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ABCDEF… interpolation test. Realtime vector graphics at 120 FPS, no pixels. Original HP font.

ertdfgcvb 𓅰 𓅬 𓅭 𓅮 𓅯

21,699 subscribers

12,812 次观看 • 5 个月前 •via X (Twitter)

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No hate to OP, but here's the same section of Bloody Stream but at the original 24 fps just to illustrate how much 60 fps interpolation ruins the animation

No hate to OP, but here's the same section of Bloody Stream but at the original 24 fps just to illustrate how much 60 fps interpolation ruins the animation

🐤Pë€P🐥 COMM SLOTS ON HOLD

498,426 次观看 • 3 个月前

X-Y-(Z), third chapter. JS code streaming itself in realtime from the browser to an HP vector display. No pixels, phosphor only. ♫ “Relay” by Gooooose (One of my favorite tracks of 2025, used with permission) Bit-bang magic by Alphonse🌱

X-Y-(Z), third chapter. JS code streaming itself in realtime from the browser to an HP vector display. No pixels, phosphor only. ♫ “Relay” by Gooooose (One of my favorite tracks of 2025, used with permission) Bit-bang magic by Alphonse🌱

ertdfgcvb 𓅰 𓅬 𓅭 𓅮 𓅯

21,163 次观看 • 4 个月前

“120 FPS isn’t objectively better, that’s subjective.” No it’s not? It’s objectively better. So is 60 FPS no longer objectively better than 30 FPS? So framerate is a subjective issue, Mr. “I don’t play games at 30FPS?”

“120 FPS isn’t objectively better, that’s subjective.” No it’s not? It’s objectively better. So is 60 FPS no longer objectively better than 30 FPS? So framerate is a subjective issue, Mr. “I don’t play games at 30FPS?”

Josh the Editor

169,044 次观看 • 8 个月前

An extremely complex example of Vector Feathering in César yet crazy fast render! — No detail loss at zoom — It's interactive, and layers follow the mouse, imitating real light — Shadows and highlights have a convincing penumbra — 400+ FPS — Works on old, weak machines

An extremely complex example of Vector Feathering in César yet crazy fast render! — No detail loss at zoom — It's interactive, and layers follow the mouse, imitating real light — Shadows and highlights have a convincing penumbra — 400+ FPS — Works on old, weak machines

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30,793 次观看 • 1 年前

More progress of my Deep Dark Forest Physcological Horror Experience This is standalone version running at 120/140fps 4k Native Epic Settings RTX4090 NO Lumen, NO Raytrace, NO Baking, NO Pathtrace, NO DLSS Lighting with Ultra Dynamic Sky Unreal Engine 5.5.4 Nanite and Ambient Occlution with SSR reflections All is realtime #ue5 #UnrealEngine5 #unrelengine #realism #lighting #forest #nanite #UDS #quixel #realtime #videogames #horror #mood #witch Unreal Engine 80 LEVEL ArtStation.com Quixel

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Pasquale Scionti

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Smooth 135+ FPS on my Rig with no drop all ultra settings! Will probably get around 110-120 with DLSS OFF! The cutscenes are unfortunately locked at 30 FPS! Great game none the less, fantastic character, out of the world boss fight and best FF combat till date ngl

Smooth 135+ FPS on my Rig with no drop all ultra settings! Will probably get around 110-120 with DLSS OFF! The cutscenes are unfortunately locked at 30 FPS! Great game none the less, fantastic character, out of the world boss fight and best FF combat till date ngl

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making my threejs / webgpu star renderer (planetarium?) more of a real thing. renders 44 million stars from the gaia star catalog at 120 fps. there's no LOD/culling. every star in frame is drawn. next is adding a nice API for agents to write "tours" that play automatically

making my threejs / webgpu star renderer (planetarium?) more of a real thing. renders 44 million stars from the gaia star catalog at 120 fps. there's no LOD/culling. every star in frame is drawn. next is adding a nice API for agents to write "tours" that play automatically

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Assassin's Creed Valhalla - 170 GB, hour+ long download on Steam at 120 Mbit/s With Via you are in game in <5 min with <5 gigs of data on disk. Ubisoft's Anvil engine tech seems pretty good to me, good loading code and no fps stutters due to streaming.

Assassin's Creed Valhalla - 170 GB, hour+ long download on Steam at 120 Mbit/s With Via you are in game in <5 min with <5 gigs of data on disk. Ubisoft's Anvil engine tech seems pretty good to me, good loading code and no fps stutters due to streaming.

Ryan Flaherty

19,443 次观看 • 2 年前

We're preparing for the second playtest of MIGHTREYA! Based on user feedback from the previous test, we've fixed bugs and added new features. We’d love to have your support again—please join us for the next round! To join the playtest, please join our Discord server. A selection of the new features we’ve added -Camera Assist (Hard Lock-On) -Takedown Converted to QTE -Duplicate Move Penalty -Expanded Graphics Options: V-Sync, FPS Cap, AI Upscaling -Enemy Warp Recovery When Stuck in Terrain -Stage Progression Navigation -Boss Guard Gauge Recovery and HP Adjustment -Boss Takedown Implementation -Boss Intro Cutscene -UI Operation Guide -Simplified Command List -Tutorial Level -One-Point Tutorials -Streaming Comment Features: Move Requests, Super Chat Integration -Cut-In Animation for Level 5 Charged Moves -Sub-Attack Motion and Performance Adjustments -Forced Transformation Cancel at Low HP -Option: Toggle PlayStation-Style Button Icons -Score and Result System Finalized: Displays Final Rank -Lock-On Distance Adjustment

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Wishlist MIGHTREYA on Steam

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Behold the CSX 4602—one of only two full-copper Shelby Cobras ever built. Hand-formed from 17 polished copper panels, this 1997 continuation 427 S/C is a rolling masterpiece. 550 HP from a 468ci side-oiler V8. 218 original miles. Sold for $990,000 at Mecum Indy 2025. No paint. No plating. Just pure, mirror-bright copper that ages like fine art.

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𝐃𝐔𝐓𝐂𝐇

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I think it's time we all get a reality check on Diddy Kong Racing 64 for the Sega Dreamcast, including myself... So here it is: original DKR for N64 vs our ported DKR for DC, direct comparison with FPS counters enabled. The truth is that I've been staring at the FPS meter all weekend, obsessing over every little dip, vectorizing, loop unrolling, and optimizing every little expensive-looking loop I can see in the codebase to win back 0.1FPS at a time... totally losing sight of what the basis for comparison even was from the original... So how long has it been since you've played the original on N64? I'm not talking about via emulation with higher FPS and fancy upscaling. I'm talking about directly, on an actual, physical Nintendo 64. Full disclosure... prolly been half a decade now, for me! Anyway, the devs behind the decomp were kind enough to share with me some of their performance and timing analysis captures... so I figured we'd take a step back and have a little fun! I had no idea it was running at only 20FPS tops, capped at 15FPS for the WizPig races!? I just remember pissing myself at having never seen a game look that good or realistic in my entire life back in 1997 when DKR came out for the N64... All I know is I want to do this game justice... but the truth is that even though we're still cooking, already the Dreamcast runs consistently at 10FPS higher and runs at 4x the screen resolution with progressive scan output... What do you guys think? How's our port comparing?🤔

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I’m starting to think that what began as "let’s try a Magnific that doesn’t change faces" might end up being Magnific’s biggest hit since we started doing AI stuff. The more I test it, the more use cases I find. For example, I’ve been playing around with historical photos and… joder, it actually works really well for restoration. It even preserves the original noise nicely without going all “plastic denoise” on everything! [Maybe the weapon area could be improved, but textils are so good, no?] M. Precision feels like a whole new dimension: “as faithful to the original image as possible, but filling in the missing detail in the new pixels.” Not there yet, but starting to feel like magic 🪄 I think it’s time to release the kraken for beta… hoping to start beta testing tomorrow!

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65,256 次观看 • 1 年前

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

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