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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.show more

Pablo Vela

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19,527 views • 9 months ago •via X (Twitter)

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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 views • 3 months ago

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 adding others, such as HOCAP/Assembly 101 2. Looking to see if it also makes sense to port to webdatasets RRD 3. I've started including visualizing confidence — green (high), yellow (medium), red (low). More info on Friday

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 adding others, such as HOCAP/Assembly 101 2. Looking to see if it also makes sense to port to webdatasets RRD 3. I've started including visualizing confidence — green (high), yellow (medium), red (low). More info on Friday

Pablo Vela

34,253 views • 1 year ago

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 views • 2 months ago

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

40,864 views • 3 months ago

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 views • 4 months ago

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

Adeniyi.sui

13,419 views • 1 month ago

My Polymarket bot takes 4 input parameters: > number of shares to buy > total price threshold that allows the hedge > dump threshold > number of minutes from the start of the round during which Leg 1 is allowed Profitability depends entirely on these parameters, so to determine the optimal parameter set, I need to backtest across many different configurations. The issue is that the API returns empty historical data for the “Bitcoin Up or Down” market (this happens for some markets). My solution is to build a bot that records live market data and saves it to a JSON file. I’ll let it run for a few days, then use this recorded data for backtesting. I’ll share the backtest results, so you can get a clear idea of the best-performing parameters.

My Polymarket bot takes 4 input parameters: > number of shares to buy > total price threshold that allows the hedge > dump threshold > number of minutes from the start of the round during which Leg 1 is allowed Profitability depends entirely on these parameters, so to determine the optimal parameter set, I need to backtest across many different configurations. The issue is that the API returns empty historical data for the “Bitcoin Up or Down” market (this happens for some markets). My solution is to build a bot that records live market data and saves it to a JSON file. I’ll let it run for a few days, then use this recorded data for backtesting. I’ll share the backtest results, so you can get a clear idea of the best-performing parameters.

The Smart Ape 🔥

38,100 views • 7 months ago

"The data that we've been working with as people now becomes extremely useful in a world of agents. You're having this rebirth of the file system." Aaron Levie on Navigators with Paul Klein IV on why unstructured data, the 90% of enterprise data that has always been the hardest to automate, is now the biggest unlock for AI agents. And why the real AI conversation is a context conversation.

"The data that we've been working with as people now becomes extremely useful in a world of agents. You're having this rebirth of the file system." Aaron Levie on Navigators with Paul Klein IV on why unstructured data, the 90% of enterprise data that has always been the hardest to automate, is now the biggest unlock for AI agents. And why the real AI conversation is a context conversation.

Box

66,225 views • 1 month ago

✨ Massive Pipeline Refactor → One Framework for Ego + Exo Datasets, Visualized with Rerun 🚀 After a deep refactoring and cleanup, my entire egocentric/exocentric pipeline is now fully modular. One codebase handles different sensor layouts and generates a unified, multimodal timeseries RRD file that you can open instantly in Rerun. --- The first three datasets that are already supported 1. Assembly101 – 4 ego Quest‑style fisheye cams + 8 exo pinhole cams 2. HO‑Cap – 1 ego HoloLens pinhole cam + 8 exo pinhole cams 3. EgoDex – 1 ego Apple Vision Pro pinhole cam Unified geometry: Each frame now logs _both_ camera intrinsics / extrinsics and COCO Whole-Body 133-kp keypoints in the same stream. Everything is canonicalized at import time, so there’s zero OpenCV vs OpenGL guess-work—Rerun reads it all in the correct coordinate system automatically. --- Why this matters - Consistent schema ✚ live visuals – Rerun’s deep link between data & rendering means every experiment comes with a built‑in viewer. No more ad‑hoc OpenCV/matplotlib hacks just to sanity‑check a dataset. - Multi‑terabyte friendly – The next step is bulk‑ingest these giants into Rerun and wrap them in a Gradio UI for point‑and‑click exploration, as I've already done for EgoDex!

✨ Massive Pipeline Refactor → One Framework for Ego + Exo Datasets, Visualized with Rerun 🚀 After a deep refactoring and cleanup, my entire egocentric/exocentric pipeline is now fully modular. One codebase handles different sensor layouts and generates a unified, multimodal timeseries RRD file that you can open instantly in Rerun. --- The first three datasets that are already supported 1. Assembly101 – 4 ego Quest‑style fisheye cams + 8 exo pinhole cams 2. HO‑Cap – 1 ego HoloLens pinhole cam + 8 exo pinhole cams 3. EgoDex – 1 ego Apple Vision Pro pinhole cam Unified geometry: Each frame now logs _both_ camera intrinsics / extrinsics and COCO Whole-Body 133-kp keypoints in the same stream. Everything is canonicalized at import time, so there’s zero OpenCV vs OpenGL guess-work—Rerun reads it all in the correct coordinate system automatically. --- Why this matters - Consistent schema ✚ live visuals – Rerun’s deep link between data & rendering means every experiment comes with a built‑in viewer. No more ad‑hoc OpenCV/matplotlib hacks just to sanity‑check a dataset. - Multi‑terabyte friendly – The next step is bulk‑ingest these giants into Rerun and wrap them in a Gradio UI for point‑and‑click exploration, as I've already done for EgoDex!

Pablo Vela

20,836 views • 1 year ago

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

Santiago

142,696 views • 1 year ago

Here is the play in question by Ryan, and as much as this is obvious that the catcher didn't secure this ... the rule in place for scorers is if a thorw BOUNCES, it is a throwing error. I do not like applying this in this type of instance on a throw from the outfield, but right now that's how the rule reads. Only the committee can change it on appeal, and they would have to do that against the writter rule (to the consternation of those in charge I'm certain). But letter of the law ... the throwing error is correct.

Here is the play in question by Ryan, and as much as this is obvious that the catcher didn't secure this ... the rule in place for scorers is if a thorw BOUNCES, it is a throwing error. I do not like applying this in this type of instance on a throw from the outfield, but right now that's how the rule reads. Only the committee can change it on appeal, and they would have to do that against the writter rule (to the consternation of those in charge I'm certain). But letter of the law ... the throwing error is correct.

MLB Scoring Changes

232,327 views • 1 year ago

This Is Absolutely Mind Blowing ⚠️ Barack Obama’s New Movie, Leave The World Behind, When Downloaded & Put Into An Editing Program Has 4 Audio Files Instead Of The Standard 1 One Of The Audio Files Is Just A Frequency 🚨 “Low Frequency Infrasound” Other Movies Do Not Have This. The Guy Has Done Thousands Of Movies & Never Seen This “When you open this this movie right here is Jurassic Park, for instance. Okay? I'm just showing this to people who have never seen this. For the last 15 years that I have been using Sony Vegas and other editing software and have gone through downloading movies And using them in our own video production, 100% of the time, it's either an MKV or a MP For occasionally, you'll download a torrent that's in AVR, but not very often. And this is what it will always look like. One video file right here. One audio file right here always. That's been my experience. Now let me show you what happened when we downloaded and put, Sony put the, uh, movie Leave the World Behind onto Sony Vegas. Here you go. Stunning. Stunning. One video file, 4 audio files opened up. And this one, This 4th audio file seems to be the weapon. This is a low ex uh, ELF, Uh, Low Frequency Infrasound. Infrasonic wave right here. And, um, so we We are gonna let me put the camera back on again. So this is big news just just so people realize. In in 15 years of video editing, I have never downloaded now speaking specifically a movie file. Okay. A Hollywood movie from a torrent downloading it in either MKV, MP4, occasionally AVI. 100% of the time, over a thousands movies I've done this with, you have video file, audio file, period. This is the very first time I've ever seen anything like this with the 4 files. Bonnie's gonna go into now talking about the The infrasonic sound waves and what we found out about that and the fact that there's one here. We're in the point is we're going to go back after Bonnie gives you some information about what it is we're dealing with, how this is a weapon. Then we're gonna go back, uh, towards the end of the live stream here. And I'm gonna hit specific places where this weapon was used against anybody who watches it. Okay.”

This Is Absolutely Mind Blowing ⚠️ Barack Obama’s New Movie, Leave The World Behind, When Downloaded & Put Into An Editing Program Has 4 Audio Files Instead Of The Standard 1 One Of The Audio Files Is Just A Frequency 🚨 “Low Frequency Infrasound” Other Movies Do Not Have This. The Guy Has Done Thousands Of Movies & Never Seen This “When you open this this movie right here is Jurassic Park, for instance. Okay? I'm just showing this to people who have never seen this. For the last 15 years that I have been using Sony Vegas and other editing software and have gone through downloading movies And using them in our own video production, 100% of the time, it's either an MKV or a MP For occasionally, you'll download a torrent that's in AVR, but not very often. And this is what it will always look like. One video file right here. One audio file right here always. That's been my experience. Now let me show you what happened when we downloaded and put, Sony put the, uh, movie Leave the World Behind onto Sony Vegas. Here you go. Stunning. Stunning. One video file, 4 audio files opened up. And this one, This 4th audio file seems to be the weapon. This is a low ex uh, ELF, Uh, Low Frequency Infrasound. Infrasonic wave right here. And, um, so we We are gonna let me put the camera back on again. So this is big news just just so people realize. In in 15 years of video editing, I have never downloaded now speaking specifically a movie file. Okay. A Hollywood movie from a torrent downloading it in either MKV, MP4, occasionally AVI. 100% of the time, over a thousands movies I've done this with, you have video file, audio file, period. This is the very first time I've ever seen anything like this with the 4 files. Bonnie's gonna go into now talking about the The infrasonic sound waves and what we found out about that and the fact that there's one here. We're in the point is we're going to go back after Bonnie gives you some information about what it is we're dealing with, how this is a weapon. Then we're gonna go back, uh, towards the end of the live stream here. And I'm gonna hit specific places where this weapon was used against anybody who watches it. Okay.”

Wall Street Apes

3,769,180 views • 2 years ago

I got a smart meter recently and saw you can download a HDF file with the data so I had the idea of writing a script that could parse that and show the data in a useful manner. However I discovered that someone has already done this and done a really good job on it. The video explains how it works. In simple terms it uses the ESB smart meter data and shows a breakdown of how much data you are using and when and also recommends plans and estimates what each one would cost based on the data. The tool is available at easier to read on Laptop or Tablet or then your phone to Landscape.

I got a smart meter recently and saw you can download a HDF file with the data so I had the idea of writing a script that could parse that and show the data in a useful manner. However I discovered that someone has already done this and done a really good job on it. The video explains how it works. In simple terms it uses the ESB smart meter data and shows a breakdown of how much data you are using and when and also recommends plans and estimates what each one would cost based on the data. The tool is available at easier to read on Laptop or Tablet or then your phone to Landscape.

Carlow Weather

298,521 views • 2 years ago

This is Anagha Rajesh. And she is crazy. She wants to store data in bacterial DNA. Her startup is called BioCompute. And last year, they actually did it. They stored data in DNA and retrieved it in their tiny lab in Bengaluru. This is a huge moment. But why is she even doing it? Because DNA is the ultimate storage tool available to us. Just 1 gm of DNA can store about 215 petabytes of data - that's like storing over 2 million movies in 4K. On top of that - this data can last for literally 1000s of years. Right now, they still need to figure out a way to make the reading and writing process faster and cheaper. But if BioCompute solves this problem - we could theoretically store all the data created in the world every year in the palm of our hands. And that would be insane. P.S. Check out this video from vyom going to Anagha's lab and actually doing it.

This is Anagha Rajesh. And she is crazy. She wants to store data in bacterial DNA. Her startup is called BioCompute. And last year, they actually did it. They stored data in DNA and retrieved it in their tiny lab in Bengaluru. This is a huge moment. But why is she even doing it? Because DNA is the ultimate storage tool available to us. Just 1 gm of DNA can store about 215 petabytes of data - that's like storing over 2 million movies in 4K. On top of that - this data can last for literally 1000s of years. Right now, they still need to figure out a way to make the reading and writing process faster and cheaper. But if BioCompute solves this problem - we could theoretically store all the data created in the world every year in the palm of our hands. And that would be insane. P.S. Check out this video from vyom going to Anagha's lab and actually doing it.

Varun Guru

254,172 views • 1 month ago

🚨 VWP SANDBOX now LIVE on View live VWP data from all NOAA radar sites + terminal sites. Explore them in 3D or 2D to understand more about how hodographs and dynamics work! Toggle on dark mode or light mode for your viewing pleasure, as well as download the geojson file of the VWP to save and come back to it later by loading it back in to the system! This works a lot better on computer but I tried my best at getting mobile at least somewhat usable. For more, go to the "How To Use" section.

🚨 VWP SANDBOX now LIVE on View live VWP data from all NOAA radar sites + terminal sites. Explore them in 3D or 2D to understand more about how hodographs and dynamics work! Toggle on dark mode or light mode for your viewing pleasure, as well as download the geojson file of the VWP to save and come back to it later by loading it back in to the system! This works a lot better on computer but I tried my best at getting mobile at least somewhat usable. For more, go to the "How To Use" section.

James Pettus

30,106 views • 1 year ago

The Digital ID Will Change the Lives of Every Australian - For the Worse ... As much as the Government attempts to downplay the importance of introducing a single central digital identifier for all Australians, the truth is that this legislation is the most significant I've encountered during my time in the Senate. It's the glue that holds together the digital control agenda by which every Australian will be controlled, corralled, exploited and then gagged when they speak or act in opposition. The government knows Digital ID will be compulsory by the device of preventing access to government services, banking services, air travel and major purchases for any Australian who does not have a Digital ID. The Digital ID will, in effect, create a live data file of your movements, purchases, accounts and associates containing reference to every piece of data being held in the private and government sector as a first step in a wider agenda. Tech giants have been building huge data files on every Australian for years. Those huge data files that contain every website you visited, every post you made on their social media, everything you have ever bought online. Keywords scanned from conversations overheard by Siri and Alexa in your home are now unmasked. Until now, that data was anonymised using a unique identifier rather than name and address, which has always been there as well. However, tech companies were not allowed to use it or share data with others that included the person's name and address. Until Now. Look for the tech giants to ask for your Digital ID as a requirement of using their service. The point of that exercise is to ensure they put the right name on the right data treasure trove. This is why the Liberal Party have moved amendments to the Digital ID Bill to bring private corporations into this roll out earlier. All those treasure troves of data worth billions, trillions, that have been accumulated for years illegally, by retailers, tech and data companies - all that unrealised profit just sitting there has been too much of a temptation for the Liberal Nationals to resist and is now joined with Labor in pushing Digital ID. There will be no escape from the digital ID. Australians now have a digital version of "papers please" and Australians will never be the same.

The Digital ID Will Change the Lives of Every Australian - For the Worse ... As much as the Government attempts to downplay the importance of introducing a single central digital identifier for all Australians, the truth is that this legislation is the most significant I've encountered during my time in the Senate. It's the glue that holds together the digital control agenda by which every Australian will be controlled, corralled, exploited and then gagged when they speak or act in opposition. The government knows Digital ID will be compulsory by the device of preventing access to government services, banking services, air travel and major purchases for any Australian who does not have a Digital ID. The Digital ID will, in effect, create a live data file of your movements, purchases, accounts and associates containing reference to every piece of data being held in the private and government sector as a first step in a wider agenda. Tech giants have been building huge data files on every Australian for years. Those huge data files that contain every website you visited, every post you made on their social media, everything you have ever bought online. Keywords scanned from conversations overheard by Siri and Alexa in your home are now unmasked. Until now, that data was anonymised using a unique identifier rather than name and address, which has always been there as well. However, tech companies were not allowed to use it or share data with others that included the person's name and address. Until Now. Look for the tech giants to ask for your Digital ID as a requirement of using their service. The point of that exercise is to ensure they put the right name on the right data treasure trove. This is why the Liberal Party have moved amendments to the Digital ID Bill to bring private corporations into this roll out earlier. All those treasure troves of data worth billions, trillions, that have been accumulated for years illegally, by retailers, tech and data companies - all that unrealised profit just sitting there has been too much of a temptation for the Liberal Nationals to resist and is now joined with Labor in pushing Digital ID. There will be no escape from the digital ID. Australians now have a digital version of "papers please" and Australians will never be the same.

Malcolm Roberts 🇦🇺

217,182 views • 2 years ago

Ok I went a little too hard on this Biologicals trial building. I’ve always been a big fan of biologicals, completely underutilized imo, likely because it has a bad reputation often marketed by companies as the cure for plant AIDS. I have a Biostimulant, lignin-derived, that sounds like a real dinger if it can do what it is supposed to do, so I have negotiated a good price per acre that ensures the manufacturer isn’t chasing the “hit it and quit it” mentality retailers in agriculture seem to have towards farmers. I am tracking everyone in the trial and open sourcing all the data in real time. No compilation afterwards to stroke the data, all raw and interpreted in real time. If the product doesn’t do anything, the retailer never sells that product ever again. Even easier for the farmers, I set up a chat bot in Telegram so to get the data in your dashboard and online is take a picture or type in your data. This is borne out of curiosity and dismay towards all the scammers. I’m really hoping this does some good. There is a link at the end of the video that took me an uncomfortable amount of time to make so I’m forcing you to watch it to get the link. More product data there.

Ok I went a little too hard on this Biologicals trial building. I’ve always been a big fan of biologicals, completely underutilized imo, likely because it has a bad reputation often marketed by companies as the cure for plant AIDS. I have a Biostimulant, lignin-derived, that sounds like a real dinger if it can do what it is supposed to do, so I have negotiated a good price per acre that ensures the manufacturer isn’t chasing the “hit it and quit it” mentality retailers in agriculture seem to have towards farmers. I am tracking everyone in the trial and open sourcing all the data in real time. No compilation afterwards to stroke the data, all raw and interpreted in real time. If the product doesn’t do anything, the retailer never sells that product ever again. Even easier for the farmers, I set up a chat bot in Telegram so to get the data in your dashboard and online is take a picture or type in your data. This is borne out of curiosity and dismay towards all the scammers. I’m really hoping this does some good. There is a link at the end of the video that took me an uncomfortable amount of time to make so I’m forcing you to watch it to get the link. More product data there.

bu/ac

29,889 views • 3 months ago

So here’s the deal, THE TREASURY DEPARTMENT DATA IS COMPROMISED. Actors without security clearance have been given access to scrape your data. Elon Musk now controls your data and it represents the same level of control exerted by the Chinese government’s social credit system. Musk is now able to profile every name on the treasury data systems. There is no way to sugar coat the fact that your data is no longer secure. It can now be used to target you and not for your benefit, be sure of that. Democrats and advocacy groups can take all the legal action they like, THE DATA IS COMPROMISED and you can’t put the genie back in the bottle. This data can now be used to literally social engineer services to target any perceived enemy of ‘the state’. Call me a conspiracy theorist if you like, but then sit back have a think if any of what I am suggesting is not possible. I analyse big data for a living and I have no solution to this problem and neither does any Democrat telling you it can be fixed.

𝔗𝔯𝔲𝔱𝔥 𝔐𝔞𝔱𝔱𝔢𝔯𝔰

16,366 views • 1 year ago

Today we're launching File Uploads (in beta)! You can now upload files to folders in Granola and Granola's AI will analyze the files as well as your meetings. This is super helpful for getting all the relevant context in one place. We're still figuring out the right interactions, so play with it and let us know what you think.

Today we're launching File Uploads (in beta)! You can now upload files to folders in Granola and Granola's AI will analyze the files as well as your meetings. This is super helpful for getting all the relevant context in one place. We're still figuring out the right interactions, so play with it and let us know what you think.

Granola

39,779 views • 1 year ago

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 views • 5 months ago