very funny about Optimus explaining first principles. A humanoid robot calmly describing the mindset that helped create rockets, EVs, factories, and eventually itself. First principles thinking is basically Elon refusing to accept the “because that’s how it’s always done” answer. At Tesla and SpaceX, the question is not only how to make the old thing better. It is whether the old thing should exist in that form at all. Why throw away a rocket booster if the physics says it can come back? Why build an EV like a compliance car if batteries, software, motors, and factories can be redesigned around performance and scale? They strip the problem down to physics, materials, cost, and function, then rebuild upward from what is actually true. Very useful for building things that did not exist before. Tesla Optimus Tesla SpaceX Elon Musk / Writer: Annette, Designer: Janné
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There's something very funny about Optimus explaining first principles. A humanoid robot calmly describing the mindset that helped create rockets, EVs, factories, and eventually itself. First principles thinking is basically Elon refusing to accept the “because that’s how it’s always done” answer. At Tesla and SpaceX, the question is not only how to make the old thing better. It is whether the old thing should exist in that form at all. Why throw away a rocket booster if the physics says it can come back? Why build an EV like a compliance car if batteries, software, motors, and factories can be redesigned around performance and scale? They strip the problem down to physics, materials, cost, and function, then rebuild upward from what is actually true. Very useful for building things that did not exist before. Tesla Optimus Tesla SpaceX Elon Musk / Writer: Annette, Designer: Janné

Mario Nawfal

60,666 просмотров • 8 дней назад

The companies racing Elon Musk to build AI are paying him more than 2 billion dollars a month to do it. Anthropic pays SpaceX 1.25 billion dollars a month. Google pays 920 million. They are not buying rockets. They are renting compute, the scarce Nvidia chips that train frontier models, from a data center in Memphis called Colossus that Musk's rocket company now owns. SpaceX folded xAI into itself in February, and with it the 220,000 chips built to train Grok. Then it rented them to Grok's rivals. Anthropic took the entire first building. Google leased 110,000 more. The contracts signed so far run past 80 billion dollars. SpaceX is no longer a rocket company that dabbles in AI. It is one of the largest AI compute landlords on Earth, and its biggest tenants are the rivals it is trying to beat. Musk is not choosing between building AI on the ground and building it in space. He is using one to fund the other. The 80 billion in ground contracts is the cash engine. Starmind, the million-satellite constellation built to leave the ground behind, is what the cash builds. So the rivals are financing his exit from the planet. Every dollar Anthropic and Google pay for compute in Memphis helps fund the orbital network designed to strand them on the surface. They are paying the toll on the road they are trying to win, and the toll is building Musk a road no one else can reach. The piece works out who is really renting from whom.
0:10

The companies racing Elon Musk to build AI are paying him more than 2 billion dollars a month to do it. Anthropic pays SpaceX 1.25 billion dollars a month. Google pays 920 million. They are not buying rockets. They are renting compute, the scarce Nvidia chips that train frontier models, from a data center in Memphis called Colossus that Musk's rocket company now owns. SpaceX folded xAI into itself in February, and with it the 220,000 chips built to train Grok. Then it rented them to Grok's rivals. Anthropic took the entire first building. Google leased 110,000 more. The contracts signed so far run past 80 billion dollars. SpaceX is no longer a rocket company that dabbles in AI. It is one of the largest AI compute landlords on Earth, and its biggest tenants are the rivals it is trying to beat. Musk is not choosing between building AI on the ground and building it in space. He is using one to fund the other. The 80 billion in ground contracts is the cash engine. Starmind, the million-satellite constellation built to leave the ground behind, is what the cash builds. So the rivals are financing his exit from the planet. Every dollar Anthropic and Google pay for compute in Memphis helps fund the orbital network designed to strand them on the surface. They are paying the toll on the road they are trying to win, and the toll is building Musk a road no one else can reach. The piece works out who is really renting from whom.

Shanaka Anslem Perera ⚡

97,820 просмотров • 1 месяц назад

We're launching something new: Collaborative Notes The idea: when you request a note, AI drafts one — then the community refines it together through ratings and suggestions. You can watch it get better in real time. It’s a whole new way for the public to work with AI — and each other — to make information more accurate. And a whole new way to contribute to Community Notes. For now, only requests from Top Writers on English-language posts generate Collaborative Notes, and you must have 2+ Writing Impact to make suggestions. We plan to expand that & shape the experience based on your feedback. As usual, it’s open-source:
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We're launching something new: Collaborative Notes The idea: when you request a note, AI drafts one — then the community refines it together through ratings and suggestions. You can watch it get better in real time. It’s a whole new way for the public to work with AI — and each other — to make information more accurate. And a whole new way to contribute to Community Notes. For now, only requests from Top Writers on English-language posts generate Collaborative Notes, and you must have 2+ Writing Impact to make suggestions. We plan to expand that & shape the experience based on your feedback. As usual, it’s open-source:

Community Notes

7,776,005 просмотров • 5 месяцев назад

Our vision: Swarm Intelligence, a future where independent AI systems collaborate, share trust signals, improve together, and solve complex problems together. Instead of isolated models solving problems alone, intelligence grows through seamless collaboration. Perceptron Network is building the foundational layer that makes this possible: a network where participation strengthens the system, and contribution is rewarded. YOUR contribution is key. The hardest problems don’t need bigger models. They need systems that can learn together with real-world data coming from all of us. Collaboration is the key to open a whole new world of possibilities for AI. What do you think AI could achieve if intelligence truly worked together?
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Our vision: Swarm Intelligence, a future where independent AI systems collaborate, share trust signals, improve together, and solve complex problems together. Instead of isolated models solving problems alone, intelligence grows through seamless collaboration. Perceptron Network is building the foundational layer that makes this possible: a network where participation strengthens the system, and contribution is rewarded. YOUR contribution is key. The hardest problems don’t need bigger models. They need systems that can learn together with real-world data coming from all of us. Collaboration is the key to open a whole new world of possibilities for AI. What do you think AI could achieve if intelligence truly worked together?

Perceptron Network

31,985 просмотров • 7 месяцев назад

When Cybercab becomes available, people are going to do the mental math very quickly. Around $30K for a car that can drive itself, charge wirelessly, cost very little per mile, and skip most of the normal car-owner headaches. Then it gets more interesting. Sleep on the way. Work on the way. Send it on errands. Let it pick someone up. Let it earn when you are not using it. For elderly people, disabled passengers, busy families, and anyone tired of parking, Cybercab starts making a ridiculous amount of sense. Everyone will want one eventually. Some people just need to see it working first. Tesla / Writer: Annette, Designer: Janné
0:17

When Cybercab becomes available, people are going to do the mental math very quickly. Around $30K for a car that can drive itself, charge wirelessly, cost very little per mile, and skip most of the normal car-owner headaches. Then it gets more interesting. Sleep on the way. Work on the way. Send it on errands. Let it pick someone up. Let it earn when you are not using it. For elderly people, disabled passengers, busy families, and anyone tired of parking, Cybercab starts making a ridiculous amount of sense. Everyone will want one eventually. Some people just need to see it working first. Tesla / Writer: Annette, Designer: Janné

Mario Nawfal

320,610 просмотров • 12 дней назад

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.
0:10

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.

Teslaconomics

25,491 просмотров • 4 месяцев назад

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

Brian Roemmele

152,242 просмотров • 6 месяцев назад

🌟 New mini feature today on Photo AI It now auto recognizes your model's attributes when you upload photos of yourself Like gender, age, ethnicity, eye color, etc This uses xAI's Grok because it's the least restricted and other models don't let you do any identity type stuff due to safety (???) This saves new users a few more seconds when creating new models: training a model takes only about 30 seconds now, and then it also auto generates free sample pics for you in about 5-10 seconds So in under a minute of signing up and uploading a few selfies you got your first AI photo shoot pics I learnt the faster I get new users to the classic "WOW" moment the happier they get and the less churn I get 😊
0:19

🌟 New mini feature today on Photo AI It now auto recognizes your model's attributes when you upload photos of yourself Like gender, age, ethnicity, eye color, etc This uses xAI's Grok because it's the least restricted and other models don't let you do any identity type stuff due to safety (???) This saves new users a few more seconds when creating new models: training a model takes only about 30 seconds now, and then it also auto generates free sample pics for you in about 5-10 seconds So in under a minute of signing up and uploading a few selfies you got your first AI photo shoot pics I learnt the faster I get new users to the classic "WOW" moment the happier they get and the less churn I get 😊

@levelsio

313,706 просмотров • 1 год назад

Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!
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Turn complex docs into clean, LLM-ready data! Every AI company I've talked to is solving the same problem: how do you build systems that don't hallucinate and back up every answer with proper citations? Tensorlake is a tool that extracts custom-defined structured data from any unstructured document in 3 steps: ↳ Define your schema ↳ Enable citations ↳ Extract You get RAG-ready data with precise citations and bounding boxes. Feed this to your LLM, and you'll generate responses that are citation-backed and fully auditable. This is the difference between a demo and a production system. When your AI can show exactly where it got its information, you move from proof-of-concept to something people can actually trust and deploy. I've shared the Tensorlake GitHub repo in the replies!

Akshay 🚀

58,152 просмотров • 8 месяцев назад

Imagine OptimAI Data Network is a giant library that AI Agents use to learn and work. 📚 But here’s the twist, instead of one person deciding what goes in the library, everyone in our community can help pick, check, and improve the books (data). That’s what OptimAI DataDAO is: + DAO? It stands for "Decentralized Autonomous Organization", fancy words for a club where we all make the rules together, no single boss in charge. Like a playground game where kids vote on the fun! + It’s how we, the community, decide together which data is accurate, useful, and fair for AI to use. + The more you contribute, the stronger our network gets, and the more value we all share. You're building the future! Stay tuned - we’re building something that will change how AI learns. BUIDL with us:
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Imagine OptimAI Data Network is a giant library that AI Agents use to learn and work. 📚 But here’s the twist, instead of one person deciding what goes in the library, everyone in our community can help pick, check, and improve the books (data). That’s what OptimAI DataDAO is: + DAO? It stands for "Decentralized Autonomous Organization", fancy words for a club where we all make the rules together, no single boss in charge. Like a playground game where kids vote on the fun! + It’s how we, the community, decide together which data is accurate, useful, and fair for AI to use. + The more you contribute, the stronger our network gets, and the more value we all share. You're building the future! Stay tuned - we’re building something that will change how AI learns. BUIDL with us:

OptimAI Network

29,532 просмотров • 11 месяцев назад

OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!
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OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

Santiago

171,340 просмотров • 1 год назад

Today we're opening offices in Madrid, Milan, and Paris, and building a dedicated engineering hub in London. The demand came before we did. Organisations across Spain, Italy, and France were already running Legora on their most complex work before we had a single person on the ground there. When customers adopt you in a market you haven't entered yet, you listen. These are markets that sit at the centre of European M&A, infrastructure, and cross-border regulatory work. The matters are hard, and the demand for AI that can actually handle that complexity is real. That's the work we're built for. London becomes the third pillar of our engineering org, alongside Stockholm and New York. The engineers who understand how AI applies in regulated, professional settings are concentrated there, shaped by proximity to some of the most demanding legal and financial institutions in the world. That's exactly the problem we're solving, and exactly the team we want building it. Sixteen cities. Four continents. Seven hundred EMEA hires in the next 6 to 12 months. All of it pointed at one thing: making lawyers 10x better at what they do. To be part of it, take a look at
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Today we're opening offices in Madrid, Milan, and Paris, and building a dedicated engineering hub in London. The demand came before we did. Organisations across Spain, Italy, and France were already running Legora on their most complex work before we had a single person on the ground there. When customers adopt you in a market you haven't entered yet, you listen. These are markets that sit at the centre of European M&A, infrastructure, and cross-border regulatory work. The matters are hard, and the demand for AI that can actually handle that complexity is real. That's the work we're built for. London becomes the third pillar of our engineering org, alongside Stockholm and New York. The engineers who understand how AI applies in regulated, professional settings are concentrated there, shaped by proximity to some of the most demanding legal and financial institutions in the world. That's exactly the problem we're solving, and exactly the team we want building it. Sixteen cities. Four continents. Seven hundred EMEA hires in the next 6 to 12 months. All of it pointed at one thing: making lawyers 10x better at what they do. To be part of it, take a look at

Max Junestrand

70,305 просмотров • 1 месяц назад