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GLM 5.3 Flash running locally makes a Minecraft black hole mod We ran GLM 5.3 Flash Q4 locally on a rented 4x RTX PRO 6000 box and asked it to make a lightning gun mod for the real game using the Fabric API Output: -7.6M tokens -Time: ~9 hours... show more

atomic.chat

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glm 5.3 flash is 7.5x cheaper, but 3.4x slower than gemini 3.7 flash Z.ai glm 5.3 flash – shipped aug 26, $0.07/$0.25 per 1m Google DeepMind gemini 3.7 flash – shipped aug 13, $0.38/$1.88 per 1m we put the two models on one job: write one html file that draws an animated 3d scene in the browser. no images, no downloads, and it has to look the same on every load. the setup: three scenes – a glass aquarium in a lit room, the solar system, a night city under a thunderstorm. identical brief word for word, reasoning effort high, 64k output cap. the numbers below are not the whole run. they cover the three scenes we kept – the best one per task from each model, the ones in the video. - total generation time for the three scenes #1 gemini 3.7 flash – 10m 36s #2 glm 5.3 flash – 36m 30s - tokens spent on those three scenes #1 glm 5.3 flash – 110k #2 gemini 3.7 flash – 111k - cost of those three scenes #1 glm 5.3 flash – $0.027 #2 gemini 3.7 flash – $0.202 observations: • glm's first 10 attempts: 7 blank pages. it kept inventing short random helpers and forgetting to define one of them. the fix was one line in the brief: use exactly one random helper, named rand(), and don't invent shorthands next to it. next 12 attempts: 11 alive, 0 crashes. • glm spends 66% of its output on reasoning, gemini 57%. that is the whole speed gap. • gemini's storm came back as a black rectangle in 4 of 6 runs. glm's best storm has a branching bolt, lit rain and wet asphalt – for $0.01. conclusion: same three scenes, same token spend – glm 5.3 flash billed $0.027 and took 36m 30s, gemini 3.7 flash billed $0.202 and took 10m 36s. glm wins gemini on price and made the best storm of the whole run follow thehype. for 24/7 ai news, analysis and breakdowns

glm 5.3 flash is 7.5x cheaper, but 3.4x slower than gemini 3.7 flash Z.ai glm 5.3 flash – shipped aug 26, $0.07/$0.25 per 1m Google DeepMind gemini 3.7 flash – shipped aug 13, $0.38/$1.88 per 1m we put the two models on one job: write one html file that draws an animated 3d scene in the browser. no images, no downloads, and it has to look the same on every load. the setup: three scenes – a glass aquarium in a lit room, the solar system, a night city under a thunderstorm. identical brief word for word, reasoning effort high, 64k output cap. the numbers below are not the whole run. they cover the three scenes we kept – the best one per task from each model, the ones in the video. - total generation time for the three scenes #1 gemini 3.7 flash – 10m 36s #2 glm 5.3 flash – 36m 30s - tokens spent on those three scenes #1 glm 5.3 flash – 110k #2 gemini 3.7 flash – 111k - cost of those three scenes #1 glm 5.3 flash – $0.027 #2 gemini 3.7 flash – $0.202 observations: • glm's first 10 attempts: 7 blank pages. it kept inventing short random helpers and forgetting to define one of them. the fix was one line in the brief: use exactly one random helper, named rand(), and don't invent shorthands next to it. next 12 attempts: 11 alive, 0 crashes. • glm spends 66% of its output on reasoning, gemini 57%. that is the whole speed gap. • gemini's storm came back as a black rectangle in 4 of 6 runs. glm's best storm has a branching bolt, lit rain and wet asphalt – for $0.01. conclusion: same three scenes, same token spend – glm 5.3 flash billed $0.027 and took 36m 30s, gemini 3.7 flash billed $0.202 and took 10m 36s. glm wins gemini on price and made the best storm of the whole run follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

15,946 次观看 • 9 天前

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

23,951 次观看 • 7 天前

Im done with GLM-5.3 Flash 🤯 I went deep into the guts of GLM-5.3 Flash. Ripped out every expert, every memory head, every quantization scale, looked at all of it up close, then put the model back together so you can see the inside too. 🎉 GLM-5.3 Flash NVFP4 is now in Weight Atlas. 320B total, 18B active, 45 language layers and a 24 block Vision tower. And none of it is read from the config. I ran the deployed NVFP4 checkpoint itself on 4x RTX PRO 6000 and measured what it does. - 12,096 expert cells, 42 layers x 288 experts, each with exact REAP importance from 12.59M tokens per layer, route share and output contribution. Slice it by 14 domains, russian and vision included - one chart invalidated an assumption I had. Route count does not track expert importance, Spearman -0.222 against exact REAP. The ranking itself is stable, 0.994 split half - 2,176 KDA memory heads, each with its own half life, from 0.25 tokens to 2.7M - 19 billion NVFP4 block scales, plus the deployed FC2 QDQ error: median 21.05 dB and 21.7% of values turn into exact zeros - a causal check of REAP at inference, weights untouched. Dropping the top 2% experts changes 79% of sequences, dropping the bottom 2% changes 64% Every number comes from a capture I checksummed, 15 artifacts, and the limits are written next to each chart. Sound on I will record a video walk through every chart later. For now go poke around inside it:

Im done with GLM-5.3 Flash 🤯 I went deep into the guts of GLM-5.3 Flash. Ripped out every expert, every memory head, every quantization scale, looked at all of it up close, then put the model back together so you can see the inside too. 🎉 GLM-5.3 Flash NVFP4 is now in Weight Atlas. 320B total, 18B active, 45 language layers and a 24 block Vision tower. And none of it is read from the config. I ran the deployed NVFP4 checkpoint itself on 4x RTX PRO 6000 and measured what it does. - 12,096 expert cells, 42 layers x 288 experts, each with exact REAP importance from 12.59M tokens per layer, route share and output contribution. Slice it by 14 domains, russian and vision included - one chart invalidated an assumption I had. Route count does not track expert importance, Spearman -0.222 against exact REAP. The ranking itself is stable, 0.994 split half - 2,176 KDA memory heads, each with its own half life, from 0.25 tokens to 2.7M - 19 billion NVFP4 block scales, plus the deployed FC2 QDQ error: median 21.05 dB and 21.7% of values turn into exact zeros - a causal check of REAP at inference, weights untouched. Dropping the top 2% experts changes 79% of sequences, dropping the bottom 2% changes 64% Every number comes from a capture I checksummed, 15 artifacts, and the limits are written next to each chart. Sound on I will record a video walk through every chart later. For now go poke around inside it:

Alexey Fateev

39,590 次观看 • 2 天前

SITUATION EXPLAINED: A stealth model on OpenRouter is beating Fable and Sol on coding, and nobody knows who made it. • Ox Alpha has a 1M token context window, text, image, and video input, free for a week, with capacity for 100 trillion tokens a day • On a DeepSWE subset it hit 80%, against Fable's 65%, GLM-5.3's 62%, and Sol's 52% • The tokenizer is one-for-one identical to GLM's, and a live test asking whether Taiwan is part of China returned a nearly identical answer • This is the 5th anonymous drop in six months, and the previous four were all claimed by Chinese labs: Zhipu, Xiaomi, Ant, and Meituan Theo Jaffee: " There's no way it's a Flash model. If that's a Flash model, then holy shit, the entire United States is cooked. If that's a Flash model, we need to short the stock market now. Not financial advice."

SITUATION EXPLAINED: A stealth model on OpenRouter is beating Fable and Sol on coding, and nobody knows who made it. • Ox Alpha has a 1M token context window, text, image, and video input, free for a week, with capacity for 100 trillion tokens a day • On a DeepSWE subset it hit 80%, against Fable's 65%, GLM-5.3's 62%, and Sol's 52% • The tokenizer is one-for-one identical to GLM's, and a live test asking whether Taiwan is part of China returned a nearly identical answer • This is the 5th anonymous drop in six months, and the previous four were all claimed by Chinese labs: Zhipu, Xiaomi, Ant, and Meituan Theo Jaffee: " There's no way it's a Flash model. If that's a Flash model, then holy shit, the entire United States is cooked. If that's a Flash model, we need to short the stock market now. Not financial advice."

MTS

380,380 次观看 • 14 天前

Travis Taylor: "Our Universe lives inside a Black Hole" 😱 “The math keeps pointing to the same place. A black hole between 5 and 10 solar masses, with the best fit right around 7.2.” He arrived at the model by starting with something unexpected: the way video game world maps handle boundaries. That led him to event horizons, then to a Hawking radiation analogy that locked the numbers in place. The paper and the calculations line up on a stellar mass black hole as the container for everything we observe. If the model holds, the edge of our observable universe is not just a light-travel limit. It is the event horizon of a black hole whose mass sits in that narrow 5 to 10 solar mass window, peaking near 7.2. The same mathematics that describe the interior geometry of such a black hole also reproduce the expansion history and the cosmic horizon we measure. It is a clean, testable claim built from the paper’s equations rather than speculation. The numbers either fit or they do not. So far they do.

Travis Taylor: "Our Universe lives inside a Black Hole" 😱 “The math keeps pointing to the same place. A black hole between 5 and 10 solar masses, with the best fit right around 7.2.” He arrived at the model by starting with something unexpected: the way video game world maps handle boundaries. That led him to event horizons, then to a Hawking radiation analogy that locked the numbers in place. The paper and the calculations line up on a stellar mass black hole as the container for everything we observe. If the model holds, the edge of our observable universe is not just a light-travel limit. It is the event horizon of a black hole whose mass sits in that narrow 5 to 10 solar mass window, peaking near 7.2. The same mathematics that describe the interior geometry of such a black hole also reproduce the expansion history and the cosmic horizon we measure. It is a clean, testable claim built from the paper’s equations rather than speculation. The numbers either fit or they do not. So far they do.

Interstellar

75,305 次观看 • 25 天前

Hubble Records Wandering Black Hole That Swallowed a Star NASA astronomers have recorded a rare event using the Hubble Space Telescope and other observatories — a tidal disruption event (TDE) involving a wandering black hole. It occurred 600 million light years from Earth and provided new insights into the behavior of black holes. The object was spotted due to gravitational lensing — the distortion of light from distant bodies under the influence of gravity. As it approached the star, the black hole tore it apart, creating a bright accretion disk that radiated in ultraviolet and visible light. Unlike typical black holes in the centers of galaxies, this one was located 2,600 light years from the core, where a supermassive black hole was already located. Despite their relative proximity, the two holes do not yet form a single system, but they may merge in the future.

Hubble Records Wandering Black Hole That Swallowed a Star NASA astronomers have recorded a rare event using the Hubble Space Telescope and other observatories — a tidal disruption event (TDE) involving a wandering black hole. It occurred 600 million light years from Earth and provided new insights into the behavior of black holes. The object was spotted due to gravitational lensing — the distortion of light from distant bodies under the influence of gravity. As it approached the star, the black hole tore it apart, creating a bright accretion disk that radiated in ultraviolet and visible light. Unlike typical black holes in the centers of galaxies, this one was located 2,600 light years from the core, where a supermassive black hole was already located. Despite their relative proximity, the two holes do not yet form a single system, but they may merge in the future.

Black Hole

72,990 次观看 • 1 年前

Hubble Records Wandering Black Hole That Swallowed a Star NASA astronomers have recorded a rare event using the Hubble Space Telescope and other observatories — a tidal disruption event (TDE) involving a wandering black hole. It occurred 600 million light years from Earth and provided new insights into the behavior of black holes. The object was spotted due to gravitational lensing — the distortion of light from distant bodies under the influence of gravity. As it approached the star, the black hole tore it apart, creating a bright accretion disk that radiated in ultraviolet and visible light. Unlike typical black holes in the centers of galaxies, this one was located 2,600 light years from the core, where a supermassive black hole was already located. Despite their relative proximity, the two holes do not yet form a single system, but they may merge in the future.

Hubble Records Wandering Black Hole That Swallowed a Star NASA astronomers have recorded a rare event using the Hubble Space Telescope and other observatories — a tidal disruption event (TDE) involving a wandering black hole. It occurred 600 million light years from Earth and provided new insights into the behavior of black holes. The object was spotted due to gravitational lensing — the distortion of light from distant bodies under the influence of gravity. As it approached the star, the black hole tore it apart, creating a bright accretion disk that radiated in ultraviolet and visible light. Unlike typical black holes in the centers of galaxies, this one was located 2,600 light years from the core, where a supermassive black hole was already located. Despite their relative proximity, the two holes do not yet form a single system, but they may merge in the future.

Black Hole

28,483 次观看 • 1 年前

DFlash makes Qwen 2.2x faster with no quality loss! We ran the same Qwen3.6-27B locally three ways on one RTX 6000: baseline, MTP, DFlash. The tasks only differ in one thing - how predictable the next word is: quicksort, describe a file in JSON, a logic puzzle, a sci-fi story. Outputs: Baseline: 44 tok/s · 1.00x MTP: 65 tok/s · 1.45x · 71% accepted DFlash: 98 tok/s · 2.20x · 30% accepted Baseline writes one token per step. MTP works inside the model itself and guesses 3 tokens ahead. DFlash is a separate small model that writes 15 tokens at once, and the big model only checks them. In JSON the same words repeat all the time, so most guesses were right: 152 tok/s, 3.4x speedup. In the story 9 guesses out of 10 were wrong. DFlash did all that extra work for nothing and became slower than baseline: 42 vs 44 tok/s. MTP guesses only 3 tokens, so a wrong guess costs very little: 46 tok/s and the win in that round. The output is identical in all three modes - DFlash is the pick for tasks with predictable output, like coding, and for chat and creative writing MTP works better. DFlash is now natively integrated into Atomic Chat on llama.cpp - speed up your Qwen models!

DFlash makes Qwen 2.2x faster with no quality loss! We ran the same Qwen3.6-27B locally three ways on one RTX 6000: baseline, MTP, DFlash. The tasks only differ in one thing - how predictable the next word is: quicksort, describe a file in JSON, a logic puzzle, a sci-fi story. Outputs: Baseline: 44 tok/s · 1.00x MTP: 65 tok/s · 1.45x · 71% accepted DFlash: 98 tok/s · 2.20x · 30% accepted Baseline writes one token per step. MTP works inside the model itself and guesses 3 tokens ahead. DFlash is a separate small model that writes 15 tokens at once, and the big model only checks them. In JSON the same words repeat all the time, so most guesses were right: 152 tok/s, 3.4x speedup. In the story 9 guesses out of 10 were wrong. DFlash did all that extra work for nothing and became slower than baseline: 42 vs 44 tok/s. MTP guesses only 3 tokens, so a wrong guess costs very little: 46 tok/s and the win in that round. The output is identical in all three modes - DFlash is the pick for tasks with predictable output, like coding, and for chat and creative writing MTP works better. DFlash is now natively integrated into Atomic Chat on llama.cpp - speed up your Qwen models!

atomic.chat

82,776 次观看 • 1 个月前

What Happens When a Black Hole Does More Than Fall? In General Relativity, a rotating Black Hole is described by the Kerr Metric. Its spin does something strange to spacetime... it drags the geometry around with it. The horizon sits at r₊ = M + √(M² - a²) but outside it there is another surface, the Ergosphere, r_E(θ) = M + √(M² - a²cos²θ) Inside that region, Spacetime is being swept around so strongly that remaining still is no longer possible. Every particle, every light thread, every local observer is forced to rotate with the geometry. For this scene, the C-metric acceleration remains alive in the background and the Black Hole still moves through the fabric, pulled along an axial defect. But the main event is Kerr frame dragging. The fabric twists. The Ergosphere breathes. Light paths shear into spirals. Particles that try to cross the region get carried around before falling inward. #GeneralRelativity #BlackHole #KerrMetric #FrameDragging #Ergosphere #Spacetime #EinsteinEquations #Astrophysics #Physics

What Happens When a Black Hole Does More Than Fall? In General Relativity, a rotating Black Hole is described by the Kerr Metric. Its spin does something strange to spacetime... it drags the geometry around with it. The horizon sits at r₊ = M + √(M² - a²) but outside it there is another surface, the Ergosphere, r_E(θ) = M + √(M² - a²cos²θ) Inside that region, Spacetime is being swept around so strongly that remaining still is no longer possible. Every particle, every light thread, every local observer is forced to rotate with the geometry. For this scene, the C-metric acceleration remains alive in the background and the Black Hole still moves through the fabric, pulled along an axial defect. But the main event is Kerr frame dragging. The fabric twists. The Ergosphere breathes. Light paths shear into spirals. Particles that try to cross the region get carried around before falling inward. #GeneralRelativity #BlackHole #KerrMetric #FrameDragging #Ergosphere #Spacetime #EinsteinEquations #Astrophysics #Physics

Mathelirium

27,112 次观看 • 1 个月前

A White Hole is a Black Hole Running Backward A Black Hole is famous because it lets things fall in, but never lets them come back out. A White Hole is the time-reversed version of that idea. In the full Schwarzschild solution of General Relativity, the Black-Hole region and the White-Hole region appear as two different causal parts of the same exact geometry. Outside the horizon, the metric is the usual Schwarzschild metric ds² = -(1 - 2M/r)dt² + (1 - 2M/r)⁻¹dr² + r²dΩ². The horizon sits at rₛ = 2M, using G = c = 1. For an ordinary Black Hope, future-directed paths can cross this surface inward. Once they do, they cannot return to the outside universe. For a White Hole, the arrow is reversed and particles and light can come out from the horizon, but outside observers cannot enter it. No one has observed a physical White Hole. It may be an ideal solution rather than an object that nature actually forms. But as Mathematics, it is one of the strangest lessons of General Relativity: The equations allow a horizon that throws the universe outward. #WhiteHoles #GeneralRelativity #Schwarzschild #Spacetime #BlackHoles #Cosmology #Physics #Mathematics #Astrophysics #Astronomy

A White Hole is a Black Hole Running Backward A Black Hole is famous because it lets things fall in, but never lets them come back out. A White Hole is the time-reversed version of that idea. In the full Schwarzschild solution of General Relativity, the Black-Hole region and the White-Hole region appear as two different causal parts of the same exact geometry. Outside the horizon, the metric is the usual Schwarzschild metric ds² = -(1 - 2M/r)dt² + (1 - 2M/r)⁻¹dr² + r²dΩ². The horizon sits at rₛ = 2M, using G = c = 1. For an ordinary Black Hope, future-directed paths can cross this surface inward. Once they do, they cannot return to the outside universe. For a White Hole, the arrow is reversed and particles and light can come out from the horizon, but outside observers cannot enter it. No one has observed a physical White Hole. It may be an ideal solution rather than an object that nature actually forms. But as Mathematics, it is one of the strangest lessons of General Relativity: The equations allow a horizon that throws the universe outward. #WhiteHoles #GeneralRelativity #Schwarzschild #Spacetime #BlackHoles #Cosmology #Physics #Mathematics #Astrophysics #Astronomy

Mathelirium

44,259 次观看 • 2 个月前

What if we could RECREATE the REFLECTION of the FLASH seen in the GLASS during the Charlie Kirk assassination? Literally recreate it on site. The timing of the flash seen in the reflection of the glass is what makes it so interesting. What else would cause a flash that bright in the middle of the day? This could at least determine a lot of good information and confirm or deny theories that are out there. Just to get an idea of where the FLASH came from, which could identify a possible shooter or 2nd shooter location. The reason I say this is because it lines up perfectly with the sound of the gun, meaning you see the flash first, then you hear the shot, which would make sense because light travels faster than sound. This way we could get an idea of where it came from in the first place and determine if it's a good lead or not and then check the crowd or building locations. It could be recreated with a camera flash or something with bright enough lumens. This could clear up at least one theory or confirm it and lead us in the right direction of where the shooter could have been. Please comment with your thoughts and ideas. We need to get someone out there to the location with the Proper equipment. At least we could confirm or deny some theories. Video in bottom left shows the flash.

What if we could RECREATE the REFLECTION of the FLASH seen in the GLASS during the Charlie Kirk assassination? Literally recreate it on site. The timing of the flash seen in the reflection of the glass is what makes it so interesting. What else would cause a flash that bright in the middle of the day? This could at least determine a lot of good information and confirm or deny theories that are out there. Just to get an idea of where the FLASH came from, which could identify a possible shooter or 2nd shooter location. The reason I say this is because it lines up perfectly with the sound of the gun, meaning you see the flash first, then you hear the shot, which would make sense because light travels faster than sound. This way we could get an idea of where it came from in the first place and determine if it's a good lead or not and then check the crowd or building locations. It could be recreated with a camera flash or something with bright enough lumens. This could clear up at least one theory or confirm it and lead us in the right direction of where the shooter could have been. Please comment with your thoughts and ideas. We need to get someone out there to the location with the Proper equipment. At least we could confirm or deny some theories. Video in bottom left shows the flash.

The SCIF

108,760 次观看 • 11 个月前

tencent hunyuan just shipped their third major model in six months. that pace is hard to keep up with. Hy4 preview is now live in workbuddy, and it's fully open source. the specs: 770B total params, 49B active, 1M+ token context window. but the number that matters is where it's pointed — real work. coding, office tasks, research, documents. here's the part i find interesting. tencent didn't build this in isolation. they put the training data together with their own experts across software engineering, gaming, finance and security, then designed the model side by side with products like workbuddy. model and product shaping each other instead of one being bolted onto the other. and it shows up in testing. 163 internal experts ran a blind test across 203 engineering tasks. hy4 preview scored 2.99/4 — ahead of kimi k3 at 2.94 and glm 5.3 at 2.92. on public benchmarks it clearly beats glm 5.2 and sits about level with glm 5.3. then the pricing. $6 per million input tokens, $18 per million output, $0.3 per million on cache hits. flagship results at a price you don't have to think about. and for the next two weeks it's free inside workbuddy.

tencent hunyuan just shipped their third major model in six months. that pace is hard to keep up with. Hy4 preview is now live in workbuddy, and it's fully open source. the specs: 770B total params, 49B active, 1M+ token context window. but the number that matters is where it's pointed — real work. coding, office tasks, research, documents. here's the part i find interesting. tencent didn't build this in isolation. they put the training data together with their own experts across software engineering, gaming, finance and security, then designed the model side by side with products like workbuddy. model and product shaping each other instead of one being bolted onto the other. and it shows up in testing. 163 internal experts ran a blind test across 203 engineering tasks. hy4 preview scored 2.99/4 — ahead of kimi k3 at 2.94 and glm 5.3 at 2.92. on public benchmarks it clearly beats glm 5.2 and sits about level with glm 5.3. then the pricing. $6 per million input tokens, $18 per million output, $0.3 per million on cache hits. flagship results at a price you don't have to think about. and for the next two weeks it's free inside workbuddy.

Fakhr

55,316 次观看 • 7 天前

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

atomic.chat

152,526 次观看 • 3 个月前

notes on my reinforcement learning for rocket league side quest so far: i’m in awe that — we live in a time where i can train a reinforcement learning model that is learning to play rocket league and at the same time, test community made rocket league models. where, both processes can run in parallel & locally on my hardware. in the game window — i’m playing against a reinforcement learning ppo model that’s running locally. and in the vscode window — i’m training my own reinforcement learning model (just a dummy example script for now). also, the rocket league botting ecosystem has incredible tooling where you can test out different reinforcement learning ideas against other algorithms + it has great documentation for linux and windows. it’s honestly a beautiful hidden gem of a rabbit hole :)

notes on my reinforcement learning for rocket league side quest so far: i’m in awe that — we live in a time where i can train a reinforcement learning model that is learning to play rocket league and at the same time, test community made rocket league models. where, both processes can run in parallel & locally on my hardware. in the game window — i’m playing against a reinforcement learning ppo model that’s running locally. and in the vscode window — i’m training my own reinforcement learning model (just a dummy example script for now). also, the rocket league botting ecosystem has incredible tooling where you can test out different reinforcement learning ideas against other algorithms + it has great documentation for linux and windows. it’s honestly a beautiful hidden gem of a rabbit hole :)

naklecha

46,795 次观看 • 1 年前

six months ago this wasn't happening on 8gb vram. running unsloth's Q4_K_XL quant of gemma 4 26b-a4b-it-qat, a sparse MoE model with only 4b active params on a single rtx 4060 laptop gpu, 8gb vram, 20+ tok/s decode. no cloud, no api, no offload hacks. just a gaming laptop on battery. what makes it fit: google's QAT (quantization aware training), plus MTP (multi token prediction) support in the latest llama.cpp builds. that combo is the single biggest unlock for local inference on low vram. rtx 3060, rtx 3070, gtx 1070, gtx 1080, rtx 4050, rtx 4060, rtx 5050, rtx 5060 — any 6-8gb consumer gpu, old or new — this model runs on it. world cup season, so i told it to build a soccer themed flappy bird clone. one shot, zero iteration, fully playable. six months ago an 8gb model could barely clone vanilla flappy bird. now it's shipping a themed game from a sparse MoE model running locally on a laptop battery. inference benchmarks: - decode throughput: 30 tok/s - context: 64k. this is the real unlock. 64k ctx is what makes a hermes agent loop viable locally on this model, not just single-turn chat. llama.cpp flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 -cmoe --port 8080 game's deployed on my own site, built and shipped end to end with open source llm, zero closed source api dependency in the pipeline. link in the description. gguf weights on huggingface, link in the comments. pull it down, run it on whatever 8gb card is sitting in your rig. try the game and tell me your score and what you want in v2. local llms on consumer gpus stopped being a meme.

six months ago this wasn't happening on 8gb vram. running unsloth's Q4_K_XL quant of gemma 4 26b-a4b-it-qat, a sparse MoE model with only 4b active params on a single rtx 4060 laptop gpu, 8gb vram, 20+ tok/s decode. no cloud, no api, no offload hacks. just a gaming laptop on battery. what makes it fit: google's QAT (quantization aware training), plus MTP (multi token prediction) support in the latest llama.cpp builds. that combo is the single biggest unlock for local inference on low vram. rtx 3060, rtx 3070, gtx 1070, gtx 1080, rtx 4050, rtx 4060, rtx 5050, rtx 5060 — any 6-8gb consumer gpu, old or new — this model runs on it. world cup season, so i told it to build a soccer themed flappy bird clone. one shot, zero iteration, fully playable. six months ago an 8gb model could barely clone vanilla flappy bird. now it's shipping a themed game from a sparse MoE model running locally on a laptop battery. inference benchmarks: - decode throughput: 30 tok/s - context: 64k. this is the real unlock. 64k ctx is what makes a hermes agent loop viable locally on this model, not just single-turn chat. llama.cpp flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 -cmoe --port 8080 game's deployed on my own site, built and shipped end to end with open source llm, zero closed source api dependency in the pipeline. link in the description. gguf weights on huggingface, link in the comments. pull it down, run it on whatever 8gb card is sitting in your rig. try the game and tell me your score and what you want in v2. local llms on consumer gpus stopped being a meme.

Alok

61,660 次观看 • 2 个月前

FABLE 5 JUST REBUILT FORTNITE FROM 1 PROMPT IN 20 MINUTES. EPIC NEEDED HUNDREDS OF ENGINEERS AND 6 YEARS. The test was simple: Fable 5 vs GLM 5.2, one prompt each, a 100-player battle royale running in the browser with no engine and no team. GLM went first and checked all 8 milestones. The game loaded with a battle bus, a generated map, Tilted Towers, even health and shield bars ripped straight from 2018. Then it fell apart. 2 FPS on landing, S walking forward and W walking backward, a gun that fires upward and a camera that refuses to look up. First fight ended with the enemy standing above physically impossible to aim at. Then Fable 5 took the same prompt on max effort. Different game entirely. Real character models instead of beans, hit reactions lighting up on contact, a shadow tracking every player, movement that just works out of the box. One follow-up prompt “add more Fortnite elements” and it shipped gliders, chests, shield potions, medkits and a sniper without breaking a thing. Final circle at 3:28 AM. Storm closing, 0 bullets left, box to box with medkit spam and a last-second swap to the SMG. Victory Royale in a game an AI wrote from a single sentence. GLM checked more boxes. Fable built something you could actually play and no benchmark on earth measures that gap. Epic needed 6 years. Fable 5 needed 1 prompt.

FABLE 5 JUST REBUILT FORTNITE FROM 1 PROMPT IN 20 MINUTES. EPIC NEEDED HUNDREDS OF ENGINEERS AND 6 YEARS. The test was simple: Fable 5 vs GLM 5.2, one prompt each, a 100-player battle royale running in the browser with no engine and no team. GLM went first and checked all 8 milestones. The game loaded with a battle bus, a generated map, Tilted Towers, even health and shield bars ripped straight from 2018. Then it fell apart. 2 FPS on landing, S walking forward and W walking backward, a gun that fires upward and a camera that refuses to look up. First fight ended with the enemy standing above physically impossible to aim at. Then Fable 5 took the same prompt on max effort. Different game entirely. Real character models instead of beans, hit reactions lighting up on contact, a shadow tracking every player, movement that just works out of the box. One follow-up prompt “add more Fortnite elements” and it shipped gliders, chests, shield potions, medkits and a sniper without breaking a thing. Final circle at 3:28 AM. Storm closing, 0 bullets left, box to box with medkit spam and a last-second swap to the SMG. Victory Royale in a game an AI wrote from a single sentence. GLM checked more boxes. Fable built something you could actually play and no benchmark on earth measures that gap. Epic needed 6 years. Fable 5 needed 1 prompt.

West Lord

80,912 次观看 • 2 个月前

I had an early access to GPT-6 Astra and I can say, everyone in the world now has a 3D designer at their fingertips. I gave it an image of a house and asked to create it in 3D with all the details including toys, appliances and furniture. This a full 3D model reconstruction in Blender with geometry that you can manually tweak and run 60fps as a locally rendered "game" on device.

I had an early access to GPT-6 Astra and I can say, everyone in the world now has a 3D designer at their fingertips. I gave it an image of a house and asked to create it in 3D with all the details including toys, appliances and furniture. This a full 3D model reconstruction in Blender with geometry that you can manually tweak and run 60fps as a locally rendered "game" on device.

Tom Krcha

1,268,511 次观看 • 1 天前

Timelapse #156 (36 hrs) - Worked with the tiny corp on getting GLM 5.2 running on 8xMI300X (sglang won here) - Launched KernelBench-Mega and updated Kernelbench-Hard with h100 and b200 sweeps - Took care of boring business stuff - Did some training sweeps for specialized technical vocab audio model - Some bugs with putting kernelbench-mega and hard on cloud instances so had to do some reruns. Learned a lot though - Setting up my own local rl infra and profiling concurrency 128 rollouts with vllm. Became clear to me that I need to serve in nvfp4, use MoE only for throughput, reap so training doesn’t OOM, dig into the vllm kernel graph itself to not underutilize my hardware from poor flashinfer/cutlass selections for my rtx pro 6000 sm120 architecture - Might do online distillation from glm 5.2 but for now taking it one step at a time - Slept for a bit then woke up and showered - Fixed an issue with SGLang tensor parallel deadlock on GLM 5.2 architecture with MTP enabled - GLM 5.2 inference is 2-3x faster than coding plans and running on amd boxes - Spent time with family - Hung out with some friends - Recorded some yoctogpt lectures with the revamped notebook (high taste btw) - Setting up dflash training for GLM 5.2

Timelapse #156 (36 hrs) - Worked with the tiny corp on getting GLM 5.2 running on 8xMI300X (sglang won here) - Launched KernelBench-Mega and updated Kernelbench-Hard with h100 and b200 sweeps - Took care of boring business stuff - Did some training sweeps for specialized technical vocab audio model - Some bugs with putting kernelbench-mega and hard on cloud instances so had to do some reruns. Learned a lot though - Setting up my own local rl infra and profiling concurrency 128 rollouts with vllm. Became clear to me that I need to serve in nvfp4, use MoE only for throughput, reap so training doesn’t OOM, dig into the vllm kernel graph itself to not underutilize my hardware from poor flashinfer/cutlass selections for my rtx pro 6000 sm120 architecture - Might do online distillation from glm 5.2 but for now taking it one step at a time - Slept for a bit then woke up and showered - Fixed an issue with SGLang tensor parallel deadlock on GLM 5.2 architecture with MTP enabled - GLM 5.2 inference is 2-3x faster than coding plans and running on amd boxes - Spent time with family - Hung out with some friends - Recorded some yoctogpt lectures with the revamped notebook (high taste btw) - Setting up dflash training for GLM 5.2

Elliot Arledge

45,447 次观看 • 2 个月前

OpenClaw 2.0 is available on Atomic Bot! We ran a battle: OpenClaw 2.0 vs Hermes Agent on GLM 5.3. Four prompts: movie scenes with the OpenClaw mascot in the lead role. Outputs: OpenClaw 2.0: ~2.1M tokens, ~$4.5, 10 self-fixes Hermes Agent: ~2.9M tokens, ~$4, 20 self-fixes OpenClaw screenshotted its own frames at exact timestamps, counted pixels to check its work, and filed fixes like "coat tail whip direction was inverted". Hermes went further and built itself a render harness: it measured frame times, checked the loop seam was invisible, and audited every scene against the spec before we ever opened it. That double-checking is where its extra tokens went. Run OpenClaw 2.0 in the cloud via Atomic Bot in one click!

OpenClaw 2.0 is available on Atomic Bot! We ran a battle: OpenClaw 2.0 vs Hermes Agent on GLM 5.3. Four prompts: movie scenes with the OpenClaw mascot in the lead role. Outputs: OpenClaw 2.0: ~2.1M tokens, ~$4.5, 10 self-fixes Hermes Agent: ~2.9M tokens, ~$4, 20 self-fixes OpenClaw screenshotted its own frames at exact timestamps, counted pixels to check its work, and filed fixes like "coat tail whip direction was inverted". Hermes went further and built itself a render harness: it measured frame times, checked the loop seam was invisible, and audited every scene against the spec before we ever opened it. That double-checking is where its extra tokens went. Run OpenClaw 2.0 in the cloud via Atomic Bot in one click!

atomicbot.ai

66,450 次观看 • 3 天前

OFFICIAL: CFB27 Modders just Completed UNLOCKED Road To Glory and made it a true sandbox mod! These 4th mod will truly SHOCK you. These all the mods you need to be using right now 🔥 1. The Ice House Free Flow Gameplay mod + CujoMatty Clean Screen Mod! This brings faster game speed, cleaner player interactions and less animation based gameplay along with the clean screen mod that removes a lot of the unnecessary indicators on the field! 2. Pandaku RTG Editor and 2 way player mod! This mod truly makes RTG fun giving you the ability to edit every rating, ability development trait, depth chart, and more! Finally you are able to play both ways on the same team no matter position so if you even want to be a WR/LB it’s now possible AND your stats do fully track both ways! Tag this on with Trix RTG editor that allows your player to have long hair out the helmet! 3. Orckestra CFB27 Unlocked Mod! This mod brings NEW gear customization options such as turtlenecks, new colored visors, guardian caps, new cleats, and more! 4. Ducky Legend on Campus Mod! This mod is INSANE…. It gives your RTG player a iphone that you can use to text other your teammates or even trash talk other players on which creates better dialogue than we see in the base game, dynamic twitter that reacts to things that happen in your RTG universe, and fully customizable profile and wallpaper to make it feel like yours. ALL these mods and even more are FREE only available in the CFB modding discord! This is UNBELIEVABLE 👀

RISCO 🧨

259,136 次观看 • 1 个月前