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KIMI K3 VS OPUS 4.8 SIMULATING A 3D PARTICLE SYSTEM same prompt, same target: thousands of particles reacting to gravity and to each other in real time → K3: more organic distribution, smoother motion, particles actually cluster and drift like something physical → Opus: more rigid pattern, less natural,... show more

Jade

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32,648 次观看 • 5 天前 •via X (Twitter)

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LongCat performed Opus 4.8 and GPT 5.5 level on real physics tasks for $0! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics Prompts: - A cannon demolishing a brick wall - A bowling ball knocking down the pins - A tornado that sucks in random objects Outputs: LongCat: 18,015 tokens, $0.00 Opus 4.8: 18,872 tokens, $0.48 GPT 5.5: 32,588 tokens, $0.98 GLM 5.2: 31,062 tokens, $0.09 On the physics LongCat came out ahead of Opus 4.8 and GLM 5.2 - cleaner collisions, nothing clipping or falling through. On detail and rendering it matched GPT 5.5, the best looking of the four. Getting this quality for free is wild!

LongCat performed Opus 4.8 and GPT 5.5 level on real physics tasks for $0! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics Prompts: - A cannon demolishing a brick wall - A bowling ball knocking down the pins - A tornado that sucks in random objects Outputs: LongCat: 18,015 tokens, $0.00 Opus 4.8: 18,872 tokens, $0.48 GPT 5.5: 32,588 tokens, $0.98 GLM 5.2: 31,062 tokens, $0.09 On the physics LongCat came out ahead of Opus 4.8 and GLM 5.2 - cleaner collisions, nothing clipping or falling through. On detail and rendering it matched GPT 5.5, the best looking of the four. Getting this quality for free is wild!

atomic.chat

105,319 次观看 • 27 天前

Fable 5 absolutely crushed the HTML5 physics contest, but cost 6x more than Opus 4.8 and 39× more than GLM 5.2 in that test. Test was done on atomic[.]chat, a desktop app that runs LLMs locally. The test asked 4 models to generate self-contained canvas demos with believable motion and collisions. The scenes were not simple animations because every crash needed gravity, force, timing, and contact handling. Outputs: - Fable 5: 62,158 tokens, $3.12 - GPT 5.5: 37,753 tokens, $1.14 - Opus 4.8: 22,280 tokens, $0.56 - GLM 5.2: 36,246 tokens, $0.08

Fable 5 absolutely crushed the HTML5 physics contest, but cost 6x more than Opus 4.8 and 39× more than GLM 5.2 in that test. Test was done on atomic[.]chat, a desktop app that runs LLMs locally. The test asked 4 models to generate self-contained canvas demos with believable motion and collisions. The scenes were not simple animations because every crash needed gravity, force, timing, and contact handling. Outputs: - Fable 5: 62,158 tokens, $3.12 - GPT 5.5: 37,753 tokens, $1.14 - Opus 4.8: 22,280 tokens, $0.56 - GLM 5.2: 36,246 tokens, $0.08

Rohan Paul

205,771 次观看 • 25 天前

🤯KIMI K3 ABSOLUTELY MOGS! BEATING Opus 4.8, GPT 5.5, and even Fable 5 in multiple benchmarks. They scored 1688 on GDPval-AA v2 🔥 This is a completely different breed of open-source models Kimi creates better games and front-end designs than Fable 5, but it's 8x cheaper! The results coming out are truly impressive! I will be testing this out further and posting multiple tests today. Stay tuned!

🤯KIMI K3 ABSOLUTELY MOGS! BEATING Opus 4.8, GPT 5.5, and even Fable 5 in multiple benchmarks. They scored 1688 on GDPval-AA v2 🔥 This is a completely different breed of open-source models Kimi creates better games and front-end designs than Fable 5, but it's 8x cheaper! The results coming out are truly impressive! I will be testing this out further and posting multiple tests today. Stay tuned!

Mark Santos

127,267 次观看 • 11 天前

Kimi K3 vs GPT-5.6 vs Claude Fable 5 A game is one of the hardest places to fake working code. A web app can hide bugs behind a polished screenshot. A game can't. Bad collision, clipping, jitter, broken physics, awkward controls, or a snowboard sinking into the terrain become obvious within seconds. That's why I like using games to evaluate coding models. I gave Kimi K3 a single prompt to build a snowboard game in Godot, and just 20 seconds of gameplay told me more than a page of benchmark scores ever could. Benchmarks matter, but watching a model build something interactive reveals a different side of its capabilities. If the gameplay feels right, the systems work together, and the experience holds up under real input, that's a much more convincing demonstration than any leaderboard.

Kimi K3 vs GPT-5.6 vs Claude Fable 5 A game is one of the hardest places to fake working code. A web app can hide bugs behind a polished screenshot. A game can't. Bad collision, clipping, jitter, broken physics, awkward controls, or a snowboard sinking into the terrain become obvious within seconds. That's why I like using games to evaluate coding models. I gave Kimi K3 a single prompt to build a snowboard game in Godot, and just 20 seconds of gameplay told me more than a page of benchmark scores ever could. Benchmarks matter, but watching a model build something interactive reveals a different side of its capabilities. If the gameplay feels right, the systems work together, and the experience holds up under real input, that's a much more convincing demonstration than any leaderboard.

FHILY👑

35,156 次观看 • 4 天前

BREAKING: Anthropic just dropped Opus 4.8—and it is a MONSTER We've been testing for about a week Every 📧 and our verdict is they could've just called it Opus 5, it's that good. Here's our vibe check: - Beats GPT-5.5 on Senior Engineer bench. On our toughest benchmark Opus 4.8 scores a 63—a hair higher than GPT-5.5's score of 62, and a full 30 points higher than Opus 4.7. It tackled a ground-up rewrite of a production codebase, and actually built something that works. HOWEVER: Coding performance varied a lot at different reasoning levels. We recommend using it on xhigh for best results. - Incredibly good writer. Opus 4.8 scored a 79.6 on our writing benchmark—measuring models on real-world writing tasks we do all of the time like essay writing, promo email writing, and more. It beats GPT-5.5 by 6 points. It produces well-written prose with fewer "AI-isms". It's also very good at writing in your voice given the right context. HOWEVER: Writing performance also varied with reasoning levels. Medium reasoning had higher incidence of AI-isms—we found best results with high. - Beast at knowledge work. Opus 4.8 is very good at general knowledge work tasks like report creation, research and more. It produced the best PowerPoint one-shot we've ever seen on our deck generation benchmark. - Emotionally intelligent, willing to question the frame. I've also found it to be quite good at talking through psychological or interpersonal issues. It has a high EQ, and it's also good at not glazing and helping to expand your perspective. Its thought process feels extremely rich and dynamic. THE BAD: These days a model is only as good as its harness, and Codex is still a far superior harness to the Claude Desktop app. This has kept me using Codex + GPT-5.5 as my daily driver, but I am flipping back and forth a lot more between Codex and Claude. Anthropic is back baby! Read the rest on Every 📧:

BREAKING: Anthropic just dropped Opus 4.8—and it is a MONSTER We've been testing for about a week Every 📧 and our verdict is they could've just called it Opus 5, it's that good. Here's our vibe check: - Beats GPT-5.5 on Senior Engineer bench. On our toughest benchmark Opus 4.8 scores a 63—a hair higher than GPT-5.5's score of 62, and a full 30 points higher than Opus 4.7. It tackled a ground-up rewrite of a production codebase, and actually built something that works. HOWEVER: Coding performance varied a lot at different reasoning levels. We recommend using it on xhigh for best results. - Incredibly good writer. Opus 4.8 scored a 79.6 on our writing benchmark—measuring models on real-world writing tasks we do all of the time like essay writing, promo email writing, and more. It beats GPT-5.5 by 6 points. It produces well-written prose with fewer "AI-isms". It's also very good at writing in your voice given the right context. HOWEVER: Writing performance also varied with reasoning levels. Medium reasoning had higher incidence of AI-isms—we found best results with high. - Beast at knowledge work. Opus 4.8 is very good at general knowledge work tasks like report creation, research and more. It produced the best PowerPoint one-shot we've ever seen on our deck generation benchmark. - Emotionally intelligent, willing to question the frame. I've also found it to be quite good at talking through psychological or interpersonal issues. It has a high EQ, and it's also good at not glazing and helping to expand your perspective. Its thought process feels extremely rich and dynamic. THE BAD: These days a model is only as good as its harness, and Codex is still a far superior harness to the Claude Desktop app. This has kept me using Codex + GPT-5.5 as my daily driver, but I am flipping back and forth a lot more between Codex and Claude. Anthropic is back baby! Read the rest on Every 📧:

Dan Shipper 📧

353,763 次观看 • 2 个月前

Opus 4.6 vs GPT 5.4 (High) (1/9) prompt: Build a single-file HTML/CSS/JS (no libs) demo that uses SVG to simulate a plant growing: stem extends, leaves sprout + unfurl with springy/windy “physics”, then seamlessly loops forever. For the initial impressions I'm really impressed by GPT, for speed they both felt about the same, but gpt was still half cheaper than opus. I also much prefer the design and animation that gpt produced, physics on the leaves are super cool and it also loops pretty nicely whilst opus just fades out the plant. Still got a bunch of tests to run but this is really exciting.

Opus 4.6 vs GPT 5.4 (High) (1/9) prompt: Build a single-file HTML/CSS/JS (no libs) demo that uses SVG to simulate a plant growing: stem extends, leaves sprout + unfurl with springy/windy “physics”, then seamlessly loops forever. For the initial impressions I'm really impressed by GPT, for speed they both felt about the same, but gpt was still half cheaper than opus. I also much prefer the design and animation that gpt produced, physics on the leaves are super cool and it also loops pretty nicely whilst opus just fades out the plant. Still got a bunch of tests to run but this is really exciting.

Dev Ed

660,154 次观看 • 4 个月前

🚨 BREAKING: Physics just broke its own rulebook. For decades, every particle in the universe fit into 2 types • Bosons (share space) • Fermions (avoid each other) Now? Scientists just created something in between. A new class of particles called anyons that can behave partly like both. Even crazier In 1D systems, you can tune their behavior like a dial between order and exclusion. This means Particle identity isn’t fixed Quantum behavior is adjustable Reality depends on dimensional constraints Think about that. If the rules change based on dimension. then what we call “fundamental” might just be a limit case. So the real question is Are particles truly different… or just different expressions of the same underlying structure? Follow for deeper physics that challenges the foundations.

🚨 BREAKING: Physics just broke its own rulebook. For decades, every particle in the universe fit into 2 types • Bosons (share space) • Fermions (avoid each other) Now? Scientists just created something in between. A new class of particles called anyons that can behave partly like both. Even crazier In 1D systems, you can tune their behavior like a dial between order and exclusion. This means Particle identity isn’t fixed Quantum behavior is adjustable Reality depends on dimensional constraints Think about that. If the rules change based on dimension. then what we call “fundamental” might just be a limit case. So the real question is Are particles truly different… or just different expressions of the same underlying structure? Follow for deeper physics that challenges the foundations.

TheNewPhysics

21,603 次观看 • 3 个月前

Fable 5 totally crushed our new contest, but it cost 6x more than Opus 4.8! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: — A train derailing off a broken bridge into the water — Two cars jumping off ramps and colliding mid-air over a canyon — A monster truck crushing a row of parked cars Outputs: Fable 5: 62,158 tokens, $3.12 GPT 5.5: 37,753 tokens, $1.14 Opus 4.8: 22,280 tokens, $0.56 GLM 5.2: 36,246 tokens, $0.08 Fable 5 did all three scenes at A+. The crashes looked real, things fell and broke the right way, and nothing went through the ground or floated. GPT 5.5 was the closest to Fable. In the Bigfoot show, we think GPT was even a little better. GLM 5.2 did not win any scene, but it was the cheapest by far. Fable is the best pick for quality, but you pay more for it.

Fable 5 totally crushed our new contest, but it cost 6x more than Opus 4.8! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: — A train derailing off a broken bridge into the water — Two cars jumping off ramps and colliding mid-air over a canyon — A monster truck crushing a row of parked cars Outputs: Fable 5: 62,158 tokens, $3.12 GPT 5.5: 37,753 tokens, $1.14 Opus 4.8: 22,280 tokens, $0.56 GLM 5.2: 36,246 tokens, $0.08 Fable 5 did all three scenes at A+. The crashes looked real, things fell and broke the right way, and nothing went through the ground or floated. GPT 5.5 was the closest to Fable. In the Bigfoot show, we think GPT was even a little better. GLM 5.2 did not win any scene, but it was the cheapest by far. Fable is the best pick for quality, but you pay more for it.

atomic.chat

2,835,681 次观看 • 25 天前

🚨 BREAKING: Gravity doesn’t actually “pull” you. That’s just how it looks. What’s really happening is deeper… Space-time isn’t empty. It behaves more like a field with structure. And when mass appears… it doesn’t just “bend space” it changes how time flows. Think of it like this: If time flows evenly → everything stays stable If time compresses → motion is forced toward that region That motion is what we call gravity. In Einstein’s model: mass curves space-time But here’s the missing piece: What is doing the curving? In my framework: Mass = a region where time is compressed Gravity = objects moving along time gradients Motion = the system trying to rebalance So you’re not being “pulled down”… You’re being carried along a flow of time. This explains something most people miss: Gravity always points one way. Because time only flows one way. And the deeper implication is wild: If gravity is just time imbalance… then controlling time flow = controlling gravity. The real question is: Are we discovering gravity… or learning how to engineer it? Follow for more this goes deeper.

🚨 BREAKING: Gravity doesn’t actually “pull” you. That’s just how it looks. What’s really happening is deeper… Space-time isn’t empty. It behaves more like a field with structure. And when mass appears… it doesn’t just “bend space” it changes how time flows. Think of it like this: If time flows evenly → everything stays stable If time compresses → motion is forced toward that region That motion is what we call gravity. In Einstein’s model: mass curves space-time But here’s the missing piece: What is doing the curving? In my framework: Mass = a region where time is compressed Gravity = objects moving along time gradients Motion = the system trying to rebalance So you’re not being “pulled down”… You’re being carried along a flow of time. This explains something most people miss: Gravity always points one way. Because time only flows one way. And the deeper implication is wild: If gravity is just time imbalance… then controlling time flow = controlling gravity. The real question is: Are we discovering gravity… or learning how to engineer it? Follow for more this goes deeper.

TheNewPhysics

21,772 次观看 • 3 个月前

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

Akshay 🚀

63,438 次观看 • 6 天前

Mysterious “Particle Fog” Reported Across the Globe, Social Media Erupts Reports of a strange, chemical-smelling “particle fog” are surfacing worldwide, with people in Florida, Pennsylvania, California, Texas, Canada, Germany, The United Kingdom and more sharing similar experiences. Described as a thick, rain-like mist, social media users claim it causes illness and appears unnatural. “That is not rain; those are particles coming out,” one user noted in a viral clip. The phenomenon has sparked widespread concern, with many questioning how the same unusual fog is appearing in multiple countries simultaneously. Could this be a global environmental anomaly or something more sinister?

Mysterious “Particle Fog” Reported Across the Globe, Social Media Erupts Reports of a strange, chemical-smelling “particle fog” are surfacing worldwide, with people in Florida, Pennsylvania, California, Texas, Canada, Germany, The United Kingdom and more sharing similar experiences. Described as a thick, rain-like mist, social media users claim it causes illness and appears unnatural. “That is not rain; those are particles coming out,” one user noted in a viral clip. The phenomenon has sparked widespread concern, with many questioning how the same unusual fog is appearing in multiple countries simultaneously. Could this be a global environmental anomaly or something more sinister?

News is Dead

41,542 次观看 • 1 年前

FABLE 5 JUST RECREATED NEW YORK CITY IN BLENDER. I connected Blender to Fable 5 and in like 20 minutes, it remade the entire cityscape of NYC It started by getting data of the buildings from public sources, THEN began building it (imo this is a smarter move than what Opus 4.8 would've done) So theoretically, this entire build is up to scale too Fable 5, welcome back.

FABLE 5 JUST RECREATED NEW YORK CITY IN BLENDER. I connected Blender to Fable 5 and in like 20 minutes, it remade the entire cityscape of NYC It started by getting data of the buildings from public sources, THEN began building it (imo this is a smarter move than what Opus 4.8 would've done) So theoretically, this entire build is up to scale too Fable 5, welcome back.

ashen

222,715 次观看 • 25 天前

Turn your imagination into motion on Grok. How? Prompt: A mysterious beautiful woman sits calmly on a throne made of stars floating in deep space. Galaxies slowly swirl around her like a crown and her dress is made of nebula fabric that gently moves like cosmic smoke. Soft glowing cosmic rim light illuminates her face and detailed realistic skin texture is visible. After a moment she slowly rises from the throne and begins a graceful slow ballet movement in zero gravity. As she stands, the star throne dissolves into thousands of glowing particles and orbiting lights. She performs a soft elegant spin and extends her arms, and her motion causes surrounding galaxies and stardust to follow her movement as if she controls gravity. Her hair flows naturally in weightlessness. No fast dancing — slow, serene ballet, quiet power. The background is deep black space with colorful nebulae and distant suns. Cinematic camera with a slow floating push-in at the beginning, then a gentle orbit around her during the ballet spin, shallow depth of field, smooth motion, no shaking. Scene timing: 0-3 seconds she sits on the star throne with galaxies slowly moving, 3-6 seconds she stands and the throne dissolves into particles, 6-10 seconds she performs a slow ballet spin in zero gravity while galaxies react to her motion. Her movements subtly bend space around her as gravity responds to her presence. Realistic physics, volumetric particles, detailed fabric simulation, no distortion, no extra limbs, no flickering. 4K cinematic quality, 24fps film look, slow motion, calm graceful movement like underwater.

Turn your imagination into motion on Grok. How? Prompt: A mysterious beautiful woman sits calmly on a throne made of stars floating in deep space. Galaxies slowly swirl around her like a crown and her dress is made of nebula fabric that gently moves like cosmic smoke. Soft glowing cosmic rim light illuminates her face and detailed realistic skin texture is visible. After a moment she slowly rises from the throne and begins a graceful slow ballet movement in zero gravity. As she stands, the star throne dissolves into thousands of glowing particles and orbiting lights. She performs a soft elegant spin and extends her arms, and her motion causes surrounding galaxies and stardust to follow her movement as if she controls gravity. Her hair flows naturally in weightlessness. No fast dancing — slow, serene ballet, quiet power. The background is deep black space with colorful nebulae and distant suns. Cinematic camera with a slow floating push-in at the beginning, then a gentle orbit around her during the ballet spin, shallow depth of field, smooth motion, no shaking. Scene timing: 0-3 seconds she sits on the star throne with galaxies slowly moving, 3-6 seconds she stands and the throne dissolves into particles, 6-10 seconds she performs a slow ballet spin in zero gravity while galaxies react to her motion. Her movements subtly bend space around her as gravity responds to her presence. Realistic physics, volumetric particles, detailed fabric simulation, no distortion, no extra limbs, no flickering. 4K cinematic quality, 24fps film look, slow motion, calm graceful movement like underwater.

Mario Nawfal

765,362 次观看 • 5 个月前

🚨 BREAKING What if mass… isn’t a fixed property? Scientists just found evidence that mass can change. Not in theory. Inside real matter. They detected a rare state called an η′-mesic nucleus where a particle gets trapped inside a nucleus… and its mass shifts. Let that sink in. Same particle. Same physics. Different environment… different mass. That means: Mass isn’t just “what something is.” It’s how it interacts with the vacuum around it. And the vacuum? It’s not empty. It’s a hidden structure that gives particles their properties. Mass may come from the vacuum The vacuum changes in dense matter So mass itself can change This is huge. Because it flips the idea that mass is fundamental. Instead: mass might be emergent. In my view: Mass isn’t a property… it’s a response to time/field structure. Change the structure… you change the mass. So the real question is: Are particles “things”… or just stable patterns in a deeper field? Follow me physics isn’t discovering matter… it’s discovering what creates it.

🚨 BREAKING What if mass… isn’t a fixed property? Scientists just found evidence that mass can change. Not in theory. Inside real matter. They detected a rare state called an η′-mesic nucleus where a particle gets trapped inside a nucleus… and its mass shifts. Let that sink in. Same particle. Same physics. Different environment… different mass. That means: Mass isn’t just “what something is.” It’s how it interacts with the vacuum around it. And the vacuum? It’s not empty. It’s a hidden structure that gives particles their properties. Mass may come from the vacuum The vacuum changes in dense matter So mass itself can change This is huge. Because it flips the idea that mass is fundamental. Instead: mass might be emergent. In my view: Mass isn’t a property… it’s a response to time/field structure. Change the structure… you change the mass. So the real question is: Are particles “things”… or just stable patterns in a deeper field? Follow me physics isn’t discovering matter… it’s discovering what creates it.

TheNewPhysics

17,272 次观看 • 3 个月前

Sakura vs Jinx, created with the AI Seedance 2.0. Artificial intelligence is evolving at an impressive speed. It is already capable of generating a fight between a 2D character and a 3D character in a way that feels natural and believable. What is even more surprising is that this can be created in just a few minutes with a single prompt. Something that previously required a large team, significant resources, and a long production time can now be generated with just one well-crafted prompt.

Sakura vs Jinx, created with the AI Seedance 2.0. Artificial intelligence is evolving at an impressive speed. It is already capable of generating a fight between a 2D character and a 3D character in a way that feels natural and believable. What is even more surprising is that this can be created in just a few minutes with a single prompt. Something that previously required a large team, significant resources, and a long production time can now be generated with just one well-crafted prompt.

nachos2d

239,984 次观看 • 4 个月前

Sakana Fugu surprisingly performed near GLM 5.2 level but 17× more expensive! We gave the same prompt to 4 models: build a complete live Trader Desk with both frontend and backend components, real-time market data fetched from external APIs for 8 symbols, and a custom dark-theme UI. Outputs: Fugu Ultra — 22,225 t, $0.51 Opus 4.8 — 15,802 t, $0.31 GPT-5.5 — 11,474 t, $0.26 GLM 5.2 — 13,677 t, $0.03 Fugu created the most polished and feature-rich trading desk in the run. GLM 5.2 was very close behind, with a similarly complete multi-panel interface and live data, but at a much lower cost. Opus and GPT also performed well, delivering solid results with a better balance between quality and cost

Sakana Fugu surprisingly performed near GLM 5.2 level but 17× more expensive! We gave the same prompt to 4 models: build a complete live Trader Desk with both frontend and backend components, real-time market data fetched from external APIs for 8 symbols, and a custom dark-theme UI. Outputs: Fugu Ultra — 22,225 t, $0.51 Opus 4.8 — 15,802 t, $0.31 GPT-5.5 — 11,474 t, $0.26 GLM 5.2 — 13,677 t, $0.03 Fugu created the most polished and feature-rich trading desk in the run. GLM 5.2 was very close behind, with a similarly complete multi-panel interface and live data, but at a much lower cost. Opus and GPT also performed well, delivering solid results with a better balance between quality and cost

atomic.chat

743,178 次观看 • 1 个月前

Double-Slit Experiment ✍️ It shows that tiny things like electrons act like ripples in water instead of solid marbles. When a particle is fired at two slits, it doesn't just go through one or the other; it travels as a wave of probability that passes through both at the same time. These two waves then overlap and interfere with each other, creating a pattern of light and dark stripes on a back wall. This shows that at the quantum level, reality isn't "set" until we actually measure it. It behaves as a spread-out wave of possibilities until the moment it hits a target. Video 📸 : umtiquinhodefisica

Double-Slit Experiment ✍️ It shows that tiny things like electrons act like ripples in water instead of solid marbles. When a particle is fired at two slits, it doesn't just go through one or the other; it travels as a wave of probability that passes through both at the same time. These two waves then overlap and interfere with each other, creating a pattern of light and dark stripes on a back wall. This shows that at the quantum level, reality isn't "set" until we actually measure it. It behaves as a spread-out wave of possibilities until the moment it hits a target. Video 📸 : umtiquinhodefisica

ScieVision

60,696 次观看 • 3 个月前

Opus 4.6 vs GPT-5.4 (4/9) prompt: Build a production-quality 3D flight-tracking web app using React + Vite + Three.js (react-three-fiber + drei) that visualizes live OpenSky aircraft data on a rotatable 3D Earth, with real-time plane motion, smooth interpolation, altitude-accurate positioning, and polished lighting/post-processing. Both models did really well on this one and honestly I’m impressed with both. GPT-5.4 had the nicer post-processing out of the box. I really liked the subtle light shimmer on the airplanes when rotating the planet, and the camera work when clicking a plane felt better overall. Opus 4.6 though had a few details I liked more. It automatically went and found a much nicer Earth texture on GitHub, while with GPT-5.4 I had to reprompt it to go look for a better one. I also preferred Opus’s plane model overall, it just looked more polished, whereas GPT-5.4’s plane asset looked a bit funny. One thing I noticed with GPT-5.4 is that when you click into the plane, the camera sometimes clips through the planet, which breaks the effect a bit. Opus handled that part more cleanly. Overall this felt like a strong result from both, just with different strengths. GPT-5.4 felt better on presentation and post-processing, while Opus had better asset choices and a more premium-looking Earth/plane combo.

Opus 4.6 vs GPT-5.4 (4/9) prompt: Build a production-quality 3D flight-tracking web app using React + Vite + Three.js (react-three-fiber + drei) that visualizes live OpenSky aircraft data on a rotatable 3D Earth, with real-time plane motion, smooth interpolation, altitude-accurate positioning, and polished lighting/post-processing. Both models did really well on this one and honestly I’m impressed with both. GPT-5.4 had the nicer post-processing out of the box. I really liked the subtle light shimmer on the airplanes when rotating the planet, and the camera work when clicking a plane felt better overall. Opus 4.6 though had a few details I liked more. It automatically went and found a much nicer Earth texture on GitHub, while with GPT-5.4 I had to reprompt it to go look for a better one. I also preferred Opus’s plane model overall, it just looked more polished, whereas GPT-5.4’s plane asset looked a bit funny. One thing I noticed with GPT-5.4 is that when you click into the plane, the camera sometimes clips through the planet, which breaks the effect a bit. Opus handled that part more cleanly. Overall this felt like a strong result from both, just with different strengths. GPT-5.4 felt better on presentation and post-processing, while Opus had better asset choices and a more premium-looking Earth/plane combo.

Dev Ed

279,565 次观看 • 4 个月前

Hugo Gonzalez on the Celtics: “What can I say? This is the [winningest] franchise in the whole NBA. It’s a privilege to be part of a franchise like this. It’s more or less like where I used to be in Real Madrid, they are the most winningest club in Europe, too. So having that type of transition and similarities and everything is something that is going to be easier.”

Hugo Gonzalez on the Celtics: “What can I say? This is the [winningest] franchise in the whole NBA. It’s a privilege to be part of a franchise like this. It’s more or less like where I used to be in Real Madrid, they are the most winningest club in Europe, too. So having that type of transition and similarities and everything is something that is going to be easier.”

Justin Turpin

60,435 次观看 • 1 年前

🚨 PHYSICISTS JUST SPLIT A SINGLE PHOTON AND IT TURNED INTO AN IMPROBABLE SWARM OF PARTICLES. In a striking experiment, researchers have shown that a photon can be split apart in such a way that it produces a large number of particles, creating what they describe as a “mixture from zero to infinity.” Instead of the usual clean splitting into two photons (as seen in spontaneous parametric down-conversion), this process generated a complex, broad swarm of particles. The result challenges conventional intuition about how photons behave when pushed into extreme nonlinear regimes. Why this matters: • It demonstrates a rare and complex form of photon splitting that was previously very difficult to observe cleanly • Such processes could help simulate high-energy particle physics in table-top experiments • It opens new possibilities for generating exotic quantum states of light • It provides deeper insight into nonlinear quantum electrodynamics (QED) in strong fields The deeper implication: Photons are usually thought of as indivisible quanta of light. But under the right extreme conditions, a single photon can effectively “break apart” into many particles. This isn’t just a curiosity it touches on fundamental questions about the nature of light and matter, and could eventually lead to new tools for quantum technologies and for studying physics that normally requires particle accelerators. We’re seeing light behave in ways that blur the line between a single quantum and a many-particle system. How do you think being able to controllably split photons into swarms of particles could impact quantum optics or fundamental physics research? Follow for more frontier quantum physics and breakthroughs in light-matter interaction.

🚨 PHYSICISTS JUST SPLIT A SINGLE PHOTON AND IT TURNED INTO AN IMPROBABLE SWARM OF PARTICLES. In a striking experiment, researchers have shown that a photon can be split apart in such a way that it produces a large number of particles, creating what they describe as a “mixture from zero to infinity.” Instead of the usual clean splitting into two photons (as seen in spontaneous parametric down-conversion), this process generated a complex, broad swarm of particles. The result challenges conventional intuition about how photons behave when pushed into extreme nonlinear regimes. Why this matters: • It demonstrates a rare and complex form of photon splitting that was previously very difficult to observe cleanly • Such processes could help simulate high-energy particle physics in table-top experiments • It opens new possibilities for generating exotic quantum states of light • It provides deeper insight into nonlinear quantum electrodynamics (QED) in strong fields The deeper implication: Photons are usually thought of as indivisible quanta of light. But under the right extreme conditions, a single photon can effectively “break apart” into many particles. This isn’t just a curiosity it touches on fundamental questions about the nature of light and matter, and could eventually lead to new tools for quantum technologies and for studying physics that normally requires particle accelerators. We’re seeing light behave in ways that blur the line between a single quantum and a many-particle system. How do you think being able to controllably split photons into swarms of particles could impact quantum optics or fundamental physics research? Follow for more frontier quantum physics and breakthroughs in light-matter interaction.

TheNewPhysics

25,874 次观看 • 1 个月前