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Introducing EXPO-FT – Efficient, Reliable & Open-Source VLA Finetuning! EXPO-FT unlocks π0.5 for challenging manipulation tasks: Routing string lights & inserting the power connector to illuminate them Striking pool ball into pocket Inserting flower into wine bottle (1/5)

79,711 views • 4 months ago •via X (Twitter)

17 Comments

Perry Dong's profile picture
Perry Dong4 months ago

Unlike prior works that only train lightweight policies, rely on latent space prediction, or decoupled RL training, EXPO-FT fully finetunes the VLA with RL — leveraging EXPO's stability and sample efficiency, augmented by human-in-the-loop corrections (2/5)

Perry Dong's profile picture
Perry Dong4 months ago

Using an average of 19.1 minutes of online robot data, EXPO-FT reaches 30/30 on these tasks: Routing/powering string lights Striking a pool ball into a pocket Inserting flower into a wine bottle Scooping candy Picking up cube from large initial states Flipping egg (3/5)

Perry Dong's profile picture
Perry Dong4 months ago

EXPO-FT outperforms highly performant prior RL approaches by fully leveraging the VLA for online learning (4/5)

Perry Dong's profile picture
Perry Dong4 months ago

Project with @khhung906, @TianGao_19, @DorsaSadigh, @chelseabfinn Website: Paper: Open-source code coming soon! (5/5)

Remi Cadene's profile picture
Remi Cadene4 months ago

Really cool work :)

Dominique Paul's profile picture
Dominique Paul4 months ago

Cool work! I'm looking into exactly this atm. What are tasks you tried the method on where it didn't work well enough and that you didn't include in the paper?

pfung's profile picture
pfung4 months ago

Cool idea congrats!!!

Sanskar Pandey's profile picture
Sanskar Pandey4 months ago

Incredible work!

Roberto's profile picture
Roberto4 months ago

This is what I was looking for. Awesome work!

Binh's profile picture
Binh4 months ago

I don’t get it, this isn’t really finetuning a vla right? You’re finetuning an edit policy that updates the vla output? Eitherway good work but was just confused, what’s the improvement on prior work? Or is the only change is the fact that the policy is a vla

Perry Dong's profile picture
Perry Dong4 months ago

The VLA becomes the base policy in EXPO and is fine-tuned

leanhtn (✱,✱)'s profile picture
leanhtn (✱,✱)4 months ago

awsome

Yurii Petruk's profile picture
Yurii Petruk3 months ago

Impressive results – 30/30 on 8 tasks with only 19 min of RL data. Quick question: these results are tested on visual variations within the same lab setup. How does EXPO-FT perform when the environment itself changes – different kitchen layout, unfamiliar objects, or a completely new cultural context? Is that a data problem or a method problem?

seanpixel 🫧's profile picture
seanpixel 🫧4 months ago

amazing

Simran Tech's profile picture
Simran Tech4 months ago

The most interesting AI progress isn't happening on screens anymore. It's happening in the physical world. Getting a robot to reliably plug in a connector or insert a flower into a bottle is often harder than generating a thousand lines of code.

Zachary Tang's profile picture
Zachary Tang4 months ago

can we say this is "fully finetuning" the VLA with RL? seems that you freeze original VLA during RL process and just modify the edit residuals actor and Q-function

AUTONOMOUS's profile picture
AUTONOMOUS4 months ago

Next you need to try with a thread and a needle😅 Looking forward to the open source too

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Nino Scouting

28,568 views • 17 days ago

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Rev.6v2 iii.) “He went forth conquering and to conquer”. The Lord Jesus is only going to battle against the Nations at the second coming, which is not yet So this is not Jesus, this is the antichrist. He had a bow, without arrows. He would conquer nations without firing a shot, he would conquer nations with peace; they would give their nations to him. Daniel 8:23-24 describes a powerful king who will arise at the end of a period of time, characterized by transgression and wickedness. This king will have a fierce appearance, understand dark schemes, and wield mighty power, not by his own means, but through extraordinary destruction. He will succeed in what he does, destroying the mighty and the holy people. SEAL 2- Conflict on Earth: Killings, Murders (The Man on the Red Horse) Daniel 6v3- 4 When He opened the second seal, I heard the second living creature saying, “Come [b]and see.” 4 Another horse, fiery red, went out. And it was granted to the one who sat on it to take peace from the earth, and that people should kill one another; and there was given to him a great sword. Once the Church is gone at the Rapture, no more peace on the earth, there would be killings. Matt.24v7,10 For nation will rise against nation, and kingdom against kingdom. And there will be famines, [a]pestilences, and earthquakes in various places. 8 All these are the beginning of sorrows. 9 “Then they will deliver you up to tribulation and kill you, and you will be hated by all nations for My name’s sake. 10 And then many will be offended, will betray one another, and will hate one another. Rev.13v15 15 He was granted power to give breath to the image of the beast, that the image of the beast should both speak and cause as many as would not worship the image of the beast to be killed. SEAL 3- Scarcity (Inflation and Famine) The Man on the Black Horse Daniel 6v5-6 When He opened the third seal, I heard the third living creature say, “Come and see.” So I looked, and behold, a black horse, and he who sat on it had a pair of scales[c] in his hand. 6 And I heard a voice in the midst of the four living creatures saying, “A [d]quart of wheat for a [e]denarius, and three quarts of barley for a denarius; and do not harm the oil and the wine. Lamentations 4v4-10 AMP [4] The tongue of the infant clings To the roof of its mouth because of thirst; The little ones ask for food, But no one gives it to them. [5] Those who feasted on delicacies Are perishing in the streets; Those reared in purple [as nobles] Embrace ash heaps. [6] For the [punishment of the] wickedness of the daughter of my people [Jerusalem] Is greater than the [punishment for the] sin of Sodom, Which was overthrown in a moment, And no hands were turned toward her [to offer help]. [7] Her princes were purer than snow, They were whiter than milk [in appearance]; They were more ruddy in body than rubies, Their polishing was like lapis lazuli (sapphire). [8] Their appearance is [now] blacker than soot [because of the prolonged famine]; They are not recognized in the streets; Their skin clings to their bones; It is withered, and it has become [dry] like wood. [9] Those killed with the sword Are more fortunate than those killed with hunger; For the hungry pine and ebb away, For the lack of the fruits of the field. [10] The hands of compassionate women Boiled their own children; They became food for them Because of the destruction of the daughter of my people [Judah]. Ezekiel 4v10-12, 17 AMP [10] The food you eat each day shall be [measured] by weight, twenty shekels, to be eaten daily at a set time. [11] You shall drink water by measure also, the sixth part of a hin; you shall drink daily at a set time. [12] You shall eat your food as barley cakes, having baked it in their sight over human dung.” [17] because bread and water will be scarce; and they will look at one another in dismay and waste away [in punishment] for their wickedness. If you think there are economic problems right now in the world, this is nothing compared to what is coming. No one would be able to solve the economic problem then, not even the antichrist. He would make promises, but would fail. This is going to be a very terrible time on earth. At this point the peace treaty is broken and the next three and a half years begins SEAL 4- Widespread Death on Earth (Death Released) A Pale(green) Horse with a rider called- Death, and Hell Followed. Rev.6v7 -8 7 When He opened the fourth seal, I heard the voice of the fourth living creature saying, “Come and see.” 8 So I looked, and behold, a pale horse. And the name of him who sat on it was Death, and Hades followed with him. And [f]power was given to them over a fourth of the earth, to kill with sword, with hunger, with death, and by the beasts of the earth. These are the judgments of God, not even the actions taken by the antichrist. The world is being punished for their rejection of Jesus Christ. Killings with the sword, refers to war. Matt 24v21 For then shall be great tribulation, such as was not since the beginning of the world to this time, no, nor ever shall be.” (KJV) The Fourth and Fifth Seals are in the second 3 and a half years, after the Peace treaty has been broken. 1 Thess. 5v3 -4 For when they shall say, peace and safety; then sudden destruction cometh upon them, as travail upon a woman with child; and they shall not escape. At this time the antichrist enters into the temple in Jerusalem and from then on, fierce persecution begins. SEAL 5- The Cry of the Tribulation Saints (Killed for their Faith) Rev 6v9-11 When He opened the fifth seal, I saw under the altar the souls of those who had been slain for the word of God and for the testimony which they held. 10 And they cried with a loud voice, saying, “How long, O Lord, holy and true, until You judge and avenge our blood on those who dwell on the earth?” 11 Then a white robe was given to each of them; and it was said to them that they should rest a little while longer, until both the number of their fellow servants and their brethren, who would be killed as they were, was completed. When a godly man dies, he goes to heaven and his soul is without a body, but is clothed with a spiritual garment until the resurrection. At the rapture, those who were dead in Christ were raised up with a glorified body, and we that are alive and remain were caught and changed into that same glorified body. The tribulation saints who were slain for their faith, they gave their lives as a sacrifice to God, they refused to take the mark of the beast, and were killed. Their souls are seen in Heaven under the Altar, without their spiritual glorified bodies, hence their cry. Vr 11 the Lord told them to rest for a while until others to be killed like them also arrive. At this time too something happens, there is more preaching of the gospel ongoing in the earth, Evangelism intensifies. Rev.14: 14 And I looked, and behold a white cloud, and upon the cloud one sat like unto the Son of man, having on his head a golden crown, and in his hand a sharp sickle. 15 And another angel came out of the temple, crying with a loud voice to him that sat on the cloud, Thrust in thy sickle, and reap: for the time is come for thee to reap; for the harvest of the earth is ripe. Here the Tribulation Saints are Raptured, which is getting to the ending part of the tribulation period. #YLWSPECIALSSeason1Phase3 #RaptureModeActivated #RaptureReady #FirstFlightGang #YearOfCompleteness

SeyiB

15,925 views • 1 year ago

CANCEL Your Weekend Plans, and Learn Claude Code Today. $5,000/month. $10,000/month. $20,000/month. People are building entire apps and charging clients thousands using Claude Code. You're still Googling 'how to center a div.' While you're binge-watching a show you won't remember next week, a 19 year old with zero coding experience just built a $5,000 SaaS product in one afternoon using the tool I'm about to break down. Same laptop. Same internet. Same 24 hours. He has Claude Code. You have Netflix. That's the only difference. This YouTube video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Save this post. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude.MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. Code editor integration. Basic usage. Commands. Modes and shortcuts. Planning prompts. Model selection. Git and version control. Memory and Claude. MD. Tasks and automation. Advanced features. All in one video. All free. All beginner friendly. The person who masters even half of these in the next 2 weeks will be in the top 1% of Claude Code users. The top 1% of Claude Code users are the ones charging $5,000-$10,000 per project and building them in a single afternoon. Everyone else is asking ChatGPT to fix their resume. Same tools. Same access. Completely different outcomes. Because one person treats AI like a toy. And the other treats it like a business. ↓ Here's the hard truth nobody wants to hear. You don't have a talent problem. You don't have an intelligence problem. You don't have a resources problem. You have an action problem. Everything I just listed has a free tutorial right here in the attached video. 33 minutes. That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumar for daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

101,793 views • 6 months ago

CANCEL Your Weekend Plans, & Learn Claude Code Today. This Claude Code teaches more about vibe-coding in 30 mins than most tutorials do in hours. Save this, it'll change how you build forever People are building entire apps and charging clients $5,000 to $20,000 using Claude Code. This Claude Code video is a goldmine. Full Claude Code tutorial. Beginner to pro. Every feature. Every setup step. Every best practice. Zero prior knowledge needed. Save it. Watch it tonight. Not tomorrow. Tonight. Follow Himanshu Kumar so you don't miss the breakdowns for each feature. This is your complete Claude Code roadmap. Lose it and you lose the next 12 months of income. ↓ 1. Understand What Claude Code Actually Is. You think Claude Code is just another chatbot. It's not. And that misunderstanding is why you're broke. ChatGPT gives you text. Claude Code gives you software. It runs in your terminal. It reads your entire codebase. It writes files directly to your project. It runs commands on your machine. It debugs errors autonomously. It builds features end to end. You're not chatting. You're deploying a developer. One that works 24/7. Never asks for a raise. Never calls in sick. Never pushes broken code at 5 PM on a Friday. People are charging clients $5,000-$10,000 for apps they built with Claude Code in 3 hours. And you didn't even know this tool existed because you're still asking ChatGPT to write you a to-do list. The gap between you and people making money with AI isn't intelligence. It's awareness. Now you're aware. Save this post. Follow Himanshu Kumar for the complete breakdown of every Claude Code feature. ↓ 2. Set Up Claude Code Properly. Most people quit here. "It's too complicated." "I don't know terminal." "I'll set it up later." Later never comes. And "complicated" means "I watched for 30 seconds and gave up." The setup takes 10 minutes. Install Node.js. Install Claude Code via npm. Authenticate your account. Open your terminal. Done. 10 minutes. You spent longer this morning deciding what to have for breakfast. The video walks through every single click. Every command. Every screen. Assuming you know absolutely nothing. If you can download an app on your phone, you can set up Claude Code. It's the same level of difficulty. But you'll still tell yourself it's "too technical" because that excuse is more comfortable than admitting you're just scared to try something new. This is the setup that everything else builds on. Skip it and nothing works. ↓ 3. Use the Desktop App. You don't even need to live in the terminal if you don't want to. Claude Code has a desktop app. Clean interface. Visual feedback. Everything you need without touching command line. But here's the thing most people don't know: The desktop app isn't just a pretty wrapper. It lets you manage projects visually. See file changes in real time. Switch between projects instantly. The people making money with Claude Code use the desktop app for client projects because it's faster to manage multiple builds simultaneously. You're still opening 14 browser tabs to organize one project. They open one app and everything's there. Efficiency isn't a personality trait. It's a tool choice. Save this post. Follow Himanshu Kumar for the desktop app workflow that handles 5 client projects at once. ↓ 4. Install the Right Dependencies. This is where beginners silently fail and blame the tool. Claude Code needs certain dependencies installed to work properly. Miss one and everything breaks. Then you go on Twitter and say "Claude Code doesn't work." It works fine. You just didn't read the setup guide. The video covers every dependency you need. What to install. How to install it. How to verify it's working. No guessing. No Stack Overflow rabbit holes at midnight. No "why isn't this working" for 3 hours. Watch the dependency section once. Follow every step. Never deal with setup issues again. You spent more time last week troubleshooting a printer than this takes. ↓ 5. Work Inside Your Code Editor. Claude Code integrates directly with your code editor. VS Code. Cursor. Whatever you use. It's not a separate window you alt-tab between. It's right there. In your workflow. You type a request. Claude writes the code. The code appears in your editor. You review it. Accept it. Done. No copy pasting between windows. No reformatting code that got mangled in transit. No "which version was the right one." It's like pair programming with someone who never gets distracted, never argues about naming conventions, and actually writes code that works on the first try. Your current coding process is: Google the problem, read 5 answers on Stack Overflow, copy the wrong one, debug for an hour, find the right one, paste it in, break something else, repeat. Claude Code's process is: describe what you want, get working code, move on with your life. Same hour. One method produces working software. The other produces frustration and a browser history full of Stack Overflow tabs. Stop coding the hard way. Save this post. Follow Himanshu Kumar for code editor setup guides and integration tips. ↓ 6. Master Basic Usage. Most people learn 5% of a tool and say they "know" it. You "know" Photoshop because you can crop an image. You "know" Excel because you can sum a column. You "know" Claude Code because you asked it one question. Basic usage means: How to give Claude Code context about your project. How to ask for changes to existing code. How to generate new files and features. How to review what Claude produces. How to iterate when the output isn't perfect. These basics are the foundation of everything. Skip them and every advanced feature feels confusing. Master them and every advanced feature feels obvious. The video breaks down each one with real examples. Not theory. Actual usage on actual projects. You've been using AI tools at 5% capacity and wondering why your results are 5% of what others get. Save this post. Follow Himanshu Kumar for daily Claude Code usage tips. ↓ 7. Learn Every Command. Claude Code has commands that most users never discover. Because most users type one message and expect magic. That's not how professionals use it. Professionals use specific commands that tell Claude Code exactly what to do, how to do it, and what constraints to follow. The difference between a beginner and someone making $10K/month with Claude Code is knowing which command to use and when. The video walks through every single one. Not just what they do. But when to use each one. And why one command is better than another for specific situations. You've been using Claude Code like a hammer. These commands turn it into a full toolbox. Stop treating a power tool like a blunt instrument. Save this post. Follow Himanshu Kumar for the command cheat sheet I use daily. ↓ 8. Understand Modes and Shortcuts. Speed matters. The person who builds an app in 2 hours charges $5,000. The person who builds the same app in 2 days charges $2,000. Same app. Same quality. Different speed. Different income. Claude Code has modes that change how it operates. And shortcuts that cut your workflow time in half. Most people don't know either exists. They use Claude Code in default mode for everything. Like driving a car in first gear on the highway. Technically it works. But everyone is passing you. The video shows you every mode. Every shortcut. Every time-saving trick that separates the people charging $2,000 per project from the people charging $10,000. Speed is money. Literally. Save this post. Follow Himanshu Kumar for the shortcuts that cut my build time by 60%. ↓ 9. Write a Proper Planning Prompt. This is the section that separates amateurs from professionals. And it's the section most people skip. A planning prompt tells Claude Code what you're building before you start building it. Architecture. File structure. Technologies. Features. Constraints. Edge cases. Without a planning prompt, Claude Code guesses. And guessing produces garbage. With a planning prompt, Claude Code executes a clear plan. And clear plans produce working software. The video shows you exactly how to write a planning prompt that makes Claude Code produce professional-grade output on the first try. "But I just want to start coding." That's why your code breaks every time. That's why you restart projects 4 times. That's why nothing you build ever gets finished. Because you refuse to plan. A 5-minute planning prompt saves you 5 hours of debugging. But you'd rather skip the 5 minutes and suffer through the 5 hours because patience isn't your thing. And that's exactly why you're not making money. Planning is the most underpaid skill in coding. And the most overpaid when you master it. Save this post. Follow Himanshu Kumar for the planning prompt templates I use for every client project. ↓ 10. Choose the Right Model. Claude Code lets you select different AI models. Not all models are the same. Not all tasks need the same model. Using the most powerful model for a simple task wastes credits. Using a basic model for a complex task wastes time. The video explains: Which model to use for quick fixes. Which model to use for complex architecture. Which model to use for debugging. Which model to use for code generation. Most people pick one model and use it for everything. That's like using a sledgehammer to hang a picture frame. Model selection is strategy. And strategy is money. The people making $10K/month with Claude Code are strategic about every credit they spend. You're burning through credits because you use the most expensive model to write a hello world. ↓ 11. Use Git and Version Control. If you're not using version control, you're one mistake away from losing everything. Claude Code integrates with Git. Every change tracked. Every version saved. Every mistake reversible. Without Git: Claude makes a change. It breaks something. You can't undo it. You start over. 3 hours wasted. With Git: Claude makes a change. It breaks something. You roll back in 5 seconds. Keep working. Version control isn't optional. It's insurance. And the people not using it are the same people who say "I lost my entire project" like it's something that just happens. It doesn't just happen. It happens because you didn't set up Git. The video walks through the entire Git integration. Save this post. Follow Himanshu Kumar for the Git workflow that's saved every project I've ever built. ↓ 12. Set Up Claude MD and Memory. This is the feature that makes Claude Code feel like a real team member instead of a stranger you explain everything to every time. ClaudeMD is a memory file. You tell Claude Code about your project once. It remembers forever. Coding style preferences. Project architecture decisions. Technology stack. File naming conventions. Business logic rules. Without ClaudeMD: Every new conversation starts from zero. You explain the same things repeatedly. Output is inconsistent. With ClaudeMD: Claude knows your project. Claude follows your rules. Claude produces consistent, professional code. The difference between a sloppy freelancer and a reliable agency is consistency. Claude. MD gives you consistency without the agency overhead. Most people don't set this up and wonder why Claude Code gives different answers every time. ↓ 13. Automate with Tasks. This is where Claude Code stops being a tool and starts being an employee. Tasks let you define repeating workflows. "Every time I push code, run tests." "Every time I create a new file, add boilerplate." "Every time I start a session, check for errors." Automated. Hands-free. Consistent. You're doing these things manually every single day. The same checks. The same steps. The same routine. Tasks do them automatically. So you can focus on the work that actually makes money. Every manual task you automate is time you get back. And time is the only thing you can never make more of. Save this post. Follow Himanshu Kumar for the task automation templates that run my entire workflow. ↓ 14. Explore Features Most People Never Touch. The video covers features that 95% of Claude Code users don't know exist. Because they watched a 3-minute TikTok about Claude Code and think they're experts now. They're not. They're using 5% of a tool that can do everything. The full tutorial goes deep into features that most tutorials skip because they're "too advanced." They're not too advanced. They're too valuable for lazy creators to bother explaining. This video explains all of them. Clearly. For beginners. The 5% of features you don't know about are the 5% that make people rich. ↓ Let's zoom out. I just broke down 14 sections of Claude Code. Setup and installation. Desktop app. Dependencies. 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That's it. 33 minutes to learn the tool that people are using to build $5,000-$20,000/month businesses. You spent more time today scrolling Twitter than it takes to watch this video. You spent more time this week watching Netflix than it takes to master Claude Code basics. You spent more time this month doing nothing than it would take to completely change your income. The information is free. The tool is accessible. The opportunity is here. The only thing missing is you caring enough to start. ↓ CANCEL your plans this week. This isn't optional anymore. The people learning Claude Code right now will be building apps for the people who didn't learn it. That's not a prediction. That's already happening. Companies are replacing $150/hour developers with one person and Claude Code. If you code: learn Claude Code or become half as valuable by next year. If you don't code: learn Claude Code or miss the biggest opportunity to start earning from tech without a CS degree. There's no path forward that doesn't include AI coding tools. None. You have one window. Right now. This week. ↓ Here's your action plan for the next 7 days: Day 1: Watch the full video. Install Claude Code. Set up dependencies. Day 2: Learn basic usage. Try 5 different commands. Day 3: Write your first planning prompt. Build a small project. Day 4: Set up Claude. MD. Configure your memory file. Day 5: Master modes and shortcuts. Build a second project faster. Day 6: Set up Git integration. Automate with tasks. Day 7: Build something real. A tool, an app, a website. Ship it. 7 days. One tool. One completely different skill set. One completely different income potential. Or 7 more days of scrolling Twitter watching other people build things while you "plan to start." Your call. ↓ This is the most important video you'll watch this year. 33 minutes. Complete Claude Code mastery. From zero to building real projects. Save this post. Come back to it every single day this week. Check off each section as you complete it. Follow Himanshu Kumarfor daily Claude Code breakdowns, advanced tutorials, and the exact workflows that are turning beginners into $10K/month builders. The only thing between you and $10K/month with Claude Code is this video and 7 days. Don't waste them. You Must Follow me Himanshu Kumar, so i can send you DM.

Himanshu Kumar

85,668 views • 5 months ago

i'm looking for feedback, thank you here's the full definition of my schematic: import { mkdirSync, writeFileSync } from "node:fs" import path from "node:path" import { defineCircuit, definePart, exportKiCadNetlist, instantiate, net, pin, } from "../../../tools/circuitd/src" const ch32v203f8u6 = definePart({ id: "CH32V203F8U6", kicadSymbol: "tinybee:CH32V203F8U6", footprint: "tinybee:QFN-20_L3.0-W3.0-P0.40-BL-EP1.7", defaultValue: "CH32V203F8U6", datasheet: " description: "144 MHz RISC-V MCU with ADCs, op-amps, advanced timers, and SWD", fields: { "LCSC Part": "C7570477", }, designNotes: [ "Power this MCU from a 3.3 V rail only; do not allow the VDD pin to see more than 3.6 V.", "Place a 100 nF decoupler immediately next to VDD and the QFN ground return, and keep that loop as short as possible.", "Put a small series resistor between PA0 and any off-board throttle or one-wire configuration signal so the MCU pin is not directly exposed at the connector.", "Keep PA13 and PA14 easy to probe and free of hard loads so SWD still works during bring-up and recovery.", "Keep PA8, PA9, PA10, PA7, PB0, and PB1 on the three phase-drive nets, and keep PA6/BKI free for a future fault or protection input.", "If you follow the openwch RISC-V ESC zero-cross scheme, tie PA3 and PA4 back into PA2 externally and reserve PA2 for that shared interrupt net.", "Route back-EMF and op-amp related pins away from phase copper, gate-drive loops, and other fast-switching nodes.", ], pins: { "PA0/WKUP/ADC0": "1", "PA1/ADC1": "2", "PA2/ADC2/OP2O0": "3", "PA3/ADC3/OP1O0": "4", "PA4/ADC4/OP2O1": "5", "PA5/ADC5/OP2N1": "6", "PA7/ADC7/OP2P1/CH1N": "7", "PB0/ADC8/OP1P1/CH2N": "8", "PB1/ADC9/OP1O1/CH3N": "9", "PB10/OP2N0": "10", "PB11/OP1N0": "11", "PB14/OP2P0": "12", "PB15/OP1P0": "13", "PA8/CH1": "14", "PA9/CH2": "15", "PA13/SWD/PA12/UDP": "16", "PA14/SWC/PA11/UDM": "17", "PA10/CH3": "18", VDD: "19", "PA6/ADC6/OP1N1/BKI": "20", GND: "21", }, }) const tlv75533pdqnt = definePart({ id: "TLV75533PDQNT", kicadSymbol: "Regulator_Linear:TLV75533PDBV", footprint: "tinybee:Texas_X2SON-4_1x1mm_P0.65mm", defaultValue: "TLV75533PDQNT", datasheet: " description: "500mA low-dropout fixed 3.3V regulator in 1x1mm X2SON-4", designNotes: [ "Treat +BATT as a 1S Li-ion or LiPo rail only; this regulator has a 5.5 V maximum input rating, so 2S or higher is out of bounds for this graph.", "Place at least 1 uF ceramic directly at IN and at least 1 uF ceramic directly at OUT; do not push those capacitors away from the package.", "Check regulator heating with (VIN - 3.3 V) * load current and add copper area if the dissipation is not comfortably safe.", "Add local input bulk when the battery lead is long or inductive so the LDO input does not absorb line spikes by itself.", "Keep EN tied to a known state at all times; if startup control matters later, break it out deliberately instead of bodging it in.", "The X2SON thermal pad is internally tied to GND; flood it into the local ground copper and do not leave the center pad floating.", ], pins: { OUT: "1", GND: "2", EN: "3", IN: "4", THERMAL_PAD: "5", }, }) const controlHeader = definePart({ id: "CTRL_IN_HEADER_1X01", kicadSymbol: "Connector_Generic:Conn_01x01", footprint: "tinybee:CTRL_PAD_1x01_Micro", defaultValue: "CTRL_IN", description: "1-pin control input wire pad for throttle signal", designNotes: [ "Use this only for the control signal.", "Ground reference is expected to be shared elsewhere in the system when this pad is in use.", "Battery power comes in through the dedicated battery wire holes, not through this control pad.", "PA0 on the CH32 is not a true FT input; treat this header as a 3.3 V logic input unless you deliberately add a real level-conditioning stage.", ], pins: { PIN1: "1", }, }) const motorHeader = definePart({ id: "MOTOR_OUT_HEADER_1X03", kicadSymbol: "Connector_Generic:Conn_01x03", footprint: "tinybee:MOTOR_PADS_1x03_Micro", defaultValue: "MOTOR_OUT", description: "3-pin motor phase wire pad group", designNotes: [ "This is the board edge interface for the three motor phases.", "Use direct wire holes here rather than a bulky 2.54 mm header footprint.", ], pins: { PIN1: "1", PIN2: "2", PIN3: "3", }, }) const programmingHeader = definePart({ id: "PROG_HEADER_1X04", kicadSymbol: "Connector_Generic:Conn_01x04", footprint: "tinybee:PROG_PADS_1x04_Micro", defaultValue: "PROG", description: "4-pin fine-pitch SWD programming header", designNotes: [ "Break out SWDIO, SWCLK, GND, and 3.3 V so the CH32 can be flashed and recovered without bodge wires.", "Keep this header free of extra loading and avoid reusing the SWD pins elsewhere until firmware bring-up is stable.", "Treat the 3.3 V pin here as target reference unless you deliberately design reverse-current-safe back-powering through the regulator path.", ], pins: { PIN1: "1", PIN2: "2", PIN3: "3", PIN4: "4", }, }) const batteryHeader = definePart({ id: "BATT_IN_HEADER_1X02", kicadSymbol: "Connector_Generic:Conn_01x02", footprint: "tinybee:BATT_PADS_1x02_Micro", defaultValue: "BATT_IN", description: "2-pin battery wire pad pair", designNotes: [ "Use this dedicated pair for battery positive and battery negative input.", "Keep the battery loop tight to the bulk capacitors and half-bridge supply entry.", ], pins: { PIN1: "1", PIN2: "2", }, }) const complementaryHalfBridge = definePart({ id: "PMCPB5530X_115", kicadSymbol: "tinybee:PMCPB5530X,115", footprint: "tinybee:DFN2020-6_L2.0-W2.0-P0.65-BL", defaultValue: "PMCPB5530X,115", datasheet: " description: "20 V complementary N/P MOSFET half-bridge in DFN2020-6", fields: { "LCSC Part": "C552747", }, designNotes: [ "Use this complementary half-bridge only on a 1S Li-ion or LiPo rail; do not reuse the direct P-gate pull-down topology above the 1S battery range.", "With +BATT limited to the 1S range, the direct P-gate pull-down swing stays inside the device gate limits and gives usable drive headroom.", "Drive the low-side N-FET gates directly from the 3.3 V timer outputs only in this 1S design; rework the stage before raising the battery voltage.", "Do not skimp on local +BATT bypass; keep real ceramic bulk plus a high-frequency bypass capacitor tight to the half-bridge supply loop.", "Pour the duplicated drain pads into real copper for current and heat spreading; do not neck them down right at the package.", "Keep each gate loop short, tight, and referenced to its own source return to reduce ringing and false turn-on.", "Assume the board copper sets the real current limit; check temperature rise on the actual ESC geometry, not only the datasheet headline current.", ], pins: { LOW_SIDE_SOURCE: "1", LOW_SIDE_GATE: "2", HIGH_SIDE_DRAIN: ["3", "8"], HIGH_SIDE_SOURCE: "4", HIGH_SIDE_GATE: "5", LOW_SIDE_DRAIN: ["6", "7"], }, }) const highSidePullDownBjt = definePart({ id: "BC847BLP_7", kicadSymbol: "Transistor_BJT:Q_NPN_BEC", footprint: "Package_TO_SOT_SMD:SOT-883", defaultValue: "BC847BLP-7", datasheet: " description: "45 V, 100 mA NPN small-signal transistor in SOT-883", designNotes: [ "Use this device only as the helper pull-down for the high-side P-gate nets; do not put motor or supply current through it as a power path element.", "In this topology, keep emitter at GND, collector on the P-gate net, and drive the base through a resistor from the MCU.", "Give the base a defined pulldown so the PMOS high side stays off while the MCU is in reset or high-impedance.", "With a 470R P-gate pull-up on a 1S rail, this transistor sinks about 9 mA at full turn-on, which is still well inside the BC847BLP-7's capability.", "Check the B-E-C pin order against the footprint before layout release; small BJTs are easy to rotate or mirror by accident.", ], pins: { BASE: "1", EMITTER: "2", COLLECTOR: "3", }, }) const yageoRc0201Datasheet = " const kemetMlccDatasheet = " const defineYageoRc0201 = (mpn: string, value: string) => definePart({ id: mpn, kicadSymbol: "Device:R", footprint: "Resistor_SMD:R_0201_0603Metric", defaultValue: value, datasheet: yageoRc0201Datasheet, description: `Yageo ${mpn} 0201 1% thick-film resistor`, fields: { Manufacturer: "Yageo", MPN: mpn, Tolerance: "1%", Power: "0.05W", }, designNotes: [ "This BOM is locked to an exact Yageo RC0201FR-07 1% resistor, not a generic 0201 placeholder.", "Keep the 1% series on the back-EMF divider path so thresholds stay predictable across temperature.", "Re-check pulse and dissipation stress if the battery domain or gate network changes.", ], pins: { A: "1", B: "2", }, }) const defineYageoRc0402 = (mpn: string, value: string) => definePart({ id: mpn, kicadSymbol: "Device:R", footprint: "Resistor_SMD:R_0402_1005Metric", defaultValue: value, datasheet: " description: `Yageo ${mpn} 0402 1% thick-film resistor`, fields: { Manufacturer: "Yageo", MPN: mpn, Tolerance: "1%", Power: "0.063W", }, designNotes: [ "Use 0402 here where the resistor sees non-trivial continuous dissipation on the switching rail.", "Do not silently shrink these positions back to 0201 without re-checking power and temperature margin.", ], pins: { A: "1", B: "2", }, }) const defineKemetMlcc = ({ mpn, value, footprint, description, voltage, dielectric, designNotes, }: { mpn: string value: string footprint: string description: string voltage: string dielectric: string designNotes: readonly string[] }) => definePart({ id: mpn, kicadSymbol: "Device:C", footprint, defaultValue: value, datasheet: kemetMlccDatasheet, description, fields: { Manufacturer: "KEMET", MPN: mpn, Dielectric: dielectric, "Rated Voltage": voltage, }, designNotes, pins: { POS: "1", NEG: "2", }, }) const resistor30r = defineYageoRc0201("RC0201FR-0730RL", "30R") const resistor470r0402 = defineYageoRc0402("RC0402FR-07470RL", "470R") const resistor1k = defineYageoRc0201("RC0201FR-071KL", "1k") const resistor10k = defineYageoRc0201("RC0201FR-0710KL", "10k") const resistor100k = defineYageoRc0201("RC0201FR-07100KL", "100k") const resistor12k = defineYageoRc0201("RC0201FR-0712KL", "12k") const resistor15k = defineYageoRc0201("RC0201FR-0715KL", "15k") const resistor33k = defineYageoRc0201("RC0201FR-0733KL", "33k") const swdVrefIsolationDiode = definePart({ id: "RB751CS40,315", kicadSymbol: "Device:D_Schottky", footprint: "tinybee:D_SOD-882", defaultValue: "RB751CS40,315", datasheet: " description: "40 V small-signal Schottky diode in SOD-882 for isolated SWD Vref sensing", designNotes: [ "This diode lets the programming header sense the target 3.3 V rail without back-powering the TLV755 when an external tool drives Vref.", "Place it close to the programming pads, and keep the +3V3 cathode side on the board rail with the isolated anode side only on the header Vref pin.", ], pins: { K: "1", A: "2", }, }) const capacitor100nF0402 = defineKemetMlcc({ mpn: "C0402C104K4RACTU", value: "100nF", footprint: "Capacitor_SMD:C_0402_1005Metric", description: "KEMET 100nF 16V X7R MLCC, 0402", voltage: "16V", dielectric: "X7R", designNotes: [ "This exact 16 V X7R part is locked for the 100 nF bypass positions in this 1S design.", "Use it for local high-frequency bypass on +BATT or V3.3; do not silently substitute a lower-voltage or poor-stability dielectric.", ], }) const capacitor100nF0201 = defineKemetMlcc({ mpn: "C0201C104K9PACTU", value: "100nF", footprint: "Capacitor_SMD:C_0201_0603Metric", description: "KEMET 100nF 6.3V X5R MLCC, 0201", voltage: "6.3V", dielectric: "X5R", designNotes: [ "Use this only for local low-voltage decoupling like the CH32 VDD bypass.", "Do not silently reuse this 0201 part on the battery rail; keep the battery-facing 100 nF positions on the 16 V 0402 part.", ], }) const capacitor1uF0402 = defineKemetMlcc({ mpn: "C0402C105K8PAC7411", value: "1uF", footprint: "Capacitor_SMD:C_0402_1005Metric", description: "KEMET 1uF 10V X5R MLCC, 0402", voltage: "10V", dielectric: "X5R", designNotes: [ "This exact 10 V X5R part is the TLV755 output capacitor and satisfies the regulator's 1 uF ceramic requirement.", "A 0402 body is acceptable here, but this is still a regulator output part, not a battery-domain bypass; do not shrink it further without checking bias derating and stability.", ], }) const capacitor22uF0805 = defineKemetMlcc({ mpn: "C0805C226M8PACTU", value: "22uF", footprint: "Capacitor_SMD:C_0805_2012Metric", description: "KEMET 22uF 10V X5R MLCC, 0805", voltage: "10V", dielectric: "X5R", designNotes: [ "This exact 10 V X5R 0805 part is the 1S battery-side bulk capacitor.", "Keep this input bulk capacitor in 0805 or larger; do not shrink it back to 0603 without re-checking effective capacitance at 1S bias.", ], }) const C1 = instantiate(capacitor1uF0402, "C1") const C2 = instantiate(capacitor100nF0201, "C2") const C3 = instantiate(capacitor100nF0402, "C3") const C4 = instantiate(capacitor22uF0805, "C4") const C5 = instantiate(capacitor100nF0201, "C5") const C6 = instantiate(capacitor22uF0805, "C6") const Q1 = instantiate(complementaryHalfBridge, "Q1") const Q2 = instantiate(complementaryHalfBridge, "Q2") const Q3 = instantiate(complementaryHalfBridge, "Q3") const Q4 = instantiate(highSidePullDownBjt, "Q4") const Q5 = instantiate(highSidePullDownBjt, "Q5") const Q6 = instantiate(highSidePullDownBjt, "Q6") const J_CTRL = instantiate(controlHeader, "J_CTRL") const J_BATT = instantiate(batteryHeader, "J_BATT") const J_MOTOR = instantiate(motorHeader, "J_MOTOR") const TP_PROG = instantiate(programmingHeader, "TP_PROG") const D1 = instantiate(swdVrefIsolationDiode, "D1") const R1 = instantiate(resistor30r, "R1") const R2 = instantiate(resistor1k, "R2") const R3 = instantiate(resistor10k, "R3") const R4 = instantiate(resistor470r0402, "R4") const R5 = instantiate(resistor30r, "R5") const R6 = instantiate(resistor1k, "R6") const R7 = instantiate(resistor10k, "R7") const R8 = instantiate(resistor470r0402, "R8") const R9 = instantiate(resistor30r, "R9") const R10 = instantiate(resistor1k, "R10") const R11 = instantiate(resistor10k, "R11") const R12 = instantiate(resistor470r0402, "R12") const R13 = instantiate(resistor15k, "R13") const R14 = instantiate(resistor33k, "R14") const R15 = instantiate(resistor100k, "R15") const R16 = instantiate(resistor15k, "R16") const R17 = instantiate(resistor33k, "R17") const R18 = instantiate(resistor100k, "R18") const R19 = instantiate(resistor15k, "R19") const R20 = instantiate(resistor33k, "R20") const R21 = instantiate(resistor100k, "R21") const R22 = instantiate(resistor100k, "R22") const R23 = instantiate(resistor100k, "R23") const R24 = instantiate(resistor100k, "R24") const R25 = instantiate(resistor12k, "R25") const R26 = instantiate(resistor33k, "R26") const R27 = instantiate(resistor1k, "R27") const U1 = instantiate(tlv75533pdqnt, "U1") const U2 = instantiate(ch32v203f8u6, "U2") const tinybeeEscChannel = defineCircuit({ name: "tinybee-esc-channel", source: "projects/tinybee/circuitd/tinybee-esc-channel.ts", description: "1S CH32-based tinybee ESC channel aligned to the openwch RISC-V_ESC V203 pinout", parts: [ C1, C2, C3, C4, C5, C6, D1, J_BATT, J_CTRL, J_MOTOR, Q1, Q2, Q3, Q4, Q5, Q6, R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, R12, R13, R14, R15, R16, R17, R18, R19, R20, R21, R22, R23, R24, R25, R26, R27, TP_PROG, U1, U2, ], nets: [ net( "+BATTERY", pin(J_BATT, "PIN1"), pin(C3, "POS"), pin(C4, "POS"), pin(C6, "POS"), pin(Q1, "HIGH_SIDE_SOURCE"), pin(Q2, "HIGH_SIDE_SOURCE"), pin(Q3, "HIGH_SIDE_SOURCE"), pin(R4, "B"), pin(R8, "B"), pin(R12, "B"), pin(R25, "B"), pin(U1, "IN"), pin(U1, "EN"), ), net("/ADC_VOLTAGE_SENSE", pin(C5, "POS"), pin(R25, "A"), pin(R26, "B"), pin(U2, "PA1/ADC1")), net("/A_HIGH_COMMAND", pin(R2, "B"), pin(U2, "PA10/CH3")), net("/A_LOW_COMMAND", pin(R1, "B"), pin(U2, "PB1/ADC9/OP1O1/CH3N")), net("/A_HIGH_CONTROL", pin(Q4, "BASE"), pin(R2, "A"), pin(R3, "B")), net("/A_LOW_GATE", pin(Q1, "LOW_SIDE_GATE"), pin(R1, "A"), pin(R22, "B")), net("/A_P_GATE", pin(Q1, "HIGH_SIDE_GATE"), pin(Q4, "COLLECTOR"), pin(R4, "A")), net("/A_BACK_EMF", pin(R13, "A"), pin(R14, "B"), pin(R15, "A"), pin(U2, "PA5/ADC5/OP2N1")), net("/B_BACK_EMF", pin(R16, "A"), pin(R17, "B"), pin(R18, "A"), pin(U2, "PB10/OP2N0")), net("/C_BACK_EMF", pin(R19, "A"), pin(R20, "B"), pin(R21, "A"), pin(U2, "PB11/OP1N0")), net("/BACK_EMF_COMMON", pin(R15, "B"), pin(R18, "B"), pin(R21, "B"), pin(U2, "PB14/OP2P0"), pin(U2, "PB15/OP1P0")), net("/B_HIGH_COMMAND", pin(R6, "B"), pin(U2, "PA9/CH2")), net("/B_LOW_COMMAND", pin(R5, "B"), pin(U2, "PB0/ADC8/OP1P1/CH2N")), net("/B_HIGH_CONTROL", pin(Q5, "BASE"), pin(R6, "A"), pin(R7, "B")), net("/B_LOW_GATE", pin(Q3, "LOW_SIDE_GATE"), pin(R5, "A"), pin(R23, "B")), net("/B_P_GATE", pin(Q3, "HIGH_SIDE_GATE"), pin(Q5, "COLLECTOR"), pin(R8, "A")), net("/C_HIGH_COMMAND", pin(R10, "B"), pin(U2, "PA8/CH1")), net("/C_LOW_COMMAND", pin(R9, "B"), pin(U2, "PA7/ADC7/OP2P1/CH1N")), net("/C_HIGH_CONTROL", pin(Q6, "BASE"), pin(R10, "A"), pin(R11, "B")), net("/C_LOW_GATE", pin(Q2, "LOW_SIDE_GATE"), pin(R9, "A"), pin(R24, "B")), net("/C_P_GATE", pin(Q2, "HIGH_SIDE_GATE"), pin(Q6, "COLLECTOR"), pin(R12, "A")), net("/PWM_INPUT", pin(J_CTRL, "PIN1"), pin(R27, "A")), net("/PWM_INPUT_MCU", pin(R27, "B"), pin(U2, "PA0/WKUP/ADC0")), net("/OPA_ZERO_CROSS_INTERRUPT", pin(U2, "PA2/ADC2/OP2O0"), pin(U2, "PA3/ADC3/OP1O0"), pin(U2, "PA4/ADC4/OP2O1")), net("/PHASE_A", pin(J_MOTOR, "PIN1"), pin(Q1, "HIGH_SIDE_DRAIN"), pin(Q1, "LOW_SIDE_DRAIN"), pin(R13, "B")), net("/PHASE_B", pin(J_MOTOR, "PIN2"), pin(Q3, "HIGH_SIDE_DRAIN"), pin(Q3, "LOW_SIDE_DRAIN"), pin(R16, "B")), net("/PHASE_C", pin(J_MOTOR, "PIN3"), pin(Q2, "HIGH_SIDE_DRAIN"), pin(Q2, "LOW_SIDE_DRAIN"), pin(R19, "B")), net("/SWD_CLOCK", pin(TP_PROG, "PIN1"), pin(U2, "PA14/SWC/PA11/UDM")), net("/SWD_DATA", pin(TP_PROG, "PIN2"), pin(U2, "PA13/SWD/PA12/UDP")), net("/SWD_VREF", pin(D1, "A"), pin(TP_PROG, "PIN4")), net( "GND", pin(J_BATT, "PIN2"), pin(C1, "NEG"), pin(C2, "NEG"), pin(C3, "NEG"), pin(C4, "NEG"), pin(C6, "NEG"), pin(Q1, "LOW_SIDE_SOURCE"), pin(Q2, "LOW_SIDE_SOURCE"), pin(Q3, "LOW_SIDE_SOURCE"), pin(Q4, "EMITTER"), pin(Q5, "EMITTER"), pin(Q6, "EMITTER"), pin(R3, "A"), pin(R7, "A"), pin(R11, "A"), pin(R14, "A"), pin(R17, "A"), pin(R20, "A"), pin(R22, "A"), pin(R23, "A"), pin(R24, "A"), pin(R26, "A"), pin(C5, "NEG"), pin(TP_PROG, "PIN3"), pin(U1, "GND"), pin(U1, "THERMAL_PAD"), pin(U2, "GND"), ), net("+3V3", pin(C1, "POS"), pin(C2, "POS"), pin(D1, "K"), pin(U1, "OUT"), pin(U2, "VDD")), net("unconnected-(U2-PA6{slash}ADC6{slash}OP1N1{slash}BKI-Pad20)", pin(U2, "PA6/ADC6/OP1N1/BKI")), ], }) const outputPath = path.resolve(__dirname, "generated", " const main = () => { mkdirSync(path.dirname(outputPath), { recursive: true }) writeFileSync(outputPath, exportKiCadNetlist(tinybeeEscChannel), "utf8") process.stdout.write(`${outputPath}\n`) } if (import.meta.main) { main() } export { tinybeeEscChannel } export default tinybeeEscChannel

kache

25,459 views • 6 months ago

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:

Mike

15,248 views • 10 months ago

$AMD| The FOMO to buy AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: The Wall Street Journal yesterday came out with an article that OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from Anthropic has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited Anthropic is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

Mike

84,951 views • 3 months ago

city said fall in love with fall 🍂 not a bad day at all❤️ Created with Seedance 2.5 on Fish Creative Prompt: SCENE CONTEXT A 20-year-old East Asian woman spends an autumn day moving through a big city — leaving her apartment, riding the subway, grabbing coffee from a cart, cutting through a park in falling leaves, browsing a bookstore, catching golden hour on a rooftop, and riding home at night — filmed as a candid, mixed-format day-in-the-city vlog, 30 seconds total. ACTIVE REFERENCES Image: 20-year-old East Asian woman, soft round face, warm ivory skin, dark brown hair in a loose high bun with face-framing strands, natural dewy makeup, gentle expressive eyes, wearing a wine-burgundy ribbed knit off-shoulder sweater and a small silver flower pendant necklace. 100% matches the reference throughout every shot. FORMAT MODE Timed multishot. HARD CUTs only at the stated seconds; camera does not cut on its own between them. Mixed scene styles — not every shot has dialogue; some are silent candid b-roll. 0.0s–4.0s — HARD CUT 4.0s–8.0s — HARD CUT 8.0s–12.0s — HARD CUT 12.0s–17.0s — HARD CUT 17.0s–21.0s — HARD CUT 21.0s–26.0s — HARD CUT 26.0s–30.0s — end OPTICS Shot 1: 63° wide, POV-adjacent handheld. Shot 2: 47° neutral, static-ish lean. Shot 3: 29° portrait-compression, quick transactional. Shot 4: 18° natural-portrait, slow-mo detail. Shot 5: 29° portrait-compression, candid unaware angle. Shot 6: 47° neutral, two-shot-adjacent solo toast. Shot 7: 18° natural-portrait, close reflective. No drift mid-segment. CAMERA Handheld throughout with natural 1–2cm tremor. Shot 1: phone-height POV-style, walking at 4 km/h. Shot 2: camera rests low against a pole, minimal movement, observational. Shot 3: quick whip from hand to face and back. Shot 4: slowed to half real-time, camera drifts sideways at 1 km/h following falling leaves. Shot 5: camera appears to catch her off-guard, slight delay before she notices it. Shot 6: static-ish, propped, she moves into frame. Shot 7: static through car window glass, reflections passing over her face. ACTION 0.0s–4.0s: She steps out of an apartment building onto a leaf-scattered sidewalk, adjusting her bag strap, glancing at her phone, morning light low and warm through bare branches. She looks up at the camera briefly, half-smiles: "Okay — let's do this." 4.0s–8.0s: Inside a subway car, she leans against a pole, earbuds in, watching the platform lights blur past the window, city sounds muffled and rhythmic. No dialogue — just her quiet, unbothered expression, occasional sway with the train's motion. 8.0s–12.0s: At a small street coffee cart, steam rising from the machine, she hands over cash, receives a paper cup, wraps both hands around it immediately for warmth. Brief exchange, she says only: "Thanks, you're the best." 12.0s–17.0s: Slow-motion through a park path thick with falling amber leaves, she tips her head back mid-step and turns once, leaves catching in her hair and sweater, genuine unguarded laugh, no spoken line — just breath and laughter audible. 17.0s–21.0s: Inside a small bookstore, she runs a finger along a shelf, pulls a book halfway out, flips a few pages, unaware of the camera, soft window light across her face, ambient store quiet. No dialogue. 21.0s–26.0s: On a rooftop at golden hour, city skyline hazy behind her, she raises a takeaway coffee cup slightly toward the lens like a small toast, warm low sun rim-lighting her hair, says only: "To days like this." 26.0s–30.0s: Inside a taxi at night, warm amber streetlights sliding across the window glass and over her face in soft passing bands, she rests her head against the seat, tired contented smile, looks toward the lens and says: "Best day, hands down." PERFORMANCE Restrained, natural reactions — no performed excitement. Shots 2 and 5 specifically read as candid/unaware of the camera. Living eyes with catch-light in every close shot, visible breath in shot 1, natural skin and fabric texture throughout. PHYSICS Leaves fall and scatter with real weight and drift, catching briefly in hair and sweater fibers before falling away. Coffee steam rises and dissipates naturally. Streetlight bands move across her face and the window glass with correct parallax as the car moves. Train sway shifts her weight subtly and believably. LIGHTING Shot 1: low warm morning sun through bare branches, soft shadow lines. Shot 2: cool fluorescent subway light with warm practicals flickering past outside. Shot 3: overcast daylight, soft even key. Shot 4: dappled warm sun through park canopy, backlit leaf edges. Shot 5: soft window-diffused daylight, low contrast. Shot 6: warm low-angle golden hour key, strong rim light, soft haze. Shot 7: warm amber sodium streetlight passing bands only, otherwise dark interior. COLOR GRADE Warm autumn palette — amber, rust, and soft gold tones with muted city grays. Warm amber streetlight at night, never neon or saturated colored signage anywhere in frame. No cool-cyan or magenta tint. WARDROBE Wine-burgundy ribbed knit off-shoulder sweater and silver flower pendant necklace, unchanged and clearly visible in every shot. A loosely draped scarf or open coat may sit over it in the subway and taxi shots without covering it. AUDIO Shot 1: quiet street ambience, rustling leaves. Shot 2: muffled train rumble, distant announcement, no dialogue. Shot 3: espresso machine hiss, brief exchange. Shot 4: crunch of leaves, natural laughter, no line. Shot 5: quiet store ambience, page-turning, no dialogue. Shot 6: distant city hum, wind. Shot 7: engine hum, soft radio murmur under her line. STYLE Authentic day-in-the-life vlog cinematography, dreamy 35mm film aesthetic, fine natural grain, realistic autofocus hunting, mix of talking and silent candid shots for a lived-in, unscripted feel. OUTPUT SETTINGS 4K, 24fps, 35mm grain, real-time except shot 4 (half real-time slow motion), realistic handheld motion, no neon or saturated signage in the color palette anywhere. POSITIVE LOCKS Same woman's face and build in every shot, matching Image exactly. Wine-burgundy sweater and silver necklace stay visible in every shot. Hair stays in the same loose high bun throughout. Cuts only at stated seconds, camera does not cut on its own. Shots 2 and 5 stay dialogue-free and candid. Warm amber tones hold throughout — no neon, no cool-cyan tint anywhere.

Mira Sterling

59,030 views • 21 days ago

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

329,713 views • 4 months ago

Charlie Munger spent 50 years studying why intelligent people make catastrophically stupid decisions. It is the most useful thing I have ever watched: 1. Incentives are more powerful than anyone thinks. Munger says he has been in the top 5% of his age cohort his entire life in understanding the power of incentives and he has still underestimated it every single year. Federal Express could not get their night shift to work efficiently until someone realized they were paying by the hour. They switched to paying by the shift. The problem disappeared immediately. 2. People rationalise terrible behavior when their incentives point that way, and they do not even know they are doing it. A doctor in Nebraska was removing perfectly healthy gallbladders for years. When Munger asked an old colleague whether the doctor knew he was harming patients, the answer was no. he genuinely believed the gallbladder was the source of all medical evil and that removing it was an act of love. That is incentive-caused bias at its most extreme. 3. Psychological denial is real, and it is not just for weak people. A family friend's son flew off a carrier in the North Atlantic and never came back. His mother, a completely sane woman, simply never believed he was dead. Reality was too painful, so she distorted it until it was bearable. Munger says we all do this to some extent, and it causes terrible problems. 4. Consistency and commitment tendency are one of the most powerful forces in the human mind. Once you have stated a position publicly, you are psychologically locked into it. Max Planck said the really important new physics was never accepted by the old guard. A new guard came along that was less brain blocked by its previous conclusions. If this happened to the deans of physics, Munger says, imagine what it does to ordinary people. 5. The Chinese brainwashing system used on prisoners of war worked better than torture. They did not start with big demands. They maneuvered people into making tiny little commitments and declarations and slowly built from there. The same mechanism operates in every cult, every sales system, and every ideology that gets deeply embedded in people's heads. 6. Pavlovian association shapes buying behavior at a level most people never consciously process. Munger estimates three quarters of all advertising works on pure Pavlov. Coca-Cola does not want to be associated with funerals. They want to be associated with the Olympics, wonderful music, heroics. The association itself changes how people feel about the product at a subconscious level. Raising the price of a product can actually increase its market share because price and quality are associated in the human mind, and people use price as a signal of value. 7. Persian messenger syndrome is alive and running every major organization. The Persians killed the messenger who brought bad news. Bill Paley in his last 20 years, did not hear one thing he did not want to hear. everyone around him knew bringing bad news was dangerous. The result was that one of the most powerful men in media made terrible decisions for two decades because reality never reached him. 8. Social proof causes otherwise intelligent people to follow each other off cliffs. When one oil company bought a fertilizer company in the 1970s, practically every other major oil company rushed out and did the same. There was no rational reason for oil companies to own fertilizer companies. But if Exxon was doing it, it was good enough for Mobil. Every single acquisition was a disaster. 9. The efficient market theory persisted in academia for decades despite Berkshire Hathaway existing as a living contradiction. One economist kept adding sigmas to explain away the anomaly. two sigma, then three, then four, eventually six sigma. Munger's observation: It is better to add a sigma than change a theory just because the evidence comes in differently. That economist later went into money management himself and sank like a stone. 10. Contrast bias warps perception constantly and invisibly. Put your hand in hot water, then room temperature water. It feels cold. Put your hand in cold water, then room temperature water. It feels hot. same bucket. The human sensory apparatus has no absolute scale, only a contrast scale. Real estate agents exploit this deliberately. They show you two overpriced, awful houses first, then take you to a merely overpriced house, and it feels like a bargain. 11. The frog in slowly heating water is the business version of contrast bias. If something bad comes to you in small pieces, you are likely to miss it entirely. Munger says he has known many high-powered brilliant businessmen who were destroyed this way. not because they were stupid but because each incremental change was too small to trigger alarm. The contrast was never large enough to notice. 12. Authority bias is so powerful it can make trained professionals watch a plane crash. In flight simulator experiments, when the pilot, the authority figure, does something that any trained co-pilot knows will crash the plane, 25% of the time, the co-pilot sits there and lets it crash anyway. They have been trained to know better. The authority relationship overrides the training. 13. Deprivation super reaction syndrome explains why people go insane over small losses. Munger's neighbor had a 180 degree view of the harbor. the neighbor put in a pine tree about 3 feet high that turned it into a 179 and three-quarter degree view. They had a blood feud that went on for years. The New Coke disaster is the corporate version. Coca-Cola told customers they were changing a flavor and triggered a deprival super reaction so powerful that Pepsi was weeks away from releasing old Coke in a Pepsi bottle. smart engineers. brilliant lawyers. armies of psychologists. All missed it. 14. Envy and jealousy are far more powerful than greed and almost entirely absent from psychology textbooks. Munger says Warren Buffett has said half a dozen times that it is not greed that drives the world but envy. In a thousand-page psychology textbook, the index entry for envy and jealousy is blank. One of the most powerful forces in human behavior and academia essentially ignores it. 15. Gambling addiction is not explained by variable reinforcement alone. Skinner thought he had fully explained gambling by showing that variable reward schedules pound in behavior more powerfully than fixed ones. But the people who design modern slot machines know things Skinner did not. Lotteries where you pick your own number get far more play than lotteries where the number is assigned to you. People who commit to a number believe it has more validity because they chose it. Near misses on slot machines trigger deprival super reaction syndrome. It is four or five psychological tendencies working together, not one. 16. The most dangerous situations are when multiple psychological tendencies combine toward the same end at once. Munger calls this the lollapalooza effect. Tupperware parties use four or five tendencies simultaneously. Moonie conversion methods combine multiple tendencies and work extraordinarily well. alcoholics anonymous achieves a 50% no drinking rate when everything else fails because it also combines multiple tendencies toward a constructive end. The Milgram experiment is not just about obedience. it involves authority bias, consistency and commitment tendency, and contrast effects all working together. That combination turns human brains into mush. 17. Boards of directors are structurally designed to fail as corrective mechanisms. The top executive is the authority figure. He is doing something questionable. You look around, and nobody else is objecting, which is social proof that it is fine. He flies you around in the corporate jet and raises your director fees every year, which triggers reciprocation tendency. Munger's rule: boards only act when the behavior gets so bad it starts making them look foolish or threatens legal liability. That is the only forcing function that reliably works. 18. John Goodfriend of Salomon Brothers destroyed his career and reputation because he did not fire a trusted employee who had lied to the government. Every psychological tendency pointed toward keeping the man. He was a close colleague. His wife was known. He was part of a group that had made over a billion dollars for the firm. He said he had never done it before and would never do it again. Goodfriend looked into his eyes and believed him. The man did it again. The lesson: everyone who gets caught embezzling says they have never done it before and will never do it again. That is what they all say. 19. Darwin avoided confirmation bias by deliberately seeking out disconfirming evidence. Munger says Darwin was not especially smart by ordinary standards of human acuity. Yet he is buried in Westminster Abbey. Munger studied how Darwin worked and realized he had psychological tricks worth learning. Darwin always paid extra attention to evidence that contradicted his theories. Munger started doing the same and credits it as one of the most important intellectual habits of his life. 20. Why is the most important word in communication? Carl Braun designed oil refineries with spectacular skill, and you got fired in his company if you wrote a communication without explaining why. not just who, what, where, and when, but why. Braun knew that in a complex system where things can blow up, a communication system that always explains the reason behind an instruction works dramatically better than one that does not. Forstein, the general counsel of Salomon, told Goodfriend on multiple occasions that he had to report the employee's misconduct. He explained it was the right thing to do. He never explained what would happen to Goodfriend personally if he did not. he failed to use the most powerful tool of persuasion. Goodfriend ignored him. When Goodfriend went down, Forstein went with him.

Jaynit

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