Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

loading demo ...

SAM WAITIN

4,151 subscribers

74,957 Aufrufe • vor 2 Jahren •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

9 Kommentare

Profilbild von SAM WAITIN

SAM WAITINvor 2 Jahren

Full song ::

Profilbild von ❯ julie

❯ julievor 2 Jahren

THAT VISUAL GOES SO HARD WTF

Profilbild von HWTsuchiyaNathan

HWTsuchiyaNathanvor 2 Jahren

wait is that the mobil 1 rally championship intro? sick!

Profilbild von Hero2004

Hero2004vor 2 Jahren

This banger song got me like this…

Profilbild von Acapower®

Acapower®vor 2 Jahren

👀🇲🇾

Profilbild von marci

marcivor 2 Jahren

lets go another nuphory and sam banger!!

Profilbild von ZpL

ZpLvor 2 Jahren

Rally championship 2000 my beloved

Profilbild von GEORGE DROYD

GEORGE DROYDvor 2 Jahren

HARD

Profilbild von TomFum

TomFumvor 2 Jahren

That tunes a vibe. Love it

Ähnliche Videos

‘IT’S COOL’ new demo loading! 🤟🏽

‘IT’S COOL’ new demo loading! 🤟🏽

Tarius 🐉

26,506 Aufrufe • vor 1 Jahr

Demo loading.... People are you readdddyyyy? ✊

Demo loading.... People are you readdddyyyy? ✊

Harrydarling💖

66,667 Aufrufe • vor 1 Jahr

Expo + React Server Components (preview) 🚀 Just open sourced a new movies app demo using Expo Router and React server functions. Native data fetching, loading states, and error handling have never been this easy! → Runs on iOS, Web, and Android

Expo + React Server Components (preview) 🚀 Just open sourced a new movies app demo using Expo Router and React server functions. Native data fetching, loading states, and error handling have never been this easy! → Runs on iOS, Web, and Android

Evan Bacon 🥓

72,268 Aufrufe • vor 1 Jahr

🚨BREAKING NEWS: Jasonafex’s Amorous 2 demo is a broken piece of shit released in desperation as Patreon subscribers flee. Frozen screens. Sonic 06 tier loading times that end in the game crashing. It’s an AI “vibe coded” nightmare.

🚨BREAKING NEWS: Jasonafex’s Amorous 2 demo is a broken piece of shit released in desperation as Patreon subscribers flee. Frozen screens. Sonic 06 tier loading times that end in the game crashing. It’s an AI “vibe coded” nightmare.

PandaTheMan

176,973 Aufrufe • vor 1 Jahr

I’ve seen quite a lot of debate recently about loading times and smoke and mirrors in space games - so thought I’d show you all the #NoMansSky debug camera .. a nice demo of how seamless and powerful our engine is! PS5 running here, with video from an iPhone… 3 planets in 50 seconds.

I’ve seen quite a lot of debate recently about loading times and smoke and mirrors in space games - so thought I’d show you all the #NoMansSky debug camera .. a nice demo of how seamless and powerful our engine is! PS5 running here, with video from an iPhone… 3 planets in 50 seconds.

Martin Griffiths

469,427 Aufrufe • vor 2 Jahren

Saw Aaryan gooey toast concept (via Ali Bey) and thought "I need this now!" 😩 So I vibed code it overnight and shipped it as an npm package goey-toast — morphing toast notifications for React - Organic pill → blob → pill animations - Promise toasts with loading → success/error - Action buttons, custom styling, full API - Built on Sonner + Framer Motion npm install goey-toast Demo & docs: GitHub:

Saw Aaryan gooey toast concept (via Ali Bey) and thought "I need this now!" 😩 So I vibed code it overnight and shipped it as an npm package goey-toast — morphing toast notifications for React - Organic pill → blob → pill animations - Promise toasts with loading → success/error - Action buttons, custom styling, full API - Built on Sonner + Framer Motion npm install goey-toast Demo & docs: GitHub:

Alfredo Natal

49,954 Aufrufe • vor 8 Monaten

On-line demo version of steam on September 31st. List of producers: huazi🔞 TeriyakiHotD🌭 PoorQian SenLin森禁止使用与任何途径，否则走法律责任】 Megatron→Optimus Prime左右固定位 #megop The following is the screen display and cover loading picture of the real game ↓

On-line demo version of steam on September 31st. List of producers: huazi🔞 TeriyakiHotD🌭 PoorQian SenLin森禁止使用与任何途径，否则走法律责任】 Megatron→Optimus Prime左右固定位 #megop The following is the screen display and cover loading picture of the real game ↓

huazi🔞

26,193 Aufrufe • vor 1 Jahr

//LOADING THE DEMO... This game prototype was made in 15 minutes with AI. All it took was a single prompt and a free tool. And it already features monetization with $PLAI, ready to launch for thousands of players when polished. You can be a gamedev. You can ship the next hit. And it won't have to take years. Take your first step. Join the closed alpha now:

//LOADING THE DEMO... This game prototype was made in 15 minutes with AI. All it took was a single prompt and a free tool. And it already features monetization with $PLAI, ready to launch for thousands of players when polished. You can be a gamedev. You can ship the next hit. And it won't have to take years. Take your first step. Join the closed alpha now:

PlayMind

32,156 Aufrufe • vor 1 Jahr

There are many great space sims out there. Some feature beautiful ship interiors, some have stunningly realistic galaxies, and others have impressive simulated ship systems. There's even software that allows you to soar through the cosmos seamlessly without any loading screens, but what the space sim genre lacks is a title that features all of the above. Until now, that is... Demo: Discord: Ko-Fi: ( You can also buy the Kickstarter tier rewards here) #spacegame #VR #gaming #pcgames

There are many great space sims out there. Some feature beautiful ship interiors, some have stunningly realistic galaxies, and others have impressive simulated ship systems. There's even software that allows you to soar through the cosmos seamlessly without any loading screens, but what the space sim genre lacks is a title that features all of the above. Until now, that is... Demo: Discord: Ko-Fi: ( You can also buy the Kickstarter tier rewards here) #spacegame #VR #gaming #pcgames

Starship Simulator

11,097 Aufrufe • vor 2 Jahren

I rebuilt an older Vercel book search demo with Next.js 16.3 Instant Navigations: Instant Navigations lets server-rendered routes respond immediately when you click, even on a throttled connection: 𝚌𝚘𝚗𝚜𝚝 𝚗𝚎𝚡𝚝𝙲𝚘𝚗𝚏𝚒𝚐 = { 𝚌𝚊𝚌𝚑𝚎𝙲𝚘𝚖𝚙𝚘𝚗𝚎𝚗𝚝𝚜: 𝚝𝚛𝚞𝚎, 𝚙𝚊𝚛𝚝𝚒𝚊𝚕𝙿𝚛𝚎𝚏𝚎𝚝𝚌𝚑𝚒𝚗𝚐: 𝚝𝚛𝚞𝚎 }; Cache Components prerenders static, cached, and fallback UI. Partial Prefetching brings that reusable route UI to the browser before navigation. Add 𝚙𝚛𝚎𝚏𝚎𝚝𝚌𝚑={𝚝𝚛𝚞𝚎} when a link should also resolve its URL-dependent content before the click. Anything still missing loads on the server and streams into its Suspense boundary. The filters use 𝚞𝚜𝚎𝚃𝚛𝚊𝚗𝚜𝚒𝚝𝚒𝚘𝚗 to fade the catalog while the server updates and 𝚞𝚜𝚎𝙾𝚙𝚝𝚒𝚖𝚒𝚜𝚝𝚒𝚌 to apply the next filter values immediately. Rebuilding the demo also improved initial loading. First Contentful Paint was 44% faster and Largest Contentful Paint was 33% faster than the original. See the source and learn more below ↓

I rebuilt an older Vercel book search demo with Next.js 16.3 Instant Navigations: Instant Navigations lets server-rendered routes respond immediately when you click, even on a throttled connection: 𝚌𝚘𝚗𝚜𝚝 𝚗𝚎𝚡𝚝𝙲𝚘𝚗𝚏𝚒𝚐 = { 𝚌𝚊𝚌𝚑𝚎𝙲𝚘𝚖𝚙𝚘𝚗𝚎𝚗𝚝𝚜: 𝚝𝚛𝚞𝚎, 𝚙𝚊𝚛𝚝𝚒𝚊𝚕𝙿𝚛𝚎𝚏𝚎𝚝𝚌𝚑𝚒𝚗𝚐: 𝚝𝚛𝚞𝚎 }; Cache Components prerenders static, cached, and fallback UI. Partial Prefetching brings that reusable route UI to the browser before navigation. Add 𝚙𝚛𝚎𝚏𝚎𝚝𝚌𝚑={𝚝𝚛𝚞𝚎} when a link should also resolve its URL-dependent content before the click. Anything still missing loads on the server and streams into its Suspense boundary. The filters use 𝚞𝚜𝚎𝚃𝚛𝚊𝚗𝚜𝚒𝚝𝚒𝚘𝚗 to fade the catalog while the server updates and 𝚞𝚜𝚎𝙾𝚙𝚝𝚒𝚖𝚒𝚜𝚝𝚒𝚌 to apply the next filter values immediately. Rebuilding the demo also improved initial loading. First Contentful Paint was 44% faster and Largest Contentful Paint was 33% faster than the original. See the source and learn more below ↓

Aurora Scharff

56,397 Aufrufe • vor 1 Monat

GeoLibre Desktop v0.7.0 is out. This release adds a big round of data loading, identify, editing, settings, and processing improvements: - GPX layers from URL or local file, with waypoints, tracks, and routes loaded as separate selectable layers - Delimited text layers from URL or local file using longitude and latitude fields - WFS layer support in the Add Data dialog - WMS GetFeatureInfo identify support - Inline attribute table editing - Wider, scrollable attribute tables and identify popups - Plugin state persistence in project files - Whitebox toolbox backed by a managed Python sidecar (700+ geoprocessing tools) GitHub: Website: Live demo: #geospatial #opensource

GeoLibre Desktop v0.7.0 is out. This release adds a big round of data loading, identify, editing, settings, and processing improvements: - GPX layers from URL or local file, with waypoints, tracks, and routes loaded as separate selectable layers - Delimited text layers from URL or local file using longitude and latitude fields - WFS layer support in the Add Data dialog - WMS GetFeatureInfo identify support - Inline attribute table editing - Wider, scrollable attribute tables and identify popups - Plugin state persistence in project files - Whitebox toolbox backed by a managed Python sidecar (700+ geoprocessing tools) GitHub: Website: Live demo: #geospatial #opensource

Qiusheng Wu

17,171 Aufrufe • vor 4 Monaten

How should you search, filter, and paginate data with Next.js? This demo has 50,000 books in a Postgres database. • Page Load: When the page loads, we see the React Suspense fallback. This loading skeleton is displayed until the first page of books is retrieved from the database. • Searching: The search input has a 200ms debounce. After 200ms of inactivity, the form submits, updating the URL state with `?q={search}`. The Server Component reads `searchParams` and queries the database. On form submission, a React transition starts, allowing us to read the pending status with `useFormStatus` to display an inline loading state. • State Preservation: Navigating to an individual book page retains the search input state. Reloading the page or sharing the link preserves the search results. • Client-side Filtering: Filtering authors in the left sidebar is done client-side. Authors are fetched by a Server Component and passed as props to the sidebar. Changing the input value updates React state and re-renders the sidebar. • Optimistic Updates: The sidebar’s selected authors are optimistically updated with `useOptimistic`. Checkbox selections update instantly without waiting for the URL to change. • State Preservation: Navigating to an individual book page retains the sidebar filter input and selected author state across navigations, giving it an app-like feel. • Pagination: Navigating between pages updates the URL state, triggering the Server Component to query the database for the specific page of books. We also fetch the total book count to show the total number of pages. This demo isn't perfect yet (still working on it) but it's been a fun playground for some of these patterns. You can imagine a similar experience for thousands of movies, cars, products, or any other very large dataset. Demo → Code →

How should you search, filter, and paginate data with Next.js? This demo has 50,000 books in a Postgres database. • Page Load: When the page loads, we see the React Suspense fallback. This loading skeleton is displayed until the first page of books is retrieved from the database. • Searching: The search input has a 200ms debounce. After 200ms of inactivity, the form submits, updating the URL state with `?q={search}`. The Server Component reads `searchParams` and queries the database. On form submission, a React transition starts, allowing us to read the pending status with `useFormStatus` to display an inline loading state. • State Preservation: Navigating to an individual book page retains the search input state. Reloading the page or sharing the link preserves the search results. • Client-side Filtering: Filtering authors in the left sidebar is done client-side. Authors are fetched by a Server Component and passed as props to the sidebar. Changing the input value updates React state and re-renders the sidebar. • Optimistic Updates: The sidebar’s selected authors are optimistically updated with `useOptimistic`. Checkbox selections update instantly without waiting for the URL to change. • State Preservation: Navigating to an individual book page retains the sidebar filter input and selected author state across navigations, giving it an app-like feel. • Pagination: Navigating between pages updates the URL state, triggering the Server Component to query the database for the specific page of books. We also fetch the total book count to show the total number of pages. This demo isn't perfect yet (still working on it) but it's been a fun playground for some of these patterns. You can imagine a similar experience for thousands of movies, cars, products, or any other very large dataset. Demo → Code →

Lee Robinson

237,013 Aufrufe • vor 2 Jahren

Virtual Textures in the Browser. This is a WebGL 2.0 implementation I did in 2023. The car model is from Sketchfab by Pavel Matoušek. It's a worst-case for virtual textures because the texture is generated from photogrametry software and has pathologically bad UV layout. The texture is 16,384 x 16,384 pixels, which is 180 Mb of PNG data. Loading this in Blender would crash Blender for me back then. The GPU memory requirement for this texture is 1 Gb. The VT displays the model after loading just 34 Kb (one tile). The other tiles are fetched in via a smart priority queue in the background as you move camera around the scene. The physical page texture is 2k, which is 16 times smaller than the source. The solution supports multiple separate virtual textures, and has GLTF integration. Texture filtering as well as anisotropy are fully supported as well. You can try the demo yourself here: Here's a writeup I did while working on this: The video recorded on a very crummy internet connection and I intentionally reload the page in the middle. The delay comes from the geometry download, actual texture part is near-instant.

Virtual Textures in the Browser. This is a WebGL 2.0 implementation I did in 2023. The car model is from Sketchfab by Pavel Matoušek. It's a worst-case for virtual textures because the texture is generated from photogrametry software and has pathologically bad UV layout. The texture is 16,384 x 16,384 pixels, which is 180 Mb of PNG data. Loading this in Blender would crash Blender for me back then. The GPU memory requirement for this texture is 1 Gb. The VT displays the model after loading just 34 Kb (one tile). The other tiles are fetched in via a smart priority queue in the background as you move camera around the scene. The physical page texture is 2k, which is 16 times smaller than the source. The solution supports multiple separate virtual textures, and has GLTF integration. Texture filtering as well as anisotropy are fully supported as well. You can try the demo yourself here: Here's a writeup I did while working on this: The video recorded on a very crummy internet connection and I intentionally reload the page in the middle. The delay comes from the geometry download, actual texture part is near-instant.

Alex Goldring

19,368 Aufrufe • vor 9 Monaten

【日常 MV RP企画】この投稿をリポストよろしくお願いします！私は浮かびながら沈むが納得いくRP数でメイキング映像公開！各種サブスクリプションページ日常 now loading…（L.R.Baggs AEG-1 sound demo） NATASHA NBSG Nylon（sound demo） --5月東名阪ツアー-- 2026.05.02 (土) 誕生までのカウントダウン-大阪会場:心斎橋 JUZA 開場/開演:17:00 / 17:30 2026.05.03 (日) 誕生までのカウントダウン-名古屋会場:名古屋MUSICFARM 開場/開演:17:00 / 17:30 2026.05.08 (金) 誕生-東京- 1部会場:原宿 La Donna 開場/開演:12:30 / 13:30 誕生-東京- 2部会場:原宿 La Donna 開場/開演:17:00 / 18:00 予約はこちら #私は浮かびながら沈む #日記帳 #新アー写 #アルバム #MV

【日常 MV RP企画】この投稿をリポストよろしくお願いします！私は浮かびながら沈むが納得いくRP数でメイキング映像公開！各種サブスクリプションページ日常 now loading…（L.R.Baggs AEG-1 sound demo） NATASHA NBSG Nylon（sound demo） --5月東名阪ツアー-- 2026.05.02 (土) 誕生までのカウントダウン-大阪会場:心斎橋 JUZA 開場/開演:17:00 / 17:30 2026.05.03 (日) 誕生までのカウントダウン-名古屋会場:名古屋MUSICFARM 開場/開演:17:00 / 17:30 2026.05.08 (金) 誕生-東京- 1部会場:原宿 La Donna 開場/開演:12:30 / 13:30 誕生-東京- 2部会場:原宿 La Donna 開場/開演:17:00 / 18:00 予約はこちら #私は浮かびながら沈む #日記帳 #新アー写 #アルバム #MV

私は浮かびながら沈む

84,089 Aufrufe • vor 6 Monaten

Reasoning from scratch round 2: In this video, I cover the text generation process in LLMs and KV caching (to prepare the base model before adding reasoning techniques in the upcoming ones). 00:00 Introduction and reasoning model demo 01:55 How to work through the book 05:00 Chapter 2 overview 08:25 Checking PyTorch and hardware support 10:26 Apple silicon and MPS caveats 15:00 Cloud GPU options 16:08 Tokens and tokenization 18:20 Qwen3 and the Reasoning From Scratch package 23:05 Encoding and decoding text 26:24 Downloading weights and selecting a device 31:01 Loading the pretrained Qwen3 model 34:32 How LLMs generate text 36:47 Input tensors and batch dimensions 41:48 Running the model in inference mode 44:11 Logits and next-token predictions 49:21 Greedy decoding with argmax 52:28 Building a streaming text generator 01:01:28 Generating text and handling end-of-sequence tokens 01:06:00 Benchmarking text generation 01:14:34 How KV caching works 01:17:22 Adding KV caching and measuring the speedup 01:24:31 Model compilation with torch.compile 01:30:33 Combining compilation with KV caching 01:32:53 Comparing CPU and GPU performance 01:35:32 Recap and next steps

Reasoning from scratch round 2: In this video, I cover the text generation process in LLMs and KV caching (to prepare the base model before adding reasoning techniques in the upcoming ones). 00:00 Introduction and reasoning model demo 01:55 How to work through the book 05:00 Chapter 2 overview 08:25 Checking PyTorch and hardware support 10:26 Apple silicon and MPS caveats 15:00 Cloud GPU options 16:08 Tokens and tokenization 18:20 Qwen3 and the Reasoning From Scratch package 23:05 Encoding and decoding text 26:24 Downloading weights and selecting a device 31:01 Loading the pretrained Qwen3 model 34:32 How LLMs generate text 36:47 Input tensors and batch dimensions 41:48 Running the model in inference mode 44:11 Logits and next-token predictions 49:21 Greedy decoding with argmax 52:28 Building a streaming text generator 01:01:28 Generating text and handling end-of-sequence tokens 01:06:00 Benchmarking text generation 01:14:34 How KV caching works 01:17:22 Adding KV caching and measuring the speedup 01:24:31 Model compilation with torch.compile 01:30:33 Combining compilation with KV caching 01:32:53 Comparing CPU and GPU performance 01:35:32 Recap and next steps

Sebastian Raschka

67,101 Aufrufe • vor 1 Monat

This Viper pilot pushed the F-16 to its absolute limit in a demo flight that made the sky feel small. She rolled in low and fast, afterburner roaring as the jet accelerated past 500 knots. Then he pulled back hard, loading the wings with G’s that crushed his body into the seat while the G-suit fought to keep blood in his brain. The nose snapped up, the world tilted 90 degrees, and the Viper climbed vertically, vapor trails spiraling off the wings as the jet fought gravity and air density. He leveled off only to dive back down, rolling inverted and pulling into another high-G turn that blurred the horizon. Every maneuver was razor-sharp—precision born from thousands of hours of training, trust in the machine, and the will to stay conscious when most would black out. This is what American air power looks like in the hands of a pilot who refuses to back down: raw speed, brutal force, and control that borders on impossible. How does seeing this kind of performance make you realize the skill and courage behind the pilots who keep the skies ours? Follow for more daily mind-blowing American wins.

This Viper pilot pushed the F-16 to its absolute limit in a demo flight that made the sky feel small. She rolled in low and fast, afterburner roaring as the jet accelerated past 500 knots. Then he pulled back hard, loading the wings with G’s that crushed his body into the seat while the G-suit fought to keep blood in his brain. The nose snapped up, the world tilted 90 degrees, and the Viper climbed vertically, vapor trails spiraling off the wings as the jet fought gravity and air density. He leveled off only to dive back down, rolling inverted and pulling into another high-G turn that blurred the horizon. Every maneuver was razor-sharp—precision born from thousands of hours of training, trust in the machine, and the will to stay conscious when most would black out. This is what American air power looks like in the hands of a pilot who refuses to back down: raw speed, brutal force, and control that borders on impossible. How does seeing this kind of performance make you realize the skill and courage behind the pilots who keep the skies ours? Follow for more daily mind-blowing American wins.

R͓̽Y͓̽a͓̽n͓̽C͓̽e͓̽y͓̽

51,103 Aufrufe • vor 6 Monaten