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Less than 48 hours ago, OpenAI dropped ChatGPT with Scheduled Tasks. And people can't stop building creative automation task to have AI agents work while they sleep. 10 wild examples: 1. Daily stock market analysis

Min Choi

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436,008 views • 1 year ago •via X (Twitter)

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BREAKING: OpenAI just launched ChatGPT Agent It allows ChatGPT to think, plan, and execute complex tasks on its own virtual computer while you do other things I had early access, and ChatGPT Agent built me a complete early retirement plan in 20 minutes: > Found local tax laws (Vancouver) > Analyzed average monthly spend rates > Calculated savings needed to retire at 30 > Researched optimal investment allocations > Found tax optimization strategies I'd never heard of > Built multiple FIRE scenarios > Created a downloadable presentation with results This would've cost me $5,000+ from a financial advisor and taken weeks I think with ChatGPT Agent now, and especially as it gains access to more tools, we're finally going to see the rise of a new AI skill category in *Agent Management* Agents are finally becoming capable of doing real work autonomously, so anyone who learns how to effectively orchestrate agents will have a huge advantage

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Rowan Cheung

653,957 views • 1 year ago

I’m incredibly proud to share that OpenAI chose Brex to power their global spend and financial operations. When you're building at the frontier of AI and scaling global teams and infrastructure at an unprecedented pace like OpenAI is, Finance can't be the thing that slows you down. You need spend visibility the moment it happens, controls that enforce themselves, and agentic workflows that eliminate the manual work so your team stays focused on driving the business forward. We were so impressed by OpenAI’s rigor in evaluating every solution in the market, and whether they align to the agentic future OpenAI is building. Their decision to run on Brex is a huge testament to our AI roadmap and vision for the future of Finance. We started Brex around a simple idea: companies shouldn't have to choose between speed and control. There's no company in the world where that tradeoff matters more than OpenAI. We are honored to support them as they build the future. The best AI companies in the world, including OpenAI, Anthropic, Cursor, Vercel, Granola, Sierra, and Mercor choose Brex over every alternative for that exact reason. If you want to understand who’s truly building the future of AI in Finance, follow the customers you admire the most – not the hype.

I’m incredibly proud to share that OpenAI chose Brex to power their global spend and financial operations. When you're building at the frontier of AI and scaling global teams and infrastructure at an unprecedented pace like OpenAI is, Finance can't be the thing that slows you down. You need spend visibility the moment it happens, controls that enforce themselves, and agentic workflows that eliminate the manual work so your team stays focused on driving the business forward. We were so impressed by OpenAI’s rigor in evaluating every solution in the market, and whether they align to the agentic future OpenAI is building. Their decision to run on Brex is a huge testament to our AI roadmap and vision for the future of Finance. We started Brex around a simple idea: companies shouldn't have to choose between speed and control. There's no company in the world where that tradeoff matters more than OpenAI. We are honored to support them as they build the future. The best AI companies in the world, including OpenAI, Anthropic, Cursor, Vercel, Granola, Sierra, and Mercor choose Brex over every alternative for that exact reason. If you want to understand who’s truly building the future of AI in Finance, follow the customers you admire the most – not the hype.

Pedro Franceschi

62,546 views • 6 months 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 • 3 months ago

GPT-5.5 is MUCH more reliable on longer running tasks - for the first time with any model. As we speak I have a migration running for over 7+ hours - this literally never happened before, the models would maybe run for 30 mins or of you really shout at them for 2-3 hours. Last night I went to sleep, set a long running task, then queued up 10 prompts to 'keep it going'. It did not stop after the first prompt and kept going for 8+ hours and I woke up to all the same prompts still queued up. The ability to run for a long time, in combination with ability to validate with computer use & other tools, makes it much more useful for building real applications.

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Peter Gostev

105,642 views • 5 months ago

while all of AI twitter is arguing about Fable 5.1 vs GPT-6 Astra, 95% of people out in public don't know either one exists. The gap is wild. Explaining doesn't work. Showing them AI built games doesn't work. Software developers are blown away because AI can do our job better and faster. - when robots build an entire house in 24 hours, the construction worker will get it. - when robots cook the food perfect every time, the chefs will understand. - when robots run into a burning building and save a family, the firefighters will understand too. people don't get it yet because they can't see what is coming as clearly as we can. we have been impacted, and what we see has far exceeded our expectations.

while all of AI twitter is arguing about Fable 5.1 vs GPT-6 Astra, 95% of people out in public don't know either one exists. The gap is wild. Explaining doesn't work. Showing them AI built games doesn't work. Software developers are blown away because AI can do our job better and faster. - when robots build an entire house in 24 hours, the construction worker will get it. - when robots cook the food perfect every time, the chefs will understand. - when robots run into a burning building and save a family, the firefighters will understand too. people don't get it yet because they can't see what is coming as clearly as we can. we have been impacted, and what we see has far exceeded our expectations.

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102,263 views • 15 days ago

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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

🚀Just launched: Amazon Q, the most capable GenAI-powered assistant is generally available today: Customers are using Q to transform how their teams get work done. When employees chat with Amazon Q, it provides immediate, relevant information and advice to help streamline tasks, speedup decision-making, and help spark creativity and innovation at work. . Early indications signal Amazon Q could help our customers’ employees become more than 80% more productive at their jobs; and with the new features we’re planning on introducing in the future, we think this will only continue to grow. 🟠 Amazon Q Developer allows developers to spend more time coding and less time on maintenance and performing other tedious, repetitive tasks. Q assists developers and IT professionals (IT pros) with all of their tasks—from coding, testing, and upgrading applications, to troubleshooting, performing security scanning and fixes, and optimizing AWS resources. Q also comes with Q Developer Agents which can autonomously perform range of tasks and we expect it to be the state of the art accuracy in benchmarks like SWE-Bench. 🟠 Amazon Q Business empowers employees to be more data-driven, and helps customers make better, faster decisions using company knowledge and data. Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in enterprise systems 🟠 Amazon Q Apps, a new and powerful capability of Amazon Q Business, enables employees to use natural language to quickly and securely build their own generative AI applications to automate daily tasks without requiring any prior coding experience. Employees simply describe the type of app they want, in natural language, and Q Apps will quickly generate an app that accomplishes their desired task, helping them streamline and automate their daily work with ease and efficiency.

🚀Just launched: Amazon Q, the most capable GenAI-powered assistant is generally available today: Customers are using Q to transform how their teams get work done. When employees chat with Amazon Q, it provides immediate, relevant information and advice to help streamline tasks, speedup decision-making, and help spark creativity and innovation at work. . Early indications signal Amazon Q could help our customers’ employees become more than 80% more productive at their jobs; and with the new features we’re planning on introducing in the future, we think this will only continue to grow. 🟠 Amazon Q Developer allows developers to spend more time coding and less time on maintenance and performing other tedious, repetitive tasks. Q assists developers and IT professionals (IT pros) with all of their tasks—from coding, testing, and upgrading applications, to troubleshooting, performing security scanning and fixes, and optimizing AWS resources. Q also comes with Q Developer Agents which can autonomously perform range of tasks and we expect it to be the state of the art accuracy in benchmarks like SWE-Bench. 🟠 Amazon Q Business empowers employees to be more data-driven, and helps customers make better, faster decisions using company knowledge and data. Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in enterprise systems 🟠 Amazon Q Apps, a new and powerful capability of Amazon Q Business, enables employees to use natural language to quickly and securely build their own generative AI applications to automate daily tasks without requiring any prior coding experience. Employees simply describe the type of app they want, in natural language, and Q Apps will quickly generate an app that accomplishes their desired task, helping them streamline and automate their daily work with ease and efficiency.

Swami Sivasubramanian

25,216 views • 2 years ago

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 views • 2 months ago

Every single person should build this crypto x AI workflow. It took me weeks to get this right, and I can't wait to show you guys what I've been building! I started vibe-coding a market-trading/arbitrage/research bot, and the build is finally almost complete. It includes: • Research dashboard for stock analysis • Trading integrations (paper/backtesting) • Risk audits • Daily snapshot • Claude integrations Been chipping away over the past couple of weeks, and can already tell this will take my research to the next level. I'll have a full guide dropping on both YouTube and here on X soon for how to build your own!

Every single person should build this crypto x AI workflow. It took me weeks to get this right, and I can't wait to show you guys what I've been building! I started vibe-coding a market-trading/arbitrage/research bot, and the build is finally almost complete. It includes: • Research dashboard for stock analysis • Trading integrations (paper/backtesting) • Risk audits • Daily snapshot • Claude integrations Been chipping away over the past couple of weeks, and can already tell this will take my research to the next level. I'll have a full guide dropping on both YouTube and here on X soon for how to build your own!

Miles Deutscher

20,674 views • 3 months ago

We are releasing AutoResearchExam, a benchmark on open-ended machine learning and engineering tasks. Our benchmark covers seven research areas including model training, data curation, AI safety and interpretability. In each task, we give agents 24 hours with a CPU or GPU machine to develop and improve their solutions through experiments and feedback. We measure both speed and quality with a combined score. Our benchmark has a unique feature: testing if agents create improvements that hold up on data they never see. We find that AI research agents often overfit as they try to improve. We see an interesting head-to-head comparison at the frontier: Astra starts the strongest and holds the lead for up to 19 hours but Fable 5.1 catches up and gets the top performing spot in the final hours. Qwen3.8 Max, Gemini 3.8 Flash and Grok 4.6 all sit on the cost-performance Pareto frontier, giving strong options at lower API budgets. Anthropic's Opus and Fable retain nearly all their validation performance on hidden tests, with gaps of 1.1% and 2.9%. Astra's improvement over Sol extends to generalization too, with that gap falling from 6.9% to 1.7%. (1/n)

We are releasing AutoResearchExam, a benchmark on open-ended machine learning and engineering tasks. Our benchmark covers seven research areas including model training, data curation, AI safety and interpretability. In each task, we give agents 24 hours with a CPU or GPU machine to develop and improve their solutions through experiments and feedback. We measure both speed and quality with a combined score. Our benchmark has a unique feature: testing if agents create improvements that hold up on data they never see. We find that AI research agents often overfit as they try to improve. We see an interesting head-to-head comparison at the frontier: Astra starts the strongest and holds the lead for up to 19 hours but Fable 5.1 catches up and gets the top performing spot in the final hours. Qwen3.8 Max, Gemini 3.8 Flash and Grok 4.6 all sit on the cost-performance Pareto frontier, giving strong options at lower API budgets. Anthropic's Opus and Fable retain nearly all their validation performance on hidden tests, with gaps of 1.1% and 2.9%. Astra's improvement over Sol extends to generalization too, with that gap falling from 6.9% to 1.7%. (1/n)

Alex Dimakis

2,030,514 views • 12 days ago

99% of people are stuck with ChatGPT, while the pros use "hidden gems" to stay ahead. The fastest way to build a competitive edge is to adopt elite tools before they go mainstream. 1/5 Oncely — Hidden AI gems with great deals 👉 Explore: Stop searching and start using. Oncely curates high-quality, underrated AI products that solve real problems. You get exclusive access to niche tools and massive lifetime discounts you won’t find elsewhere. If you love finding gold before everyone else, this is it.

99% of people are stuck with ChatGPT, while the pros use "hidden gems" to stay ahead. The fastest way to build a competitive edge is to adopt elite tools before they go mainstream. 1/5 Oncely — Hidden AI gems with great deals 👉 Explore: Stop searching and start using. Oncely curates high-quality, underrated AI products that solve real problems. You get exclusive access to niche tools and massive lifetime discounts you won’t find elsewhere. If you love finding gold before everyone else, this is it.

Choya

42,025 views • 8 months ago

A CHINESE DEVELOPER CREATED THE "NEXT INTERSTELLAR" WITH AI, AND NETFLIX IS OFFERING $5,000,000 TO BUY THE RIGHTS He dropped a solo-generated cinematic trailer online, and within 48 hours. Streaming giants like Netflix and Amazon Prime flooded his inbox with over 10 massive multi-million dollar acquisition deals. The production engine driving this is pure insanity: Scripting & visual world-building: automated via LLM agents Hyper-realistic sci-fi rendering & lighting: 100% neural generated Dynamic cinematic physics & camera motion: executed on local AI rigs If you want to replicate this exact pipeline and build your own cinematic AI movie, read the full step-by-step workflow breakdown in the article below 👇

A CHINESE DEVELOPER CREATED THE "NEXT INTERSTELLAR" WITH AI, AND NETFLIX IS OFFERING $5,000,000 TO BUY THE RIGHTS He dropped a solo-generated cinematic trailer online, and within 48 hours. Streaming giants like Netflix and Amazon Prime flooded his inbox with over 10 massive multi-million dollar acquisition deals. The production engine driving this is pure insanity: Scripting & visual world-building: automated via LLM agents Hyper-realistic sci-fi rendering & lighting: 100% neural generated Dynamic cinematic physics & camera motion: executed on local AI rigs If you want to replicate this exact pipeline and build your own cinematic AI movie, read the full step-by-step workflow breakdown in the article below 👇

Rich Odin

18,408 views • 1 month ago

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

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

Brian Roemmele

152,242 views • 8 months ago

Had the opportunity while in London to work with a quant on one of my models. Fully 100% mechanical edge over 10 years data. We are still making refinements. This was all done in less than 24 hours as well. One of the coolest experiences I ever had. I remember manually backtesting 6 years data on FX Replay years ago a lot has changed. I preach trading with data and I’m a nerd for this stuff excited to continue to make the edge even greater.

Had the opportunity while in London to work with a quant on one of my models. Fully 100% mechanical edge over 10 years data. We are still making refinements. This was all done in less than 24 hours as well. One of the coolest experiences I ever had. I remember manually backtesting 6 years data on FX Replay years ago a lot has changed. I preach trading with data and I’m a nerd for this stuff excited to continue to make the edge even greater.

tylerg

13,457 views • 3 months ago