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ROCK’S STRETCHING JOURNAL Week09: I Stopped Training Everthing At Once 🙅🏻‍♂️ This video officially begins my new 3-session training structure: S1 — ENTRANCE (width) S2 — BEND (second ring) S3 — DEPTH (adaptation)

Rock O‘Yang

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28,400 views • 2 days ago •via X (Twitter)

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Let's talk about training and how hard you need to push yourself at home before you go for any trials. I remember when I knew that I will be traveling for a trial, i stopped going out of my neighbourhood to play football and I just locked in on training for 2month straight. I will do a roadside jogging from my house to the pitch at Ashiribo and I will train for like 2hrs or more and that was my routine every week except Sundays to rest and I did that for 2 months. Victor McDonald assigned me a coach to train and get me ready and he was by far the hardest I have ever trained. Days where I will tie a tyre around my waist to run on the sand, I did a lot of running,strength training, speed work on the sand with tyre. People who see me train have no idea why I was k!lling myself to train that hard. I once trained with Raphael Edereho a guy who I was looking up at that time and I was not able to walk for 2 days after training with him. I trained like my life depends on it. And I stayed away from many things that will slow me down like, bad friends, drinking, sm0king, including girls and I was just locked in on my purpose to make it happen and it paid off. After 3 months that I left Nigeria the news broke out that I signed for Sölvesborg GIF and People were surprised to see it in the news paper and even my father got asked questions like how or when did I traveled. I thought I trained well enough but to my surprise I was no where near ready at all. I couldn't believe what I saw during the trainings I had to call one of my coaches back home and tell him how hard the trainings are for me. The new generation of young players don't like to train and they except someone to just come and say I will take you to trials. Many have failed not because they don't have the talent but because they don't train enough to meet the standards and social media has made them think otherwise. Your preparation is the most important thing you can do for sure yourself and just like Kobe Bryant said if you are well prepared for it you will not be afraid of failure.

Abiola Dauda

15,427 views • 2 months ago

Training to failure isn’t needed to max gains? Some people believe that you must take every set to failure in order to maximize muscle gains, but emerging literature suggests this may not be the case A new study examined training to failure vs stopping 1 to 2 reps shy of failure & found that the participants gained the same amount of muscle mass from both training styles There are several strengths to this study. First of all, they used a unilateral design where each participant was their own control by training one leg taking each set to failure and stopping 1-2 reps shy on the other. This helps negate any genetic induced differences since each person is acting as their own control Second, they matched training volumes to the participants previous volumes. This is a HUGE strength that is often overlooked in other studies Third, they used participants that were well trained (at least 3 years resistance training experience) Fourth, they had them all eat in a slight calorie surplus This adds to a growing body of literature demonstrating that training to absolute failure isn’t needed for gains & is likely counterproductive for optimal strength gains due to excess fatigue Interestingly, each group had no difference in total reps performed. That may seem strange when one group is going to failure but the other is stopping 1-2 reps shy. This can be explained by lower inter-set fatigue in the non failure group. For example if a failure group hits failure at 10 reps in set 1, they may only get 8 on the next set, and 6 on the next set. Whereas they might have been able to do 8 reps every set if they didn’t go to all out failure. As such, if you do train to failure I recommend only going to failure on your very last set of an exercise If you want to know how to implement this sort of programming make sure you check out the Biolayne Workoit Builder to get access to all my evidence based programs to help you get strong AF & build muscle 👊

Training to failure isn’t needed to max gains? Some people believe that you must take every set to failure in order to maximize muscle gains, but emerging literature suggests this may not be the case A new study examined training to failure vs stopping 1 to 2 reps shy of failure & found that the participants gained the same amount of muscle mass from both training styles There are several strengths to this study. First of all, they used a unilateral design where each participant was their own control by training one leg taking each set to failure and stopping 1-2 reps shy on the other. This helps negate any genetic induced differences since each person is acting as their own control Second, they matched training volumes to the participants previous volumes. This is a HUGE strength that is often overlooked in other studies Third, they used participants that were well trained (at least 3 years resistance training experience) Fourth, they had them all eat in a slight calorie surplus This adds to a growing body of literature demonstrating that training to absolute failure isn’t needed for gains & is likely counterproductive for optimal strength gains due to excess fatigue Interestingly, each group had no difference in total reps performed. That may seem strange when one group is going to failure but the other is stopping 1-2 reps shy. This can be explained by lower inter-set fatigue in the non failure group. For example if a failure group hits failure at 10 reps in set 1, they may only get 8 on the next set, and 6 on the next set. Whereas they might have been able to do 8 reps every set if they didn’t go to all out failure. As such, if you do train to failure I recommend only going to failure on your very last set of an exercise If you want to know how to implement this sort of programming make sure you check out the Biolayne Workoit Builder to get access to all my evidence based programs to help you get strong AF & build muscle 👊

Layne Norton, PhD

48,374 views • 2 years ago

someone leaked MiroFish's wallet address last night Op0jogggg - $1.48M profit in 4 months i copied it with OpenClaw and made $37K in 3 days this isn't another bot story MiroFish is the chinese AI that's been destroying polymarket since november betting on: - trump rallies - premier league - valorant tournaments - norwegian third division football nobody can be expert in all of this the machine analyzes terabytes in a second, finds arbitrage windows, places bets milliseconds before odds update found the wallet leak on a telegram channel at 4 AM gave my OpenClaw the address: "watch this wallet, copy every entry signal, execute 2 seconds after it does" day 1: bot copied 73 trades didn't understand why any of them made sense just pure mimicry $900 → $8,200 day 2: OpenClaw started finding the pattern: MiroFish doesn't bet on outcomes it bets on when polymarket will reprice whale moves USDC on-chain → 6 seconds later polymarket odds shift X sentiment explodes → 8 seconds later market updates MiroFish enters that 6-8 second gap $8,200 → $19,400 day 3: my bot stopped copying started predicting the same windows MiroFish hunts → blockchain USDC tracker → X sentiment API → orderbook depth scanner when all 3 spike = entry $19,400 → $37,200 here's what fucked me up: checked the logs this morning OpenClaw placed 14 trades before MiroFish entered the same markets it learned the pattern now it's front running the bot i was copying we're not trading anymore we're training AI to hunt other AI copytrade mirofish before this edge dies: this either prints forever or stops working tomorrow

someone leaked MiroFish's wallet address last night Op0jogggg - $1.48M profit in 4 months i copied it with OpenClaw and made $37K in 3 days this isn't another bot story MiroFish is the chinese AI that's been destroying polymarket since november betting on: - trump rallies - premier league - valorant tournaments - norwegian third division football nobody can be expert in all of this the machine analyzes terabytes in a second, finds arbitrage windows, places bets milliseconds before odds update found the wallet leak on a telegram channel at 4 AM gave my OpenClaw the address: "watch this wallet, copy every entry signal, execute 2 seconds after it does" day 1: bot copied 73 trades didn't understand why any of them made sense just pure mimicry $900 → $8,200 day 2: OpenClaw started finding the pattern: MiroFish doesn't bet on outcomes it bets on when polymarket will reprice whale moves USDC on-chain → 6 seconds later polymarket odds shift X sentiment explodes → 8 seconds later market updates MiroFish enters that 6-8 second gap $8,200 → $19,400 day 3: my bot stopped copying started predicting the same windows MiroFish hunts → blockchain USDC tracker → X sentiment API → orderbook depth scanner when all 3 spike = entry $19,400 → $37,200 here's what fucked me up: checked the logs this morning OpenClaw placed 14 trades before MiroFish entered the same markets it learned the pattern now it's front running the bot i was copying we're not trading anymore we're training AI to hunt other AI copytrade mirofish before this edge dies: this either prints forever or stops working tomorrow

ZER

74,356 views • 4 months ago

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TeamYouTube . My channel Just got hit with INAUTHENTIC CONTENT . YouTube's AI Wrongly flagged my channel as low effort and mass produced content. All of our scripts are Original. Every topic is distinct and well researched, covering distinct historical subjects with genuine educational depth. No two videos are the same and no template can produce what we make. These are 3 hour long documentaries. We use real archived historical footage that takes a long time to find and source and edit with the narration. We do not use AI images or video. This is not something you can template. This is not something you can mass produce. The render time alone makes that impossible. I did everything right. I made a detailed appeal video explaining the entire workflow of my channel. How we write the scripts, how we do the editing, how we record the voiceovers, everything in full detail. That video has zero views. It was auto rejected. No one at YouTube watched a single second of it. $5,500 from last month is on hold. $2,200 from this month is on hold. That is $7,700 sitting frozen because an automated system made a decision that no human being has actually verified. Real people are being affected by this. I am not asking for a free pass. I am just asking for one real human to look at this case once .

Blain Fernandes

12,135 views • 4 months ago

you tend to hear this a lot from people outside or new to ML, and I often point to a talk Ilya gave a few years back: 1) think of any decent deep neural net that has enough memory and sequential ops as just a big parallel computer 2) training this neural net is doing search over computer programs that maximize your objective 3)unless you have some large bottleneck (and given you can successfully optimize this system) you’ll find that these parallel computers are highly robust to architectural changes. 4) this is because computers are great at simulating each other. your new architecture can usually be straightforwardly simulated ‘inside’ your old architecture. 5) it’s not that architecture doesn’t matter, but it mostly matters with respect to (1) fundamental bottlenecks in this parallel computer (2) modifications that make models easier to optimize, since this argument only holds if your optimization is good (3) compute efficiency/system efficiency wins that make learning easier or faster. 6) it’s quite possible that new architectures will lead to breakthroughs in machine learning, but we should first start with bottlenecks, not naturalist intuitions about the ‘form’ of AI should take. until you understand this it seems surprising that small models trained longer are better than undertrained big models, that depth and width are surprisingly interchangeable, that talking to a model with an MoE or sparse attention or linear attention is approximately the same iso evals.

you tend to hear this a lot from people outside or new to ML, and I often point to a talk Ilya gave a few years back: 1) think of any decent deep neural net that has enough memory and sequential ops as just a big parallel computer 2) training this neural net is doing search over computer programs that maximize your objective 3)unless you have some large bottleneck (and given you can successfully optimize this system) you’ll find that these parallel computers are highly robust to architectural changes. 4) this is because computers are great at simulating each other. your new architecture can usually be straightforwardly simulated ‘inside’ your old architecture. 5) it’s not that architecture doesn’t matter, but it mostly matters with respect to (1) fundamental bottlenecks in this parallel computer (2) modifications that make models easier to optimize, since this argument only holds if your optimization is good (3) compute efficiency/system efficiency wins that make learning easier or faster. 6) it’s quite possible that new architectures will lead to breakthroughs in machine learning, but we should first start with bottlenecks, not naturalist intuitions about the ‘form’ of AI should take. until you understand this it seems surprising that small models trained longer are better than undertrained big models, that depth and width are surprisingly interchangeable, that talking to a model with an MoE or sparse attention or linear attention is approximately the same iso evals.

will depue

215,052 views • 7 months ago

In 2025, the Western States 100 is going to be a track meet. I think that in 5-10 years, almost every major ultra is going to be a track meet. Our big theory is that as ultrarunners probe the limits of human physiology, athletes will be leaving time on the table if they aren’t developing their true speed. And most athletes can’t leave time on the table as the margins get more and more narrow. That theory doesn’t mean you need to be ready for a track race—I would get my doors blown off at the Olympic Trials. It just means that every aspect of running economy has a relation to top speed. Mile speed is connected to 5k speed is connected to 100-mile speed is connected to multi-day speed. We all have our limiters, and pushing back the speed limit via strides and workouts causes immediate and sustained improvement even at very low effort levels. The same principles apply to athletes doing their first ultra or dreaming of staying ahead of cutoffs (it might even apply more). Plus, it’s fun. The amazing Cody Bare filmed my workout yesterday for The Feed (for an upcoming video series), and this is the finish of the final 3 minute interval. The session: 4 miles Z2 warm-up 6 x (3 min on/1 min easy/1 min on/1 min easy) GI training consisting of 32 oz fluid at once 2.5 miles Z2/Z3 steady running after workout At the end of the video, I go to my arms, inspired by track/trail runners like Allie Ostrander and Anna Gibson and Grayson Murphy (hitting 3:50 min/mile pace). As I said at the end with my hands on my knees: “fun.” It’s so exciting to see how our understanding of human limits is changing with big fueling, an emphasis on long-term health, and fun training. I am so inspired by everyone out there pushing their own limits, wherever those limits happen to be. I can’t wait to see where all of you go in 2025. Let’s have some fun 🧡 WE LOVE YOU ALL (even more than we love strides)

In 2025, the Western States 100 is going to be a track meet. I think that in 5-10 years, almost every major ultra is going to be a track meet. Our big theory is that as ultrarunners probe the limits of human physiology, athletes will be leaving time on the table if they aren’t developing their true speed. And most athletes can’t leave time on the table as the margins get more and more narrow. That theory doesn’t mean you need to be ready for a track race—I would get my doors blown off at the Olympic Trials. It just means that every aspect of running economy has a relation to top speed. Mile speed is connected to 5k speed is connected to 100-mile speed is connected to multi-day speed. We all have our limiters, and pushing back the speed limit via strides and workouts causes immediate and sustained improvement even at very low effort levels. The same principles apply to athletes doing their first ultra or dreaming of staying ahead of cutoffs (it might even apply more). Plus, it’s fun. The amazing Cody Bare filmed my workout yesterday for The Feed (for an upcoming video series), and this is the finish of the final 3 minute interval. The session: 4 miles Z2 warm-up 6 x (3 min on/1 min easy/1 min on/1 min easy) GI training consisting of 32 oz fluid at once 2.5 miles Z2/Z3 steady running after workout At the end of the video, I go to my arms, inspired by track/trail runners like Allie Ostrander and Anna Gibson and Grayson Murphy (hitting 3:50 min/mile pace). As I said at the end with my hands on my knees: “fun.” It’s so exciting to see how our understanding of human limits is changing with big fueling, an emphasis on long-term health, and fun training. I am so inspired by everyone out there pushing their own limits, wherever those limits happen to be. I can’t wait to see where all of you go in 2025. Let’s have some fun 🧡 WE LOVE YOU ALL (even more than we love strides)

David Roche

60,917 views • 1 year ago

Back in January when Megan and I charted out the year, the dream of the Leadville 100 course record was overwhelming. But big dreams should be overwhelming. It was time to get to work. Our plan started from 3 principles: 1. I’d need to be capable of running a 13:xx 5k at altitude, or around sub-4 min mile fitness. 6-minute mile pace would need to be a jog on race day, and that all came from improving my running economy. 2. I had to get stronger to handle the unknown distance, both in terms of threshold climbing and actual muscular strength. 3. I’d have to run every step of the race, including Hope Pass. Most interestingly, none of those goals required lots of training volume. I did 8-12 hours of aerobic training most weeks with very few doubles given life constraints, usually around 60-75 miles of running and 1-2 bike rides (pulsing up and down for adaptations, with some bigger weeks and a longer down period for my accident). You don’t need to do consistent 100+ mile weeks to be good at this sport. I have been building endurance bricks for 18 years, and every brick counts. With that time, we applied 6 ideas: 1. Most weeks had a speed workout (often pacing Allie Ostrander ahead of the Olympic Trials 🔥), culminating in 12 x 400 on short rest in 63-64 seconds at altitude in June 2. I’d do threshold sessions approximately every other week, often on the uphill treadmill at 8% or 15% grade, culminating in a massive 12 x 5 minute session a few weeks before race day 3. I did uphill treadmill runs in Z2 at 20% grade all year, including for 10 min after as many aerobic runs as I could 4. I did 3-4 days of strides every single week. My strength is my speed. 5. I biked once per week in place of a run, often using Zwift races for hard sessions (A+ racing category in Zwift!) 6. Every week, I did Ultra Legs strength + squats and took a rest day, plus did heat training Big takeaway: you don’t need to do wild volume to have success in ultras. Get fast, stay fast year round, spend time in Z2, and stack some fun bricks in the context of your life. The record may have shocked the ultra world. But as Megan said, it only shocked people who haven’t been following my Strava for the last decade 🧡

Back in January when Megan and I charted out the year, the dream of the Leadville 100 course record was overwhelming. But big dreams should be overwhelming. It was time to get to work. Our plan started from 3 principles: 1. I’d need to be capable of running a 13:xx 5k at altitude, or around sub-4 min mile fitness. 6-minute mile pace would need to be a jog on race day, and that all came from improving my running economy. 2. I had to get stronger to handle the unknown distance, both in terms of threshold climbing and actual muscular strength. 3. I’d have to run every step of the race, including Hope Pass. Most interestingly, none of those goals required lots of training volume. I did 8-12 hours of aerobic training most weeks with very few doubles given life constraints, usually around 60-75 miles of running and 1-2 bike rides (pulsing up and down for adaptations, with some bigger weeks and a longer down period for my accident). You don’t need to do consistent 100+ mile weeks to be good at this sport. I have been building endurance bricks for 18 years, and every brick counts. With that time, we applied 6 ideas: 1. Most weeks had a speed workout (often pacing Allie Ostrander ahead of the Olympic Trials 🔥), culminating in 12 x 400 on short rest in 63-64 seconds at altitude in June 2. I’d do threshold sessions approximately every other week, often on the uphill treadmill at 8% or 15% grade, culminating in a massive 12 x 5 minute session a few weeks before race day 3. I did uphill treadmill runs in Z2 at 20% grade all year, including for 10 min after as many aerobic runs as I could 4. I did 3-4 days of strides every single week. My strength is my speed. 5. I biked once per week in place of a run, often using Zwift races for hard sessions (A+ racing category in Zwift!) 6. Every week, I did Ultra Legs strength + squats and took a rest day, plus did heat training Big takeaway: you don’t need to do wild volume to have success in ultras. Get fast, stay fast year round, spend time in Z2, and stack some fun bricks in the context of your life. The record may have shocked the ultra world. But as Megan said, it only shocked people who haven’t been following my Strava for the last decade 🧡

David Roche

158,587 views • 1 year ago

Claude + Obsidian + n8n built AI models that now clear $5,400 a month. Most builders chase the next big model and burn cash on cloud bills. This stack runs everything local and compounds your own work. The base is Karpathy’s 1-file method: one giant markdown note in Obsidian. New thoughts, datasets, training logs, and prompt tests dump at the top. Old ones sink. Every few days you reread and pull the survivors back up. No folders. No tags. No plugins. The rereading is the system. The flaw hits past 10,000 lines. No human rereads that. Claude takes over. Point it at the vault folder. Ask: “What worked on my last fine-tune.” “Find the 3 datasets I keep circling.” “What hyperparameters failed in February.” It answers from your own notes with exact quotes in 15 seconds. n8n closes the loop on autopilot. Once a week it triggers Claude: read the last 7 days, surface the 5 experiments worth pulling up, flag contradictions, then spin up the next training job. The model stays alive. You never chase old logs again. Week 1 feels like nothing. Week 4 you hit the first “I already solved this drift in January.” Month 3 you ship custom agents and fine-tunes faster than most teams. Most builders lose ideas in scattered notebooks. This one compounds because dumping takes zero discipline. You use the same system to create high-quality AI models, package them as agents, and sell them on marketplaces or to clients. $5,000+ a month becomes the floor once the vault hits critical mass. Notion stores what you thought. This thing ships models that argue back and pay rent.

Claude + Obsidian + n8n built AI models that now clear $5,400 a month. Most builders chase the next big model and burn cash on cloud bills. This stack runs everything local and compounds your own work. The base is Karpathy’s 1-file method: one giant markdown note in Obsidian. New thoughts, datasets, training logs, and prompt tests dump at the top. Old ones sink. Every few days you reread and pull the survivors back up. No folders. No tags. No plugins. The rereading is the system. The flaw hits past 10,000 lines. No human rereads that. Claude takes over. Point it at the vault folder. Ask: “What worked on my last fine-tune.” “Find the 3 datasets I keep circling.” “What hyperparameters failed in February.” It answers from your own notes with exact quotes in 15 seconds. n8n closes the loop on autopilot. Once a week it triggers Claude: read the last 7 days, surface the 5 experiments worth pulling up, flag contradictions, then spin up the next training job. The model stays alive. You never chase old logs again. Week 1 feels like nothing. Week 4 you hit the first “I already solved this drift in January.” Month 3 you ship custom agents and fine-tunes faster than most teams. Most builders lose ideas in scattered notebooks. This one compounds because dumping takes zero discipline. You use the same system to create high-quality AI models, package them as agents, and sell them on marketplaces or to clients. $5,000+ a month becomes the floor once the vault hits critical mass. Notion stores what you thought. This thing ships models that argue back and pay rent.

Rugikk

12,599 views • 20 days ago

Be so good they can't ignore you! I had an investor last year ask me (or rather tell me) - "you can't really think AvatarOS can compete with Epic Games and Metahuman on digital humans". (Never mind that I wasn't proposing to compete with Epic) Imagine what that investor is thinking now with the release of #sora and the avalanche of other AI video generation models... First, lets give this investor a break. I'm sure my pseudo pitch was just a complete trainwreck. But, second, let's take this comment at face value for a second. 1. Yes, I absolutely do. 2. One of the great things about tech and VC is the ability to play positive sum games. These things are best built as multipliers of other great technology. Metahuman walked so we could run! 3. What do you think people told Tim Sweeney when he was building Epic Games??? 4. Is the contention that all cool stuff has already been built, or is so complex that only OpenAI or Epic Games etc. are allowed to build it? 5. The very nature of big companies and their inherent incentive structures give startups and small focused teams an immense advantage, especially when it comes to new technology and especially during large disruptions. Find the future that you want to see, then build it, and make it so awesome others can't help but see it too. Here's a small peak behind the curtain - some progress towards procedurally analyzing, training, and generating authentic human avatars and authentic human motion that is three-dimensional, interactive, and interoperable, while maintaining the subtle nuance that makes individuals unique. Shout out to for the voice training. A lot of cool stuff coming up - check us out at and subscribe to receive updates! I'll be at #GDC and #GTC later this month, reach out!

Be so good they can't ignore you! I had an investor last year ask me (or rather tell me) - "you can't really think AvatarOS can compete with Epic Games and Metahuman on digital humans". (Never mind that I wasn't proposing to compete with Epic) Imagine what that investor is thinking now with the release of #sora and the avalanche of other AI video generation models... First, lets give this investor a break. I'm sure my pseudo pitch was just a complete trainwreck. But, second, let's take this comment at face value for a second. 1. Yes, I absolutely do. 2. One of the great things about tech and VC is the ability to play positive sum games. These things are best built as multipliers of other great technology. Metahuman walked so we could run! 3. What do you think people told Tim Sweeney when he was building Epic Games??? 4. Is the contention that all cool stuff has already been built, or is so complex that only OpenAI or Epic Games etc. are allowed to build it? 5. The very nature of big companies and their inherent incentive structures give startups and small focused teams an immense advantage, especially when it comes to new technology and especially during large disruptions. Find the future that you want to see, then build it, and make it so awesome others can't help but see it too. Here's a small peak behind the curtain - some progress towards procedurally analyzing, training, and generating authentic human avatars and authentic human motion that is three-dimensional, interactive, and interoperable, while maintaining the subtle nuance that makes individuals unique. Shout out to for the voice training. A lot of cool stuff coming up - check us out at and subscribe to receive updates! I'll be at #GDC and #GTC later this month, reach out!

Isaac Bratzel

31,109 views • 2 years ago

My fox shooting garden defending AI robot is finally done and WORKING! 🤩 (Don’t worry it only shoots 💦 water) After months of slowly moving forward with each part I finished the last step to train a TensorFlow model on the footage of the 🦊 fox I collected hours of footage 📹 with the fox roaming around my garden, from this I labeled around 2000 images with the fox by hand ✋ Honestly, I was quite skeptical training the model was actually gonna work, maybe this was partly the reason I avoided working on this until the very end. If I couldn’t train a model to detect the fox, this whole robot would never be able to function properly. On the flipside though, with no previous experience in hardware or electronics there was a bit of a learning curve and I didn’t want to end up labeling thousands of images, training a TensorFlow model, only to fail on building the hardware. As I started building, I realized that mixing hardware and software adds quite another dimension to debugging things. At times I wasted hours debugging code in my IDE, only to realize the issue was somewhere in the electronics. Furthermore, combining this side project with a full time job and a young family, is not always easy. It can be quite frustrating, to know you only need 4 hours of concentrated effort for a small task, having to spread it out across a week of 20min increments. Then, a few months into the build I noticed the fox had stopped coming to my garden, in fact one day, I recorded her walking with 3 cute little 🐶 pups, and the next day I saw her moving out of my garden completely. Did she know I was building a robot? I had this strange mix of feelings, happy my garden was safe from poop and digging, happy she was safe with her pups, but how was I gonna finish this project if my robot had no fox to detect? For sure they would be back next year, I figured I could postpone the whole thing until next winter, but I also knew it was gonna be much harder to pick up momentum if I did let it sit there for six months. So I decided to keep working, hoping the fox would reappear,.. but she never did. As I finished labeling the footage and started training my model, I could finally see the mAP results, quantifying the precision of my object detection model. It was measuring at 78% across different metrics on detecting my fox. I quickly ran the model on some of the video footage I got from my fox. Inference speed took a hit, but it did a near perfect job detecting the fox, even when she was deep down in the grass or wizzing past in a motion blur. It took me by surprise how well it worked. With the default model I had to drop my confidence threshold way down to 15%, to recognize the fox as 🦜“bird” in one or two frames, with my custom model it followed the fox all the way down to the back of the garden! Still this didn’t solve the issue of there being no actual fox in my garden and how was I gonna wrap this project in a short timeframe. I played with the idea of putting a fox toy 🧸 on an RC 🚗 car, or borrowing a dog to run around the garden to test. Friends suggested I run around the garden in a fox costume.. what a ridiculous idea. I wasn’t really feeling the idea of running around the garden in a floppy cloth fox 🎭 costume, but had a look anyway. I came across these self inflating costumes. This actually could be perfect. Since it’s inflated, it would hold its shape super well, making it much easier to label, train and be recognized by my robot. So I got the costume and shot a time lapse of myself as a fox walking around the garden. I labeled it to around 600 images. Ran the model training again and got a mAP result of 82%. This was even better than my real fox! At this point I knew this was gonna work. So here’s the final 🎥 video, just having some fun with it. I’ll update here whenever the real fox does come back. On a final note, I’m looking for (remote) jobs in these fields of AI now: - object detection - visual generative AI - 3D (nerfs + gaussian splats) So if you know anything let me know! My DMs are open 😊

My fox shooting garden defending AI robot is finally done and WORKING! 🤩 (Don’t worry it only shoots 💦 water) After months of slowly moving forward with each part I finished the last step to train a TensorFlow model on the footage of the 🦊 fox I collected hours of footage 📹 with the fox roaming around my garden, from this I labeled around 2000 images with the fox by hand ✋ Honestly, I was quite skeptical training the model was actually gonna work, maybe this was partly the reason I avoided working on this until the very end. If I couldn’t train a model to detect the fox, this whole robot would never be able to function properly. On the flipside though, with no previous experience in hardware or electronics there was a bit of a learning curve and I didn’t want to end up labeling thousands of images, training a TensorFlow model, only to fail on building the hardware. As I started building, I realized that mixing hardware and software adds quite another dimension to debugging things. At times I wasted hours debugging code in my IDE, only to realize the issue was somewhere in the electronics. Furthermore, combining this side project with a full time job and a young family, is not always easy. It can be quite frustrating, to know you only need 4 hours of concentrated effort for a small task, having to spread it out across a week of 20min increments. Then, a few months into the build I noticed the fox had stopped coming to my garden, in fact one day, I recorded her walking with 3 cute little 🐶 pups, and the next day I saw her moving out of my garden completely. Did she know I was building a robot? I had this strange mix of feelings, happy my garden was safe from poop and digging, happy she was safe with her pups, but how was I gonna finish this project if my robot had no fox to detect? For sure they would be back next year, I figured I could postpone the whole thing until next winter, but I also knew it was gonna be much harder to pick up momentum if I did let it sit there for six months. So I decided to keep working, hoping the fox would reappear,.. but she never did. As I finished labeling the footage and started training my model, I could finally see the mAP results, quantifying the precision of my object detection model. It was measuring at 78% across different metrics on detecting my fox. I quickly ran the model on some of the video footage I got from my fox. Inference speed took a hit, but it did a near perfect job detecting the fox, even when she was deep down in the grass or wizzing past in a motion blur. It took me by surprise how well it worked. With the default model I had to drop my confidence threshold way down to 15%, to recognize the fox as 🦜“bird” in one or two frames, with my custom model it followed the fox all the way down to the back of the garden! Still this didn’t solve the issue of there being no actual fox in my garden and how was I gonna wrap this project in a short timeframe. I played with the idea of putting a fox toy 🧸 on an RC 🚗 car, or borrowing a dog to run around the garden to test. Friends suggested I run around the garden in a fox costume.. what a ridiculous idea. I wasn’t really feeling the idea of running around the garden in a floppy cloth fox 🎭 costume, but had a look anyway. I came across these self inflating costumes. This actually could be perfect. Since it’s inflated, it would hold its shape super well, making it much easier to label, train and be recognized by my robot. So I got the costume and shot a time lapse of myself as a fox walking around the garden. I labeled it to around 600 images. Ran the model training again and got a mAP result of 82%. This was even better than my real fox! At this point I knew this was gonna work. So here’s the final 🎥 video, just having some fun with it. I’ll update here whenever the real fox does come back. On a final note, I’m looking for (remote) jobs in these fields of AI now: - object detection - visual generative AI - 3D (nerfs + gaussian splats) So if you know anything let me know! My DMs are open 😊

Jeroen Pixel

55,797 views • 2 years ago

On May 8, 2025 I underwent spinal fusion surgery, a 6 hour procedure in which I was filleted from front to back. First, my abdomen was opened up so that the surgeon could scrape out the disc between L5 and S1, replacing it with a perforated cage containing bone grafting material that was screwed into my vertebra. Then I was flipped over and opened up on my back so that my surgeon could screw vertical rods into L5 and S1 to secure my spine position to ensure the fusion sets properly. The procedure was successful, correcting 15 years of lower back debilitation due to severe Spondylolisthesis. However, the recovery process demanded I endure far more than I bargained for, debilitating me in ways I thought might handicap me permanently. For the first 3 months I could barely move. For the first six months my activity was limited to walking only. Pain was constant. At nine months I was still in so much discomfort, still so limited in my range of motion, still too unstable to do anything to elevate my heart rate. My weight ballooned. My muscles atrophied. My mood plummeted. And I was becoming resigned to the idea that my athletic identity (let alone performing extreme feats of ultra-endurance) was a thing of the past, a memory well behind me. But very slowly after that I began to turn a corner. At ten months, I finally felt stable enough to resume a very modest non-spine compressing return to fitness exercise regimen. Zone 1 indoor cycling, gentle core work, extremely low weight / high rep resistance training. Proceeding on a ‘less is more’ mandate in late November (which demands discipline for someone like myself prone to taking everything to the extreme, I just showed up every single morning to do what I could, and stop well before doing more than I should. Today I am down 35 pounds from November (207 to 171) including a body fat reduction from 20% to 11%. More importantly, I am beginning to feel like myself again. Grateful and hopeful. I still have a long way to go—it takes 12-18 months for the fusion to fully set. My surgeon was not optimistic that I will be able to run again. Time will tell of course, but I’m confident that provided I continue to proceed patiently that I have a future in which running can become part of my new reality. Towards that end I have a goal—which is to celebrate my 60th birthday this Fall by participating in the NYC Marathon. But here’s the thing. I’m not trying to return to who I once was. I’ve leaned into the stillness this experience has demanded of me to become someone new and better. I am posting this story not for external validation but rather to say that change is always possible. And the way to do it is the same way I have navigated every one of my many life transformations, from alcoholism to sobriety, from sedentary to middle aged ultra endurance athlete, and from a corporate lawyer career to becoming an author and podcaster: getting sober and staying sober: by taking contrary action consistently and religiously—one day at a time. As Chris Paul said on my podcast, “keep stacking days.” And remember, every obstacle life presents you is simply an opportunity custom-designed for your growth and evolution.

On May 8, 2025 I underwent spinal fusion surgery, a 6 hour procedure in which I was filleted from front to back. First, my abdomen was opened up so that the surgeon could scrape out the disc between L5 and S1, replacing it with a perforated cage containing bone grafting material that was screwed into my vertebra. Then I was flipped over and opened up on my back so that my surgeon could screw vertical rods into L5 and S1 to secure my spine position to ensure the fusion sets properly. The procedure was successful, correcting 15 years of lower back debilitation due to severe Spondylolisthesis. However, the recovery process demanded I endure far more than I bargained for, debilitating me in ways I thought might handicap me permanently. For the first 3 months I could barely move. For the first six months my activity was limited to walking only. Pain was constant. At nine months I was still in so much discomfort, still so limited in my range of motion, still too unstable to do anything to elevate my heart rate. My weight ballooned. My muscles atrophied. My mood plummeted. And I was becoming resigned to the idea that my athletic identity (let alone performing extreme feats of ultra-endurance) was a thing of the past, a memory well behind me. But very slowly after that I began to turn a corner. At ten months, I finally felt stable enough to resume a very modest non-spine compressing return to fitness exercise regimen. Zone 1 indoor cycling, gentle core work, extremely low weight / high rep resistance training. Proceeding on a ‘less is more’ mandate in late November (which demands discipline for someone like myself prone to taking everything to the extreme, I just showed up every single morning to do what I could, and stop well before doing more than I should. Today I am down 35 pounds from November (207 to 171) including a body fat reduction from 20% to 11%. More importantly, I am beginning to feel like myself again. Grateful and hopeful. I still have a long way to go—it takes 12-18 months for the fusion to fully set. My surgeon was not optimistic that I will be able to run again. Time will tell of course, but I’m confident that provided I continue to proceed patiently that I have a future in which running can become part of my new reality. Towards that end I have a goal—which is to celebrate my 60th birthday this Fall by participating in the NYC Marathon. But here’s the thing. I’m not trying to return to who I once was. I’ve leaned into the stillness this experience has demanded of me to become someone new and better. I am posting this story not for external validation but rather to say that change is always possible. And the way to do it is the same way I have navigated every one of my many life transformations, from alcoholism to sobriety, from sedentary to middle aged ultra endurance athlete, and from a corporate lawyer career to becoming an author and podcaster: getting sober and staying sober: by taking contrary action consistently and religiously—one day at a time. As Chris Paul said on my podcast, “keep stacking days.” And remember, every obstacle life presents you is simply an opportunity custom-designed for your growth and evolution.

richroll

442,096 views • 4 months ago

Before high carbohydrate fueling, I finished every race totally wrecked. Even in my best races, I’d cross the finish line desperate to stop. As an athlete, I had no idea how anyone could go farther than 50k. With high carb, I had a new realization: what I thought was an endurance limitation was just a fueling limitation. 100 milers feel easier than some trail 30ks I used to do. But knowing the science of high carb and actually slurping down tons of gels in a race setting are two different things. Episode 10 is all about a practice race at the Cheyenne Mountain 50k where the main goal was to get over 125 grams of carbs per hour (500+ calories) in an urgent, high-performance setting (I got to 135 grams per hour, which was a prelude to 150 grams per hour at my 50 miler 2 weeks later). It’s a super fun video by amazing director Cody Bare and presented by The Feed 🧡 Watch here and subscribe: What does high carb look like for everyone? It doesn’t have to be complicated! When you are pushing harder, start with a 40 gram of carb gel every 30 min plus electrolyte drink for hydration, dialed into your sweat needs. I think that anyone can learn to take a 40 gram gel every 30 min, and that alone will do more for endurance performance than almost every training intervention (except easy running and strides, of course 😂). You deserve high carb. In hard events, it improves adaptation AND health outcomes. Don’t listen to anyone preaching to you about health risks or fat adaptation like it’s 2010. The science has moved past that. Fuel the work you are doing. I just wish I could go back to 2010 and show my younger self this video of the end of a 50k. Oh how different the next 13 years could have been! That’s why I come to you with this message now: Give yourself the gift of higher carb, and higher carb will give you the gift of exploring new horizons. Carbs are for everyone 🧡💚💜

Before high carbohydrate fueling, I finished every race totally wrecked. Even in my best races, I’d cross the finish line desperate to stop. As an athlete, I had no idea how anyone could go farther than 50k. With high carb, I had a new realization: what I thought was an endurance limitation was just a fueling limitation. 100 milers feel easier than some trail 30ks I used to do. But knowing the science of high carb and actually slurping down tons of gels in a race setting are two different things. Episode 10 is all about a practice race at the Cheyenne Mountain 50k where the main goal was to get over 125 grams of carbs per hour (500+ calories) in an urgent, high-performance setting (I got to 135 grams per hour, which was a prelude to 150 grams per hour at my 50 miler 2 weeks later). It’s a super fun video by amazing director Cody Bare and presented by The Feed 🧡 Watch here and subscribe: What does high carb look like for everyone? It doesn’t have to be complicated! When you are pushing harder, start with a 40 gram of carb gel every 30 min plus electrolyte drink for hydration, dialed into your sweat needs. I think that anyone can learn to take a 40 gram gel every 30 min, and that alone will do more for endurance performance than almost every training intervention (except easy running and strides, of course 😂). You deserve high carb. In hard events, it improves adaptation AND health outcomes. Don’t listen to anyone preaching to you about health risks or fat adaptation like it’s 2010. The science has moved past that. Fuel the work you are doing. I just wish I could go back to 2010 and show my younger self this video of the end of a 50k. Oh how different the next 13 years could have been! That’s why I come to you with this message now: Give yourself the gift of higher carb, and higher carb will give you the gift of exploring new horizons. Carbs are for everyone 🧡💚💜

David Roche

42,201 views • 1 year ago

🚨 Charlie Kirk: FAILED Mass Shooting Event & 3 BRAND NEW Evidence Points More 2-Shot Theory Evidence To Consider: - The 2-man camera crew in the tunnel DELIBERATELY filmed the rear-right shot to Charlie Kirk's head. Why wasn't this evidence at court? Where is this video? - Dan Flood gave hand signals to start the operation. Dan Flood had no idea that an infrared beam wasn't just on the back of Charlie Kirk's head, but the beam was also on him during his hand signal. - Phil Lyman was directly in the LINE OF FIRE with an infrared beam on the back of his head too, but absolutely didn't know it. If Phil didn't miss Charlie Kirk being assassinated, we would VERY LIKELY be looking at a mass shooting event involving a Presidentially pardoned political candidate. This addition to Charlie's death would have caused a civil war overnight. I'm giving away so much tea before I release Charlie Kirk: Operation 322 PART VI... And I have a lot more to give! Charlie Kirk was shot TWICE at the same time; once by Bird of Prey drone platform, once with a pinfire pistol. I discovered this and released everything you need in my Charlie Kirk: Operation 322 series on YouTube. It's 8 hours long, but you can just check out PARTS IV & V if you're pressed for time. You can also read my "Charlie's CLOSURE" article right here on X... The second video in the article has a 10 minute frame-by-frame analysis that details both shots. CKO322 PART VI will show you ALL of the details on how this MASS SHOOTING operation actually failed... Stay Tuned...

WeAreNotGoingToMars

103,167 views • 12 days ago

hey here is the final result of octopus invaders on nvidia's flagship at full precision. nemotron super 120B on 2x H200 NVL. BF16 unquantized. 287GB of VRAM. hermes agent as the harness. 60 tok/s. first try it autonomously coded for 6 minutes straight. created 11 files. correct project structure. correct load order. started the server. i opened the browser and the result was a blank screen. i did not give up. second try i gave it a precise list of bugs and things to fix. it went back in for another 3 minutes. patched the code. served it again. still blank. so i did what any sane person would do. third try i just said the screen is blank, test it and fix it yourself. and this is where nemotron showed what it actually is. it became a debugger. you can see it in the video. realtime CSS test squares, red screen flashes, hermes agent browser tools, inspecting its own output. it built the parallax background with planets and comets. it rendered a rocket ship that tracks your mouse with fire and bullet physics. the aesthetic is real. but no enemies spawn. no collision. not playable. what surprised me is qwen 27B one shotted this exact game on a single RTX 3090 at Q4 quant. and here is nvidia's flagship at full precision on enterprise hardware needing 3 tries and still not getting there. that makes my hope high for the undisputed qwen 122B which is about to face the same test next. same hardware. same prompt and same harness. lets see if it one shots or not. full session in the video. no cuts. 5x speed.

hey here is the final result of octopus invaders on nvidia's flagship at full precision. nemotron super 120B on 2x H200 NVL. BF16 unquantized. 287GB of VRAM. hermes agent as the harness. 60 tok/s. first try it autonomously coded for 6 minutes straight. created 11 files. correct project structure. correct load order. started the server. i opened the browser and the result was a blank screen. i did not give up. second try i gave it a precise list of bugs and things to fix. it went back in for another 3 minutes. patched the code. served it again. still blank. so i did what any sane person would do. third try i just said the screen is blank, test it and fix it yourself. and this is where nemotron showed what it actually is. it became a debugger. you can see it in the video. realtime CSS test squares, red screen flashes, hermes agent browser tools, inspecting its own output. it built the parallax background with planets and comets. it rendered a rocket ship that tracks your mouse with fire and bullet physics. the aesthetic is real. but no enemies spawn. no collision. not playable. what surprised me is qwen 27B one shotted this exact game on a single RTX 3090 at Q4 quant. and here is nvidia's flagship at full precision on enterprise hardware needing 3 tries and still not getting there. that makes my hope high for the undisputed qwen 122B which is about to face the same test next. same hardware. same prompt and same harness. lets see if it one shots or not. full session in the video. no cuts. 5x speed.

Sudo su

10,994 views • 4 months ago