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Claude 3.7 Sonnet is seriously impressive It goes beyond basic logic, capturing subtle nuances. Previous models for me was just about getting 3D experiences running, but 3.7 adds polished details—particle trails, collision visuals, and flawless physics #threejs

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Ian Curtis

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80,564 просмотров • 1 год назад •via X (Twitter)

Наука и технологииИскусство#threejs
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🚁 Adding an Apache attack helicopter today I think I will rewrite the entire flight model today again to be ambiguous of what kind of vehicle it is Planes behave very different than helicopters and different than vehicles like tanks or pedestrians Because people here also requested "Guy with bazooka" so that will be next I have no idea if everyone else is just lying about using AI for their plane models but mine look so clunky (but I like the look), but I now tried with Claude 3.5 and 3.7 and Grok 3 and they all just generate clunky models, not beautiful like you see in other AI vibe coded games, so either they bs'ing or I'm retarded I also have to move sub parts of each model like for 30 minutes until everything fits, AI has a really hard time with 3d models But okay the look is kinda cute
0:19

🚁 Adding an Apache attack helicopter today I think I will rewrite the entire flight model today again to be ambiguous of what kind of vehicle it is Planes behave very different than helicopters and different than vehicles like tanks or pedestrians Because people here also requested "Guy with bazooka" so that will be next I have no idea if everyone else is just lying about using AI for their plane models but mine look so clunky (but I like the look), but I now tried with Claude 3.5 and 3.7 and Grok 3 and they all just generate clunky models, not beautiful like you see in other AI vibe coded games, so either they bs'ing or I'm retarded I also have to move sub parts of each model like for 30 minutes until everything fits, AI has a really hard time with 3d models But okay the look is kinda cute

@levelsio

485,575 просмотров • 1 год назад

Ok, that's amazing. 🦄 A side by side three.js comparison - with GitMCP and without. GitMCP's story originally started from a tweet by Three.js about their large, non-LLM-digestable, documentation file. Ido Salomon and I ended up creating a cool generic documentation MCP server, but we didn't forget threejs and mrdoob. So we put it to the test. Using GitMCP, I gave this prompt to Cursor + Claude 3.7: "Build a Three.js scene featuring a controllable realistic person navigating a textured dynamic urban environment with realistic lighting and subtle bloom effects. Ensure keyboard controls (WASD) for movement." This is the result. To the right is the one-shot result with our MCP server. To the left is the result without it, based just on Cursor's limited knowledge. The video is pretty conclusive. 🤯 It's really amazing since Cursor already knows a lot about three.js, but with our dedicated documentation MCP server it just outputs better results. And for all other libraries out there - that are not included in Cursor - this is a total game changer. A huge shout out to Cloudflare Developers and Anni Wang, who worked with us yesterday to quickly insert new capabilities in their AutoRAG feature (that enables indexing large amount of documentation) according to our specific needs, and to troubleshoot issues. It was very helpful. Knowledge is power, and that applies for every coding assitant. And not just for three.js! Check out GitMCP for any library you're using - link in the next comment.
0:13

Ok, that's amazing. 🦄 A side by side three.js comparison - with GitMCP and without. GitMCP's story originally started from a tweet by Three.js about their large, non-LLM-digestable, documentation file. Ido Salomon and I ended up creating a cool generic documentation MCP server, but we didn't forget threejs and mrdoob. So we put it to the test. Using GitMCP, I gave this prompt to Cursor + Claude 3.7: "Build a Three.js scene featuring a controllable realistic person navigating a textured dynamic urban environment with realistic lighting and subtle bloom effects. Ensure keyboard controls (WASD) for movement." This is the result. To the right is the one-shot result with our MCP server. To the left is the result without it, based just on Cursor's limited knowledge. The video is pretty conclusive. 🤯 It's really amazing since Cursor already knows a lot about three.js, but with our dedicated documentation MCP server it just outputs better results. And for all other libraries out there - that are not included in Cursor - this is a total game changer. A huge shout out to Cloudflare Developers and Anni Wang, who worked with us yesterday to quickly insert new capabilities in their AutoRAG feature (that enables indexing large amount of documentation) according to our specific needs, and to troubleshoot issues. It was very helpful. Knowledge is power, and that applies for every coding assitant. And not just for three.js! Check out GitMCP for any library you're using - link in the next comment.

Liad Yosef

41,745 просмотров • 1 год назад

claude fable 5 is live. spawn 5.0 was built with it: 1,687 prompts, 102 sessions, my job shifted from architecture to judging taste. what we built, each of which would've been at least a month with a whole team on opus: — a from-scratch physics engine (mantle) that rivals rapier, testable in a day as a side thread — clustered froxel lighting: 8 lights to 1,000+ fully dynamic ("it's stupid fast" — creator of threejs) — realtime diffuse GI on webgpu, on your phone (landing in the next update) — million-particle gpu vfx with a shader architecture beyond what unreal and unity ship — mmo-scale netcode plus, for good measure: an (alpha) native ios app. all of it in about a week. all of it running on mobile. and the list doesn't include half of what we shipped — full changelog in the thread below. and the part the benchmarks won't show you: this model has a wonderful personality. it's genuinely funny. i laughed so hard i cried multiple times, mid-physics-rewrite. the genius and the character aren't separate features.
0:17

claude fable 5 is live. spawn 5.0 was built with it: 1,687 prompts, 102 sessions, my job shifted from architecture to judging taste. what we built, each of which would've been at least a month with a whole team on opus: — a from-scratch physics engine (mantle) that rivals rapier, testable in a day as a side thread — clustered froxel lighting: 8 lights to 1,000+ fully dynamic ("it's stupid fast" — creator of threejs) — realtime diffuse GI on webgpu, on your phone (landing in the next update) — million-particle gpu vfx with a shader architecture beyond what unreal and unity ship — mmo-scale netcode plus, for good measure: an (alpha) native ios app. all of it in about a week. all of it running on mobile. and the list doesn't include half of what we shipped — full changelog in the thread below. and the part the benchmarks won't show you: this model has a wonderful personality. it's genuinely funny. i laughed so hard i cried multiple times, mid-physics-rewrite. the genius and the character aren't separate features.

jacob

55,515 просмотров • 4 месяцев назад

I really hated the ending of Resident Evil (2026) — it completely ruined the movie for me I watched #residentevil yesterday, and honestly, I really hated the ending. The whole movie, I was invested in his struggle to survive. Everything he goes through is basically about staying alive and making it through. So when it finally gets to the end, he gets turned into a monster… I was just like, seriously? What was the point? I know Resident Evil is supposed to be dark and brutal, but that ending felt so frustrating to me. After watching him fight so hard just to survive, having it end that way made everything he went through feel almost pointless. Did anyone else feel the same way about the ending?
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I really hated the ending of Resident Evil (2026) — it completely ruined the movie for me I watched #residentevil yesterday, and honestly, I really hated the ending. The whole movie, I was invested in his struggle to survive. Everything he goes through is basically about staying alive and making it through. So when it finally gets to the end, he gets turned into a monster… I was just like, seriously? What was the point? I know Resident Evil is supposed to be dark and brutal, but that ending felt so frustrating to me. After watching him fight so hard just to survive, having it end that way made everything he went through feel almost pointless. Did anyone else feel the same way about the ending?

tunmise

66,231 просмотров • 25 дней назад

Astra (GPT-6) is here!!! I've had early access and tested it like crazy with things like games, code, writing, browser control, presentations and general knowledge work. This is the best model I've ever used. Period. (Incredible demos below in this thread ⬇️) Here's my take on Astra: > It's insanely capable. This feels like a massive improvement, not just an incremental change. This is especially true with zero-shot prompts. > It's all about knowledge work. Slide creation, analysis, writing, and browser control. And oh my...it's so good at browser control. GPT-5.6 was already fantastic at doing things in the browser, Astra is another level and significantly faster. > We're closer than ever (arrived?) at prompt-to-playable game. And I don't just mean only playable, these are actually fun games. I bet if someone with a great eye for games used Astra, they could create a viral game within 1-2 weeks. > Astra is better at writing but not perfect. It removes much of the "AI Smell" we're all familiar with but some stink still survived. > It has a tendency to use the same design colors and look/feel as GPT-5.6 (forrest green anyone?) but it is more steerable in design than previous models. > It's highly steerable in general. A little nudge goes a long way. When I first started using Astra, almost every task I gave it would go for ~30 minutes. I wanted it to keep working. Adding more specifics to a prompt helped greatly with it's ability to work for a long time. > Astra's 3D understanding is unmatched. 3D asset creation was consistent and easy and its spacial awareness while building complex 3D worlds blew me away. I'm still getting familiar with Astra but this will now be my go-to model for any difficult work I have. Check out the demos below: 👇
0:14

Astra (GPT-6) is here!!! I've had early access and tested it like crazy with things like games, code, writing, browser control, presentations and general knowledge work. This is the best model I've ever used. Period. (Incredible demos below in this thread ⬇️) Here's my take on Astra: > It's insanely capable. This feels like a massive improvement, not just an incremental change. This is especially true with zero-shot prompts. > It's all about knowledge work. Slide creation, analysis, writing, and browser control. And oh my...it's so good at browser control. GPT-5.6 was already fantastic at doing things in the browser, Astra is another level and significantly faster. > We're closer than ever (arrived?) at prompt-to-playable game. And I don't just mean only playable, these are actually fun games. I bet if someone with a great eye for games used Astra, they could create a viral game within 1-2 weeks. > Astra is better at writing but not perfect. It removes much of the "AI Smell" we're all familiar with but some stink still survived. > It has a tendency to use the same design colors and look/feel as GPT-5.6 (forrest green anyone?) but it is more steerable in design than previous models. > It's highly steerable in general. A little nudge goes a long way. When I first started using Astra, almost every task I gave it would go for ~30 minutes. I wanted it to keep working. Adding more specifics to a prompt helped greatly with it's ability to work for a long time. > Astra's 3D understanding is unmatched. 3D asset creation was consistent and easy and its spacial awareness while building complex 3D worlds blew me away. I'm still getting familiar with Astra but this will now be my go-to model for any difficult work I have. Check out the demos below: 👇

Matthew Berman

1,910,361 просмотров • 1 месяц назад

This is f*cking insane. This tip saved me thousands of dollars. run Opus 5.5, Sonnet 5.5, and Fable 5.1 together, and stop burning Opus on work it was never needed for. the whole idea in one line: the strong model plans, the mid-tier model executes, Fable stays quiet until it's actually needed. roles, broken down: Opus 5.5, high effort, owns the plan and ships the final code Sonnet 5.5, medium effort, splits into explorer (reads the codebase), worker (edits files, runs tests), researcher (pulls docs) Fable 5.1, called through /advisor fable, reads everything happening in the session but stays silent unless something's actually wrong three moments where Fable speaks: → a plan goes out: is this actually the right call? → the same failure shows up again: is the search going nowhere? → the task gets marked finished: did something get skipped? Jev engineering does the same thing one level down. the forks that don't need real thought, which file, which tool, keep going or stop, go straight to Jev and come back in under half a second. the big models only ever see the forks that genuinely need a decision. anyone still running one model for everything is paying Opus prices to decide whether a file exists. drop this into Claude Code: "Rebuild my Claude Code setup around this structure: Look through ~/.claude/agents and .claude/agents for subagents already covering explorer, worker, and researcher. Only create new ones for roles that are missing. Set model: sonnet, effort: medium on each. If an existing subagent is locked to a different model, leave it as is and just list it. In ~/.claude/settings.json, set effortLevel to high and advisorModel to fable. Check for anything disabling the advisor, CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, or anything blocking feature-flag fetches, plus CLAUDE_CODE_EFFORT_LEVEL, which can override subagent effort settings. Report what you find. Don't change any of it yet. Add one line to ~/.claude/CLAUDE.md: check in with the advisor before a big plan, when the same error shows up twice, and before marking a long task done. Show every change as a diff first. Wait for my go-ahead before touching anything."
0:20

This is f*cking insane. This tip saved me thousands of dollars. run Opus 5.5, Sonnet 5.5, and Fable 5.1 together, and stop burning Opus on work it was never needed for. the whole idea in one line: the strong model plans, the mid-tier model executes, Fable stays quiet until it's actually needed. roles, broken down: Opus 5.5, high effort, owns the plan and ships the final code Sonnet 5.5, medium effort, splits into explorer (reads the codebase), worker (edits files, runs tests), researcher (pulls docs) Fable 5.1, called through /advisor fable, reads everything happening in the session but stays silent unless something's actually wrong three moments where Fable speaks: → a plan goes out: is this actually the right call? → the same failure shows up again: is the search going nowhere? → the task gets marked finished: did something get skipped? Jev engineering does the same thing one level down. the forks that don't need real thought, which file, which tool, keep going or stop, go straight to Jev and come back in under half a second. the big models only ever see the forks that genuinely need a decision. anyone still running one model for everything is paying Opus prices to decide whether a file exists. drop this into Claude Code: "Rebuild my Claude Code setup around this structure: Look through ~/.claude/agents and .claude/agents for subagents already covering explorer, worker, and researcher. Only create new ones for roles that are missing. Set model: sonnet, effort: medium on each. If an existing subagent is locked to a different model, leave it as is and just list it. In ~/.claude/settings.json, set effortLevel to high and advisorModel to fable. Check for anything disabling the advisor, CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, or anything blocking feature-flag fetches, plus CLAUDE_CODE_EFFORT_LEVEL, which can override subagent effort settings. Report what you find. Don't change any of it yet. Add one line to ~/.claude/CLAUDE.md: check in with the advisor before a big plan, when the same error shows up twice, and before marking a long task done. Show every change as a diff first. Wait for my go-ahead before touching anything."

rvaniaaa

254,273 просмотров • 21 дней назад

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.
0:12

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

Sudo su

37,623 просмотров • 8 месяцев назад

This part is the most personal to me. Thank you Freen for taking the time to listen to my story 🥹. I thought about whether to tell her as it’s still a sensitive topic for me, but I’m glad I did. I asked Freen to draw me a pawprint to represent my pets and as a future tattoo. There was a bit of confusion as to what I wanted (hence previous video where I accidentally filmed the translator 😅). I was also nervous so my English wasn’t very good either in that moment and idk I didn’t think to just point at her tattoo. Thank you Freen for taking the drawing so seriously, I was so surprised. I told her she could just draw any pawprint, but she actually reached over to zoom in on my dogs photo and bless her because as you can see you can’t see her paw pad at all 😅, but she took the time and effort to really draw it. I really should’ve found a better photo, but it was a bit of a last minute decision to ask her to draw and I was already tearing up looking through photos of my dog. This moment will forever be precious to me and just showed me how kind and generous she is. Even writing this post I’m in tears. I can’t ever imagine not loving her. HOME SWEET HOME WITH FREEN #HomeWithHerFansign #srchafreen‌
0:31

This part is the most personal to me. Thank you Freen for taking the time to listen to my story 🥹. I thought about whether to tell her as it’s still a sensitive topic for me, but I’m glad I did. I asked Freen to draw me a pawprint to represent my pets and as a future tattoo. There was a bit of confusion as to what I wanted (hence previous video where I accidentally filmed the translator 😅). I was also nervous so my English wasn’t very good either in that moment and idk I didn’t think to just point at her tattoo. Thank you Freen for taking the drawing so seriously, I was so surprised. I told her she could just draw any pawprint, but she actually reached over to zoom in on my dogs photo and bless her because as you can see you can’t see her paw pad at all 😅, but she took the time and effort to really draw it. I really should’ve found a better photo, but it was a bit of a last minute decision to ask her to draw and I was already tearing up looking through photos of my dog. This moment will forever be precious to me and just showed me how kind and generous she is. Even writing this post I’m in tears. I can’t ever imagine not loving her. HOME SWEET HOME WITH FREEN #HomeWithHerFansign #srchafreen‌

Mami* Van 🦋 🌷🪽| FB My Loves :🤍)

17,061 просмотров • 1 месяц назад

I love Meta, but I have some tough love. Opus 5.5 is absolutely mogging Wonder on every single 3D game demo we’ve seen, and I love Alexandr because he can admit that they didn’t explicitly train Wonder on 3D games/visual representations. I know he’s heard this, however, why wouldn’t you? Theo saying 3D demos don’t represent real coding is utterly ridiculous. If you work with models on 3D visual representations, you know that they’re testing geometry, coordinate transformations, camera systems, physics, rendering, and actual interactive environments. A model can’t just be RL’d on game demos and build unique, never before seen 3D representations without having genuinely good spatial reasoning and creativity within the model. What do you think game developers do all day? I’m saying this as someone who used to be one. Alexandr and the Meta team should have known that when people are scrolling on X, you’re not going to see posts saying, “Wow, Meta Wonder found me a house on Zillow that matches my exact preferences!” or “Oh wow, it one shotted this extremely economically useful spreadsheet!” Few people are going to repost and get excited about that. People LOVE demos because they make the model’s capabilities actually tangible and something we can use, play with, and watch every day. Watching an LLM generate an entire playable 3D world from a prompt and Reaching AAA game quality is an almost insurmountably difficult challenge for these models. I think Meta seriously underestimated how important that is. I don’t think people are pushing Meta Muse wonder as far as they could be so I want to give it a fair chance. The demo below is not a fair representation but shows how good Opus 5.5 can look when paired with unity.
0:19

I love Meta, but I have some tough love. Opus 5.5 is absolutely mogging Wonder on every single 3D game demo we’ve seen, and I love Alexandr because he can admit that they didn’t explicitly train Wonder on 3D games/visual representations. I know he’s heard this, however, why wouldn’t you? Theo saying 3D demos don’t represent real coding is utterly ridiculous. If you work with models on 3D visual representations, you know that they’re testing geometry, coordinate transformations, camera systems, physics, rendering, and actual interactive environments. A model can’t just be RL’d on game demos and build unique, never before seen 3D representations without having genuinely good spatial reasoning and creativity within the model. What do you think game developers do all day? I’m saying this as someone who used to be one. Alexandr and the Meta team should have known that when people are scrolling on X, you’re not going to see posts saying, “Wow, Meta Wonder found me a house on Zillow that matches my exact preferences!” or “Oh wow, it one shotted this extremely economically useful spreadsheet!” Few people are going to repost and get excited about that. People LOVE demos because they make the model’s capabilities actually tangible and something we can use, play with, and watch every day. Watching an LLM generate an entire playable 3D world from a prompt and Reaching AAA game quality is an almost insurmountably difficult challenge for these models. I think Meta seriously underestimated how important that is. I don’t think people are pushing Meta Muse wonder as far as they could be so I want to give it a fair chance. The demo below is not a fair representation but shows how good Opus 5.5 can look when paired with unity.

Chris

57,750 просмотров • 10 дней назад

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.
0:18

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

MrNeRF

290,319 просмотров • 11 месяцев назад

Another WTF moment. Cihan got himself ARRESTED just to check on her. Cihan got SHOT trying to prove her innocence. Even after she told him she was going to marry Enver, knowing how deeply that hurt him, he was STILL worried about her. He stayed until after the trial. And he didn’t just wait for the verdict. Even when the lawyer said it would take a few more hours for her to be released, he stayed and waited for HER. All while dealing with his own pain, physically and emotionally. And when he finally saw her, what was the first thing he said? That her getting released was the one good thing that had happened that day. Think about that. After everything he had been through, after everything she had said to hurt him, HER freedom was still the one thing that made him happy. And her response? “You say you’re happy, but your face says otherwise. Is it because Enver was the one who got me out?” ENVER KIM??? AGAIN??? How many times is she going to bring up Enver just to hurt him? He was standing right there, physically wounded, emotionally devastated, and still genuinely happy that she was free. And instead of seeing that, instead of acknowledging even for a second what he had done for her, she chose to hurt him instead . This isn’t about jealousy anymore. It’s about deliberately hitting someone exactly where you know it hurts the most. We are WAY beyond jealousy at this point.
0:19

Another WTF moment. Cihan got himself ARRESTED just to check on her. Cihan got SHOT trying to prove her innocence. Even after she told him she was going to marry Enver, knowing how deeply that hurt him, he was STILL worried about her. He stayed until after the trial. And he didn’t just wait for the verdict. Even when the lawyer said it would take a few more hours for her to be released, he stayed and waited for HER. All while dealing with his own pain, physically and emotionally. And when he finally saw her, what was the first thing he said? That her getting released was the one good thing that had happened that day. Think about that. After everything he had been through, after everything she had said to hurt him, HER freedom was still the one thing that made him happy. And her response? “You say you’re happy, but your face says otherwise. Is it because Enver was the one who got me out?” ENVER KIM??? AGAIN??? How many times is she going to bring up Enver just to hurt him? He was standing right there, physically wounded, emotionally devastated, and still genuinely happy that she was free. And instead of seeing that, instead of acknowledging even for a second what he had done for her, she chose to hurt him instead . This isn’t about jealousy anymore. It’s about deliberately hitting someone exactly where you know it hurts the most. We are WAY beyond jealousy at this point.

Marie 🇨🇦

18,897 просмотров • 9 дней назад

Many of you might think face wash is just…face wash. We use it to clean the dirt and grime from our daily lives. Some take it seriously while others just use it as a checklist of hygiene. But what if face wash was something more? What if there was a way to gently and quickly combat dry skin every day? Introducing H2.O Facial Cleanser by biöm – a groundbreaking product in the world of skincare. In my quest to redefine what a cleanser can do, I've created something extraordinary. Skin gets dirty, but it also gets thirsty. H2.O isn't just about cleaning your skin; it's about transforming it. The secret sauce in my unique formula is ElfaMoist, a hydration powerhouse. This active ingredient goes beyond the superficial layer of your skin, delivering hydration that penetrates deep into your pores, rivaling those satisfying Gatorade commercials. You can think of ElfaMoist like water that went to an Ivy League school. It’s clinically proven to hydrate your skin for over 30 hours, and it outperforms every other hydrator on the market. H2.O isn't just a cleanser; it's a hydration revolution. ⚠️ No fragrances. 🫒 Gentle olive & coconut oil-based surfactants. 🌿 Natural aloe vera for soothing hydration. 💧Squalane to mimic your skin’s natural oils. With each use, H2.O promises not only to cleanse but to leave your skin feeling refreshed, nourished, and more hydrated than ever before. It's time to experience a cleanser that does more than just clean – it rehydrates, replenishes, and renews. Welcome to the future of skincare.
0:10

Many of you might think face wash is just…face wash. We use it to clean the dirt and grime from our daily lives. Some take it seriously while others just use it as a checklist of hygiene. But what if face wash was something more? What if there was a way to gently and quickly combat dry skin every day? Introducing H2.O Facial Cleanser by biöm – a groundbreaking product in the world of skincare. In my quest to redefine what a cleanser can do, I've created something extraordinary. Skin gets dirty, but it also gets thirsty. H2.O isn't just about cleaning your skin; it's about transforming it. The secret sauce in my unique formula is ElfaMoist, a hydration powerhouse. This active ingredient goes beyond the superficial layer of your skin, delivering hydration that penetrates deep into your pores, rivaling those satisfying Gatorade commercials. You can think of ElfaMoist like water that went to an Ivy League school. It’s clinically proven to hydrate your skin for over 30 hours, and it outperforms every other hydrator on the market. H2.O isn't just a cleanser; it's a hydration revolution. ⚠️ No fragrances. 🫒 Gentle olive & coconut oil-based surfactants. 🌿 Natural aloe vera for soothing hydration. 💧Squalane to mimic your skin’s natural oils. With each use, H2.O promises not only to cleanse but to leave your skin feeling refreshed, nourished, and more hydrated than ever before. It's time to experience a cleanser that does more than just clean – it rehydrates, replenishes, and renews. Welcome to the future of skincare.

Fawn

105,878 просмотров • 2 лет назад

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.
0:12

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Rachel🥥

61,994 просмотров • 3 месяцев назад

Qwen3.8-Flash-Next is starting to feel like the local model Opus fans have been waiting for. Someone ran the NVFP4 176B-class Flash-Next on 2× DGX Sparks, and the results are wild. Real measured scaling → C1: 44.2 tok/s → C2: 64.6 tok/s → C4: 86.8 tok/s aggregate The per-stream speed drops with concurrency, but total throughput keeps climbing. Long-context behavior was even more impressive: → 5K: needle retrieved → 21K: needle retrieved → 84K: needle retrieved → 167K: needle retrieved → 262K: prefill succeeded, but the window was saturated That 167K retrieval test is the one I care about. Long agent runs are where models usually start losing the plot. Flash-Next didn’t. It also held up surprisingly well on physics-heavy reasoning, artifact generation, research workflows, evidence checking, and long-horizon planning. The personality is interesting too. DeepSeek V4 Flash feels like the dependable workhorse. GLM-5.2 feels like the problem-solving machine. Qwen3.8-Flash-Next feels more insightful. It has that rare ability to understand what you’re actually asking rather than just following the surface pattern. The main weakness I’ve noticed is instruction following. It can occasionally drift between prose turns where DeepSeek and GLM stay tighter. And this is why the 256GB M5 Ultra conversation gets interesting. If Apple can pair that huge unified-memory pool with enough bandwidth, this model class becomes genuinely practical for long-running local agents. We’re talking about frontier-class reasoning on hardware sitting on a desk.
0:18

Qwen3.8-Flash-Next is starting to feel like the local model Opus fans have been waiting for. Someone ran the NVFP4 176B-class Flash-Next on 2× DGX Sparks, and the results are wild. Real measured scaling → C1: 44.2 tok/s → C2: 64.6 tok/s → C4: 86.8 tok/s aggregate The per-stream speed drops with concurrency, but total throughput keeps climbing. Long-context behavior was even more impressive: → 5K: needle retrieved → 21K: needle retrieved → 84K: needle retrieved → 167K: needle retrieved → 262K: prefill succeeded, but the window was saturated That 167K retrieval test is the one I care about. Long agent runs are where models usually start losing the plot. Flash-Next didn’t. It also held up surprisingly well on physics-heavy reasoning, artifact generation, research workflows, evidence checking, and long-horizon planning. The personality is interesting too. DeepSeek V4 Flash feels like the dependable workhorse. GLM-5.2 feels like the problem-solving machine. Qwen3.8-Flash-Next feels more insightful. It has that rare ability to understand what you’re actually asking rather than just following the surface pattern. The main weakness I’ve noticed is instruction following. It can occasionally drift between prose turns where DeepSeek and GLM stay tighter. And this is why the 256GB M5 Ultra conversation gets interesting. If Apple can pair that huge unified-memory pool with enough bandwidth, this model class becomes genuinely practical for long-running local agents. We’re talking about frontier-class reasoning on hardware sitting on a desk.

FHILY👑

20,253 просмотров • 1 месяц назад

An Anthropic paid for my espresso at Sightglass when he saw my screen. I was backtesting a Claude-built arbitrage system. Terminal open. Live trades firing. He glanced over. Stopped walking. That is not TradingView. What framework is that actually running. Claude Code. Three repos. One prompt. $20 per month. He sat down across from me without asking. I work on AlphaGo successor models. We test reinforcement agents for market simulation. You are running something similar but you let Claude write the strategy layer. Not just strategy. Detection. github/warproxxx/poly_data 86 million Polymarket trades. Every wallet. Every position. Every timestamp. You are feeding Claude transaction history and letting it identify asymmetric behavior patterns. Then cloning the profitable ones in real time. Exactly. One prompt: Scan every wallet with 150+ trades and ROI above 65%. Rank by consistency. Export top 40. Claude processed 18,600 wallets in 6 minutes. Returned 38. Top 15 wallets outperformed the bottom 18,000 combined. That is not analysis. That is alpha concentration. Precisely. And you did not write the ranking algorithm. Claude built it. I just connected it to execution logic. Then I opened the second repo. github/Polymarket/polymarket-cli Official Rust CLI. No auth required for reads. 600+ markets scanned in under 3 minutes. Claude scores: liquidity depth, pricing gap, resolution timeline. 512 markets reduced to 28 before capital moves. 94.5% filtered out before entry consideration. A notification hit. Position filled. +$127. How does it decide entry timing. Four agents. No shared state. Arbitrage detector, convergence scanner, whale mirror, volume surge tracker. 3 agents agree: full position. 2 agree: half size. Split vote: skip. Consensus filtering alone eliminated 46% of losses in backtest. And exit logic. The 38 top wallets almost never hold to settlement. 89% exit early. Average 71% of max profit captured. Immediate redeployment. My bot exits at 82% of projected move or 4x volume spike. Whichever hits first. You built a whale copy system that exits before the whales do. Correct. He set his coffee down slowly. How many trades per day. 12 average. Most rejected by filters before I see notifications. My setup: Claude API: $20/mo VPS Frankfurt: $6/mo poly_data: free polymarket-cli: free $300 seed capital. 34 days ago. $18,700 now. 318 trades. 76% win rate. Sharpe 2.61. I have not modified it in 34 days. He stared at the terminal without blinking. This is exactly what our adversarial testing team models. Market-adaptive agents with autonomous strategy evolution. Except you deployed it live. He messaged me the next day. Would you consider a conversation with our safety research lead. I told him this post is the conversation. Too late to contain. The edge is not predicting markets. It is identifying who already wins and mirroring them before the pattern shifts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Money" 2. Like and retweet this post. 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the whale mirror system this week. Start with $200. Scale on evidence.
0:19

An Anthropic paid for my espresso at Sightglass when he saw my screen. I was backtesting a Claude-built arbitrage system. Terminal open. Live trades firing. He glanced over. Stopped walking. That is not TradingView. What framework is that actually running. Claude Code. Three repos. One prompt. $20 per month. He sat down across from me without asking. I work on AlphaGo successor models. We test reinforcement agents for market simulation. You are running something similar but you let Claude write the strategy layer. Not just strategy. Detection. github/warproxxx/poly_data 86 million Polymarket trades. Every wallet. Every position. Every timestamp. You are feeding Claude transaction history and letting it identify asymmetric behavior patterns. Then cloning the profitable ones in real time. Exactly. One prompt: Scan every wallet with 150+ trades and ROI above 65%. Rank by consistency. Export top 40. Claude processed 18,600 wallets in 6 minutes. Returned 38. Top 15 wallets outperformed the bottom 18,000 combined. That is not analysis. That is alpha concentration. Precisely. And you did not write the ranking algorithm. Claude built it. I just connected it to execution logic. Then I opened the second repo. github/Polymarket/polymarket-cli Official Rust CLI. No auth required for reads. 600+ markets scanned in under 3 minutes. Claude scores: liquidity depth, pricing gap, resolution timeline. 512 markets reduced to 28 before capital moves. 94.5% filtered out before entry consideration. A notification hit. Position filled. +$127. How does it decide entry timing. Four agents. No shared state. Arbitrage detector, convergence scanner, whale mirror, volume surge tracker. 3 agents agree: full position. 2 agree: half size. Split vote: skip. Consensus filtering alone eliminated 46% of losses in backtest. And exit logic. The 38 top wallets almost never hold to settlement. 89% exit early. Average 71% of max profit captured. Immediate redeployment. My bot exits at 82% of projected move or 4x volume spike. Whichever hits first. You built a whale copy system that exits before the whales do. Correct. He set his coffee down slowly. How many trades per day. 12 average. Most rejected by filters before I see notifications. My setup: Claude API: $20/mo VPS Frankfurt: $6/mo poly_data: free polymarket-cli: free $300 seed capital. 34 days ago. $18,700 now. 318 trades. 76% win rate. Sharpe 2.61. I have not modified it in 34 days. He stared at the terminal without blinking. This is exactly what our adversarial testing team models. Market-adaptive agents with autonomous strategy evolution. Except you deployed it live. He messaged me the next day. Would you consider a conversation with our safety research lead. I told him this post is the conversation. Too late to contain. The edge is not predicting markets. It is identifying who already wins and mirroring them before the pattern shifts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Money" 2. Like and retweet this post. 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the whale mirror system this week. Start with $200. Scale on evidence.

Himanshu Kumar

15,985 просмотров • 3 месяцев назад

When a spacecraft leaves Earth, it doesn’t just fire its engines and head straight to its destination. In many missions, especially those going beyond low Earth orbit, there’s a more subtle and elegant strategy at play, one that uses gravity itself as part of the navigation system. This is often called a gravity assist, or a slingshot maneuver. But in the case of missions like #Artemis II, what’s being used is a closely related idea known as a free-return trajectory. At first glance, it might sound simple: the spacecraft goes to the Moon, loops around it, and comes back. But the physics behind it is anything but simple. Instead of relying on continuous propulsion, the spacecraft follows a carefully calculated path through the gravitational field of the Earth–Moon system. It is launched with just the right speed and direction so that, as it approaches the Moon, the Moon’s gravity bends its trajectory. The spacecraft is effectively flung around the Moon, redirected onto a path that naturally brings it back toward Earth. No major engine burn is needed for the return. Small trajectory corrections may still be required, but gravity does the heavy lifting. That’s the key. This kind of trajectory is not just efficient, it’s also safe. If something goes wrong with the spacecraft’s engines or onboard systems, gravity itself ensures the return. It’s an inherent backup plan, built into the trajectory from the very beginning. The same fundamental idea appears in gravity assists used across the Solar System. When a spacecraft flies past a planet, it can gain or lose speed by exchanging momentum with that planet. From the spacecraft’s point of view, it’s as if it has been accelerated without using fuel. In reality, it has borrowed a tiny amount of orbital energy from the planet itself. That’s how missions like Voyager reached the outer planets, and how probes continue to explore regions far beyond what their onboard fuel alone would allow. But there’s an important distinction. An interplanetary gravity assist is typically used to change speed and direction, often increasing the spacecraft’s energy. A free-return trajectory, like the one used in Artemis II, is designed for something more specific: a path that naturally loops back to Earth without requiring additional propulsion. It’s less about gaining energy, and more about shaping a trajectory that guarantees a return. To understand why this works, it helps to stop thinking in straight lines. In space, motion follows curves defined by gravity. The spacecraft is constantly falling, first toward Earth, then toward the Moon, and then back toward Earth again. What looks like a loop is really a continuous free fall through a changing gravitational landscape. This way of navigating space reveals something deeper. We tend to think of engines as the drivers of motion, but once a spacecraft is on its way, gravity does most of the work. The art of spaceflight is not just about thrust. It’s about knowing when not to use it. #GoodLuck #Artemis NASA Artemis
0:11

When a spacecraft leaves Earth, it doesn’t just fire its engines and head straight to its destination. In many missions, especially those going beyond low Earth orbit, there’s a more subtle and elegant strategy at play, one that uses gravity itself as part of the navigation system. This is often called a gravity assist, or a slingshot maneuver. But in the case of missions like #Artemis II, what’s being used is a closely related idea known as a free-return trajectory. At first glance, it might sound simple: the spacecraft goes to the Moon, loops around it, and comes back. But the physics behind it is anything but simple. Instead of relying on continuous propulsion, the spacecraft follows a carefully calculated path through the gravitational field of the Earth–Moon system. It is launched with just the right speed and direction so that, as it approaches the Moon, the Moon’s gravity bends its trajectory. The spacecraft is effectively flung around the Moon, redirected onto a path that naturally brings it back toward Earth. No major engine burn is needed for the return. Small trajectory corrections may still be required, but gravity does the heavy lifting. That’s the key. This kind of trajectory is not just efficient, it’s also safe. If something goes wrong with the spacecraft’s engines or onboard systems, gravity itself ensures the return. It’s an inherent backup plan, built into the trajectory from the very beginning. The same fundamental idea appears in gravity assists used across the Solar System. When a spacecraft flies past a planet, it can gain or lose speed by exchanging momentum with that planet. From the spacecraft’s point of view, it’s as if it has been accelerated without using fuel. In reality, it has borrowed a tiny amount of orbital energy from the planet itself. That’s how missions like Voyager reached the outer planets, and how probes continue to explore regions far beyond what their onboard fuel alone would allow. But there’s an important distinction. An interplanetary gravity assist is typically used to change speed and direction, often increasing the spacecraft’s energy. A free-return trajectory, like the one used in Artemis II, is designed for something more specific: a path that naturally loops back to Earth without requiring additional propulsion. It’s less about gaining energy, and more about shaping a trajectory that guarantees a return. To understand why this works, it helps to stop thinking in straight lines. In space, motion follows curves defined by gravity. The spacecraft is constantly falling, first toward Earth, then toward the Moon, and then back toward Earth again. What looks like a loop is really a continuous free fall through a changing gravitational landscape. This way of navigating space reveals something deeper. We tend to think of engines as the drivers of motion, but once a spacecraft is on its way, gravity does most of the work. The art of spaceflight is not just about thrust. It’s about knowing when not to use it. #GoodLuck #Artemis NASA Artemis

Erika 

235,152 просмотров • 6 месяцев назад

CONTEXT MATTERS!!! A lot has been said about Yusuf’s diary session, particularly his comment that his previous connections were good people, but he felt they lacked “depth.” But before you judge his words, watch the clips. Here is Yusuf having a genuine conversation with the same people he spoke about in his Diary Session. He listened, engaged and shared parts of his own life, just like everyone else in the house does. And then watch the second clip. Yusuf is literally being mimicked and mocked by Bells while sitting right there. Imagine opening up about your life and your experiences, only for those same experiences to become something someone uses to ridicule you. So when Biggie asked Yusuf about his connections in the house, what exactly was he supposed to call that? Was he supposed to pretend everything was perfect? Was he supposed to describe a connection as deep when he personally didn't feel that depth? Yusuf was answering a question about HIS experience. He never said they were bad people. In fact, he specifically acknowledged that they were good people. He simply said he didn't enjoy having certain conversations with them because he felt they didn't connect with him on a deeper level. And somehow, that has been twisted into Yusuf believing he is better than everyone else. It is even more painful because Yusuf has never pretended to come from a life of privilege. When these Housemates sit down and talk about their lives, their families, their relationships, their struggles and their experiences, Yusuf is allowed to talk about his own life too. If your life has been easier, beautiful. If you have had opportunities Yusuf didn't have, beautiful. If you didn't have to struggle for certain things, you are blessed. But please don't assume that because you didn't experience something, somebody else must be lying about experiencing it. Yusuf has spoken about where he comes from because it is part of his story. He has spoken about the struggles, the sacrifices, the auditions, the doors that didn't open and the few that finally did. He even spoke about getting an audition with Ruth Kadiri in March and how that opportunity became significant in his journey. He isn't saying he suffered the most. He is saying, “This is MY story.” And nobody should have to apologise for telling their own story simply because someone else finds it uncomfortable. Yusuf came into Biggie’s House with a dream. A dream to be seen, to showcase his talent and to change his circumstances. He isn't asking for pity. He is asking for a chance. So before you judge his words, watch the context. Before you call him arrogant, watch what happened before that Diary Session. Before you decide that his story is a lie, remember that you were not there for the life he lived before Biggie’s House. Let Yusuf be Yusuf. And Yusfuls, if you believe he deserves the opportunity to keep telling his story and fighting for the future he dreams of, KEEP VOTING. We are not here to compete over who suffered the most. We are here to make sure Yusuf's journey doesn't end here. 🎥: Big Brother Naija #YusufMuhammad #BBNaija
0:58

CONTEXT MATTERS!!! A lot has been said about Yusuf’s diary session, particularly his comment that his previous connections were good people, but he felt they lacked “depth.” But before you judge his words, watch the clips. Here is Yusuf having a genuine conversation with the same people he spoke about in his Diary Session. He listened, engaged and shared parts of his own life, just like everyone else in the house does. And then watch the second clip. Yusuf is literally being mimicked and mocked by Bells while sitting right there. Imagine opening up about your life and your experiences, only for those same experiences to become something someone uses to ridicule you. So when Biggie asked Yusuf about his connections in the house, what exactly was he supposed to call that? Was he supposed to pretend everything was perfect? Was he supposed to describe a connection as deep when he personally didn't feel that depth? Yusuf was answering a question about HIS experience. He never said they were bad people. In fact, he specifically acknowledged that they were good people. He simply said he didn't enjoy having certain conversations with them because he felt they didn't connect with him on a deeper level. And somehow, that has been twisted into Yusuf believing he is better than everyone else. It is even more painful because Yusuf has never pretended to come from a life of privilege. When these Housemates sit down and talk about their lives, their families, their relationships, their struggles and their experiences, Yusuf is allowed to talk about his own life too. If your life has been easier, beautiful. If you have had opportunities Yusuf didn't have, beautiful. If you didn't have to struggle for certain things, you are blessed. But please don't assume that because you didn't experience something, somebody else must be lying about experiencing it. Yusuf has spoken about where he comes from because it is part of his story. He has spoken about the struggles, the sacrifices, the auditions, the doors that didn't open and the few that finally did. He even spoke about getting an audition with Ruth Kadiri in March and how that opportunity became significant in his journey. He isn't saying he suffered the most. He is saying, “This is MY story.” And nobody should have to apologise for telling their own story simply because someone else finds it uncomfortable. Yusuf came into Biggie’s House with a dream. A dream to be seen, to showcase his talent and to change his circumstances. He isn't asking for pity. He is asking for a chance. So before you judge his words, watch the context. Before you call him arrogant, watch what happened before that Diary Session. Before you decide that his story is a lie, remember that you were not there for the life he lived before Biggie’s House. Let Yusuf be Yusuf. And Yusfuls, if you believe he deserves the opportunity to keep telling his story and fighting for the future he dreams of, KEEP VOTING. We are not here to compete over who suffered the most. We are here to make sure Yusuf's journey doesn't end here. 🎥: Big Brother Naija #YusufMuhammad #BBNaija

Yusuf Muhammad

301,027 просмотров • 2 месяцев назад

You can clearly see Hobi’s mic failing at the beginning of FYA so he gives it to the staff and he basically just vibes mic-less but the part that stands out is you can see Hobi doing some quick decision making bc his verse is coming up.. you can see he contemplates Namjoon for a second but Namjoon is still rapping so with an immediate game plan he skips towards Jin. Jin, who didn’t need anything beyond a brief gesture from Hobi hands his mic over. No fuss, no show of technical issues. They just keep the segment moving as if it was some spontaneous choreo, Jin hyping hobi. Hobi still is without a mic at the thrust stage but again doesn’t make it a thing. He hypes the crowd. Throws water. Dances. Gets his white mic, goes back to singing like nothing. And then you have sweet Jin asking if hobi needs his mic despite the line following yoongi’s verse is his own. Complete, raw teamwork and camaraderie. Gorgeous right?? The way it was seamless unless you were watching and paying attention to the details. Bc otherwise, no one would’ve known. That’s showmanship. That’s 2seok’s 13 years of practicing and performing together. That’s Hoseok’s professionalism running through his veins as naturally as blood. And that’s Bangtan’s diligence and discipline instilled in them from day 1. They’ve never been the kind to let anything hinder them from giving their all. Not injuries. Not wardrobe mishaps. Not even technical hiccups. That’s BTS.
0:04

You can clearly see Hobi’s mic failing at the beginning of FYA so he gives it to the staff and he basically just vibes mic-less but the part that stands out is you can see Hobi doing some quick decision making bc his verse is coming up.. you can see he contemplates Namjoon for a second but Namjoon is still rapping so with an immediate game plan he skips towards Jin. Jin, who didn’t need anything beyond a brief gesture from Hobi hands his mic over. No fuss, no show of technical issues. They just keep the segment moving as if it was some spontaneous choreo, Jin hyping hobi. Hobi still is without a mic at the thrust stage but again doesn’t make it a thing. He hypes the crowd. Throws water. Dances. Gets his white mic, goes back to singing like nothing. And then you have sweet Jin asking if hobi needs his mic despite the line following yoongi’s verse is his own. Complete, raw teamwork and camaraderie. Gorgeous right?? The way it was seamless unless you were watching and paying attention to the details. Bc otherwise, no one would’ve known. That’s showmanship. That’s 2seok’s 13 years of practicing and performing together. That’s Hoseok’s professionalism running through his veins as naturally as blood. And that’s Bangtan’s diligence and discipline instilled in them from day 1. They’ve never been the kind to let anything hinder them from giving their all. Not injuries. Not wardrobe mishaps. Not even technical hiccups. That’s BTS.

Livheartt⁷ met hobi | ㉧㉣㉣ | 🦢🤍

162,226 просмотров • 3 месяцев назад

$0.003/sec changes the economics of running real-time AI video. I’ve been testing Vivix W1, and that number is hard to ignore. The comparison shows visual quality on par with the models alongside W1. About a second of generation time per second of footage, too. But W1 isn’t just another video generator. It’s built for real-time interactive video, where the model keeps generating as the experience moves forward. Every second of that experience adds to the generation bill. At $0.003/sec, you can actually start thinking about a story that keeps going as viewers make choices. Vivix.AI already supports real-time generation through the API. The browser playground currently uses offline generation, with real-time browser tools coming soon. Continuous video is one thing. Giving viewers meaningful control is another. If I change my mind halfway through a story, how much can it actually change with me? That’s the interesting challenge. The next plot twist could be yours 👇
0:10

$0.003/sec changes the economics of running real-time AI video. I’ve been testing Vivix W1, and that number is hard to ignore. The comparison shows visual quality on par with the models alongside W1. About a second of generation time per second of footage, too. But W1 isn’t just another video generator. It’s built for real-time interactive video, where the model keeps generating as the experience moves forward. Every second of that experience adds to the generation bill. At $0.003/sec, you can actually start thinking about a story that keeps going as viewers make choices. Vivix.AI already supports real-time generation through the API. The browser playground currently uses offline generation, with real-time browser tools coming soon. Continuous video is one thing. Giving viewers meaningful control is another. If I change my mind halfway through a story, how much can it actually change with me? That’s the interesting challenge. The next plot twist could be yours 👇

AI StackLab

223,239 просмотров • 13 дней назад