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i trained a computer vision model for beach volleyball analysis — using roboflow rapid + SAM3 + python for detection of players, ball, net — automated stat collection and dataviz for ball touches, speed, height, net crosses could expand this prototype for individual player stats, court coverage heatmap, automated... show more

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740,957 views • 8 months ago •via X (Twitter)

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i trained a computer vision model to count chinups roboflow rapid + SAM3 detected the chinup bar - hands - head through natural language prompts then I wrote a python script to count completed reps (head above bar) and annotate the video idea for a gamified fitness app? automated tracking of your workout, with rep counts per exercise added to your database. this gives progress stats over time, and a leaderboard with your friends these new vision models are neat because they can detect exercise equipment, allowing tracking of dumbbell workouts, bench press, squat, etc.

i trained a computer vision model to count chinups roboflow rapid + SAM3 detected the chinup bar - hands - head through natural language prompts then I wrote a python script to count completed reps (head above bar) and annotate the video idea for a gamified fitness app? automated tracking of your workout, with rep counts per exercise added to your database. this gives progress stats over time, and a leaderboard with your friends these new vision models are neat because they can detect exercise equipment, allowing tracking of dumbbell workouts, bench press, squat, etc.

AA

1,181,503 views • 9 months ago

i trained a computer vision model to train me at tennis it tracks ball speed, forehand vs backhand strokes, shot placement, body position when i strike the ball how this works: - input video from an iphone - CV pipeline orchestrated with roboflow agent: data labelling using Astra 6, detection done with a fine-tuned RF-DETR model - video annotation using python + openCV goal is to make an app that records each practice session, tracks progress over time, and gives feedback on improving my technique more to come!

i trained a computer vision model to train me at tennis it tracks ball speed, forehand vs backhand strokes, shot placement, body position when i strike the ball how this works: - input video from an iphone - CV pipeline orchestrated with roboflow agent: data labelling using Astra 6, detection done with a fine-tuned RF-DETR model - video annotation using python + openCV goal is to make an app that records each practice session, tracks progress over time, and gives feedback on improving my technique more to come!

AA

851,536 views • 10 days ago

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

zostaff

120,370 views • 4 months ago

AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. You upload a regular match video. No sensors, no GPS trackers, just camera footage. The neural network finds every player, referee, and ball on its own. Every frame, in real time. KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint. Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters. Speed of every player. Distance covered. Ball possession percentage. All calculated automatically. Four hours of tutorial from zero to a working system. The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run. Sports analytics is no longer behind closed doors. AI leveled the playing field.

AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. You upload a regular match video. No sensors, no GPS trackers, just camera footage. The neural network finds every player, referee, and ball on its own. Every frame, in real time. KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint. Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters. Speed of every player. Distance covered. Ball possession percentage. All calculated automatically. Four hours of tutorial from zero to a working system. The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run. Sports analytics is no longer behind closed doors. AI leveled the playing field.

zostaff

1,076,853 views • 5 months ago

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

zostaff

230,988 views • 5 months ago

Seen a post today… Stats lie. Show me the film! No stat for how deep you shoot that 3 or level of difficulty. No stat for the IQ it took to let go of that ball on the trap. No stat for turning around and immediately knowing it’s time for an inbound play. No stat for communication. No stat for scoring on 2 or 3 D1 level talent players, the points are same BUT different. No stat line for a player scoring on a triple team while the stands are chanting over rated. No stat for that timely screen or that hockey pass. No stat for motor bc you played all game and still drained the deep 3…. Recruits: Stats don’t tell the whole story! They tell a piece of that story. Stay the course!

Seen a post today… Stats lie. Show me the film! No stat for how deep you shoot that 3 or level of difficulty. No stat for the IQ it took to let go of that ball on the trap. No stat for turning around and immediately knowing it’s time for an inbound play. No stat for communication. No stat for scoring on 2 or 3 D1 level talent players, the points are same BUT different. No stat line for a player scoring on a triple team while the stands are chanting over rated. No stat for that timely screen or that hockey pass. No stat for motor bc you played all game and still drained the deep 3…. Recruits: Stats don’t tell the whole story! They tell a piece of that story. Stay the course!

Amber Mathurin ™️

12,657 views • 2 years ago

Introducing the “Get” action in basketball to lacrosse! The “Get” is a 2man action where a player passes the ball to another player and sprints to go “Get” it back on a handoff. This action has similar concepts and reads to pick and roll, but the picker is actually the ball handler! In lacrosse, we have been experimenting with Gets because it creates so many interesting situations and reads for the players. The way we teach it in small sided games is to have one offensive player declare “I’m shut” and the defense has to face-guard him. From there we pick for the shut off player trying to spring them free for the Get. The ball handler has to engage and control their defender, anticipating the shut player trying to get open. The shut player can backdoor if being overplayed towards the get (rejecting the get), receive a flip, or continue curling around the ball getting open for a late pass. The ball handler can feed the reject, flip the ball, or fake the flip to attack the goal or make a late pass to the shut payer curling to the goal. I’ve seen examples of this run in men’s and women’s DI lacrosse at Ohio State and Northwestern, respectively and I’ve run this myself when I was coaching HS girls lacrosse. You will also see on this reel a sick application for Gets vs. a common big-little invert coverage where the short stick doesn’t go behind with his man and plays “Safety” in front of the net. Gets are fun for the players, are incredible for teaching your players 2man game on and off ball, dealing with double teams, and creativity with passing, and can be used to score goals. I hope you enjoy it! Can you think of other applications for Gets?

Introducing the “Get” action in basketball to lacrosse! The “Get” is a 2man action where a player passes the ball to another player and sprints to go “Get” it back on a handoff. This action has similar concepts and reads to pick and roll, but the picker is actually the ball handler! In lacrosse, we have been experimenting with Gets because it creates so many interesting situations and reads for the players. The way we teach it in small sided games is to have one offensive player declare “I’m shut” and the defense has to face-guard him. From there we pick for the shut off player trying to spring them free for the Get. The ball handler has to engage and control their defender, anticipating the shut player trying to get open. The shut player can backdoor if being overplayed towards the get (rejecting the get), receive a flip, or continue curling around the ball getting open for a late pass. The ball handler can feed the reject, flip the ball, or fake the flip to attack the goal or make a late pass to the shut payer curling to the goal. I’ve seen examples of this run in men’s and women’s DI lacrosse at Ohio State and Northwestern, respectively and I’ve run this myself when I was coaching HS girls lacrosse. You will also see on this reel a sick application for Gets vs. a common big-little invert coverage where the short stick doesn’t go behind with his man and plays “Safety” in front of the net. Gets are fun for the players, are incredible for teaching your players 2man game on and off ball, dealing with double teams, and creativity with passing, and can be used to score goals. I hope you enjoy it! Can you think of other applications for Gets?

JM3 SPORTS

12,424 views • 1 year ago

Fernandez: Can He Recover for the Goal? When I do analysis on goals, teams and more importantly, highlighting individuals, it's never meant to be from a place of badness, just analysis and some opinion. I seen a ton of comments from this mornings video about Manny Fernandez on this goal but I think there is lots happening that afforded him the benefit of the doubt: > Good Pressure Initially > Trips Over Player He Presses > Trips Over Gassama Recovering > Recovers Footing Too Late for Me There is a full host of individuals, that if working together as a team, then they all correct and cover for any individual errors in this goal. Football is all opinion and maybe there is still more Manny Fernandez could do but it looks like a full team approach.

Fernandez: Can He Recover for the Goal? When I do analysis on goals, teams and more importantly, highlighting individuals, it's never meant to be from a place of badness, just analysis and some opinion. I seen a ton of comments from this mornings video about Manny Fernandez on this goal but I think there is lots happening that afforded him the benefit of the doubt: > Good Pressure Initially > Trips Over Player He Presses > Trips Over Gassama Recovering > Recovers Footing Too Late for Me There is a full host of individuals, that if working together as a team, then they all correct and cover for any individual errors in this goal. Football is all opinion and maybe there is still more Manny Fernandez could do but it looks like a full team approach.

John Walker

16,391 views • 1 month ago

$BTC Statistical Study using Claude - A Beginner's Workflow Here's an example of a z-score study on $BTC - still tinkering so don't take this as overtly useful information but the creation of a dashboard for the visualization of statistical data is phenomenal. my current workflow: > import $BTC time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.

$BTC Statistical Study using Claude - A Beginner's Workflow Here's an example of a z-score study on $BTC - still tinkering so don't take this as overtly useful information but the creation of a dashboard for the visualization of statistical data is phenomenal. my current workflow: > import $BTC time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.

Stoic

19,782 views • 1 year ago

this is the workflow for 90% of the assets i create for myself or for clients - start in a diffusion model to get the best aesthetics - bring it into a multimodal model (Nano Banana Pro) in Higgsfield to create a second frame and/or fix details - use a high quality video model that supports start and end frames (Kling 3.0 Omni in Higgsfield) to create a sick shot in <60 seconds model literacy and knowing which ones to grab and when is one of the biggest turning points I see in AI practices. it starts to feel more like another language instead of a black box that you are always just gambling on

this is the workflow for 90% of the assets i create for myself or for clients - start in a diffusion model to get the best aesthetics - bring it into a multimodal model (Nano Banana Pro) in Higgsfield to create a second frame and/or fix details - use a high quality video model that supports start and end frames (Kling 3.0 Omni in Higgsfield) to create a sick shot in <60 seconds model literacy and knowing which ones to grab and when is one of the biggest turning points I see in AI practices. it starts to feel more like another language instead of a black box that you are always just gambling on

techbimbo

16,681 views • 1 month ago

Sora 2 API + n8n for UGC videos ... absolutely insane 🤯 this n8n workflow generates realistic influencer-style videos from just a product image. No watermark. Fully automated. (and I'm giving it away for free) Here's what this system does: → Analyzes your product image with AI vision → Creates the perfect influencer persona to promote it → Generates multiple UGC video scripts automatically → Uses OpenAI's Sora 2 to create realistic videos → Outputs videos ready for Instagram, TikTok, and Facebook The results? What used to take weeks and thousands of dollars now happens in minutes for under $5 per video. This isn't just about cutting costs – it's about scaling your creative production and driving your ROAS 50%+ higher due to on-demand, high-converting video content. Brands and agencies today spend $10,000+ per month hiring creators, shipping products, waiting weeks, and hoping for a few decent videos back. This changes the whole game for $1 per video. Want the full n8n template, all of the prompts, and a full step-by-step setup video? 1. Like & RT this post 2. Follow me (so I can dm you) 3. Comment "UGC" below I'll send you the entire system for free.

Sora 2 API + n8n for UGC videos ... absolutely insane 🤯 this n8n workflow generates realistic influencer-style videos from just a product image. No watermark. Fully automated. (and I'm giving it away for free) Here's what this system does: → Analyzes your product image with AI vision → Creates the perfect influencer persona to promote it → Generates multiple UGC video scripts automatically → Uses OpenAI's Sora 2 to create realistic videos → Outputs videos ready for Instagram, TikTok, and Facebook The results? What used to take weeks and thousands of dollars now happens in minutes for under $5 per video. This isn't just about cutting costs – it's about scaling your creative production and driving your ROAS 50%+ higher due to on-demand, high-converting video content. Brands and agencies today spend $10,000+ per month hiring creators, shipping products, waiting weeks, and hoping for a few decent videos back. This changes the whole game for $1 per video. Want the full n8n template, all of the prompts, and a full step-by-step setup video? 1. Like & RT this post 2. Follow me (so I can dm you) 3. Comment "UGC" below I'll send you the entire system for free.

David Roberts

138,113 views • 11 months ago

Behind the ball is how you get depth AND length out front. (Not one or the other) Here’s what it actually gives you: 1. You swing more connected and deeper. 2. The barrel enters earlier and stays in longer. You get both. 3. Depth behind what buys you length out front. Most guys are picking one or the other. 4. You sling the barrel through. You’re not shoving it or yanking it into the zone. 5. You actually use the ground. Behind the ball you turn. If you’re not using the ground, you’re probably sliding and pulling everything towards the ball. 6. Your bat speed becomes usable. Pushed and pulled bat speed still reads fast vs one pitch one speed. Only one of them shows up on the barrel, in the zone, on time vs pitching. — Cue for your next round: let the ball get deeper than usual, and turn behind a wall. You can scoot close to a net in front of you to get the feeling, too. Then vs moving ball. > Save this for when you need it > Send this to a hitter who needs it right now (And make sure you’re following)

Behind the ball is how you get depth AND length out front. (Not one or the other) Here’s what it actually gives you: 1. You swing more connected and deeper. 2. The barrel enters earlier and stays in longer. You get both. 3. Depth behind what buys you length out front. Most guys are picking one or the other. 4. You sling the barrel through. You’re not shoving it or yanking it into the zone. 5. You actually use the ground. Behind the ball you turn. If you’re not using the ground, you’re probably sliding and pulling everything towards the ball. 6. Your bat speed becomes usable. Pushed and pulled bat speed still reads fast vs one pitch one speed. Only one of them shows up on the barrel, in the zone, on time vs pitching. — Cue for your next round: let the ball get deeper than usual, and turn behind a wall. You can scoot close to a net in front of you to get the feeling, too. Then vs moving ball. > Save this for when you need it > Send this to a hitter who needs it right now (And make sure you’re following)

Trey Hannam

26,774 views • 2 months ago

I generated this AI ad for a local lawn service company in 30 mins with the new AI system my team and I are building. Insane 🤯 The "before" and "after" pictures are real, but brought to life with Image → Video. A creator would charge $200-$500 to make this for you. Our system did it for $12 worth of credits. Comment "Fabric" below if you want me to DM you early access to the system before we release it to public (must follow for auto dm)

I generated this AI ad for a local lawn service company in 30 mins with the new AI system my team and I are building. Insane 🤯 The "before" and "after" pictures are real, but brought to life with Image → Video. A creator would charge $200-$500 to make this for you. Our system did it for $12 worth of credits. Comment "Fabric" below if you want me to DM you early access to the system before we release it to public (must follow for auto dm)

David Roberts

34,326 views • 9 months ago

A LOT of people asked for this and I definitely should NOT be leaking this but f*ck it a lot Using Google Omni + GPT 2 Image to build an automated Doctor page factory is a gold mine I cracked the formula to make the most realistic AI videos in literally minutes As well as completely automating the scripting Using this to push traffic to peptide affiliate offers is making affiliates $40k/month and I’m LEAKING the whole system here’s what’s your getting: > my Claude skill for automated scripting > a full VIDEO tutorial on how I made this insanely realistic AI videos in minutes > the exact peptide offer printing $40k +/month RT + reply ‘FB’ and I’ll send all of it to you (must follow so I can dm)

A LOT of people asked for this and I definitely should NOT be leaking this but f*ck it a lot Using Google Omni + GPT 2 Image to build an automated Doctor page factory is a gold mine I cracked the formula to make the most realistic AI videos in literally minutes As well as completely automating the scripting Using this to push traffic to peptide affiliate offers is making affiliates $40k/month and I’m LEAKING the whole system here’s what’s your getting: > my Claude skill for automated scripting > a full VIDEO tutorial on how I made this insanely realistic AI videos in minutes > the exact peptide offer printing $40k +/month RT + reply ‘FB’ and I’ll send all of it to you (must follow so I can dm)

Pounds

24,872 views • 1 month ago

Everyone is using Macros WRONG in #CollegeFootball27 🤯 Most players are filling all 10 macro slots with complete coverage shells. 😴 ...Really only 5 since you need one for the left and one for the right. Instead, think of your macros as building blocks. For example, instead of creating a full Cover 3 Cloud macro, create a simple Cover 3 Right macro. Now you can stack it onto: ✅ A stock Cover 2 to create Cover 3 Cloud ✅ A stock Cover 4 Quarters to create a (reversed) Cover 6 ✅ Other mini macros to build dozens of unique coverage shells 👉You can even create building blocks for annoying adjustments. For example you could create a "Send 4 Swap" macro that instantly flips your blitz side. Instead of making three separate adjustments every snap, you could press one button and basically finish the setup. This approach lets you combine stock coverages, full macros, and mini macros together, turning 10 macro slots into 30-40 different defensive setups while saving a ton of button presses. 🎥 Full breakdown in the video. (for real this time) This was part of Swolosimo's Emergency Lab Update that dropped yesterday. 📌 Bookmark this for later and follow Victory Formation GG for more College Football 27 tips.

Everyone is using Macros WRONG in #CollegeFootball27 🤯 Most players are filling all 10 macro slots with complete coverage shells. 😴 ...Really only 5 since you need one for the left and one for the right. Instead, think of your macros as building blocks. For example, instead of creating a full Cover 3 Cloud macro, create a simple Cover 3 Right macro. Now you can stack it onto: ✅ A stock Cover 2 to create Cover 3 Cloud ✅ A stock Cover 4 Quarters to create a (reversed) Cover 6 ✅ Other mini macros to build dozens of unique coverage shells 👉You can even create building blocks for annoying adjustments. For example you could create a "Send 4 Swap" macro that instantly flips your blitz side. Instead of making three separate adjustments every snap, you could press one button and basically finish the setup. This approach lets you combine stock coverages, full macros, and mini macros together, turning 10 macro slots into 30-40 different defensive setups while saving a ton of button presses. 🎥 Full breakdown in the video. (for real this time) This was part of Swolosimo's Emergency Lab Update that dropped yesterday. 📌 Bookmark this for later and follow Victory Formation GG for more College Football 27 tips.

Victory Formation GG

140,101 views • 2 months ago

Giveaway below 👇 one of my favourite Cbd products includes the muscle balm which I have used for a couple of years now. If you would like to win one of these just watch & repost the video and tell me why you would like to try it. This is a great product for sports recovery and easing of the muscles. To purchase this or any other product take a look at and use code Tiss40 for 40 percent off. Have a lovely evening and will pick winners in the morning 👍 #supremecbd #ad

Giveaway below 👇 one of my favourite Cbd products includes the muscle balm which I have used for a couple of years now. If you would like to win one of these just watch & repost the video and tell me why you would like to try it. This is a great product for sports recovery and easing of the muscles. To purchase this or any other product take a look at and use code Tiss40 for 40 percent off. Have a lovely evening and will pick winners in the morning 👍 #supremecbd #ad

Matt Le Tissier ✝️

94,899 views • 1 year ago

When I was in pro ball, even throwing in the mid 90s, I never did this. But I honestly think it’s one of the biggest things that’s going to help me get to 100 miles an hour. One of the most underrated ways to throw harder is to implement cross training. Now, cross training in terms of pitching just means you’ll be using something other than the baseball to throw. For example, you see Yamamoto in the big leagues right now. He attributes a lot of his velocity gains to javelin throwing, and he’s thrown the javelin for many, many years. There’s another pitcher who’s one of the best pitchers in college baseball right now in Japan who’s up to 102 miles an hour and attributes a lot of his success to throwing the volleyball and the volleyball teaching him how to move. For myself, I use a football to clean up my arm action. When you use different implements, it’ll give you a new stimulus. It can clean up a lot of the mechanics that you wouldn’t be able to with the baseball itself. For myself and my training to throw 100 miles an hour, I use all of these implements for my arm action. Now, I have a tendency of forearm flyout where my forearm flies out this way, and so I want to keep it a little bit more directed toward the plate. Some of the best ways to do this is to throw the javelin. Because to throw the javelin far, you can’t be flying out. You need to go straight through. The football as well needs to be straight through. Also, just hitting the volleyball. I love watching volleyball players hit the volleyball because the upper mechanics are so efficient. So if you feel stuck in your training, start incorporating cross training.

When I was in pro ball, even throwing in the mid 90s, I never did this. But I honestly think it’s one of the biggest things that’s going to help me get to 100 miles an hour. One of the most underrated ways to throw harder is to implement cross training. Now, cross training in terms of pitching just means you’ll be using something other than the baseball to throw. For example, you see Yamamoto in the big leagues right now. He attributes a lot of his velocity gains to javelin throwing, and he’s thrown the javelin for many, many years. There’s another pitcher who’s one of the best pitchers in college baseball right now in Japan who’s up to 102 miles an hour and attributes a lot of his success to throwing the volleyball and the volleyball teaching him how to move. For myself, I use a football to clean up my arm action. When you use different implements, it’ll give you a new stimulus. It can clean up a lot of the mechanics that you wouldn’t be able to with the baseball itself. For myself and my training to throw 100 miles an hour, I use all of these implements for my arm action. Now, I have a tendency of forearm flyout where my forearm flies out this way, and so I want to keep it a little bit more directed toward the plate. Some of the best ways to do this is to throw the javelin. Because to throw the javelin far, you can’t be flying out. You need to go straight through. The football as well needs to be straight through. Also, just hitting the volleyball. I love watching volleyball players hit the volleyball because the upper mechanics are so efficient. So if you feel stuck in your training, start incorporating cross training.

Josh Gessner

29,140 views • 11 months ago

I turned myself into an attractive 28 year old influencer to promote my toothpaste brand. With Wan 2.2 + this AI workflow, I can turn 1 video shoot into infinite UGC variations featuring different avatar personas. Need your ad to speak to old men? Moms? Truckers? Funeral home operators? Well, now you can hit every cohort with a single winning ad concept. Here's how it works: • Film yourself once reading any script • AI generates diverse avatars for your target audience • Wan 2.2 video model swaps you out with realistic characters • ElevenLabs clones voices to match each persona • Result: 50+ ad variations from 1 filming session Marketers are using this exact system to test across dozens of demographics without hiring actors or reshooting content. Saves 20+ hours per campaign and cuts production costs by 90%. Want the complete prompts and a step-by-step tutorial video? Tap "👍 Like" to save this. Follow, RT, and comment 'AVATAR' below. I'll DM you the full workflow + all prompts for free.

David Roberts

189,169 views • 10 months ago