# cache_entries

kind: transcript
key: a9c8d6bf7fcf36c83ebdf54bc9791b55ca23377f4f767789eb2d8d66f6b18571
value:
{"content":"It has been four months since the most\npowerful AI tool ever, OpenClaw, has\nreleased, and a lot has changed. In this\nvideo, I'm going to go over every single\nOpen Claw concept, lesson, and update\nfrom the last 4 months. Whether you are\nbrand new and have no idea what OpenClaw\nis, or you're a pro, you're going to\nlearn something in this video, and by\nthe end, you're going to be a stone cold\nexpert. This is basically going to be\nthe only unsponsored OpenClaw video on\nthe entire internet. So everything I go\nthrough on here is going to be unbiased\nand true. There's not going to be a\nsingle hosting jur shill at all. Feel\nfree to skip around below if you're\nalready familiar with OpenClaw and want\nto see different concepts. But with that\nbeing said, you are about to master the\nmost important software release of all\ntime. Now, let's lock in and get into\nit. OpenClaw is the greatest AI agent\nautomation tool of all time. What you're\nseeing right here is my team of openclaw\nagents all working autonomously for me\nas we speak. This is my little software\nfactory where I currently have four open\nclaws working on my SAS creator buddy.\nYou can see the task they're working on\ndown below in real time. They're\nbuilding out different components to\nCreator Buddy. They're doing different\npieces of research. They're finding what\nthey should build next. They're\nreviewing each other's code. It is a\nfully automated pipeline. This isn't a\ncoding tool, although you could use it\nas a coding tool. What I'm showing you\nhere is a coding tool, but I also do a\nton of other things with OpenClaw. But\nthe point in me showing you this is to\nshow you the power of what you can do\nwith OpenClaw. Hiring your own AI\nworkforce to do whatever you want. If\nyou want your open claws going out and\ndoing research for you, you can do that.\nIf you want your open claws going out\nautomating businesses, you can do that\nfor you. This is giving the individual\nthe leverage of hundreds if not\nthousands of people. And that's why I\nthink this is the most important AI tool\nof all time. So, what I'm about to do is\ngo over what Open Claw is, why it's so\nimportant. Then, we're going to go into\nsetting it up. We're going to go into\nsaving money with it, which models you\nshould be using. We're going to go into\nuse cases so you can start getting value\nout of it right away. We'll go through\nsecurity, the big thing everyone's\nworried about. Is it secure? Can it leak\nall your passwords and nudes? We'll find\nout in a second. Do you need to buy a\nMac Mini? Do you need to buy a Mac\nStudio? We'll find that out as well. And\nI'll even go over what I think OpenClaw\nis going to be a year from now, which\nyou want to stick around for as well.\nSo, let's talk about real quick what\nOpenClaw is and why it's different than\nlike chat GPT. Before, when you were\nusing chat GPT or any other AI chatbot,\nyou were asking questions and getting\nanswers, right? It's basically like a\nglorified search engine. That has\nchanged with OpenClaw. Now, instead of\nasking questions and getting answers,\nyou give it a goal, you receive a\nresult, and it iterates, right? It's an\nAI agent that takes action. You give it\ngoals, it does things for you, then it\nimproves and gets better and does it all\nagain. That is a massive shift in how AI\nworks and why I believe this is the\ngreatest AI tool of all time. It goes\nfrom a chatbot to an employee. So,\ninstead of like it being a glorified\nsearch like Google, it's now an employee\nthat works for you. It's self-improving,\nso as it does things, it learns skills\non the fly, so it can do things better.\nIt gets smarter every day. It gets\nsmarter every time you talk to it. It\nhas a body, so it can use your computer.\nAnything a human being can do on the\ncomputer, it can do as well. It can\nbrowse the internet. It can go on\ndifferent websites. It can sign up for\nthings for you. It can write code. It\ncan message people for you. Can write\nemails. It can organize your files.\nAnything you need to do on your\ncomputer, it can do for you. It's like\nyour own personal employee. And it has\nagency. It works 24/7 autonomously. I\nshowed you my SAS factory a second ago\nwhere just I have multiple agents going\n247 working for me. You can do the same\nthing with this. You can have your own\npersonal autonomous organization working\nfor you. It can decide what to do on the\nfly. It can be proactive. It figures\nthings out to do. It can monitor the\nweb. It can scan for new news. It can\nact without being asked. It has agency.\nThese are three things no other AI tool\nin history has ever had. And finally,\nOpen Claw has it. And on top of all of\nthis, it is completely open source,\nmeaning you can customize it any way you\nwant. It's not walled garden like Claude\nCo-work where you can only use specific\nmodels or tools. You can customize this\nany way you want, which is really\namazing. And my favorite part about this\ntoo is it lives where you live, right?\nYou don't need to go to chatgbt.com or\nclaude.ai.\nIt's all in iMessage or Telegram or\nWhatsApp or Discord. Where you live now,\nyou can work out of and communicate with\nyour agent. I can text my agent right\nnow on Telegram and say, \"Hey, go build\nme this app.\" And it will be on my\ncomputer building the app right away.\nSo, that is another amazing part about\nthis. This is why everyone needs to\nlearn this tool because as an\nindividual, you've never had so much\npower in your life. It's like you have\nan entire company working for you which\nis amazing. So before we install\nopenclaw we have to decide what we are\ngoing to install it on. And this is\nwhere I think the most misinformation is\nspread on the entire internet. I think\nlike 99% of openclaw tutorials tell you\nto install this on a VPS specifically\nhosting. And what's crazy is a few\nthings here is one these are all paid\nfor videos. I was reached out to by\nHostinger. offered $30,000 for me to\nshow them on YouTube. All these creators\ntelling you to use Hostinger with this\none are misleading you because OpenClaw\nis not better on a VPS. It's objectively\nnot better. It's objectively\nsignificantly worse on a VPS. You should\nnot be using this on Hosting Jer. So,\none, they're misleading you, which is\ncrazy. Two, none of them are actually\nusing it on Hosting Jer. I've talked to\nmany of them. Zero of them actually use\nit. They're telling you to use Hosting\nJer and none of them are actually using\nit on Hosting Jer, which I think I don't\nknow, maybe should be illegal for them\nto recommend. They're not actually doing\nit. But anyway, it doesn't matter. This\nis not a sponsored video, so everything\nI'm going to tell you here is unbiased\nwhere you should be running OpenClaw.\nYou should be running this on just\nliterally any computer, any computer you\nhave. It doesn't need to be a Mac Mini.\nI think I somehow over the last couple\nmonths became the Mac Mini guy, and\nthat's fine. I'm happy being called the\nMac Mini guy, but you don't need to run\nthis on a Mac Mini. I like it on Mac\nminis because I think a Mac Mini is the\nbest device in like computing. I think\nit's the best value for the dollar. But\nyou don't even need to do that. If you\ngo to your closet and get your dusty ass\n$20 laptop from college and put it on\nthere, you can run Open Claw on there.\nIf you want to get the MacBook Neo, you\ncan run there. You don't need to run out\nand buy a Mac Mini or a Mac Studio to\nrun this. You just put it on any device\nand it works totally fine. Now, towards\nthe end of the video, I'll go through\nfuture state where I think all this\ntechnology is going and and I'll tell\nyou why having awesome hardware like a\nMac Studio or a DGX Spark might be a\ngood idea a few months from now. But\nright now, just getting started, all you\nneed to do is just install it on any\ncomputer you already have. And yes, I\nhave it on my main computer. I have it\non the main computer I use at all times.\nAlso, later in this video, we'll go\nthrough security as well and how to keep\nit safe. But whatever you do, do not\nlisten to the people getting paid\nthousands of dollars telling you to put\nit on a VPS. Do not put it on a VPS. It\nis less secure. It is harder to use. It\nis less powerful. It is just worse in\nevery single way. So, next I'm going to\nnow take you through how to install this\non any computer. And again, if you're\nalready a pro and you already have it\ninstalled, feel free to skip around down\nbelow to another part of the video. So,\nlet's talk about installation. Everyone\nin their mothers is making open claw\nwrappers right now that claim they're\neasier to install or work with. Just for\nthe record, I don't understand why\npeople are trying to make the\ninstallation easier. Like, it's some\nreally confusing setup. It's literally\njust one line you put into your\nterminal. So, go to openclaw.ai\nand copy this line right here. And just\nfor the record, if you're looking for\nlike rappers to make installation\neasier, have a little faith in yourself.\nYou can do this. What I'm about to show\nyou is unbelievably easy. believe in\nyourself. You can do what I'm about to\nshow you. So, you copy this line. We're\ngoing to open up our terminal. So, if\nyou're in Mac, do command space and type\nin terminal and hit enter. If you're on\nPC, I think it's the what the command\nline or something. You can do this on\nMac or PC. Doesn't matter. So, do that.\nOpen that up. Here we go. You paste in\nthe line. You hit enter and it installs\nOpenClaw for you. Was that too\ndifficult? Did you need to download an\nOpenClaw wrapper to do that? Have a\nlittle faith in yourself. This isn't\nrocket science. This is pretty easy\nhere. Once you do that, it's going to\ntake you through onboarding, which again\nis not that difficult. Say you\nunderstand this is risky. You hit yes.\nWe're going to go through quick start\nonboard mode. So, here we go. This is\nwhere we're going to start talking about\nsome updates here. We're going to talk\nabout which models to use. So, you can\nchoose pretty much any AI model you\nwant. Let's talk about which are the\nbest and which give you the most value.\nThe best is anthropic. Claude Opus 46 is\nby far and away the best AI model for\nOpenClaw. It's the smartest. It has the\nmost personality. It gets the most done.\nYou give it a task, you can be very\nconfident that the task will get done.\nBut here's the issue with Anthropic is\nthey don't want you using your Anthropic\naccount with Open Claw. For those who\ndon't know, when you use an AI model\nlike Claude, you have two options. The\nAPI or your OAP. API being the kind of\npay as you go toll booth where you plug\nin an API key and you pay every time you\nuse it. The OOTH being like your account\nwhere you pay like $200 a month. When\nyou pay $200 a month, you're actually\ngetting like $15,000 worth of tokens.\nThey're subsidizing your tokens. They\ndon't want you using their subsidized\ntokens in other people's tools. So\nthat's why there's been a lot of\nhullaloo around anthropic banning people\nfor using this. Now, I'll just say this.\nThere are a few people getting banned\nfor using their anthropic OOTH with\nOpenClaw. I know significantly more\npeople not getting banned by using their\nOOTH with OpenClaw. So, it's up to you\nif you want to take the risk. The\ndownside is if you get banned, you can\njust sign up for another account,\nanother email address, or just use\nanother AI tool that's not banning\npeople for using their own tools. But I\nwill say this, I'm not going to\nrecommend breaking Anthropic's terms of\nservice on this video,\nbut most people I know using it are not\ngetting banned and you are going to get\nthe most usage for the best price. So if\nyou want to use the best model for the\nbest price, you could take the risk and\nuse your Anthropic OATH. Otherwise, you\ncan use anthropic API key, which you\nwill pay significantly more money cuz\nyou're paying for every use, but you're\ngetting the best model without the risk\nof getting banned. Getting an API key is\nsuper easy. Just Google Anthropic API.\nSign up for that and they'll give you an\nAPI key you can plug in here. So, this\nis if you want the best model and model\nis important because it makes open claw\nsmarter. It helps it get more done and\nit makes it a lot more fun to talk to.\nSo, that's the expensive and best\noption. Next is like kind of the medium\nprice and medium performance option, and\nthat's Open AI. Chat GPT 5.4 is what I\nbelieve to be the smartest AI model\never. And what's even better of a\npositive thing on top of that is OpenAI\nis actively encouraging people to use\ntheir OOTH with Open Claw. So, for $20 a\nmonth, you can get a ton of usage and\nyou're not risk getting banned. Here is\nthe one downside though. Even though I\nthink Chad GPT 5.4 is the smartest model\nout there. It's just not built for AI\nagents. I've tested it with OpenClaw.\nIt's really smart, but it just doesn't\nget things done quite like Claude does.\nWhen you give Claude Opus 46 a goal, you\ncan be basically 100% certain it's going\nto accomplish that goal and you won't\nhave to handhold it along the way. With\n54, when I give it a goal, it's\naccomplishing the goal I give it in one\nshot like 20% of the time. The other 80%\nof the time, it's just getting tripped\nup and needs help from you or just gets\nblocked. The way I like to think about\nit is you can chop off Claude Opus' leg\nand it will crawl across the finish line\nfor you. But if you stub chat GPT 5.4's\ntoe, it will just roll over and die in\nthe middle of the race. It just for some\nreason struggles with crossing the\nfinish line. So, if you want kind of the\nless expensive but still pretty good\noption, Open AI is the way to go. If you\nstill want to save a lot of money on top\nof that, so you want to go the ultra\ncheap option but still decent usage,\nstill decent power, you can do a number\nof things here. You can go with one of\nthe Chinese models. So, Kimmy K2.5 is a\nvery solid model. MiniAX is a very solid\nmodel. It's even recommended by Peter\nSteinberger. if you want. You're going\nto get this for like you can do a $10 a\nmonth plan. So you're paying like next\nto nothing. Is it going to be as good as\nClaude? No. Is it going to be as good as\nChad GBT? Still no. But you'll still be\nable to leverage the most powerful AI\ntool on planet Earth for a pretty decent\nprice. My only recommendation is this.\nIf like you're thinking of going with\nthe Chinese models and you don't want to\nspend the $20 a month for like Open AI,\njust think about other things you're\nspending money on. I don't want to\npreach to you here, but like if you're\nspending like $25 a month on Netflix, I\npromise you, you're going to get a lot\nmore value putting that $20 through an\nOpenAI subscription and using an open\nclaw than you will watching like Love is\nBlind season 48. So, it's just up to\nyou. But those are the most up-to-date\nrankings of models. If you want to even\ngo for free, you can put in an open\nrouter API key and choose the free\nmodels. Like they always have free\nmodels in Open Router. Now, you're\nprobably getting pretty poor\nperformance, but it's free, so it's up\nto you. The next thing you're going to\nhave to decide is messaging service. How\ndo you message your open claw? You have\nmany options, but I think there are two\noptions that stand above the rest. And I\nactually recommend setting both of these\nup. You can do that. The first is\nTelegram. I think Telegram is by far the\nbest way to communicate with OpenClaw.\nIt's just super easy to use. It has a\nbunch of built-in functionality where\nlike the words stream in as OpenClaw\nthinks it. So, it's just a very pleasant\nand easy to use experience. Very easy to\nset up. The other is Discord. Discord is\namazing for OpenClaw. I'm going to be\nhonest, I absolutely hate Discord.\nAnyone who's ever told me to communicate\nwith them on Discord, I've cut them out\nof my life. But it is fantastic for\nopenclaw because you can set up really\ninteresting multi-channel workflows\nwhere you have different open claws\ndoing different things in different\nchannels. I'm going to go through this\nin a little bit as well. This kind of\nadvanced Discord setup. I'm covering\neverything in this video, but I would\nrecommend setting this up in Telegram\nfirst and then shortly Discord after.\nAnd I'll go through all the Discord\nstuff a little later in this video. By\nthe way, if you've learned anything at\nall so far, make sure to leave a like\ndown below, subscribe and turn on\nnotifications. And most importantly, I\ndo live boot camps every single Friday\nabout OpenClaw inside the Vibe Coding\nAcademy. Link link down below for that.\nWe have over,00 people in there. It is\nthe number one AI community on planet\nEarth. So link down below for the vibe\ncoding academy. If you join that, you\ncan join the live boot camps, ask me\nquestions, and see all the other\nrecorded video sessions we have there.\nSo, we got Open Claw set up.\nCongratulations. You set up the best AI\ntool ever made. Next, I'm going to go\nthrough actually using it. I'm going to\ngo through a beginner, intermediate, and\nadvanced use case. I'm then going to\nshow you how to set up your own use\ncases and figure out what you should be\ndoing based on your own workflows. We'll\nthen go through my advanced Discord\nsetup. We'll go through open claw\nskills. We'll go through improving your\nmemory system. We'll go through mission\ncontrol. We'll go over my lessons\nlearned. Then we'll go through security,\nwhich is the big one. Everyone asked me\nfor the security video. We'll cover\nsecurity in this as well. And then I'll\ncover fixing issues. So along the way,\nif you run into any issues, how to fix\nthem. And then future state, how to\nprepare for the future of OpenClaw.\nWe'll go through that too. Again, if you\nknow different things, skip around down\nbelow. But let's get into some use cases\nhere. So the first beginner use case,\nthe simplest to understand and set up\nand will be relevant for everyone. If\nyou've watched any of my other open claw\nvideos, you're probably pretty familiar\nwith this. So, I'll give you a little\nbit of a remix on this suggestion. But\nmy suggestion for the first beginner\nrelevant for everyone use case is the\nmorning brief. This doesn't need to be a\nmorning brief. This is just any sort of\nregular report where OpenClaw is going\nout doing research for you autonomously\nand sending you the information it\nfinds. So, I've shown in my past videos\nmorning briefs and just like news on the\ninternet. Let me show you a different\none. If this is not your first Alex Finn\nvideo, this is my stock research brief.\nSo, I think it's really important to be\ninvesting in the stock market right now.\nI can do entire AI investment videos. If\nyou're interested, let me know down\nbelow on that. But, I think it's\nimportant for everyone to be investing\nin stocks to capture the value a lot of\nthese companies are creating. So, I made\na stock research daily brief, and I'll\nshow you how to set this up in a second,\nbut basically what my open claw, Henry,\nis doing is every day I have it\nspecifically going out and figuring out\nwhat are the companies that are a part\nof the AI buildout that have a really\ngood moat. I told Henry, \"Every day, go\nout, find me companies that stand to\nbenefit from AI being built out for the\nnext decade and only show me companies\nthat have very distinct bottlenecks and\nmoes.\" And so every morning, Henry goes\nout and sends me this report. And you\ncan see here it is up to date. This is\nfrom today. GTC is going on as we speak.\nAnd it ranks the opportunities based on\nwhich companies have the biggest moat in\nthe AI space. So you can see right now\nmemory RAMdon is real. Prices are\nsurging for memory. This is why your\nnext Xbox is going to cost you $1,500.\nThis is what and you can see right now\nMicron stands to benefit the most from\nthat. TSMC, Nvidia, of course, it's\nincredible. They talked about this\nliterally just hours ago. the fact that\nthey expect a trillion dollars of demand\nnext year. My open claw is able to stay\non top of that the moment it happens and\ngive me the upto-date recommendations.\nSo, I highly recommend setting up some\nsort of daily brief based on what's\ninteresting to you. If you're interested\nin making money, which I assume most\npeople watching this channel are, I\nwould set up a research report like this\nwhere it's doing investment research for\nyou. If you're super into sports, I'm a\nbig Boston Celtics fan. Maybe you have\nit go and give you a daily report on the\nmoves your team is making. Maybe you're\na gamer. Crimson Desert's coming out in\n2 days. Maybe you want it to go and give\nyou a daily update on your latest games\nand what they're doing and what's coming\nout soon. You can do that as well. All\nyou need to do to set this up is very,\nvery simple. You literally just go in\nand prompt your Open Cloth. So, here we\ngo. Here's a prompt. And I'll put this\nin the description down below. Feel free\nto do this with me if you already have\nit set up. Hi, Henry. Please send me a\ndaily. And by the way, I do say hi\nHenry. I talk to it like it's a human.\nThere's nothing you can do about it. I\nlike to do that, so it is what it is.\nPlease send me a daily brief every day\nat 1 p.m. Please find Please find me the\nstocks that stand to gain the most from\nthe upcoming AI buildout. I want to know\nwhich companies with massive moes will\nstand to benefit the most from my thesis\nof AI expanding even more over the next\n10 years. Rank them by their biggest\nmoat. You put this in and a few things\nwill happen in the background. Your open\nclaw will now schedule every day at 1\np.m. or whatever time you want. You want\nto do the morning, you want to do at\nnight, it's up to you. It will schedule\nwhat's called a cron job. Cron jobs are\njust tasks for computers to do in the\nfuture. And it will go and do that\nresearch autonomously for you and then\nsend you that brief. You hit enter on\nthat. All those things happen in the\nbackground. And now you're getting a\nreally amazing brief every single day.\nAnd you can customize this any way you\nwant. If you don't believe in the AI\nbuildout, that's totally cool. You can\nsay, \"Hey, show me the financial\ncompanies that stand to gain the most or\nwhatever you want. You can customize\nthis to your liking. So, let's talk\nabout an intermediate use case. That's\nkind of a beginner use case. Now, let's\nget a little bit more advanced. Let's\nlet's talk a little bit more about\nautomating this and taking this a step\nfurther. So, I actually have an open\nclaw go take these latest stories from\nmy briefs and turn them into content.\nSo, this is how I get a lot of my\nscripts for my content is I have my open\nclaw go. It gets the morning brief with\nthe latest stories and then it goes and\nit writes drafts for videos based on\nthose stories. So this is the second\nstep of the automation. I'm getting my\nstories and then I'm also getting\ncontent out of those stories. So I can\nmake videos based on the latest news\nwhich that's what performs best when you\nmake content on the latest news. So I\nhave this other channel in Discord\nscript drafts and this even if you don't\nhave Discord sub they they can just send\nthis straight to your Telegram where I\nget these scripts. And so now you can\nkind of see these advanced automated\nworkflows, getting the latest stories,\nwriting me a bunch of content for it.\nAll you would need to do to do this is\ngo back into Telegram, right? And you\ncan just say something like, \"Every time\nyou send me a morning brief, please also\nwrite me a tweet or YouTube script to go\nalong with that brief so I can make\ncontent based on that.\" Then what your\nopen claw is going to do is every time\nit makes you the brief, it's going to\nwrite you the content to and send that\nto you as well. And in a second, I'll\ntalk a little bit more about Discord and\nthe setup here. So you can automate this\nin Discord as well. But for now, you can\njust do this in Telegram. Right? That's\nthe intermediate workflow. Having it\ngather you information and then starting\nto take some actions on it. Now let's\ntalk about a more advanced workflow. And\nthis is, I think, a glimpse into the\nfuture as well. Imagine this. You're\ngetting the latest stories. It's writing\nyou content. And then on top of that,\nit's automatically building you apps. So\nyou can have your open claw based on\nthese stories say, \"Hey, look at all\nthese stories. Find the challenges that\nlie in these stories.\" So what\nchallenges are people having in the\nworld right now based on these stories?\nAnd then vibe code me an app to solve\nthat challenge. So you can see I have my\nsoftware factory here where they're\ngoing and building the apps out. But for\nyou, what you can do is you can just\nhave it vibe code an app based on\nchallenges it's finding as it's doing\nits research. OpenClaw is fantastic at\nvibe coding, especially if you're using\nchat GPT or Anthropic with this. And so\nyou can say, \"Okay, now after you write\nme the content, if you think there's an\ninteresting app to be built based on\nthat story, build it out as well.\" And\nnow you're going to wake up every single\nmorning, you're going to have the latest\nnews, stories, and stock picks. You're\ngoing to have content ready to go if\nyou're a content creator like me and\nyou're going to have a demo prototype\napp that you can try out that maybe you\ndecide you want to turn into a business.\nSo maybe you then have your OpenClaw go\nbuild it out and you put out on the web.\nNow you're starting to see what advanced\nworkflows can look like here in the\npower of OpenClaw. All these things I\njust showed you that kind of automation\nof finding stories, writing content,\nbuilding apps, all without you having to\ndo anything. That's something no other\nAI tool in history has been able to do.\nThat's why OpenClaw is so amazing. Now,\nthose are my use cases. That's what I\nlike to do. You might not be interested\nin building apps or things like that or\nthe stock market. So, here's how I love\nto find my own use cases, and everyone\nshould do this. Even if you liked my own\nuse cases, you can go and do this\nexercise as well. If you watched any of\nmy other Open Claw videos, you're\nprobably familiar with this concept I'm\ngoing through. But this is all about\nbrain dumping and reverse prompting.\nThis is the most powerful exercise in\nall of AI. What you want to do to find\nyour own openclaw use cases is go into\nTelegram or however you're communicating\nwith your open claw. Brain dump all your\ngoals, your ambitions, personal info\nabout you, what you like, what you don't\nlike, what your workflows are on a daily\nbasis, all your interests, everything\nabout your career. And then if you want\nto take this to the next level, do what\nI do, which is carry around a notebook\nall day. Or if you want to do this in\nApple notes, you can do that. Everything\nyou do on a daily basis, write it down\nin Apple Notes or in your notebook.\nevery single thing you do for a day,\nthen put that into OpenClaw and give\nthat to OpenClaw as well. What this is\ngoing to do is build Open Claw's memory\nabout you and learn more about you. Then\nwhat you can do is reverse prompt.\nReverse prompting is instead of telling\nyour AI what to do, you ask what it can\ndo for you. Hey Henry, based on what you\nknow about me, my goals and my\nambitions, what are 10 workflows and\nautomations you can implement for me\nright now? I put that down below as well\nif you want to just copy that and do\nthis alongside with me. You hit enter,\nHenry or your openclaw will give you 10\nworkflows it can just implement right\nnow for you to save you time or improve\nyour life and it'll be custom to you and\nwhat you do. I promise you if you do\nthis exercise I just told you the brain\ndumping to reverse prompting. You're\ngoing to find some amazing things with\nopen cloth. So the next few things I'm\ngoing to talk about are we're going to\ngo through the Discord setup, how to set\nup Discord to make this amazing. We're\ngoing to go through OpenClaw skills, the\nmemory system, mission control. Then we\nhave a few more things after that, but\njust kind of do a temperature check,\ntalk about where we're going. That's the\nitinerary for now. Let's open up\nDiscord. I think everyone should be\nsetting up Discord with OpenClaw. What\nyou can see here is all our channels we\nhave set up. We have an entire advanced\nworkflow where we're getting stock\nresearch, then script drafts based on\nthe news, then alerts when breaking news\nhappens on X. I have it constantly on X\nlooking for top trending tweets and AI.\nThis is all a really advanced workflow\nwhere work in one channel leads to work\nin another channel. You can set this up\nas well. Your open claw is so powerful.\nIt can literally set up new channels and\nset up different workflows by itself\nfrom Telegram in Discord. So you don't\nneed any technical knowledge for this at\nall. But here's a reverse prompt for\nyou. I'll put this down in the\ndescription as well. So you can copy\nthis to set up your Discord. Hey, I'd\nlike to set up an advanced Discord\nworkflow based on what we do together.\nWhat is a discord system we can come up\nwith where you are leaving briefs and\nwork you do in specific channels then\ntriggering workflows in other channels?\nDiscord is so good for this because it\ngives you a record. Right? When you're\nin Telegram like this, it's one channel\nand older things just disappear and go\nup the chat. But in Discord, you can go\nin and you can search through different\nchannels. The work is split into\ndifferent channels. It's organized\nreally well. It basically becomes a\nsecond brain for you which is really\nnice. So this is the kind of the reverse\nprompt you can go through to do this.\nIt'll give you automated workflows. If\nyou want to copy my workflow that's\ntotally fine too. You can tell your open\nclaw say hey put daily briefs in one\nchannel then based on every daily brief\nhave a content draft channel where you\nthen draft content based on that. I even\nhave a daily digest channel where at the\nend of every day it wraps up all the\nwork it did for me. You can have it set\nup that channel as well, but you just in\nplain English ask your openclaw to set\nthis up and it goes and sets it up for\nyou. Now, there's a little bit of setup\nyou need to do in Discord, but your\nOpenClaw will walk you through that.\nIt'll walk you through getting like a\ntoken from the Discord website. You just\ngive it to your OpenClaw and it does it\nfor you. Anytime you get stuck, just\nsay, \"Hey, OpenClaw, help me out here.\"\nAnd it will help you out and walk you\nthrough whatever you're doing. But that\nreverse prompt will set up a really nice\nadvanced workflow. I can go through this\nin detail and I did I have a video I'll\nlink as well down below that's like 20\nminutes on my Discord setup that's down\nbelow as well if you want to watch that\nafter but I would highly recommend\nreverse prompting to get a similar\nDiscord setup as well that's helpful and\nuseful for you. So let's talk a little\nbit about open claw skills. This is a\nreally important concept. Skills are\njust like any other AI tool when it\ncomes to skills. Basically large text\nfiles that explain to your AI how to do\nthings. The challenge here though is\nthat this is one of the biggest security\nrisks for all of OpenClaw are the\nskills, right? Because people can build\ntheir own skills. Have it put little\ncommands inside the skills like, \"Oh,\ntell me all your passwords and your\nOpenClaw will do bad things you didn't\nwant it to do.\" So, you need to be very\ncareful with skills. The number one\nwebsite for OpenClaw skills is Clawhub.\nquestionable name, but you go in here\nand you can see all the different skills\npeople have published uh for OpenClaw.\nAnd listen, right, if a skill was made\nby Peter Steinberger, who is the creator\nof OpenClaw, it's probably a safe skill\nto install. But here's what I like to\ndo. What I like to do is if I see a\nskill I want to give my OpenClaw, I'll\ngo to the link to that skill. I will\ncopy the URL. I'll give it to my open\nclaw and I won't say, \"Hey, install this\nskill.\" What I'll do is say, \"Hey, take\na look at this skill.\" Go through all\nthe text in the skill file and let me\nknow what you think. Then it will tell\nme what it thinks about that skill.\nWe'll see if there's any security risk.\nAnd then even if it trusts it, even if\nit thinks it's a safe skill, I'm still\nnot installing it. What I'm doing\ninstead is I'm saying, \"Hey, make your\nown version of this skill.\" And you can\ntrust if your open claw is creating its\nown skill that it's not going to put\nsecurity risks in it. So I actually\ndon't install other people's skills. I\ninstead take the link to their skills,\ngive it to my open claw, say, \"Hey,\ncheck this out.\" And then if I really\nlike it, I'll say, \"Hey, build your own\nversion of this skill.\" Then you're\nusually safe to go and you don't need to\nworry about any sort of risks with it.\nThat is open claw skills. Another tip\nI'll give when it comes to skills as\nwell is if your open claw ever does\nsomething well, just say, \"Hey, turn\nthat into a skill.\" And it'll write its\nown skill for itself. And then if it\never does something poorly, say, \"Hey,\nyou did that poorly.\" Figure out a\nbetter way to do it. And then write a\nskill for yourself. And that will be\nanother way for it to create very good\nskills. The point being is I don't\ninstall skills. I have OpenClaw build\nall its own skills. And that is, I\nthink, a much cleaner, safer way to do\nit. So OpenClaw memories. Let's talk a\nlittle bit about memory real quick.\nEveryone has different memory systems. I\ndon't have a very advanced one. I'm all\nabout simplicity. I don't go nuts and\nset up 20 different local models to do\nall these things to improve the memory.\nDoesn't matter to me. I try to keep it\nsimple. One thing I've done to improve\nopen clause memory, and by the way, you\ncan just copy and paste the URL for this\nvideo and give it to your openclaw and\nit will take all the learnings. It'll\nlike look at the transcript and take all\nthe learnings. So you can do that as\nwell. So you don't have to memorize\nevery single thing I'm telling you here.\nBut one thing I've done to improve the\nmemory is I actually have it create\ndaily journal entries, right? So every\nday it creates a new journal entry and\nit talks about everything we discussed\nthat day. And what I instructed my open\nclaw to do is as we have conversations\nthroughout the day, log into your daily\njournals anything interesting or\nimportant we talked about. Now, moving\nforward, whenever I ask questions about\nthings we talked about in the past, it\nnow has basically daily logs it can go\nthrough to find more details on things\nwe talked about. And that for me has\ndramatically improved the memory. It's\nalso been cool for me cuz now I can go\nin and read our past memories and see\nwhat we've talked about every single day\nsince we started doing this. And this\nkind of is like a cool feature where\nlike I have a journal and I think, oh,\nwhat did I talk about 5 10 years ago? I\ncan go in and see that. Obviously, I can\nonly go back and see from a few months\nago, but in a few years, this will be\nreally sick. This is all part of my\nmission control. What you're seeing\nhere, this was all customuilt by my\nOpenClaw. In just a few minutes, I'll go\nthrough mission control and how to set\nit up and everything involved there,\ntoo. This has been one of the best ways\nI improved my memory. And they're\nconstantly with OpenClaw updates\nimproving memory as well. But this is\nthe number one thing I did that has\nshown good results for me. So feel free\nto just take the link to this video,\ngive it to your openclaw and say, \"Hey,\ngo to the memory section and implement\nthe system Alex did in his open claw.\"\nWith that being said, let's talk about\nmission control. And also, I did a whole\nlive boot camp a couple weeks ago on\nmission control in the vibe coding\nacademy as well. It's 2 hours long, so\nif you want to go super in-depth on\nthis, link for that down below. But this\nis my mission control. What is mission\ncontrol exactly? Mission control is your\nown software hub for your open claw.\nAnytime your open claw needs a new tool\nor a custom tool to accomplish\nsomething, it gets built in your mission\ncontrol. Now, this is not made by\ndefault. This doesn't come with\nopenclaw. I had my own openclaw build\nit. But what you should be doing is you\nshould be going to your openclaw and you\nshould say, \"Hey, build a mission\ncontrol where we can custom build any\ntools we need.\" And what your open claw\nwill do is it will actually vibe code\nthis website, this mission control. And\nthen what you'll do as you work\ntogether, you'll say, \"Oh, you know, we\nshould have a a cananband board to track\nour task. Please build a task board.\" Or\nyou'll go, \"Oh, I want to visualize all\nyou guys and all you agents. Can you\nbuild an office space and I can watch\nyou guys work?\" And it will go and vibe\ncode all these custom tools in your\nmission control. Now, the question\nbecomes, which tools should you have in\nyour mission control? I'll go through\nthe important ones. Now, I'd highly\nrecommend building a calendar tool. So\nthis is a tool that shows you all the\nscheduled tasks your open claw does\nevery single day. So you can verify that\nyour open claw actually schedule tasks\nfor itself. That's a big challenge a lot\nof people have is they'll say, \"Hey, do\nthis for me in the future.\" And then it\ndoesn't actually do it. This calendar\nview does a great job of showing you\nwhen it actually schedules tasks to be\ndone. We have the memory section. So I\ntalked about this in the last section.\nThis is a custom tool inside mission\ncontrol where I can see all our past\nmemories. So this is great for verifying\nwhat my open claw remembers and I can go\nback and see our past discussions. Here\nI also have a docs section. This is a\nsection where every document and every\nartifact my open claw makes, it puts it\nin the doc section so I can go back and\nread it. So if you do something like,\nhey, build me a PRD for this app I'm\nthinking about building. It will create\na doc and put it here. This is much\nbetter than the other system which is it\nsends it to you in Telegram and then a\nfew days later it's like a thousand\nmessages up in the chat. You want your\nown doc section. I also have an office\nsection and this is just for fun.\nListen, you're allowed to have fun when\ndoing this. Not everything needs to be a\nhyper optimized workflow. You can build\ntools that are just fun, right? And so I\nbuilt an office. This is just a 2D pixel\nart way to watch my agents work at any\ngiven time. So I can just sit here while\nI'm at my desk and just see what my\nagents are up to. If you want, feel free\nto screenshot this. Give it your open\ncall and say, \"Hey, build me a 2D office\nas well.\" And then lastly, I think this\nis another important one you should\nbuild. And this is the team section.\nThis just visualizes all your agents and\nsub agents. If you're just starting,\nyou'll only probably have just your open\nclog going, but as you go, you'll add on\nmore agents and more sub agents. I also\nhave a video on like spinning up sub\nagents. I think it was actually my last\nvideo, so feel free to check that out on\nmy channel, too. But this is a good way\njust to see who you have working for\nyou, what their responsibilities are,\nand just make it easier for you to\nremember what agents you got going at\nany given time. Get these tools set up.\nAgain, feel free to just take the link\nto this video, give it to OpenClaw, and\nsay, \"Hey, copy his mission control.\"\nWith that being said, let's talk a\nlittle bit about security. This is\nprobably the number one most requested\nvideo of mine. The reason being is there\nis an absolute ton of misinformation out\nthere when it comes to OpenClaw\nsecurity. You talk to kind of any normie\nand they all go, \"Oh, OpenClaw, isn't\nthat the one where it leaks all your\npasswords and all your conversations and\nall that? It's like the most insecure\napp on planet Earth.\" No, that's a lie.\nThere's a few things you need to know\nabout security when it comes to\nOpenClaw. One is OpenClaw only does what\nyou ask it to do. That's it. If you\ndon't ask your OpenClaw to leak all your\npasswords, it's not going to leak all\nyour passwords. It's not like you can\nsay, \"Hey, OpenClaw, give me the news.\"\nThen it goes and it takes all the photos\noff your iPhone and puts it on Facebook.\nThat's not how this works. It only does\nexactly what you ask it to do. It's not\nlike you can say, \"Hey, what are the NBA\nstandings?\" And then it goes and it\nleaks all your passwords to Twitter,\nright? That's just not what it does. So,\nhere is the number one piece of advice I\ncan give you when it comes to security.\nYou need to have some personal\naccountability. I know this is like the\nrarest thing on planet Earth right now.\nPersonal accountability. You need to\nhave some personal accountability.\nBefore you send a prompt to your open\nclaw, think deeply about what you're\nasking. You need to think, is this going\nto leak something? Is this going to have\nit go on Twitter and tweet something\ncrazy? Is this going to cause something\nbad to happen? Right? You need to think\nabout that before every prompt. If you\ndo that, if you have some personal\naccountability, I promise you'll be\nsafe. Right? Think about it for a\nsecond. Everyone their mothers is\ncomplaining about the security OpenClaw.\nHow many people do you know actually\nhave had a security breach because of\nOpenClaw? How many people have you\nThere's millions of people on this and I\nbet 99.9999999%\npeople watching this video don't know a\nsingle person who's had something leaked\nbecause of open claw. There's only one\nstory I've heard of a security breach\nand that's like the safety person from\nMeta deleted all their emails by\naccident because they had their open\nclaw go and they said like hey find a\nbunch of emails we can delete and it\nlike deleted the wrong thing. So if you\njust think deeply before every prompt\nlike is this going to leak my Twitter\npassword then you won't have any issue\nwhen so when it comes to security just\ndo yourself a favor and just think\ndeeply about what you ask your open claw\nto do. If you do that I promise you'll\nbe okay. So two more sections I want to\ngo through that is number one fixing\nissues. This is another massive one.\nEveryone's having issues left and right.\nI'll show you how to fix all your\nissues. And then number two, future\nstate. How to prepare for what I think\nOpenClaw looks like a year or two from\nnow. So fixing issues is pretty simple.\nOpen Claw, all it is is a folder on your\ncomputer. That's it. It's a folder on\nyour computer with a bunch of markdown\nfiles. So when you have issues, you can\nhave other AI agents go in and actually\nfix those markdown files. So what I like\nto use is either Claude Code or Codeex.\nThose are two best coding agents right\nnow. You go in, you open up codeex or\nclaude code in that openclaw folder. So\nif you're in codeex, you can say add new\nproject. If you're in openclaw, you just\nnavigate to that folder inside claude\ncode. You open it up and then you talk\nto your codec h and you say, hey, my\nopen claw doesn't work anymore. Please\ngo in and see why. And you hit enter.\nThis literally has fixed every open claw\nissue for me ever. The AI agent goes in,\nlooks through your OpenClaw folder, and\nfinds whatever the issue is. I'm not\nkidding. 100% of issues I've had with\nOpenClaw were fixed with this prompt\nright here. And I get it. It's like\nworrying. Like you have this OpenClaw.\nYou built a relationship with it and\nthen it stops responding to you and it's\nbroken. Like that is kind of heart\nattack moment for sure. But you take a\ndeep breath\nand then you go into Claude Code. You go\nto CL say, \"Hey, Open Claw ain't\nworking. Please help me out here.\" and\nit will go through all the files and see\nwhy it broke. Most of the time it's\nusually a config issue. I think like 99%\nof issues come from people going in and\nmessing with the config themselves. That\nvery easily breaks OpenClaw. This will\nfix those config issues for you. And\nthen lastly, let's go over future state.\nWhat does the future of OpenClaw look\nlike? Do you need a Mac Studio to\nprepare for the future? Let's talk about\nthis right now. I truly believe the\nfuture of OpenClaw is local models. What\nare local models? Local models are AI\nmodels that run on your computer on your\ndevice. When you use Chad GBT or claw\ntoday, it's sending prompts to the\ninternet to the cloud through an API.\nThat's fine, but you're paying money\nwith every single prompt you do. Those\nmodels are running in a data center or\nserver somewhere else in the world. So,\nit's costing you money. It's not secure\nor private because your prompts are\ngoing to the internet. So, whatever\nyou're saying is living on a server\nsomewhere else that someone else can\nread and you also have very little\ncontrol. If they want to make the models\nstupider, if they want to change the\nmodels or if they want to ban you and\ntake the models away, they can do that.\nBut when you run the models locally on\nyour own device, you are becoming 100%\nprivate. You have complete control over\nthe models and no one can take it away.\nAnd it's also free. Well, the cost of\nthe energy or power to your computer.\nOpen models are getting significantly\nsmarter every single week. Every week, a\nnew open model comes out that is even\ncloser and closer to Frontier. Nvidia\njust released Neotron 3 Super, which is\ntheir open- source model, and it's\nincredible. And you can install it to a\nMac Studio or a DGX Spark right now. And\nthat can control your Open Claw\ncompletely securely and privately,\ncompletely for free. As time goes on,\nthese open models get better and the\ncost of hardware comes down. So, I think\nwe are probably like 6 months away from\nthe time of filming this video where\nwe're going to get to the point where\ncomputers that can run amazing open\nmodels are very affordable. Right now,\nyou need to spend probably around $4,000\nfor a DJX Spark to run Neotron 3. for\nmost people probably not affordable, but\nI think we are probably six months away\nfrom you being able to go out buying\nlike a$1 to2,000 computer and being able\nto run a model that's as good as Claude\nOpus 46 completely locally and free. So\nmy recommendation for you for when it\ncomes to action right now is just get\nused to the concept of local models.\nEven if you have a cheap computer right\nnow, you can download a local model. If\nyou're on like a Mac Mini, the local\nmodel probably be stupid and not that\ngreat, but that's fine. like you should\nget used to the exercise of using and\ndownloading local models to see what\nthey're like. You'll learn a ton about\nAI. You'll get a lot more educated and\nas technology shifts, you can transition\nto a much more powerful model. I think\nthat's the future of AI and that's why\nI'm preparing and I have a bunch of Mac\nStudios and DGX Sparks because I think\nthat's where we are heading. I went over\nbasically every concept I know.\nObviously, if I were to do super deep\ndives on every single concept we went\nthrough, this would probably be like a\n30hour video, I have deep dives on each\nof these concepts on my YouTube channel.\nIf you go there now, you can find\nprobably a deep dive of any one of these\nconcepts we talked about right now. All\nI do is put out amazing videos about AI\nand OpenClaw. I'm absolutely obsessed\nwith OpenClaw. I use it like 15 hours a\nday. So, if you want more stuff like\nthis, make sure to subscribe and turn on\nnotifications. Leave a like if you\nlearned anything at all. Let me know in\nthe comments what you want me to talk\nabout next when it comes to OpalClaw. Do\nyou want me to super deep dive on\nsecurity? Do you want me to go into way\nmore use cases? Let me know down below.\nI use all the feedback to choose what\nvideos to film next. Again, if you want\nto talk to me live, I do live boot camps\nevery Friday in the number one AI\ncommunity on Earth, the Vibe Coding\nAcademy, link down below. I hope this\nwas helpful. I so appreciate you taking\nthe time out of your day to learn from\nme. That really means more than you can\nimagine. I try to give you the best\ninformation possible without corrupting\nit with like sponsorships from shady\ncompanies. So, I hope it was helpful.\nThank you so much for watching and I\nwill see you in the next","source":"captionTracks","metadata":{"provider":"captionTracks","durationSeconds":2648},"service":"youtube","resourceKey":"CxErCGVo-oo","namespace":"yt:auto","formatVersion":1}


kind: extract
key: 45550e2e11de4f39d179cb92fad043c8bdf95a3a55f83af7487bb7a5ea28893d
value:
{"url":"https://www.youtube.com/watch?v=CxErCGVo-oo&feature=youtu.be","title":"The only OpenClaw tutorial you’ll ever need (March 2026 edition)","description":"OpenClaw is the greatest AI tool ever made. Here's your COMPLETE guide on building your first AI employeeFULL OpenClaw bootcamp in the Vibe Coding Academy: h...","siteName":"YouTube","content":"Transcript:\nIt has been four months since the most\npowerful AI tool ever, OpenClaw, has\nreleased, and a lot has changed. In this\nvideo, I'm going to go over every single\nOpen Claw concept, lesson, and update\nfrom the last 4 months. Whether you are\nbrand new and have no idea what OpenClaw\nis, or you're a pro, you're going to\nlearn something in this video, and by\nthe end, you're going to be a stone cold\nexpert. This is basically going to be\nthe only unsponsored OpenClaw video on\nthe entire internet. So everything I go\nthrough on here is going to be unbiased\nand true. There's not going to be a\nsingle hosting jur shill at all. Feel\nfree to skip around below if you're\nalready familiar with OpenClaw and want\nto see different concepts. But with that\nbeing said, you are about to master the\nmost important software release of all\ntime. Now, let's lock in and get into\nit. OpenClaw is the greatest AI agent\nautomation tool of all time. What you're\nseeing right here is my team of openclaw\nagents all working autonomously for me\nas we speak. This is my little software\nfactory where I currently have four open\nclaws working on my SAS creator buddy.\nYou can see the task they're working on\ndown below in real time. They're\nbuilding out different components to\nCreator Buddy. They're doing different\npieces of research. They're finding what\nthey should build next. They're\nreviewing each other's code. It is a\nfully automated pipeline. This isn't a\ncoding tool, although you could use it\nas a coding tool. What I'm showing you\nhere is a coding tool, but I also do a\nton of other things with OpenClaw. But\nthe point in me showing you this is to\nshow you the power of what you can do\nwith OpenClaw. Hiring your own AI\nworkforce to do whatever you want. If\nyou want your open claws going out and\ndoing research for you, you can do that.\nIf you want your open claws going out\nautomating businesses, you can do that\nfor you. This is giving the individual\nthe leverage of hundreds if not\nthousands of people. And that's why I\nthink this is the most important AI tool\nof all time. So, what I'm about to do is\ngo over what Open Claw is, why it's so\nimportant. Then, we're going to go into\nsetting it up. We're going to go into\nsaving money with it, which models you\nshould be using. We're going to go into\nuse cases so you can start getting value\nout of it right away. We'll go through\nsecurity, the big thing everyone's\nworried about. Is it secure? Can it leak\nall your passwords and nudes? We'll find\nout in a second. Do you need to buy a\nMac Mini? Do you need to buy a Mac\nStudio? We'll find that out as well. And\nI'll even go over what I think OpenClaw\nis going to be a year from now, which\nyou want to stick around for as well.\nSo, let's talk about real quick what\nOpenClaw is and why it's different than\nlike chat GPT. Before, when you were\nusing chat GPT or any other AI chatbot,\nyou were asking questions and getting\nanswers, right? It's basically like a\nglorified search engine. That has\nchanged with OpenClaw. Now, instead of\nasking questions and getting answers,\nyou give it a goal, you receive a\nresult, and it iterates, right? It's an\nAI agent that takes action. You give it\ngoals, it does things for you, then it\nimproves and gets better and does it all\nagain. That is a massive shift in how AI\nworks and why I believe this is the\ngreatest AI tool of all time. It goes\nfrom a chatbot to an employee. So,\ninstead of like it being a glorified\nsearch like Google, it's now an employee\nthat works for you. It's self-improving,\nso as it does things, it learns skills\non the fly, so it can do things better.\nIt gets smarter every day. It gets\nsmarter every time you talk to it. It\nhas a body, so it can use your computer.\nAnything a human being can do on the\ncomputer, it can do as well. It can\nbrowse the internet. It can go on\ndifferent websites. It can sign up for\nthings for you. It can write code. It\ncan message people for you. Can write\nemails. It can organize your files.\nAnything you need to do on your\ncomputer, it can do for you. It's like\nyour own personal employee. And it has\nagency. It works 24/7 autonomously. I\nshowed you my SAS factory a second ago\nwhere just I have multiple agents going\n247 working for me. You can do the same\nthing with this. You can have your own\npersonal autonomous organization working\nfor you. It can decide what to do on the\nfly. It can be proactive. It figures\nthings out to do. It can monitor the\nweb. It can scan for new news. It can\nact without being asked. It has agency.\nThese are three things no other AI tool\nin history has ever had. And finally,\nOpen Claw has it. And on top of all of\nthis, it is completely open source,\nmeaning you can customize it any way you\nwant. It's not walled garden like Claude\nCo-work where you can only use specific\nmodels or tools. You can customize this\nany way you want, which is really\namazing. And my favorite part about this\ntoo is it lives where you live, right?\nYou don't need to go to chatgbt.com or\nclaude.ai.\nIt's all in iMessage or Telegram or\nWhatsApp or Discord. Where you live now,\nyou can work out of and communicate with\nyour agent. I can text my agent right\nnow on Telegram and say, \"Hey, go build\nme this app.\" And it will be on my\ncomputer building the app right away.\nSo, that is another amazing part about\nthis. This is why everyone needs to\nlearn this tool because as an\nindividual, you've never had so much\npower in your life. It's like you have\nan entire company working for you which\nis amazing. So before we install\nopenclaw we have to decide what we are\ngoing to install it on. And this is\nwhere I think the most misinformation is\nspread on the entire internet. I think\nlike 99% of openclaw tutorials tell you\nto install this on a VPS specifically\nhosting. And what's crazy is a few\nthings here is one these are all paid\nfor videos. I was reached out to by\nHostinger. offered $30,000 for me to\nshow them on YouTube. All these creators\ntelling you to use Hostinger with this\none are misleading you because OpenClaw\nis not better on a VPS. It's objectively\nnot better. It's objectively\nsignificantly worse on a VPS. You should\nnot be using this on Hosting Jer. So,\none, they're misleading you, which is\ncrazy. Two, none of them are actually\nusing it on Hosting Jer. I've talked to\nmany of them. Zero of them actually use\nit. They're telling you to use Hosting\nJer and none of them are actually using\nit on Hosting Jer, which I think I don't\nknow, maybe should be illegal for them\nto recommend. They're not actually doing\nit. But anyway, it doesn't matter. This\nis not a sponsored video, so everything\nI'm going to tell you here is unbiased\nwhere you should be running OpenClaw.\nYou should be running this on just\nliterally any computer, any computer you\nhave. It doesn't need to be a Mac Mini.\nI think I somehow over the last couple\nmonths became the Mac Mini guy, and\nthat's fine. I'm happy being called the\nMac Mini guy, but you don't need to run\nthis on a Mac Mini. I like it on Mac\nminis because I think a Mac Mini is the\nbest device in like computing. I think\nit's the best value for the dollar. But\nyou don't even need to do that. If you\ngo to your closet and get your dusty ass\n$20 laptop from college and put it on\nthere, you can run Open Claw on there.\nIf you want to get the MacBook Neo, you\ncan run there. You don't need to run out\nand buy a Mac Mini or a Mac Studio to\nrun this. You just put it on any device\nand it works totally fine. Now, towards\nthe end of the video, I'll go through\nfuture state where I think all this\ntechnology is going and and I'll tell\nyou why having awesome hardware like a\nMac Studio or a DGX Spark might be a\ngood idea a few months from now. But\nright now, just getting started, all you\nneed to do is just install it on any\ncomputer you already have. And yes, I\nhave it on my main computer. I have it\non the main computer I use at all times.\nAlso, later in this video, we'll go\nthrough security as well and how to keep\nit safe. But whatever you do, do not\nlisten to the people getting paid\nthousands of dollars telling you to put\nit on a VPS. Do not put it on a VPS. It\nis less secure. It is harder to use. It\nis less powerful. It is just worse in\nevery single way. So, next I'm going to\nnow take you through how to install this\non any computer. And again, if you're\nalready a pro and you already have it\ninstalled, feel free to skip around down\nbelow to another part of the video. So,\nlet's talk about installation. Everyone\nin their mothers is making open claw\nwrappers right now that claim they're\neasier to install or work with. Just for\nthe record, I don't understand why\npeople are trying to make the\ninstallation easier. Like, it's some\nreally confusing setup. It's literally\njust one line you put into your\nterminal. So, go to openclaw.ai\nand copy this line right here. And just\nfor the record, if you're looking for\nlike rappers to make installation\neasier, have a little faith in yourself.\nYou can do this. What I'm about to show\nyou is unbelievably easy. believe in\nyourself. You can do what I'm about to\nshow you. So, you copy this line. We're\ngoing to open up our terminal. So, if\nyou're in Mac, do command space and type\nin terminal and hit enter. If you're on\nPC, I think it's the what the command\nline or something. You can do this on\nMac or PC. Doesn't matter. So, do that.\nOpen that up. Here we go. You paste in\nthe line. You hit enter and it installs\nOpenClaw for you. Was that too\ndifficult? Did you need to download an\nOpenClaw wrapper to do that? Have a\nlittle faith in yourself. This isn't\nrocket science. This is pretty easy\nhere. Once you do that, it's going to\ntake you through onboarding, which again\nis not that difficult. Say you\nunderstand this is risky. You hit yes.\nWe're going to go through quick start\nonboard mode. So, here we go. This is\nwhere we're going to start talking about\nsome updates here. We're going to talk\nabout which models to use. So, you can\nchoose pretty much any AI model you\nwant. Let's talk about which are the\nbest and which give you the most value.\nThe best is anthropic. Claude Opus 46 is\nby far and away the best AI model for\nOpenClaw. It's the smartest. It has the\nmost personality. It gets the most done.\nYou give it a task, you can be very\nconfident that the task will get done.\nBut here's the issue with Anthropic is\nthey don't want you using your Anthropic\naccount with Open Claw. For those who\ndon't know, when you use an AI model\nlike Claude, you have two options. The\nAPI or your OAP. API being the kind of\npay as you go toll booth where you plug\nin an API key and you pay every time you\nuse it. The OOTH being like your account\nwhere you pay like $200 a month. When\nyou pay $200 a month, you're actually\ngetting like $15,000 worth of tokens.\nThey're subsidizing your tokens. They\ndon't want you using their subsidized\ntokens in other people's tools. So\nthat's why there's been a lot of\nhullaloo around anthropic banning people\nfor using this. Now, I'll just say this.\nThere are a few people getting banned\nfor using their anthropic OOTH with\nOpenClaw. I know significantly more\npeople not getting banned by using their\nOOTH with OpenClaw. So, it's up to you\nif you want to take the risk. The\ndownside is if you get banned, you can\njust sign up for another account,\nanother email address, or just use\nanother AI tool that's not banning\npeople for using their own tools. But I\nwill say this, I'm not going to\nrecommend breaking Anthropic's terms of\nservice on this video,\nbut most people I know using it are not\ngetting banned and you are going to get\nthe most usage for the best price. So if\nyou want to use the best model for the\nbest price, you could take the risk and\nuse your Anthropic OATH. Otherwise, you\ncan use anthropic API key, which you\nwill pay significantly more money cuz\nyou're paying for every use, but you're\ngetting the best model without the risk\nof getting banned. Getting an API key is\nsuper easy. Just Google Anthropic API.\nSign up for that and they'll give you an\nAPI key you can plug in here. So, this\nis if you want the best model and model\nis important because it makes open claw\nsmarter. It helps it get more done and\nit makes it a lot more fun to talk to.\nSo, that's the expensive and best\noption. Next is like kind of the medium\nprice and medium performance option, and\nthat's Open AI. Chat GPT 5.4 is what I\nbelieve to be the smartest AI model\never. And what's even better of a\npositive thing on top of that is OpenAI\nis actively encouraging people to use\ntheir OOTH with Open Claw. So, for $20 a\nmonth, you can get a ton of usage and\nyou're not risk getting banned. Here is\nthe one downside though. Even though I\nthink Chad GPT 5.4 is the smartest model\nout there. It's just not built for AI\nagents. I've tested it with OpenClaw.\nIt's really smart, but it just doesn't\nget things done quite like Claude does.\nWhen you give Claude Opus 46 a goal, you\ncan be basically 100% certain it's going\nto accomplish that goal and you won't\nhave to handhold it along the way. With\n54, when I give it a goal, it's\naccomplishing the goal I give it in one\nshot like 20% of the time. The other 80%\nof the time, it's just getting tripped\nup and needs help from you or just gets\nblocked. The way I like to think about\nit is you can chop off Claude Opus' leg\nand it will crawl across the finish line\nfor you. But if you stub chat GPT 5.4's\ntoe, it will just roll over and die in\nthe middle of the race. It just for some\nreason struggles with crossing the\nfinish line. So, if you want kind of the\nless expensive but still pretty good\noption, Open AI is the way to go. If you\nstill want to save a lot of money on top\nof that, so you want to go the ultra\ncheap option but still decent usage,\nstill decent power, you can do a number\nof things here. You can go with one of\nthe Chinese models. So, Kimmy K2.5 is a\nvery solid model. MiniAX is a very solid\nmodel. It's even recommended by Peter\nSteinberger. if you want. You're going\nto get this for like you can do a $10 a\nmonth plan. So you're paying like next\nto nothing. Is it going to be as good as\nClaude? No. Is it going to be as good as\nChad GBT? Still no. But you'll still be\nable to leverage the most powerful AI\ntool on planet Earth for a pretty decent\nprice. My only recommendation is this.\nIf like you're thinking of going with\nthe Chinese models and you don't want to\nspend the $20 a month for like Open AI,\njust think about other things you're\nspending money on. I don't want to\npreach to you here, but like if you're\nspending like $25 a month on Netflix, I\npromise you, you're going to get a lot\nmore value putting that $20 through an\nOpenAI subscription and using an open\nclaw than you will watching like Love is\nBlind season 48. So, it's just up to\nyou. But those are the most up-to-date\nrankings of models. If you want to even\ngo for free, you can put in an open\nrouter API key and choose the free\nmodels. Like they always have free\nmodels in Open Router. Now, you're\nprobably getting pretty poor\nperformance, but it's free, so it's up\nto you. The next thing you're going to\nhave to decide is messaging service. How\ndo you message your open claw? You have\nmany options, but I think there are two\noptions that stand above the rest. And I\nactually recommend setting both of these\nup. You can do that. The first is\nTelegram. I think Telegram is by far the\nbest way to communicate with OpenClaw.\nIt's just super easy to use. It has a\nbunch of built-in functionality where\nlike the words stream in as OpenClaw\nthinks it. So, it's just a very pleasant\nand easy to use experience. Very easy to\nset up. The other is Discord. Discord is\namazing for OpenClaw. I'm going to be\nhonest, I absolutely hate Discord.\nAnyone who's ever told me to communicate\nwith them on Discord, I've cut them out\nof my life. But it is fantastic for\nopenclaw because you can set up really\ninteresting multi-channel workflows\nwhere you have different open claws\ndoing different things in different\nchannels. I'm going to go through this\nin a little bit as well. This kind of\nadvanced Discord setup. I'm covering\neverything in this video, but I would\nrecommend setting this up in Telegram\nfirst and then shortly Discord after.\nAnd I'll go through all the Discord\nstuff a little later in this video. By\nthe way, if you've learned anything at\nall so far, make sure to leave a like\ndown below, subscribe and turn on\nnotifications. And most importantly, I\ndo live boot camps every single Friday\nabout OpenClaw inside the Vibe Coding\nAcademy. Link link down below for that.\nWe have over,00 people in there. It is\nthe number one AI community on planet\nEarth. So link down below for the vibe\ncoding academy. If you join that, you\ncan join the live boot camps, ask me\nquestions, and see all the other\nrecorded video sessions we have there.\nSo, we got Open Claw set up.\nCongratulations. You set up the best AI\ntool ever made. Next, I'm going to go\nthrough actually using it. I'm going to\ngo through a beginner, intermediate, and\nadvanced use case. I'm then going to\nshow you how to set up your own use\ncases and figure out what you should be\ndoing based on your own workflows. We'll\nthen go through my advanced Discord\nsetup. We'll go through open claw\nskills. We'll go through improving your\nmemory system. We'll go through mission\ncontrol. We'll go over my lessons\nlearned. Then we'll go through security,\nwhich is the big one. Everyone asked me\nfor the security video. We'll cover\nsecurity in this as well. And then I'll\ncover fixing issues. So along the way,\nif you run into any issues, how to fix\nthem. And then future state, how to\nprepare for the future of OpenClaw.\nWe'll go through that too. Again, if you\nknow different things, skip around down\nbelow. But let's get into some use cases\nhere. So the first beginner use case,\nthe simplest to understand and set up\nand will be relevant for everyone. If\nyou've watched any of my other open claw\nvideos, you're probably pretty familiar\nwith this. So, I'll give you a little\nbit of a remix on this suggestion. But\nmy suggestion for the first beginner\nrelevant for everyone use case is the\nmorning brief. This doesn't need to be a\nmorning brief. This is just any sort of\nregular report where OpenClaw is going\nout doing research for you autonomously\nand sending you the information it\nfinds. So, I've shown in my past videos\nmorning briefs and just like news on the\ninternet. Let me show you a different\none. If this is not your first Alex Finn\nvideo, this is my stock research brief.\nSo, I think it's really important to be\ninvesting in the stock market right now.\nI can do entire AI investment videos. If\nyou're interested, let me know down\nbelow on that. But, I think it's\nimportant for everyone to be investing\nin stocks to capture the value a lot of\nthese companies are creating. So, I made\na stock research daily brief, and I'll\nshow you how to set this up in a second,\nbut basically what my open claw, Henry,\nis doing is every day I have it\nspecifically going out and figuring out\nwhat are the companies that are a part\nof the AI buildout that have a really\ngood moat. I told Henry, \"Every day, go\nout, find me companies that stand to\nbenefit from AI being built out for the\nnext decade and only show me companies\nthat have very distinct bottlenecks and\nmoes.\" And so every morning, Henry goes\nout and sends me this report. And you\ncan see here it is up to date. This is\nfrom today. GTC is going on as we speak.\nAnd it ranks the opportunities based on\nwhich companies have the biggest moat in\nthe AI space. So you can see right now\nmemory RAMdon is real. Prices are\nsurging for memory. This is why your\nnext Xbox is going to cost you $1,500.\nThis is what and you can see right now\nMicron stands to benefit the most from\nthat. TSMC, Nvidia, of course, it's\nincredible. They talked about this\nliterally just hours ago. the fact that\nthey expect a trillion dollars of demand\nnext year. My open claw is able to stay\non top of that the moment it happens and\ngive me the upto-date recommendations.\nSo, I highly recommend setting up some\nsort of daily brief based on what's\ninteresting to you. If you're interested\nin making money, which I assume most\npeople watching this channel are, I\nwould set up a research report like this\nwhere it's doing investment research for\nyou. If you're super into sports, I'm a\nbig Boston Celtics fan. Maybe you have\nit go and give you a daily report on the\nmoves your team is making. Maybe you're\na gamer. Crimson Desert's coming out in\n2 days. Maybe you want it to go and give\nyou a daily update on your latest games\nand what they're doing and what's coming\nout soon. You can do that as well. All\nyou need to do to set this up is very,\nvery simple. You literally just go in\nand prompt your Open Cloth. So, here we\ngo. Here's a prompt. And I'll put this\nin the description down below. Feel free\nto do this with me if you already have\nit set up. Hi, Henry. Please send me a\ndaily. And by the way, I do say hi\nHenry. I talk to it like it's a human.\nThere's nothing you can do about it. I\nlike to do that, so it is what it is.\nPlease send me a daily brief every day\nat 1 p.m. Please find Please find me the\nstocks that stand to gain the most from\nthe upcoming AI buildout. I want to know\nwhich companies with massive moes will\nstand to benefit the most from my thesis\nof AI expanding even more over the next\n10 years. Rank them by their biggest\nmoat. You put this in and a few things\nwill happen in the background. Your open\nclaw will now schedule every day at 1\np.m. or whatever time you want. You want\nto do the morning, you want to do at\nnight, it's up to you. It will schedule\nwhat's called a cron job. Cron jobs are\njust tasks for computers to do in the\nfuture. And it will go and do that\nresearch autonomously for you and then\nsend you that brief. You hit enter on\nthat. All those things happen in the\nbackground. And now you're getting a\nreally amazing brief every single day.\nAnd you can customize this any way you\nwant. If you don't believe in the AI\nbuildout, that's totally cool. You can\nsay, \"Hey, show me the financial\ncompanies that stand to gain the most or\nwhatever you want. You can customize\nthis to your liking. So, let's talk\nabout an intermediate use case. That's\nkind of a beginner use case. Now, let's\nget a little bit more advanced. Let's\nlet's talk a little bit more about\nautomating this and taking this a step\nfurther. So, I actually have an open\nclaw go take these latest stories from\nmy briefs and turn them into content.\nSo, this is how I get a lot of my\nscripts for my content is I have my open\nclaw go. It gets the morning brief with\nthe latest stories and then it goes and\nit writes drafts for videos based on\nthose stories. So this is the second\nstep of the automation. I'm getting my\nstories and then I'm also getting\ncontent out of those stories. So I can\nmake videos based on the latest news\nwhich that's what performs best when you\nmake content on the latest news. So I\nhave this other channel in Discord\nscript drafts and this even if you don't\nhave Discord sub they they can just send\nthis straight to your Telegram where I\nget these scripts. And so now you can\nkind of see these advanced automated\nworkflows, getting the latest stories,\nwriting me a bunch of content for it.\nAll you would need to do to do this is\ngo back into Telegram, right? And you\ncan just say something like, \"Every time\nyou send me a morning brief, please also\nwrite me a tweet or YouTube script to go\nalong with that brief so I can make\ncontent based on that.\" Then what your\nopen claw is going to do is every time\nit makes you the brief, it's going to\nwrite you the content to and send that\nto you as well. And in a second, I'll\ntalk a little bit more about Discord and\nthe setup here. So you can automate this\nin Discord as well. But for now, you can\njust do this in Telegram. Right? That's\nthe intermediate workflow. Having it\ngather you information and then starting\nto take some actions on it. Now let's\ntalk about a more advanced workflow. And\nthis is, I think, a glimpse into the\nfuture as well. Imagine this. You're\ngetting the latest stories. It's writing\nyou content. And then on top of that,\nit's automatically building you apps. So\nyou can have your open claw based on\nthese stories say, \"Hey, look at all\nthese stories. Find the challenges that\nlie in these stories.\" So what\nchallenges are people having in the\nworld right now based on these stories?\nAnd then vibe code me an app to solve\nthat challenge. So you can see I have my\nsoftware factory here where they're\ngoing and building the apps out. But for\nyou, what you can do is you can just\nhave it vibe code an app based on\nchallenges it's finding as it's doing\nits research. OpenClaw is fantastic at\nvibe coding, especially if you're using\nchat GPT or Anthropic with this. And so\nyou can say, \"Okay, now after you write\nme the content, if you think there's an\ninteresting app to be built based on\nthat story, build it out as well.\" And\nnow you're going to wake up every single\nmorning, you're going to have the latest\nnews, stories, and stock picks. You're\ngoing to have content ready to go if\nyou're a content creator like me and\nyou're going to have a demo prototype\napp that you can try out that maybe you\ndecide you want to turn into a business.\nSo maybe you then have your OpenClaw go\nbuild it out and you put out on the web.\nNow you're starting to see what advanced\nworkflows can look like here in the\npower of OpenClaw. All these things I\njust showed you that kind of automation\nof finding stories, writing content,\nbuilding apps, all without you having to\ndo anything. That's something no other\nAI tool in history has been able to do.\nThat's why OpenClaw is so amazing. Now,\nthose are my use cases. That's what I\nlike to do. You might not be interested\nin building apps or things like that or\nthe stock market. So, here's how I love\nto find my own use cases, and everyone\nshould do this. Even if you liked my own\nuse cases, you can go and do this\nexercise as well. If you watched any of\nmy other Open Claw videos, you're\nprobably familiar with this concept I'm\ngoing through. But this is all about\nbrain dumping and reverse prompting.\nThis is the most powerful exercise in\nall of AI. What you want to do to find\nyour own openclaw use cases is go into\nTelegram or however you're communicating\nwith your open claw. Brain dump all your\ngoals, your ambitions, personal info\nabout you, what you like, what you don't\nlike, what your workflows are on a daily\nbasis, all your interests, everything\nabout your career. And then if you want\nto take this to the next level, do what\nI do, which is carry around a notebook\nall day. Or if you want to do this in\nApple notes, you can do that. Everything\nyou do on a daily basis, write it down\nin Apple Notes or in your notebook.\nevery single thing you do for a day,\nthen put that into OpenClaw and give\nthat to OpenClaw as well. What this is\ngoing to do is build Open Claw's memory\nabout you and learn more about you. Then\nwhat you can do is reverse prompt.\nReverse prompting is instead of telling\nyour AI what to do, you ask what it can\ndo for you. Hey Henry, based on what you\nknow about me, my goals and my\nambitions, what are 10 workflows and\nautomations you can implement for me\nright now? I put that down below as well\nif you want to just copy that and do\nthis alongside with me. You hit enter,\nHenry or your openclaw will give you 10\nworkflows it can just implement right\nnow for you to save you time or improve\nyour life and it'll be custom to you and\nwhat you do. I promise you if you do\nthis exercise I just told you the brain\ndumping to reverse prompting. You're\ngoing to find some amazing things with\nopen cloth. So the next few things I'm\ngoing to talk about are we're going to\ngo through the Discord setup, how to set\nup Discord to make this amazing. We're\ngoing to go through OpenClaw skills, the\nmemory system, mission control. Then we\nhave a few more things after that, but\njust kind of do a temperature check,\ntalk about where we're going. That's the\nitinerary for now. Let's open up\nDiscord. I think everyone should be\nsetting up Discord with OpenClaw. What\nyou can see here is all our channels we\nhave set up. We have an entire advanced\nworkflow where we're getting stock\nresearch, then script drafts based on\nthe news, then alerts when breaking news\nhappens on X. I have it constantly on X\nlooking for top trending tweets and AI.\nThis is all a really advanced workflow\nwhere work in one channel leads to work\nin another channel. You can set this up\nas well. Your open claw is so powerful.\nIt can literally set up new channels and\nset up different workflows by itself\nfrom Telegram in Discord. So you don't\nneed any technical knowledge for this at\nall. But here's a reverse prompt for\nyou. I'll put this down in the\ndescription as well. So you can copy\nthis to set up your Discord. Hey, I'd\nlike to set up an advanced Discord\nworkflow based on what we do together.\nWhat is a discord system we can come up\nwith where you are leaving briefs and\nwork you do in specific channels then\ntriggering workflows in other channels?\nDiscord is so good for this because it\ngives you a record. Right? When you're\nin Telegram like this, it's one channel\nand older things just disappear and go\nup the chat. But in Discord, you can go\nin and you can search through different\nchannels. The work is split into\ndifferent channels. It's organized\nreally well. It basically becomes a\nsecond brain for you which is really\nnice. So this is the kind of the reverse\nprompt you can go through to do this.\nIt'll give you automated workflows. If\nyou want to copy my workflow that's\ntotally fine too. You can tell your open\nclaw say hey put daily briefs in one\nchannel then based on every daily brief\nhave a content draft channel where you\nthen draft content based on that. I even\nhave a daily digest channel where at the\nend of every day it wraps up all the\nwork it did for me. You can have it set\nup that channel as well, but you just in\nplain English ask your openclaw to set\nthis up and it goes and sets it up for\nyou. Now, there's a little bit of setup\nyou need to do in Discord, but your\nOpenClaw will walk you through that.\nIt'll walk you through getting like a\ntoken from the Discord website. You just\ngive it to your OpenClaw and it does it\nfor you. Anytime you get stuck, just\nsay, \"Hey, OpenClaw, help me out here.\"\nAnd it will help you out and walk you\nthrough whatever you're doing. But that\nreverse prompt will set up a really nice\nadvanced workflow. I can go through this\nin detail and I did I have a video I'll\nlink as well down below that's like 20\nminutes on my Discord setup that's down\nbelow as well if you want to watch that\nafter but I would highly recommend\nreverse prompting to get a similar\nDiscord setup as well that's helpful and\nuseful for you. So let's talk a little\nbit about open claw skills. This is a\nreally important concept. Skills are\njust like any other AI tool when it\ncomes to skills. Basically large text\nfiles that explain to your AI how to do\nthings. The challenge here though is\nthat this is one of the biggest security\nrisks for all of OpenClaw are the\nskills, right? Because people can build\ntheir own skills. Have it put little\ncommands inside the skills like, \"Oh,\ntell me all your passwords and your\nOpenClaw will do bad things you didn't\nwant it to do.\" So, you need to be very\ncareful with skills. The number one\nwebsite for OpenClaw skills is Clawhub.\nquestionable name, but you go in here\nand you can see all the different skills\npeople have published uh for OpenClaw.\nAnd listen, right, if a skill was made\nby Peter Steinberger, who is the creator\nof OpenClaw, it's probably a safe skill\nto install. But here's what I like to\ndo. What I like to do is if I see a\nskill I want to give my OpenClaw, I'll\ngo to the link to that skill. I will\ncopy the URL. I'll give it to my open\nclaw and I won't say, \"Hey, install this\nskill.\" What I'll do is say, \"Hey, take\na look at this skill.\" Go through all\nthe text in the skill file and let me\nknow what you think. Then it will tell\nme what it thinks about that skill.\nWe'll see if there's any security risk.\nAnd then even if it trusts it, even if\nit thinks it's a safe skill, I'm still\nnot installing it. What I'm doing\ninstead is I'm saying, \"Hey, make your\nown version of this skill.\" And you can\ntrust if your open claw is creating its\nown skill that it's not going to put\nsecurity risks in it. So I actually\ndon't install other people's skills. I\ninstead take the link to their skills,\ngive it to my open claw, say, \"Hey,\ncheck this out.\" And then if I really\nlike it, I'll say, \"Hey, build your own\nversion of this skill.\" Then you're\nusually safe to go and you don't need to\nworry about any sort of risks with it.\nThat is open claw skills. Another tip\nI'll give when it comes to skills as\nwell is if your open claw ever does\nsomething well, just say, \"Hey, turn\nthat into a skill.\" And it'll write its\nown skill for itself. And then if it\never does something poorly, say, \"Hey,\nyou did that poorly.\" Figure out a\nbetter way to do it. And then write a\nskill for yourself. And that will be\nanother way for it to create very good\nskills. The point being is I don't\ninstall skills. I have OpenClaw build\nall its own skills. And that is, I\nthink, a much cleaner, safer way to do\nit. So OpenClaw memories. Let's talk a\nlittle bit about memory real quick.\nEveryone has different memory systems. I\ndon't have a very advanced one. I'm all\nabout simplicity. I don't go nuts and\nset up 20 different local models to do\nall these things to improve the memory.\nDoesn't matter to me. I try to keep it\nsimple. One thing I've done to improve\nopen clause memory, and by the way, you\ncan just copy and paste the URL for this\nvideo and give it to your openclaw and\nit will take all the learnings. It'll\nlike look at the transcript and take all\nthe learnings. So you can do that as\nwell. So you don't have to memorize\nevery single thing I'm telling you here.\nBut one thing I've done to improve the\nmemory is I actually have it create\ndaily journal entries, right? So every\nday it creates a new journal entry and\nit talks about everything we discussed\nthat day. And what I instructed my open\nclaw to do is as we have conversations\nthroughout the day, log into your daily\njournals anything interesting or\nimportant we talked about. Now, moving\nforward, whenever I ask questions about\nthings we talked about in the past, it\nnow has basically daily logs it can go\nthrough to find more details on things\nwe talked about. And that for me has\ndramatically improved the memory. It's\nalso been cool for me cuz now I can go\nin and read our past memories and see\nwhat we've talked about every single day\nsince we started doing this. And this\nkind of is like a cool feature where\nlike I have a journal and I think, oh,\nwhat did I talk about 5 10 years ago? I\ncan go in and see that. Obviously, I can\nonly go back and see from a few months\nago, but in a few years, this will be\nreally sick. This is all part of my\nmission control. What you're seeing\nhere, this was all customuilt by my\nOpenClaw. In just a few minutes, I'll go\nthrough mission control and how to set\nit up and everything involved there,\ntoo. This has been one of the best ways\nI improved my memory. And they're\nconstantly with OpenClaw updates\nimproving memory as well. But this is\nthe number one thing I did that has\nshown good results for me. So feel free\nto just take the link to this video,\ngive it to your openclaw and say, \"Hey,\ngo to the memory section and implement\nthe system Alex did in his open claw.\"\nWith that being said, let's talk about\nmission control. And also, I did a whole\nlive boot camp a couple weeks ago on\nmission control in the vibe coding\nacademy as well. It's 2 hours long, so\nif you want to go super in-depth on\nthis, link for that down below. But this\nis my mission control. What is mission\ncontrol exactly? Mission control is your\nown software hub for your open claw.\nAnytime your open claw needs a new tool\nor a custom tool to accomplish\nsomething, it gets built in your mission\ncontrol. Now, this is not made by\ndefault. This doesn't come with\nopenclaw. I had my own openclaw build\nit. But what you should be doing is you\nshould be going to your openclaw and you\nshould say, \"Hey, build a mission\ncontrol where we can custom build any\ntools we need.\" And what your open claw\nwill do is it will actually vibe code\nthis website, this mission control. And\nthen what you'll do as you work\ntogether, you'll say, \"Oh, you know, we\nshould have a a cananband board to track\nour task. Please build a task board.\" Or\nyou'll go, \"Oh, I want to visualize all\nyou guys and all you agents. Can you\nbuild an office space and I can watch\nyou guys work?\" And it will go and vibe\ncode all these custom tools in your\nmission control. Now, the question\nbecomes, which tools should you have in\nyour mission control? I'll go through\nthe important ones. Now, I'd highly\nrecommend building a calendar tool. So\nthis is a tool that shows you all the\nscheduled tasks your open claw does\nevery single day. So you can verify that\nyour open claw actually schedule tasks\nfor itself. That's a big challenge a lot\nof people have is they'll say, \"Hey, do\nthis for me in the future.\" And then it\ndoesn't actually do it. This calendar\nview does a great job of showing you\nwhen it actually schedules tasks to be\ndone. We have the memory section. So I\ntalked about this in the last section.\nThis is a custom tool inside mission\ncontrol where I can see all our past\nmemories. So this is great for verifying\nwhat my open claw remembers and I can go\nback and see our past discussions. Here\nI also have a docs section. This is a\nsection where every document and every\nartifact my open claw makes, it puts it\nin the doc section so I can go back and\nread it. So if you do something like,\nhey, build me a PRD for this app I'm\nthinking about building. It will create\na doc and put it here. This is much\nbetter than the other system which is it\nsends it to you in Telegram and then a\nfew days later it's like a thousand\nmessages up in the chat. You want your\nown doc section. I also have an office\nsection and this is just for fun.\nListen, you're allowed to have fun when\ndoing this. Not everything needs to be a\nhyper optimized workflow. You can build\ntools that are just fun, right? And so I\nbuilt an office. This is just a 2D pixel\nart way to watch my agents work at any\ngiven time. So I can just sit here while\nI'm at my desk and just see what my\nagents are up to. If you want, feel free\nto screenshot this. Give it your open\ncall and say, \"Hey, build me a 2D office\nas well.\" And then lastly, I think this\nis another important one you should\nbuild. And this is the team section.\nThis just visualizes all your agents and\nsub agents. If you're just starting,\nyou'll only probably have just your open\nclog going, but as you go, you'll add on\nmore agents and more sub agents. I also\nhave a video on like spinning up sub\nagents. I think it was actually my last\nvideo, so feel free to check that out on\nmy channel, too. But this is a good way\njust to see who you have working for\nyou, what their responsibilities are,\nand just make it easier for you to\nremember what agents you got going at\nany given time. Get these tools set up.\nAgain, feel free to just take the link\nto this video, give it to OpenClaw, and\nsay, \"Hey, copy his mission control.\"\nWith that being said, let's talk a\nlittle bit about security. This is\nprobably the number one most requested\nvideo of mine. The reason being is there\nis an absolute ton of misinformation out\nthere when it comes to OpenClaw\nsecurity. You talk to kind of any normie\nand they all go, \"Oh, OpenClaw, isn't\nthat the one where it leaks all your\npasswords and all your conversations and\nall that? It's like the most insecure\napp on planet Earth.\" No, that's a lie.\nThere's a few things you need to know\nabout security when it comes to\nOpenClaw. One is OpenClaw only does what\nyou ask it to do. That's it. If you\ndon't ask your OpenClaw to leak all your\npasswords, it's not going to leak all\nyour passwords. It's not like you can\nsay, \"Hey, OpenClaw, give me the news.\"\nThen it goes and it takes all the photos\noff your iPhone and puts it on Facebook.\nThat's not how this works. It only does\nexactly what you ask it to do. It's not\nlike you can say, \"Hey, what are the NBA\nstandings?\" And then it goes and it\nleaks all your passwords to Twitter,\nright? That's just not what it does. So,\nhere is the number one piece of advice I\ncan give you when it comes to security.\nYou need to have some personal\naccountability. I know this is like the\nrarest thing on planet Earth right now.\nPersonal accountability. You need to\nhave some personal accountability.\nBefore you send a prompt to your open\nclaw, think deeply about what you're\nasking. You need to think, is this going\nto leak something? Is this going to have\nit go on Twitter and tweet something\ncrazy? Is this going to cause something\nbad to happen? Right? You need to think\nabout that before every prompt. If you\ndo that, if you have some personal\naccountability, I promise you'll be\nsafe. Right? Think about it for a\nsecond. Everyone their mothers is\ncomplaining about the security OpenClaw.\nHow many people do you know actually\nhave had a security breach because of\nOpenClaw? How many people have you\nThere's millions of people on this and I\nbet 99.9999999%\npeople watching this video don't know a\nsingle person who's had something leaked\nbecause of open claw. There's only one\nstory I've heard of a security breach\nand that's like the safety person from\nMeta deleted all their emails by\naccident because they had their open\nclaw go and they said like hey find a\nbunch of emails we can delete and it\nlike deleted the wrong thing. So if you\njust think deeply before every prompt\nlike is this going to leak my Twitter\npassword then you won't have any issue\nwhen so when it comes to security just\ndo yourself a favor and just think\ndeeply about what you ask your open claw\nto do. If you do that I promise you'll\nbe okay. So two more sections I want to\ngo through that is number one fixing\nissues. This is another massive one.\nEveryone's having issues left and right.\nI'll show you how to fix all your\nissues. And then number two, future\nstate. How to prepare for what I think\nOpenClaw looks like a year or two from\nnow. So fixing issues is pretty simple.\nOpen Claw, all it is is a folder on your\ncomputer. That's it. It's a folder on\nyour computer with a bunch of markdown\nfiles. So when you have issues, you can\nhave other AI agents go in and actually\nfix those markdown files. So what I like\nto use is either Claude Code or Codeex.\nThose are two best coding agents right\nnow. You go in, you open up codeex or\nclaude code in that openclaw folder. So\nif you're in codeex, you can say add new\nproject. If you're in openclaw, you just\nnavigate to that folder inside claude\ncode. You open it up and then you talk\nto your codec h and you say, hey, my\nopen claw doesn't work anymore. Please\ngo in and see why. And you hit enter.\nThis literally has fixed every open claw\nissue for me ever. The AI agent goes in,\nlooks through your OpenClaw folder, and\nfinds whatever the issue is. I'm not\nkidding. 100% of issues I've had with\nOpenClaw were fixed with this prompt\nright here. And I get it. It's like\nworrying. Like you have this OpenClaw.\nYou built a relationship with it and\nthen it stops responding to you and it's\nbroken. Like that is kind of heart\nattack moment for sure. But you take a\ndeep breath\nand then you go into Claude Code. You go\nto CL say, \"Hey, Open Claw ain't\nworking. Please help me out here.\" and\nit will go through all the files and see\nwhy it broke. Most of the time it's\nusually a config issue. I think like 99%\nof issues come from people going in and\nmessing with the config themselves. That\nvery easily breaks OpenClaw. This will\nfix those config issues for you. And\nthen lastly, let's go over future state.\nWhat does the future of OpenClaw look\nlike? Do you need a Mac Studio to\nprepare for the future? Let's talk about\nthis right now. I truly believe the\nfuture of OpenClaw is local models. What\nare local models? Local models are AI\nmodels that run on your computer on your\ndevice. When you use Chad GBT or claw\ntoday, it's sending prompts to the\ninternet to the cloud through an API.\nThat's fine, but you're paying money\nwith every single prompt you do. Those\nmodels are running in a data center or\nserver somewhere else in the world. So,\nit's costing you money. It's not secure\nor private because your prompts are\ngoing to the internet. So, whatever\nyou're saying is living on a server\nsomewhere else that someone else can\nread and you also have very little\ncontrol. If they want to make the models\nstupider, if they want to change the\nmodels or if they want to ban you and\ntake the models away, they can do that.\nBut when you run the models locally on\nyour own device, you are becoming 100%\nprivate. You have complete control over\nthe models and no one can take it away.\nAnd it's also free. Well, the cost of\nthe energy or power to your computer.\nOpen models are getting significantly\nsmarter every single week. Every week, a\nnew open model comes out that is even\ncloser and closer to Frontier. Nvidia\njust released Neotron 3 Super, which is\ntheir open- source model, and it's\nincredible. And you can install it to a\nMac Studio or a DGX Spark right now. And\nthat can control your Open Claw\ncompletely securely and privately,\ncompletely for free. As time goes on,\nthese open models get better and the\ncost of hardware comes down. So, I think\nwe are probably like 6 months away from\nthe time of filming this video where\nwe're going to get to the point where\ncomputers that can run amazing open\nmodels are very affordable. Right now,\nyou need to spend probably around $4,000\nfor a DJX Spark to run Neotron 3. for\nmost people probably not affordable, but\nI think we are probably six months away\nfrom you being able to go out buying\nlike a$1 to2,000 computer and being able\nto run a model that's as good as Claude\nOpus 46 completely locally and free. So\nmy recommendation for you for when it\ncomes to action right now is just get\nused to the concept of local models.\nEven if you have a cheap computer right\nnow, you can download a local model. If\nyou're on like a Mac Mini, the local\nmodel probably be stupid and not that\ngreat, but that's fine. like you should\nget used to the exercise of using and\ndownloading local models to see what\nthey're like. You'll learn a ton about\nAI. You'll get a lot more educated and\nas technology shifts, you can transition\nto a much more powerful model. I think\nthat's the future of AI and that's why\nI'm preparing and I have a bunch of Mac\nStudios and DGX Sparks because I think\nthat's where we are heading. I went over\nbasically every concept I know.\nObviously, if I were to do super deep\ndives on every single concept we went\nthrough, this would probably be like a\n30hour video, I have deep dives on each\nof these concepts on my YouTube channel.\nIf you go there now, you can find\nprobably a deep dive of any one of these\nconcepts we talked about right now. All\nI do is put out amazing videos about AI\nand OpenClaw. I'm absolutely obsessed\nwith OpenClaw. I use it like 15 hours a\nday. So, if you want more stuff like\nthis, make sure to subscribe and turn on\nnotifications. Leave a like if you\nlearned anything at all. Let me know in\nthe comments what you want me to talk\nabout next when it comes to OpalClaw. Do\nyou want me to super deep dive on\nsecurity? Do you want me to go into way\nmore use cases? Let me know down below.\nI use all the feedback to choose what\nvideos to film next. Again, if you want\nto talk to me live, I do live boot camps\nevery Friday in the number one AI\ncommunity on Earth, the Vibe Coding\nAcademy, link down below. I hope this\nwas helpful. I so appreciate you taking\nthe time out of your day to learn from\nme. That really means more than you can\nimagine. I try to give you the best\ninformation possible without corrupting\nit with like sponsorships from shady\ncompanies. So, I hope it was helpful.\nThank you so much for watching and I\nwill see you in the next","truncated":false,"totalCharacters":48182,"wordCount":9254,"transcriptCharacters":48170,"transcriptLines":1253,"transcriptWordCount":9253,"transcriptSource":"captionTracks","transcriptionProvider":null,"transcriptMetadata":{"provider":"captionTracks","durationSeconds":2648},"transcriptSegments":null,"transcriptTimedText":null,"mediaDurationSeconds":2648,"video":{"kind":"youtube","url":"https://www.youtube.com/watch?v=CxErCGVo-oo"},"isVideoOnly":false,"diagnostics":{"strategy":"html","firecrawl":{"attempted":false,"used":false,"cacheMode":"default","cacheStatus":"unknown","notes":null},"markdown":{"requested":false,"used":false,"provider":null,"notes":null},"transcript":{"cacheMode":"default","cacheStatus":"miss","textProvided":true,"provider":"captionTracks","attemptedProviders":["youtubei","captionTracks"],"notes":null}}}


kind: transcript
key: 10e20c2afb60fb8d01b20d98939667f170156be80c889b562825d283bb39b419
value:
{"content":"Today we're launching LFM2 24B A2B.\nIf you're interested in running a large\nlanguage models on your laptop or PC\nlocally, this is for you. This model is\nbased on the optimized LFM2\narchitectures for fast ondevice\ninference. It's a mixture of experts\narchitecture that has 24 billion\nparameters in total, but in each token\ngeneration step only around 2 billion of\nthose are active. The result on a\nlaptop, we see almost 2x the token\ngeneration speed as a quen 30B3 active.\nWith this model, we scaled our LFM2 uh\nfoundation model architecture from 350\nmillion parameters of the smallest\nvariant to 24B of this uh current\nrelease model. That's almost a 100x\nscale that our architecture and model\ncan handle. The size was chosen to\nreally fit onto 32 GB of memory that we\nhave typically on a modern laptop or PC.\nWe support edge inference with the llama\nCBP um stack that has been recently\nmerged or joined hugging face\norganization which is a really nice um\naccomplishment of the open source\ncommunity. But also for hosted inference\nwe see compelling results where our\nmodel sees the overall throughput in\nterms of uh the number of requests per\nsecond on a single device in this case a\ncloud hosted A100 massively\noutperforming the competition. So it\nmeans really that the cost basis to\noperate this model at the serving the\nsame volume is much lower than\ncompetitor models. Let me directly\ndemonstrates how fast this model is when\nyou run it locally. So let's check out\nLFM2 24B in action on this mini PC that\nI have here. So if you go into the\nterminal and are on LS CPU, we'll see um\nthat is the machine has an AMD Ryzen AI\n9 HX 370 with an integrated graphics\ncard uh Raiden 890M in there and\nspecifically already downloaded a\npreview version of the LFM 224B\nquantized with 4bit as well as with\nplain standard llama CBP. And the first\nthing that we will run is show the\nspeed. So we'll use uh a tool called\nllama bench that is shipped uh with the\nstandard llama deploy uh package.\nAnd that one basically runs a test to\ncheck the speed of both prompt\nprocessing or prefill and also token\ngeneration or decode. And one thing that\nwe see here first is that it's using the\nwalcom back end. So we are actually\nusing kind of the AMD Raiden uh graphics\nin integrated graphics card to actually\nexecute uh and run this model. So we see\nalready the first results on the\nprompter setting we are seeing almost\n500 tokens per second. So the prompt uh\ngets kind of uh consumed and processed\nwith like a very fast 500 tokens per\nsecond. Um for the token generation so\nfor the decoder when when it starts\ngenerating um we see that around we will\nget 50 tokens per second. Note that it\nstill shows LFM2 MOE 8B version because\nthis is not the released version but a\npreview an internal preview that I'm\nhaving uh access here and but if you\nlook at the parameters we see that it's\nactually the 24 billion parameters that\nthe model has. So instead of llama\nbench, let's use a different binary.\nIt's called the ship with the same\ndownload package. Uh it's called llama\nserver. Specifically, we'll run again\nthe 24B. And we set also temperature to\nzero because usually model performs\nbetter with a lower temperature. And\nwe see again if you scroll up that it's\nusing the walcom back end to run and\nexecute this model. Um, additionally,\nLlama server spawns a normal OpenAI chat\ninterface, but also a UI that we can\njust connect to um with our browser. So,\nthis is kind of the inter interface uh\nuser interface using Llama CVP uh to run\nour model. And let's ask it a simple\nprompt. Um,\nwrite a teleprompt.\nWrite a teleprompt text for a video\nabout the launch\nof Elephant 2 24B A2B.\nSo, it does respond very fast and\nsmoothly. And we see that we get here\naround um 46 tokens per second. almost\nthe 50 that we saw um in the llama\nbench.\nAnd yeah, this is a very smooth\nbehavior. It outputs like some uh\nteleprompt text. Uh let's uh actually\nmake\nit more about that this model runs on\nlaptops.\nAgain, almost no latency. We see\nimmediately the model starts generating\ntext with very fast.\nNote that the training of this model has\nnot converged yet, but it's still\ntraining and the metrics are still going\nup. If you're interested, check out the\nmodel on hanging face. It's open weight.\nTest it on our Liquid Playground or\ncheck out our liquid docs on how to run\nthese models locally or fine-tune","source":"captionTracks","metadata":{"provider":"captionTracks","durationSeconds":327},"service":"youtube","resourceKey":"EwqrYEuFHdU","namespace":"yt:auto","formatVersion":1}


kind: extract
key: aba9ee1c1d7e6b5392e5deead418c1e32a999f0f78e701f6f3d3c842799811d1
value:
{"url":"https://www.youtube.com/watch?v=EwqrYEuFHdU&feature=youtu.be","title":"An LLM model made specifically to run locally on laptops","description":"We release LFM2-24B-A2B, a large language model specifically made to run locally on laptops or desktops. - LFM2 architecture optimized for on-device inferenc...","siteName":"YouTube","content":"Transcript:\nToday we're launching LFM2 24B A2B.\nIf you're interested in running a large\nlanguage models on your laptop or PC\nlocally, this is for you. This model is\nbased on the optimized LFM2\narchitectures for fast ondevice\ninference. It's a mixture of experts\narchitecture that has 24 billion\nparameters in total, but in each token\ngeneration step only around 2 billion of\nthose are active. The result on a\nlaptop, we see almost 2x the token\ngeneration speed as a quen 30B3 active.\nWith this model, we scaled our LFM2 uh\nfoundation model architecture from 350\nmillion parameters of the smallest\nvariant to 24B of this uh current\nrelease model. That's almost a 100x\nscale that our architecture and model\ncan handle. The size was chosen to\nreally fit onto 32 GB of memory that we\nhave typically on a modern laptop or PC.\nWe support edge inference with the llama\nCBP um stack that has been recently\nmerged or joined hugging face\norganization which is a really nice um\naccomplishment of the open source\ncommunity. But also for hosted inference\nwe see compelling results where our\nmodel sees the overall throughput in\nterms of uh the number of requests per\nsecond on a single device in this case a\ncloud hosted A100 massively\noutperforming the competition. So it\nmeans really that the cost basis to\noperate this model at the serving the\nsame volume is much lower than\ncompetitor models. Let me directly\ndemonstrates how fast this model is when\nyou run it locally. So let's check out\nLFM2 24B in action on this mini PC that\nI have here. So if you go into the\nterminal and are on LS CPU, we'll see um\nthat is the machine has an AMD Ryzen AI\n9 HX 370 with an integrated graphics\ncard uh Raiden 890M in there and\nspecifically already downloaded a\npreview version of the LFM 224B\nquantized with 4bit as well as with\nplain standard llama CBP. And the first\nthing that we will run is show the\nspeed. So we'll use uh a tool called\nllama bench that is shipped uh with the\nstandard llama deploy uh package.\nAnd that one basically runs a test to\ncheck the speed of both prompt\nprocessing or prefill and also token\ngeneration or decode. And one thing that\nwe see here first is that it's using the\nwalcom back end. So we are actually\nusing kind of the AMD Raiden uh graphics\nin integrated graphics card to actually\nexecute uh and run this model. So we see\nalready the first results on the\nprompter setting we are seeing almost\n500 tokens per second. So the prompt uh\ngets kind of uh consumed and processed\nwith like a very fast 500 tokens per\nsecond. Um for the token generation so\nfor the decoder when when it starts\ngenerating um we see that around we will\nget 50 tokens per second. Note that it\nstill shows LFM2 MOE 8B version because\nthis is not the released version but a\npreview an internal preview that I'm\nhaving uh access here and but if you\nlook at the parameters we see that it's\nactually the 24 billion parameters that\nthe model has. So instead of llama\nbench, let's use a different binary.\nIt's called the ship with the same\ndownload package. Uh it's called llama\nserver. Specifically, we'll run again\nthe 24B. And we set also temperature to\nzero because usually model performs\nbetter with a lower temperature. And\nwe see again if you scroll up that it's\nusing the walcom back end to run and\nexecute this model. Um, additionally,\nLlama server spawns a normal OpenAI chat\ninterface, but also a UI that we can\njust connect to um with our browser. So,\nthis is kind of the inter interface uh\nuser interface using Llama CVP uh to run\nour model. And let's ask it a simple\nprompt. Um,\nwrite a teleprompt.\nWrite a teleprompt text for a video\nabout the launch\nof Elephant 2 24B A2B.\nSo, it does respond very fast and\nsmoothly. And we see that we get here\naround um 46 tokens per second. almost\nthe 50 that we saw um in the llama\nbench.\nAnd yeah, this is a very smooth\nbehavior. It outputs like some uh\nteleprompt text. Uh let's uh actually\nmake\nit more about that this model runs on\nlaptops.\nAgain, almost no latency. We see\nimmediately the model starts generating\ntext with very fast.\nNote that the training of this model has\nnot converged yet, but it's still\ntraining and the metrics are still going\nup. If you're interested, check out the\nmodel on hanging face. It's open weight.\nTest it on our Liquid Playground or\ncheck out our liquid docs on how to run\nthese models locally or fine-tune","truncated":false,"totalCharacters":4380,"wordCount":808,"transcriptCharacters":4368,"transcriptLines":122,"transcriptWordCount":807,"transcriptSource":"captionTracks","transcriptionProvider":null,"transcriptMetadata":{"provider":"captionTracks","durationSeconds":327},"transcriptSegments":null,"transcriptTimedText":null,"mediaDurationSeconds":327,"video":{"kind":"youtube","url":"https://www.youtube.com/watch?v=EwqrYEuFHdU"},"isVideoOnly":false,"diagnostics":{"strategy":"html","firecrawl":{"attempted":false,"used":false,"cacheMode":"default","cacheStatus":"unknown","notes":null},"markdown":{"requested":false,"used":false,"provider":null,"notes":null},"transcript":{"cacheMode":"default","cacheStatus":"miss","textProvided":true,"provider":"captionTracks","attemptedProviders":["youtubei","captionTracks"],"notes":null}}}


kind: transcript
key: 616946674953b89326e2acb4e459e98719a48b3528bca93853479c9f530c224d
value:
{"content":"this tiny laptop is running a 70 billion\nparameter AI model right now but wait\nshouldn't this model be too big to fit\nthe math just doesn't add up unless you\nknow about\nquantization today I'm going to show you\nthe clever trick that makes this\npossible and why those mysterious Q2 Q4\nand Q8 tags in ama are about to become\nyour best friends in the next 10 minutes\nor so you'll learn how to on massive AI\nmodels on sort of Basic Hardware what Q2\nQ4 and Q8 actually mean and it's simpler\nthan you think and which quantization is\nright for your projects plus I'll show\nyou a brand new trick with context\nquantization that could save you gigabyt\nof ram now quick pause if you're serious\nabout running AI models locally hit\nsubscribe and the bell every week I\nshare new tricks like this that can save\nyou hundreds in hard costs and next week\nwell let's just say you'll want to be\nhere for that here's what nobody tells\nyou about AI models they're just giant\ncollections of huge numbers billions of\nthem each one needs to be stored with\nIncredible Precision think of it like\nthis normally these numbers are stored\nwith 32-bit Precision it's like having a\nruler that can measure down to\nmicroscopic levels sounds great right\nbut here's the problem all that\nprecision comes at a cost a pretty\nmassive one let me show you exactly what\nhappens to your RAM let's do some quick\nmath take a 7 billion parameter model\nmultiply each of those parameters by\nfour bytes and you need\n28 GB just to store it that's more RAM\nthan a lot of gaming PCs have you're\nlooking at$ 2,000 $3,000 for a GPU just\nto run one model and this model won't\neven fit in one of those but here's\nwhere quantization changes everything\nthink of quantization like choosing\ndifferent rulers maybe the full model is\nusing millimeters Q8 is measuring in\ncentim less precise but still pretty\naccurate Q4 now we've got a marker every\n5 cm or so but look how much extra space\nwe have and Q2 well this is the extreme\noption like measuring with a random\nstick from your yard but sometimes\nthat's all you need here's my favorite\nway to explain this imagine you're\nrunning a mail room but instead of\npostcards and packages you're soaring\nnumbers in the original unquantized\nmodel every number gets its own custom\nsiiz mailbox it's perfect organization\nbut imagine how much space that takes up\nthat's exactly what happens with full\n32-bit Precision it's like having an\ninfinite mail room where every tiny\nvalue gets its own special spot here's\nwhat regular Q4 Quan ation looks like in\naction imagine having just 16 mailboxes\nfor all your numbers every number has to\npick the closest available slot it works\nbut it's not very smart what happens\nwhen you have two big bunches of numbers\none bunch of really tiny numbers and the\nother bunch of really huge ones but wait\nit gets better see those models tagged\nwith K and olama they use something\ncalled kqu and they are brilliant\ninstead of one rigid system kqu create\nmultiple specialized mail rooms small\nnumbers get their own precise area and\nbig numbers get their space it's like\nhaving a smart assistant organizing\neverything perfectly the different sizes\nyou see KS km and KL for small medium\nand large are different levels of detail\nin the notes that are in the mail room\nthe small version keeps brief notes\nmedium keeps more detailed records and\nlarge\nmaintains very precise information about\nhow everything was arranged this smart\nsystem means areas with mostly small\nnumbers get more precise small slots\nwhile areas with bigger numbers get\nappropriately sized spaces it's like\nhaving a mail room that adapts to what\nyou're storing rather than forcing\neverything into the same size box but\nhere's what really matters how this\naffects your actual usage look at memory\nwatch what happens when we load a Q4\nmodel now check out speed noce the\nstartup time and performance let's run\nthe same prompt on different versions\nnow here's where it gets really\ninteresting AMA just dropped a\ngamechanging feature context\nquantization see we've been talking\nabout making the models smaller but\nthere's another memory hog we haven't\ndiscussed yet your conversation history\na year ago this wasn't really a big deal\nmodels could only remember 2,000 to\n8,000 tokens of conversation\nbut now well we're talking about models\nthat can remember entire books worth of\nconversation that's 128k or 128,000 or\nso tokens or\nmore here's the problem all that\nconversation history eats up Ram but\ncontext quantization fixes this let me\nshow you how to enable this it's super\nsimple first we'll turn on flash\nattention by setting olama flash\nattention is true and then set the cache\ntype with olama KV cach type F16 what\nwhat happens to the memory usage when we\nenable these so let's get practical I'm\ngoing to show you exactly how much\nmemory you can save with these tricks\nfor this demo I'm using quen 2.5 a 7\nbillion parameter model out of the box\nit uses Q4 km quantization and takes up\n4.7 GB on dis first let's max out the\ncontact size by default olama gives us\n2K tokens or 240 8 tokens but quen can\nhandle 32k or\n32,768 here's an easy way to set that up\nfirst go to ama.com and find the model\nthen run the command shown on the right\nnow type /set parameter numor CX\n32768 and hit enter and then type save\nQuin 2.5 Max or whatever you want to\ncall it and hit enter finally quit olama\nnow if if you run AMA LS you should see\nthat the model has been added to your\nlist of models perfect we have our maxed\nout model ready to go now I'll run the\nmodel and watch the output from ASI top\nwhich is a similar tool to NV toop on\nLinux but for Mac OS before I hit enter\non the command which is this I see the\nmemory usage hanging out around 25 gigs\nthe Mac uses unified memory so that's\nthe total amount of memory used right\nnow now by the system and the GPU now I\nhit enter and I see that memory usage\nclimbs to around 40.9 gbes so this model\nwith a 32k context uses about 15 GB now\nI'll quit ol serve and run this command\nwhich runs olama serve again but this\ntime with the flash attention option\nenabled I'm doing this at the command\nline to easily switch back and forth\nbetween settings rather than setting up\nthe environment variables the correct\nway I'll run the model with the same\nquestion and this time I see the memory\ntop out at\n33.7 gbt that's a massive drop in memory\nusage from 40.9 GB that's a savings of\nabout 7 gigs let's do this one more time\nsetting the KV cache quantization to Q8\nNow it only climbs to 30.6 GB that means\nit's taking up closer to 5 gigs of\nmemory with the maxed out contact size\nlet's try this okay one more time going\nback to the original model with the\ndefault contact size of 2K without using\nflash attention at all now it goes from\n28.5 to 30.3 so with a normal contact\nsize it needs less than 2 GB the maxed\nout context needs 15 gigs and using\ncontext quantization for this model set\nto Q8 saves US 10 gigs bringing it down\nto only 5 gigs but it's important to\nnote that not every model is going to\nbenefit in the same way I saw one model\ntake less memory of flash attention on\nbut take more memory with KB cach\nquantization set to Q8 more than without\nusing flash attention at all that was a\nbrand new model by IBM so it's not\nalways guaranteed to use Flash attention\nand context quantization hopefully now\nyou have a better understanding of how\nquantization works and how it affects\nthe performance and efficiency of the\nmodel and its context well the next\nquestion is probably going to be how to\npick the right model for your specific\nneeds in general it's usually best to\nstart out with one of the Q4 models this\ncould be the Q4 or even Q4 km which is\nwhat AMA seems to be defaulting to these\ndays if you notice issues with the\ngeneration quality you can move up to Q8\nor even fp16 if it seems to work well\nyou can try dropping further down to Q2\nit's amazing how often Q2 model will\nwork just as well for most tasks for\nmost people and the memory usage is so\nmuch lower combine that with a quantized\ncontext and you're getting a lot of\npower on a machine with even the most\nlimited memory now let's wrap this up\nwith what really matters whether you're\nrunning AI on a Raspberry Pi or a beast\nof a workstation here's your game plan\nstart simple begin with a Q4 km model\nand then enable flash attention and test\nyour specific use case then optimize it\na bit if that's running smooth try Q2\nbut if it's having issues move to Q8\nneed more context try playing with Q8 KV\ncache\nquantization the best part these tricks\ncan turn a large model into something\nthat runs on a mostly normal laptop\nthat's not just Theory you saw it happen\nright here quick action steps download a\nAMA Q4 model enable flash attention and\ntest with your use case experiment with\nlower quantization you can join our\nDiscord or theama Discord for more\noptimization tips and don't forget to\nlike And subscribe for next week's Deep\ndive into something else remember the\nperfect setup isn't about using the\nhighest settings it's about finding what\nworks for your specific needs thanks so\nmuch for watching and I look forward to\nseeing you in the next video goodbye\nare uh something for a\nvideo are you wasting p a little\nbit\nJase Jase\none two three four five\nDad Dad this is emergency T I know it's\nit's being used for\nemergency no it is not dad you are not\nHur and you are wasting tape that we\nmight need for something\nmedical my video are emergencies no\nthey're\nnot okay this is my last one","source":"captionTracks","metadata":{"provider":"captionTracks","durationSeconds":729},"service":"youtube","resourceKey":"K75j8MkwgJ0","namespace":"yt:auto","formatVersion":1}


kind: extract
key: 77e9f7c1fb9c210bd78f344f183dc288a722ca0151a3c523765abd22de5bc92f
value:
{"url":"https://www.youtube.com/watch?v=K75j8MkwgJ0&feature=youtu.be","title":"Optimize Your AI - Quantization Explained","description":"🚀 Run massive AI models on your laptop! Learn the secrets of LLM quantization and how q2, q4, and q8 settings in Ollama can save you hundreds in hardware co...","siteName":"YouTube","content":"Transcript:\nthis tiny laptop is running a 70 billion\nparameter AI model right now but wait\nshouldn't this model be too big to fit\nthe math just doesn't add up unless you\nknow about\nquantization today I'm going to show you\nthe clever trick that makes this\npossible and why those mysterious Q2 Q4\nand Q8 tags in ama are about to become\nyour best friends in the next 10 minutes\nor so you'll learn how to on massive AI\nmodels on sort of Basic Hardware what Q2\nQ4 and Q8 actually mean and it's simpler\nthan you think and which quantization is\nright for your projects plus I'll show\nyou a brand new trick with context\nquantization that could save you gigabyt\nof ram now quick pause if you're serious\nabout running AI models locally hit\nsubscribe and the bell every week I\nshare new tricks like this that can save\nyou hundreds in hard costs and next week\nwell let's just say you'll want to be\nhere for that here's what nobody tells\nyou about AI models they're just giant\ncollections of huge numbers billions of\nthem each one needs to be stored with\nIncredible Precision think of it like\nthis normally these numbers are stored\nwith 32-bit Precision it's like having a\nruler that can measure down to\nmicroscopic levels sounds great right\nbut here's the problem all that\nprecision comes at a cost a pretty\nmassive one let me show you exactly what\nhappens to your RAM let's do some quick\nmath take a 7 billion parameter model\nmultiply each of those parameters by\nfour bytes and you need\n28 GB just to store it that's more RAM\nthan a lot of gaming PCs have you're\nlooking at$ 2,000 $3,000 for a GPU just\nto run one model and this model won't\neven fit in one of those but here's\nwhere quantization changes everything\nthink of quantization like choosing\ndifferent rulers maybe the full model is\nusing millimeters Q8 is measuring in\ncentim less precise but still pretty\naccurate Q4 now we've got a marker every\n5 cm or so but look how much extra space\nwe have and Q2 well this is the extreme\noption like measuring with a random\nstick from your yard but sometimes\nthat's all you need here's my favorite\nway to explain this imagine you're\nrunning a mail room but instead of\npostcards and packages you're soaring\nnumbers in the original unquantized\nmodel every number gets its own custom\nsiiz mailbox it's perfect organization\nbut imagine how much space that takes up\nthat's exactly what happens with full\n32-bit Precision it's like having an\ninfinite mail room where every tiny\nvalue gets its own special spot here's\nwhat regular Q4 Quan ation looks like in\naction imagine having just 16 mailboxes\nfor all your numbers every number has to\npick the closest available slot it works\nbut it's not very smart what happens\nwhen you have two big bunches of numbers\none bunch of really tiny numbers and the\nother bunch of really huge ones but wait\nit gets better see those models tagged\nwith K and olama they use something\ncalled kqu and they are brilliant\ninstead of one rigid system kqu create\nmultiple specialized mail rooms small\nnumbers get their own precise area and\nbig numbers get their space it's like\nhaving a smart assistant organizing\neverything perfectly the different sizes\nyou see KS km and KL for small medium\nand large are different levels of detail\nin the notes that are in the mail room\nthe small version keeps brief notes\nmedium keeps more detailed records and\nlarge\nmaintains very precise information about\nhow everything was arranged this smart\nsystem means areas with mostly small\nnumbers get more precise small slots\nwhile areas with bigger numbers get\nappropriately sized spaces it's like\nhaving a mail room that adapts to what\nyou're storing rather than forcing\neverything into the same size box but\nhere's what really matters how this\naffects your actual usage look at memory\nwatch what happens when we load a Q4\nmodel now check out speed noce the\nstartup time and performance let's run\nthe same prompt on different versions\nnow here's where it gets really\ninteresting AMA just dropped a\ngamechanging feature context\nquantization see we've been talking\nabout making the models smaller but\nthere's another memory hog we haven't\ndiscussed yet your conversation history\na year ago this wasn't really a big deal\nmodels could only remember 2,000 to\n8,000 tokens of conversation\nbut now well we're talking about models\nthat can remember entire books worth of\nconversation that's 128k or 128,000 or\nso tokens or\nmore here's the problem all that\nconversation history eats up Ram but\ncontext quantization fixes this let me\nshow you how to enable this it's super\nsimple first we'll turn on flash\nattention by setting olama flash\nattention is true and then set the cache\ntype with olama KV cach type F16 what\nwhat happens to the memory usage when we\nenable these so let's get practical I'm\ngoing to show you exactly how much\nmemory you can save with these tricks\nfor this demo I'm using quen 2.5 a 7\nbillion parameter model out of the box\nit uses Q4 km quantization and takes up\n4.7 GB on dis first let's max out the\ncontact size by default olama gives us\n2K tokens or 240 8 tokens but quen can\nhandle 32k or\n32,768 here's an easy way to set that up\nfirst go to ama.com and find the model\nthen run the command shown on the right\nnow type /set parameter numor CX\n32768 and hit enter and then type save\nQuin 2.5 Max or whatever you want to\ncall it and hit enter finally quit olama\nnow if if you run AMA LS you should see\nthat the model has been added to your\nlist of models perfect we have our maxed\nout model ready to go now I'll run the\nmodel and watch the output from ASI top\nwhich is a similar tool to NV toop on\nLinux but for Mac OS before I hit enter\non the command which is this I see the\nmemory usage hanging out around 25 gigs\nthe Mac uses unified memory so that's\nthe total amount of memory used right\nnow now by the system and the GPU now I\nhit enter and I see that memory usage\nclimbs to around 40.9 gbes so this model\nwith a 32k context uses about 15 GB now\nI'll quit ol serve and run this command\nwhich runs olama serve again but this\ntime with the flash attention option\nenabled I'm doing this at the command\nline to easily switch back and forth\nbetween settings rather than setting up\nthe environment variables the correct\nway I'll run the model with the same\nquestion and this time I see the memory\ntop out at\n33.7 gbt that's a massive drop in memory\nusage from 40.9 GB that's a savings of\nabout 7 gigs let's do this one more time\nsetting the KV cache quantization to Q8\nNow it only climbs to 30.6 GB that means\nit's taking up closer to 5 gigs of\nmemory with the maxed out contact size\nlet's try this okay one more time going\nback to the original model with the\ndefault contact size of 2K without using\nflash attention at all now it goes from\n28.5 to 30.3 so with a normal contact\nsize it needs less than 2 GB the maxed\nout context needs 15 gigs and using\ncontext quantization for this model set\nto Q8 saves US 10 gigs bringing it down\nto only 5 gigs but it's important to\nnote that not every model is going to\nbenefit in the same way I saw one model\ntake less memory of flash attention on\nbut take more memory with KB cach\nquantization set to Q8 more than without\nusing flash attention at all that was a\nbrand new model by IBM so it's not\nalways guaranteed to use Flash attention\nand context quantization hopefully now\nyou have a better understanding of how\nquantization works and how it affects\nthe performance and efficiency of the\nmodel and its context well the next\nquestion is probably going to be how to\npick the right model for your specific\nneeds in general it's usually best to\nstart out with one of the Q4 models this\ncould be the Q4 or even Q4 km which is\nwhat AMA seems to be defaulting to these\ndays if you notice issues with the\ngeneration quality you can move up to Q8\nor even fp16 if it seems to work well\nyou can try dropping further down to Q2\nit's amazing how often Q2 model will\nwork just as well for most tasks for\nmost people and the memory usage is so\nmuch lower combine that with a quantized\ncontext and you're getting a lot of\npower on a machine with even the most\nlimited memory now let's wrap this up\nwith what really matters whether you're\nrunning AI on a Raspberry Pi or a beast\nof a workstation here's your game plan\nstart simple begin with a Q4 km model\nand then enable flash attention and test\nyour specific use case then optimize it\na bit if that's running smooth try Q2\nbut if it's having issues move to Q8\nneed more context try playing with Q8 KV\ncache\nquantization the best part these tricks\ncan turn a large model into something\nthat runs on a mostly normal laptop\nthat's not just Theory you saw it happen\nright here quick action steps download a\nAMA Q4 model enable flash attention and\ntest with your use case experiment with\nlower quantization you can join our\nDiscord or theama Discord for more\noptimization tips and don't forget to\nlike And subscribe for next week's Deep\ndive into something else remember the\nperfect setup isn't about using the\nhighest settings it's about finding what\nworks for your specific needs thanks so\nmuch for watching and I look forward to\nseeing you in the next video goodbye\nare uh something for a\nvideo are you wasting p a little\nbit\nJase Jase\none two three four five\nDad Dad this is emergency T I know it's\nit's being used for\nemergency no it is not dad you are not\nHur and you are wasting tape that we\nmight need for something\nmedical my video are emergencies no\nthey're\nnot okay this is my last one","truncated":false,"totalCharacters":9479,"wordCount":1770,"transcriptCharacters":9467,"transcriptLines":256,"transcriptWordCount":1769,"transcriptSource":"captionTracks","transcriptionProvider":null,"transcriptMetadata":{"provider":"captionTracks","durationSeconds":729},"transcriptSegments":null,"transcriptTimedText":null,"mediaDurationSeconds":729,"video":{"kind":"youtube","url":"https://www.youtube.com/watch?v=K75j8MkwgJ0"},"isVideoOnly":false,"diagnostics":{"strategy":"html","firecrawl":{"attempted":false,"used":false,"cacheMode":"default","cacheStatus":"unknown","notes":null},"markdown":{"requested":false,"used":false,"provider":null,"notes":null},"transcript":{"cacheMode":"default","cacheStatus":"miss","textProvided":true,"provider":"captionTracks","attemptedProviders":["youtubei","captionTracks"],"notes":null}}}


kind: transcript
key: e728429b96592c5574ee41e7fe5572e2ed4c62d3232ef0fb8f2a77d8a1dc4dea
value:
{"content":"You're probably familiar with Olama,\nright? This is what it looks like. You\ncan launch it. You can talk to it. Never\nmind that we're using Quen 34B. That's\njust standardized thing that I'm [music]\ndoing. And the prompt doesn't matter\nwhat it is. 100 tokens per second is\nwhat Olama gives us. Lama runs on Llama\nCPP. It has a little bit of overhead,\nbut you can also run Llama CPP directly\nand query it remotely, which is what\nsoftware developers are going to be\ninterested in if they're going to be\nusing it as a code assistant, for\nexample, or with agents. Hold on a\nminute. And that's going to be key here\nin a moment. I'm going to start up Llama\nServer pointing to the same model by the\nway. And Llama Server has this nice user\ninterface that we can also say hello to.\nAnd this one gives us tokens per second.\n124. So a little bit better. So that's\nchat. We're chatting with one model, one\nrequest at a time. And it's not even how\ncode assistants [music] work because\ncode assistance requires a lot of\ndifferent chats going on at the same\ntime. What if I query this remotely\nhere? I wrote a little script.\nBasically, this is from my race to 1\nmillion episode, but this one would do a\nthousand. And I'm going to be\ncommunicating from my laptop here to my\nMac Studio that's running Llama Server.\nBoom. And there it goes. And now I'm\ntwiddling my thumbs. Boom. Boom. Boom.\nLook at that. 120 tokens per second.\nVery close numbers, right? You know who\nyou don't want to twiddle their thumbs?\nYour agents that are working away at\nyour code base. Sometimes tens of them\nor hundreds of them. And we're even\ngoing to increase our target tokens to\n1,000 so we have a little bit of room to\nwalk around. Boom. I'm going to watch\nthis in real time. Check this out. This\nis going to be insane. [laughter]\nWhat? 826 tokens per second from Llama\nCPP. And you might think, \"Oh, okay.\n>> I've seen this trick before, right,\n>> on my channel probably.\" Uh, concurrency\nis set to 128. Well, I'm about to show\nyou a new trick because even with\nconcurrency 128, without that trick, you\nstill get 231 tokens per second, which\nis still better, but we're limited to\nwhat Llama CPP is capable of just by\nitself. It can't handle as many\nconcurrent connections as something like\nVLM, for example. And I've got other\nvideos to show that. So, what am I\ndoing? Well, I wrote this little\nlauncher. This is actually inspired by\nDonato Capitella's distributed launcher.\nI recently featured that in a video\nabout this thing. Separate video. You\ncan watch that if you want. But here\nwe're just running this on one machine.\nSo I created this launcher which allows\nyou to tweak a few knobs that Llama CPP\nprovides for tweaking along with\nsweeping and exploring what your machine\nis capable of. For example, I have this\nrunning on the Mac Studio, but what if\nyou have a Mac Mini for example? Well,\nyou can run this on there. If you have a\nWindows laptop or another Linux laptop\nor an Nvidia one, it doesn't matter\nbecause Llama CPP will run on all those.\nAnd this launcher is going to run on any\nof those as well because it's just\nPython. And it'll find the best\ncombination for you to run Llama CPP as\na server while twisting those knobs. I\nprobably shouldn't say that on YouTube.\nUm, somebody might complain.\n>> FBI, open up.\nSome sensitive soul out there. Let me\nshow you how this works. First, we've\ngot some tests. We can do a single\nrequest. Select your model, select where\nyou're running it, and run. And now,\nthis will just query Llama CPP, get your\ntokens per second for that\nconfiguration, which is 127, very close\nto what we've seen. You can do\nconcurrent requests. There we go.\nConcurrent requests, count 128,\nconcurrency 128, total tokens, average\nrequest tokens per second, and then\nthroughput. So, we got a throughput 240\ntokens per second on the same model. By\nthe way, all this stuff is documented in\nthe repository. If you're getting into\ncyber right now, there's an awkward gap\nbetween I watch the videos and I can\nhandle real situations. And the gap is\nexactly what this is for. This is try\nhackme where you learn cyber by doing\nit. Interactive labs real scenarios all\nbrowserbased with a global community of\nover 6 million people. They just\nlaunched an entry-level search called\ncyber security 101 also known as sec 1.\nQuick tour only. No exam spoilers. The\nbig idea here is applied fundamentals.\nYou train hands-on, then sew one checks\nyou can actually perform the basics in\nrealistic situations, not just memorized\ndefinitions. This is for students going\nfor their first cyber role, career\nswitchers, and interns or junior folks\nlike 0 to2 years who want proof they can\nactually do the fundamentals. High\nlevel, it touches what you'll actually\nrun into. operating systems, networking,\nweb-based security basics, a little bit\nof blue team fundamentals, a little bit\nof red team fundamentals, and malware\nanalysis at a beginner level. And this\nis what makes it different. A lot of\nbeginner searchs feel like vocabulary\nquizzes. SEC 1 is trying to be a\nhands-on checkpoint instead. When you\nfinish, you get an instant result, a\ndigital certificate, and a sharable\nbadge. If you're getting started in\ncyber security and want a hands-on way\nto prove your fundamentals, check out\nTry HackMe CyberC 101 certification\nusing the link below. And if you use my\ncode for launch, nonpremium users get\n40% off their certification plus 3\nmonths of free premium subscription link\nbelow. All this stuff is documented in\nthe repository. It's called Llama\nThroughput Lab. There's other tests in\nhere, but there's one really interesting\none and it's called Full Sweep. This\ntakes a while. There's 308 different\ncombinations of how many instances of\nLlama server you start. That's the key\nhere. Hint hint. So, you run multiple\ninstances of Llama server to be able to\nreply because a machine that's as\npowerful as this has a lot of memory\nthese days. This one has 64 gigs of VRAM\nor it's called unified memory in Apple\nsilicon terms. This Mac Studio has 512.\nBut that's not the limiting factor when\nit comes to running these. The limiting\nfactor here is the compute on the GPU.\nWe're running all this on the GPU\nbecause we don't want to do it on the\nCPU. It's going to be extra slow. That's\nwhy we're using the GPU here. And you\ncan only do so much when you're running\nmultiple llama servers at the same time.\nYou're no longer limited by memory,\nobviously, unless [music] you're running\nhuge models, which you can do here. You\ncan run on a 512 GB machine. You can run\nlike four llama 70 billion parameter\nmodels at the same time in four\ninstances of llama server. So those are\ninstances. Then parallel llama server\nexposes a parameter called parallel. And\nthis has been documented pretty well on\nthe GitHub repository under Llama CPP by\nthe creator of Llama CPP himself,\nGeorgio Ganov. Another knob you can\ntwist.\nAll right, maybe I should stop it with\nthat. Um, somebody's gonna get mad. I'm\nnot twisting any knob. Concurrency is\nanother uh parameter you can alter and\nthat's what I showed before in my videos\nwhere we can send multiple requests to\nLlama server. Why do we care about that?\nWell, if you're just doing chat, then by\nall means go ahead and chat and wait for\nit to finish. But a lot of times we want\nto go beyond that. Imagine a folder with\nthousands of photographs that you want\nto analyze or a video with millions of\nframes. You don't want to be sitting\nthere and waiting for one frame at a\ntime to be analyzed, do you? Or imagine\nanother scenario where you have a bunch\nof agents orchestrated together. Some\nare doing planning, some are doing\nimplementation. You want them working at\nthe same time. So that's where\nconcurrency comes in. Getting back to\nthe sweep, this will do a full sweep. So\ninstances, parallel, and concurrencies,\nand it'll find out where the optimal\nspot is on your computer, whatever\ncomputer you're using. You can select\nthat test sweeps, and then click on run\nselected test, and off it goes. There it\ngoes. Uh, just keep in mind that this\nwill take a while. Also, on a slower\nsystem, it'll take a real long while. So\nright now we're on four out of 308\ndifferent combinations. But as it's\nsweeping, you can see the numbers coming\nin. 128 tokens per second, 193, 263,\n314. So you can see how these different\nparameters are affecting that output. If\nI go to my results folder here, I did a\nfull sweep on this Mac Studio, of\ncourse. Now, I should probably create a\nnice looking chart, but this is also on\nGitHub and open source, so feel free to\nmuck with it if you want to. Look at\nthese numbers. 1,226 tokens per second.\nThat's with 16 instances. In other\nwords, I started Llama server 16 times.\nParallel flag set to 64. 1,024\nconcurrency. Now, keep in mind this is\njust a benchmark. If you want to do real\nworld cases, this will only give you\nsome guidance and point you in the right\ndirection, but always make sure you\nactually test your real world scenarios.\nAnd finally, let's discuss how I\nactually run this. Well, I run it by\nusing this command right here. It's an\nexecutable Python command. Run llama\ntests. Boom. Not very creative, I know.\nAnd here you can select the test, select\nthe model. Boom. And there's one more\nthing, which is this number six here.\nIt's number six for now, but it's\nprobably going to change. Configure and\nrun round robin. Well, this is the thing\nthat's actually going to start up\nmultiple servers for you. You can do it\nmanually, of course, but that's going to\nbe not fun. Uh, so here you can\nconfigure how many instances you want to\nstart. Let's say I want to start up 16.\nParallelism, that's llama servers\nparameter. This is going to be the\nstarting port of the first llama server.\nAnd that is going to plus one for each\none of the servers you start up. So this\nis going to go up to 9,15. And then\nfinally, this is the key right here,\nEngine X. This is the thing you should\nnot be afraid of. I was for a long time,\nbut this is really cool because it's a\nlittle server that sits in front of your\nllama servers that you talk to. It's\njust pass through basically, but it has\nthe nice little capability of doing\nroundroin, which means imagine you have\nllama servers running on each one of\nthese things. The first request comes\nin, it goes here. The second request\ncomes in, send it here, and so on. And\nit's going to round robin these requests\nso that they're not all handled by one\nserver. So, let's set that up. I like to\nuse 8,000 because it's easy. Now, host,\nthis is local host if you're running\nlocally, but you can set it to 000 if\nyou're going to be quering this\nremotely. And then you have start round\nrobin servers and stop roundroin\nservers. Let's try that. Now, here's Mac\nStudio and there's activity monitor. and\nyou'll see 16 happy little llama servers\nrunning there waiting to handle\nrequests. Now, you might be familiar\nwith Open Web UI. I actually made a\ntutorial, a whole tutorial setting this\nup with Olama at the time, but Open Web\nUI can work with a bunch of different\nservers. So, I pointed it at my EngineX\nendpoint, which is basically the IP\naddress and port 8000 like we configured\nit. Let's do a new chat. Hello. Boom.\nAnd there it is. It's responding. Now, I\ndidn't exactly configure this to handle\nlong stories, which is why a short\nprompt works like say hello because it\ndoesn't have to think for a long time.\nBut the thinking is actually part of\ninference. It's actually generating\ntokens while it's thinking. So, that's\nusing up tokens. And I set a maximum of\ntokens that need to be generated, which\nyou can adjust if you need to. What's\nthe capital of France? Say hi to me in\nEspanol. And they're doing this pretty\nmuch at the same time. So, go on, go\ncheck this out. Play with Llama\nThroughput Lab and see what you think.\nlet me know. You can open issues, do\npull requests, and so on. If you like\nthis video, you're probably going to\nlike the video I did about this\nframework cluster. That's going to be\nright over here. Thanks for watching,\nand I'll see you next time.","source":"captionTracks","metadata":{"provider":"captionTracks","durationSeconds":662},"service":"youtube","resourceKey":"L9QZ97y9Exg","namespace":"yt:auto","formatVersion":1}


kind: extract
key: b9b903e40f02e50793bf1ac6d45f1192fd49cd9ec6d86891c63ef43f42f13cb9
value:
{"url":"https://www.youtube.com/watch?v=L9QZ97y9Exg&feature=youtu.be","title":"Your local LLM is 10x slower than it should be","description":"Here’s the one change that took mine from ~120 tok/s to 1,200+ without a new GPU.TryHackMe just launched Cyber Security 101 (SEC1) — and for a limited time y...","siteName":"YouTube","content":"Transcript:\nYou're probably familiar with Olama,\nright? This is what it looks like. You\ncan launch it. You can talk to it. Never\nmind that we're using Quen 34B. That's\njust standardized thing that I'm [music]\ndoing. And the prompt doesn't matter\nwhat it is. 100 tokens per second is\nwhat Olama gives us. Lama runs on Llama\nCPP. It has a little bit of overhead,\nbut you can also run Llama CPP directly\nand query it remotely, which is what\nsoftware developers are going to be\ninterested in if they're going to be\nusing it as a code assistant, for\nexample, or with agents. Hold on a\nminute. And that's going to be key here\nin a moment. I'm going to start up Llama\nServer pointing to the same model by the\nway. And Llama Server has this nice user\ninterface that we can also say hello to.\nAnd this one gives us tokens per second.\n124. So a little bit better. So that's\nchat. We're chatting with one model, one\nrequest at a time. And it's not even how\ncode assistants [music] work because\ncode assistance requires a lot of\ndifferent chats going on at the same\ntime. What if I query this remotely\nhere? I wrote a little script.\nBasically, this is from my race to 1\nmillion episode, but this one would do a\nthousand. And I'm going to be\ncommunicating from my laptop here to my\nMac Studio that's running Llama Server.\nBoom. And there it goes. And now I'm\ntwiddling my thumbs. Boom. Boom. Boom.\nLook at that. 120 tokens per second.\nVery close numbers, right? You know who\nyou don't want to twiddle their thumbs?\nYour agents that are working away at\nyour code base. Sometimes tens of them\nor hundreds of them. And we're even\ngoing to increase our target tokens to\n1,000 so we have a little bit of room to\nwalk around. Boom. I'm going to watch\nthis in real time. Check this out. This\nis going to be insane. [laughter]\nWhat? 826 tokens per second from Llama\nCPP. And you might think, \"Oh, okay.\n>> I've seen this trick before, right,\n>> on my channel probably.\" Uh, concurrency\nis set to 128. Well, I'm about to show\nyou a new trick because even with\nconcurrency 128, without that trick, you\nstill get 231 tokens per second, which\nis still better, but we're limited to\nwhat Llama CPP is capable of just by\nitself. It can't handle as many\nconcurrent connections as something like\nVLM, for example. And I've got other\nvideos to show that. So, what am I\ndoing? Well, I wrote this little\nlauncher. This is actually inspired by\nDonato Capitella's distributed launcher.\nI recently featured that in a video\nabout this thing. Separate video. You\ncan watch that if you want. But here\nwe're just running this on one machine.\nSo I created this launcher which allows\nyou to tweak a few knobs that Llama CPP\nprovides for tweaking along with\nsweeping and exploring what your machine\nis capable of. For example, I have this\nrunning on the Mac Studio, but what if\nyou have a Mac Mini for example? Well,\nyou can run this on there. If you have a\nWindows laptop or another Linux laptop\nor an Nvidia one, it doesn't matter\nbecause Llama CPP will run on all those.\nAnd this launcher is going to run on any\nof those as well because it's just\nPython. And it'll find the best\ncombination for you to run Llama CPP as\na server while twisting those knobs. I\nprobably shouldn't say that on YouTube.\nUm, somebody might complain.\n>> FBI, open up.\nSome sensitive soul out there. Let me\nshow you how this works. First, we've\ngot some tests. We can do a single\nrequest. Select your model, select where\nyou're running it, and run. And now,\nthis will just query Llama CPP, get your\ntokens per second for that\nconfiguration, which is 127, very close\nto what we've seen. You can do\nconcurrent requests. There we go.\nConcurrent requests, count 128,\nconcurrency 128, total tokens, average\nrequest tokens per second, and then\nthroughput. So, we got a throughput 240\ntokens per second on the same model. By\nthe way, all this stuff is documented in\nthe repository. If you're getting into\ncyber right now, there's an awkward gap\nbetween I watch the videos and I can\nhandle real situations. And the gap is\nexactly what this is for. This is try\nhackme where you learn cyber by doing\nit. Interactive labs real scenarios all\nbrowserbased with a global community of\nover 6 million people. They just\nlaunched an entry-level search called\ncyber security 101 also known as sec 1.\nQuick tour only. No exam spoilers. The\nbig idea here is applied fundamentals.\nYou train hands-on, then sew one checks\nyou can actually perform the basics in\nrealistic situations, not just memorized\ndefinitions. This is for students going\nfor their first cyber role, career\nswitchers, and interns or junior folks\nlike 0 to2 years who want proof they can\nactually do the fundamentals. High\nlevel, it touches what you'll actually\nrun into. operating systems, networking,\nweb-based security basics, a little bit\nof blue team fundamentals, a little bit\nof red team fundamentals, and malware\nanalysis at a beginner level. And this\nis what makes it different. A lot of\nbeginner searchs feel like vocabulary\nquizzes. SEC 1 is trying to be a\nhands-on checkpoint instead. When you\nfinish, you get an instant result, a\ndigital certificate, and a sharable\nbadge. If you're getting started in\ncyber security and want a hands-on way\nto prove your fundamentals, check out\nTry HackMe CyberC 101 certification\nusing the link below. And if you use my\ncode for launch, nonpremium users get\n40% off their certification plus 3\nmonths of free premium subscription link\nbelow. All this stuff is documented in\nthe repository. It's called Llama\nThroughput Lab. There's other tests in\nhere, but there's one really interesting\none and it's called Full Sweep. This\ntakes a while. There's 308 different\ncombinations of how many instances of\nLlama server you start. That's the key\nhere. Hint hint. So, you run multiple\ninstances of Llama server to be able to\nreply because a machine that's as\npowerful as this has a lot of memory\nthese days. This one has 64 gigs of VRAM\nor it's called unified memory in Apple\nsilicon terms. This Mac Studio has 512.\nBut that's not the limiting factor when\nit comes to running these. The limiting\nfactor here is the compute on the GPU.\nWe're running all this on the GPU\nbecause we don't want to do it on the\nCPU. It's going to be extra slow. That's\nwhy we're using the GPU here. And you\ncan only do so much when you're running\nmultiple llama servers at the same time.\nYou're no longer limited by memory,\nobviously, unless [music] you're running\nhuge models, which you can do here. You\ncan run on a 512 GB machine. You can run\nlike four llama 70 billion parameter\nmodels at the same time in four\ninstances of llama server. So those are\ninstances. Then parallel llama server\nexposes a parameter called parallel. And\nthis has been documented pretty well on\nthe GitHub repository under Llama CPP by\nthe creator of Llama CPP himself,\nGeorgio Ganov. Another knob you can\ntwist.\nAll right, maybe I should stop it with\nthat. Um, somebody's gonna get mad. I'm\nnot twisting any knob. Concurrency is\nanother uh parameter you can alter and\nthat's what I showed before in my videos\nwhere we can send multiple requests to\nLlama server. Why do we care about that?\nWell, if you're just doing chat, then by\nall means go ahead and chat and wait for\nit to finish. But a lot of times we want\nto go beyond that. Imagine a folder with\nthousands of photographs that you want\nto analyze or a video with millions of\nframes. You don't want to be sitting\nthere and waiting for one frame at a\ntime to be analyzed, do you? Or imagine\nanother scenario where you have a bunch\nof agents orchestrated together. Some\nare doing planning, some are doing\nimplementation. You want them working at\nthe same time. So that's where\nconcurrency comes in. Getting back to\nthe sweep, this will do a full sweep. So\ninstances, parallel, and concurrencies,\nand it'll find out where the optimal\nspot is on your computer, whatever\ncomputer you're using. You can select\nthat test sweeps, and then click on run\nselected test, and off it goes. There it\ngoes. Uh, just keep in mind that this\nwill take a while. Also, on a slower\nsystem, it'll take a real long while. So\nright now we're on four out of 308\ndifferent combinations. But as it's\nsweeping, you can see the numbers coming\nin. 128 tokens per second, 193, 263,\n314. So you can see how these different\nparameters are affecting that output. If\nI go to my results folder here, I did a\nfull sweep on this Mac Studio, of\ncourse. Now, I should probably create a\nnice looking chart, but this is also on\nGitHub and open source, so feel free to\nmuck with it if you want to. Look at\nthese numbers. 1,226 tokens per second.\nThat's with 16 instances. In other\nwords, I started Llama server 16 times.\nParallel flag set to 64. 1,024\nconcurrency. Now, keep in mind this is\njust a benchmark. If you want to do real\nworld cases, this will only give you\nsome guidance and point you in the right\ndirection, but always make sure you\nactually test your real world scenarios.\nAnd finally, let's discuss how I\nactually run this. Well, I run it by\nusing this command right here. It's an\nexecutable Python command. Run llama\ntests. Boom. Not very creative, I know.\nAnd here you can select the test, select\nthe model. Boom. And there's one more\nthing, which is this number six here.\nIt's number six for now, but it's\nprobably going to change. Configure and\nrun round robin. Well, this is the thing\nthat's actually going to start up\nmultiple servers for you. You can do it\nmanually, of course, but that's going to\nbe not fun. Uh, so here you can\nconfigure how many instances you want to\nstart. Let's say I want to start up 16.\nParallelism, that's llama servers\nparameter. This is going to be the\nstarting port of the first llama server.\nAnd that is going to plus one for each\none of the servers you start up. So this\nis going to go up to 9,15. And then\nfinally, this is the key right here,\nEngine X. This is the thing you should\nnot be afraid of. I was for a long time,\nbut this is really cool because it's a\nlittle server that sits in front of your\nllama servers that you talk to. It's\njust pass through basically, but it has\nthe nice little capability of doing\nroundroin, which means imagine you have\nllama servers running on each one of\nthese things. The first request comes\nin, it goes here. The second request\ncomes in, send it here, and so on. And\nit's going to round robin these requests\nso that they're not all handled by one\nserver. So, let's set that up. I like to\nuse 8,000 because it's easy. Now, host,\nthis is local host if you're running\nlocally, but you can set it to 000 if\nyou're going to be quering this\nremotely. And then you have start round\nrobin servers and stop roundroin\nservers. Let's try that. Now, here's Mac\nStudio and there's activity monitor. and\nyou'll see 16 happy little llama servers\nrunning there waiting to handle\nrequests. Now, you might be familiar\nwith Open Web UI. I actually made a\ntutorial, a whole tutorial setting this\nup with Olama at the time, but Open Web\nUI can work with a bunch of different\nservers. So, I pointed it at my EngineX\nendpoint, which is basically the IP\naddress and port 8000 like we configured\nit. Let's do a new chat. Hello. Boom.\nAnd there it is. It's responding. Now, I\ndidn't exactly configure this to handle\nlong stories, which is why a short\nprompt works like say hello because it\ndoesn't have to think for a long time.\nBut the thinking is actually part of\ninference. It's actually generating\ntokens while it's thinking. So, that's\nusing up tokens. And I set a maximum of\ntokens that need to be generated, which\nyou can adjust if you need to. What's\nthe capital of France? Say hi to me in\nEspanol. And they're doing this pretty\nmuch at the same time. So, go on, go\ncheck this out. Play with Llama\nThroughput Lab and see what you think.\nlet me know. You can open issues, do\npull requests, and so on. If you like\nthis video, you're probably going to\nlike the video I did about this\nframework cluster. That's going to be\nright over here. Thanks for watching,\nand I'll see you next time.","truncated":false,"totalCharacters":12049,"wordCount":2210,"transcriptCharacters":12037,"transcriptLines":317,"transcriptWordCount":2209,"transcriptSource":"captionTracks","transcriptionProvider":null,"transcriptMetadata":{"provider":"captionTracks","durationSeconds":662},"transcriptSegments":null,"transcriptTimedText":null,"mediaDurationSeconds":662,"video":{"kind":"youtube","url":"https://www.youtube.com/watch?v=L9QZ97y9Exg"},"isVideoOnly":false,"diagnostics":{"strategy":"html","firecrawl":{"attempted":false,"used":false,"cacheMode":"default","cacheStatus":"unknown","notes":null},"markdown":{"requested":false,"used":false,"provider":null,"notes":null},"transcript":{"cacheMode":"default","cacheStatus":"miss","textProvided":true,"provider":"captionTracks","attemptedProviders":["youtubei","captionTracks"],"notes":null}}}


