Grockbot is the absolute best AI agent out there right now. If used correctly, you will have an entire fleet of AI agents working for you 247, 365. In this video, I'm going to show you how to set it up so you get the absolute most productivity from this incredible AI agent. If you stick with me until the end of the video, you will have an army of AI agents taking care of your entire life for you all around the clock. Now, let's lock in and get into it. We're going to cover everything in this video. We're going to cover setting it up, setting up your first bots, first use cases, how I'm using it, should you rip out Hermes agent or Open Claw or not. But first, I just want to give you a quick update on why this is such an incredible AI agent. Feel free to skip around down below if you want to get to specific parts of the video. I've been using Grockbot literally non-stop for the past week now, and I just can't get enough of it. Every day I discover new things, new ways to use it, better ways to automate my life. And if this is your first Grobot video you're watching, this is what it looks like. By the way, it's basically like this kind of iMessage format where you have a list of bots over on the lefth hand side. These are completely custom bots. You can name them, give them whatever picture you want, give them a title, description, and they all work together as a team. They communicate with each other. They message each other. They have their own tools. Each one has their own computer. So if you go to any of these agents, you can actually see the computer they can work on. These are fully working computers. You can open up a browser, use them any way you want. Each bot you create has its own computer that lives in the cloud. It doesn't need to live on your computer, although it can control your computer if you'd like. It's just a really, really amazing simple experience. And because of this simple experience, I'm able to get so much more productivity done. But I'll go over this in a sec. I'll go over how to set up each of these bots, what you should name them, what should they do, what role should they be, what use cases you should do. We'll go over that in a second. But here's the reason why it's so good. It is by far the best out ofthe-box experience for an AI agent ever. You literally just open it up, you download it, you install it, you have your first bot, and you just get to work. You don't have to choose models. You don't have to choose anything. It just out ofthe-box works. And I think for a majority of people out there, this is the ideal experience. This is how AI agent should work. You shouldn't need to configure a 100 different things. Give it a 100 different permissions. Choose a different model for every single thing it does. Choose a 100 different tools. It should just work. Grockbot is the only AI agent I've used that just straight up works. It has extremely opinionated workflows, more so than basically any other AI tool I've used in the world. What does that mean, extremely opinionated workflows? Basically, it's gotten rid of most of the customization that every other AI tool gives you. For instance, with Hermes, you can choose whatever model you want. You can choose if it works in the cloud or if it works locally. You can choose if it uses sub aents. You can choose the thinking level. You can choose the context window. you right. Everything you do has a hundred different choices you can make on how it does it. Not with Grockbot. With Grockbot, it has an opinion on how you should do things. It doesn't let you edit all those things. It decides what's the best way to do things and does it that way. I think this is a supremely better way to use AI tools. 95% of people that use AI tools don't want to think about which model and context window and thinking level to do absolutely everything they do. They just want to give it a task and then it does the task the best way. That's how Grockbot works. It's opinionated about everything. Every other AI tool, oh, should I do this in the cloud? Should I do it locally? Should I do it in the terminal? Should I do it in the desktop app? Grockbot just decides, no, we are going to do everything inside this virtual machine. everything these bots do will be on their own computer. And that kind of opinionated workflow makes using Grockbots so much simpler. It's cloud agent first. Again, as I said, everything happens in the cloud computer. I'm going to be honest, at first I wasn't a huge fan going into this. I just want all the work done on my computer. Why do I want everything done on the cloud? That feels like there's like separation between me and what the bots are doing. But after using Grockbot for the last week and after all the work getting done in the cloud, I love it. I'm cloudpilled. There's this natural security separation. There's this natural scoping that happens when everything happens in the cloud. I don't have to worry about my bots having access to accounts they shouldn't have access to. If I want a bot to manage a certain account, I just go onto their virtual computer and log in for them. I don't have to worry about them getting access to the wrong things. So I am 100% cloud build now. Zero config. As I said, you never decide which model to use. You never decide reasoning levels, none of that. Hermes, you have 100 drop downs for every single thing you do. With with codecs, everything has 100 different drop downs to configure how to do things. None of that exists here. It's multi- aent first. Again, I didn't know I needed this, but after using it, now I know I need it. By default, every agent messages the other agent when it has work to do. What I mean by that is if I'm talking to my building agent, my coding agent, and I'm talking about, oh, can you code this thing that I tweeted about the other day? It'll be, okay, yeah, let me check in with your content agent and see what you tweeted about, right? It's by default thinks, okay, who can I talk to to get the best context from them? And so, you'll just be using your bots and you'll see, oh, this bot message this bot, this bot messaged this bot, and they all talk to each other just kind of by default, which is really amazing. This is like a thing you have to turn on with your other agents with Grockbot just default really at the end of the day. Why it's so great is it just works with very little setup with very little config. It just works and that's what makes Grockbot so good. All right, so let's get into setting it up. So we're going to cover first things you should do in Grockbot. We're going to cover which bots you should set up. We'll cover a couple different use cases. I'll cover everything I'm doing inside of my Grockbot just maybe as a little inspiration for you. Then we'll cover should you rip out Hermes? What should you leave for Hermes? What should you leave for Open Claw? How do those two live together? Let's get straight into setup here. So, when you start your Grockbot experience, you will have one bot. I recommend making this your CEO, your chief of staff. You rightclick it, you pin it to the top so it's always at the top. Then I would go into edit profile and I would give it the title chief of staff, CEO, whatever you want. Here's why you want like a CEO, chief of staff bot because makes the entire Grockbot experience far simpler. You're going to end up as you go adding more and more bots on your lefthand side here, right? And there's nothing wrong with that. You're allowed to have I've seen people like literally like a hundred bots. You're allowed to have a lot of bots. There's nothing wrong with that. But it might get annoying if you have to scroll through hundreds of bots and choose which one to talk to. That's why you want a CEO bot because the agentto agent communication between the different agents is so good. You can just rely on talking to just one bot and allowing them to distribute the work to delegate the work. it'll figure out which bots have the right tools, the right plugins, the right context, the right roles, and then delegate that work to the correct bots. So, yes, sometimes I still do talk directly to some of my other bots, but most of the time I just go to Slate and I say, "Hey, here's what I want to get done. Determine who should be doing this work." And be like, "Oh, that's coding work. I'll give it to Build. Oh, that's this work. Oh, that's Oh, you're sending an email. I'm going to give that to Cindy." And it just makes it much easier for you to work with. Also, by the way, a majority of people I see using Grockbot just like make the names of each their bot like content, email, coding, right? That's like the name of all their bots. I highly recommend against that. I've been saying this for a long time. I think fun should be like a big part of using AI. Like, I think you should be having fun when using AI. It'll make you use it more. It'll help you be more productive. It'll just give you more joy as you use these tools. I find it a lot more fun when you give your bots like real names. Now, there's a couple exceptions here. Last 30 days is a skill for my friend Matt Van Horn. We'll talk about that in a second. That just that set the name by itself. Build. Okay, that's not exactly a human name, but I'm going to change that soon. But everything else is real names. It's a lot more fun when you're talking to your bots and it feels like you're talking to a human being. So, as you set up these bots, I'd recommend giving them like real human names. It's just going to be a lot more fun. You're going to enjoy the process. So, when you're setting up your CEO bot, give it whatever name you want. The title is what shows up next, a name. So, that's it's going to allow you to give it like human names and then you can look at the title to see what it is. If you name your bot like coder, it's just boring. But if you give it a real name and then you make the title like your CTO, then you can know what it is very easily by looking at your side here. Then the description is basically the context that gets sent with every prompt, right? This is like the system prompt to the bot. You can start off with like a basic description of CEO for whoever you are. I'm going to go in a second and show you the easiest way to set up these descriptions, the bots, and all that. But just want to make sure your first bot you have, you pin it to the top, give it a nice name, one you won't mind seeing over and over again. Don't name it after your ex-boyfriend, your ex-girlfriend, and then title it your chief of staff. Once that's set up, we're going to go into a world famous process I invented and talk about all the time, which is the brain dump to reverse prompt process. So, this is the first thing I do with every single AI tool I get my hands on, every single AI agent I set up, the brain dump to reverse prompt. This makes it so you don't have to think about setup. The AI does all of the thinking and setting up for you. So, step one, brain dump, you tell the agent all about yourself. We're going to go into all our passions, goals, ambitions, what we want to get rid of in our lives, what we want to do more of in our lives. You just brain dump it all in. Right after this, we're going to do the reverse prompt, which is, "Hey, what can you do for me? How would you set yourself up? What bots would you create? What routines would you create?" Basically say, based on everything you know about me, what's the best way you can help me out? So, first, put in your brain dump. I'm Alex Finn. I'm the founder and CEO of Creator Buddy and Henry Intelligent Machines. I'm an entrepreneur and content creator. I have a YouTube channel and XC account that talks about AI. I also have the Vibe Coding Academy, number one AI community on the internet. By the way, join that full boot camp on Grockbot coming up. Join, be a part of that. You'll learn a ton. Link down below. You hit enter on that. Your new CEO is going to learn all about you. Then you do the reverse prompt I talked about. Based on what you know about me, how would you set up Grockbot? Which bot should we set up? What should be their roles, responsibilities, and routines? How can we maximize our productivity as much as possible? Then you hit enter on that and it's going to recommend a bunch of bots to set up. The glorious thing about Grockbot is it can control itself. So you can say that's great. Set all those up for me and then your CEO bot will go and actually create the bots, give the names, give the descriptions, the titles and all that and you'll be good to go. That will give you a base of where to start with. There are many other things you want to do here. So I'm about to go through the plugins, the tools you need. We'll go through routines a little bit and then I'll show you everything I'm doing so you can get inspired by some of these use cases and set them up for yourself. This plug-in section in the bottom left here, this is basically like your MCPs, your tools, all that. They just put them all in one place, which is really, really smart. Again, simplification, just making things work out of the box. There's some basic ones you want to set up, kind of self-explanatory, Gmail and Google Calendar. I'd also highly recommend setting up X so you can pull posts very very easily from X. It is great with social media content. But here's the one I've actually liked the most and this is one I highly recommend you set up yourself and that is Agent Mail. We are not sponsored by Agent Mail. I just discovered them like 3 days ago, but it has made my Grothbot so much more powerful. And I'd highly recommend you do the same thing here. Set up Agent Mail. It's completely free. You get like three free inboxes with the free tier. But here's how I'd use it. Basically, what agent mail does is give your AI agents their own email address, like their own inbox, their own email address, everything really, really easily. It's way better than setting up Gmail because when you set up new Gmail accounts, there's so much security and backup and and all this like complexity. Agent Mail, you literally just sign up in like 10 seconds, you have an inbox for all your agents. But we're going to use this inbox that you create with agent mail for all your agents so that when you invite them to your different accounts, they can just have their own account. You don't need to share your own accounts with them. So for instance, Dusty is my community manager for the Vibe Coding Academy. Dusty answers people's posts, helps them out with technical questions, sends them DMs when they have questions, gives them recommendations for things to build, does a whole lot of amazing things, but I don't have Dusty logged into my admin account in the Vibe Coding Academy in school. I invited Dusty to his own account. So, as you can see here, this is Dusty's computer. Inside of the computer, I invited Dusty his agent mail email to my community and I made him a moderator. Now he can do anything in my community he wants through his own computer. So I don't give the bots my accounts. I don't give the bots access to anything. Everything is done through their own email address. You just invite them to your team on whatever apps you're using and then you give them whatever permissions they need. This is a way more secure way of interacting with your bots, giving them accounts, things like that. It keeps things separated and using Agent Mail made it like a hundred times easier. Again, we're not sponsored. I've never talked to anyone from Agent Mail in my entire life. I just discovered them over the weekend. Just made working with my bot so much easier because they have now their own accounts. I can just say, "Hey, I invited you to my community through your agent mail account. Go accept it and now you're a moderator and go do things in there." And it just works and it does it. So, Agent Mail, very important plugin everyone should be installing. If you do any sort of coding work, which I assume you do if you're watching this channel, Verscell plugin is a mustave. Makes it super easy for your bots to push code to Verscell, update your projects, things like that. So, also add Versel as a plugin right here, too. One really cool thing about plugins as well and the way Grockbot handles things is plugins are basically everything, right? As I said earlier, they're MCPs. They're also skills. So, I install skills and they go into your plugins as well. So, this is a really cool way to manage all your skills, MCPs, plugins, everything in one place. I really recommend the last 30day skill. This is made by my friend Matt Van Horn. It is like the best researching skill out there. I'll leave a link to it down below. Basically, the way it works is you can give a topic or a subject and it uses like the API for every single social media site on planet Earth. like reverse engineered all the APIs and gives you a rundown of what people are saying, how they're using it, the latest news, all of that. And it's like the best deep research when it comes to trends I've ever done. So, last 30 days skill, get that installed as well. It just makes like the research from your agent so much better. I'm having research cursor origin now, which is Cursor's GitHub competitor. If you want a video on that, let me know down below if you're interested. Also, let me know down below what parts of Grockbot you're really interested in. I'll do like deep dive videos. Do you want like a super duper use case video? I'll do that next if you want. Let me know down below. Also, leave a like, subscribe, and turn on notifications. If you learned anything at all so far, all I do is make amazing videos about AI. Join the Finn fam. Hit subscribe down below. Tons of amazing videos coming out very, very soon. So, we talked about initial setup, reverse prompting, brain dumping. We talked about the skills and plugins you need installed. What I'm going to do now is I'll go through my use cases. I'll go through my bots right here. You can go ahead and steal any of my use cases I'm doing or maybe just inspires you on different things you can be doing with Grockbot as well. So, the first bot I use a ton is Build. Build is like my network administrator/coder. Basically does all my technical work for me. If you're anything like me, you have a bunch of different devices. Maybe you have your iPhone, your iPad, you have a computer, maybe a Mac Mini, maybe you have other computers on top of that. Build is my network administrator. I gave it access to my tail scale network. All you need to do is say, "Hey, my network's on tail scale. Please access it." If you have multiple computers, highly recommend setting up tails scale as well. Basically lets your AI agents go across all your devices and do whatever needs across them. But for instance, I had build my technical bot go last night, install Quen 3827B on my 5090 computer. Then it built a game for me right here on my Mac Studio using that model that was running locally on my 5090. So it can go across any of my devices, manage any of them, load models up, build apps, do any sort of coding. It's hooked into Verscell so it can see all my code bases, all my projects I'm working on. I have like a personal operating system I maintain. I need a little fix in it. Today I went to Bill. I said, "Hey, in my personal operating system, can you edit this?" It's basically like my CTO. Anytime I need to do any technical work at all, I go to build and it works really well. The reason why like this is better than having like one mono agent that just handles everything is build just has context just around my technical work, just around my computers, how things are set up in my projects. That's it. Like the description it has just describes my projects and computers. So when we give it prompts and we tell it to do things, it only has to pull from a very small description, from a very small context. When you have one agent doing everything, doing your programming, your content, this and that, its skills, its description, its system prompt is massive. And the bigger the system prompt gets for an agent, the slower it becomes, the more expensive it becomes, and the stupider it becomes. This is the beauty of the architecture of Grockbot is all your skills, all your description system prompts are split up nicely between bots. So when you message the right bot, it's quick, it's cheap, it's easy because it just knows what it needs to know. Just the system prompt it needs to know, just the role it needs to know, the tools it needs to know. That's it. Nothing else. That's why this architecture of Grockbot is so great. Then we have Barry. Barry is my content engine. Barry keeps me up to date on trends, breaking AI news. It's always watching X for me, looking for breaking news and things like that. Barry has access to X API. Barry does a few things for me. One is it helps me write all my newsletters. Newsletters take a long time to write. Barry helps me write them. It takes my posts, my YouTube videos, repurpos them into newsletters for me. It also keeps an eye on any AI major products. Right? So, this is another routine here. We didn't really cover this too much. Routines are basically just your cron jobs. You can reverse prompt your routines as well. Go to an age say, "Hey, what are the best routines we can set up?" It'll set up the routines as well. Every 30 minutes from 7:00 a.m. to 11:30 p.m. Barry goes and checks the SpaceX Twitter account. It checks Anthropic. It checks Open AAI and it sees if they made any new major releases recently and then lets me know about it, which is amazing. As you can see, SpaceX just shipped a connector inside Grock live now. Boom. I get that breaking news the moment it happens. So, even if you're not into content, I still recommend having a berry of your own that just keeps an eye on X and lets you know the moment new things happen. Then we have Dusty, who we talked about a little earlier. Dusty is my Vibe Coding Academy moderator. Anytime someone posts a technical question that'd be appropriate for an AI to answer, Dusty answers it. People aren't as engaged after a few days after they join, Dusty will message them and say, "Hey, here's a few ideas based on my research about you of things you can build." And Dusty just keeps people engaged, keeps things moving, post new AI news in the community every day. It just takes so much work off my plate. And because I invited Dusty through that agent mail account, it's a moderator. It can do whatever it wants. It doesn't have admin access cuz it's not using my account, but Dusty has his own account. If you need any sort of community management, Grockbot is like the perfect candidate for that. Cindy is my revenue ops. Cindy's basic job is monitoring my email and finding sponsorship opportunities and replying to people. My number one pet peeve in life. I don't know what it is. I don't know if there's something wrong with me. I absolutely hate email. I hate email. When I get a popup on my phone, it's an email for me. I get like anxious. Like I hate it. I hate getting emails. I hate reading emails. I hate replying to emails. The downside of this is I don't get many sponsorships because of that. I get hundreds of emails a day from companies trying to sponsor my videos. I don't reply to any of them just because I hate emails so much. Cindy does that for me. So Cindy's hooked into my business account. Cindy goes through the hundreds of emails I get a day, researches who's emailing me, if they're legit, if they're just a scam. By the way, if you get into YouTube making, you're going to get 40,000 scam emails a day. Be careful out there. But Cindy researches who scammer, who's real, and at the end of the day gives me a spreadsheet of the legit opportunities that might be real sponsorship opportunities. So Cindy is amazing. If you're anything like me, you hate email. Set up a bot to monitor whatever email accounts important to you when it comes to business and alert you when important business emails come in. Get yourself a Cindy. And then I have Reed. Reed is my last bot right now. Reed is my experimentter. I have Reed go and experiment on the internet all day. This is going to sound a little strange, but I have Reed going running experiments, finding what people are saying online, build products based on what they're saying, post those products online to see if people click it, use it, see where there's demand. I basically have Reed going out, finding demands, running experiments, seeing what people are into on the internet. Reed's kind of like my go-to guy for just finding business opportunities online. Slate manages Reed, so I don't go to Reed and say, "Hey, do this, do that." I just tell Slate, "Hey, have Reed try different things out and see if there's different business opportunities online." Then Slate just watches Reed do all those things all day. So, he's kind of my experimentter. These are my main use cases right now. Based on those bots I showed you, I can manage like 90% of my business. I'm coming up with new bots all the time. So, if you stick with the channel, you subscribe down below. I'm This is the first of many Grockbot videos I'm going to do. So, you'll see how my setup evolves over time. I'm going to be adding way more bots to this. Hermes versus Grockbot. Which should you use? Which should you rip out? These are two different agents. It's not one or the other. It isn't Hermes this or Grockbot that. Hermes is a completely customizable agent running on any model that can do anything you want. If I load up Quen 38 on my 5090 computer, it's now powering my Hermes, right? Because Hermes is fully customizable. I can't change models in Grockbot. You could only use Grock for Grockbot. So, there's still going to be things you need to do that require customization, that require cheaper models, require your agent going and doing things on your computer, tinkering, changing things around. That's where Hermes comes in. That's what I would use Hermes for. But for basic day-to-day knowledge work, Grockbot's the goat right now. Grockbot is the best way to do it. So, I do not recommend ripping out Hermes. Keep Hermes. Use it for cheaper models. Use it for changing things on your computer, doing admin things on your computer, things that require deep customization, and then use Grockbot for your general knowledge work. I'm absolutely in love with Grockbot. I'm sure you are too if you started using it. I hope this was helpful. Let me know down below what you want my next video to be about. Any aspects, Grockbot, use cases, things like that. Let me know. Hope this was helpful.
Okay, so what is the best coding agent available today? Is it Codex, Cursor, or Claude Code? I think that is the wrong question to ask and a lot of people focus on that. The right question is how do you combine them together to build powerful software? And in this video, I want to really show you my own workflow that I personally use where I combine the strength of different coding agents within a modern IDE to build some really amazing features. For example, this is an educational tool that I'm currently building for my son, and it really lets you explore different organs, different systems in the body. So, my goal for this video is to show you a workflow which is really easy to follow, and but it's going to help you build some really great software that you can actually ship to your users all within a modern IDE. Now, in the rest of the video, I'll show you my workflow, how to use the strengths of different coding agents in your IDE. For this, we're going to be using PyCharm from JetBrains, who are also the kind sponsors of this video. However, the principles are going to apply to any framework or IDE that you are working with. But, JetBrains does offer some really neat extra features. First, you can bring any coding agent of your choice either through your subscription or through an API. So, you're not locked into a specific vendor. Anything that supports agent communication protocol can be integrated in here. Second, and I think this is a big one. This enables you to bring the IDE context into your coding agent, which is not available in a CLI-based coding agent. Okay, so what exactly we're going to be building? We're going to look at a very common scenario where you often provide specs or requirements to your coding agent and it is going to build something based on those requirements. But often you're not sure whether the code implementation actually follows your full specs or not. That's the problem we're going to be tackling in this video. Okay, so the app I showed you in the beginning was implemented based on these requirements. Now, it's a really big build, so I'm not sure whether the coding agent that I used actually followed the set of requirements fully or not. The output looks correct, but I think there are a lot of missing pieces. So, we're going to use different coding agents with different capabilities in order to ensure that the agents actually follow our set of requirements. Now, as I said, this new AI in the IDE feature lets you connect multiple different agents in the same IDE interface. So, right now, if you look at, we have Quad Code, Client, CodeX, Cursive, even I have connected Kimicy and I. Or you can just bring in a coding agent of your choice as long as it supports the agent communication protocol. So, here is a list of all the different coding agents that you can connect through ACP. This is pretty comprehensive list. This includes both proprietary and open source coding agents. Now, when you start, you will first need to configure your agents as using either an API or through their subscription. Now, right now I'm selecting CodeX. Now, if we send in our first message, it's asking me to install and continue. So, we are going to install CodeX on this machine. Okay, you basically get access to all of the different features that a specific agent actually provides you here. So, for example, we can select which coding model that we want to use. Let's say we want to go with uh GPT-5.6-Soul, which is great for code review. And then I'm going to put it on high. Now, in terms of the coding agent itself, it gives you all of the different capabilities. So, for example, right now you can select the model, the effort level. Since we're doing a code review, I would select uh the reasoning effort to be high. And then you can give access to the agent, whether you want full access or read only, or you want the agent to ask you for every action. Right? Similarly, if you go to something like cloud code, again uh you have different modes that you can set here. You can also use any skills that you have already installed with the agent, which is pretty neat. And then you'll be able to set up the model and reasoning level. Now, I am not using Fable for this because this is a bio-related uh application, and usually that flags the classifier. But, what we're going to do is we're going to use GPT-5.6-Soul in order to find any discrepancies. And then we're going to use the Kimi code to actually implement those. Okay, so in here I'm asking it to uh look at the uh build.md, which has the specs and uh the rest of the code, to make sure there's a feature parity. Also, you can enable or disable the IDE context. Right now, it's enabled, so it will be able to see whatever I am doing in the IDE. All right, so we send this in. So, right now codex is planning on doing the full audit, reading the specific files that it needs to. We're going to come back when it's finished. Okay, so while the GPT-5.6 is doing the analysis, a couple of things that I know are missing is that I asked the agent in the initial spec to include glowing particles in these animations, but seems like it forgot to implement those. But, it said that the implementation is complete. Okay, so the first phase of analysis is complete and it's actually a much stronger implementation than I thought. Uh but, it did find some missing features that are not implemented. And that's why you usually want to use a completely different agent in order to verify the work. And I even recommend a different class of agent. So, if you do uh implementation with, let's say, topic models, it's good to use OpenAI models for verification. Now, overall, the implementation is pretty strong. However, it did find some missing pieces. So, for example, that uh particle flow piece is missing and it was able to identify it, which is pretty neat, right? Uh based on this analysis, I asked it to come up with an implementation plan only for P0 and P1 for the sake of this video to keep it simple. So, here's the implementation plan that it came up with. It also gives you a diff of every change that it's making. So, this is really helpful, uh especially in a full IDE. You can easily look at what the agent is doing. Then, you can just take uh this and use another coding agents like Kim i CLI uh to actually implement this. Kim i CLI using KT is a lot less expensive, but it's a pretty capable model. Now, you have two different options. Either you can just switch here. And this way it's going to have full context of all of the conversation or you can just go to a new chat session. Uh so for example, I created a new chat session and in here I simply asked it to look at the feature implementation plan and test and validate and document the implementation that is going to be doing. Now, uh for the model we selected Gemini say light. Uh I have already connected this through my Gemini subscription. I chose Gemini 1.5 thinking for this and right now is the default permissions. So at the moment the agent is working through the implementation. Okay, the first pass is complete. However, the verifier found that the implementer still had gaps in the implementation and that's why it's very important to use a secondary model to check the implementation correctness based on the specifications that you're providing. This is the biggest lever that you have when it comes to agentic coding. You don't want to just rely on the word of a model in regards to its work. Either you want to verify it or if it's a substantial work, you want a secondary agent to verify the work and then provide feedback. Now in this case I took that second part of the feedback and basically provided it to the implementer and the implementer has implemented fix for those issues that were highlighted. I'm using the same prompt with Codex solved on high setting again just to verify the implementation. And then it basically independently runs everything and verifies them again. So this time with the second pass a lot closer to what we want. There are some minor things that we still need to fix, but this is in a very good shape now. So here I asked it to run the app for me to test it out. Okay, so the major issue was with the organs and now it has implemented that pretty neat article effect uh list uh functionality is there. So, this is really good, right? Now, personally, I like this iterative refinement and improvement. Still want to personally test out at the implementation. I have found that this works really well for me uh because even though I am spending some time up front, it actually saves me a lot of time down the line. And for this, especially having an IDE where you can actually uh check what exactly is being changed and worked on is extremely critical. So, I usually follow this planner, implementer, and verify loop. But, do let me know if there is any pattern that you have found that works really well for you. I would love to learn from the community. So, the question is not which coding agent is the best. I think it's how you are able to build workflows for your specific needs that combines the strengths of multiple different coding agents for you in a modern IDE. Do let me know your thoughts and I hope you found this video useful. Thanks for watching and as always, see you in the next one.
Here are six incredible open- source projects that you can install right now. Let's get into the first one, Unsloth. It started as a way to make LLM fine-tuning locally much easier for the average person like me. Now, it has evolved into a powerhouse of a tool for everything from fine-tuning to running inference, training models, and everything in between. So, let me show you a little bit about it. Again, it is free. It is open source. It comes in Windows, Mac, Linux, everything you need. You can run and train all the latest open- source models including Kimmy K3, Miniax, Quen 38, Muse Glimmer, which just launched by Meta, Deepseek V4, Gemma by Google, everything. It is also now a fullyfledged agent UI. So, it looks just like Chat GBT. Of course, you could type anything in here, but of course, it is running a local model. It has web search. You can plug in tools. You can have MCP. It has memory. Everything that you need all in one place and all local. All of the features that you've come to really depend on and love from products like Codeex and Cloud Code and Cursor are all built into Unsloth, but again, everything just runs locally. It supports all different types of hardware. You can actually remote access into it, which is really nice. So you have it running on one computer, you can open it up from another and control that computer from anywhere in the world, which is very similar to what Codeex and Cloud Code and Cursor also do. You can run and train image models, video models, text models, basically everything really just out of the box. It works extremely well. And one thing that I absolutely love is if you're trying to dip your toe into training and fine-tuning, it can be extremely intimidating. There are so many different settings, so many different ways that things can go wrong. But Unsloth makes it really easy and you don't need to code at all. It's basically just a point-and-click interface that allows you to do training, to do fine-tuning, and it walks you through it and just makes it all really easy. And by the way, I'm going to drop all of the links to all of these projects down below. Next is a project called diagram design. Diagram design gives your agent the ability to create diagrams that are actually good-looking and don't have arrows that are all broken and overlapping and really nice, really simple and easy to use. And they have a ton of different diagram options for you. Here's a flowchart. Here's an architecture chart, a state machine, a timeline. Basically, everything you could possibly need. Quadrant, all easily available. You just give it to your agent. You can install it as a plugin in Cloud Code, plugin in Codeex, in Pi, and you can install it in Hermes Agent, which is what I'm going to show you how to do right now. And today I'm hosting Hermes on Hostinger, who's also sponsoring this video. So, check this out. I already have Hermes set up for myself in Hostinger, which means it is hosted in the cloud. I don't need to actually have my computer on and running all day long. I simply type install this, give it the GitHub URL, and then hit enter. So, it installed it as a skill. It only took 1 minute. And now I can create a diagram. Let me show you how to do that. Create a diagram for Hermes agent to show the architecture. And of course, it knew to use the diagram design skill. And while that's going, let me just explain why hosting and hosting in the cloud is so important. You don't have to keep your computer on. And in fact, that was fast. You can run background processes 24 hours a day. It's actually a one-click install to get Hermes running. So, if Hermes was a little bit intimidating to you, this is a great solution. One click and you're in. The diagram design skill worked flawlessly. This looks beautiful. And don't forget to use code Matthew B in the checkout to get 10% off. If you sign up for 24 months, you get the biggest discount. So, go check it out. Link down below. Next is Obsidian Skills. And if you're not familiar with Obsidian, you should definitely check it out. It's basically a simple note-taking app, but everything is done in markdown. Obsidian kind of reminds me of this image. What you see when you first get into it and start using it is this little bit on top. But with those simple primitives, you can get so much functionality out of it. I sync it across all of my devices. It is really cool. And now you can give your agent access to your Obsidian and let it use it in all these different kind of cool ways. Obsidian skills uses the agent skills specification, so it can be used with any agent, including Hermes, which I just showed you, Claude Code, Codeex, Cursor, Grockbot, all of the above. I've seen people do incredible things with Obsidian, like using it as a wiki, using it as a knowledge base for their agent, basically throwing everything into it and using it as kind of the brain of the agent. And obviously agents work really well with markdown and Obsidian is all about markdown. So it works really cleanly with your agent and your agent can just use it. It can store all of these things locally if you want. It is very much local first. If you want to sync across all your devices like I do, you can also do that quite easily. And now again with Obsidian Skills, your agent has access to knowing how to do all of it. All right. Next from Jack Dorsey's company, the creator of Twitter. He also created Square and now it's called Block. They have an open-source Slack alternative. This is called Buzz and it's basically like Slack but made to be agent native, made to be AI first. And the nice thing is it is completely open- source coming in at 27,000 stars after like a week of being published. So, this is what it looks like. If you're familiar with Slack, you'll probably be very familiar with this. But the thing is here is the key. Your agents are first class citizens in Buzz. So you have your team and you have your agents working in the same channels working on the same work and they are both users first class users of Buzz. And I think that's kind of the distinctive feature of Buzz is it's very much agents are working with you with your human team. It is self-hostable which is obviously quite nice. They are very much privacy and security first, which obviously if you're wanting to host all of this stuff locally, you're probably thinking quite deeply about. And it has all the features you're used to, emojis, replies, threads, channels, all of the things you're used to. And then also, you get the agents as first class citizens. It also deeply embeds workflows and automations directly into it. It is not really just a chat app. It very much lets you build out automations directly into it. This is meant to be the central hub for your work. They track everything being done in Buzz, which is really interesting. It's a noster, which I've never heard of. Nostaster relay. Every message, reaction, workflow, step, review, approval, and get event is a signed event in one log. Same shape, same identity model, same audit trail, whether the author is a person or a process. And when they say process, they mean agent, workflow, etc. It's all the same. I really like this structure. You can bring any model you want, open source, closed source, hosted, local, whatever you want. And because it is backed by a major company in block and by Jack Dorsey, you know, they're going to continue to iterate on it and make it better over time. And it is a newish product, so go test it out, see if you like it. Next is Ego Light, an open source project that is self-described as the fastest browser to give to your agents. so your agents can control your browser. This is Egoite. It has 10,000 stars, so relatively under the radar right now. It describes itself as the fastest browser for AI agents to run browser automation. Built for sharing your loggedin browser state with your AI agents like Codeex or Cloud Code without disturbing you. Zero cost, zero configuration. Wonderful. So this is what it looks like. This is codeex. You type / browser. So it gets invoked kind of like a skill. Let's actually see it run. So this is the ego browser right here and we can actually see it running. So it has that nice blue glimmer. You can actually see the arrow the cursor and there it goes. Super fast and especially if you're going to be using it with the new cerebrous powered soul, it's going to be even faster. So very cool, free, open source. Go check it out. And the last project is Modly. Coming in at just under 6,000 stars. It is image to 3D design. Local open- source AI powered image to 3D mesh generation. So whether you're doing uh asset generation or you're into 3D printing like I am, you can basically just take any image, load it in, and it will create a 3D mesh that you can then use for your 3D printing or your game or whatever other use you're using that asset for. It runs entirely locally, which is very, very nice. And it's not really compute inensive either. So, you can use this with your desktop GPU most likely. It has support for Windows, Linux, and Mac and many different GPUs. So, if you're into 3D modeling, go try this out. Seems very cool. I made another video that covers a ton more. Go check it out right
Claude Fable 5 just completely changed how AI videos get made, and every video you're watching right now was made by it. Four ads and a week of vlogs, and I didn't edit a single frame. I just approved steps in one chat. I've been testing this setup for the past week, and nothing else I've tried even comes close. And so, in this video, I'll show you exactly how to make AI videos with Fable 5 from one brief to a finished batch, so you can make your first one today. The idea is you stop making the videos yourself and start directing an agent that makes them for you. With a normal video model, you write a prompt and take what comes back. But, here you hand over the whole job and approve the plan, the cast, and each render as it builds. Higgsfield supercomputer runs all of Higgsfield from a single chat, so you describe what you want once, and it plans the steps out, matching each one to the right model and rendering the lot. And the reason it can take on a brief this big is that it's running on Claude Fable 5. The way it works is that Fable 5 is the planner, and each step gets handed to whatever specialist fits. An image model for the frames, a video one for the renders, and a separate model for the voices. So, Fable 5's real job is lining them all up and passing the right thing to each one. Normally, four ads like these would mean jumping between a few different tools and an editor. A week of vlogs on top of that is days of work. Here, all of it happens in one chat. The only piece I make myself is the product, so that's where we start. Every ad is going to reuse that one product shot, so it's worth getting right before anything else. Everything today runs on Higgsfield, and the link is in the description if you want to follow along step-by-step. So, before I even open supercomputer, I'll go to the image tab, pick GPT image 2, set it to a one-to-one square, and describe the product I want. For this video, the product is a simple mug, a matte cream ceramic one with a little line art sun on the front. And I've got a plain product shot of the mug on a white background, which every ad is built around from here on. You make it once and reuse it, because if you let the agent regenerate the mug for every ad, the glaze and the shape come out a bit different each time, and your four ads stop looking like the same product. The lighting on this shot matters more than it looks. Keep it flat and with no harsh shadows or reflections, because the more the lighting is locked into the product shot, the harder it is for the agent to relight it convincingly in a sunny kitchen or a dim reading corner later. I use GPT image two for it because it holds fine product detail well. The shape and the little sun come out exactly right, which counts for a lot when this one image is about to appear in every ad. I keep it one-to-one on a plain background, too, since a simple product plate is the easiest thing for the model to drop into any scene later. Now, I'll open Supercomputer, and there are only two settings to change here. I'll pick approve before generation, so after every prompt it lays out its plan and pauses until I hit run instead of spinning on its own. I'll set it to Claude Fable 5, since that's the part that reads the brief and plans every step. And then I hand it a single brief that has to come back as four ads that all feel different. I'll give it that brief in one message. The mug goes in first, so it's locked as the product from line one. Then I tell it what I want. UGC style ads, four of them, each a different angle, morning coffee, cozy reading, office desk, and a gift idea. All casual and honest, like real creators shot them. I want them vertical, 9:16, so they're ready for reels and shorts. And I ask it to walk me through each step, so I can sign off as it goes. This is why I made the image first, instead of just describing the mug in the brief. If you only describe it, the agent pictures it a bit differently every time. An attached photo gives it one fixed thing to [music] copy, so the mug stays identical across all four ads. From that one message, it comes back with a full plan. Four ads and four different avatars, each one worked out down to the hair and the styling. Everyone gets its own setting and its own script. The casting is properly varied. The coffee ad gets a late 20s woman with ash blonde bangs in a bright kitchen, and the reading one a softer brunette on a couch with a throw and books. For the office ad, it picks a 30-something guy with light stubble at a lived-in desk. And the gift one a bearded guy boxing it up under string lights. The scripts are just as different. [music] One leans on the mug making the morning feel calmer. Another plays it as a break from a desk full of monitors. And the gift version sells it as the present that always works. So, no two of them sound the same. It all comes down to the brief. I tell it to start, and it builds the first avatar, a late 20s woman in a bright kitchen. Once Once it she's good, it puts that frame together, her holding the mug with the sun clearly on it, and starts rendering that first video while it moves on to the next creator. That start frame step is what keeps each ad consistent. It locks the creator's face and the mug into one still image first. So, when the video renders, it has a fixed target to animate instead of reinventing her frame by frame. [music] I make it check in at each step, mostly to save money. Catch a bad avatar right here and it's a one-line fix. Miss it and let it run and you've paid to render four ads you'll just throw out. The second creator is a woman with soft brown waves and a reading mug. And once I approve her, it goes straight into her start frame and renders the video off that. Then I tell it to build the last two at once instead of stopping to check with me on each and it works the same way through both. The avatar first, then the start frame, then the video straight off that frame. So, now there are four creators who all look different, two women and two men with a finished ad each. Then I change direction. I tell it I want them looking at the camera and talking. The video's at 10 seconds and the scripts tighten to fit. [music] And it regenerates all four as 10-second on-camera videos with proper lip sync, rewriting each script shorter to fit, and it never once starts over. I push it to 10 seconds and on-camera on purpose. 10 seconds is long enough to make one point and short enough that nobody scrolls off before it's done. And having them talk to camera is what makes it read as a real person. >> This is the mug I reach for first. It's just a simple ceramic thing with a sun on it, but pouring my coffee into it makes the whole morning feel calmer. This mug is my perfect reading companion. Tea in one hand, book in the other. The little sun on the side just fits the vibe. >> My desk used to be all screens. This mug didn't change much, but having it next to my keyboard makes the work day feel less robotic. Whenever I don't know what to get someone, I get this mug. Simple, beautiful. Everyone deserves a cup that makes them smile. >> Watching the four back, it's clearly the same mug in everyone, just re-lit for each scene. Ads are one thing, but the same agent will build you a whole content series. Now, let's do something completely different, a week of casual vlogs. This time I want a brand new creator instead of reusing any of the ad avatars. So, I have it build her character sheet first, full body and face, and then each day's video comes straight off that sheet, so there's no start frame to make this time. It's seven vlogs, one for each day of her week, 10 seconds each and all on camera. Then I'll paste the brief in. It plans her out, too, a warm mid-20s woman with dark hair and light freckles. Then it maps out 7 days for her, a morning routine, a gym session, errands around town, cooking dinner, then a night out with friends, a day outdoors, and a slow reset day at home. I'll approve, and it builds the character sheet, then generates all seven vlogs off that one sheet, so it's the same face all week. Get the character sheet right, and you've basically got a creator you own. Once she exists, you can put her in any video from here on. So, it's worth nudging her face and build until they're right before you generate a single day. It's also why the vlogs come together quicker than the ads did. The ads needed a fresh start frame for every video, since each one was a different creator holding a product. Here it's one person all week. So, the character sheet on its own is enough for the model to hold her face the whole way through. The sheet keeps her face locked, but the clothes and the settings can still change day-to-day. If you want a tighter look, put that in the brief, too. This vlog style is a different job to the ads. It's how you'd run a personal brand or a faceless channel. I do want to fix the voice, though, so I ask it to put a single consistent voice across all seven days. It offers me a few presets that suit a young creator. I pick one called Quinn, and it applies that same voice to every vlog. Preset voices like that are quick, and they fit, but they can sound a little generic. So, for a real channel, you'd probably clone your own voice instead. This is just the fast way to keep the whole week sounding like one person. >> Morning coffee. Let's see if I can be a functioning human today. Same walk every day, and I still love it. Three stops, and I've already lost the list. Post office, groceries, and one thing I definitely forgot. Wish me luck. Only got like 40 minutes today, so we're keeping it short. Legs, then I'm out. Honestly, the hardest part is just showing up. Attempting to cook something that isn't pasta. Wish me luck. Said I'd be ready at 8:00. It's 8:15. Meeting the girls in 20 minutes. This is 5:00 now. Needed some sun. Found a spot and honestly, might just stay here forever. Doing absolutely nothing today and I've never felt better. Laundry's going, tea's made. That's the whole plan. Reset day. >> That's a full week of content built from one character sheet and one voice, with me doing nothing but describing it. Once you've got a recipe that works, you don't have to baby sit it forever. You can take it off confirm and let it run a bigger batch unattended, which is how a week of vlogs turns into a month without you approving every frame. Because it remembers between sessions, the mug, the creators and the voice all carry over to your next chat. So you're not rebuilding your product or your cast every time you come back. For anyone making content at any volume, this is the change that matters. One person can now brief and approve what used to take a small team and the hard part stops being the production and becomes coming up with the ideas. The whole batch, all 11 videos, cost a fraction of what a single shoot or an agency day would, because you're paying in credits per render rather than day rates and revisions. It suits anyone who needs volume and consistency more than one cinematic hero video, a brand running UGC or a creator posting daily. And if you only ever wanted one perfect 30-second film, you'd still art direct that yourself. But for a steady stream of content, this is the fastest route I've found. So you hand Claude Fable 5 and the supercomputer your own product or your own week and it builds the batch while you approve each step. And the part I can't do for you is pointed at your own product. [music] What it builds from a brief of your own is the part I want you to see. Open Higgsfield through the first link in the description and hand it your first brief today. Thank you for watching and I'll see you in the next one.
What's up, engineers? Andy Devdan here. Like me, you've probably gotten sick and tired of Opus 5's insanely verbose responses and its overuse of phrases like loadbearing, worth stating plainly, here's the honest truth, and a bunch of others. Or maybe you're tired of the Anthropic team trying to take credit in your Git commit messages for intelligence you paid for. Or maybe you notice Opus 5 is burning your cash with way more output tokens than any model before it. You're not alone. Myself and many engineers feel the exact same way. Obus 5 is one of the best state-of-the-art ultra smart models and one of the worst state-of-the-art models ever released because it talks like a complete smartass. In this video, we turn smartass Opus 5 into a precise senior engineer that's enjoyable to work with. How are we going to do that? We're going to use one of the most important skills any engineer using agents can learn. You know what it is? It's prompt engineering. The skill that was once a complete joke is now the most important skill for [music] any engineer to master to scale their impact with agents. If you hear someone say prompt engineering is dead, completely ignore them. They have no idea what they're talking about, I've been engineering for 15 years now, and one thing that all the best engineers I've worked with know how to do extraordinarily well is this. Above all, they know how [music] to communicate with their technology. And guess how you communicate with agents? Yes, prompts. There are two ways [music] to prompt engineer your agents. Most engineers only use one when the second is most powerful by far. By the end of [music] this video, you'll have the prompt engineering expertise to make your AI agent communicate like a precise cracked senior engineer. No matter the model that's running today, it's Opus 5. Tomorrow [music] it'll be something else. Let's fix Opus 5 with system prompt engineering. What are the two ways to prompt your agents? There's, of course, the user prompt, which you're very familiar with, the single task at hand. But then there's the system prompt, which is the law for every task you hand your agent. Most engineers fixate on skills, skills, skills, skills slash this slash that slash plan/grill/re. Don't get me wrong, skills are powerful. The user prompt is powerful, but the system prompt is vastly more useful and most engineers never even touch it. Because the system prompt is where every single word you write is multiplied over every single user prompt. The system prompt is essential for setting up great communication patterns with your agents and for reducing those expensive Opus 5 output token costs dramatically. The system prompt is where the real leverage is. Let me show you exactly what I mean. in VS Code. Here we have the simple three file structure. I've clone down Zuck's the future is for everyone. We're going to do what all of us degenerates do nowadays. We just summarize a long article like this. We're not going to read this whole thing. We don't have the time. We're too busy with 10 million agents open in our terminal window. To do that, we're going to use a standard powerful clawed opus 5 agent. As we work through this, we're going to constantly improve the system prompt we're passing in to clawed code. I have this simple command here called compare. So, let's go ahead and fire it off inside of a terminal. We're running herder. This is my terminal multiplexer of choice. You'll see exactly why in a second. If I type J, you can see all the commands in this directory. And what I'll do is just type J compare. And we'll start with zero. I now have a brand new workspace open. Let's go and hop into it and see what's going on. You can see here I'm running two cloud code instances side by side. On the left we have smartass opus 5 and on the right we have senior opus 5. Right now these are both in smartass mode. This is just the default system prompt and the default clawed opus 5 model with no tweaks, no changes, no prompt engineering to adjust and improve the system. As you can see all of the ticks and problems and verbosity of this model are coming alive right now. Zuck's blog is very very long, but you can see here these models spin for a long time. The one on the left is really really going off here with a lot of information. Okay, so 53 seconds, 35 seconds. Most of that was just output text. Look at how many output tokens my agent just torched responding to me. Most of us truly were not reading this whole thing. Just like we're not reading Zuck's blog, we need concise, compressed information so we can move on or decide to invest more. A lot of engineering with agents is about figuring out where to spend your time. So let's improve the system by prompt engineering the system prompt. So in VS code I have senior opus 5 system prompt and this is getting passed directly into that herder pane. You can see here we have one version that's just claude code opus out of the box and then we have the other where we're pending the system prompt file using this argument flag. So that's what we're going to do here. And so if we open up this file right now we have nothing in there. So we just got the default response out of it. Let's improve it. Let's write a great system prompt to steer the behavior of every single run, every single input and every single output from our model. What we're building here is a clear concise communication document. I am typing this by hand. When I'm working with pieces of autonomous software that's going to be multiplied over many, many, many runs, I do it by hand. If you don't understand your technology, if you're just vibes slopping everything, you're not going to get the results who someone paying attention is going to get. We're going to start really simple here. We're appending this to the system prompt. Clear, concise, actionable communication. And then we're going to talk through the purpose. First things first, you and I maintain a no BS, clear, concise, actionable relationship. Notice how I'm talking to my agent. I'm not giving it a role. I'm just talking to my agent as almost if I'm talking to like another engineer, okay? And and kind of setting the standards. We maintain a no BS, clear, concise, actionable relationship. Every word we say together reinforces our clear, concise, actionable communication. I'll jump through some of this. We're here to solve problems and create value. And our communication reflects that. And then I'm going to start the next section, instructions. And this is going to be where we dial into several ideas. We'll have four sections and then one kind of recap section. Um, this will be the examples and then we'll work through these sections one by one. But I'm writing this out to be able to reference these sections so that I can clearly communicate inside the system prompt what I want my agent to focus on. Okay? So, pay close attention to the details throughout instructions to maintain our great communication patterns. And then I'm going to do something here that I think is really important when you're prompt engineering. I'm going to explain why. Why? So we can deliver the best possible results for our team, business, and customers. Just walking through things here really, really cleanly, really concisely with my agent. Let's pause for a second here. Let me just actually delete this and just kick this off again. So let me save that text there. Let's go back into our terminal here. Let's close this down. Let's fire off our J compare uh with purpose. So it's going to just rerun that exact same thing. Already we have one kind of clear change in our system. And you can see here at the top we do have that reference append system prompt. We're attaching that. But already we have made a small change in our purpose. This is a good start. As you can see here, it's not going to change a lot of the ticks and the problems inside the model. As you can see, there's loadbearing right there. That's one of the classic ones with Opus 5. It's using a lot of dashes, but it is communicating a little bit better. You can see that one came in at 35 seconds. Not a huge difference at all from previous runs, but you can see here if we looked through these, we would probably see some glimpses of clear communication, but we need more than this. This is all very high level. Let's get more midlevel, more lowlevel with our system prompt. So, I'm going to copy this in. And now we have instructions. Okay, so purpose, instructions. Let's start giving some real [music] clear communication patterns. Now we get into like concrete prompt engineering patterns that are repeatable across all of your work. For instance, here's one clear pattern. Positive patterns and negative patterns. And so what does this section do? Replicate the and I'm going to just write out a H4 here. Positive patterns. And of course we'll have that and then we'll have negative patterns. And so we're just going to walk through this. And you know, as you can imagine, I'm going to clearly reference this section as behavioral references. So, I'm saying replicate the positive patterns. And then I'm going to, of course, say avoid the negative patterns. So, we want to replicate these and we want to ignore and not do these. Okay? So, we're doing both positive, do this, and negative. Don't do this. And now, we can just start writing out these bullets. What do you want your agent to do? What do you want them to not do? I'm just going to start from the top. And this is one of the important things that is missed a lot. So, I'll say I always see the last thing you write first. Place the most important information there. As you're working with these agents, you have likely noticed this, too. You see the last thing. So, it's important that the last thing you see is the most important. So, you can, if you want to, skip all the other stuff. What else do we want the agent to keep doing? Positive patterns. Use plain specific language. State each fact once. There's no need to repeat anything. Match the level of detail to the level of task and request. Okay, so this keeps the agent aligned with the amount of detail and effort you're putting in. Challenge incorrect assumptions directly and explain why. We do not want any sickle fancy here in our responses. We want a useful engineering partner. Optimize for clarity and engineering value, not quotability. I don't want to have a good conversation. I want to deliver engineering value with clear, concise communication patterns. Okay. One more note here on domain terminology. Use the simplest domain terminology that compresses information. So just some positive patterns here. You want to write what you want to see. And then of course the negative patterns. You know exactly what we want to add to this. Avoid words and phrases in this list. Okay. And then here we can just go crazy with all the obnoxious things that this model writes. We can do things like loadbearing. That's going to be at the top here. We can do things like worth stating plainly. Here's the honest truth, the [snorts] real tension. [laughter] And then there's carry the argument. There are a bunch of these. Put whichever ones here you want. A couple things I don't like. Personally, I don't like avoid analogies. Discuss what's right in front of us. Not a huge fan of analogies when working. I just want to, you know, focus on the thing right in our face. Of course, do not overuse em dashes or dash chaining. I think some of these are fine. My writing style personally has a decent amount of EM dashes. I have had to personally dial these back because I don't want people to think I'm just spamming out using models when I'm replying. I think it's really rude if it looks like you're just responding with AI with zero thought in specific circumstances. Do not flatter, praise, validate, or agree without reason. Again, no sick fancy. Just kind of re-emphasizing the idea. Uh, do not use decorative headings, emoji, or motivative language. Again, I just want this model to talk like a cracked senior engineer. Let's just solve the problem at hand. Avoid semicolons, fragments, non-standard punctuation. I just want a normal conversation. This is one really important section in your prompt engineering. You can use this when you're building plans. For the system prompt specifically, this has a lot of weight to it. We're explicitly talking with our model saying, "Replicate these things. Avoid these patterns." So, how else can we prompt engineer our system prompt for consistent results across every single execution? Before we do that, let's actually run this. Let's see how this is developing. Okay, so I'm going to go ahead and just flip this, place this here, go ahead and just rerun, trash this session, and let's run it again. So, now we have pause negative. All right, so let's rerun a new pair of cloud code again. On the left we have our smartass opus. No changes. And on the right we're going to have our smart senior concise engineering opus. Okay. So let's see how it does. Now right away you can see something very very cool. Very few dashes here. We do have a couple dashes showing up. I don't see loadbearing anywhere [laughter] which is good. I'll do a quick search on that in a second. But there's a bunch of these obnoxious keywords that are not getting referenced. 31 second execution. So we are speeding things up a bit. And by speed I mean it's outputting fewer tokens. So, we still have some dashes. We didn't say never use them. We said use fewer. The language is looking a bit clearer. We have clear headers. You know, no analogies. We do have a dash chain here, private by default. Maybe you're okay with that. Maybe you don't want to see that. That's up to you. You can prompt engineer that however you like. You can see here we're making some improvement. And we're starting to save on the output tokens. Even in your subscription, your output tokens still churn up the most of your usage. So, it's important to keep that in mind. We can do this at the system prompt level. Why? because we want to affect every single input and output from the agent. Your prompts in and its responses out. If you have something that you want to apply globally, that is the time to reach for the system prompt. So, let's keep moving. How else can we improve our communication patterns with our agents? So, number two, this is a really powerful one that I'm a huge fan of and I think you will be too. Check this out. We can use reference points. What is this? we use. And again, I'm talking to my agent, right? I'm talking directly to them. I'm not giving them a weird funky role. I'm just talking to them. These models are getting smarter. And even the lower class workhorse models, the A and B tier models that aren't your S tier state-of-the-art models, they respond really, really well to this additional direction. We use reference points to communicate quickly with each other. And now, what do I mean by that? What is a reference point? Use numbered lists and markdown headings when they improve navigation when presenting three or more findings. And this is stuff like uh decisions, options, risks, questions, or actions. Assign everyone a short code. Okay, so what does that mean? So use D1 for decisions. And what I'm really looking for here is D1, D2, and then DN. You can imagine this going on, right? So I'll do this and do a dot dot dot. And I'll skip over this in the video so you don't have to wait for me to type all this out. And then we do dot dot dot for continuation. When we don't have something here, I want it to invent new references for sections we don't have. Preserve same codes throughout the conversation. Do not create codes for short, simple answers. This is really cool. So you can see this in action right away, right? Let's go ahead and fire off our system and see how the system prompt materially impacts every single prompt. Because again, that's the scale of what we're dealing with here. The system prompt is the law for your agents. It affects every single task. Let's delete the previous session. Bam. And let's rerun. This is going to be our ref points. So now we have that new workspace. We're comparing side by side. And of course, smartass on the left and we have our senior opus slowly improving with each change we make. So you can see here we have the P's, we have the Rs coming up. And so we have risks there. And now our agent is even referencing one of the Rs R six. Okay, so now we can just jump to the reference. Our agent isn't repeating itself. It's not wasting time in tokens and it's repeating the risks here. And then if we scroll back up to the PS, I assume these are the promises that Meta says it's going to actually make. What is Meta saying it's going to ship? It doesn't really matter what the P stands for. The fact is that we can reference them now instantly. Okay, so for instance, like I can continue the prompt here, right? So, if I said talk more about R6, and of course, my agent knows exactly what I'm talking about when I say R six because we've created this quick language together. This is risk number six in this section, existential RSI, self-improvement risk that Zuckerberg is talking about here. And our agent is just going to kind of continue breaking things down here, right? What R six actually says, the section claims and sequence. There it goes. And it's going to break down that section more. We can jump into it. And you can see here we have another reference here. We have FS. These are findings. That's exactly what we've encoded here. Fs for findings. And so our agent is just using our reference system. And again, like the key I really want to communicate here with you for your engineering work is like um great communication is great engineering and vice versa. Knowing how to properly communicate with your technology now more than ever is a massive advantage you can use. It's so underutilized and it's so powerful. You can see here we're starting to improve things. We're adding layer by layer. Let's add some more really important [music] layers. What is the third improvement to our system? Let's go ahead and dial into this. Hard operational boundaries. Okay, one of the very annoying things with some of these state-of-the-art models is that they will do things that they have not been requested. This is part of their reinforcement learning. This is part of the loops that they're going through when these models are being trained where they're just taught to find the answer at all costs, no matter what it takes. Opus 5 is like really guilty of this. Comment down below. Let me know if you've experienced this. Opus 5 will just find and call out and reference problems you did not even ask for remotely and it's trying to pull everything together. It's trying to do as much as possible. But in that um it actually loses focus pretty quickly. So hard operational boundaries solve that problem. Let's write that out. So in addition to clearly communicating, it's important that we clearly communicate our work operational boundaries. What do I mean by that? So deliver only what was requested at the intended scope. That single sentence really is this right? But we can add more detail. Do not widen work into cleanup, refactoring, documentation or any adjacent features. Just stay focused. That's what I'm saying here. Stay focused. Do not speculate on abstractions for future requirements. Do not claim completion without evidence. Very, very important. These models are really good at that. Now, this is not a great line to add, but going to add it anyway. And then here's another one. So annoying when these models do this. Never add a co-author to a commit message. Like nice marketing trick for Enthropic, but they got to stop doing that for completed work. Concisely restate, but do not overload with response detail. There's a better way to say that, but you kind of get what I'm saying. Opus 5 specifically will respond with everything it's done and kind of like do a big recap. Don't really want that. And let's go ahead and stack another one on here. This is a really powerful one that I think is very useful and it really starts veering into the fact that the system prompt can help you control your inputs and your outputs. This one is aliases. So, let me break this down. Aliases are reminders of great communication and patterns we want to uphold. When you see these exact aliases, expand them and act as if their expansions were given to you directly. And then we want to clear this up to make sure we're being very clear. If these are referenced in a longer string, they are not aliases. Do not expand. Okay, so these are exactly what you think they are. They're short codes. They're bash aliases. They're expansions. They're commands inside your system prompt. And if you want to, these can reference your commands, your skills, and other things you have. But you can also just write them in line like this. STR equals simplify, compress, and repeat your response. Let's write a couple more and then we'll demo these. Okay, so Eli, you already know what this is going to be. Explain this like I'm We don't want to do five. Explain this like I'm 18. Simplify your language. Shorten your response. What else? Uh, one of my favorites. Focus on what matters most here. What's the true signal? What's the true value? Boil your response down into the most important thing we need to focus on. And then ref. Let's just do one more here to reference our previous work. Cuz once you start building up your system prompt, you can reference other sections. So I'm going to say I'll rewrite your responses with reference points, which is exactly what we dialed in above. So now let's try our improved enhanced system prompt. Same deal. Open the terminal. kill this session with our aliases. So, we're going to compare, boot these up, and now our agent has a little bit more guidance into exactly what it's going to do. Okay, so you can see there we have C123. That's our core thesis. We have our risks addressed. This is a much better generation really using all of our reference points. Very nice. And done in 30 seconds, done in 30 seconds on the left. So, you know, sometimes the generations are just fast. These are nondeterministic systems, so they're not always going to be generated faster. And in fact, we can improve that, which we'll do in a second here. But remember, we just added those aliases. So, let's run scr simplify, compress, and repeat. This is effectively a micro scale you're giving your agent. So, there we go. It's compressing that. It's simplifying it, and it's repeating it. So much more simplified. I can act on this information a lot faster. Even if our agent responds with a lot, which again we'll tweak in a second, we can improve on that. So, let's run another right focus. This is focus on what matters the most here. here. So our agent sees that and it's going to do it. Now this should be a lot more concise. There we go. The signal alignment is being redefined from the model holds the lab's value to the agent holds yours. Everything else is downstream of that. This is a speaking pattern from Opus that you might want to get rid of. Totally up to you. We have why it matters and then pulling some memory stuff here. And then we have our final paragraph there. Right. A couple things I want to do here. And once you like set up this foundation inside of your system prompt, you can really start to control it and fine-tune it. These patterns and these sections are really important because we can now collapse and we can jump into anyone we need to very very quickly by hand. We are controlling the experience of our agent. Again, I really want to stress this point. The more multiplicative the thing you're working on is going to be for the rest of your work, the more you should stop, slow down. Don't just throw a prompt at this. Even if it's a state-of-the-art model, as you can see, the state-of-the-art models have problems. They might accomplish your goal, but they'll add or do a bunch of other We're being really, really clear here. We're doing this by hand. We're slowing down. I know a lot of people are using Whisper Flow. They're just talking in to their device. There is going to be a level of detail that is missed, and you're going to be very exposed and very leaning on the normal distribution of what the models can do, and you're going to be inside that zone. If you're just viro prompting things, there's a time and place for that, but the high leverage places is when you want to slow down and step in to the loop. And I mean old school, hands-on. So anyway, let's look at this. So I want to do some positive. Where do I want to put this? This is definitely going to be positive. And I want to say if you can communicate the idea in one paragraph instead of two, do so. And I want to say without losing valuable information, do so. Same idea for one sentence versus two sentences. So I'm just saying be a little bit more compact, be a little bit more concise. This is important. Do not repeat yourself. State every idea once. Only repeat if it's relevant to subsequent queries. Yeah, something like that. Because we really don't want repetition. And then I want to add another positive pattern here. Don't use overloaded terms that could mean more than one thing. Use the simplest word that satisfies the idea you're trying to communicate or I should say simplest words and maybe PN that. You might be like, "Wow, you're getting really detailed here." And yeah, that's exactly right. I am getting really detailed here. And we can see the results of that. So, let's boot this up again. And this is going to be uh fine-tuned. And now let's go ahead and see what this execution looks like. And to be clear here, we are using append system prompt, not overwrite system prompt. But let's see how uh this execution runs now. Fewer dashes, no loadbearing. We have our reference points policy ask again with reference points. And our sentences are simpler. They're more concise. They're not getting overloaded where the argument is the weakest. Still outputting a lot here. This is a pretty big one. 35 seconds. So, not great, but it is going to be better than this generation on the left. Again, these are nondeterministic systems, so it's not going to be consistent. 43 versus 35. Not bad. We could probably do better in our compression. If this happened though, in this specific case, we can just throw an STR at this to get a simplify, compress, repeat, or we can do an ELI. Throw an Eli at it. explain this like I'm 18 and there you can see the language is really going to get boiled down and sometimes this is just useful right it doesn't mean you're an idiot if you want simpler concise language it means you're effective but you can see here even still the agent is saying a bunch of stuff so I might run a str after this simplify compress and repeat so as we start stacking up the user prompts in between every execution of a user prompt the system prompt is still going to be ever present so you can see argument really broken down here AI is safest when everyone has it not when a Few model labs control it. Other labs say AI is dangerous. Lock it down. He says when just a few people have it with enormous power, that's never gone well. One safe AI fails because people want different things. Any single AI has to pick whose values win. He's got a lot of really great arguments in here. Highly recommend you read and then compress if you need to uh the kind of core ideas coming out of Zuck's argument here. With these like uh CEO posts, it's always like understand the incentives. when you're not leading the AI race, of course, you would have a narrative something like this that you would want to share. I agree with him, frankly, on a lot of this stuff, but it is very, let's say, positional of him to put out something like this. That's not the focus of this video, though. I'll link that in the description for you if you haven't seen that yet. One more thing I want to show you that can really, really dial in the performance of your models, and it's a prompt engineering technique as old as time. Again, if someone tells you prompt engineering is dead, don't listen to them. The PI coding agent has a small system prompt. Cloud Code just got rid of a lot of their system prompt, that is not the signal to not use a system prompt. That is the wrong signal. They're doing that because the models can just do a bunch of stuff without needing it. If you want the model to do specific things, to perform in specific ways, to not torch your output tokens, to have cool unique capability like aliases or reference points or hard operational boundaries or patterns that you want to see and words and concepts you don't want to see, you must system prompt engineer. The system prompt is the most effective place to prompt engineer because it's applied across everyone of your prompts going into your agent and the responses coming out of your agents. Okay, so one more section here for you. Check this out. Examples to kind of put it all together. Here are concrete examples of how we do and do not communicate together. Replicate how we do communicate and avoid how we do not communicate. a lot like our positive and negative prompting section except the main differentiator here is we're giving real examples. So, it's just like training data. User is legacy JSON still referenced. To-do, no, the only match is the file itself. There are no imports, blah blah blah blah blah or documentation links. Very good. Um, we could also, you know, make this tighter. The only match of the file is itself. We could make this the to-do. And then here's what not to do. Great question. I will research the repository and blah blah blah blah blah blah. Just do the work. Respond concisely. Right. And then here's another one. Engineering recommendation. And it looks like I'm missing my user prompt here. So, I'm just going to add that here. User. Should we add reddus to this system? Write something just like this to-do. Do not add reddus here. This process has one writer, stores from SQL, and has no cross host coordination requirement. Reddus adds a failure domain without solving the correct constraint, the current constraint. What not to do? You are absolutely right. Blah blah blah blah blah. Again, just a really simple prompt engineering technique that is older than time at this point. Um, I was writing these examples back when GPT 3.5 was the model, GPT4, when there was really no anthropic. This is still relevant today. And that's how you know something is a valuable skill to learn when years ago it was still relevant. These concepts, these patterns, these principles, great engineering is great communicating. These things just kind of don't go away. That's how you know you have something valuable to hold on to. Now, we can continue doing this. And kind of one big idea, one Easter egg for whoever's still watching, engineers that are really trying to absorb this value. One thing I want to mention here is that you can boot up a model. Let's say you want to boot up a cloud fable model and you want to run that same prompt that we're running. So let me just go ahead and pull that explain this. Copy paste. And so Fable 5 I think is a great model. I think it responds well. It doesn't have as many ticks and problems and repeated patterns and verbosity as Opus 5 has. So what I like to do is in context distillation. In context distillation is exactly what you think. That's what examples are, right? It's in context distillation. Do this, don't do that. And you can do this by pulling out specific context, specific responses from models that you like. For instance, like this oneline core thesis here. Maybe we like this. We just copy this out and we create another one here. Summarizing a blog. And then user is summarize the blog. and then XYZ. We just template this out. And then we have our to-do text. And then we can paste the response from the model that we like. And we can go even further. We can tweak the response from the model that we like. Get rid of a couple of lines. Make sure it's formatted properly. So we can do a dash search to get rid of all of our dashes. All of our framing. Maybe we like that one. We keep that one in. Super intelligence. Super intelligence. Super intelligence. This should be more concise and compact like this. So maybe we like this version. Then we say not to do text. Text. We hop back over to the previous version, right? We go to our smartass opus and we copy all this. This is not what we want to see. So, just a bunch of extra information and we want to see a concise, simple response. Really just get to it. And really, I would want to see something like this, a little bit more broken up, easier to read. Even Fable has some long kind of run-on sentences here. Might break this up a little bit more into a couple section. And then we would fire this off. We have a new version here. Let's go ahead and run this. It's super correct senior opus. And then we can run our comparison here. So now our agent has examples of how we like to communicate. It has reference points. We have cleaned up the language. We taught it to respond concisely with fewer em dashes. It's not loadbearing anymore. We have reference points. This one does not have reference points. So thankfully we've encoded a way to add those. So we could just type ref. And now our short aliases are going to expand the response. There you go. You can see we now have references. You'll notice this response from Smartass took 41 seconds. Our original response over here took just 22 seconds. So, we are saving our output tokens. And again, if you need to spend output tokens, spend them. But in this case, you know, when we're uh summarizing things where we're just getting concise responses, we don't want all of that. You know, you can see here we have a nice response. We use our ref alias, which we have encoded into the system prompt. If we just search for ref equals, you can see exactly what this expands into. You could point this to a scale. You could point this to a command. Whatever you want to do here, you can do. My whole point here is to communicate with you that if you really want to control your agent and get the best results, you must know how to use your tool. A lot of engineers are overleveraged on the user prompt and you get literally less leverage. You get less output by fixating on the user prompt. Opus 5 and all models coming out, they are going to be smart asses. Quite literally smart. What we want to do is keep the smart but drop the ass. [laughter] With system prompts, you can do that across every user prompt you write. You can use these exact prompt engineering patterns to guide Opus 5 and whichever state of the art models come next. If we don't like this response, we can shoot up another alias at it. Eli, explain it like I'm 18 or whatever age you want to set your uh response reasoning level to. You can add an Eli 5, you can add an Eli 10, whatever you want to do. Great engineering is about great communicating, right? great communication with your team of engineers, with your agents, and most importantly with yourself. And this is how you can tap into that. You've noticed this if you're using agents on a daily basis for many tasks. You and I, the developer, are the bottleneck. It's not the model. It's not the tools. It's not anything else. The hard part now is communicating quickly, concisely, and in the most value accreative way. I'll leave this cracked senior engineer prompt linked in the description for you. It's super super simple. You can do the comparison side by side and see yourself improving every time you add something by running the just compare command. I'll also add my classic readme and just install and all the quick start so that you don't have to waste your time doing any setup and configuration. I want this to be as simple and quick and easy for you to use. On the channel, we've been focused on much higher levels of abstraction on top of agents like software factories, agent sandboxes. Uh check out last week's video if you want to dive into some really advanced agentic engineering concepts. I'll link that in the description as well. But every once in a while, it's going to be really important for us to step back into the fundamentals. You don't want to miss the atomic units, the primitives that make up the larger powerful composition pieces because if you stack a bunch of agents together and you burn a bunch of tokens doing in half the time by just putting together the right system prompt, you want to be doing that instead. Don't waste time using really, really clever solutions when there's a much simpler solution. And engineering is about getting the job done, not looking cool, not doing what everyone else is [music] doing, not listening to the idiots that say prompt engineering is dead. That returns false. Your agent is just another tool. If you understand your tools, [music] you'll understand the results you can get from your tools. Use your system prompt as the law for every [music] task you send to your agents and use your user prompt for individual tasks. If you got value out of this video and you made it to the end, like, subscribe, drop a comment, and let me [music] know how you're leveraging your system prompt to get outsized results and to clean up some of the insane verbosity from models like Opus 5. You know where to find me every single Monday. Stay focused and keep building.
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Most people spend hours watching tutorials and taking notes, only to forget most of what they learned by the next day. That's because consuming information isn't the same as actually learning it. But Claude can dramatically speed up the way you learn by turning any learning material into an interactive learning system that helps you understand, remember, and apply what you learn. By the end of this video, you'll have a complete workflow you can use to learn any skill. We begin by creating a separate Claude project for learning. I also add a short description so the purpose of the project stays clear. A normal chat can answer one question. This project keeps the teaching rules and course materials together. The first mistake is starting with a vague request. It doesn't know my level, my goal, or my deadline. It can't tell whether I need exam prep or a working skill, or whether I learn faster from examples than from questions. So, I open the project instructions and explain how I want Claude to teach. This prompt can controls the whole lesson instead of one answer. First, Claude checks what I already understand. Then it chooses an explanation that matches my level. The next rule stops the finished solution from appearing immediately. That matters because copying an answer isn't the same as learning the method. The rule about mistakes is just as useful. A good tutor explains where your thinking went wrong. It doesn't simply delete your answer and replace it. The final rule gives us something useful for the next session. At the end, the project records what worked and what still needs practice. These instructions can also change for different subjects. In language practice, the tutor corrects mistakes and brings new vocabulary back. On a coding lesson, it asks for my explanation first, then shows the fix. The goal is deciding which parts Claude handles. The learner should still do the thinking that builds the skill. I test the setup with one simple topic. The first reply asks what I already know about prices and demand. The next question covers why I need the topic. I explain that I want to understand the idea and use it inside Excel. One final question checks how much study time is available. I give it 7 days with 30 minutes each day. Claude confirms that budget before it builds a single lesson. That small check keeps the plan realistic because the schedule starts with I actually have. That totals 3 and 1/2 hours focus study across the week. Instead of writing a huge course, Claude builds a short path around that goal. The first lesson uses one simple example. Day one covers the basic price and demand relationship with round numbers. Day three moves into the elasticity idea behind that relationship. Day six ties everything back into the spreadsheet work I actually need. Later lessons at practice and a final check. Day seven ends with a short revenue exercise inside my own spreadsheet. The schedule isn't locked after day one. A rough first exercise can push the spreadsheet task back while an easy session moves the practical work forward. Claude adjust the order around what actually happens instead of pretending the first plan was perfect. That's the first real difference between a chatbot and a tutor. A chatbot answers the question you type. A tutor changes the lesson around what you need. All right, let's park the study agent for a minute because we're staying inside Claude anyway. I do all my thinking in Claude, but every serious deck hit the same wall. The charts and tables never looked presentable. Quick heads-up, this part is sponsored by Perceptus. It's a deck platform that plugs right into Claude. And honestly, I use it almost every day. Claude is great for the outline and the research, but it falls short on real charts and financial breakdowns. So, you export it and rebuild everything by hand in PowerPoint. That easily eats an hour of your day. Perceptus kills that step with a connector right inside Claude. It works from any chat, so whatever you research turns into business grade slides. So, I asked it for something I actually needed. I typed, "Build me a 12-slide deck on my content roadmap." Seconds later, I had clean slides with my messy notes sorted into real sections and titles. It even added a chart for the growth numbers, and the whole PowerPoint file stays fully editable. That's the part one really love. Perceptus doesn't just design the slides, it structures the whole deck for you. So, if you're still fixing Claude's decks by hand, this is the fix. And my timing here is kind of lucky because they just shipped a new model called Perceptus 2.0. Design Arena already ranked it number one in its category, ahead of tools like Gamma, and even the Claude models. There's a link in the description if you want to try it. Okay, let's jump back into Claude and keep building our study agent. The next step is giving Claude something real to work with. For this example, I've got a short marketing guide, a page of audience notes, and a sample campaign task. All three files cover the same product, but they don't tell the same story. The guide explains how to choose an audience and build one clear message. My notes focus on freelancers managing several client projects. The campaign task targets small creative teams launching a shared workspace. Most people would ask Claude to summarize everything. That would only create another document I still need [music] to study. A better first step is asking Claude to organize the material around the task. The source map pulls out only the sections I need. Out of nine guide sections, only four end up on the map. It links audience segmentation to the brief [music] and value proposition to the core message. It also shows that the call to action must support a free trial. Claude even quotes the exact line from the brief that mentions the trial. Then Claude catches the conflict between the files. My notes describe freelancers while the brief targets small creative teams. That's a real conflict, not just two different writing styles for the same audience. That matters because the strongest message changes with the audience. For freelancers, the guide suggests protect and focus time. For small teams, it suggests keeping shared work visible. Claude also flags what the files cannot answer yet. The final offer, launch date, and proof points are still missing. Claude lists all three gaps as open questions instead of guessing a launch date or a price point. The useful part is how quickly I can check the map later. I don't need to re-open every file whenever a question comes up. That gives me a clear steady order before I make any campaign decisions. It also keeps the lesson tied to a real task instead of another generic summary. Next, I decide which source Claude should trust for each answer. Now Claude knows which source should guide each decision. The brief defines the audience and the campaign goal. The guide provides the method while my notes add useful context. A personal note can quietly override the actual task. Once that source order is clear, every later answer becomes easier to check. I can trace a recommendation back to the brief, the guide, or my notes. When Claude has no source, the gap stays visible instead of turning into a confident guess. This is more useful than a general summary. Claude shows what matters and where it comes from. I can scan the map in seconds and know exactly which file supports each decision. That same setup works with a professional course or internal documentation. Start with the files for one skill or one project. When two sources disagree, decide which one Claude should trust. When a detail is missing, make Claude flag it instead of filling the gap. I pinned that rule into the project instructions, so every future chat follows it. That's what makes practical learning more useful than collecting random information. Inside AI Master, the academy turns that approach into structured lessons. It includes over 200 lessons across roughly 30 hours of practical training. You can test each workflow immediately on your own project. AI Master also puts leading language models inside one workspace. That makes model comparison easier and keeps the workflow in one place. Generation also uses lower token costs than many separate subscriptions. Text models are only one part of the platform. You can generate images and voiceovers inside the same workspace. The platform also supports video generation at lower token costs. The character tools keep one hero visually consistent across different generations. You can publish that character directly on the platform. You earn whenever other people use your published character. Generated content can also be shared with the wider community. Sharing helps that content reach more people. The platform also runs a full content factory. You build your own channel inside it. And AI agents plan the schedule, write ready to record scripts, fill in the descriptions, and generate the thumbnails. More than 12,000 people already use AI Master. The community helps users find feedback and new connections. It also helps creators meet collaborators and build an audience. The landing page shows real cases from current users. The demo video includes real reactions from people using the platform. Wow, thank you so much AI Master Pro. Big shout out, it helped me so much. I love this service and I'm enjoying using it. Getting access takes only a few clear steps. Use the link below and open the selling landing page. Click buy and choose the annual subscription. The annual option includes the strongest available discount. The purchase includes money-back guarantee. After checkout, open the confirmation email and you're in. Okay, back to the learning agent. Now, I want Claude to teach one idea without giving me another long lecture. A shop sells 100 items for $20 each. The owner raises the price to $25. Monthly sales then fall to 70 items. Price multiplied by units sold gives $2,000. The next question moves to the new revenue. My first instinct is to focus only on the higher price. Each sale is now worth $5 more, so earning more sounds reasonable. Instead of giving the answer, Claude points out the missing sales volume. Using both new values gives $1,750. [music] Claude makes me type the multiplication myself before it confirms the number. Revenue actually fell by $250. Follow-up question asks why that happened. The extra $5 didn't cover the 30 lost sales. That explanation is more useful than memorizing a textbook definition. The lesson changes only one number. Instead of 70 sales, the shop now sells 90 items. That produces $2,250. This time, the higher price does increase revenue because the price stays at $25, the comparison is clean. Claude isn't giving me another random example. It changes one variable so I can see exactly what caused the result. That checks whether the idea transfers to new situation. It's not just another explanation with different wording. The final question asks whether lower revenue always means lower profit. We still need information about costs before making that claim. There is no need to turn this into another finance lesson. The question simply checks whether I notice missing information. This teaching style works far beyond economics, SQL, a new language, a certification you're cramming for. The same basic pattern stays in place. You get a question built in what you already know and you have to attempt it before any hint shows up. You can also change the difficulty during the lesson. When the questions feel easy, ask Claude to remove the hints. I try that here and the next question arrives with no hint at all. When the lesson moves too quickly, ask for a smaller example. You don't need to restart the whole conversation. The next step can change while the learning goal stays the same. The learner still has to reach the answer. That also makes the pace feel more natural. Claude can slow down when I'm missing a step then move faster once the pattern is clear. I'm not locked into one teaching speed for the whole topic. Now I want to use the same idea inside a real spreadsheet. The first task is calculating revenue for every order. I could ask Claude to fill the whole column immediately. That would finish the spreadsheet but it wouldn't teach me much. Instead, Claude should guide my first attempt. Claude first asks which values create revenue. Each row already contains the price and number of units. I enter a simple formula in the first revenue cell. The formula multiplies price by units for the current row. When I copy it down, both row references move correctly. I spot check row 11 and the formula correctly shows E11 * F11. The next task [music] compares each revenue value with the target in K2. I write an IF formula using the revenue cell and K2. The first result looks correct, but the problem appears after I copy the formula. The revenue reference should move because every row uses a different value, while the target should stay in K2 because every row uses the same rule. Claude doesn't replace the formula immediately. It asks which reference should remain fixed. That question points me toward an absolute reference. Now the revenue reference moves, while the target stays locked. I copy that corrected formula down the entire order list. Every row still points back to the same target cell in K2. I checked 11 rows and the reference held steady each time. The important part isn't memorizing where the dollar signs go. I need to understand which value changes and which one stays fixed. Next, I ask for a task that combines several conditions. Claude begins by asking which column contains the values I want to add. >> [music] >> The revenue column becomes the sum range. Then I choose the region column and the selected region. The final conditions define the start and end of the month. The first day condition includes the first day, while the second stops before the next month begins. Using the next month as the boundary avoids guessing the final calendar day. >> [music] >> It also works when different months have different lengths. The same boundary trick still works if I check quarterly totals instead of monthly ones. Claude then changes the region and month. I have to update the inputs without rebuilding the formula. It also asks me to predict the new total before the sheet shows it. I switch the criteria to the West region and the month to June. The total updates immediately without touching a single formula reference. The spreadsheet is still doing the calculation. Claude is helping me understand the decisions behind it. This is where the difference between help and automation becomes clear. Claude can explain why a formula breaks, but I still choose the reference and test it. The spreadsheet stays useful because I understand what changed instead of trusting a finished result I never built. That is the balance I want from a learning assistant. It should support the work without removing the part I need to practice. After a lesson, most people create notes that repeat the original material. That wastes time because the easy parts get just as much attention. A useful review should focus on the moments where your thinking broke down. Claude can see which answers I changed, where I needed a hint, and which ideas I still couldn't explain clearly. >> [music] >> It can use that history to build a much more focused revision guide. The guide spends most of its space on the areas where I struggled. In this session, [music] one section returns to the conflict between two audience sources. It reminds me which file should guide the final decision. Then it gives me a new example where two sources disagree. This time the conflict sits between a pricing sheet and an old sales deck. But the same review system changes completely with another skill. For language practice, Claude can reuse vocabulary inside a short dialogue. It can ask me to complete the next line before showing a correction. A later card can bring back the same phrase in a different situation. For coding, Claude can show me a small function. Then it can ask what that function returns. Another card can hide one mistake inside the code. Instead of giving the fix, Claude asks me to explain which line caused the problem. That's much more useful than reading the same explanation again. The format changes, but Claude still follows the skills I need to practice. This only works when I make a real attempt first. If I copy every answer, Claude has no useful mistakes to work with. They questions give you generic notes, and passive reading does the same. I still need to explain the idea and test my own answer. The active recall questions now become interactive flashcards. One card asks me to explain an idea in my own words. Another asks me to predict what happens next. A different card asks me to find and correct a mistake. >> [music] >> One useful detail is that Claude doesn't need my answer to match its wording. I can explain the idea in plain English, and it checks the reasoning instead. That makes the cards feel more like practice and less like memorizing a definition. Cards answered correctly with high confidence disappear from the session. The card about the audience conflict stays in rotation for two more rounds, and I only stop seeing it once I answer with high confidence. Weaker cards return later with a different example. The goal isn't memorizing the sentence Claude wrote. It's building a review loop around mistakes I actually make. Reviewing those mistakes helps, but it doesn't prove I can use the skill. A short quiz puts that to the test in a new situation. The goal isn't collecting more questions inside the chat. I want to see whether the same ideas still work in a new situation. The Claude artifact gives that test its own interactive space, but the artifact is only the format. What matters is how Claude arranges the questions. I used the same three marketing files and the work from this session, giving Claude real material for every question. The first questions check ideas I should already remember. Then the quiz moves into new campaign situations. One question asks whether a message pulled from audience notes should be used for the current campaign. I choose it because the message sounds well-formed. Claude marks the answer wrong and explains that the notes are exploratory and target freelancers, while the approved brief targets small creative teams. The feedback stays short enough to keep the quiz moving, and then it goes straight to the next question. The next campaign example asks me to choose the strongest message for a launch team whose work is scattered across chats, documents, and trackers. I have to connect that specific problem to the message instead of choosing generic productivity copy. Another question shows a colleague drafting subject lines and choosing a send time before the audience, message, and offer are locked. I need to identify why that sequence creates polished work aimed at the wrong target. The next question asks how many days the free trial lasts. None of the uploaded files includes that detail, so the correct answer is that the available sources can answer it. That checks whether I can spot a real gap instead of guessing. Another scenario asks whether I should invent a customer testimonial. I choose the wrong answer and Claude reminds me that testimonials need approved evidence. The same setup also works for certification prep, where Claude can turn course rules into short workplace questions. After the final question, the quiz shows my score. I finish with six correct answers out of eight questions. The two misses are the source authority question and the testimonial scenario. Six correct answers out of eight puts the score at 75% and it also shows which two skills need more practice. Those results give Claude a clear starting point for the next session. A new quiz can keep the scale but change the situation. Claude keeps my two weak skills, but writes a completely different campaign scenario. The score itself isn't the useful part. What matters is where I still get stuck, because that shapes the next round of practice. I ask Claude to build that practice around my weak areas. The PDF doesn't repeat the whole lesson. It starts with the source mistake and the proof we still need, and each section gives me one correction and one quick check. The Doc X plan spreads those weak areas across seven short sessions. Each session still stays under 30 minutes, matching the original weekly plan, and later days bring them back inside new campaign situations. The final practice file depends on the skill. For language practice, it could include short speaking prompts. For coding, Claude could create debugging exercises. Here, it creates an Excel workbook, because Excel was the practical skill. One exercise checks a copied formula with a fixed reference. That's the same absolute reference formula I built earlier in this session. Another asks me to build a monthly total from a new table. The new table uses different column positions, so I can't just reuse my references. The workbook holds two Excel exercises, one built around absolute references and one around a fresh SUMIFS table. Claude leaves both exercise sheets unfinished. The answers stay on a separate sheet called instructor check. That gives me another real attempt before I see the solution. Finally, I save a short learning log for the next session. The log records what I can now do without help and tells Claude what should come back next time. When I return, Claude can start with the next useful question. It doesn't need to repeat the entire first lesson. Claude becomes much more useful when it understands your goal. It also needs to know which materials matter. Clear rules help it guide you without taking over the work. Start with the smallest version that solves a real problem. One project, the files you already have, one lesson. Every prompt from this video stays on screen so you can copy the setup. And AI Master Pro is still sitting there if you want the models, the agents, and the courses in one place. Your call. Like and subscribe to the channel. I'll see you in the next one.
What it actually becomes once you treat
it as more of an operator, right? Not
just a coding assistant or a chatbot.
It's called operating systems, that's
the vernacular of the AI space. I call
them command centers. So, we have like
20 command centers across the whole
business. Each one is like a subject
matter expert domain for that specific
thing. So, if I were to open my other
tab here, I have a tax [music] operating
system, I have a finance operating
system, I have a YouTube operating
system, I have a school operating
system. Every single part of the domain,
the business, even personal. I literally
have a an entire domain that helps me
get out of jail free card. And I have a
thing called {slash} date night that
will go through every single the top 10
most recent posts, go through all of
them, watch, score them on ambiance and
novelty, and send me a top three every
Friday. And it's really a matter of just
like digging and deciding what is the
goal of this operating system, how will
I maintain it, and how will I get
infinite leverage from it.
Chat GPT just looked at my schedule, my calendar, found the best opening, and added a 30-minute break so I can relax and recharge. All right, now we're talking. I can go out there. But seriously though, I didn't even open Google Calendar in this entire transaction, and these things were quite hard to achieve just a few weeks ago. You needed custom setups and da da da da da da. Now, Chat GPT is all you need, or Claude that is, okay? So, this is what plugins can essentially do inside of the new Chat GPT work, also in Claude connectors, but today we're looking at Chat GPT work as most people are using Chat GPT, and I want to open your third eye to what's possible with these AI tools because it's unbelievable. So, here's the deal at its core. A plugin can teach Chat GPT a specialized workflow and connect it to an outside service. So, you can basically be like, "Hey, you can see my Google Calendar now just like my assistant or or my phone or my own eyes when I open it, and you need to be inside of Chat GPT work to do this, okay?" So, once you have that plugin set up, you can well, schedule. It can help me make changes. It can hop between other applications. You just need to be inside of Chat GPT work for this, and I know many of you will notice already, but these capabilities, they've been getting better, and I wasn't a big fan of plugins, and now I kind of am. You tab over to Chat GPT work, then you go to plugins down here. You say connect plugins. You can see already Google Calendar here cuz I selected it, but if you go to connect plugins, if it's not up here, you should be able to find it. Yeah, worst case you go to browse all plugins, and then in here, well, very worst case I have to search for it. So, you know, calendar. There you go, Google Calendar. Click that. Click the three dots. Okay, mine is already installed, but you know what? For you, just cuz I love you so much, I will de-install this. Now, I will install again, and you should see something similar to this. Continue to Google Calendar. I select account with my main calendar, and then the beautiful thing is these permissions are only to access the calendar. It's not going to be looking at everything across Gmail, etc. That's a different plugin, but if you want to see it, you could click here and see what services you're giving to it. These are the six. All right, I'm good with that. I'm going to click continue. And there it is. Now we're connected. I could try it in chat, and the way you do that, well, now it's already preset, but the way you would get to this from a naked chat, is you need to use the add button. This is a bit different from Claude. So, just say add calendar. There it is. This is how you would access it, okay? And hey, if you're wondering what else you could do with ChatGPT your work, well, I put together a PDF with the top 10 prompts that you could try today within there. And you can have it completely for free right now. First link in the video description. All right, back to the video. So, now I could ask, "What does my schedule look like?" And for anybody who's been watching the channel for a while, you'll know I've been an avid hater of these connectors, these plugins, because they just weren't reliable. You ask this type of question, and it was like, "Well, you actually don't have anything going on today." Or well, there's two events today, when in reality there's three. And getting two out of three things is not good enough. They were not reliable, okay? Now, several things changed. The models changed. They reworked normal ChatGPT to ChatGPT work that thinks a bit more, is a bit more thoughtful. The MCP standards underneath also changed, although I'm not even sure if this one is updated to the new one. The point is there has been several improvements over time, where ChatGPT with uh Google Calendar login a year ago was bad, trash, unusable. I told you this straight up. I didn't used to recommend it. Now, it just gets it. It knows exactly where everything is. It can call multiple things. It used to be in a way that you could call one thing and get that reliably, but if you wanted multiple, mm, messy. Now, you could do something like using voice input. Okay, every time I'm in meetings for more than 2 hours, put in a 30-minute recharge block. Now, you'll notice that today's day has passed, but this is smart. This is ChatGPT work, and it thinks through the problem through a wider lens than you might have specified. That's how it is. It kind of plans for the problem and looks to resolve the entire category of work, not just this one instance. That's the product, okay? So, as you see, it first didn't just look at my day that has passed, but it set up a schedule task where it's like, "Okay, I'll review your Google Calendar for the upcoming 7 days, and whenever there's more than two consecutive busy meeting hours, I'll schedule a 30-minute busy calendar event titled to recharge, okay?" And it set the frequency to check every hour, which I think is totally overkill, but, you know, good starting point. And then, it just prompted itself, essentially, and continued and said, "Okay, recording block mastery da da da da da weekly ah." So, now it didn't change anything because I'm already beyond all of these meetings, but it set up a schedule task that should do this for my calendar. And this is the power of plugins, but with together with schedule tasking work, it's just unbelievable. Like, you could set up a few rules like this that just run your calendar. And let me show you, if I go here into scheduled, and then I would take this post meeting recharge one that just got created, and I say run now here under the three dots, there it is. It's going to do that for the entire week, and then I could change the frequency, right? I could say, "Hey, I want this to run once a week or once a day, maybe." There it is. Added them. And right there in my Thursday, we have the recharge block. These plugins work so well now that you should be thinking about your life in a new way now. Together with this ChatGPT work product or Claude co-work or whatever you might be using, you can set up a few rules like this for reoccurring problems that just solve this category of problem for $20 a month. And considering the fact that paid plans are roughly used by 1% of the working population on planet Earth, it's ridiculous to me that the edge you can get in terms of productivity and ease by eliminating all these annoying admin tasks, if you use this correctly, it's just an amazing opportunity, and I wanted to show you in this video. Nice and easy. And finally, I'll say, if you have a task yourself that you set up some schedule task for a plugin that you use all all time, leave a comment below. I really want to use which ones you use. Let's learn from each other. Let's share the love and share the knowledge. That's pretty much it. My name is Igor Pogany. I hope this was helpful to you and I will see you very soon.
قر س مختسن ب قاسر قزم قازر قازر Монда шар Miss ش Где лежат? اجي جم شرم Жи بج G ب А س Лдажал бетай ا برن قرب بز زم م او ماي قزم ماي قم قم Oh
If you've ever made an animated video
with AI, you already know the problem.
It looks like AI, faces change between
shots, hair moves on its own, the eyes
are empty, and viewers click off the
second they see it. Everything on your
screen right now is AI. Same scene, four
completely different looks, and none of
them have that problem because the
workflow I'm about to show you catches
everything that gives AI away before you
ever generate a frame. So, in this video
I'm walking you through the seven steps
that take you from a blank page to
scenes like these in minutes. Let's get
into it. Now, the most important step is
to generate the story, and here's the
one I am using for this build. It's
about a man called Victor Lustig who
conned scrap metal dealers and tried to
sell them the Eiffel Tower. All right,
so now the next step is to choose a
style based on the story we selected.
I'm going with faceted 3D because this
is a story about a man lying to people's
faces, so I need a face I can actually
watch him do it with. Now, picking the
right style took me the longest, so I
put everything I found into one place.
Let me walk you through it because we'll
be pulling from it for the rest of this
video. So, this is the style library I
built for you, 28 styles, and each one
is tagged with the niche it actually
works in. So, if you know your niche,
start there and it'll show you what
fits. And if you already know the look
you want, go straight to it. Now, open
any style and you get three prompts. One
makes your character sheet, one makes
your storyboard, one makes your video.
They run in order, so whatever comes out
of the first image gets used to generate
the next. Inside each of them, there's
one section marked as the only part you
write. That's your story, a paragraph or
two. Everything around it stays exactly
as it is. And if you want to take it
further, copy the prompt into Claude or
ChatGPT with your story attached to it
and ask it to merge the two. It will
generate refined prompts for you. Then
it is just copy and paste. That's what I
will be doing for storyboard and video
prompt. Real quick, if you want this
style library with all the prompts,
comment the word styles and I'll send it
over. So, let's go ahead and start with
the character sheet. I'll build it in
this first style, faceted 3D painterly,
then show you the same sheet in a
completely different style. So, you can
see this work with whatever you choose.
For this tutorial, I'll be using Design
AI and you can use the link to sign up
and follow along. Once you come into the
platform, the left side gives you home,
projects, asset and result and your AI
tools and the middle is just a chat box.
Go ahead and copy the character sheet
prompt, drop it in here, then scroll
down and paste your story into that one
section we just talked about. Now, the
settings. I'm switching over to GPT
image 2. 0 {comma} 16 by 9, 2K and
quality on medium and watch this. The
second I switch the model, the character
limit drops and my story is too long to
fit. So, I'm cutting it back to the
three sentences that carry the plot and
that fits. Let's go ahead and generate
and we'll wait for it to finish and
there it is. Now, look at what you
actually got because this is a lot more
than a character. Every person in the
story has their own block with different
angles and expressions and your
locations are built out along the
bottom. So, that's one generation and
the whole world of your video is already
decided. And now, before we move on, let
me show you one more thing. So, I'm
going back into the pack, opening
claymation and copying its character
sheet prompt. Same story, same settings.
The only thing that changes is the style
block. Drop your story in and hit
generate and there you go. Same story,
same characters, completely different
world. That's the whole point of the
library. Every style has its own version
of this prompt. So, whichever one you
pick, this still works. I'm going to
delete this one though because we're
staying with faceted 3D for the rest of
this build. So, the storyboard is next
and this is where that sheet starts
doing its actual job. Click on chat
editor on the character sheet you just
made and drop in the second prompt. Now,
this is where people mess it up, so stay
with me. You'll see image one written in
a few spots inside the prompt. That's a
live [music] link to your image and you
have to point every one of them at your
character sheet. Miss even one and the
model stops looking at your sheet and
starts making the face up, which is
exactly how you end up with a different
guy in panel four. So, let me just
quickly tag all of them and generate and
look at [music] that. Six panels, three
across and two down and every face in
there is the same face of the sheet.
Your whole scene is mapped out and you
haven't made a single second of video
yet, but you've now got two references
sitting in your project and only one of
them is going to give you the better
video. All right, the next step is to
generate our video. I will be using the
latest Students 2.5 model. Now, Students
2.5 can generate your video from either
the character sheet or the storyboard.
You can attach both, but it does
sometimes mix them up. So, pick one and
I'm going to run it both ways so you can
see which one actually comes out better.
Now, go ahead and click into AI video
and set it to reference. Model is
Students 2.5, 16 by 9, 720p, but can go
up to 1080p
and pull the duration all the way up to
30 seconds. Then, paste in prompt three,
the clip prompt, and the same rule
carries over. So, tag your image
everywhere it's required. One thing
you'll notice inside this prompt is that
it blocks speech completely. Ambience
and sound effects only. Zero human voice
for the full 30 seconds. That's
deliberate and I'll come back to it in
the voice step. For this first run, I'm
attaching the storyboard. Let's see what
happens.
>> [snorts]
>> 30 seconds, one generation, one
continuous shot. The camera moves, the
light holds, the faces hold. That's six
clips of work coming out in a single
take. Now I'm running the same prompt
with the character sheet attached.
And here's the difference. Both came out
strong, but the character sheet version
gave me lip movement I never asked for
and the storyboard came back cleaner,
which makes sense because the storyboard
had already told it what happens in
every beat, so there was less left to
guess at. So, the storyboard is what you
animate from and the character sheet
stays as the thing that built it. Go
ahead and click download. That's your
video, but it's still silent. And
silence is where most of these builds
quietly fall apart. So, let's generate
the voiceover. I'm using 11 Labs for
this. So, once you are into the platform
at the left side, click into
text-to-speech and paste your script
into the box. I'm using Roger's voice
for this one. Now, remember the clip
prompt blocking speech? This is why. You
already saw it happen back in the
character sheet version. The mouth moved
on its own. Students can do lip sync.
And it does it well when you actually
ask for it. But, one mouth landing
slightly off is the single thing anyone
remembers out of a 10-minute video. And
the animated history channels that
actually work are narrated anyway. Night
Shift, Bible Project, Yarnhub, all of
them run narration as the spine and let
the visuals carry the acting. Hit
generate and it hands you two takes. I
like the first one, so let's go ahead
and download that. All right. The last
step is to stitch everything together
and it's the easy one. Open CapCut,
click import, and bring in both files,
your video and your voiceover. Drop the
video onto the timeline, drop the audio
on the track underneath it, and line the
two of them up against each other. And
that is done. So, that's every step
stacked into one one piece. Let's let's
play it back.
>> In 1925, Victor Lustig walked into a
Paris hotel and told a room full of
scrap metal dealers something
unbelievable.
The government was secretly selling the
Eiffel Tower.
He convinced them he was an official,
convinced one dealer to pay him, and
walked away with the money.
Then, Lustig did something even crazier.
He came back and ran the exact same scam
again.
>> So, that's the whole thing, story,
style, character sheet, storyboard,
animation, voice, assembly. And the
reason it's seven steps and not one
prompt is that these problems don't all
come from the same place. I spent a long
time trying to fix all of it inside the
prompt, and it just doesn't work that
way. So, everything you just watched was
built inside Design AI, who sponsored
this video. Sedans 2.5 sits in there
alongside everything else I used. So,
the link is in the description if you
want to build this one alongside me. And
if you want the style library, every
style I've tested with all the prompts,
comment the word styles and I'll send it
over. The thing I do next, if I were
you, is pick your story before you pick
anything else. It's the least exciting
step, and it's the one that decides
everything after it. I'll see you in the
next one.
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If you use Suno, you got an email this week. And if you skimmed it, I'd go back and read it properly because that completely [music] changes what you're actually allowed to do with your Suno songs. Starting September 3rd, Suno is putting a monthly limit on how many songs you can download. Here's how it breaks down. On the free plan of Suno, you get seven downloads. No, not seven a month, seven total for lifetime. And they're for personal [music] use only. On Pro, it's 20 a month. On Premiere, 60 a month. And if you're working inside Suno Studio, those don't count against you. And no, unused downloads don't roll over to the month. [music] Yeah, that sounds pretty strict. And here's the part I'd underline. Under the new terms, >> [music] >> your commercial rights are tied to those downloads. You can only use a song commercially if you've downloaded it properly under your plan's allowance. Now, if you're someone who's making music just for fun, you might not need to touch that ceiling. But if you're making music for scoring your YouTube videos, taking client work, or building a catalog, 20 finished tracks per month can be pretty tight. There's more in the update. Suno's now watermarking and fingerprinting [music] generated audio, so tracks can be identified once they leave the platform. >> [music] >> There's automatic scanning of uploads and lyrics for copyright, and the terms of service got rewritten around commercial rights and how disputes get handled. Suno's reasoning for all this is that people were mass exporting AI tracks onto streaming platforms at scale. That's a real problem, and I'm not going to pretend it isn't. But this fix landed on paying customers, and the reaction from creators has been rough. If that's where you're at, and you're looking around at what else is out there, I've been using something else for a few months now, and I want to walk [music] you through it. I'm not saying it's better than Suno, but this could genuinely be one of the best alternatives that navigates around the current AI music landscape. It's called Mozart AI, and it's built on a different idea. And right now, that difference matters. Mozart is built for musicians, by musicians. It started from one number, 85% of music never gets finished. They wanted those songs finished. So, they didn't build a song generator. They built a workstation that takes the tedious [music] parts and leaves the creative decisions with you. No training on copyrighted music, either. Only licensed data sets. Because the last thing a producer wants is a one-click [music] song. Enough talk. Let's get on with it. This is the main create screen. They call it vibe sessions. You describe what you want, and it generates. You can type in your lyrics, or use the AI assistant to generate some lyrics right here, as well. There's an instrumental toggle if you don't want vocals at all. Your library sits [music] on the right. Everything you've made, ready to pull back up. Same idea you already know. Their latest music model is called Hans 2.0, and it's [music] their best one yet. Some people have claimed it to be even superior to Suno's V5.5 model. And in my previous [music] video, I actually tested it against Suno on identical prompts, and it surprisingly held its own. Let's hear something. I'll open the lyrics co-writer with [music] my idea, generate the lyrics, and hit create. >> So, go on without [music] me. Count stars for two. Our whole [music and singing] is raining till the ocean's through. Don't you look back. Don't [music] you say my name. Just remember Tuesday. >> Yeah, that is as good as it gets. The vocals are very realistic. The lyrics are exactly built on the mood [music] that we asked for and it delivers. Now, here's where it gets more interesting. This is personas. Similar to Suno's voice feature, a persona is a saved sound identity. Think of it as your voice and style captured so you can reuse it across every song you make. You can give it the source audio, upload a file, pull something from your library, or just record directly in the browser. >> [music] >> Once it's built in vibe sessions, you can select that persona for your next song. There's a strength slider so you control how much it colors the track [music] and you can publish a persona for other people to use which earns you credits. There's a whole gallery of them if you want to browse what others have made. That's the difference between generating a song and building a sound that's actually yours across everything [music] you release. Next, we have layers. This is something that Suno doesn't directly have as a separate feature. In this workflow, Mozart AI lets you use text prompts to generate musical loops and stems that automatically complement your track's existing key, BPM, and rhythm. Let's create a reggae groove. >> [music] [music] >> Both variants sound great. This is a very useful feature, especially [music] for generating ideas for your beats. Next, we have the big one, studio. This is a proper multi-track timeline, tempo, time signature, metronome, key, record and play controls. Separate tracks stacked up and you can see the waveforms. Every [music] track has its own track agent. You click into a track, type what you want in plain English, and it generates onto that track alone. Nothing else moves. There's a synth in here, too, with real controls, envelope, filter, [music] reverb, delay, and presets for pads, leads, plucks, bass. There's a piano roll for MIDI. So, if you want to get in there and move notes around by hand, you can. There's an agent side to it, as well. Once a [music] song exists, click three dots and go to edit with agent, and you can just talk to it. "Brighten the chorus. Add a saxophone solo. Extend this with a 30-second guitar section." It tells you what it did and lets you review the changes before you keep them. Mastering is the same. Ask it to master for streaming, and it handles loudness and clarity targeting specifically [music] for streaming platforms. And what's more, it also makes music videos. It'll either generate a character from the song or take an image you upload, build a storyboard, and then build a cinematic sequence to match. Here's one it made for our initial country song on the Titanic sinking. >> Count the stars for two. I'll hold this railing till the ocean's through. >> [music] >> That's not exactly on par yet with those AI video giants, but it does a good job at bringing life to your idea instead of a static image. It has understood the context of the scene well, and the imagery is honestly beautiful. However, the lip sync needs to be improved, and it would be nice to have more dynamic. And with that, we come to downloads. On Mozart's paid plans, downloads aren't rationed monthly. There's no counter sitting in the corner telling you you've got four left until the 3rd. Commercial rights come with creator, pro, and premium plans. And here's the bit I like. Once a song is generated while you're subscribed, [music] those rights are yours permanently. You keep them even if you downgrade later or cancel completely. [music] They don't get taken back. I'll be straight with you though. Generating still costs [music] credits, so it's not unlimited in every sense of the word, but there's no monthly ceiling on how many of your finished songs you're allowed to earn from. Right now, that's the difference that actually matters. Look, I'm not telling you to delete your Suno account. Suno is still excellent at what it does, and if 20 downloads a month covers how you work, you're fine. But if that number is a problem for you, if you're doing client work or scoring or building out a catalog, it's [music] worth having a second option open before September 3rd rather than after. And Mozart AI is a genuinely strong contender. Go make a few tracks and judge it yourself. That's the only way you'll know. Also, there's a link in the description that gets you 15% off the annual plan if you want to try it properly. And tell me in the comments what you want next. [music] A full deep dive on studio, a different alternative, or a video on getting the most out of Suno's new limits. That's it for me. See you in the next one.
Hey everyone, I think Grockbot is the future of personal agents and I'm going to show you how to build five of my favorite bots. In this video, we're gonna build an advisor to create and manage your other bots. A YouTube researcher to find out videos in your interest. An ex Scout to surface the most insightful and funny tweets from your timeline. A digital Marie Condo to clean up your email and find paid subscriptions to cancel. A personal concurge to help you get good prices on travel and vacations. And as a bonus, we're going to build a gamer to see if Grockbot can play classic games like Doom and Red Alert. We'll finish with a discussion about privacy and whether Grockbot should be your new AI daily driver. All right, but first, let me show you what makes Grockbot different. A few weeks ago, I tweeted that chat GBT was one feature away from building the AI product that I really want, which is a dedicated personal computer in the cloud for running AI agents. Lee from the cursor team DM'd me shortly after to test Grockbot. What exactly is a dedicated cloud computer? You know the meme of people walking around with their laptop half open to keep their agents running? You don't have to do that anymore if you're using Grockbot. Basically, Grockbot lives in a computer sitting somewhere in SpaceX AI servers instead of in your home. It has its own browser and operating system that you can use and logging to your favorite apps. Now, let's do a quick comparison of Hermes, Chat GBT, and Grockbot. Hermes I run on my Mac Mini at home. It's on 24/7 and it gives it a persistent computer, but you have to go buy the machine and set everything up for yourself. You can technically also run Hermes on a virtual private server, but that again requires more setup work. Now, chat GPT work uses plugins and a cloud browser, but the problem is that browser at is right now cannot stay signed in to your favorite apps. The UX for chat GBD work also feels scattered across chat work and codecs. Let's take a quick look here. It's just kind of like a mess of chat threads here. There's chat GPT codeex. There's like another tab up here for work and chat. And it's just all really confusing even to someone like me. In contrast, Scrotbot gives you a persistent cloud computer right out of the box. And I think it's the easiest of the three to set up and get going. and his interface, as you can see here, also feel a lot more focused and delightful. I love the little bots and the animations that they make when they start working. Right, let's say look up the weather. And you can see here that there's some pretty cool subtle animations. Each bot has a different personality and style. And this is the work of Kurser's great design team, including Jenny Wen and other folks on her team that I've interviewed in the past. Overall, I think this UX feels more like talking to a coworker than getting lost in a 100 chat threads. So, can Grockbot actually replace Chat GBT and Codeex, which is my current daily driver? Let's run through some of my favorite bots right now. So, the first bot that you should create after you install Grockbot is an advisor. Here I have my advisor ped to the very top of my Grockbot list. With your adviser, you want to start by telling it about your work and life and then actually ask it to give you some ideas for boss to create. Here's what I told my adviser. I'm a creator and founder of Behind the Craft. I have two daughters and I live in the Bay Area. I publish newsletter posts and YouTube videos focus on practical AI tutorials and podcast interviews. And I also have a membership portal over here. So based on all this, suggest five bots that can help me save me time or money. And for each explain what it would do in a few sentences. As you can see here, it created five bot ideas. A daily AI news scout, YouTube comment miner, membership concurge, social media poster, and podcast prepper. Right now, what many people don't realize is that you can actually get one bot, in this case, our advisor bot, to help us create the rest of our bots. Here I asked my adviser to build a single bot that both researches other channels and mine's comments and let's call it YouTube researcher. Right? So now let's go ahead and check out the YouTube researcher use case. You can see that the advisor kicked off the YouTube researcher bot with this instruction to create a morning brief to send me YouTube intel every morning. But I want to give it more specific instructions. Right? First, I asked it to monitor specific channels in my niche. And let's scroll down here. And it's starting to monitor channels. And here is its first attempt at a brief. You can see here that the brief is like just like very verbose and hard to follow. It's like a lot of stuff here. It did end up pulling my YouTube comments and finding themes there. So, that's great. But, this is just like too much to process, right? So, I gave it some more instructions. I told it to give me a report in the specific format which is first list your top three content ideas. Then give me the top five outliers for other channels and then give me the top performing overall. Limit your research to the last 14 days because YouTube with AI stuff is very topical and also add the top common themes back to the report. And by the way, this is how you should be working with AI to refine its output instead of trying to oneshot something. Now you see here that the report is much more concise and easier to follow. Let's take a quick look. Top three content ideas. It wants me to make a video on my spec scale that I plan to do soon. It tells me to make a video on Grockbot that we're making right now. And here are the top outliers from the other channels. Outlier meaning that it beat that channel's performance for the last 14 days. Yep, pretty interesting topics here that we can follow up on. and top performing overall and also the top common themes and it did more here and then I told you to send a full report again just to make sure it looks good and now what I can do is basically there's a daily job that sends me this report every morning right just this morning for example it sent me a new report with the top angles the watch list and comments and I basically have a YouTube researcher that proactively finds interesting topics for me to make videos on and gives me feedback back based on my user's comments. Okay, so this is the process that we're going to follow for pretty much the rest of the boss that we're going to make, which is we're going to kick it off with an initial prompt. We're going to iterate back and forth with it to make his output good, and then we're going to schedule a routine or job that runs daily, weekly, or monthly to have it proactively do work for us. Now, let's actually see how we can follow the same process with our X Scoutbot. So, Grockbot is part of Space X AI, right? So, it should have firstparty access to X and Twitter data. And even though I have an unhealthy addiction to X, I still miss plenty of great content or bookmark tweets that I never look at again. Here's the prompt that I gave my exbot. Find the most viral ex posts from the last seven days in my niche and create a weekly report with the top 10 tweets from people I follow, engaged with, or bookmarked grouped into a few categories. Include a full tweet copy link and your analysis and end with three content ideas for me to tweet about. Next, you can see here that it found my ex account and it automatically created a routine to do this every morning. And here is the initial output, right? So it found themes around playbooks that people save not just like from Greg. 23 ways I use AI agents to grow my startup. Chinese developer from loop to graph engineering and more tweets from Greg. Another theme that I found is just about Grockbot codeex and chat GPT work compared and actually used right a tweet from Riley and from Ben who works on the cursor team on the most loved internal Grockbot use cases and more. And the third theme is around creative workflows that I actually am interested in tasing. And they ended this report with the three things to tweet about next. Some ideas here. And just for fun, I asked you to also give me the top five funniest tweets from my timeline. I think digging through it. And the number one funniest tweet is as usual, OpenAI and Anthropic kind of sniping at each other and kind of joking, right? And then there's some more funny tweets down here. all kind of in my AI knee niche. You can see here that it set up a routine automatically and it sent me a new report this morning. Now, let's actually tell it to do something else. I'm going to turn on voice here. Can you send me this report to peterbehindthecraft.com and also make sure you include a full tweet copy in your report. Send to my email right now. All right. So, let's see if Ashi is able to send this report over email because I don't want to have to open Grockbot each time to see it. I want it just in my inbox every morning. Awesome. Here's a report from Grothbot. You can see here that it has this themes. It's included the full tweet copy. You can scroll down here and yeah, I I think this looks pretty good, right? And then it has down here, I'm sure, the top three things to tweet about next. Again, the process here is to give Grockbot initial prompt, have it pull up the information, iterate with it to get the output and the report right, and then either tell it to send you a daily job through Grockbot itself or through your email. In this case, I prefer email because I like to wake up with the top tweets to consume directly in my inbox instead of having to open a separate app. And by the way, it's able to send me an email because I connected to a bunch of plugins, including the Gmail plug-in. Now, let's move on to a fun bot that I call Marie Condo that cleans up your digital clutter. There are three places where clutter piles up, right? Your email, Google Drive, and your pay subscriptions. Now, I've connected both Gmail and Drive in plugins to Grubbot. And then I gave it this prompt. Audit my Gmail, Google Drive, and recurring email receipts and create a cleanup plan. Find newsletters I already open. Find large or abandoned drive files. Try to identify pay subscriptions from email receipts. And also you can hook up Mercury MCP to get this data. Mercury is the bank that I use for my business. And then group everything into these different categories. Use a number list. And crucially, I told it to not move, delete, or unsubscribe or cancel anything without my approval. Right? The last line is really important for a bot that cleans up your files. You always want to review what it plans to delete before letting it remove anything. And you can see here that first Grockbot confirmed that it's connected to Google Drive and Gmail. And then it actually connected to Mercury MCP. I had to sign in on the remote cloud computer to make this work. And then it gave me this initial report. This is a massive list of stuff to clean up in my email subscriptions to review and other things that it should not touch. Right? Honestly, this list is like pretty overwhelming. It's incredibly long. And I gave it some feedback of like, hey, list it properly, categorize it properly. It's still an incredibly long list. Right? Again, this stuff is not going to get it right in one shot. And eventually I told it to just show me max 10 items in each list and get rid of all the random labels that it has. So now this is much more digestible. It has a bunch of emails here, Google Drive and pay subscriptions. And I gave it one final piece of feedback to only show me emails to unsubscribe to, Google Drive files to delete and pay subscriptions that I want to cancel. And then here's the update list that I came up with. Now it has this list as a number list and my feedback is to actually get it to take action which is email unsubscribe to three and four 5 6 7 and 8 Google Drive delete the extra tax return delete some of these large files and pay subscriptions cancel 11 and 15 right and just as a general tip it's always good to ask Grothbot or AI to give you its response in a number list like this to make it super easy for you to just tell it to do things by just referring to the number in the list instead of have to type everything over again. I use this pattern all the time. All right, so now Crockbot is actually taking action, right? This is where the magic happens. You can see here is already unsubscribed to a bunch of senders. It's trashed three Google Drive files that we asked it to and now it's cancelling Lovable and Equipped Foods, which is a protein company. Now to cancel lovable I first have to sign to the cloud computer with my lovable credentials which I did manually and we found out that actually lovable is already scheduled to be cancelled. So lovable is done. Then it asked me to sign to equip foods and I did that and then it cancelled uh the protein subscription for me. By the way, Equip Foods is a great protein company. I'm only canceling because I have too many protein powders at home already. Uh so it basically did all this work, right? trashed a bunch of stuff. It's unsubscribed to a bunch of emails and it's canceled a bunch of subscriptions to save me money. And you can see that it did all this in around five minutes when it would have taken me probably 30 minutes to an hour to do all this manually. Marie Condo is very very useful. Of course, and there's something missing with this bot. I think we should ask it to can you talk like Marie Condo from now on? Give me an example because right now it sounds too much like a robot and not so much like Marie Condo and let's see what it comes up with. All right. So now uh Marie Condo is actually going to talk like Marie Condo. You can see Hermis and the singing have completed their work. We thank them and place them in the trash where they may rest. Equip foods no longer sparks joy and release prime protein subscription with gratitude. [laughter] Yeah. So now we can set up Marie Condo to maybe talk to us every week or every month to clean up our digital files and spark joy, right? So this is definitely a a bot that I recommend you setting up. Just remember to ask it to give it output in a number list instead of just doing the cleanup for you so that you can review it first, right? You don't want it to accidentally delete some important file. Now let's cover the personal concurge bot. This is a bot that I want to use for all my vacation and travel plans. It has access to my vacation document where I've listed my December trip to Japan. Here's a quick preview of the document. It has the full iterity for Japan that I created with AI as well. Let's go back to Grockbot. And what I want Grockbot to do is I haven't quite booked my flights to Japan yet. So, I wanted to monitor for price changes and alert me on what routes are the best deal. Here's a prompt that I give it. Read my vacation document and monitor the exact flight legs for my family trip. Get the dates, the best options for each flight leg. Check regularly and let me know when the price improves. You see here that I found my flight legs and is using Google flights to check the prices. Now, you may be wondering what's the advantage of using Grockbot for this instead of just setting up Google flight price alerts. And the value here is that Grockbot can understand my whole trip based on my document and decide what's a better option for my family. If you scroll down here, the big finding is that a Tokyo round trip is about $2,700 cheaper than the open jaw flights that my dog prefers. Right? My dog prefers it to fly from SFO to Tokyo and then from Tokyo to Fukoka, which we're going to, and then from Fukuoka back to SFO. But actually, Grockbot found a better itinerary. It's about $2,700 cheaper to just fly round trip to Tokyo instead of doing this itinerary that I laid out, right? A simple Google price alert would not have found this. And now I ask it to check every morning at 9:00 a.m. to see if the price improves. And you can see here that the price is still around the same. The Tokyo round trip is still much cheaper. Eventually, I can ask Rockbot to also just go ahead and book the flight for me or check into the flight when the time comes. And generally speaking, I think it's always a good idea to have a travel thread or travel bot to both help you plan travel ahead of time and also when you're at a location to help you book amusement parks, to help you figure out what to do every single day. So, I imagine I'm going to be using this personal travel concurge a lot. All right, let's test one more use case with Grockbot. Because Grockbot gives me a dedicated cloud computer, I thought it would be fun to ask it to install and let me play some retro games. I asked it to install Red Alert, Doom, and Commander King. And you can see here that it found the files and installed it. So, why don't we just ask it now? Let's say open Doom for us to play. And let's see how things work out. All right, looks like Doom is up. Let's open it in our virtual cloud computer. And here it is. And here we can start a new game. Let's pick this episode. And yeah, let's say Hurt Me Plant Penty. Here's Doom. And here's kind of where Grockbot falls apart a little bit. Unfortunately, because it's on a virtual cloud computer, the mouse isn't quite configured right to actually play Doom. For some reason, it's looking at the floor all the time, and I can't seem to adjust the mouse to actually look up. All right. So, Doom doesn't quite work here right now. Now, let's actually try playing some other game. Let's try asking to play Commander King. I'm not sure if you guys know this, but Commander King is an awesome platform game that I played in my youth. And now it's loading Commander King. So, let's see what it comes up with. Awesome. This is Commander King. Let's see if it plays well or not. Let's start a new game. One player normal difficulty. And here we go. Here we go. It's a very basic platformer, but I remember really enjoy it in my youth. How do I jump? I forgot how to jump. Oh, so control is jump. And yeah, it plays better than Doom for sure, but there is some lag in the keyboard and mouse that makes it difficult to actually control the character. Okay, so um yeah, that's just for fun. But basically, Grockbot is not replacing your gaming PC or GeForce now yet. But the fact that the agent can install and launch these games on its own computer gives you a sense of how open-ended this could become and how much potential there is here. Right? So maybe Space X AI can use all the data centers and GPUs in space to actually deliver AAA games through this virtual cloud computer. That would be a dream, right? And while we're at it, I open this file manager here. And here's all the stuff that we installed into our virtual cloud computer. It kind of looks like Windows 3.1 a little bit. I'm not sure what kind of thing it is. There's Japan flights. There's other stuff here, right? And then there's games that we've installed. And also we have Chrome and a terminal. So that's kind of our virtual cloud computer. All right. Now, All right. Now before we wrap, I want to talk about probably the biggest hurdle for Grobbox adoption, which is trust. When I see a Google sign screen like this on my laptop, I don't really think twice before signing in. But because this appeared on the virtual cloud computer, I hesitated a bit because how do I know that nobody else is seeing this screen on the virtual cloud computer? Now, if you go to the Grogbot website, if you scroll all the way down here, there is a note about privacy here, which is how does Grockbot handle my privacy? Grabbot uses the same cursor SSO off and privacy mode you already trust. Your cloud computers encrypted in transit and at rest and there's no AI training on top of it. Right? So, you know, I'm willing to give Grockbot and the virtual cloud computer access to all this stuff because I'm an early AI adopter, but I can see normal people struggling to understand what this cloud computer thing even is and kind of hesitate to sign into their favorite apps on this device. So, I think the AI Asian platform that figures out trust will be the first to get mass adoption. But overall, I think Grockbot is a clear sign of the future. We're moving away from manually using our keyboard and mouse to do work on our laptops to using our voice to orchestrate a bunch of agents that live in a dedicated cloud computer. And Grockbot is the first product to actually enable this. Now, it's not quite my daily driver yet because I think Chat GPT still offers more for $20 a month while Grockbot requires paying $200 a month to use on a regular basis. But I think Grockbot has much cleaner UI than chat right now and is also very very capable. And overall, it's just great to be in a world where cursor and SpaceX AI are just as viable a competitor as OpenAI and Anthropic. I think cursor may even have the edge if it can continue to support multiple models from all providers. So, Grockbot is available for free and I definitely recommend downloading and trying it to see a glimpse of the future. I'll include some of the prompts from this video in the pin comment below. And I also have an exclusive interview with the Cursor team on how they built Grockbot coming up in the next few weeks. Overall, I'm really impressed by Grockbot. I think the team really cooked here and I can't wait to hear the story behind how they built this. So, please like and subscribe if you enjoy this video and I'll see you next time.
Most people using AI in the slowest way possible. They open Chat Gypt, they copy an email, they paste it in, they ask for a reply, then they copy the reply back into Gmail, and somehow after all of that, they call it an automation. But the real future of AI is not you babysitting a chatbot. It is AI sitting inside the tools you already use, quietly doing the admin work before you even ask. And that is exactly what today's sponsor, Fixer, is trying to do. Fixer is an AI assistant for your inbox. It works with Gmail and Outlook, organizes your emails, draft replies in your voice, and can even take meeting notes for you. So, in this video, I'm going to show you what Fixer actually does, how the setup actually works, how it fits into a real workday, and why tools like this are probably where the AI productivity is going next. Because the goal is not to have more tools, the goal is to have less admin. And because Fixer is sponsoring today's video, they're actually giving me an exclusive link in which you can get 25% off your first month of subscription and a 7-day free trial so you can try out the software risk-free. So, if you run a business, freelance, manage clients, work in sales, or just have a job where people constantly email you, your inbox eventually becomes a second job. You wake up, open Gmail, and immediately get hit with a wall of unread messages. Some need a reply. Some are just FYI. Some are newsletters you forgot you signed up for in 2021. And some are actually important, but they are buried under 40 random notifications. The problem is not just email. The problem is the tiny decisions emails force you to make all day. Should I reply to this now? Is this urgent? Do I need to schedule something? What did we agree in this meeting? That is where Fixer comes in. Fixer is basically an AI executive assistant that lives inside your existing email workflow and helps surface important messages and tasks. Not another dashboard, not another tab you need to remember, not another AI tool where you have to engineer the perfect prompt. It plugs it into Gmail or Outlook and starts helping with the repetitive part of communication. And according to Fixer, it is already managing over 350,000 inboxes for mostly client-f facing professionals, people who live in their email all day. So, let's start at the beginning. The setup. Setup. Connecting your email. This part is genuinely simple, which matters because most productivity tools die at the setup screen. You go to Fixer's site and you click start with Gmail or start with Outlook. And here's where you just grant permission. And once you do that, that's basically it. You just allow Fixer access. And Fixer describes this as one-click setup with no configuration needed. No rules to build, no folders to design, no settings menu you have to figure out before it does anything. Now, once you're connected, Fixes starts learning how you write. It looks at your past emails. It picks up your tone, your phrasing, and how long your replies usually are. That is what powers the reply drafting later. So, the more email history it can see, the more the draft sounds like you. One practical tip is to give it a little time after connecting. The first hour or so, it is getting oriented, and after that, the features start kicking in. There's a 7-day free trial, so you can test it out on your real inbox and back out if it's not for you. Feature one, automatic inbox organization. The first thing that you'll notice is that your inbox looks different. Instead of treating every email like it deserves the same attention, Fixers sorts incoming mail into categories automatically. Emails that need a reply, emails that are just an FYI, marketing and newsletter style messages, notifications. And this matters because most inboxes fail for one simple reason. Everything looks equally important. A client email, a calendar update, a receipt, a newsletter, and a random automated notification all sit in the same pal. So your brain has to sort everything manually. That's not deep work. That is digital housekeeping. With Fixer, you open your inbox and immediately see what actually needs your attention, and you're not locked into its default either. You can adjust the categories, turn ones that you want and turn off the ones that you don't. You can also set email rules. So if you want messages from your boss or a key client to always land in your torespond list, you can tell fix it once and it handles it from then on. So just come on over to categorization custom rules and then in this box I can essentially say any email that comes in from contactigrid.com I can put this in FYI to respond any section that I wish and this is one of the biggest shifts with AI productivity. The most useful AI tools are not always the ones that generate the flashiest content. Sometimes the useful ones are tools that remove 100 small decisions from your day. You're no longer digging, you're deciding. Feature two, draft replies in your voice. The second big feature, and this is the one that most people come for, is reply drafting. For emails that need a response, Fixer writes a draft and has it waiting for you. You see, most people use AI to write their emails, but they do it manually. They copy the email to the chatbot, then they write, write a professional response. The AI gives them something that sounds like a LinkedIn comment from a corporate bot, and nobody wants that. What Fix It is doing is different. Because it studied your past emails, the drafts are meant to match your tone and style. So, if you're short and direct, the replies are short and direct. If you're more polished and formal [music] with clients, it should adapt to that. It also uses the context. If you've had prior conversations with that person, it pulls that in. So, the reply actually makes sense instead of being polite but empty. So, here's the workflow in practice. You open the email, the draft is immediately already there, and you do one of three things. You either send it if it's good, you mark a quick edit if it's a one line off, or you delete it and write your own if the AI missed its mark. And that's the key point here. AI doesn't send emails for you. It drafts them. You review, edit, and approve everything before it goes out. And that matters because for most people, the scary part of AI email tools is not whether they can write, it's whether they might send something stupid without permission. Instead of starting every reply from a blank page, you're starting from a draft that is most of the way there. That might only save a few minutes per email, but if you reply to 20 emails a day, it compounds very quickly. Feature three, meeting notes and follow-ups. The next part is meetings. Because email is only one half of the admin problem. The other half is what happens after calls. You join a meeting, you talk for 30 minutes, someone says, "Great, let's follow up on that." And then nobody writes the follow-up properly. Or someone writes notes in a random document, or the action items disappear into the void. Fixer has a noteaker that can join your calls, transcribe the conversation, and create summaries with key decisions and action item. It supports Google Meet and Microsoft Teams, which makes sense because those are the meeting tools a lot of Gmail and Outlook users already live in. And this is useful for a very specific reason. Most people do not need more meeting recordings. They need the useful part of the meeting extracted. What was decided? Who needs to do what? What should the follow-up email say? And because everything is transcribed, it's searchable. You can find exactly what someone said by searching the text instead of scrubbing through recording. But here's the part that ties it all together. Because Fixer also lives in your inbox, it can draft the follow-up email based on the meeting. The great talking to you. Here's what we agreed. Here are the next steps. Email. Meeting happens. Notes are created. Action items are pulled out. Follow-up draft is ready. And again, you review it before anything goes out. That is the difference between AI as a novelty and AI as infrastructure. One gives you a call output. The other removes a step from your actual workflow. Feature four is fixer chat and scheduling. Now if you go beyond the basic plan, fixer has more advanced features. On the professional plan, you get fixer chat which gives you instance answers from your inbox and meeting notes. And this is the part that becomes really powerful if you deal with a lot of context. Because your inbox is basically a private chat knowledge database, client history, project updates, meeting decisions, feedback, but normally all of that is trapped inside search. You have to remember the right keyword, who sent it, and roughly when it happened. And if you don't remember those things, good luck. With a chat layer over communication history, you can just ask a person, "What did we do with this client? Which emails still need a reply? What are my important emails?" The professional plan also helps scheduling across teams and time zones. So, instead of endless just Tuesday work, no, how about Thursday back and forth? It helps coordinate the time for you. You can also upload files to train it further. And there's a HubSpot integration if you run client work through a CRM. This is where AI assistants clearly are heading. Not just answering general questions, sitting on top of your work, and helping you operate faster. That is a much more useful category than write me a poem about productivity. At least unless your inbox is somehow full of poems, in which case you have a different problem. Security and control. Now, because this is email, we have to talk about security and control. You're not connecting an AI tool to a random notes app. You're connecting it to your inbox. So, this part matters. Fixer is Google verified and Microsoft verified. And you must understand that Fixer has passed the security reviews required for those platforms. It is also SOCK 2 type2 certified, ISO/IEC27001 certified, GDPR compliant, and that data is encrypted at every stage and that your email data is not used to train external AI models. But the practical point for viewers is this. You stay in control. Fixer drafts emails, it doesn't send them for you. You can review the drafts, you can correct the categories, and if you delete your Fixer account, Fixer will revert your inbox to how it was before. And that doesn't mean you should ignore privacy. You should always read the permissions before connecting any AI tool to your email. But for a tool in this category, these are the kind of controls you want to see. So, who is Fixer for? I wouldn't say this is for someone who gets three emails a day. If your inbox is already clean, you probably don't need an AI assistant living. This is for people whose inbox has become a part of their job. Consultants, recruiters, sales teams, creators working with sponsors, founders, agency owners. Basically, anyone who spends a meaningful part of their day sorting messages, writing replies, scheduling meetings, and trying to remember what happened on calls. If your email is just communication, Fixer might be useful. But if your email is your operating system, this is where it makes the most sense because a product is not really selling AI email. It's selling a cleaner workday, fewer open loops, fewer blank replies, fewer forgotten follow-ups. Here's a quick breakdown of what it costs because that matters. Pixer has a 7-day free trial on every plan, so you can test it before paying. The starter plan is listed at $22 per user per month when build annually or $30 per user per month monthly. that gives you your organization for one inbox and calendar, draft replies in your invoice and meeting notes. That's the core experience and for most individuals that is enough. The professional plan is listed at $37.50 per month when build annually or $50 per user per month monthly. That adds multiple inboxes and calendars, scheduling across teams, time zones, fixer chat, the HubSpot integration, uploaded files to train fixer, and onboarding with a specialist. If you're juggling multiple inboxes or running client work through a CRM, this is the one. And then there's an enterprise plan for larger teams with bespoke pricing and features like SSO, dedicated account management, and custom security tools. So if you're testing it as an individual, start with a free trial and connect one inbox. Don't over complicate it. Just use it for a few days and ask one question. Did this reduce the amount of time I spent in email? Because that is the only metric that matters here. So that is fixer, an AI assistant that organizes your inbox, drafts replies in your voice, and takes meeting notes inside the workflow you already use. And I think this category is important because it shows where AI is going next. Now, if you want to try Fixer, you can try them for free with a 7-day free trial and 25% off your first month with the link in the description.
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In just a couple of seconds Spain is about to get its first total solar eclipse in over 100 years. And we're here with NASA to film it from space. But we almost didn't do this video because it turns out there are a great couple videos on solar eclipses on YouTube, and we just thought we couldn't add anything. But then we started looking. [laughs] Oh my God, that's insane! And it turns out... Oh, I can't, I just gotta go watch this. That's insane. So, Henry, Emilia and I still had some unanswered questions like, why is the Northern Hemisphere getting more total eclipses than the southern hemisphere? Why does the sun suddenly turn white only during pure totality? And how come we still haven't figured out what causes these elusive shadow bands on the ground? Oh, that's so weird. Okay, so we are on the way to Burgos, Spain, because it's going to be right in the center of the moon's shadow. So perfect for totality. And we're also meeting up with NASA and a bunch of other science groups because they're launching a couple of balloons up to the edge of space. So they will actually be filming the eclipse, and hopefully they can answer some of our questions. Vamos! Oh my God! It's getting occluded, oh no! Angela? - Yes. Oh, lovely to meet you. I’m Emilia. Oh, so nice to meet you. This is Gregor. Nice to meet you, thanks for having us. So, can I ask you? Yeah. Are those total solar eclipses on your toes? Yes. Totally geeking it up. Yeah, I guess, first question right off the bat. How many eclipses have you seen? So this will be my fourth eclipse and the third total eclipse. There's lots of folks here who have seen many more than me. Eight or even as many as 12. That's crazy. So how rare is it that Spain is getting two total solar eclipses in less than 12 months? If you picked any random point on any place on the earth, it's about every 300 years that the eclipse will repeat there. But then there are some places, you know, where there happens to be occurrences, because there is kind of a pattern to the eclipses. You can see one of those patterns if you map out the paths of all total solar eclipses between 2000 BCE and 3000 CE. Notice that the Northern Hemisphere consistently gets more eclipses, about 15% more than the Southern Hemisphere. Now for there to be a total solar eclipse where the moon completely covers the sun, you want it to be bigger than the sun in the sky. Now, the biggest factor for that is the moon's elliptical orbit. Sometimes it's further from the Earth, sometimes closer. So over the course of the month, its apparent area increases by up to 30%. But the Earth's orbit around the sun is also elliptical. So from our point of view, the sun's area is about 7% smaller in July than it is in January. Which means there is a higher chance of a total solar eclipse around July. But that's also when it's mostly the north half of Earth that is tilted towards the sun, the Northern Hemisphere summer. So that's where the total eclipses tend to fall. Now the Southern Hemisphere is actually going to get more annular eclipses where the moon is slightly smaller than the sun so you have this ring of sunlight around it. And that's because during the southern summer the sun is actually closer. All of this is actually going to flip, because the Earth precesses around its axis and its elliptical orbit also slowly shifts around the sun. In about 9500 years, it will be the Southern Hemisphere that gets more total solar eclipses. Now, there is another interesting pattern that shows up when you look at all types of solar eclipses, including annular and partial ones too. So I crunched some numbers, like 5000 years of eclipses. And if you look at how many happened per year, there's never a year without some sort of solar eclipse, which kind of feels mind boggling. You at least have to get two and sometimes you can get five. How do you get five? - Yeah, yeah I know. Check this out, so: The moon's orbit is tilted about five degrees out of the Earth sun plane. So most times the moon passes in front of the sun It's at the wrong height to cast a shadow on Earth. And that's why we don't get a solar eclipse every month. But there are these two nodes along the moon's orbit where it does actually cross the Earth-Sun plane. And throughout the year, both of these nodes are going to land nicely in between the Earth and the Sun. This happens about six months apart, and it opens a window of around 34 days where solar eclipses become possible. And these are called eclipse seasons. This is huge window to get eclipses. - Yeah. So as long as you get a new moon somewhere inside of this eclipse window, you get an eclipse. But it turns out you have to get at least one. The reason is the eclipse season is 34 days, but a new moon happens every 29.5 days. Of course. So you can't go through an eclipse season without the new moon happening at least once. However you roll the dice, it always has to land inside the eclipse season at least once. So you always get two a year. Great. Yeah, right? Now, if the new moon falls towards the middle of an eclipse season, it usually produces a total or an annular eclipse, and you can only fit one of those types of eclipses into a season. So you can get about two per year. Yeah, this doesn't seem rare, definitely not once in a lifetime. Yeah, but it gets worse because if you also count partial eclipses, those happen more towards the edges. And because they happen to the edges. You can get two in a single eclipse season. Or you can get four a year. But wait, then how do you get five? Yeah. The thing is, the eclipse seasons themselves also drift around the Earth's orbit. So if you start on January 1st and you get an eclipse really, really soon into the year, you can hit like a partial and then another partial. And then as you're moving, these eclipse seasons are shifting. So you get another one and another one and then you think you're done. But the eclipse season has actually already shifted into December. And then you get a final one. So you can get up to five. Woah. Okay, wait, I realized I forgot to tell you my favorite fact out of this, out of this whole discussion, and that's that if you look at the eclipse season, when the eclipse happens, the solar eclipse here, and you rewind by about two weeks, you'll realize that now the Earth is in between the sun and the moon. So you get a lunar eclipse. So you get a lunar eclipse, which means every time there is a solar eclipse, it is accompanied by a lunar eclipse either two weeks before or after. Oh, that's so cool. Yeah. So there's one happening in two weeks. Oh, amazing. Yeah, yeah, it's really cool. I actually tried to find an answer to this question, but if you look at timelapses taken during the eclipse. You'll see that the partial parts are usually like yellowish in color. And then the totality is white. If you Google on Google you get like a little animation and it turns white. Yellow, and then it's white and then it's yellow again. I could not find a satisfying scientific explanation for why that happens. It's so bright, right? In order to take those timelapse pictures, you have to put a filter in front of it. And so the most common with those filters, it's that kind of reddish orange color. So it's just the most common filter makes it that color because the sun is white. But then it's dim enough that we can look right at it during totality. And so you see white. - Yeah It's just a filter, okay. It's just the filter. Maybe I can show you where we're at. Sure. You can kind of get set up. Hey. We're a NASA funded lab in Bozeman, Montana, and this is an extension of what's called the Nationwide Eclipse Ballooning Project. So we studied the 2023 and 2024 eclipse. So we have two teams in Reykjavik, Iceland, and we have three teams here in Spain. The Iceland teams are launching essentially weather balloons, radiosondes. And then we're launching what we call larger engineering balloons here. Look, cowboy engineering here. What are you doing? We attach this to our basically the balloon. Like right below where we're connecting the balloon to the payload. And usually we have a little bit more sophisticated weights, but we didn't want to just ship weights to Spain with us. So we took water bottles and filled them with rocks. I love it, I love it. We have a little bag of like, souvenir kind of things, like signed NASA stickers and things like that that we're going to send up on the balloon. This is going up so you can sign it. - Hey! You're signing it ‘Eclipse’? I mean, Eclipse 2026, I want to show it off I thought you were going to say like ‘Henry’ or something. So, “Henry was here”? We're not putting you in the balloon, though, so... We're gonna set up kind of in the middle just because of the wind direction at the moment. Okay. So right now the wind's going the wrong way from the forecast. Right. Blowing it that way. It’s just finding a way to be far enough that we don't hit the church. How strong does the wind have to be for you to hit the church? Like it's pretty far away, no? It is. We just have to outclimb it before we get there. Okay. It's kind of weird. I feel like it was one of those things where I'd seen pictures of them, but I didn't realize how much it just looked like a regular balloon, but just super sized. On the count of three, one, two, three. Amazing. That launch went absolutely perfectly, and right now our cameras are on their way up to the edge of space. But about 3000km away NASA's launching another eclipse mission, one mounted on a 70 year old Cold War era bomber plane. They're trying to chase the totality for as long as they can, but surprisingly, this is actually already been attempted. In 1973, the Concorde went Mach 2 and stayed in the totality for 74 minutes. Here on the ground, the longest anyone's going to be able to see the totality is two minutes and 18 seconds. And with NASA's jet going at 460mph, they're going to be able to stay in the totality for just around three minutes. It doesn't seem worth it, but they're not just trying to stay in it longer. Compared to a balloon, which is a bit shaky. A jet is much more stable, so it lets them mount much more sensitive equipment so they can study a very specific part of the sun, part of the sun, that once led to the discovery of a fake element. On the total eclipse of the 18th of August, 1868, French astronomer Jules Janssen went to India to look at a part of the sun that's normally outshone from observation, the prominences. Arcs of glowing gas at the very edge. He put a slit in front of a prism and broke their light into its constituent wavelengths. Out came five bright bands. One of them matched no element known on Earth. Two months later, the English astronomer Norman Lockyer saw the same line. He called it helium, after Helios, the Greek god of the sun. The next year, other scientists used the same method on the hotter corona and found another unexplained band. For 70 years it was thought to be an element called coronium, but in 1939 it turned out to be iron, so hot that 13 of its electrons had been ripped away. So an eclipse led to discovering both a real element and a fake one. Now, to study the eclipse, Janssen used a slit, but you actually don't have to. So here I've cut out a triangle shape in this cardboard, and we're going to see the shadow that it casts. You can see there's this projection of light that makes a triangle, not too surprising, but watch what happens as I slowly move it away from the ground. You'll see that triangle slowly morphs into a circle. I can even take this eclipse shape, which looks like an eclipse when it's close to the ground, but I pull it again and it's always a circle. Gregor, what are you seeing brother? Wait, let me switch the glasses. Oh, we're getting, like, a nice croissant shape. You know, I think it's called a crescent. No no no no no no no. A croissant, it's like very thick. You check it out. Check it. Yeah. No. It's cool. Here we take those same cardboard cutouts Here's the one with the triangle. And again, you're seeing a triangle. But look what happens when I move it away. Look, it's a crescent. How cool is that? Okay, and I can do it with another shape. I'm gonna try with this star shape, which again, close up. But if I move away? This star... Also a crescent. How sick is that? You can even do it. With something like this, with all these little circular holes. Circle. That's a circle. But even quicker. A bunch of little crescents. That's amazing. Right? Come on, come on. See, when the cardboard is high enough, you're no longer looking at the hole, but a projection of the light source itself. Light travels in straight lines, so a ray leaving the top of the sun has to angle downward to get through the hole. So it lands at the bottom here. The top goes to the bottom and the left flips to the right. Everything crosses at the hole and it comes out on the other side reversed, which means that during an eclipse, the crescents on the ground point the opposite way to the crescents in the sky. All cameras actually work like this, flipping your image. But to get this effect, you need the point to be rather small, it's like a pinhole camera. That's why all the spaces between leaves and a tree are perfect. During an eclipse, they all become crescents. And there's another crazy effect. Watch my hand. The shadows around my fingers are quite fuzzy, but if I turn my hand 90 degrees, those same shadows suddenly become much sharper. As we approach totality, every object has a sharp direction and a soft one, 90 degrees apart. Take one point source of light, on its own it casts a perfectly sharp shadow, but add a second point, also perfectly sharp, but arriving at a different angle. Now, where the two shadows overlap, you get full darkness, but out at the edges, where only one of them lands, you get half the light. If you do it for more and more points, well, those edges start to stack up into a gradient, and that's what makes the blur. And during an eclipse, you're left with this sliver shape. In the tall direction, it stays roughly the same width as normal. That's why in this direction you still get blurry shadows. But change your orientation 90 degrees and now, it's much more narrow. So now it's like a point source again. That's why the shadows in this direction are perfectly sharp. So we're a few minutes from totality. And, what we're going to do is we're going to hold up this sheet, and what we're looking for are these long moving shadows. They're sometimes called snake shadows because of how they move. So you're talking about seconds before totality that you'll see these shadow bands occur so maybe 20s, 10s before totality. And so you really have to be, you know, looking for them and aware that they're going to exist. Gosh, I'm a bit stressed about us filming these now. - Oh, you'll be fine. Like they're either going to be pronounced, you're going to be able to capture them, or they're going to be so subtle that nobody's going to see them anyway. Is that it? No, no, I think there is a little something, like it's faint. Oh, that's so weird. See? Did you see it? Like it's definitely there. I don't know, I felt like you were just moving the sheet. No. Come on. It was there, like, they are real. We saw it, we saw it. It was faint. There is still some mystery around shadow bands, but exactly what causes them, there is still some discovery there. And so, it's absolutely an open question. Right as you get to the final seconds of light, before the eclipse and you just get it's a Bailey's beads, where you just get a single point of light, or just a few points of light along the limb. The leading theory is that these narrow beams of light pass through many layers of air, of varying temperature and densities. Each of the boundaries between those layers of air refract the light, bending it this way and that. This is actually the same effect that makes starlight twinkle. But if that were the whole story, as you went up through the atmosphere, surely you wouldn't see these shadow bands. Did you hear? Because there was an experiment done in Burgos by an army engineer in 1905 in the 1905 eclipse, going up in a hydrogen balloon? Oh, wow. And he was actually looking for shadow bands. Oh, really? So they launched these balloons. They've put these big white sheets under the balloons, to try and spot it, And then they weren't seeing anything, there standing at it, and then all of a sudden, someone pointed out they were just everywhere, like, on their hands, on the basket. And they saw them everywhere, but not on the white sheet. They just they were just surrounded by it. But again, someone was already testing it, trying to test it, at altitude back in 1905, right here. That's amazing. But what's going on there? Is there another explanation? So there are, the other thing that is coming into effect there, was the actual topology of the moon itself. So those different point sources, as they line up and interfere with each other, that's where you get some of those sort of secondary effects and make them more pronounced, because you have you can imagine it like in a theater. If you could picture yourself in a theater and you have one spotlight shining, you have one point source. If you had three spotlights, you're going to get some interesting sort of overlapping shadows, and that's one of those effects that amplify that, the shadow bands. Oh that's cool. 15 seconds. Okay, quick whip the glasses on. Oh my gosh. Wow. That's so strange. Man. This is, this is so crazy. Unbelievable. I don't know what I was imagining, but this is so much more insane. Everyone is going wild. I love this guy. Oh my God, you can see the corona. And look at that, like, really bright spot on the left. Like sticking out. It's like ethereal. Is that like a solar flare? I don't, I don't know I don't know what the words are. I'm just like, this is cool. Yeah, right, - Its the 360! - Oh, the 360 yeah, you're right. There's a 360 sunset. Oh my goodness!. - I love that you're have a good time here, right. It's incred... I know you? Where are you from? - Veritasium is the channel. Jesus Christ, this guy's from Veritasium. This is amazing. It’s coming back, it’s coming back! Look at the sky. It's unbelievable. It's made my... my life. It's got me unreasonably fired up and then like, whoa! And then that ring in the sky. This is sweet. Whoooaa, I have never been this pumped up on a natural event before, my god. Bro how was that? How do you feel, man? It was insane. It was insane. Really, I don't know, I just can't explain it. Looking at it in person, it is kind of cataclysmic. You gotta cry. We're up at the crack of dawn. Chasing this balloon. It did this crazy path, and then ended up falling much closer than they thought. Still probably going to be in some farmers field somewhere. Might have to do a little hike to get it. Do we go for it? I mean, I don't know. He's going for it. And then we have our cameras, so that's good. So this is the one. Yeah. Okay. Cool. Something going on. That's crazy. That's so cool. Favorite shots of the shadow are where you can just see part of it, like receding. Yeah. So, we had about a seven hour drive to get here, so we had plenty of time to catch up on podcasts. And we ended up listening to one from today's sponsor, 80,000 hours. It was about the Fermi Paradox and some potential solutions. It was pretty intriguing. If you're into long conversations that dig into big ideas, their podcast is worth checking out. 80,000 hours is a nonprofit that's built around a simple idea. Your career is about 80,000 hours long. That's a huge chunk of your life, and it's probably your best shot at making a real impact on the world. What I like is they don't just tell you to follow your passion. Their advice is actually evidence based. It's supported by over a decade of research. The site's got career guides, deep dives into different paths, and a job board that's full of high impact roles and all of it, the podcast, the research, the guides. It's all completely free because they're a nonprofit. They're not trying to sell you anything. So if you're trying to figure out your next step or just curious how to have more impact with your work, head to 80000hours.org/Veritasium. It'll get you their free career guide, which walks you through what makes for a high impact career, it might give you some ideas you might not have considered, and it'll help you turn them into an actual plan. It'll also sign you up for their newsletter, so you'll get updates on new research and job opportunities a couple times a month. Thanks to 80,000 hours for sponsoring this one and as always, thanks to you for watching. One last thing. I wanted to thank Astrum, and if you want to learn more about The Sun's corona, you can check out their video in the description. Also, thank you to Exploratorium for letting us link into their live stream. And of course, these guys, the team from NASA borealis for all of their help. Yeahhhh. So we've taken a bunch of stickers and we flew them to the edge of space. You can see them right here. We're now all signing them. It went to what, 92,000ft? Yeah like 30km? Insane. If you guys want your hands on one, we're going to give them out to our Patreon members. It's totally free. So if you want to check that out, the link is also in the description.