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Moving Inside Your Breath Find Your Flow #Bearyoga #personaltrainer #yogaforbiggerbodies #flow #breath #bear #chub #cub #chaser #supportive #restorative #meditation #nyc #queens #brooklyn #bronx #movement
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![This prompt is seriously good, so I ran it through our agent and made a few tweaks. Agent: Shoutout to TechHalla for sharing the original prompt 🙌 Here’s the refined version: image prompt： DIRECTIVE: Produce one still that reads as a real smartphone frame filmed from inside a moving car approaching a strange highway incident. Optical consumer-phone capture through a real windshield — wide forward view, windshield glass, rain specks, A-pillar, a slice of dashboard / hood at the bottom. Lived-in phone JPEG, not cinema, not HDR, not advertising photography. FORMAT: Vertical 3:4 smartphone frame. BEAT / COMPOSITION: Looking forward through the windshield while approaching two trucks clustered close together on the LEFT side of the highway, either in the left lane or left shoulder. LEFT TRUCK — SPARKLING LEMONADE TANKER, CRITICAL: A full-size stainless-steel beverage tanker with bright yellow-and-white SPARKLING LEMONADE branding clearly readable on the tank. Large lemon graphics, yellow citrus branding, believable commercial beverage-truck design. The tanker has suffered a breakdown: a torn, jagged, ragged hole has opened in the side of the tank. A thick, pressurized jet of pale yellow carbonated lemonade is ALREADY blasting horizontally and downward through the hole onto the asphalt. Huge amount of liquid. Visible carbonation and bubbles. Pale-yellow transparent soda, NOT fuel, NOT paint. Foamy white-yellow splashes around the impact area. A wide lemonade lake is already spreading across the road. Wet asphalt reflections, spray suspended in the air, runoff flowing along the lane markings. The truck is stopped or crawling with hazard lights. RIGHT / BESIDE IT — BAKING SODA DELIVERY TRUCK, CRITICAL: Immediately beside the lemonade tanker, slightly ahead or alongside, is a closed white box delivery truck. Large unmistakable generic commercial branding on the side: “BAKING SODA” “FOOD GRADE” “SODIUM BICARBONATE” Simple blue-and-white industrial food-supply graphics, with illustrations of white baking-soda boxes and large paper sacks. Rear doors CLOSED. Side doors CLOSED. No powder spilling yet. The truck must clearly read as a baking-soda delivery vehicle, not flour, cement, salt, or detergent. CAMERA PACK: Handheld smartphone from inside the car, passenger or driver seat. Not a fixed professional dashcam. Wide smartphone lens with subtle barrel distortion. Windshield and left A-pillar visible. A small slice of dashboard / hood along the lower frame. Rain dots, minor grime and reflections on the windshield. Slight handheld breathing shake implied. Consumer smartphone sharpening and compression. Minor autofocus softness. Natural dynamic range. No cinematic depth of field. No dramatic movie lighting. ENVIRONMENT: Daylight, overcast highway. Realistic American interstate-style road. Other normal traffic farther ahead. Clear long highway vanishing point beyond the trucks. Wet asphalt. Muted cloudy sky. Road barriers, lane markings, signs and distant utility structures. PHOTOGRAPHIC CHARACTER: An accidental, unstaged smartphone capture of a bizarre roadside beverage accident. Physically grounded truck scale. Believable weight and liquid flow. The lemonade leak is the unmistakable event. The juxtaposition of a leaking SPARKLING LEMONADE tanker beside a BAKING SODA delivery truck should immediately suggest that something disastrous and absurd is about to happen. NO PERSON visible yet. NO open baking-soda doors. NO powder reaction yet. NO explosion. NO fire. NO fantasy lighting. Real phone footage aesthetic. video prompt： [STYLE + CAMERA + ATMOSPHERE] Vertical 3:4 smartphone video filmed FROM INSIDE a moving car, passenger or driver seat. CONTINUOUS SINGLE TAKE for the entire 30 seconds. The phone is handheld, NOT a bolted dashcam. Visible windshield glass, A-pillar, rain specks, faint reflections, a slice of dashboard / hood at the bottom, occasionally a tiny finger edge near the phone. Natural arm movement and slight breathing shake. Consumer smartphone footage: minor autofocus hunting, rolling shutter, digital sharpening, occasional exposure pumping, phone microphone clipping during the loudest moment, real compressed social-media-video texture. We are ARRIVING. The car is moving forward first. The trucks gradually grow larger through the windshield. Then the car brakes, slows, and stops just short of the spreading lemonade lake. NO CUTS. NO camera teleportation. ONLY digital pinch-zooms: zoom into the lemonade jet, zoom into the clear jug filling, zoom toward the baking-soda truck doors, zoom into the powder hitting the liquid, slam-zoom onto the launch, then zoom out as the woman disappears toward the highway horizon. Daylight. Overcast sky. Wet asphalt. Occasional rain droplets on the windshield. Everything should initially feel like an absurd event caught accidentally on a normal phone. Photoreal environment with coherent liquid, foam, gravity and vehicle scale. The final launch uses deliberately exaggerated slapstick physics, but remains visually photorealistic. No gore. No visible injury. No fire. No combustion explosion. [LOCKED SET / VEHICLES] LEFT ahead through the windshield: A full-size stainless-steel beverage tanker with bright yellow-and-white SPARKLING LEMONADE branding. Large lemon graphics. The words: “SPARKLING LEMONADE” and “CARBONATED LEMON DRINK” are clearly readable. A large ragged torn hole is already present in the tanker wall. A thick pale-yellow carbonated lemonade jet is ALREADY blasting from the hole when the video begins. The liquid is translucent yellow with heavy carbonation. It splashes violently onto the asphalt and has already created a large spreading lemonade lake. White-yellow foam forms where the jet strikes the pavement. The tanker remains stopped or crawling. BESIDE IT: A white commercial box truck clearly branded: “BAKING SODA” “FOOD GRADE” “SODIUM BICARBONATE” Blue-and-white industrial food-supply livery. Graphics of baking-soda cartons and large paper sacks. Rear doors are CLOSED when we arrive. No powder is leaking at the beginning. The baking-soda truck is slightly ahead of or directly alongside the lemonade tanker. The two vehicles form one obvious cluster on the LEFT side of the highway. HIGHWAY: The highway continues beyond the trucks toward a long, clear vanishing-point horizon. Normal distant traffic may exist farther ahead. No additional vehicle belongs to the woman. [LOCKED CAST] THE WOMAN: ONE woman only. Clearly adult, approximately 25–30 years old. Attractive adult woman with a naturally voluptuous figure and a noticeably full bust, realistic adult proportions. No exaggerated cartoon anatomy. Long dark hair, slightly messy from humid roadside weather. Natural makeup. Real skin texture. She wears: a fitted white ribbed spaghetti-strap camisole / tank top, secure opaque fabric, natural neckline, denim shorts, casual sneakers. Clothing remains identical for the entire clip. No wardrobe malfunction. No transparent fabric. No nudity. She has confident, reckless, chaotic roadside energy, behaving as if this absurd situation is completely normal. She is alone. No second person. She does NOT exit any vehicle. She ENTERS FRAME FROM THE RIGHT SIDE of the windshield view, already walking on foot from the roadside / shoulder. [HERO OBJECT] One giant transparent gas-station plastic drink jug / oversized Big-Gulp-scale clear cup. She carries it in her RIGHT hand. At first it is completely EMPTY. Daylight can clearly be seen through the empty transparent plastic. It stays in her right hand through the filling and drinking sequence. The jug is only lost during the final foam blast. [DIEGETIC AUDIO] Beginning: normal road hum, tires on wet pavement, wind, engine noise. Approaching the trucks: increasing loud pressurized lemonade hiss and splashing. Jug filling: liquid hammering plastic, violent fizzy splashes. Drinking: large gulps, brief coughing / breath. Truck: metal latch clunk, rear doors creaking. Reaction: rapid fizzing grows from thousands of tiny bubbles into a deep roaring foam eruption. Launch: phone microphone clips, wind rush, short Doppler scream as she rapidly travels away. No soundtrack. No cinematic score. [IMAGE REFERENCE] [ref_image] = locked opening smartphone still from inside the moving car looking through the windshield at: the SPARKLING LEMONADE tanker, the exact ragged hole, the pale-yellow lemonade jet, the BAKING SODA delivery truck, the wet highway, and the in-car windshield foreground. These visual elements are LAW. The clip begins while the car is still rolling toward that exact truck cluster. [TIMELINE — SECOND BY SECOND] 0–3s: [HOOK — ARRIVING + FIRST ZOOM] Phone is already recording. The car is moving toward the incident. Through the windshield, both trucks are clearly visible on the LEFT. The sparkling-lemonade tanker is ALREADY leaking. A powerful pale-yellow carbonated stream blasts through the ragged hole and crashes onto the highway. The lemonade lake is already widening. The vehicles visibly grow larger as we approach. Around 1s: digital pinch-zoom quickly punches toward the torn metal and lemonade jet. Bubbles and yellow-white spray are visible. Autofocus briefly hunts. Around 2.5s: the driver brakes. The phone and camera body dip slightly with the deceleration. 3–5s: [STOP + WOMAN ENTERS FROM RIGHT] The car finishes stopping before reaching the liquid. Engine idles. Natural handheld phone shake continues. Digital zoom pulls back slightly. THE WOMAN enters frame FROM THE RIGHT. She is already walking on foot. She never exits any car or truck. Her appearance stays consistent: adult woman, long dark hair, white fitted ribbed spaghetti-strap tank top, denim shorts, sneakers, naturally voluptuous figure. In her RIGHT hand is the giant completely EMPTY transparent jug. Light passes visibly through the jug. She raises it casually while staring at the lemonade tanker. Then she steps directly into the shallow spreading lemonade lake. 5–9s: [FILL — HOLD THE SHOT] She approaches the tanker leak. ONE CLEAR CONTACT: She forcefully pushes the mouth of the empty jug directly INTO the high-pressure sparkling-lemonade jet. The phone immediately slam-zooms toward the jug. HOLD THIS ACTION. Do not skip it. The jug remains underneath the stream for approximately TWO FULL SECONDS. Show the liquid level visibly rising through the transparent plastic: empty, one-quarter, half-full, almost full. Pale-yellow carbonated lemonade churns violently inside. Millions of bubbles. White foam builds around the rim. The pressurized stream continuously hammers the jug opening. Overflow splashes across her hand, forearm and tank top. The jug becomes visibly heavy. Only after this clear multi-second fill does she pull it away from the stream. She raises the now-full jug. 9–12s: [CHUG] She immediately tilts the giant jug and starts drinking. Huge exaggerated gulps. Carbonated lemonade pours toward her mouth. Some spills naturally down the front of the white tank top. She keeps drinking until most of the jug is empty. She lowers it. Gasps for air. Makes a quick surprised face from the intense carbonation. Still holding the jug. Then she notices the BAKING SODA delivery truck. She turns toward it. Phone zooms back enough to simultaneously frame: the woman, the lemonade jet, the spreading yellow puddle, and the rear of the baking-soda truck. 12–16s: [OPEN + BAKING SODA AVALANCHE] She reaches the rear of the baking-soda truck. ONE CLEAR CONTACT: She grabs the metal latch. YANKS it open. Both rear doors swing outward. Inside: large stacks of FOOD-GRADE BAKING SODA. White cardboard cartons. Huge brown-and-white paper sacks. “SODIUM BICARBONATE” labels visible. She grabs one oversized sack near the edge. Pulls it. The heavy sack tears open. A huge avalanche of white baking-soda powder pours out of the truck. Additional cartons tumble and split. A dense waterfall of white powder falls directly toward the lemonade lake. Phone digital-zooms onto the falling powder. Show individual white powder clouds and clumps striking the yellow liquid. She remains standing inside the shallow lemonade puddle with the mostly empty clear jug still in her hand. 16–18s: [REACTION STARTS] For a fraction of a second, nothing huge happens. Then: tiny bubbles begin appearing everywhere around the baking soda. Fizz races rapidly outward across the pale-yellow puddle. The entire lemonade lake starts bubbling. Yellow liquid rapidly turns cloudy and pale. Thousands of CO2 bubbles expand across the road surface. The sound changes from small fizzing into an increasingly loud rolling hiss. Foam races around the woman's shoes. She looks down. Confused. The reaction accelerates dramatically. 18–20s: [IT GOES] The carbonation and acid-base fizz become absurdly intense. The entire center of the puddle suddenly swells upward. A huge WHITE-AND-PALE-YELLOW FOAM COLUMN erupts vertically between the two trucks. No fire. No flame. No smoke explosion. Only violently expanding wet beverage foam. ONE CLEAR CONTACT: The rising foam column strikes the woman from BELOW. Her feet abruptly leave the asphalt. The giant clear jug flies from her hand. Her hair shoots upward. She is propelled vertically and then forward along the highway axis. The phone operator reacts late. A violent handheld jerk. Then a slam digital zoom catches her leaving the ground. 20–24s: [HUMAN LEMONADE ROCKET] The woman is now airborne. She becomes a dark human figure riding the huge white-yellow foam column. Her body rotates once naturally in the air. Hair and clothing react to wind. One sneaker nearly comes loose but remains on. No injury. No gore. The foam geyser tilts toward the open highway horizon. The sparkling-lemonade tanker continues feeding liquid into the reaction. Baking soda continues pouring from torn sacks. Foam hits the windshield. Wet white-yellow streaks temporarily obscure parts of the view. The phone jerks backward inside the car but NEVER leaves the car. Autofocus struggles through the wet windshield. Digital zoom hunts for the woman's airborne silhouette. 24–26s: [SHRINKS] Camera zooms back out through foam streaks on the windshield. Farther down the highway: the woman is still airborne. Now much smaller. A tiny spinning adult figure carried along a long white spray trail. The baking-soda truck remains visible behind her with both rear doors open. The sparkling-lemonade tanker continues spraying from the original hole. The reaction is still producing a huge foamy fountain. Phone digital-zooms aggressively toward the shrinking airborne figure. Pixelation and smartphone digital zoom artifacts become visible. Then zooms back out again. 26–29s: [DISAPPEARS INTO HORIZON] The woman becomes smaller and smaller. First human-sized. Then tiny. Then only a dark moving speck above the highway vanishing point. The camera desperately tracks her. Slight rolling shutter. Autofocus pumping. She finally disappears into the bright gray horizon haze. Her short distant Doppler scream fades away. 29–30s: [PAYOFF — EMPTY HORIZON] The camera holds on the exact spot where she vanished. Nothing there now. Only an empty highway horizon. Foreground remains chaotic: towering white-yellow foam between the trucks, sparkling lemonade still blasting from the tanker, baking-soda doors open, wet windshield, yellow liquid and white foam covering the highway. Final tiny digital pinch-zoom into the completely empty vanishing point. Hold. The roaring fizz continues. CUT / LOOP. [STYLE & QUALITY BOOSTERS] Photoreal handheld smartphone footage from inside a real car. Vertical 3:4. Continuous uninterrupted single take. Approaching movement first, then braking and stopping. No cinematic camera movement. No drone shot. No third-person cutaway. No external camera. Digital pinch-zooms only. Authentic consumer-phone autofocus and exposure behavior. Windshield remains part of the visual language throughout. One adult woman only. Woman enters from the RIGHT already on foot. She never exits a vehicle. Consistent face, body, hair and clothing throughout. Naturally voluptuous adult proportions. Secure opaque white spaghetti-strap tank top. Denim shorts. No nudity. Empty transparent jug visibly fills on camera for multiple seconds. Sparkling lemonade must remain pale yellow and visibly carbonated. Baking soda must remain white powder. Reaction begins where the powder physically contacts the lemonade. Reaction progresses visibly: small bubbles → rapid fizz → spreading foam → swelling foam → giant white-yellow geyser. No flame. No fireball. No black smoke. No electrical explosion. Exaggerated slapstick launch physics presented with photoreal visual rendering. The woman is launched toward the highway VANISHING POINT and visibly shrinks until she completely disappears. Identity lock to [ref_image] for: both trucks, truck positions, lemonade tanker graphics, ragged tank hole, lemonade stream, highway geometry, weather, windshield framing, A-pillar, dashboard foreground.](https://image.24vids.com/tw-2100591996427714836/amplify_video_thumb/2100591779699658753/img/pzyrATjCQ5pIWwnV.jpg)
![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:](https://image.24vids.com/tw-1994972575768727743/amplify_video_thumb/1994972220930621440/img/_5CCZJyz89oDfqQJ.jpg)
