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Generative 3D has been stuck in a representational compromise. When you serialize spatial data to fit standard architectures, you force a unidirectional causal bias. It fundamentally breaks the symmetry of 3D geometry and limits how the model understands global context. With Tripo P1.0, we stepped back to fix the...

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Yanpei Cao

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65,085 görüntüleme • 3 ay önce •via X (Twitter)

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This is what happens when you combine 3D with AI · Complete spatial, elements and character consistency · Zero morphing or artifacts · Full camera control (bullet time included) It's filmmaking of the future, and I'll show you how you can achieve this in minutes with Tripo 👇
0:38

This is what happens when you combine 3D with AI · Complete spatial, elements and character consistency · Zero morphing or artifacts · Full camera control (bullet time included) It's filmmaking of the future, and I'll show you how you can achieve this in minutes with Tripo 👇

TechHalla

48,256 görüntüleme • 5 ay önce

Volumetric, animated 3D noise shader block in Unity. Where each frame is 2D space, and the 3rd dimension is time. So that, measurement of change within a slice is 'space', and measurement of change across slices is 'time'. As a 3D block, you see the noise animate into the 3rd dimension, but when looking at only a single slice, it appears to evolve in place. Likewise, for 4D... If: this were 3D noise, then: the 4th dimension would be whatever processing/frames/states of 3D data.
0:31

Volumetric, animated 3D noise shader block in Unity. Where each frame is 2D space, and the 3rd dimension is time. So that, measurement of change within a slice is 'space', and measurement of change across slices is 'time'. As a 3D block, you see the noise animate into the 3rd dimension, but when looking at only a single slice, it appears to evolve in place. Likewise, for 4D... If: this were 3D noise, then: the 4th dimension would be whatever processing/frames/states of 3D data.

Mirza Beig

126,907 görüntüleme • 5 ay önce

✨ 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!
0:51

✨ 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 görüntüleme • 11 ay önce

This was way too easy... I created a cute 3D character in minutes instead of hours using this combo: Unbound + Tripo I’m not a 3D artist, but this made the whole process feel simple, playful, and actually accessible. Unbound helped me shape the character idea, then Tripo turned it into a 3D model without the usual complicated workflow If you are a 3D artist or you are just starting, try Unbound. It’s free, open for everyone to experiment with, and a fun way to create characters, creatures, and 3D models without feeling overwhelmed I had so much fun using it today, and I think many visual creators are going to love it. Link in the first comment 👇🏻
0:39

This was way too easy... I created a cute 3D character in minutes instead of hours using this combo: Unbound + Tripo I’m not a 3D artist, but this made the whole process feel simple, playful, and actually accessible. Unbound helped me shape the character idea, then Tripo turned it into a 3D model without the usual complicated workflow If you are a 3D artist or you are just starting, try Unbound. It’s free, open for everyone to experiment with, and a fun way to create characters, creatures, and 3D models without feeling overwhelmed I had so much fun using it today, and I think many visual creators are going to love it. Link in the first comment 👇🏻

Amira Zairi

186,621 görüntüleme • 1 ay önce

I believe that the interfaces of the future will disappear. Instead of buttons and menus, we will interact with objects. The process itself becomes the feedback. Building software is always a complex craft, but spatial computing is a different beast entirely. It demands a fusion of visual art, logic, and spatial UI where every interaction happens in the air around you. With OPERATOR, built for Apple Vision Pro, we explored this by turning music into a sculpture. The music track isn't a timeline anymore; it’s a 3D object you can walk around, touch, and modify from any angle.
0:21

I believe that the interfaces of the future will disappear. Instead of buttons and menus, we will interact with objects. The process itself becomes the feedback. Building software is always a complex craft, but spatial computing is a different beast entirely. It demands a fusion of visual art, logic, and spatial UI where every interaction happens in the air around you. With OPERATOR, built for Apple Vision Pro, we explored this by turning music into a sculpture. The music track isn't a timeline anymore; it’s a 3D object you can walk around, touch, and modify from any angle.

maxim zhestkov

102,574 görüntüleme • 4 ay önce

oh may.. 3D Gaussian Splat is so crazy now last time we need weeks to turn a room into a 3D model.. now with AI 3DGS, you do it in seconds… even zoom into the text and looks perfect give it like 6 months… you can turn a whole city into a full detailed 3D model
0:22

oh may.. 3D Gaussian Splat is so crazy now last time we need weeks to turn a room into a 3D model.. now with AI 3DGS, you do it in seconds… even zoom into the text and looks perfect give it like 6 months… you can turn a whole city into a full detailed 3D model

el.cine

142,328 görüntüleme • 6 ay önce

WE DID IT! +100,000 OVRMAPS 🚀 A major milestone in decentralized spatial computing. A huge thank you to our incredible community of mappers and our technical team! ↳ 30M sqm of 3D-mapped terrain ↳ 55TB of image data processed with cutting-edge AI models ↳ Powered by DePIN and a global network of mappers This is the future of Spatial Computing. Let’s break it down: 🧵👇
0:28

WE DID IT! +100,000 OVRMAPS 🚀 A major milestone in decentralized spatial computing. A huge thank you to our incredible community of mappers and our technical team! ↳ 30M sqm of 3D-mapped terrain ↳ 55TB of image data processed with cutting-edge AI models ↳ Powered by DePIN and a global network of mappers This is the future of Spatial Computing. Let’s break it down: 🧵👇

Over the Reality 🌐

493,928 görüntüleme • 1 yıl önce

🚨Breaking: 3D artists no longer need to model from scratch. Sketch → production asset in seconds with clean topology. Tripo just automated the hard part:
0:22

🚨Breaking: 3D artists no longer need to model from scratch. Sketch → production asset in seconds with clean topology. Tripo just automated the hard part:

AI Frontliner

118,264 görüntüleme • 4 ay önce

This is a pretty wild model! You can use it to turn an image into a 3D object with texture. The quality is out of this world! I'm not even a designer, and I've been using this nonstop for the last 2 hours. The model is Hunyuan 3D 2.1. It's open source. You'll find model weights, training/inference code, data pipelines, and architecture on their repository. You can even fine-tune it if you want! GitHub Repository: By the way, the model runs on consumer-grade GPUs. You don't need a datacenter for this! I've been using the model from the HuggingFace demo page: To use it, go to the link and upload an image. That's it! Check out the video I recorded for a couple of examples.
2:42

This is a pretty wild model! You can use it to turn an image into a 3D object with texture. The quality is out of this world! I'm not even a designer, and I've been using this nonstop for the last 2 hours. The model is Hunyuan 3D 2.1. It's open source. You'll find model weights, training/inference code, data pipelines, and architecture on their repository. You can even fine-tune it if you want! GitHub Repository: By the way, the model runs on consumer-grade GPUs. You don't need a datacenter for this! I've been using the model from the HuggingFace demo page: To use it, go to the link and upload an image. That's it! Check out the video I recorded for a couple of examples.

Santiago

44,783 görüntüleme • 11 ay önce

Most generative models predict pixels. Predicting a 3D scene instead has many benefits: the scene won’t change if you look away and come back, and it obeys the basic physical rules of 3D geometry. The simplest way to visualize the 3D scene is a depth map, where each pixel is colored by its distance to the camera. 4/n
0:20

Most generative models predict pixels. Predicting a 3D scene instead has many benefits: the scene won’t change if you look away and come back, and it obeys the basic physical rules of 3D geometry. The simplest way to visualize the 3D scene is a depth map, where each pixel is colored by its distance to the camera. 4/n

World Labs

16,605 görüntüleme • 1 yıl önce

We are releasing Carbon: a crazy fast DNA model Carbon is 275x faster than the next best model. So fast you can process the whole human genome on a single GPU in <2 days. Here are the tricks we used: When modelling DNA sequences a lot of the performance comes down to tokenizing the sequences in a smart way. BPE tokenizer struggle because there are no whitespaces and character (called base in DNA) level tokenizers waste a lot of compute on too many tokens. Carbon is built with a unique tokenizer: we split sequences in chunks of 6 bases, but during both training and inference we can work with single base resolution. That's similar to having word tokens but resolving them at the character level. All possible thanks to the DNA tokens unique structure. The architecture combined with the tokenizer makes the model 275x faster than the previous SoTA (Evo2) at this size. We built an interactive demo so you can explore how the model can generate DNA sequences, investigate the structure of genes, predict the effect of mutations, generate and fold proteins and even reconstruct parts of the tree of life.
1:07

We are releasing Carbon: a crazy fast DNA model Carbon is 275x faster than the next best model. So fast you can process the whole human genome on a single GPU in <2 days. Here are the tricks we used: When modelling DNA sequences a lot of the performance comes down to tokenizing the sequences in a smart way. BPE tokenizer struggle because there are no whitespaces and character (called base in DNA) level tokenizers waste a lot of compute on too many tokens. Carbon is built with a unique tokenizer: we split sequences in chunks of 6 bases, but during both training and inference we can work with single base resolution. That's similar to having word tokens but resolving them at the character level. All possible thanks to the DNA tokens unique structure. The architecture combined with the tokenizer makes the model 275x faster than the previous SoTA (Evo2) at this size. We built an interactive demo so you can explore how the model can generate DNA sequences, investigate the structure of genes, predict the effect of mutations, generate and fold proteins and even reconstruct parts of the tree of life.

Leandro von Werra

401,529 görüntüleme • 23 gün önce

I made a cute 3D wizard in minutes... for free The combo was simple: Unbound + Tripo. Unbound helped me shape the character idea, then Tripo turned it into a 3D model without the usual heavy workflow. The whole process felt fast, playful, and easy to experiment with If you’re a 3D artist, a visual creator, or just starting to explore 3D, try Unbound while it’s still free for a limited time. You can use it to create characters, creatures, and 3D models without feeling overwhelmed I'm sure many visual creators are going to love this Link in the first comment 👇🏻
0:38

I made a cute 3D wizard in minutes... for free The combo was simple: Unbound + Tripo. Unbound helped me shape the character idea, then Tripo turned it into a 3D model without the usual heavy workflow. The whole process felt fast, playful, and easy to experiment with If you’re a 3D artist, a visual creator, or just starting to explore 3D, try Unbound while it’s still free for a limited time. You can use it to create characters, creatures, and 3D models without feeling overwhelmed I'm sure many visual creators are going to love this Link in the first comment 👇🏻

Amira Zairi

18,116 görüntüleme • 1 ay önce

3D editing is hard: you need to ground an image + instruction and generate a faithful 3D shape in one forward pass -- no test-time optimization. So, we steer pretrained image-to-3D representations to do text-guided 3D edits; no massive 3D edit-pair dataset needed. Key trap: the “no-edit” solution is a nasty local minimum. We fix it with preference optimization, pushing the model to actually edit. Steer3D is the second work that adapts alignment ideas from LLMs to the 3D modality. SAM 3D also used DPO to improve its 3D generations.
0:22

3D editing is hard: you need to ground an image + instruction and generate a faithful 3D shape in one forward pass -- no test-time optimization. So, we steer pretrained image-to-3D representations to do text-guided 3D edits; no massive 3D edit-pair dataset needed. Key trap: the “no-edit” solution is a nasty local minimum. We fix it with preference optimization, pushing the model to actually edit. Steer3D is the second work that adapts alignment ideas from LLMs to the 3D modality. SAM 3D also used DPO to improve its 3D generations.

Georgia Gkioxari

115,844 görüntüleme • 5 ay önce

“If you’re doing a bench press do you want to start at the bottom and push up or start at the top and as soon as you get to the bottom push up? Same thing with the back hip…We don’t want to get stuck in a position, we want to flow and move through positions and get to it at the end when it’s actually time to deliver the force “🔥🤯 #the108way
1:30

“If you’re doing a bench press do you want to start at the bottom and push up or start at the top and as soon as you get to the bottom push up? Same thing with the back hip…We don’t want to get stuck in a position, we want to flow and move through positions and get to it at the end when it’s actually time to deliver the force “🔥🤯 #the108way

108 Performance

20,743 görüntüleme • 2 yıl önce

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.
2:32

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

AK

161,400 görüntüleme • 2 yıl önce

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:
0:50

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

Sherwin Bahmani

66,404 görüntüleme • 8 ay önce

Large-scale Gaussian splats have reached a new level of realism. This is a well-known temple in Bangkok, reconstructed as a high-fidelity 3D environment from 360 captures. At this level, the boundary between video and 3D starts to disappear. But what you’re looking at is not a video. It’s a dense spatial representation of a real place, where geometry, texture, and structure are preserved and made machine-readable. This kind of 3D data can power Visual AI, Robotics navigation, VPS localization, XR experiences, world models, and next-generation spatial computing systems. Built with Over the Reality.
2:46

Large-scale Gaussian splats have reached a new level of realism. This is a well-known temple in Bangkok, reconstructed as a high-fidelity 3D environment from 360 captures. At this level, the boundary between video and 3D starts to disappear. But what you’re looking at is not a video. It’s a dense spatial representation of a real place, where geometry, texture, and structure are preserved and made machine-readable. This kind of 3D data can power Visual AI, Robotics navigation, VPS localization, XR experiences, world models, and next-generation spatial computing systems. Built with Over the Reality.

Over the Reality 🌐

346,640 görüntüleme • 22 gün önce

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?
1:01

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?

Herman Trading

20,079 görüntüleme • 14 gün önce

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:
1:07

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

175,302 görüntüleme • 5 ay önce

I’ve traded millions in $SPX 0DTE and nothing has been more reliable to me intraday than this one strategy. A win rate over 80%. Clean structure. Clear entries. No guessing. It’s called the 9ON10 Flip™ 📼 Video: Attached 📝 Full breakdown: Below The 9ON10 is the 9 SMA on the 10-minute chart. I use it to read short-term control. When price is below it and the line is sloping lower, sellers are usually in control. When price is above it and the line is sloping higher, buyers are usually in control. The flip happens when price breaks back through it, then comes back to test it and hold from the other side. That hold is what makes the setup stronger. It gives you structure instead of forcing you to chase the first move. → The 9ON10 is the 9 SMA on the 10-minute chart → Above it, buyers may be in control → Below it, sellers may be in control → Pushes into it can fail in downtrends → Pullbacks into it can hold in uptrends → The flip starts when price breaks back through it → The hold is what makes the setup stronger → The back test often gives the cleaner entry → $SPX gave a clean example of the shift → $NVDA showed similar relative strength → $TSLA showed the same idea, less clean → The break gets attention, the hold gives the trade If you see value in this I’d appreciate a ❤️ sm
16:53

I’ve traded millions in $SPX 0DTE and nothing has been more reliable to me intraday than this one strategy. A win rate over 80%. Clean structure. Clear entries. No guessing. It’s called the 9ON10 Flip™ 📼 Video: Attached 📝 Full breakdown: Below The 9ON10 is the 9 SMA on the 10-minute chart. I use it to read short-term control. When price is below it and the line is sloping lower, sellers are usually in control. When price is above it and the line is sloping higher, buyers are usually in control. The flip happens when price breaks back through it, then comes back to test it and hold from the other side. That hold is what makes the setup stronger. It gives you structure instead of forcing you to chase the first move. → The 9ON10 is the 9 SMA on the 10-minute chart → Above it, buyers may be in control → Below it, sellers may be in control → Pushes into it can fail in downtrends → Pullbacks into it can hold in uptrends → The flip starts when price breaks back through it → The hold is what makes the setup stronger → The back test often gives the cleaner entry → $SPX gave a clean example of the shift → $NVDA showed similar relative strength → $TSLA showed the same idea, less clean → The break gets attention, the hold gives the trade If you see value in this I’d appreciate a ❤️ sm

spacemonkey

80,377 görüntüleme • 29 gün önce