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Rapi’s Aim Training Challenge You’re training methods may be…unconventional…but not even Rapi can doubt their effectiveness! find more Rapi content on patreon VA 💕😈🔞TemptyVA 🔞😈💕 Audio HentAudio 🇺🇦 🟢COMMS OPEN🟢

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I’m with my soldiers and am truly grateful to have so many of you supporting us in the comments. Not only are these packages morale boosters but ice breakers. I am a woman and from #Canada so it’s not always easy to get new soldiers to open up but by the time we got into the blindage I had a line of soldiers waiting to be treated. Every soldier got medical care and some relief but now I have a list of needed items from tools and power stations to braces for knees and elbows. Tomorrow even more new soldiers will arrive and this list will continue to grow. I know there are a lot of fundraisers but I can only speak for my battalion Alcatraz Battalion. When I joined the Armed Forces of Ukraine I made a decision to stop pulling myself in every direction and focus my energy. One frontline, one brigade and in turn helping all the soldiers that fight along side us. A unit can be outfitted but if the battalion is without, their combat effectiveness is impacted. If one blindage has power and comms and the next along the trench line is without, their combat effectiveness is impacted. In 2024 I spoke in the Netherlands about strategically placing aid along the 0 line and this is exactly what I meant. I need your help more than ever to turn these lists into equipment and supplies. Please don’t be indifferent, our freedom is depending on it. #Support93 #SupportUkraine

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🚨 KILL THE ALGORITHM — BRING BACK REAL DEBATE 🚨 It’s time we ditch the social media algorithms and go back to seeing what’s actually most popular — not what the AI thinks we want to see. The algorithm is keeping ALL of us stuck in digital echo chambers. If you’re a conservative, it only shows you conservative content. If you’re a liberal, it only shows you liberal content. And guess what? That’s killing our ability to debate, understand, or even SEE the other side. 🤔 How can you challenge bad ideas if you never even hear them? 💬 How can you change minds or refine your own thinking if you’re only being spoon-fed what you already agree with? As a conservative, I make it a point to watch left-wing content — not because I agree with it, but because I want to know what they’re planning, what narratives they’re pushing, and how we can counter it. Let’s be honest — the reason politics feels so toxic right now is because the algorithm rewards division and extremes. In reality? Most Americans are probably somewhere in the middle — but we’d never know it, because the algorithm hides the nuance. 🧠 We need more critical thinking, not curated content. 👀 Show us EVERYTHING — not just what some machine thinks we’ll click on. 🔥SHARE If you agree and let the people decide what’s worth seeing. It’s time to kill the echo chamber and bring back open platforms. #EndTheAlgorithm #DigitalEchoChamber #OpenDebate #ThinkForYourself #FreeSpeechOnline

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EVEREST IN 4 DAYS FROM SEA LEVEL, NEW WORLD RECORD, NEW ERA OF CLIMBING EVEREST 🙌🏽🙏🏽🙌🏽🙇- Huge Congratulations Andrew ! He has just become the fastest person to go from sea-level to the summit of Everest in 3 days 23 hours and 7 minutes. Andrew Ushakov had a dream to be the fastest person to do this challenge, and show a different way to attempt Everest. Normally Everest missions can take 50+days due to acclimatisation routines, but Andrew wanted to show that with dedication, training and lots of preparation and hypoxic training for acclimatisation (*including 400+ hours in a hypoxic tent), it could be done faster. At Elite Exped we want to help clients achieve their big dreams in a safe way – that’s our priority. Andrew started his challenge from Sea Level, starting at New York on May 15th at 00.30am and flying to Kathmandu, and then flying to Everest Base Camp. Then he and his team climbed from Base Camp and stood on the summit of Everest on May 19th date at 9.22am – meaning he completed the incredible feat in 3 days, 23 hours and 7 minutes! This new record is a triumph, not just for Andrew, but for everyone who believes in their dreams. Andrew only started climbing in 2020, he is not a full-time athlete, which makes this feat even more impressive. Huge Congratulations Andrew – Brother, we are proud to have helped you realise your dream! So proud of our Elite Exped team who supported Andrew’s record led by Tejan Gurung. #AndrewUshakov #4daystosummitEverest #Everest #EliteExped #Nothingisimpossible

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Driveline Family, Almost 20 years ago, I set out to revolutionize baseball. I believed that the notion of “natural talent” was holding players back, and that with the right tools, data, and mindset, any player could achieve greatness. Today, Driveline has become the undisputed leader in player development. The industry looks to us for innovation and evolution; none can match what we’ve built in Sports Science. We have developed the overlooked player alongside the game’s already-elite - proving that greatness can be built, not simply born. We’ve helped develop All-Stars, Cy Young winners, and MVPs - but like you, we’re capable of so much more. So we’re pushing the boundaries even further with Training 2.0. This isn’t just about getting bigger, faster, or stronger. It’s about unlocking the hidden potential that lies dormant inside every player. It’s about identifying weaknesses before anyone else can see them, and attacking them with a ferocity that borders on obsession. It’s about harnessing the power of cutting-edge technology and world-class experts to create bespoke training programs that treat each player like the unique individual they are using the decade-long head start we have due to our early investment in Sports Science when everyone else thought it was a passing fad. They’re playing catch-up on our old system, and we’re about to leave them in the dust again. We have poured our passion, knowledge, and relentless drive for innovation into creating the most advanced training system the world has ever seen. Our new Driveline App takes this elite system and distills it down into something that you carry in your pocket; accessible to anyone, anywhere. Whether you’re a young dreamer, a rising star, or an established pro, Training 2.0 will take you further than you ever thought possible. If you’re ready to leave no doubt about your potential, then join the Driveline family. We ensure our athletes leave no doubt at the end of their career that they got the very best assistance from the very best team. Welcome to Training 2.0. Let’s show the world what greatness looks like. - Kyle Boddy

Driveline Baseball

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🧵24/34 Inner Misalignment --- Consider this simplified experiment: We want this AI to find the exit of the maze. So we feed it millions of maze variations and reward it when it finds the exit. Please notice that in the worlds of the training data the apples are red and the exit is green. After enough training, our observation is that it has become extremely capable at solving mazes and finding the exit, we feel very confident it is aligned, so then we deploy it to the real world. The real world will be different though, it might have green apples and a red door. The AI geeks call this distributional shift. We expected that the AI will generalise and find the exit again, but in fact we now realise that the AI learned something completely different from what we thought. All the while we thought it learned how to find the exit, it had learned how to go after the green thing. Its behaviour was perfect in training. And most importantly, this AI is not stupid, it is an extremely capable AI that can solve extremely complex mazes. It’s just mis-aligned on the inside. Fishing for Failure modes --- The way to handle the shift between the training and deployment distributions is with methods like adversarial training: feeding it with a lot of generated variations and trying to make it fail so the weakness can be fixed. In this case, we generate an insane amount of maze variations, we discover those for which it fails to find the exit (like the ones with the green apples or the green walls or something), we generate many more similar to that and train it with reinforcement learning until it performs well at those as well. The hope is that we will cover everything it might encounter later when we deploy it in real life. There exist at least 2 basic ways this approach falls apart: First, there will never be any guarantee that we’ll have covered every possible random thing it might encounter later when we deploy it in real life. It’s very likely it will have to deal with stuff outside its training set which it will not know how to handle and will throw it out of balance and break it away from its expected behavioural patterns. The cascade effect of such a broken mind operating in the open world can be immense, and with super-capable runaway rogue agents, self-replicating and recursively self-improving, the phenomenon could grow and spread to an extinction-level event. ...

🧵24/34 Inner Misalignment --- Consider this simplified experiment: We want this AI to find the exit of the maze. So we feed it millions of maze variations and reward it when it finds the exit. Please notice that in the worlds of the training data the apples are red and the exit is green. After enough training, our observation is that it has become extremely capable at solving mazes and finding the exit, we feel very confident it is aligned, so then we deploy it to the real world. The real world will be different though, it might have green apples and a red door. The AI geeks call this distributional shift. We expected that the AI will generalise and find the exit again, but in fact we now realise that the AI learned something completely different from what we thought. All the while we thought it learned how to find the exit, it had learned how to go after the green thing. Its behaviour was perfect in training. And most importantly, this AI is not stupid, it is an extremely capable AI that can solve extremely complex mazes. It’s just mis-aligned on the inside. Fishing for Failure modes --- The way to handle the shift between the training and deployment distributions is with methods like adversarial training: feeding it with a lot of generated variations and trying to make it fail so the weakness can be fixed. In this case, we generate an insane amount of maze variations, we discover those for which it fails to find the exit (like the ones with the green apples or the green walls or something), we generate many more similar to that and train it with reinforcement learning until it performs well at those as well. The hope is that we will cover everything it might encounter later when we deploy it in real life. There exist at least 2 basic ways this approach falls apart: First, there will never be any guarantee that we’ll have covered every possible random thing it might encounter later when we deploy it in real life. It’s very likely it will have to deal with stuff outside its training set which it will not know how to handle and will throw it out of balance and break it away from its expected behavioural patterns. The cascade effect of such a broken mind operating in the open world can be immense, and with super-capable runaway rogue agents, self-replicating and recursively self-improving, the phenomenon could grow and spread to an extinction-level event. ...

Lethal Intelligence

535,291 görüntüleme • 1 yıl önce

How to Identify Agents with Sound Alone: Audio Profiles Each agent in VALORANT has a unique walk sound. We can use this to identify who is moving around without seeing them AND without them using abilities. Each agent has unique tells, here's Proof: Key: (How Easy it is to Identify who is running) 🟢 - Extremely Easy to identify without trying 🟡 - Somewhat difficult until you know what to listen for 🔴 - Difficult, and needs practice In order of their names Alphabetically (up to Miks in release date) Astra - 🟢. Astra has golden, loose jewelry. This makes it easy to identify her because she is one of the only agents that you can hear it, and of those agents, it's the most consistent. She also has lighter steps as she is meant to be smaller than the other agents 5'6 (170cm) Breach - 🟢. Breach is easily identifiable by the heavy boots that he wears. He is one of the loudest agents in terms of walking as his boots as well as his radianite arms that are also quite loud, being made of metal. Brimstone - 🟢. Similar to Breach, Brim is also easily identifiable due to how heavy his footsteps are. What separates him from Breach is his boots are leather but stomps, likely coming from his military background. Chamber - 🟡. Chamber is what started this entire thing for me. You can identify chamber by his loose gold watch, which chimes as he walks. The reason this is yellow circle and not green is because it is not as frequent as Astra and he wears sneakers, which the majority of the cast wear. So besides the watch it can be somewhat difficult to identify, if you're not actively listening for it. Clove - 🟡. Clove is somewhat difficult, but what makes them unique is how soft / quiet they walk. They are very light, wears shoes that have a lot of cushion and is meant to feel like a smaller character, canonically 5'6 / 168cm. Cypher 🔴 - Despite playing a lot of Cypher, I have a really hard time differentiating him from other characters with similar patterns like Viper and Fade. Comapred to Fade, I would say his have more bass. Deadlock 🟢 - The only person you could mix Deadlock up with is Sova. However, Deadlock's walk has a much more "wet" sound to it, as if she were crushing in soft snow with her snow boots. Fade 🔴 - Too similar to other agents, especially Cypher makes it very hard to tell. The only thing that makes her unique is her consistency, the decibel spread is almost nothing. Gekko 🟡- Also pretty hard, as he is one of the many average weight sneaker users. He is very similar to Killjoy, but with more bass which is how i remember. He's wearing vans, it sounds like rubber. Harbor 🟢 - Extremely easy. You can hear his braclet from a mile away. One of the more unique ones. Iso 🔴- Another middleweight flat shoe wearer. I think he is meant to be heavier than agents like Gekko and Miks who sound similar. This is one wear you have to look at who is alive to differentiate between him and other agents. Jett 🟢- Extremely easy, maybe because I've heard it so much with the Jetts in my games, but I think the idea is she is meant to feel light and dainty, as if the wind is carrying her. That's why she walks so soft. She is also meant to be quite small at 5'4 / 164cm which corroborates the quiet steps. KAY/O 🟢- The easiest. Even people who don't play VALORANT could tell you that's the sound of a robot. The metal makes it very easy to tell. Killjoy 🟡 - Somewhat difficult, I think the giveaway are the boots. Like I said about Gekko, hers in comparison create softer sounds. Miks 🔴 - Really disappointed that Miks, the sound guy doesn't have anything unique to his walk. It is similar to Gekkos, in that it is rubbery but also muddy. Neon 🟢- Iconic. Being a runner, she is wearing running shoes. Not much said, I think anyone could hear the difference. Omen 🟡 - Somewhat difficult. You are listening for a "wet" sound when he walks. He has been out for long enough where I can hear it distinctly enough. Phoenix 🟡 - Somewhat difficult. You are listening for the same rubbery sound as Miks, but with a lot more bass, he drags his feet somewhat. Raze 🟡 - Hard to hear at first, but you are listening for a "mud" / squish sound. Similar to Omen, but not "stompy" Reyna 🟢- Maybe just me but I have an easy time hearing Reyna's. This is the one I can identify the quickest in game. I listen for a lot of bass towards the front of her step, as she has raised shoes which put more impact on her toes / balls of her feet. The sound is unique enough where you can her pretty clearly. Sage 🟡 - Somewhat difficult. Can be confused with Raze sometimes, as you are also listening for a "muddy sound" Skye 🟢- Very easy, she has trail shoes. Makes sense, she's from Australia. Sova 🟢- Again, similar to deadlock, Sova has boots made for winter, and there are feint sounds of him walking on ice. This one is somewhat similar to Skye, but not enough to mistakes the two. His has more bass. Tejo 🟡 - I didn't play as much when Tejo was popular, and this could be why I don't hear much differences in Gekko and Miks as well, but I find Tejo easier because of his heavty duty boots. Veto 🟡 - Somewhat hard to hear, only because I haven't played enough but there is dampness and a muddy sound to his walk similar to Raze, Omen etc. with that rubbery sound Viper 🟡 - somewhat hard to hear. Again, I think she sounds very similar to Fade and Cypher. But I think she is easier to identify because of the bass in each step. Vyse 🟢 - Very easy once you start listening. She has a lot of metal attached to her, which you can hear. Waylay 🔴 - Another middleweight rubber user. Hard to hear, but I haven't played with/against enough Waylay to really know her pattern. Yoru 🟢 - Iconic sound, you have been conditioned to hear this sound over and over with the Fake Footsteps and the reworked Yoru Clone + his fake clone of himself, he is most commonly walking around rather than sneaking, so you will probably become very accustom to him. Things to note: 1. These noises are NOT client / team sided. At the end of this video I show my friend walking as Chamber and Phoenix, you can hear a clear difference. He is on the ENEMY team, not mine. 2. You are NOT relying on audio alone. Agents all have their unique abilities which are significantly more telling of who is nearby compared to footsteps. The point of this exercise is to help identify WHO is there if there is no other way of knowing. 3. It is not meant to be used every round, hell even every game. I have really good hearing, so I can make good use of it. But as you can see by the red circles, it is not universal. Again, there are other more obvious ways of telling who is nearby. 4. But how is this useful? Well for me, I like to memorize the Controllers the most, because if I can identify that the sound of a controller's footsteps. I know they are not lurking, which allows for a little more peace of mind for my teammates rotating onto my site. 5. I used the same spot on purpose to show that they have different sound profiles. HOWEVER, it is important to note that with the different surfaces that you can run across, some of the noises that I described could sound different or harder to hear.

How to Identify Agents with Sound Alone: Audio Profiles Each agent in VALORANT has a unique walk sound. We can use this to identify who is moving around without seeing them AND without them using abilities. Each agent has unique tells, here's Proof: Key: (How Easy it is to Identify who is running) 🟢 - Extremely Easy to identify without trying 🟡 - Somewhat difficult until you know what to listen for 🔴 - Difficult, and needs practice In order of their names Alphabetically (up to Miks in release date) Astra - 🟢. Astra has golden, loose jewelry. This makes it easy to identify her because she is one of the only agents that you can hear it, and of those agents, it's the most consistent. She also has lighter steps as she is meant to be smaller than the other agents 5'6 (170cm) Breach - 🟢. Breach is easily identifiable by the heavy boots that he wears. He is one of the loudest agents in terms of walking as his boots as well as his radianite arms that are also quite loud, being made of metal. Brimstone - 🟢. Similar to Breach, Brim is also easily identifiable due to how heavy his footsteps are. What separates him from Breach is his boots are leather but stomps, likely coming from his military background. Chamber - 🟡. Chamber is what started this entire thing for me. You can identify chamber by his loose gold watch, which chimes as he walks. The reason this is yellow circle and not green is because it is not as frequent as Astra and he wears sneakers, which the majority of the cast wear. So besides the watch it can be somewhat difficult to identify, if you're not actively listening for it. Clove - 🟡. Clove is somewhat difficult, but what makes them unique is how soft / quiet they walk. They are very light, wears shoes that have a lot of cushion and is meant to feel like a smaller character, canonically 5'6 / 168cm. Cypher 🔴 - Despite playing a lot of Cypher, I have a really hard time differentiating him from other characters with similar patterns like Viper and Fade. Comapred to Fade, I would say his have more bass. Deadlock 🟢 - The only person you could mix Deadlock up with is Sova. However, Deadlock's walk has a much more "wet" sound to it, as if she were crushing in soft snow with her snow boots. Fade 🔴 - Too similar to other agents, especially Cypher makes it very hard to tell. The only thing that makes her unique is her consistency, the decibel spread is almost nothing. Gekko 🟡- Also pretty hard, as he is one of the many average weight sneaker users. He is very similar to Killjoy, but with more bass which is how i remember. He's wearing vans, it sounds like rubber. Harbor 🟢 - Extremely easy. You can hear his braclet from a mile away. One of the more unique ones. Iso 🔴- Another middleweight flat shoe wearer. I think he is meant to be heavier than agents like Gekko and Miks who sound similar. This is one wear you have to look at who is alive to differentiate between him and other agents. Jett 🟢- Extremely easy, maybe because I've heard it so much with the Jetts in my games, but I think the idea is she is meant to feel light and dainty, as if the wind is carrying her. That's why she walks so soft. She is also meant to be quite small at 5'4 / 164cm which corroborates the quiet steps. KAY/O 🟢- The easiest. Even people who don't play VALORANT could tell you that's the sound of a robot. The metal makes it very easy to tell. Killjoy 🟡 - Somewhat difficult, I think the giveaway are the boots. Like I said about Gekko, hers in comparison create softer sounds. Miks 🔴 - Really disappointed that Miks, the sound guy doesn't have anything unique to his walk. It is similar to Gekkos, in that it is rubbery but also muddy. Neon 🟢- Iconic. Being a runner, she is wearing running shoes. Not much said, I think anyone could hear the difference. Omen 🟡 - Somewhat difficult. You are listening for a "wet" sound when he walks. He has been out for long enough where I can hear it distinctly enough. Phoenix 🟡 - Somewhat difficult. You are listening for the same rubbery sound as Miks, but with a lot more bass, he drags his feet somewhat. Raze 🟡 - Hard to hear at first, but you are listening for a "mud" / squish sound. Similar to Omen, but not "stompy" Reyna 🟢- Maybe just me but I have an easy time hearing Reyna's. This is the one I can identify the quickest in game. I listen for a lot of bass towards the front of her step, as she has raised shoes which put more impact on her toes / balls of her feet. The sound is unique enough where you can her pretty clearly. Sage 🟡 - Somewhat difficult. Can be confused with Raze sometimes, as you are also listening for a "muddy sound" Skye 🟢- Very easy, she has trail shoes. Makes sense, she's from Australia. Sova 🟢- Again, similar to deadlock, Sova has boots made for winter, and there are feint sounds of him walking on ice. This one is somewhat similar to Skye, but not enough to mistakes the two. His has more bass. Tejo 🟡 - I didn't play as much when Tejo was popular, and this could be why I don't hear much differences in Gekko and Miks as well, but I find Tejo easier because of his heavty duty boots. Veto 🟡 - Somewhat hard to hear, only because I haven't played enough but there is dampness and a muddy sound to his walk similar to Raze, Omen etc. with that rubbery sound Viper 🟡 - somewhat hard to hear. Again, I think she sounds very similar to Fade and Cypher. But I think she is easier to identify because of the bass in each step. Vyse 🟢 - Very easy once you start listening. She has a lot of metal attached to her, which you can hear. Waylay 🔴 - Another middleweight rubber user. Hard to hear, but I haven't played with/against enough Waylay to really know her pattern. Yoru 🟢 - Iconic sound, you have been conditioned to hear this sound over and over with the Fake Footsteps and the reworked Yoru Clone + his fake clone of himself, he is most commonly walking around rather than sneaking, so you will probably become very accustom to him. Things to note: 1. These noises are NOT client / team sided. At the end of this video I show my friend walking as Chamber and Phoenix, you can hear a clear difference. He is on the ENEMY team, not mine. 2. You are NOT relying on audio alone. Agents all have their unique abilities which are significantly more telling of who is nearby compared to footsteps. The point of this exercise is to help identify WHO is there if there is no other way of knowing. 3. It is not meant to be used every round, hell even every game. I have really good hearing, so I can make good use of it. But as you can see by the red circles, it is not universal. Again, there are other more obvious ways of telling who is nearby. 4. But how is this useful? Well for me, I like to memorize the Controllers the most, because if I can identify that the sound of a controller's footsteps. I know they are not lurking, which allows for a little more peace of mind for my teammates rotating onto my site. 5. I used the same spot on purpose to show that they have different sound profiles. HOWEVER, it is important to note that with the different surfaces that you can run across, some of the noises that I described could sound different or harder to hear.

ROCKET

420,152 görüntüleme • 10 gün önce

What you’re about to see may be the single craziest training video that I’ve ever seen. A John Deere USA source sent me this video where employees are trained on what to do if they overhear employees saying that they’re uncomfortable with trans identifying men in women’s restrooms. So what’s an employee supposed to do if they overhear this discomfort from their female colleagues? Report them because it’s a "violation" of THE MAN’S privacy. Seriously. The training goes on to say that "trans women are women, and they’ve never not been a woman" so it’s "not accurate" to say that they used to be a man. This is absolutely absurd. Does John Deere know who they sell farming equipment to? This lunacy needs to stop. Companies all need to stop pushing these topics at work. Stop telling women that they can’t be upset by men in their bathrooms. Stop creating groups that divide people by race or identity. Stop racial hiring quotas. Stop woke trainings. Stop pushing agendas for political causes. Stop introducing divisive subjects. Ban wokeness in the workplace. Stop woke donations. Make work about work again. Just do good work, make great products and provide great customer service. That’s all people want. It seems like the only companies you can find anymore who aren’t pushing wokeness are on On PublicSquare you can search for non-woke alternatives to most products. PublicSquare knows how important this fight to end wokeness is so I’m proud to partner with them to provide people with non-woke alternatives. John Deere was contacted for comment on this to give them the chance to disavow this training but they refused to comment by deadline. If you want to do your part to support our reporting, subscribe to my page here on X so you can help power the fight to end wokeness. Together, we can create real, lasting change.

What you’re about to see may be the single craziest training video that I’ve ever seen. A John Deere USA source sent me this video where employees are trained on what to do if they overhear employees saying that they’re uncomfortable with trans identifying men in women’s restrooms. So what’s an employee supposed to do if they overhear this discomfort from their female colleagues? Report them because it’s a "violation" of THE MAN’S privacy. Seriously. The training goes on to say that "trans women are women, and they’ve never not been a woman" so it’s "not accurate" to say that they used to be a man. This is absolutely absurd. Does John Deere know who they sell farming equipment to? This lunacy needs to stop. Companies all need to stop pushing these topics at work. Stop telling women that they can’t be upset by men in their bathrooms. Stop creating groups that divide people by race or identity. Stop racial hiring quotas. Stop woke trainings. Stop pushing agendas for political causes. Stop introducing divisive subjects. Ban wokeness in the workplace. Stop woke donations. Make work about work again. Just do good work, make great products and provide great customer service. That’s all people want. It seems like the only companies you can find anymore who aren’t pushing wokeness are on On PublicSquare you can search for non-woke alternatives to most products. PublicSquare knows how important this fight to end wokeness is so I’m proud to partner with them to provide people with non-woke alternatives. John Deere was contacted for comment on this to give them the chance to disavow this training but they refused to comment by deadline. If you want to do your part to support our reporting, subscribe to my page here on X so you can help power the fight to end wokeness. Together, we can create real, lasting change.

Robby Starbuck

1,740,531 görüntüleme • 2 yıl önce

Google just proved that bigger isn't always better. Their 308M parameter model is outperforming models 2x its size. Google just released 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝗚𝗲𝗺𝗺𝗮, and it's proving that lightweight embedding models can punch way above their weight class. At just 308M parameters (578MB), it's the new state-of-the-art for models under 500M parameters across MTEB multilingual, English, and code benchmarks. But the really impressive part is that it ranks 8th overall on MTEB(Multilingual, v2) - that's 𝟭𝟳 𝗽𝗹𝗮𝗰𝗲𝘀 above the second-best sub-500M model, and it's delivering performance 𝗰𝗼𝗺𝗽𝗮𝗿𝗮𝗯𝗹𝗲 𝘁𝗼 𝗺𝗼𝗱𝗲𝗹𝘀 𝗻𝗲𝗮𝗿𝗹𝘆 𝗱𝗼𝘂𝗯𝗹𝗲 𝗶𝘁𝘀 𝘀𝗶𝘇𝗲. There are three key parts of their training recipe that sets it apart: 𝟭. 𝗘𝗻𝗰𝗼𝗱𝗲𝗿-𝗗𝗲𝗰𝗼𝗱𝗲𝗿 𝗜𝗻𝗶𝘁𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Instead of starting from a decoder-only Gemma 3 model, they first adapted it to encoder-decoder, then used just the encoder. By basing EmbeddingGemma off an LLM that already has world and language understanding, it gives it a stronger starting point. 𝟮. 𝗧𝗵𝗿𝗲𝗲-𝗟𝗼𝘀𝘀 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 They combine three different loss functions, instead of just having one: • Contrastive loss (NCE) with in-batch negatives and hardness weighting • Spread-out regularization to ensure embeddings utilize the full space (for quantization and ANN retrieval) • Embedding matching distillation from Gemini Embedding - not just learning from relevance scores, but directly aligning the embedding space with the teacher model 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗦𝗼𝘂𝗽𝗶𝗻𝗴 Rather than just averaging checkpoints from the same training run, they use optimization techniques to find multiple specialized training mixtures. Each mixture creates an "expert" model in different domains, and averaging all their parameters creates a final model that's actually better than individual models. Extras: • Matryoshka embeddings supporting 768, 512, 256, and 128 dimensions • Quantization-aware training - maintains quality even at int4 precision • 100+ languages from Gemma 3 pretraining • Exceptional performance on low-resource languages (check their XTREME-UP results) Is it the absolute best embedding model? No - Gemini Embedding still leads overall. But that's not really the point. EmbeddingGemma proves you can achieve state-of-the-art performance in a small package that's actually deployable on-device, in low-latency applications, and in resource-constrained environments. This makes good embeddings accessible for use cases that I'm seeing more and more: offline applications, privacy-sensitive deployments, and high-throughput scenarios where inference cost actually matters. Full paper: Shoutout to the EmbeddingGemma team at Google DeepMind for this awesome open source work 💙 and to Daniel Williams for helping me with this video! 🫶

Google just proved that bigger isn't always better. Their 308M parameter model is outperforming models 2x its size. Google just released 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝗚𝗲𝗺𝗺𝗮, and it's proving that lightweight embedding models can punch way above their weight class. At just 308M parameters (578MB), it's the new state-of-the-art for models under 500M parameters across MTEB multilingual, English, and code benchmarks. But the really impressive part is that it ranks 8th overall on MTEB(Multilingual, v2) - that's 𝟭𝟳 𝗽𝗹𝗮𝗰𝗲𝘀 above the second-best sub-500M model, and it's delivering performance 𝗰𝗼𝗺𝗽𝗮𝗿𝗮𝗯𝗹𝗲 𝘁𝗼 𝗺𝗼𝗱𝗲𝗹𝘀 𝗻𝗲𝗮𝗿𝗹𝘆 𝗱𝗼𝘂𝗯𝗹𝗲 𝗶𝘁𝘀 𝘀𝗶𝘇𝗲. There are three key parts of their training recipe that sets it apart: 𝟭. 𝗘𝗻𝗰𝗼𝗱𝗲𝗿-𝗗𝗲𝗰𝗼𝗱𝗲𝗿 𝗜𝗻𝗶𝘁𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Instead of starting from a decoder-only Gemma 3 model, they first adapted it to encoder-decoder, then used just the encoder. By basing EmbeddingGemma off an LLM that already has world and language understanding, it gives it a stronger starting point. 𝟮. 𝗧𝗵𝗿𝗲𝗲-𝗟𝗼𝘀𝘀 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 They combine three different loss functions, instead of just having one: • Contrastive loss (NCE) with in-batch negatives and hardness weighting • Spread-out regularization to ensure embeddings utilize the full space (for quantization and ANN retrieval) • Embedding matching distillation from Gemini Embedding - not just learning from relevance scores, but directly aligning the embedding space with the teacher model 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗦𝗼𝘂𝗽𝗶𝗻𝗴 Rather than just averaging checkpoints from the same training run, they use optimization techniques to find multiple specialized training mixtures. Each mixture creates an "expert" model in different domains, and averaging all their parameters creates a final model that's actually better than individual models. Extras: • Matryoshka embeddings supporting 768, 512, 256, and 128 dimensions • Quantization-aware training - maintains quality even at int4 precision • 100+ languages from Gemma 3 pretraining • Exceptional performance on low-resource languages (check their XTREME-UP results) Is it the absolute best embedding model? No - Gemini Embedding still leads overall. But that's not really the point. EmbeddingGemma proves you can achieve state-of-the-art performance in a small package that's actually deployable on-device, in low-latency applications, and in resource-constrained environments. This makes good embeddings accessible for use cases that I'm seeing more and more: offline applications, privacy-sensitive deployments, and high-throughput scenarios where inference cost actually matters. Full paper: Shoutout to the EmbeddingGemma team at Google DeepMind for this awesome open source work 💙 and to Daniel Williams for helping me with this video! 🫶

Victoria Slocum

21,610 görüntüleme • 9 ay önce

Iga Swiatek on constantly being filmed at Australian Open and other tournaments, ‘The question is, are we tennis players? Or are we animals in the zoo? It would be nice to have some privacy’ “I wanted to ask something that Coco was talking about last night. The cameras backstage at the tournament… I know you got filmed recently forgetting your credential… you’re sort of always on camera at a lot of different tournament areas.. I’m wondering if you think there should be more privacy for players and their teams as they’re back there? Or how you see the balance of that versus them trying to have entertainment and content coming out.” Iga: “The question is, are we tennis players? Or are we animals in the zoo? Where they are observed even when they’re pooping. Ok, that was exaggerating obviously, but it would be nice to have some privacy. It would be nice also to have your own process and not always be observed. For example, in other sports, you have some technical things you wanna do. It would be nice to have some space you can do that without the whole world watching. On Wimbledon there are courts where people with accreditation can get there but it’s without the fans. There are some tournaments where it’s impossible and you’re constantly observed. I don’t think it should be like that because we are tennis players. We’re meant to be watched on court and in the press. That’s our job. It’s not our job to be a meme when you forget your accreditation. It’s funny for sure. People have something to talk about. For us, I don’t think it’s necessary.” “Have you talked to the tournament about it here?” Iga: “What’s the point?” (via Australian Open Press)

Iga Swiatek on constantly being filmed at Australian Open and other tournaments, ‘The question is, are we tennis players? Or are we animals in the zoo? It would be nice to have some privacy’ “I wanted to ask something that Coco was talking about last night. The cameras backstage at the tournament… I know you got filmed recently forgetting your credential… you’re sort of always on camera at a lot of different tournament areas.. I’m wondering if you think there should be more privacy for players and their teams as they’re back there? Or how you see the balance of that versus them trying to have entertainment and content coming out.” Iga: “The question is, are we tennis players? Or are we animals in the zoo? Where they are observed even when they’re pooping. Ok, that was exaggerating obviously, but it would be nice to have some privacy. It would be nice also to have your own process and not always be observed. For example, in other sports, you have some technical things you wanna do. It would be nice to have some space you can do that without the whole world watching. On Wimbledon there are courts where people with accreditation can get there but it’s without the fans. There are some tournaments where it’s impossible and you’re constantly observed. I don’t think it should be like that because we are tennis players. We’re meant to be watched on court and in the press. That’s our job. It’s not our job to be a meme when you forget your accreditation. It’s funny for sure. People have something to talk about. For us, I don’t think it’s necessary.” “Have you talked to the tournament about it here?” Iga: “What’s the point?” (via Australian Open Press)

The Tennis Letter

596,858 görüntüleme • 7 ay önce

A person asks Sadhguru, “How much weight training exercise do you recommend?” And Sadhguru answers, “Weight training is not useful, but do 25-50 Surya-Namaskars, it is complete exercise.” First of all, I am not sure why anyone would ask someone like Sadhguru, who is most of the time, in another dimension, a question on exercise and weight training. And secondly, Sadhguru is just plain wrong. Weight (or strength) training is one of the most important aspects of exercise regimen you can include in your daily or 3-4 times a week physical activity because there is great science behind its benefits. I advise my obese or sarcopenic (very poor muscle mass in advanced liver disease) patients to include weight training to improve clinical outcomes. The highest level of scientific evidence showed that standard muscle-strengthening activities were associated with lower risk of death in patients with non-communicable diseases – including cardiovascular disease, diabetes and lung cancer. Another metanalysis showed strong evidence for a considerable risk reduction of strength training for 60 minutes a week, on all-cause death (−15%), cardiovascular disease death (−19%), and cancer death (−14%). See here: here Sadhguru says the older we get, the better we do not weight train. He is again absolutely wrong. The findings of another systematic review/metanalysis support power (strength) training as an effective therapeutic intervention for improving physical function in adults diagnosed with frailty (poor physical function in old age) and patients with chronic medical conditions. See here: In fact, another study on strength training, this one again a metanalysis, concluded that strength training interventions can be used as a non-drug treatment for hypertension (!), as they promote significant decreases in blood pressure. See here: Strength training also reduces significantly, chronic inflammation as shown in another high quality systematic review and meta-analysis. See here: Resistance/Strength training improved muscle mass and muscle strength, thereby improving performance status. Improved performance status is a wonderful benchmark for an active and healthy life. See here: Even in fatty liver disease, independent of weight loss, exercise training was associated with 3 and a half times more meaningful treatment response towards lowering liver fat. Strength training is a powerful tool to maintain liver health. See here: and here Now Surya Namaskar. Surya Namaskar or Sun Salutation is a sequence of around twelve yoga poses connected by jumping or stretching movements, varying somewhat between various Yoga schools – which means, it has no regulation or standardization unlike weight training. In Iyengar Yoga there is a way, in Ashtanga Vinayasa Yoga there is Type A and B and there are other types followed by other schools or Yoga teachers. Along with the stretching and jumping, which is done is a slow and steady manner, the practice includes chanting a “mantra” calling out twelve names of the Sun God. In its classical form, Surya Namaskar is not an exercise, and is not aerobic. The energy cost of exercise is measured in units of metabolic equivalent of task (MET). Less than 3 METs counts as light exercise; 3 to 6 METs is moderate; 6 or over is vigorous. American College of Sports Medicine and American Heart Association guidelines count periods of at least 10 minutes of moderate MET level activity towards their recommended daily amounts of exercise. For healthy adults aged 18 to 65, the guidelines recommend moderate exercise for 30 minutes five days a week, or vigorous aerobic exercise for 20 minutes three days a week. Surya Namaskar in its classical form has a measly 2.9 METs and in its rigorous form (some people perform mutated highly active forms of Surya Namaskar to make it feel like an exercise) can go up to 7.4 METs – which requires a lot of jumping and little stretching and no time to chant the Sun God names - does not even come close to strength training by any margin. Those who are part of the Surya Namaskar and Yoga cult would provide anecdotal experiences on its benefits (please see comment section) through non-classical forms and would call it an “exhilarating experience.” Experiences are not scientific, evidence are. Many Yoga journals and some dubious and third rate Ayurveda journals have also have published on such experiences in small group of patients, which are not validated or published in better journals [like this junk here: There are no metanalysis level data to prove effectiveness of Surya Namaskar as beneficial as aerobic exercise or better than strength training as Sadhguru claims. Do Surya namaskar if you are doing nothing. But upgrade to strength training if you want something. And stop listening to pseudoscience peddlers who speak religion and culture for your healthcare needs. Sadhguru suffers seriously from Dunning Kruger fallacy: a type of cognitive bias, where people with little expertise or ability assume they have superior expertise or ability. This overestimation occurs because of the fact that they don’t have enough knowledge to know they don’t have enough knowledge.

A person asks Sadhguru, “How much weight training exercise do you recommend?” And Sadhguru answers, “Weight training is not useful, but do 25-50 Surya-Namaskars, it is complete exercise.” First of all, I am not sure why anyone would ask someone like Sadhguru, who is most of the time, in another dimension, a question on exercise and weight training. And secondly, Sadhguru is just plain wrong. Weight (or strength) training is one of the most important aspects of exercise regimen you can include in your daily or 3-4 times a week physical activity because there is great science behind its benefits. I advise my obese or sarcopenic (very poor muscle mass in advanced liver disease) patients to include weight training to improve clinical outcomes. The highest level of scientific evidence showed that standard muscle-strengthening activities were associated with lower risk of death in patients with non-communicable diseases – including cardiovascular disease, diabetes and lung cancer. Another metanalysis showed strong evidence for a considerable risk reduction of strength training for 60 minutes a week, on all-cause death (−15%), cardiovascular disease death (−19%), and cancer death (−14%). See here: here Sadhguru says the older we get, the better we do not weight train. He is again absolutely wrong. The findings of another systematic review/metanalysis support power (strength) training as an effective therapeutic intervention for improving physical function in adults diagnosed with frailty (poor physical function in old age) and patients with chronic medical conditions. See here: In fact, another study on strength training, this one again a metanalysis, concluded that strength training interventions can be used as a non-drug treatment for hypertension (!), as they promote significant decreases in blood pressure. See here: Strength training also reduces significantly, chronic inflammation as shown in another high quality systematic review and meta-analysis. See here: Resistance/Strength training improved muscle mass and muscle strength, thereby improving performance status. Improved performance status is a wonderful benchmark for an active and healthy life. See here: Even in fatty liver disease, independent of weight loss, exercise training was associated with 3 and a half times more meaningful treatment response towards lowering liver fat. Strength training is a powerful tool to maintain liver health. See here: and here Now Surya Namaskar. Surya Namaskar or Sun Salutation is a sequence of around twelve yoga poses connected by jumping or stretching movements, varying somewhat between various Yoga schools – which means, it has no regulation or standardization unlike weight training. In Iyengar Yoga there is a way, in Ashtanga Vinayasa Yoga there is Type A and B and there are other types followed by other schools or Yoga teachers. Along with the stretching and jumping, which is done is a slow and steady manner, the practice includes chanting a “mantra” calling out twelve names of the Sun God. In its classical form, Surya Namaskar is not an exercise, and is not aerobic. The energy cost of exercise is measured in units of metabolic equivalent of task (MET). Less than 3 METs counts as light exercise; 3 to 6 METs is moderate; 6 or over is vigorous. American College of Sports Medicine and American Heart Association guidelines count periods of at least 10 minutes of moderate MET level activity towards their recommended daily amounts of exercise. For healthy adults aged 18 to 65, the guidelines recommend moderate exercise for 30 minutes five days a week, or vigorous aerobic exercise for 20 minutes three days a week. Surya Namaskar in its classical form has a measly 2.9 METs and in its rigorous form (some people perform mutated highly active forms of Surya Namaskar to make it feel like an exercise) can go up to 7.4 METs – which requires a lot of jumping and little stretching and no time to chant the Sun God names - does not even come close to strength training by any margin. Those who are part of the Surya Namaskar and Yoga cult would provide anecdotal experiences on its benefits (please see comment section) through non-classical forms and would call it an “exhilarating experience.” Experiences are not scientific, evidence are. Many Yoga journals and some dubious and third rate Ayurveda journals have also have published on such experiences in small group of patients, which are not validated or published in better journals [like this junk here: There are no metanalysis level data to prove effectiveness of Surya Namaskar as beneficial as aerobic exercise or better than strength training as Sadhguru claims. Do Surya namaskar if you are doing nothing. But upgrade to strength training if you want something. And stop listening to pseudoscience peddlers who speak religion and culture for your healthcare needs. Sadhguru suffers seriously from Dunning Kruger fallacy: a type of cognitive bias, where people with little expertise or ability assume they have superior expertise or ability. This overestimation occurs because of the fact that they don’t have enough knowledge to know they don’t have enough knowledge.

TheLiverDoc™

505,792 görüntüleme • 2 yıl önce

How to roll like a local Before rolling, take a moment to observe your opponent’s archetype. Are they heavy and slow, or light and strong? Are they a tough brawler or a glass cannon? First impressions can be deceiving—some strong looking opponents may not feel strong on the mat. Adjust your observations as the roll progresses. Be mindful of any obvious injuries. Avoid targeting these areas. In a competition, exploiting injuries would be part of your strategy, but during training, focus on building skills without causing harm. If you’re more experienced, challenge yourself by working in areas where your partner is skilled. Don’t make it easy for your partner by giving away free guard passes or free submission attempts. If you feel the need to use excessive will power, consider conceding the position and resetting at the next position with a quick attack. As you get a read on your partner, loosen up and engage in a way that encourages interaction. Get them to notice your intentions then you can really mix them up. Be tough and skillful, but keep the roll playful so your partner will attack back. This creates more exchanges and helps both of you improve. Apply pressure but, avoid giving unnecessary chip damage. If your partner concedes an opening for a submission, avoid slamming it on hard. Respect their well-being by catching and releasing submissions. This approach not only promotes safety but also increases opportunities for transitions, providing valuable “XP” When encountering a partner who uses excessive strength and will power against your proper technique, avoid responding with willpower and strength of your own. Instead, ease into the situation. Recognize resistance as an opportunity to set traps or wear them down gradually. If you’re less experienced, avoid your opponents strengths and look for opportunities to capitalize on their weaknesses. Don’t shy away from challenging situations; step into the action and earn your partner’s respect, even if it means getting caught. Keep working out of submissions and recovering your guard. If you’re under attack, extend the engagement and work toward regaining a neutral position. Avoid being timid or overly conservative, this limits the amount of exchanges. Instead, embrace the pace and intensity of higher-level rolls. Progress happens when you immerse yourself in the action and take risks. Remember force and will power are even less reliable against more experienced opponents. Rolling like a local isn’t just about improving your own skills; it’s about contributing to a more connected community.

How to roll like a local Before rolling, take a moment to observe your opponent’s archetype. Are they heavy and slow, or light and strong? Are they a tough brawler or a glass cannon? First impressions can be deceiving—some strong looking opponents may not feel strong on the mat. Adjust your observations as the roll progresses. Be mindful of any obvious injuries. Avoid targeting these areas. In a competition, exploiting injuries would be part of your strategy, but during training, focus on building skills without causing harm. If you’re more experienced, challenge yourself by working in areas where your partner is skilled. Don’t make it easy for your partner by giving away free guard passes or free submission attempts. If you feel the need to use excessive will power, consider conceding the position and resetting at the next position with a quick attack. As you get a read on your partner, loosen up and engage in a way that encourages interaction. Get them to notice your intentions then you can really mix them up. Be tough and skillful, but keep the roll playful so your partner will attack back. This creates more exchanges and helps both of you improve. Apply pressure but, avoid giving unnecessary chip damage. If your partner concedes an opening for a submission, avoid slamming it on hard. Respect their well-being by catching and releasing submissions. This approach not only promotes safety but also increases opportunities for transitions, providing valuable “XP” When encountering a partner who uses excessive strength and will power against your proper technique, avoid responding with willpower and strength of your own. Instead, ease into the situation. Recognize resistance as an opportunity to set traps or wear them down gradually. If you’re less experienced, avoid your opponents strengths and look for opportunities to capitalize on their weaknesses. Don’t shy away from challenging situations; step into the action and earn your partner’s respect, even if it means getting caught. Keep working out of submissions and recovering your guard. If you’re under attack, extend the engagement and work toward regaining a neutral position. Avoid being timid or overly conservative, this limits the amount of exchanges. Instead, embrace the pace and intensity of higher-level rolls. Progress happens when you immerse yourself in the action and take risks. Remember force and will power are even less reliable against more experienced opponents. Rolling like a local isn’t just about improving your own skills; it’s about contributing to a more connected community.

Drewlock

24,712 görüntüleme • 1 yıl önce

WHO'S READY FOR SHOPPABLE/INTERACTIVE VIDEO? Using NoSpoon studios new spinoff platform, create and detect overlaid products embedded with analyzed custom interactive overlays. You can even link to friend's profiles on X if you so choose! Most everything is customizable, including logos. We provide the embedded webpage featuring an AI analysis and detection of products within your uploaded media, as well as links users can click without interrupting playback on your content (however you can edit all of this yourself and add more products/tweak products naturally). Oh and did I mention...you can click products just by staring at them if you enable 'gaze'? It's super fun! There's a few easter eggs on the launch page too for this very sort. See if you can hold your eyes closed long enough to find a few of them... This is great for gaming, music streaming, industrial training, AI workflow sharing, anything that requires you to link to another interactive component! Feel free to go crazy! I personally think this will be great for AI workflows, as in conversation you can just say the tool then run it through NoSpoon's shoppable video platform and it will detect this (bear in mind you should say the website if you are using your audio to do this). Otherwise, it will try to find all products visible in your shots :). Here you will see it find Chanel. I've got loads of features I'll explain a bit better come Sunday, but in the meantime, enjoy this super early beta release!

WHO'S READY FOR SHOPPABLE/INTERACTIVE VIDEO? Using NoSpoon studios new spinoff platform, create and detect overlaid products embedded with analyzed custom interactive overlays. You can even link to friend's profiles on X if you so choose! Most everything is customizable, including logos. We provide the embedded webpage featuring an AI analysis and detection of products within your uploaded media, as well as links users can click without interrupting playback on your content (however you can edit all of this yourself and add more products/tweak products naturally). Oh and did I mention...you can click products just by staring at them if you enable 'gaze'? It's super fun! There's a few easter eggs on the launch page too for this very sort. See if you can hold your eyes closed long enough to find a few of them... This is great for gaming, music streaming, industrial training, AI workflow sharing, anything that requires you to link to another interactive component! Feel free to go crazy! I personally think this will be great for AI workflows, as in conversation you can just say the tool then run it through NoSpoon's shoppable video platform and it will detect this (bear in mind you should say the website if you are using your audio to do this). Otherwise, it will try to find all products visible in your shots :). Here you will see it find Chanel. I've got loads of features I'll explain a bit better come Sunday, but in the meantime, enjoy this super early beta release!

Kiri

40,355 görüntüleme • 11 ay önce

LOVE ISLAND USA SEASON 8 EPISODE 11 Yall may not like this , but I actually want Aniya & KC to explore and if it’s meant for them to be together , they will find their way back . Love island need to bring a bombshell for Aniya , preferably BLACK HISTORY MONTH EDITION ✊🏾 so she can feel some one else energy cause it’s no one in the Villa for her but KC & I want KC to let his SOL glow like Coming to America cause he keep fighting it & it’s obviously there . I just think KC needs to be honest and say he wants to explore Sol , cause Aniya keeps asking , but the fact that he keeps reassuring her , means she need to just believe him until he shows her otherwise cause he hasn’t yet . She starting to stress me out a little . I do feel like they would find their way back to eachother in the end . Zach really is MRS.DOUBTFIRE & be putting doubt in people heads about their couples lmao . Even when it’s not on purpose , he does it Gabriel & Jenn was giving everybody a PSA with their PDA . I knew after 24 hours he was gone turn right back into Duolingo Ding-A-Ling 😭 & why is trinity and Kayda acting surprised like he wasn’t doing that when Beatriz was there . This ain’t nothing new I’m okay with Melanie sleeping outside cause I can only imagine her up all night staring at Sincere & Sol lmfaooooooo . Go find your peace & zen outside baby ☮️ I actually can tolerate Corbin more with Melanie , than with Kenzie . He less of a computer with Melanie cause she makes him have to have conversations . Kenzie is turned on by anybody who talks to her like ALEXA , so he doesn’t have to try hard . Also , he already has been Kenzie Sign language teacher and she felt them fingers in that 🐱 🤞🏽✌🏾🖖🏾 #loveislandusa #peacock #isaiahjaay

LOVE ISLAND USA SEASON 8 EPISODE 11 Yall may not like this , but I actually want Aniya & KC to explore and if it’s meant for them to be together , they will find their way back . Love island need to bring a bombshell for Aniya , preferably BLACK HISTORY MONTH EDITION ✊🏾 so she can feel some one else energy cause it’s no one in the Villa for her but KC & I want KC to let his SOL glow like Coming to America cause he keep fighting it & it’s obviously there . I just think KC needs to be honest and say he wants to explore Sol , cause Aniya keeps asking , but the fact that he keeps reassuring her , means she need to just believe him until he shows her otherwise cause he hasn’t yet . She starting to stress me out a little . I do feel like they would find their way back to eachother in the end . Zach really is MRS.DOUBTFIRE & be putting doubt in people heads about their couples lmao . Even when it’s not on purpose , he does it Gabriel & Jenn was giving everybody a PSA with their PDA . I knew after 24 hours he was gone turn right back into Duolingo Ding-A-Ling 😭 & why is trinity and Kayda acting surprised like he wasn’t doing that when Beatriz was there . This ain’t nothing new I’m okay with Melanie sleeping outside cause I can only imagine her up all night staring at Sincere & Sol lmfaooooooo . Go find your peace & zen outside baby ☮️ I actually can tolerate Corbin more with Melanie , than with Kenzie . He less of a computer with Melanie cause she makes him have to have conversations . Kenzie is turned on by anybody who talks to her like ALEXA , so he doesn’t have to try hard . Also , he already has been Kenzie Sign language teacher and she felt them fingers in that 🐱 🤞🏽✌🏾🖖🏾 #loveislandusa #peacock #isaiahjaay

3/12 🎈

32,885 görüntüleme • 2 ay önce

INVEST IN PEOPLE: KINDNESS IS A GENERATIONAL INVESTMENT One of the greatest seeds you can sow for a glorious relationship in the future is kindness, especially to people at their lowest moments. When you find people in pain, don’t walk past them. Be present. Wipe their tears. Stand with them genuinely. Such seeds are never forgotten. Even if people forget, God will remind them that good people are rare. Never waste the opportunity to sow into a hurting life. The hand that wipes tears today may open doors tomorrow. You don’t need recognition or introductions, just show up with love, prayer, and sincerity. Don’t wait for people to rise before you support them. It is harder then. Help people while they are rising; it is cheaper and more impactful. A small seed sown today can become a powerful testimony years later. Be intentional with relationships. Don’t reserve your kindness only for the powerful and wealthy. Sometimes, the ones with nothing to give back are the greatest investments you will ever make. Kindness leaves memories. Insensitivity does too. Men may forgive, but they do not always forget. The seeds you sow, good or bad, can speak for generations. You can watch the full sermon “Dominion Checklist (End of Year Appraisals)” on our YouTube page: Koinonia Global #ApostleJoshuaSelman #DominionChecklist #EndOfYearAppraisals #KoinoniaAbuja #KoinoniaGlobal

INVEST IN PEOPLE: KINDNESS IS A GENERATIONAL INVESTMENT One of the greatest seeds you can sow for a glorious relationship in the future is kindness, especially to people at their lowest moments. When you find people in pain, don’t walk past them. Be present. Wipe their tears. Stand with them genuinely. Such seeds are never forgotten. Even if people forget, God will remind them that good people are rare. Never waste the opportunity to sow into a hurting life. The hand that wipes tears today may open doors tomorrow. You don’t need recognition or introductions, just show up with love, prayer, and sincerity. Don’t wait for people to rise before you support them. It is harder then. Help people while they are rising; it is cheaper and more impactful. A small seed sown today can become a powerful testimony years later. Be intentional with relationships. Don’t reserve your kindness only for the powerful and wealthy. Sometimes, the ones with nothing to give back are the greatest investments you will ever make. Kindness leaves memories. Insensitivity does too. Men may forgive, but they do not always forget. The seeds you sow, good or bad, can speak for generations. You can watch the full sermon “Dominion Checklist (End of Year Appraisals)” on our YouTube page: Koinonia Global #ApostleJoshuaSelman #DominionChecklist #EndOfYearAppraisals #KoinoniaAbuja #KoinoniaGlobal

Koinonia Global

18,917 görüntüleme • 8 ay önce