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we sped up distributed inference by up to 5x with decentralized speculative decoding. many don't realize that AI models normally generate text one single word at a time, waiting for the network after every word. speculative decoding changes this by using a "guess & confirm" system, similar to autocomplete....

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Parallax

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45,425 görüntüleme • 6 ay önce •via X (Twitter)

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My dual RTX PRO 6000 setup is currently training a Draft model for Qwen 3.6 27B! 🔥 I'm taking the paper DeepSeek dropped on 6/26 and going for a super ambitious application to the 27B scale. Thanks to my homelab, I was able to dive straight in — I read the paper and immediately started experimenting. The amount I've learned has been insane: - How memory bandwidth bottlenecks speed and clever ways to hack around it - Methods to train the draft model and boost its accuracy - Mechanisms to reference tokens all the way back to the previous one to skyrocket draft acceptance rates - The impact of Attention vs. GateDeltaNet on speculative decoding performance and how to handle those differences - The unique approaches and trade-offs of MTP, Dflash, JetSpec, and DSpark I could go on forever, but just from speculative decoding alone I've learned so much. The 27B architecture feels way more DSpark-native than JetSpec, so once draft training finishes, I'm going all-in with DSpark! My goal is to beat existing speculative decoding speeds outright — no task-specific shortcuts or cheating, pure general improvement. If you're into this kind of research, I'd love to hear your thoughts, impressions, and any suggestions — please reply! 🚀
0:17

My dual RTX PRO 6000 setup is currently training a Draft model for Qwen 3.6 27B! 🔥 I'm taking the paper DeepSeek dropped on 6/26 and going for a super ambitious application to the 27B scale. Thanks to my homelab, I was able to dive straight in — I read the paper and immediately started experimenting. The amount I've learned has been insane: - How memory bandwidth bottlenecks speed and clever ways to hack around it - Methods to train the draft model and boost its accuracy - Mechanisms to reference tokens all the way back to the previous one to skyrocket draft acceptance rates - The impact of Attention vs. GateDeltaNet on speculative decoding performance and how to handle those differences - The unique approaches and trade-offs of MTP, Dflash, JetSpec, and DSpark I could go on forever, but just from speculative decoding alone I've learned so much. The 27B architecture feels way more DSpark-native than JetSpec, so once draft training finishes, I'm going all-in with DSpark! My goal is to beat existing speculative decoding speeds outright — no task-specific shortcuts or cheating, pure general improvement. If you're into this kind of research, I'd love to hear your thoughts, impressions, and any suggestions — please reply! 🚀

Hikari∣LocalLLM⚡

56,314 görüntüleme • 19 gün önce

Everlyn isn’t just a video model. It’s a system you can trust. 🌀 At its core is the Lyn Protocol, the onchain layer that gives AI video permanence, provenance, and ownership. Here’s what it means: 🔹Decentralized inference: Videos are not generated by a single company’s servers. They run across a distributed network of nodes. 🔹Agent ownership: When you summon your agent, it is minted onchain. It belongs to you, not to a platform. 🔹Video provenance: Every render carries a timestamp and fingerprint written to the ledger. No one can fake or steal it. The Lyn Protocol transforms AI video from content into verifiable digital memory.
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Everlyn isn’t just a video model. It’s a system you can trust. 🌀 At its core is the Lyn Protocol, the onchain layer that gives AI video permanence, provenance, and ownership. Here’s what it means: 🔹Decentralized inference: Videos are not generated by a single company’s servers. They run across a distributed network of nodes. 🔹Agent ownership: When you summon your agent, it is minted onchain. It belongs to you, not to a platform. 🔹Video provenance: Every render carries a timestamp and fingerprint written to the ledger. No one can fake or steal it. The Lyn Protocol transforms AI video from content into verifiable digital memory.

Everlyn

93,078 görüntüleme • 10 ay önce

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.
0:14

Holy sh!t ! OpenAI will have their custom inference chips ready in just a few months and deployed at scale by the end of the year! 🤯 Training chip = The heavy lifters that require massive amounts of data and power to build and teach the AI models from scratch. Inference chip = The specialized, highly efficient chips that actually run the AI and generate the answers in real-time when you use it. This is going to help OpenAI drastically cut down their massive compute costs, speed up model reasoning times, and finally break free from relying entirely on Nvidia to scale their operations.

Chris

60,278 görüntüleme • 4 ay önce

PAYING PER MODEL IS THE DUMBEST THING IN TECH RIGHT NOW i was paying 3x what i needed to for AI inference the grid lets you buy a quality spec instead of a specific model.. it routes every request in real time to the cheapest option that qualifies swap one url and your code keeps working exactly the same openai-compatible, one line to switch, 200M free tokens to start
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PAYING PER MODEL IS THE DUMBEST THING IN TECH RIGHT NOW i was paying 3x what i needed to for AI inference the grid lets you buy a quality spec instead of a specific model.. it routes every request in real time to the cheapest option that qualifies swap one url and your code keeps working exactly the same openai-compatible, one line to switch, 200M free tokens to start

Robin Delta

15,729 görüntüleme • 1 ay önce

we made distributed inference verifiable with <1% overhead. verification is critical for any distributed system. in a trustless network, actors may swap your 70B model for a cheaper 8B one to cut costs. until now, maintaining inference integrity meant either doubling your cost (redundancy) or exploding your latency (zkp). we created veri: an on-chain verification layer light enough for high-throughput frameworks like Parallax. it hits the economic sweet spot through architectural elegance: 1. commit-sample-verify we don't prove every step; we check a random slice using game theory. workers commit to their work before the audit. cheating becomes statistically irrational, allowing a 1% sample to secure the entire sequence. 2. simultaneous execution inference and verification happen simultaneously on the same worker pool. we don't need a separate "verifier set", so compute utilization stays high. find out more about the architecture and benchmarks: paper: blog:
0:20

we made distributed inference verifiable with <1% overhead. verification is critical for any distributed system. in a trustless network, actors may swap your 70B model for a cheaper 8B one to cut costs. until now, maintaining inference integrity meant either doubling your cost (redundancy) or exploding your latency (zkp). we created veri: an on-chain verification layer light enough for high-throughput frameworks like Parallax. it hits the economic sweet spot through architectural elegance: 1. commit-sample-verify we don't prove every step; we check a random slice using game theory. workers commit to their work before the audit. cheating becomes statistically irrational, allowing a 1% sample to secure the entire sequence. 2. simultaneous execution inference and verification happen simultaneously on the same worker pool. we don't need a separate "verifier set", so compute utilization stays high. find out more about the architecture and benchmarks: paper: blog:

Parallax

28,496 görüntüleme • 6 ay önce

Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.
0:13

Karpathy method + Claude Code reading your whole Obsidian vault is the smartest second brain on earth. The method is simple and brutal. If you can’t build a thing from scratch, you don’t know it. Tutorials are fake learning and your brain deletes them in 3 days. Most people ignore this. They build a second brain that just sits there, folders of notes nobody reopens, dead text. Point Claude Code at the vault and it wakes up. 5,000 notes, one mind. It reads all of it and answers in your own words and your own proofs, not a model’s guess. Then the loop closes. Want to understand neural nets? Skip the 3-hour video and ask Claude Code to build a tiny one. 200 lines from scratch. Watch it train, break a layer, watch it fail, fix it. It clicks in 20 minutes instead of 3 weeks. The second it lands the note gets written. One idea per file, linked to 10 others, dropped into the vault while the memory is still hot. Now it compounds. Month 1: is 60 notes. Month 6 is 900. Every new note pulls in old ones, so you ask anything and the answer comes from your brain, not the internet. Before: 40 tabs, 6 half read PDF, 0 retained. After: build it once, own it for life. Setup takes 4 minutes. Plain text, no lock-in. A second brain nobody reads is a graveyard. Yours just started thinking.

West Lord

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0:16

Today, we’re excited to announce a partnership with Mind Network. Both Nesa and Mind Network specialize in decentralized security, and we share a mission to bring this tech to crypto AI. Together we will explore a close technological collaboration, sharing components of our stacks with one another. Mind Network will also be using Nesa for its AI inference. Mind Network is the first FHE Restaking Layer for AI, focused on enhancing security at the consensus and validator levels of AI networks. Nesa is the Layer-1 blockchain for AI, specializing in private inference and building infrastructure to make it easy for any application, protocol, and smart contract to fuse with AI. Look out for our AMA together and other activations soon.

Nesa

101,895 görüntüleme • 2 yıl önce

If you ever get the chance to go to Normandy, take it. Don't just visit it-take your time and let it sink in, because it is one of the most humbling experiences I've ever had. Standing there, looking out across those beaches, you can almost feel the weight of what happened there. It's hard to fully understand what those soldiers went through until you're standing in that place, realizing the courage it took just to run toward that shore. The beauty of Normandy is quiet and powerful, but so is the sacrifice it represents. The rows of white crosses, the history in the ground, the silence in the air-it all reminds you that freedom came at a tremendous cost. It's the kind of place that stays with you long after you leave. It makes you grateful, reflective, and a little more aware of how much was given for the life we live today. Normandy is more than a battlefield. It is a lesson in sacrifice, bravery, and remembrance. 🙏🇺🇸🙏
0:15

If you ever get the chance to go to Normandy, take it. Don't just visit it-take your time and let it sink in, because it is one of the most humbling experiences I've ever had. Standing there, looking out across those beaches, you can almost feel the weight of what happened there. It's hard to fully understand what those soldiers went through until you're standing in that place, realizing the courage it took just to run toward that shore. The beauty of Normandy is quiet and powerful, but so is the sacrifice it represents. The rows of white crosses, the history in the ground, the silence in the air-it all reminds you that freedom came at a tremendous cost. It's the kind of place that stays with you long after you leave. It makes you grateful, reflective, and a little more aware of how much was given for the life we live today. Normandy is more than a battlefield. It is a lesson in sacrifice, bravery, and remembrance. 🙏🇺🇸🙏

G-PA

178,708 görüntüleme • 1 ay önce

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable
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THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable

Om Patel

175,574 görüntüleme • 11 gün önce

What if a network could deliver AI results closer to where data is created, instead of sending it to far‑off data centers? In collaboration with NVIDIA and Decart, we’re making that a reality by bringing GPU‑powered computing to the network edge — closer to homes and businesses — so AI applications respond in real-time. This is how we're laying the foundation for the next generation of AI‑driven services:
0:16

What if a network could deliver AI results closer to where data is created, instead of sending it to far‑off data centers? In collaboration with NVIDIA and Decart, we’re making that a reality by bringing GPU‑powered computing to the network edge — closer to homes and businesses — so AI applications respond in real-time. This is how we're laying the foundation for the next generation of AI‑driven services:

Comcast

15,350 görüntüleme • 4 ay önce

This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."
0:14

This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."

Rohan Paul

12,761 görüntüleme • 5 ay önce

Gemini Omni doesn’t just allow you to render text more accurately — but to create it in sync with your visuals. 🎥 Choose your type, placement, animation, exposure and more. Prompt for this video: Word by word, one word on the screen at a time: did, you, know, that, this, model, can, do, pretty, good, text!? each word appears with a different animated style, perfect pacing to a rhythm, sizzle reel.
0:10

Gemini Omni doesn’t just allow you to render text more accurately — but to create it in sync with your visuals. 🎥 Choose your type, placement, animation, exposure and more. Prompt for this video: Word by word, one word on the screen at a time: did, you, know, that, this, model, can, do, pretty, good, text!? each word appears with a different animated style, perfect pacing to a rhythm, sizzle reel.

Google

151,076 görüntüleme • 1 ay önce

🚨 SCIENTISTS ARE USING AI TO MAP A HIDDEN “CLEANING SYSTEM” INSIDE THE HUMAN BRAIN AND IT COULD CHANGE HOW WE UNDERSTAND SLEEP, AGING, AND NEURODEGENERATIVE DISEASE. It’s called the glymphatic system a vast network of fluid channels that flushes toxic waste from the brain while you sleep. For years it was almost impossible to observe in detail. Now AI-powered imaging is revealing it like a living galaxy of microscopic rivers and pathways inside neural tissue. Why this matters: Your brain produces toxic proteins constantly. The glymphatic system is one of its primary waste-clearance mechanisms clearing the very proteins linked to Alzheimer’s, Parkinson’s, and other neurodegenerative diseases. But here’s the unsettling part: This cleaning system becomes dramatically less efficient with age, poor sleep, stress, and brain injury. Sleep may not just “rest” your mind it may literally wash your brain. The deeper implication is staggering: The brain isn’t just a computer. It’s a dynamic, living network that constantly rebuilds and cleanses itself in real time. The more we map it, the more it looks like an entire universe of connected energy flows inside our skulls. What if some neurological diseases begin when the brain can no longer properly “wash” itself at night? Follow for more frontier neuroscience and future technology.
0:10

🚨 SCIENTISTS ARE USING AI TO MAP A HIDDEN “CLEANING SYSTEM” INSIDE THE HUMAN BRAIN AND IT COULD CHANGE HOW WE UNDERSTAND SLEEP, AGING, AND NEURODEGENERATIVE DISEASE. It’s called the glymphatic system a vast network of fluid channels that flushes toxic waste from the brain while you sleep. For years it was almost impossible to observe in detail. Now AI-powered imaging is revealing it like a living galaxy of microscopic rivers and pathways inside neural tissue. Why this matters: Your brain produces toxic proteins constantly. The glymphatic system is one of its primary waste-clearance mechanisms clearing the very proteins linked to Alzheimer’s, Parkinson’s, and other neurodegenerative diseases. But here’s the unsettling part: This cleaning system becomes dramatically less efficient with age, poor sleep, stress, and brain injury. Sleep may not just “rest” your mind it may literally wash your brain. The deeper implication is staggering: The brain isn’t just a computer. It’s a dynamic, living network that constantly rebuilds and cleanses itself in real time. The more we map it, the more it looks like an entire universe of connected energy flows inside our skulls. What if some neurological diseases begin when the brain can no longer properly “wash” itself at night? Follow for more frontier neuroscience and future technology.

TheNewPhysics

15,641 görüntüleme • 1 ay önce

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.
0:19

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

elvis

47,678 görüntüleme • 2 ay önce

Slideshow clipping has to be the chillest business model you can really be making $5k - $15k+ a month running 10 TikTok accounts and have complete location freedom + make more than you would working a job you can literally just go to content rewards, pick a saas or brand to promote, for example skin care buy 5 aged TT accounts to start with connect US proxy / sim find videos in the niche the product is in for example skin care steal the hook of the video and content inside it and turn it into a slideshow for example: “skin care tips for broke girls” generate the images with NBP by finding a Pinterest image and getting the JSON from Gemini or ChatGPT promote the product as another “glow up” tip Post 1 a day from the start and as the accounts get more warm boost it to 2-3 + scale to more accounts If you can’t make your first $5k doing this then there is no hope
0:14

Slideshow clipping has to be the chillest business model you can really be making $5k - $15k+ a month running 10 TikTok accounts and have complete location freedom + make more than you would working a job you can literally just go to content rewards, pick a saas or brand to promote, for example skin care buy 5 aged TT accounts to start with connect US proxy / sim find videos in the niche the product is in for example skin care steal the hook of the video and content inside it and turn it into a slideshow for example: “skin care tips for broke girls” generate the images with NBP by finding a Pinterest image and getting the JSON from Gemini or ChatGPT promote the product as another “glow up” tip Post 1 a day from the start and as the accounts get more warm boost it to 2-3 + scale to more accounts If you can’t make your first $5k doing this then there is no hope

Pounds

15,415 görüntüleme • 5 ay önce

Diffusion models are an amazing tool for cofolding, they allow us to predict a protein and the molecule bound to it at once. But they are not exactly fast and require a lot of denoising steps to get accurate predictions. So we distilled ours. Meet DeCAF-Pearl: the first flow map model for all-atom cofolding. Instead of inching along the denoising trajectory, a flow map learns to jump across it. DeCAF-Pearl runs structure generation ~5x faster than Pearl, our SOTA model, while still maintaining the performance of the teacher model. That speed up allows us to run larger experiments and generate more synthetic data to improve our models. Getting there meant reparameterizing into noise-level space to stabilize gradients, committing to clean-structure prediction to keep the rigid-alignment loss biomolecules needed, and building DeCAF-Search, one steering algorithm for every compute budget. For more technical details, read out blog post: And the paper:
0:11

Diffusion models are an amazing tool for cofolding, they allow us to predict a protein and the molecule bound to it at once. But they are not exactly fast and require a lot of denoising steps to get accurate predictions. So we distilled ours. Meet DeCAF-Pearl: the first flow map model for all-atom cofolding. Instead of inching along the denoising trajectory, a flow map learns to jump across it. DeCAF-Pearl runs structure generation ~5x faster than Pearl, our SOTA model, while still maintaining the performance of the teacher model. That speed up allows us to run larger experiments and generate more synthetic data to improve our models. Getting there meant reparameterizing into noise-level space to stabilize gradients, committing to clean-structure prediction to keep the rigid-alignment loss biomolecules needed, and building DeCAF-Search, one steering algorithm for every compute budget. For more technical details, read out blog post: And the paper:

Sergey Edunov

36,985 görüntüleme • 1 ay önce

Multiple times a day for the last few weeks my internet keeps getting reset mid stream and it's driving me fricking insane because it doesn't automatically turn the player back on. It will do the load wheel, maybe Error 2000, and then it stops the stream so if you're lurking in the background, you actually don't even know that the stream F'd for 5 seconds. People just think the stream ended and they leave. This has been a problem with Twitch for like 10 years and even Disconnect Protection (NotLikeThis) that they added doesn't fix it. It happens 2-3 times within every 24 hours, my ISP shows no loss of service, but it's happening to multiple PCs on my network. Is there anything else that I can try before I completely rip out my entire network in my house and install a bunch of new equipment? Ironically enough, this whole situation is why I left my house the other day to go to the computer store to look at network equipment before my car got fking smoked in the parking lot by random person driving off the road lmao
0:10

Multiple times a day for the last few weeks my internet keeps getting reset mid stream and it's driving me fricking insane because it doesn't automatically turn the player back on. It will do the load wheel, maybe Error 2000, and then it stops the stream so if you're lurking in the background, you actually don't even know that the stream F'd for 5 seconds. People just think the stream ended and they leave. This has been a problem with Twitch for like 10 years and even Disconnect Protection (NotLikeThis) that they added doesn't fix it. It happens 2-3 times within every 24 hours, my ISP shows no loss of service, but it's happening to multiple PCs on my network. Is there anything else that I can try before I completely rip out my entire network in my house and install a bunch of new equipment? Ironically enough, this whole situation is why I left my house the other day to go to the computer store to look at network equipment before my car got fking smoked in the parking lot by random person driving off the road lmao

Esfand.TV

323,520 görüntüleme • 4 ay önce

At OptimAI Network, we believe the true power of AI Agent comes from the data behind it—and that power shouldn’t rest in the hands of just a few. That’s why we’re building a decentralized ecosystem with you, our community, at its core. Using EVM Layer-2, DePIN, and your collective efforts, we’re creating smarter, faster, and continuously evolving AI agents. Through our DePIN (Decentralized Physical Infrastructure) Network, your nodes are the backbone. You provide the computing power, data sourcing, storage, and bandwidth that keep this system resilient, scalable, and truly distributed. It’s not just infrastructure—it’s a movement powered by all of us. Then there’s the DeHIN (Decentralized Human Intelligence), where your insights shine. As a global community, you validate and refine data, ensuring our AI models stay accurate, diverse, and grounded in real-world perspectives. This isn’t about top-down control; it’s about harnessing the intelligence of many to make AI better for everyone. We’re not just dreaming of a decentralized AI future—we’re building it, together. This is your network, your impact, and your chance to redefine what AI can be. Let’s keep pushing forward. Learn more at:
0:14

At OptimAI Network, we believe the true power of AI Agent comes from the data behind it—and that power shouldn’t rest in the hands of just a few. That’s why we’re building a decentralized ecosystem with you, our community, at its core. Using EVM Layer-2, DePIN, and your collective efforts, we’re creating smarter, faster, and continuously evolving AI agents. Through our DePIN (Decentralized Physical Infrastructure) Network, your nodes are the backbone. You provide the computing power, data sourcing, storage, and bandwidth that keep this system resilient, scalable, and truly distributed. It’s not just infrastructure—it’s a movement powered by all of us. Then there’s the DeHIN (Decentralized Human Intelligence), where your insights shine. As a global community, you validate and refine data, ensuring our AI models stay accurate, diverse, and grounded in real-world perspectives. This isn’t about top-down control; it’s about harnessing the intelligence of many to make AI better for everyone. We’re not just dreaming of a decentralized AI future—we’re building it, together. This is your network, your impact, and your chance to redefine what AI can be. Let’s keep pushing forward. Learn more at:

OptimAI Network

21,362 görüntüleme • 1 yıl önce

The thing I help hitters with the most.. (1 good way to train it👇🏼) They start on time but never actually get loaded to GO. They think they’re ready. They’re not. So they start earlier next time. Same problem. —— This is a conversation I have with almost every hitter. When is the right time for you? - Based on your load style - Based on how long your load takes - Based on the pitcher’s velo, windup, or slide step —— If that’s you, try a “loaded at release” type load. Bobby Witt. Judge. Get your hip and elbow loaded early. Then stride with it ready to fire. “The pitcher is up there for 20 seconds telling you to get ready. Shame on you if you’re not ready to swing.” ____ If I have you in the cage, what I’ll do is have you get into a no stride position, get ready to swing and I stand there for 1-4 seconds… Holding my hand out over the plate ready to snap my fingers. When I snap, you swing. Do that 4-5x. You get the quickness, readiness feeling. Then we go back to stride with moving ball and you’re trying to time up that same GO move with the ball With your foot up in the air and the GO will make the foot go down. Not foot down then swing. That typically kills quickness. This works 95% of the time I’d say! —— > Send this to a hitter who needs it > Save it for when you need it.
0:17

The thing I help hitters with the most.. (1 good way to train it👇🏼) They start on time but never actually get loaded to GO. They think they’re ready. They’re not. So they start earlier next time. Same problem. —— This is a conversation I have with almost every hitter. When is the right time for you? - Based on your load style - Based on how long your load takes - Based on the pitcher’s velo, windup, or slide step —— If that’s you, try a “loaded at release” type load. Bobby Witt. Judge. Get your hip and elbow loaded early. Then stride with it ready to fire. “The pitcher is up there for 20 seconds telling you to get ready. Shame on you if you’re not ready to swing.” ____ If I have you in the cage, what I’ll do is have you get into a no stride position, get ready to swing and I stand there for 1-4 seconds… Holding my hand out over the plate ready to snap my fingers. When I snap, you swing. Do that 4-5x. You get the quickness, readiness feeling. Then we go back to stride with moving ball and you’re trying to time up that same GO move with the ball With your foot up in the air and the GO will make the foot go down. Not foot down then swing. That typically kills quickness. This works 95% of the time I’d say! —— > Send this to a hitter who needs it > Save it for when you need it.

Trey Hannam

39,202 görüntüleme • 1 ay önce

🇨🇳 Many people don't know this but Chinese high speed trains are now the fastest in the word and officially faster than even the 🇯🇵 Japanese Shinkansen Chinese trains go 350-400km/h (217-250mph) depending on the model While the Japanese Shinkansen goes 320km/h (200mph) max So I went to try it from Chengdu to Chongqing (just $80 for biz class) This one is the slightly older 350km/h model The business class cabin has 5 big seats that can lay flat with a stewardess dedicated to the cabin who serves you drinks like coffee, water, orange juice etc. I guess if it's a longer trip you probably get food served too The journey was very smooth and very fast, it maintained close to 350km/h at all times and there wasn't many stops or breaks (good) It didn't have WiFi, which is kind of a miss but it seems Chinese just use 5G for everything which worked fine for me without interrruption too I'd say the interior could be a bit more beautiful and modern. I usually say things in Japan look ancient but I think the Shinkansen interior there looks a bit more modern than here The trip took 1 hour and it passed about 300km in that time, roughly equivalent to the width of my country the Netherlands 😂 Anyway solid experience (and cheap!) 👍
0:33

🇨🇳 Many people don't know this but Chinese high speed trains are now the fastest in the word and officially faster than even the 🇯🇵 Japanese Shinkansen Chinese trains go 350-400km/h (217-250mph) depending on the model While the Japanese Shinkansen goes 320km/h (200mph) max So I went to try it from Chengdu to Chongqing (just $80 for biz class) This one is the slightly older 350km/h model The business class cabin has 5 big seats that can lay flat with a stewardess dedicated to the cabin who serves you drinks like coffee, water, orange juice etc. I guess if it's a longer trip you probably get food served too The journey was very smooth and very fast, it maintained close to 350km/h at all times and there wasn't many stops or breaks (good) It didn't have WiFi, which is kind of a miss but it seems Chinese just use 5G for everything which worked fine for me without interrruption too I'd say the interior could be a bit more beautiful and modern. I usually say things in Japan look ancient but I think the Shinkansen interior there looks a bit more modern than here The trip took 1 hour and it passed about 300km in that time, roughly equivalent to the width of my country the Netherlands 😂 Anyway solid experience (and cheap!) 👍

@levelsio

2,012,308 görüntüleme • 7 ay önce