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Here you see the cloud + on-device split very cleanly. Doubao handles the semantics: from a single hotel entrance photo it figures out which hotel this is, that I want to book tonight, and that it will need to check the hotel’s pet policy. Then ZTE’s 7B Nebula-GUI model... show more

Taylor Ogan

38,439 subscribers

41,355 次观看 • 7 个月前 •via X (Twitter)

旅游健康养生科学技术

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This isn’t a chat overlay, it’s a true multimodal agent. It has the brand-new Snapdragon 8 Elite Gen 5 with 16GB RAM, so it can push a lot of the agentic workload on-device. Here I take a picture of a NIO battery swap station and ask, “What is this thing?” It’s running ByteDance’s Doubao model (>175M users in China): a massive, sparse MoE model with full text+vision support. It recognizes the infrastructure from the photo, grounds it to NIO’s network, and explains what it does.

Taylor Ogan

46,529 次观看 • 7 个月前

Sam Altman says the goal is to run a GPT-5 quality model locally on a single device We don't know how to do it yet, but the recent efficiency gains were also once thought impossible The vision is a new computer form factor for the AI era: "it should run locally... if you want it to"

Sam Altman says the goal is to run a GPT-5 quality model locally on a single device We don't know how to do it yet, but the recent efficiency gains were also once thought impossible The vision is a new computer form factor for the AI era: "it should run locally... if you want it to"

Haider.

197,384 次观看 • 8 个月前

I took Clicky and I made it 5x Faster So I saw that Farza 🇵🇰🇺🇸 was using uses Claude's vision to find UI elements on screen, send a screenshot, wait for coordinates back. It works, but it's slow. I replaced that with OmniParser V2 by Microsoft which is a local Object detection model trained specifically on UI elements. It runs on-device, detects every button, menu, and icon in 400ms, and gives me pixel-precise coordinates. No API call, no latency, no cost. The green highlights you see around the UI elements is the detection overlay and you can see as I am switching the screen it takes no time to detect and highlight which is pretty neat! With Local models improving by the day, Its the right direction for applications like these. Next up: video-synced tutorials where a YouTube tutorial pauses and waits for you to perform each action in the real app. I am not stopping!

I took Clicky and I made it 5x Faster So I saw that Farza 🇵🇰🇺🇸 was using uses Claude's vision to find UI elements on screen, send a screenshot, wait for coordinates back. It works, but it's slow. I replaced that with OmniParser V2 by Microsoft which is a local Object detection model trained specifically on UI elements. It runs on-device, detects every button, menu, and icon in 400ms, and gives me pixel-precise coordinates. No API call, no latency, no cost. The green highlights you see around the UI elements is the detection overlay and you can see as I am switching the screen it takes no time to detect and highlight which is pretty neat! With Local models improving by the day, Its the right direction for applications like these. Next up: video-synced tutorials where a YouTube tutorial pauses and waits for you to perform each action in the real app. I am not stopping!

Milind S

73,228 次观看 • 3 个月前

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.

Sudo su

23,816 次观看 • 7 天前

🚨 MAN TRIES TO BREAK WORLD RECORD FOR COLDEST HOTEL ROOM - THEN TURNS IT INTO A MONSOON “I set it to 45, it’s 57 degrees in here and this is the happiest day of my life.” He laughs, “I feel f*ing great. But I’m pretty sure this hotel’s making no profit. I just ran that AC bill up to $700 for one night.” Then he takes it further: “I’m trying to make a monsoon happen here.” He cranks the bathtub, fills it with steaming hot water, fog spilling through the air: “We got rain clouds forming in the hotel room. I’m gonna see if I can literally make it rain in this b***h.” He didn’t book a room. He tried to change the weather.

🚨 MAN TRIES TO BREAK WORLD RECORD FOR COLDEST HOTEL ROOM - THEN TURNS IT INTO A MONSOON “I set it to 45, it’s 57 degrees in here and this is the happiest day of my life.” He laughs, “I feel f*ing great. But I’m pretty sure this hotel’s making no profit. I just ran that AC bill up to $700 for one night.” Then he takes it further: “I’m trying to make a monsoon happen here.” He cranks the bathtub, fills it with steaming hot water, fog spilling through the air: “We got rain clouds forming in the hotel room. I’m gonna see if I can literally make it rain in this b***h.” He didn’t book a room. He tried to change the weather.

HustleBitch

6,087,901 次观看 • 8 个月前

I taught a speech model to understand context in conversation. This is what happened It adjusts voice and tone to express urgency, comfort, understanding from the dialogue. Just like a real human being 520M model. Runs locally on consumer devices How this is achieved 🧵

I taught a speech model to understand context in conversation. This is what happened It adjusts voice and tone to express urgency, comfort, understanding from the dialogue. Just like a real human being 520M model. Runs locally on consumer devices How this is achieved 🧵

Luozhu

21,782 次观看 • 4 个月前

Model 6 & 7 is the ICT universal Model that' works on every Time Frame !!! The Accuracy is on a different level entirely You need to see this to the end. Retweet it.. Bookmark it

Model 6 & 7 is the ICT universal Model that' works on every Time Frame !!! The Accuracy is on a different level entirely You need to see this to the end. Retweet it.. Bookmark it

Q𝓊𝒾𝓃𝓃 🐼

45,964 次观看 • 1 个月前

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

@levelsio

119,210 次观看 • 1 年前

Tesla Robotaxi Unsupervised gets into a hotel parking lot that blocked off by an Amazon delivery vehicle, another vehicle, and a large Sysco delivery truck. It took Robotaxi a few seconds to figure out what it was going to do and then it went to the side of the hotel to drop me off, and then went behind the hotel to get back out and on to it's next pickup. Pretty impressive!

Tesla Robotaxi Unsupervised gets into a hotel parking lot that blocked off by an Amazon delivery vehicle, another vehicle, and a large Sysco delivery truck. It took Robotaxi a few seconds to figure out what it was going to do and then it went to the side of the hotel to drop me off, and then went behind the hotel to get back out and on to it's next pickup. Pretty impressive!

JC Christopher

34,088 次观看 • 3 个月前

Another DeepSeek moment. This is the world’s first actual smart phone. It’s an engineering prototype of ZTE’s Nubia M153 running ByteDance’s Doubao AI agent fused into Android at the OS level. It has complete control over the phone. It can see the UI, choose/download apps, tap/type, call, and run multi-step task chains. Here I just say (in English) “find someone to wait in line for me” (something you can do in China), and it picks which app to open, configures the job, and hands me one confirm screen. I wouldn’t otherwise know how to do this, and here the phone just did it in a matter of seconds.

Another DeepSeek moment. This is the world’s first actual smart phone. It’s an engineering prototype of ZTE’s Nubia M153 running ByteDance’s Doubao AI agent fused into Android at the OS level. It has complete control over the phone. It can see the UI, choose/download apps, tap/type, call, and run multi-step task chains. Here I just say (in English) “find someone to wait in line for me” (something you can do in China), and it picks which app to open, configures the job, and hands me one confirm screen. I wouldn’t otherwise know how to do this, and here the phone just did it in a matter of seconds.

Taylor Ogan

936,167 次观看 • 7 个月前

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,574 次观看 • 1 个月前

I don't think Powell's gonna do it. From the CPI, you got a 95% chance of a 25 basis point reduction in September, but that's it. The economy's on fire. We're killing it and the consumer is still strong given that data last week from credit cards. You're not going to see rate reductions with this strength in the economy right now. There's nothing wrong with the economy and that's why I'm, I don't think we're going to get any rate cuts beyond the 25 bps. I look at the odds, it's 95% for 125 basis points. I am not investing on the hopes of any rate cuts this year. I don't agree on the 15% tariff or reverse tax or industrial policy on American chip makers going into China. I mean, it's a very strange policy. Pick one country, tax chips going in there. I don't like it. I would prefer to see policy like this that simply says to these companies, you're allowed to sell chips anywhere in the world you want one generation back.

I don't think Powell's gonna do it. From the CPI, you got a 95% chance of a 25 basis point reduction in September, but that's it. The economy's on fire. We're killing it and the consumer is still strong given that data last week from credit cards. You're not going to see rate reductions with this strength in the economy right now. There's nothing wrong with the economy and that's why I'm, I don't think we're going to get any rate cuts beyond the 25 bps. I look at the odds, it's 95% for 125 basis points. I am not investing on the hopes of any rate cuts this year. I don't agree on the 15% tariff or reverse tax or industrial policy on American chip makers going into China. I mean, it's a very strange policy. Pick one country, tax chips going in there. I don't like it. I would prefer to see policy like this that simply says to these companies, you're allowed to sell chips anywhere in the world you want one generation back.

Kevin O'Leary aka Mr. Wonderful

25,167 次观看 • 11 个月前

Sam Altman on GPT 5: "This morning I was testing our new model and I got a question. I got emailed a question that I didn't quite understand. And I put it in the model, this is GPT-5, and it answered it perfectly. And I really kind of sat back in my chair and I was just like, oh man, here it is moment... I felt like useless relative to the AI in this thing that I felt like I should have been able to do and I couldn't. It was really hard. But the AI just did it like that. It was a weird feeling."

Sam Altman on GPT 5: "This morning I was testing our new model and I got a question. I got emailed a question that I didn't quite understand. And I put it in the model, this is GPT-5, and it answered it perfectly. And I really kind of sat back in my chair and I was just like, oh man, here it is moment... I felt like useless relative to the AI in this thing that I felt like I should have been able to do and I couldn't. It was really hard. But the AI just did it like that. It was a weird feeling."

Chris

5,664,499 次观看 • 1 年前

everyone in iOS development should watch this. seriously, it might change the whole industry. i pointed claude code at a live ios device running on revyl, typed "test everything," and walked away. here's what's actually happening: ① you don't write the tests. no scripts, no selectors, no test plan. i never told it which screens to open or what to check. it read the app, decided what mattered, and tested it. the entire instruction was "test everything." ② it built its own test team. it looked at the app, clocked that it's basically four mini apps (rides, delivery, services, account), and split itself into 4 agents, one per surface. scoping coverage like that is usually a person's whole afternoon. it did it in seconds, unprompted. ③ all four ran at the same time, each on its own live device. this is where revyl comes in. every agent gets its own live ios session in the cloud, so four running apps get tested in parallel instead of taking turns on one simulator. serial testing turns coverage into a time tax. running all of it at once removes the tax. ④ it tests like a person, not like a script. each agent drives the app the way a user would, taps through the flows, and visually checks each screen against what it expected to see. nothing is pinned to a brittle element id, so renaming a button doesn't take down half your suite. that one detail is the most annoying thing about how we test today, and it just quietly goes away. ⑤ no xcuitest, no sims melting your laptop. i didn't write a single xcuitest script, and there were no simulators booting on my machine. the agents run on cloud devices, so coverage stops being capped by what your laptop can handle. the part that got me isn't that an agent tested an app. it's that i never told it how. i handed it a device and an intent, and it figured out the scoping, the parallelizing, and the driving on its own. if you still write and maintain mobile ui tests by hand, i'm not sure that lasts the year.

everyone in iOS development should watch this. seriously, it might change the whole industry. i pointed claude code at a live ios device running on revyl, typed "test everything," and walked away. here's what's actually happening: ① you don't write the tests. no scripts, no selectors, no test plan. i never told it which screens to open or what to check. it read the app, decided what mattered, and tested it. the entire instruction was "test everything." ② it built its own test team. it looked at the app, clocked that it's basically four mini apps (rides, delivery, services, account), and split itself into 4 agents, one per surface. scoping coverage like that is usually a person's whole afternoon. it did it in seconds, unprompted. ③ all four ran at the same time, each on its own live device. this is where revyl comes in. every agent gets its own live ios session in the cloud, so four running apps get tested in parallel instead of taking turns on one simulator. serial testing turns coverage into a time tax. running all of it at once removes the tax. ④ it tests like a person, not like a script. each agent drives the app the way a user would, taps through the flows, and visually checks each screen against what it expected to see. nothing is pinned to a brittle element id, so renaming a button doesn't take down half your suite. that one detail is the most annoying thing about how we test today, and it just quietly goes away. ⑤ no xcuitest, no sims melting your laptop. i didn't write a single xcuitest script, and there were no simulators booting on my machine. the agents run on cloud devices, so coverage stops being capped by what your laptop can handle. the part that got me isn't that an agent tested an app. it's that i never told it how. i handed it a device and an intent, and it figured out the scoping, the parallelizing, and the driving on its own. if you still write and maintain mobile ui tests by hand, i'm not sure that lasts the year.

Landseer Enga

23,963 次观看 • 1 个月前

My dream since the late 1960s: To have a device that fits in my pocket and with no other connection and contain the entirety of human wisdom. This week I got it. I have an AI model to be trapped on a south sea island with and help you survive. It only gets better from here.

My dream since the late 1960s: To have a device that fits in my pocket and with no other connection and contain the entirety of human wisdom. This week I got it. I have an AI model to be trapped on a south sea island with and help you survive. It only gets better from here.

Brian Roemmele

128,879 次观看 • 9 个月前

I want you to understand something about the trade war Trump started with China—and how this might play out. When I first started traveling to China decades ago, I was riding in a top-of-the-line Mercedes on a long drive from my luxury hotel to the factory. I casually asked, “I know this is a communist country, but it feels very capitalist to me.” The answer was simple: “You’re allowed to make money here. But you must not speak out against the government. That is forbidden.” So let that sink in for a moment… 1.

I want you to understand something about the trade war Trump started with China—and how this might play out. When I first started traveling to China decades ago, I was riding in a top-of-the-line Mercedes on a long drive from my luxury hotel to the factory. I casually asked, “I know this is a communist country, but it feels very capitalist to me.” The answer was simple: “You’re allowed to make money here. But you must not speak out against the government. That is forbidden.” So let that sink in for a moment… 1.

Christopher Webb

4,626,901 次观看 • 1 年前

I think I discovered the cheapest way to code with AI 😁 I tried GLM 4.5 on Claude Code and I'm really amazed how good this model is! I usually test with my nextjs crm app benchmark so I asked it to do that. Prompt "create a nextjs app which is a crm app. use sqlite for the database and use api routes to manage connecting to the database from the frontend" My observations - Generation feels like Sonnet 4 intelligence - Speed is great - It used create-next-app so it bootstrapped nextjs v15.5.2 - It created a sqlite database and used Prisma ORM and helped me easily add seed data - I needed a few prompts to iron out minor issues but all's good. It didn't struggle or got stuck. - Spent $0.50 for this app. Could be way cheaper with the Z.ai sub plans After trying, I can say GLM 4.5 model is up there as the best open source model for coding. Will continue using it and update here. So far so good! P.S. DM me if you want my referral link, otherwise just check Not dropping the link here so it doesn’t look like I’m hyping it. Here's a video of the experience 👇(sped up a bit)

I think I discovered the cheapest way to code with AI 😁 I tried GLM 4.5 on Claude Code and I'm really amazed how good this model is! I usually test with my nextjs crm app benchmark so I asked it to do that. Prompt "create a nextjs app which is a crm app. use sqlite for the database and use api routes to manage connecting to the database from the frontend" My observations - Generation feels like Sonnet 4 intelligence - Speed is great - It used create-next-app so it bootstrapped nextjs v15.5.2 - It created a sqlite database and used Prisma ORM and helped me easily add seed data - I needed a few prompts to iron out minor issues but all's good. It didn't struggle or got stuck. - Spent $0.50 for this app. Could be way cheaper with the Z.ai sub plans After trying, I can say GLM 4.5 model is up there as the best open source model for coding. Will continue using it and update here. So far so good! P.S. DM me if you want my referral link, otherwise just check Not dropping the link here so it doesn’t look like I’m hyping it. Here's a video of the experience 👇(sped up a bit)

Melvin Vivas

45,749 次观看 • 10 个月前

Apple built a large foundation model and fine-tuned it on multiple tasks. But they are doing something very clever: They load a single model in memory and use different adapters to specialize the model on the fly. I recorded a video to show you how to write the code to do the same thing Apple is doing. I explain everything step by step. Here is what I'll show you in the video: 1. We'll load two datasets 2. Then load a large model 3. Then, we'll fine-tune the model on both datasets I'll use LoRA to fine-tune the model. This process creates two small adapters, each specializing in solving one of the datasets. The base model's original parameters will remain unchanged. From here: 4. We'll generate a list of tasks 5. We'll load the correct adapter to solve each task The large model I'm using needs 346 MB of memory, but I only need to load it once. Each adapter is only 2.7 MB. I only need to load the base model once and pair it with any of the fine-tuned adapters. Minimum memory footprint and I can solve multiple tasks. Hope this helps!

Apple built a large foundation model and fine-tuned it on multiple tasks. But they are doing something very clever: They load a single model in memory and use different adapters to specialize the model on the fly. I recorded a video to show you how to write the code to do the same thing Apple is doing. I explain everything step by step. Here is what I'll show you in the video: 1. We'll load two datasets 2. Then load a large model 3. Then, we'll fine-tune the model on both datasets I'll use LoRA to fine-tune the model. This process creates two small adapters, each specializing in solving one of the datasets. The base model's original parameters will remain unchanged. From here: 4. We'll generate a list of tasks 5. We'll load the correct adapter to solve each task The large model I'm using needs 346 MB of memory, but I only need to load it once. Each adapter is only 2.7 MB. I only need to load the base model once and pair it with any of the fine-tuned adapters. Minimum memory footprint and I can solve multiple tasks. Hope this helps!

Santiago

84,747 次观看 • 1 年前

Draymond Green calls out Adam Silver and the NBA for the new All Star format calling it a joke to the league: “Best in the East, Best in the West it works when the game is good. We doing all this different gimmicks makes it more of a joke. It looks good for a year it’s a quick fix, but then you are like I don’t want to see that again. Because it wasn’t the game I grew up on. I can’t see this World vs USA and then equated to when KG was on the East, Shaq was on the West. I can’t do that it doesn’t make sense to me it’s not the same thing”

Draymond Green calls out Adam Silver and the NBA for the new All Star format calling it a joke to the league: “Best in the East, Best in the West it works when the game is good. We doing all this different gimmicks makes it more of a joke. It looks good for a year it’s a quick fix, but then you are like I don’t want to see that again. Because it wasn’t the game I grew up on. I can’t see this World vs USA and then equated to when KG was on the East, Shaq was on the West. I can’t do that it doesn’t make sense to me it’s not the same thing”

Ahmed/The Ears/IG: BigBizTheGod 🇸🇴

547,450 次观看 • 5 个月前

For this hackathon I taught a robot to beat you in Jenga. It sims every possible move, picks the block that's the best move, then an RL policy pushes it out. Built in MuJoCo & Three.js, running on a real AgileX Robotics arm.

For this hackathon I taught a robot to beat you in Jenga. It sims every possible move, picks the block that's the best move, then an RL policy pushes it out. Built in MuJoCo & Three.js, running on a real AgileX Robotics arm.

jonathanhawkins

39,800 次观看 • 2 个月前