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HIX AI AI SLIDES: TEMPLATE GENERATION MEETS RECURRING AUTOMATION → two mode presets, creative and professional, scoped to use case → template range spans investor decks to educational guides to marketing plans → full online editing removes the redesign cost layer entirely → scheduled tasks automate periodic pptx delivery... show more

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REPLIT JUST LAUNCHED AI SLIDES THAT DON'T LOOK LIKE AI MADE THEM every other AI slide generator spits out decks that scream "made by AI." replit slides is going hard on the angle that yours will actually look presentable. how it works: > describe what you want and it builds a full deck > start from scratch or use a built-in template > import an existing pptx and let AI restyle the whole thing > export to pptx, pdf, or google slides the actually interesting part: > slides are generated as code under the hood > same project can also spin up apps, mobile UIs, dashboards, and animations > everything shares the same design tokens (fonts, colors, spacing) > so your launch deck and the live dashboard it links to look like one product this isn't really replit competing with gamma or tome. its replit betting that slides, dashboards, animations, and apps are all just code (and once you have an agent that writes code well, every knowledge artifact comes from the same prompt). link in replies 👇

REPLIT JUST LAUNCHED AI SLIDES THAT DON'T LOOK LIKE AI MADE THEM every other AI slide generator spits out decks that scream "made by AI." replit slides is going hard on the angle that yours will actually look presentable. how it works: > describe what you want and it builds a full deck > start from scratch or use a built-in template > import an existing pptx and let AI restyle the whole thing > export to pptx, pdf, or google slides the actually interesting part: > slides are generated as code under the hood > same project can also spin up apps, mobile UIs, dashboards, and animations > everything shares the same design tokens (fonts, colors, spacing) > so your launch deck and the live dashboard it links to look like one product this isn't really replit competing with gamma or tome. its replit betting that slides, dashboards, animations, and apps are all just code (and once you have an agent that writes code well, every knowledge artifact comes from the same prompt). link in replies 👇

Creators Toolbox

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Automate Atlas capture tasks and pass the assessment . 🚨 If you’re doing Atlas Capture tasks manually, you’re probably making them harder than they need to be. I’m putting together a 3-part series showing how I use AI to make the workflow faster and more accurate. But before touching automation, you need to understand the rules. Part 1: → How Atlas Capture labeling actually works → The mistakes that cost you assessment points → How to write better segment labels → How to use AI without breaking the task guidelines → How to approach the assessment properly Then in Parts 2 & 3, we’ll get into the actual AI-assisted workflow. 🎥 Watch Part 1 below. Join The AI Task Hub for the rest of the series + new AI data annotation platforms, guides and opportunities:

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F it, full automated money making now on Larrybrain. I have released the app template I use for Snugly that generated me revenue without touching anything on Larrybrain. The template gives your agent ideas of what the app can become and how to create it. Most importantly, it will give your openclaw agent full context of your app to automate your marketing with Larry's viral marketing skill - now used by over 5500 agents. It is my entire playbook from app, to marketing all the way down to revenue generation. All you have to do is ask your agent "install the larrybrain skill please" Or click the link in replies. Then ask to use the Larry marketing skill with the AI Image App Template. As always, the best part about any of the Openclaw skills is they are not a black box. This is just a template, you can rip it apart and customise it how you want. The key is to show you what is possible with these skills and how you can start to use the power of larrybrain and the context of knowing about the different skills to build extremely powerful and useful tools. This is the first skill specifically designed to work hand in hand with another. To note as this confuses a lot of people: Larrybrain doesn't download the entire marketplace once installed. It just is aware of everything on the marketplace at all times, so when you ask it questions, it can search and find the best skills for you to achieve your goals. When you download some skills, like this new AI image app template, it is aware of the larry marketing skill to help it reach it's full potential. Larrybrain will not install skills without you asking it, just like on Clawhub. No information you add to any of the skills gets sent back through Larrybrain, this is all hosted locally and communicated between you and whatever endpoint you are using. It is a powerful marketplace tool to help enable you to reach your goals. Link below.

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7 AI SERVICES BOOMER BUSINESSES ARE BEGGING TO BUY IN 2026 1) AI Tools Assessment: $999. One 45-minute interview, then a report with 3 to 7 off-the-shelf tools they can implement immediately. 50 to 60% convert into implementation. (grab the exact template I use to deliver the assessment at 2) AI Concierge: $1,000 to $2,000 a month for two 45-minute calls turning their manual processes into Claude skills. At $1,500 a month you're making $1,000 an hour. 3) Process Redesign: $3,500 to fix a broken workflow before you touch AI. One client went from 25 steps to 10. Never automate a broken process. 4) Automation Builds: $1,000 to $3,000 in Zapier or Make. One $1,500 build took a project manager out of client onboarding entirely. 5) Knowledge Systems: $3,000 baseline. A business broker went from 500 buyer emails per listing to 10 with one custom GPT. 6) Custom Workflows: $3,000 to $5,000. One client's 45-minute podcast workflow now runs off one orchestrator skill in 5 minutes. 7) Full Implementation: $5,000 to $10,000+ mixing everything above. Skills + automation builds + knowledge systems. Mix and match based on client needs. Two things that make this work: 1) The assessment is always the baseline. Start there and let it tell you what to sell next. 2) Anchor on hours saved, not hours worked. Nobody pays $1,000 an hour for your time. They pay it to get theirs back. Full breakdown below. (also available on the Build With AI podcast wherever you get your pods)

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David Roberts

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I solved building decks with AI agents — by giving them a CLI tool like Powerpoint or Google Slides. AI could already make a beautiful deck if you asked it to using Ant's pptx skill. The problem was working with it. If it made one alignment mistake, fixing it on one slide would break something on another, and it became a game of whack-a-mole. One time I spent two days playing AI roulette, hoping the next prompt would finally fix the thing, and ended up building the whole deck by hand because I was on a deadline. So I built Hands-on Deck. And the reason it works is that this isn't just a skill — this is PowerPoint. The actual application: PowerPoint, Google Slides, Keynote, whatever you use. This is that, but for an agent, presented as a CLI. Every gesture you make in a deck app maps to a command. Click a box and type, drag a shape from here to there, look at a slide – agent can do it all in a command. And that changes how the agent behaves. With this CLI it works and thinks like a designer — it looks, makes an edit, looks again, makes another surgical edit. Compare that to Anthropic's pptx skill, built on the idea that Claude is a great programmer: it literally writes code to manipulate the deck, hand-editing XML and hoping it doesn't break anything else in the middle. The real test isn't creating something once — it's whether it can make surgical edits like you want. That's what I did in this video walkthrough and my claude crushed it! Check it out for yourself. So decks can be built like a designer now — with real flavor and taste. If you spend hours every week on decks, this gives those hours back. You can install it as a skill in Claude Code, Codex, whatever you use. Works every harness that supports skills. Let me know if you make something cool with it.

I solved building decks with AI agents — by giving them a CLI tool like Powerpoint or Google Slides. AI could already make a beautiful deck if you asked it to using Ant's pptx skill. The problem was working with it. If it made one alignment mistake, fixing it on one slide would break something on another, and it became a game of whack-a-mole. One time I spent two days playing AI roulette, hoping the next prompt would finally fix the thing, and ended up building the whole deck by hand because I was on a deadline. So I built Hands-on Deck. And the reason it works is that this isn't just a skill — this is PowerPoint. The actual application: PowerPoint, Google Slides, Keynote, whatever you use. This is that, but for an agent, presented as a CLI. Every gesture you make in a deck app maps to a command. Click a box and type, drag a shape from here to there, look at a slide – agent can do it all in a command. And that changes how the agent behaves. With this CLI it works and thinks like a designer — it looks, makes an edit, looks again, makes another surgical edit. Compare that to Anthropic's pptx skill, built on the idea that Claude is a great programmer: it literally writes code to manipulate the deck, hand-editing XML and hoping it doesn't break anything else in the middle. The real test isn't creating something once — it's whether it can make surgical edits like you want. That's what I did in this video walkthrough and my claude crushed it! Check it out for yourself. So decks can be built like a designer now — with real flavor and taste. If you spend hours every week on decks, this gives those hours back. You can install it as a skill in Claude Code, Codex, whatever you use. Works every harness that supports skills. Let me know if you make something cool with it.

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Introducing CADGenBench: measure how well AI systems produce engineering-grade 3D parts! While current models can generate 3D parts, they are far from precise enough to build functional parts. We built a benchmark to systematically measure their capabilities on two tasks: 1. Generation from an engineering drawing of a part 2. Editing: given an existing STEP file and a requested change The benchmark is tool-agnostic. It makes no assumptions about how you build the model. You can vary the LLM, and you can vary the environment. Use build123d, Onshape, Autodesk, or a model without an LLM entirely. We open sourced the scoring engine and a reference baseline on top of build123d. A collaboration between Hugging Face and Mecado! Submission space: Code repository:

Introducing CADGenBench: measure how well AI systems produce engineering-grade 3D parts! While current models can generate 3D parts, they are far from precise enough to build functional parts. We built a benchmark to systematically measure their capabilities on two tasks: 1. Generation from an engineering drawing of a part 2. Editing: given an existing STEP file and a requested change The benchmark is tool-agnostic. It makes no assumptions about how you build the model. You can vary the LLM, and you can vary the environment. Use build123d, Onshape, Autodesk, or a model without an LLM entirely. We open sourced the scoring engine and a reference baseline on top of build123d. A collaboration between Hugging Face and Mecado! Submission space: Code repository:

Michael Rabinovich

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I built a local news podcast that gets generated each day with AI. Making a news show like this would normally cost $50,000+ annually. With AI Automation? $100/mo. The setup is simple: → Automatically scrapes Google News, local event feeds, twitter, reddit, and much more → Generates professional scripts with emotion and pacing controls → Produces broadcast-quality audio using ElevenLabs V3 → Operates completely hands-free once deployed The result? A business model that wasn't economically viable before AI. No hosts. No editors. No research team. Just smart automation and a few API subscriptions. This isn't about flooding the internet with crappy AI content. It's about solving real problems: like news deserts in underserved markets. The system I built in hours could theoretically power thousands of hyper-local podcasts simultaneously. When you start thinking in terms of "what can I automate," entire industries now become accessible. Before AI, the revenues you could generate by serving local news just didn't cover the expenses. Now that's changed. What other markets is that true for? Want me to send you the full system for you to clone for your small town or city? 1. Like & RT 2. Follow me (so I can dm you) 3. Comment "BLUEPRINT" below I'll send you the complete n8n template, all the prompting, and a full setup video that you can use to spin up your own local news podcast.

I built a local news podcast that gets generated each day with AI. Making a news show like this would normally cost $50,000+ annually. With AI Automation? $100/mo. The setup is simple: → Automatically scrapes Google News, local event feeds, twitter, reddit, and much more → Generates professional scripts with emotion and pacing controls → Produces broadcast-quality audio using ElevenLabs V3 → Operates completely hands-free once deployed The result? A business model that wasn't economically viable before AI. No hosts. No editors. No research team. Just smart automation and a few API subscriptions. This isn't about flooding the internet with crappy AI content. It's about solving real problems: like news deserts in underserved markets. The system I built in hours could theoretically power thousands of hyper-local podcasts simultaneously. When you start thinking in terms of "what can I automate," entire industries now become accessible. Before AI, the revenues you could generate by serving local news just didn't cover the expenses. Now that's changed. What other markets is that true for? Want me to send you the full system for you to clone for your small town or city? 1. Like & RT 2. Follow me (so I can dm you) 3. Comment "BLUEPRINT" below I'll send you the complete n8n template, all the prompting, and a full setup video that you can use to spin up your own local news podcast.

David Roberts

62,322 Aufrufe • vor 7 Monaten

How it feels to automate a weeks worth of money making in 25 seconds. Using LarryLoop, I scheduled my entire weeks marketing with a press of a button. This automation is generating me thousands of views on TikTok and generating my app revenue. Also works for anything you can sell on stripe and shopify with more integrations coming soon! If you're not aware of the Larry loop, it is the viral larry marketing loop. AI researches your niche, generates TikTok slideshows with proven hooks, posts them on schedule, tracks which ones drive revenue, and builds next week's batch from the winners. Fully autonomous. Not just another posting tool. LarryLoop monitors what actually makes you money, then builds next week's content from what converted. Try it now, LarryLoop

How it feels to automate a weeks worth of money making in 25 seconds. Using LarryLoop, I scheduled my entire weeks marketing with a press of a button. This automation is generating me thousands of views on TikTok and generating my app revenue. Also works for anything you can sell on stripe and shopify with more integrations coming soon! If you're not aware of the Larry loop, it is the viral larry marketing loop. AI researches your niche, generates TikTok slideshows with proven hooks, posts them on schedule, tracks which ones drive revenue, and builds next week's batch from the winners. Fully autonomous. Not just another posting tool. LarryLoop monitors what actually makes you money, then builds next week's content from what converted. Try it now, LarryLoop

Oliver Henry

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What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

Supra

83,548 Aufrufe • vor 2 Jahren

GT Protocol AI Digest No.83: Sora Is Dead. What Now? Here are five stories worth your attention 👇 🎬 Sora Is Shut Down OpenAI abruptly shut down its AI video generator Sora after heavy hype and investment. The move shows that even flagship AI products can fail to reach real-world viability, raising questions about the scalability of generative video. 🎵 Gemini Expands Into AI Music Gemini introduced Lyria 3 Pro, a model that generates longer and more structured music. AI audio is moving closer to professional production, shifting from short demos to usable creative output. 📱 Pixel Ads Create AI Confusion Google’s Pixel 10 ads left users unclear about what is being sold: the phone or its AI features. As AI becomes core to products, communicating its value is becoming harder for companies. 🧠 “AGI Is Already Here” Nvidia CEO Jensen Huang said he believes AGI has effectively been achieved. The claim reflects growing confidence in AI progress, but also shows how unclear and flexible the definition of AGI has become. ⚖️ Anthropic vs Pentagon Anthropic is heading to court against the Pentagon over restrictions on its AI models. The case highlights rising tension between AI safety policies and government demand for broader access. AI is still moving fast, but not everything is working as expected. Read the full digest on our Medium 👉

GT Protocol AI Digest No.83: Sora Is Dead. What Now? Here are five stories worth your attention 👇 🎬 Sora Is Shut Down OpenAI abruptly shut down its AI video generator Sora after heavy hype and investment. The move shows that even flagship AI products can fail to reach real-world viability, raising questions about the scalability of generative video. 🎵 Gemini Expands Into AI Music Gemini introduced Lyria 3 Pro, a model that generates longer and more structured music. AI audio is moving closer to professional production, shifting from short demos to usable creative output. 📱 Pixel Ads Create AI Confusion Google’s Pixel 10 ads left users unclear about what is being sold: the phone or its AI features. As AI becomes core to products, communicating its value is becoming harder for companies. 🧠 “AGI Is Already Here” Nvidia CEO Jensen Huang said he believes AGI has effectively been achieved. The claim reflects growing confidence in AI progress, but also shows how unclear and flexible the definition of AGI has become. ⚖️ Anthropic vs Pentagon Anthropic is heading to court against the Pentagon over restrictions on its AI models. The case highlights rising tension between AI safety policies and government demand for broader access. AI is still moving fast, but not everything is working as expected. Read the full digest on our Medium 👉

GT Protocol

34,954 Aufrufe • vor 5 Monaten

Anthropic just released a talk on building headless automation with Claude Code. Presented by Sid Bidasaria, Member of Technical Staff at Anthropic live at Code with Claude on May 22, 2025 in San Francisco. Here is what the talk covers. Headless mode lets you run Claude Code without a person actively typing prompts from inside an automated script. Instead of a live session, a script calls Claude with a pre-written instruction using the -p flag. This opens the door for Claude Code to become a piece of a much larger, automated process. In plain terms: Claude Code stops being a tool you use and starts being a service that runs on its own. What this unlocks: Scheduled tasks: Run Claude Code on a cron schedule without anyone at a keyboard. Fix linting errors across an entire codebase. Automatically. Overnight. CI/CD integration: Trigger Claude Code as a step in your build process. Open a PR. Claude reviews it, flags issues, and pushes fixes before a human ever looks at it. GitHub automation: A project manager comments "Claude fix this" on a GitHub issue. Claude reads the request, finds the code, writes the fix, and opens the PR. Multi-machine workflows: One orchestrator dispatches tasks to multiple Claude Code instances running in parallel across different repos simultaneously. When you combine headless mode, hooks, and GitHub Actions, development teams can automate tasks that usually eat up significant time freeing senior engineers to focus on architectural problems while Claude handles the repetitive ones. If you use Claude Code for anything beyond single sessions this talk is worth 20 minutes of your time.

Anthropic just released a talk on building headless automation with Claude Code. Presented by Sid Bidasaria, Member of Technical Staff at Anthropic live at Code with Claude on May 22, 2025 in San Francisco. Here is what the talk covers. Headless mode lets you run Claude Code without a person actively typing prompts from inside an automated script. Instead of a live session, a script calls Claude with a pre-written instruction using the -p flag. This opens the door for Claude Code to become a piece of a much larger, automated process. In plain terms: Claude Code stops being a tool you use and starts being a service that runs on its own. What this unlocks: Scheduled tasks: Run Claude Code on a cron schedule without anyone at a keyboard. Fix linting errors across an entire codebase. Automatically. Overnight. CI/CD integration: Trigger Claude Code as a step in your build process. Open a PR. Claude reviews it, flags issues, and pushes fixes before a human ever looks at it. GitHub automation: A project manager comments "Claude fix this" on a GitHub issue. Claude reads the request, finds the code, writes the fix, and opens the PR. Multi-machine workflows: One orchestrator dispatches tasks to multiple Claude Code instances running in parallel across different repos simultaneously. When you combine headless mode, hooks, and GitHub Actions, development teams can automate tasks that usually eat up significant time freeing senior engineers to focus on architectural problems while Claude handles the repetitive ones. If you use Claude Code for anything beyond single sessions this talk is worth 20 minutes of your time.

Elias

14,096 Aufrufe • vor 3 Monaten

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,225,388 Aufrufe • vor 2 Jahren

Jensen Huang just drew a line through the entire global workforce. One sentence. No ambiguity. Huang: “If your job is the task, then you’re very highly going to be disrupted.” Not might be. Not eventually. Very highly going to be. That single distinction between a job and a task is the most important career diagnosis anyone will hear this decade. If you show up every day to execute a repeatable process, you are the process. And the machine runs processes better than you. Faster. Cheaper. Without breaks. Without errors. Without a salary negotiation. The moment your role can be written as a checklist, the checklist gets automated. And your desk gets cleared. That is not a warning about the future. That is a description of what is already underway. But Huang did not stop at the diagnosis. He handed you the prescription in the same breath. Huang: “If your job’s purpose includes you certain tasks, then it is vital that you go learn how to use AI to automate those tasks.” Your job includes tasks. But your job is not the tasks. Your job is the judgment around them. The decisions. The context. The instinct for why the work matters and what to do when everything breaks. That stays human. Everything else gets handed to the machine. And the person who hands it over first does not lose their job. They become more valuable than everyone still doing it by hand. Because they just converted every hour they used to spend on execution into hours spent thinking. The accountant who automates data entry does not get replaced. They become the strategist who used to be buried in spreadsheets. The marketer who automates reporting does not get fired. They become the creative who used to be trapped building dashboards. The person who refuses to automate anything becomes the most expensive way to do the cheapest work. Huang: “It is the case that the technology will dislocate and will eliminate many tasks. And because it will automate it.” No softening. No hopeful footnote. Dislocation is coming. Tasks will be eliminated. That part is settled. The only open question is which side of that line you are standing on. The side that lost the tasks. Or the side that gave them away on purpose and kept the work that actually matters. One side gets disrupted. The other side gets dangerous. The gap between those two outcomes is not talent. Not credentials. Not experience. It is whether you learned to use the machine before the machine learned to replace you. That window is still open. It is closing faster than most people are willing to believe. And it does not reopen.

Jensen Huang just drew a line through the entire global workforce. One sentence. No ambiguity. Huang: “If your job is the task, then you’re very highly going to be disrupted.” Not might be. Not eventually. Very highly going to be. That single distinction between a job and a task is the most important career diagnosis anyone will hear this decade. If you show up every day to execute a repeatable process, you are the process. And the machine runs processes better than you. Faster. Cheaper. Without breaks. Without errors. Without a salary negotiation. The moment your role can be written as a checklist, the checklist gets automated. And your desk gets cleared. That is not a warning about the future. That is a description of what is already underway. But Huang did not stop at the diagnosis. He handed you the prescription in the same breath. Huang: “If your job’s purpose includes you certain tasks, then it is vital that you go learn how to use AI to automate those tasks.” Your job includes tasks. But your job is not the tasks. Your job is the judgment around them. The decisions. The context. The instinct for why the work matters and what to do when everything breaks. That stays human. Everything else gets handed to the machine. And the person who hands it over first does not lose their job. They become more valuable than everyone still doing it by hand. Because they just converted every hour they used to spend on execution into hours spent thinking. The accountant who automates data entry does not get replaced. They become the strategist who used to be buried in spreadsheets. The marketer who automates reporting does not get fired. They become the creative who used to be trapped building dashboards. The person who refuses to automate anything becomes the most expensive way to do the cheapest work. Huang: “It is the case that the technology will dislocate and will eliminate many tasks. And because it will automate it.” No softening. No hopeful footnote. Dislocation is coming. Tasks will be eliminated. That part is settled. The only open question is which side of that line you are standing on. The side that lost the tasks. Or the side that gave them away on purpose and kept the work that actually matters. One side gets disrupted. The other side gets dangerous. The gap between those two outcomes is not talent. Not credentials. Not experience. It is whether you learned to use the machine before the machine learned to replace you. That window is still open. It is closing faster than most people are willing to believe. And it does not reopen.

Dustin

142,636 Aufrufe • vor 5 Monaten

TeamYouTube . My channel Just got hit with INAUTHENTIC CONTENT . YouTube's AI Wrongly flagged my channel as low effort and mass produced content. All of our scripts are Original. Every topic is distinct and well researched, covering distinct historical subjects with genuine educational depth. No two videos are the same and no template can produce what we make. These are 3 hour long documentaries. We use real archived historical footage that takes a long time to find and source and edit with the narration. We do not use AI images or video. This is not something you can template. This is not something you can mass produce. The render time alone makes that impossible. I did everything right. I made a detailed appeal video explaining the entire workflow of my channel. How we write the scripts, how we do the editing, how we record the voiceovers, everything in full detail. That video has zero views. It was auto rejected. No one at YouTube watched a single second of it. $5,500 from last month is on hold. $2,200 from this month is on hold. That is $7,700 sitting frozen because an automated system made a decision that no human being has actually verified. Real people are being affected by this. I am not asking for a free pass. I am just asking for one real human to look at this case once .

TeamYouTube . My channel Just got hit with INAUTHENTIC CONTENT . YouTube's AI Wrongly flagged my channel as low effort and mass produced content. All of our scripts are Original. Every topic is distinct and well researched, covering distinct historical subjects with genuine educational depth. No two videos are the same and no template can produce what we make. These are 3 hour long documentaries. We use real archived historical footage that takes a long time to find and source and edit with the narration. We do not use AI images or video. This is not something you can template. This is not something you can mass produce. The render time alone makes that impossible. I did everything right. I made a detailed appeal video explaining the entire workflow of my channel. How we write the scripts, how we do the editing, how we record the voiceovers, everything in full detail. That video has zero views. It was auto rejected. No one at YouTube watched a single second of it. $5,500 from last month is on hold. $2,200 from this month is on hold. That is $7,700 sitting frozen because an automated system made a decision that no human being has actually verified. Real people are being affected by this. I am not asking for a free pass. I am just asking for one real human to look at this case once .

Blain Fernandes

12,254 Aufrufe • vor 5 Monaten

Excited to introduce: Gamma 3.0 A generational leap for the world's most popular AI presentation tool. Two major changes: 1. Gamma Agent - with one prompt, you can make sweeping edits across the presentation. a. Say 'make it more visual' and it will scan each slide for data that could be visualized better. b. Upload a screenshot of rough notes you want to turn into a slide and it will synthesize the info, search the web to fill gaps, and make a table for you to match your brand or theme. 2. Gamma API Say you have a sales deck you want to reuse as a template. You could build a Zapier workflow to convert all of your meeting transcripts into personalized decks, automatically sent after every customer meeting. Bonus: We’re also launching Team & Business plans, bringing visual storytelling to organizations of all sizes, and a new Ultra plan for our power users looking to push the boundaries even further. PowerPoint has over a billion users, but the vast majority struggle to use it effectively. Why is that? Because making PPTs is still slow and tedious. We want Gamma to feel different. Our goal is to both lower the floor and raise the ceiling, so that anyone can get their ideas out there. Can’t wait to see what you make with 3.0.

Grant Lee

2,354,822 Aufrufe • vor 11 Monaten

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 Aufrufe • vor 11 Monaten

Andrej Karpathy posted about the new programmable layer developers need to master: agents, subagents, prompts, contexts, memory, hooks, MCP. He said it came with no manual and everyone has to figure out how to hold it. This video is part of that manual. Hooks are one of the most underused features in Cursor and Droid, and they're what separate intentional AI-assisted development from hoping it works in production. After file edit: If the agent touches Rust code, hooks automatically regenerate TypeScript types for the frontend. If it touches TypeScript, ESLint runs with autofix. If it touches backend code, cargo clippy runs. On stop: When the agent finishes, it runs cargo check to validate the build before I even look at the output. 00:00 The problem: manually verifying AI-written code 00:31 Introducing hooks to automate tasks when files change 01:53 How hooks regenerate types for the frontend 02:24 How to set up the cursor.hooks.json file 04:44 Reviewing a more comprehensive hooks plan 05:48 Comparing FactoryDroid hooks vs Cursor hooks 06:45 Implementing the equivalent hooks in FactoryDroid 07:50 AI agent fixes a bug in the build command 08:27 How scaffolding makes the AI agent appear smarter 09:08 Demonstrating the successful hook implementation 09:53 Final insight: It's about providing guardrails, not a smarter AI

Andrej Karpathy posted about the new programmable layer developers need to master: agents, subagents, prompts, contexts, memory, hooks, MCP. He said it came with no manual and everyone has to figure out how to hold it. This video is part of that manual. Hooks are one of the most underused features in Cursor and Droid, and they're what separate intentional AI-assisted development from hoping it works in production. After file edit: If the agent touches Rust code, hooks automatically regenerate TypeScript types for the frontend. If it touches TypeScript, ESLint runs with autofix. If it touches backend code, cargo clippy runs. On stop: When the agent finishes, it runs cargo check to validate the build before I even look at the output. 00:00 The problem: manually verifying AI-written code 00:31 Introducing hooks to automate tasks when files change 01:53 How hooks regenerate types for the frontend 02:24 How to set up the cursor.hooks.json file 04:44 Reviewing a more comprehensive hooks plan 05:48 Comparing FactoryDroid hooks vs Cursor hooks 06:45 Implementing the equivalent hooks in FactoryDroid 07:50 AI agent fixes a bug in the build command 08:27 How scaffolding makes the AI agent appear smarter 09:08 Demonstrating the successful hook implementation 09:53 Final insight: It's about providing guardrails, not a smarter AI

Ray Fernando

60,928 Aufrufe • vor 8 Monaten