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Working on adding a new dataset to the lineup. Ported ego-dex over to Rerun With rerun now stabilizing RRD format between versions (0.23 -> 0.24), this is the perfect time to start encoding all of the datasets I've been using to RRD 1. I'm starting with ego-dex and then... show more

Pablo Vela

2,977 subscribers

34,315 Aufrufe • vor 1 Jahr •via X (Twitter)

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5 Kommentare

Profilbild von Peide Huang

Peide Huangvor 1 Jahr

@rerundotio Very cool visualization!

Profilbild von ζ Pedram ζ

ζ Pedram ζvor 1 Jahr

@rerundotio Looks cool, can the spine support the arms center of gravity… that looks more like a centerline, with minimal kinematics

Profilbild von Pablo Vela

Pablo Velavor 1 Jahr

@rerundotio Yea, I’m not totally sure how the Apple Vision Pro is estimating those joints 🤷‍♂️

Profilbild von James | 🤖

James | 🤖vor 1 Jahr

@rerundotio How did you align the camera with the body model, and get the camera intrinsics?

Profilbild von Pablo Vela

Pablo Velavor 1 Jahr

@rerundotio This is directly extracted from the Apple Vision Pro. You can take a look at the paper to see what they did

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More progress! I now have two Dockerized Gradio | Rerun apps. The first one takes as input a "raw" rrd file that consists of the synchronized egocentric and exocentric MP4 files. This runs the pipeline and produces an "annotated" rrd file. This has the camera parameters, 3D joints, and projected 2D joints (with 6DOF mano soon). The second app takes this "annotated" rrd file and allows for manual labeling. This is a crucial step in addressing any major failures in the pipeline. Right now, it is only the ego view that can be modified. But I'll eventually extend to all. This results in a final "gt" rrd file. From here, the plan is to improve quality and start building a data loop. Excited to start really scaling this. I'm basically going all in on keeping my data stored as Rerun rrd files. As always, I want to emphasize how crucial it is to LOOK AT YOUR data! The rrd format makes it incredibly easy to do so. Getting the data out to use is a bit of a hassle right now, but for me, it's well worth the tradeoff.

More progress! I now have two Dockerized Gradio | Rerun apps. The first one takes as input a "raw" rrd file that consists of the synchronized egocentric and exocentric MP4 files. This runs the pipeline and produces an "annotated" rrd file. This has the camera parameters, 3D joints, and projected 2D joints (with 6DOF mano soon). The second app takes this "annotated" rrd file and allows for manual labeling. This is a crucial step in addressing any major failures in the pipeline. Right now, it is only the ego view that can be modified. But I'll eventually extend to all. This results in a final "gt" rrd file. From here, the plan is to improve quality and start building a data loop. Excited to start really scaling this. I'm basically going all in on keeping my data stored as Rerun rrd files. As always, I want to emphasize how crucial it is to LOOK AT YOUR data! The rrd format makes it incredibly easy to do so. Getting the data out to use is a bit of a hassle right now, but for me, it's well worth the tradeoff.

Pablo Vela

19,527 Aufrufe • vor 10 Monaten

We have HOT3D! I've started using Claude to port more datasets into Rerun and exoego-forge. I'd been meaning to bring in the HOT3D dataset from Meta for a while, but with Claude, it's way easier. My goal is to take any egocentric, exocentric, or both datasets and ingest them into a standardized schema. Getting everything into Rerun means we can easily query and transform data via the in-memory OSS server. This lets us generate SQL-like queries such as: "Find me all frames that only contain left hands in the leftmost camera view." Most people think of Rerun as a viewer, but this is the actual superpower. So far we have: 1. HOT3D 2. Hocap 3. UmeTrack 4. Assembly101 5. EgoDex Planning to add more, and with every addition, it gets easier as we build up agent skills and better code examples. Hoping to make it almost fully automatic for adding new datasets. The next few I'm looking at are Harmony4D and Aria Pilot Gen2 After we have enough samples, I'll work on bringing in all the different algorithms I've worked on to transform the data 🙂

We have HOT3D! I've started using Claude to port more datasets into Rerun and exoego-forge. I'd been meaning to bring in the HOT3D dataset from Meta for a while, but with Claude, it's way easier. My goal is to take any egocentric, exocentric, or both datasets and ingest them into a standardized schema. Getting everything into Rerun means we can easily query and transform data via the in-memory OSS server. This lets us generate SQL-like queries such as: "Find me all frames that only contain left hands in the leftmost camera view." Most people think of Rerun as a viewer, but this is the actual superpower. So far we have: 1. HOT3D 2. Hocap 3. UmeTrack 4. Assembly101 5. EgoDex Planning to add more, and with every addition, it gets easier as we build up agent skills and better code examples. Hoping to make it almost fully automatic for adding new datasets. The next few I'm looking at are Harmony4D and Aria Pilot Gen2 After we have enough samples, I'll work on bringing in all the different algorithms I've worked on to transform the data 🙂

Pablo Vela

35,662 Aufrufe • vor 5 Monaten

Most people think Rerun is a visualization tool. In reality, it's a database masquerading as a visualizer. I wanted to showcase this functionality by building a full data pipeline consisting of: ingestion → baseline method → eval → finetuning for SLAM on egocentric data. I'll eventually extend this to the rest of my ego/exo datasets, but I wanted to start with a smaller bunch of datasets first. Rerun allows you to expose your saved .rrd files to a catalog where you store datasets. You can query, filter, and join them like any database using DataFusion under the hood. These are the same .rrd files that are automatically generated whenever you visualize anything in Rerun and decide to save it to disk. I brought in 109 VSLAM-LAB sequences across 14 datasets into the Rerun catalog as an example. These include 7Scenes, Euroc, eth3d, and others. Now I can query them with segment_table, filter_segments, and filter_contents instead of parsing CSVs and YAML files. With a strong set of ground-truth datasets for SLAM, baseline additions become nearly automatic with agents like Opus/Codex. This unification of data and visualization is imo the largest missing part for Physical AI. Visualization becomes a natural byproduct of having your data properly structured and queryable. The catalog API is what makes it a database, not just a viewer. I initially focused on VSLAM-LAB data, but I'll migrate all the egoexo data to this format in the coming days to really show just how useful this is.

Most people think Rerun is a visualization tool. In reality, it's a database masquerading as a visualizer. I wanted to showcase this functionality by building a full data pipeline consisting of: ingestion → baseline method → eval → finetuning for SLAM on egocentric data. I'll eventually extend this to the rest of my ego/exo datasets, but I wanted to start with a smaller bunch of datasets first. Rerun allows you to expose your saved .rrd files to a catalog where you store datasets. You can query, filter, and join them like any database using DataFusion under the hood. These are the same .rrd files that are automatically generated whenever you visualize anything in Rerun and decide to save it to disk. I brought in 109 VSLAM-LAB sequences across 14 datasets into the Rerun catalog as an example. These include 7Scenes, Euroc, eth3d, and others. Now I can query them with segment_table, filter_segments, and filter_contents instead of parsing CSVs and YAML files. With a strong set of ground-truth datasets for SLAM, baseline additions become nearly automatic with agents like Opus/Codex. This unification of data and visualization is imo the largest missing part for Physical AI. Visualization becomes a natural byproduct of having your data properly structured and queryable. The catalog API is what makes it a database, not just a viewer. I initially focused on VSLAM-LAB data, but I'll migrate all the egoexo data to this format in the coming days to really show just how useful this is.

Pablo Vela

34,937 Aufrufe • vor 4 Monaten

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Pablo Vela

30,070 Aufrufe • vor 5 Monaten

I've been on a SLAM/SFM kick. It's one of the more underexplored and lacking areas when it comes to human teleop/data collections, so I've brought over Deep Patch Visual Odometry/SLAM to Rerun and Gradio. With this example, we now have 1. pycuvslam 2. pycolmap/glomap 3. mast3r-slam 4. dpvo/slam all integrated into rerun. The question becomes, which method should be used in what situations? They all make different trade-offs with different camera requirements and throughput/accuracy. What about when a new method comes out? Now that I have several different methods, I plan to use VSLAM-LAB for evaluation. It uses prefix.dev to isolate all the dependencies of each of these methods and easily compare them against each other. In particular, I'll be converting the data preprocessing, algorithm outputs, and evaluation into rerun recordings (rrd files). This will allow both programmatic querying of anything stored in the files (which method had the highest ATE-to-FPS ratio? Which dataset/sequence caused the most difficulty? etc. etc.), all with easy visual inspection using the rerun server to link them all together. Another really important side effect of this is how it impacts agents. As Karpathy said ``` LLMs are exceptionally good at looping until they meet specific goals, and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria, and watch it go. ``` by having accuracy and throughput metrics deeply tied with human inspectable artifacts. One can really accelerate agentic development with an actual understanding of how the method/data performs. I think this is another killer use case that I'll be really leaning into to make ingestion of new datasets/methods trivial with an agent. I'm making it my mission for folks to understand that rerun as a visualization tool only scratches the surface of what its true benefit is. Deep integration between data and visuals, with powerful query capabilities. I'll be focusing on the SLAM use case first and then bringing this into the full egocentric/exocentric data collection domain!

I've been on a SLAM/SFM kick. It's one of the more underexplored and lacking areas when it comes to human teleop/data collections, so I've brought over Deep Patch Visual Odometry/SLAM to Rerun and Gradio. With this example, we now have 1. pycuvslam 2. pycolmap/glomap 3. mast3r-slam 4. dpvo/slam all integrated into rerun. The question becomes, which method should be used in what situations? They all make different trade-offs with different camera requirements and throughput/accuracy. What about when a new method comes out? Now that I have several different methods, I plan to use VSLAM-LAB for evaluation. It uses prefix.dev to isolate all the dependencies of each of these methods and easily compare them against each other. In particular, I'll be converting the data preprocessing, algorithm outputs, and evaluation into rerun recordings (rrd files). This will allow both programmatic querying of anything stored in the files (which method had the highest ATE-to-FPS ratio? Which dataset/sequence caused the most difficulty? etc. etc.), all with easy visual inspection using the rerun server to link them all together. Another really important side effect of this is how it impacts agents. As Karpathy said ``` LLMs are exceptionally good at looping until they meet specific goals, and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria, and watch it go. ``` by having accuracy and throughput metrics deeply tied with human inspectable artifacts. One can really accelerate agentic development with an actual understanding of how the method/data performs. I think this is another killer use case that I'll be really leaning into to make ingestion of new datasets/methods trivial with an agent. I'm making it my mission for folks to understand that rerun as a visualization tool only scratches the surface of what its true benefit is. Deep integration between data and visuals, with powerful query capabilities. I'll be focusing on the SLAM use case first and then bringing this into the full egocentric/exocentric data collection domain!

Pablo Vela

41,151 Aufrufe • vor 4 Monaten

here's a quick showcase of the ui editor i've been working on! already starting to add more to it, and i'm super happy with the direction it's going in! let me know what else you'd like to see :)

here's a quick showcase of the ui editor i've been working on! already starting to add more to it, and i'm super happy with the direction it's going in! let me know what else you'd like to see :)

Gavin

75,911 Aufrufe • vor 2 Monaten

There's been a few cool updates recently. In particular, Rerun 0.33 released headless rendering. This, along with the Fable 5 release pushed me to work torwards making MAMMA realtime! I threw Fable at the problem, and it was able to take original implementation that was ~12 seconds / frame and get it all the way down to 40ms /frame, or nearly a 300x speedup 🏎️ How did I achieve this? TLDR: - Use rerun's headless rendering as supervision when optimizing - Save rrd file as test fixture to guide model optiziation with /goal - create an html artifact with headless rendering to provide detailed breakdown of what it did and how it actually looks like in the viewer There were a few critical bits to make sure that this ACTUALLY worked and that Fable didn't just cheat or delete something and declare victory. The first is that the original version used Rerun, this allowed us to save things to disk as an RRD file, meaning we could query the contents and use this as a sort of test fixture or golden artifact that held EXACTLY what all of the values should be. Then we can use this with /goal as a metric when doing the optimization to ensure there are no regressions. The second bit is the headless rendering, this gave us the ability to check that not only did the test fixture pass, but it also looked visually correct. This made a huge difference, and an awesome side affect of it is that we can use the headless rendering to create an implementations.html file. This gives a visual guide as to what the agent did (I walk through it in the video below) Along with this, we're working on an MCP server for rerun that allows full interactivity with the rerun viewer for your agent. So for example the agent can click, drag, move views, scroll timelines, ect. I used this to help the agent debug certain parts such as when the 2d sam masks didn't line up, or if the triangulated keypoints werent correctly matching with the optimized mesh. The agents could go, click into the view, scroll through the timeline and see where things went wrong. Fable + Headless Rendering + Rerun MCP == 300x speedup in less then a days work With these new tools, I'm planning on going back to my gaussian splatting implemntation and cleaning it up + making it fast!

There's been a few cool updates recently. In particular, Rerun 0.33 released headless rendering. This, along with the Fable 5 release pushed me to work torwards making MAMMA realtime! I threw Fable at the problem, and it was able to take original implementation that was ~12 seconds / frame and get it all the way down to 40ms /frame, or nearly a 300x speedup 🏎️ How did I achieve this? TLDR: - Use rerun's headless rendering as supervision when optimizing - Save rrd file as test fixture to guide model optiziation with /goal - create an html artifact with headless rendering to provide detailed breakdown of what it did and how it actually looks like in the viewer There were a few critical bits to make sure that this ACTUALLY worked and that Fable didn't just cheat or delete something and declare victory. The first is that the original version used Rerun, this allowed us to save things to disk as an RRD file, meaning we could query the contents and use this as a sort of test fixture or golden artifact that held EXACTLY what all of the values should be. Then we can use this with /goal as a metric when doing the optimization to ensure there are no regressions. The second bit is the headless rendering, this gave us the ability to check that not only did the test fixture pass, but it also looked visually correct. This made a huge difference, and an awesome side affect of it is that we can use the headless rendering to create an implementations.html file. This gives a visual guide as to what the agent did (I walk through it in the video below) Along with this, we're working on an MCP server for rerun that allows full interactivity with the rerun viewer for your agent. So for example the agent can click, drag, move views, scroll timelines, ect. I used this to help the agent debug certain parts such as when the 2d sam masks didn't line up, or if the triangulated keypoints werent correctly matching with the optimized mesh. The agents could go, click into the view, scroll through the timeline and see where things went wrong. Fable + Headless Rendering + Rerun MCP == 300x speedup in less then a days work With these new tools, I'm planning on going back to my gaussian splatting implemntation and cleaning it up + making it fast!

Pablo Vela

22,880 Aufrufe • vor 2 Monaten

Don't know if this will turn out to be anything, but I've been playing with the idea of adding multiplayer to TLOU 2 on PC. Here we can spawn clients and control them (locally only for now, as I'm just testing) #TheLastOfUsPartII

Don't know if this will turn out to be anything, but I've been playing with the idea of adding multiplayer to TLOU 2 on PC. Here we can spawn clients and control them (locally only for now, as I'm just testing) #TheLastOfUsPartII

Speclizer

102,009 Aufrufe • vor 8 Monaten

I've been working on the opening to the opening, if that makes sense. This is the scene leading up to the original intro cutcene at the front gate. I'm going for a more vintage horror feel this time around. I think it fits the game better.

I've been working on the opening to the opening, if that makes sense. This is the scene leading up to the original intro cutcene at the front gate. I'm going for a more vintage horror feel this time around. I think it fits the game better.

DRIG

34,236 Aufrufe • vor 26 Tagen

We have SLAM on the Robocap! 🎉 Visualized with Rerun Using NVIDIA AI Developer cuVSLAM for GPU-accelerated multicamera tracking. I basically wrote zero code myself and fully used Claude Code for this. It worked because I had so many existing examples to point to that it just wrote everything the way I would have. A few technical wins: 1. Used rattler-build from prefix.dev to package the compiled cuVSLAM CUDA binaries, which made it SUPER easy to use across repos. This also means it works on the DGX Spark (ARM64) out of the box. 2. Zero-setup experience: git clone && pixi run track-robocap auto-downloads a 100MB dataset from HuggingFace and tracks frames. 3. Real-time 3D visualization with trajectories, landmarks, pose graphs, and video playback in Rerun. Still visual-only (not visual-inertial yet), and loop closure needs some debugging. Next steps are getting this into a Gradio interface, then into daggr, and extending it to work with other datasets from exoego-forge. The last piece I'm excited about: Rerun's RRD files now support layers for incremental data. Planning to build pipelines that go from raw sensor data → slam -> human pose → depth estimation → etc. Repo here:

We have SLAM on the Robocap! 🎉 Visualized with Rerun Using NVIDIA AI Developer cuVSLAM for GPU-accelerated multicamera tracking. I basically wrote zero code myself and fully used Claude Code for this. It worked because I had so many existing examples to point to that it just wrote everything the way I would have. A few technical wins: 1. Used rattler-build from prefix.dev to package the compiled cuVSLAM CUDA binaries, which made it SUPER easy to use across repos. This also means it works on the DGX Spark (ARM64) out of the box. 2. Zero-setup experience: git clone && pixi run track-robocap auto-downloads a 100MB dataset from HuggingFace and tracks frames. 3. Real-time 3D visualization with trajectories, landmarks, pose graphs, and video playback in Rerun. Still visual-only (not visual-inertial yet), and loop closure needs some debugging. Next steps are getting this into a Gradio interface, then into daggr, and extending it to work with other datasets from exoego-forge. The last piece I'm excited about: Rerun's RRD files now support layers for incremental data. Planning to build pipelines that go from raw sensor data → slam -> human pose → depth estimation → etc. Repo here:

Pablo Vela

50,825 Aufrufe • vor 6 Monaten

An end-to-end Machine Learning system, starting with a dataset and going all the way to monitoring it in production. We start next Monday! We'll build everything using open-source tools, and I'll show you how to deploy your system on different cloud platforms. I've been running this live course for 1.5+ years now, and about 1,500 students have taken it. It's the best Machine Learning Engineering class I'm aware of by 10 miles. More information is in the attached video. Link to join below. See you next week!

An end-to-end Machine Learning system, starting with a dataset and going all the way to monitoring it in production. We start next Monday! We'll build everything using open-source tools, and I'll show you how to deploy your system on different cloud platforms. I've been running this live course for 1.5+ years now, and about 1,500 students have taken it. It's the best Machine Learning Engineering class I'm aware of by 10 miles. More information is in the attached video. Link to join below. See you next week!

Santiago

36,625 Aufrufe • vor 1 Jahr

🎙️ Coby White on embracing the bench role after being a starter in Chicago: "It's an adjustment, but I just want to win. I'm excited, I'm blessed to be here, whatever role that is." "I've been in every role since I've been in the NBA. I've been a starter, sixth man, ninth man, whatever you want to call it. No. 1 option, 15th option, it don't matter. I just love to play basketball. If I get the opportunity to play and win at a high level, I'm all for it." 🎙️ On what he likes most about the Hornets: "The energy. They continue to push me to be myself." "One thing I've been loving too is that in Chicago the last couple of years, I've been off the ball predominantly. Now I've been on the ball a lot more, with the ball in my hands and being able to play (PG) more. That's been dope for me, just getting back to that because that's the position I grew up playing."

🎙️ Coby White on embracing the bench role after being a starter in Chicago: "It's an adjustment, but I just want to win. I'm excited, I'm blessed to be here, whatever role that is." "I've been in every role since I've been in the NBA. I've been a starter, sixth man, ninth man, whatever you want to call it. No. 1 option, 15th option, it don't matter. I just love to play basketball. If I get the opportunity to play and win at a high level, I'm all for it." 🎙️ On what he likes most about the Hornets: "The energy. They continue to push me to be myself." "One thing I've been loving too is that in Chicago the last couple of years, I've been off the ball predominantly. Now I've been on the ball a lot more, with the ball in my hands and being able to play (PG) more. That's been dope for me, just getting back to that because that's the position I grew up playing."

r/CharlotteHornets

64,801 Aufrufe • vor 5 Monaten

Bismillah Ar-rahmaan Ar-Raheem. It is with great humility that I present to you Iqraa, a project that I've been working on for a couple of weeks now with the sole mission of making knowledge more accessible to Muslims in this digital era. Without doubt, trying to learn about Islam can be daunting and difficult if you don't know where to start, or tedious because there's just so much information on the internet, with that in mind, I sought out to build this as a means to bridge the gap between Muslims and the knowledge of Islam. I ask Allah to accept it from me as an act of Ibaadah and that it becomes something adopted far and wide insha'Allah Visit: to get started.

Bismillah Ar-rahmaan Ar-Raheem. It is with great humility that I present to you Iqraa, a project that I've been working on for a couple of weeks now with the sole mission of making knowledge more accessible to Muslims in this digital era. Without doubt, trying to learn about Islam can be daunting and difficult if you don't know where to start, or tedious because there's just so much information on the internet, with that in mind, I sought out to build this as a means to bridge the gap between Muslims and the knowledge of Islam. I ask Allah to accept it from me as an act of Ibaadah and that it becomes something adopted far and wide insha'Allah Visit: to get started.

𝙰𝚋𝚘𝚘 𝙷𝚊𝚊𝚛𝚒𝚝𝚑 🇴🇲

43,349 Aufrufe • vor 9 Monaten

1 micro to 70 micros. One trade +$2,200 Something you don't see many people do. Because most traders do the opposite. Starting with 1 micro, adding to the winner over and over again all the way to 70 micro leverage, using macro time for momentum 💡The other side of this trade the trader is adding to the loser over and over again When you add to the winner and increase leverage you have to go BE or a predetermined small loss. Yes you will get a lot of BE trades. But you just need one that works

1 micro to 70 micros. One trade +$2,200 Something you don't see many people do. Because most traders do the opposite. Starting with 1 micro, adding to the winner over and over again all the way to 70 micro leverage, using macro time for momentum 💡The other side of this trade the trader is adding to the loser over and over again When you add to the winner and increase leverage you have to go BE or a predetermined small loss. Yes you will get a lot of BE trades. But you just need one that works

JD

22,531 Aufrufe • vor 1 Monat

Long time no see! 🫣 It's been a while once again, but I'm back with another showcase of how Mining Simulator 3 is going.. There's so much that has happend behind the scenes and that I've been working on in the meantime. I really want to try to be a bit more active here on Twitter again <3

Long time no see! 🫣 It's been a while once again, but I'm back with another showcase of how Mining Simulator 3 is going.. There's so much that has happend behind the scenes and that I've been working on in the meantime. I really want to try to be a bit more active here on Twitter again <3

Treqy

23,333 Aufrufe • vor 4 Monaten

🎞 🎤 "This is such a nice jersey, probably one of the best looking kits with all its history(...) It's a special, special team and I'm really proud to be a part of it, to hopefully make some more history (...) I've come out of my comfort zone and I'm excited to see what it brings"

🎞 🎤 "This is such a nice jersey, probably one of the best looking kits with all its history(...) It's a special, special team and I'm really proud to be a part of it, to hopefully make some more history (...) I've come out of my comfort zone and I'm excited to see what it brings"

john stones hqs

264,614 Aufrufe • vor 20 Tagen

___ CANDACE OWENS DEBUNKED BY FORMER SPECIAL SERVICES ARMY GREEN BERET Thank you to Green Beret Nap Time for taking the time to sit down with me and explain/critique Candace's theories on Charlie Kirk, the Macrons, etc. This is part 2 of a multipart series I've started on Candace. Part 1 and all of my links are below in the comments...

___ CANDACE OWENS DEBUNKED BY FORMER SPECIAL SERVICES ARMY GREEN BERET Thank you to Green Beret Nap Time for taking the time to sit down with me and explain/critique Candace's theories on Charlie Kirk, the Macrons, etc. This is part 2 of a multipart series I've started on Candace. Part 1 and all of my links are below in the comments...

Colonel Kurtz -PopCulture/ Politics/ MarilynManson

79,258 Aufrufe • vor 9 Monaten