📷 ARGON 2nd Mini Album [GO FORWARD : Wide Dream] CONCEPT PHOTO 'YEOUN' 📀 ALBUM RELEASE 2019.10.02 #아르곤# #ARGON# #여운# #YEOUN# #2nd_mini_album# #GO_FORWARD# #Wide_Dream#

GPT Image 2 just dropped. Nano Banana 2 just aged. Same prompt used: A high-fashion editorial image depicting a woman descending an endless spiral staircase captured from an extreme low-angle perspective beneath the steps. Her legs and impossibly tall black platform heels are dramatically and unnaturally elongated through wide-angle lens distortion, creating a sculptural and surreal silhouette, while her arms maintain natural proportions. She wears a distinctly original, skin-tight black leotard paired with bold, geometric black sunglasses, embodying an elegant and architectural pose that emphasizes form and structure. The background features a minimalist, soft grey-toned concrete spiral staircase, blending brutalist architecture with surreal fashion aesthetics. The scene is illuminated with professional, fashion-editorial lighting casting subtle studio shadows and dynamic highlights that realistically render skin textures, reflective fabric sheen, and the intricate surfaces of her footwear and eyewear. The composition’s exaggerated low-angle perspective amplifies the surreal elongation of her legs and footwear, enhancing the dramatic spectral scale and creating a compelling, hyper-photorealistic visual impact with exceptional detail in facial features, hair strands, and garment textures.

Made with GPT Image 2 + Seedance 2.0 on @SocialSight Generate a 16-move choreography sheet in GPT Image ↓↓↓↓↓↓ Upload it to Seedance 2.0 as the "brain" of your dance ↓↓↓↓↓↓ Your character performs the full routine instantly ↓↓↓↓↓↓ A complete K-pop dance video. Done in minutes. GPT Image 2 prompt: A colored pencil sketch style choreography sheet infographic for a K-pop solo dance. Layout: 16 steps arranged in a clean 4x4 grid, each panel showing a different dance move. Subject: A teenage Asian girl with short blunt-cut hair and bold bangs, wearing a monochrome outfit — fitted black turtleneck, wide-leg tailored trousers, chunky white platform boots, with silver chain accessories and fingerless gloves. Style: Hand-drawn colored pencil illustration, bold outlines with sharp shading, slightly angular and graphic, monochrome base with pops of electric red and silver as accent tones. Confident, editorial energy. Movement: Each frame captures sharp, punchy dance motions — arm isolations, sharp head snaps, shoulder locks, chest contractions, low lunges, toe-point poses, dramatic wrist flicks, and a signature ending freeze — with bold red arrows showing force direction and snap timing. Design: High-contrast K-pop girl crush aesthetic, grid with thin black borders, step numbers (1–16) in bold sans-serif, short captions under each frame describing the motion. Text: Title at the top — "K-POP GIRL CRUSH – 16 COUNTS – 10 SECONDS – SHARP LOCK & POP" Environment: White studio background, dramatic downlight suggestion, minimal clean shadows. Quality: High detail, crisp composition, balanced layout, editorial dance tutorial poster. Negative prompt: blurry, low quality, extra limbs, distorted anatomy, bad proportions, messy layout, overcrowded design, text errors, watermark, soft or pastel tones.

Breaking the "Memory Wall": Optical Interconnects Emerge in GPU–HBM Packaging As a solution to the "memory wall," one of the chronic challenges in AI semiconductors, the memory and packaging industries at home and abroad are weighing an approach that decouples the GPU and high-bandwidth memory (HBM) and packages them separately. The core idea is to move the HBM—until now mounted right next to the GPU—a certain distance away, and bridge the gap with light (optics), allowing several times more HBM to be installed than is possible today. On the 22nd, a researcher at a major domestic memory maker said, "We're currently struggling to expand HBM bandwidth and capacity, so we're discussing with customers a plan to overcome the GPU's shoreline limit through optical interconnects and mount more HBM." Shoreline refers to the length of the chip's perimeter. In today's AI computing environment, the key factor dragging down compute efficiency is the data transfer speed of memory chips. While GPU performance has grown by leaps and bounds with each generation, the speed at which memory stores and supplies data has failed to keep pace—creating a structural performance barrier, the memory wall. The arrival of HBM, with its wide data pathways, put out the immediate fire, but critics continue to point out that bandwidth and transfer speeds still fall short of handling the explosive growth in AI compute. Until now, the industry has focused on stacking HBM ever higher to increase memory capacity and bandwidth within a confined footprint. But as stack counts climbed past 12 and 16 layers toward 20 and beyond, process difficulty rose exponentially. The technology hit physical limits, including the growing difficulty of meeting fixed height specifications. Vertical stacking has reached an inflection point—so much so that the JEDEC standards body has relaxed its HBM height specifications. The bigger problem is that if stack counts can't be raised, the alternative is to add more HBM horizontally around the GPU—but that, too, is impossible. In the current 2.5D packaging structure, the GPU and HBM are mounted tightly together on a single substrate. Within this structure, the number of HBM units that can be placed is strictly limited by the finite length of the GPU chip's perimeter—its shoreline. Even when more HBM is desired, there is physically no room to place it, leaving the industry in a structural deadlock. The alternative now emerging across the semiconductor industry is to separate the GPU and HBM and package them independently. It overturns the conventional chip-design principle that components must sit close together to minimize data transfer time. Instead of keeping the two chips adjacent, the approach spaces them apart and links them with overwhelmingly fast optical signals to overcome the added physical distance. Placing the HBM slightly away from the GPU within the board frees the design from the GPU's shoreline constraint. With the spatial limitation gone, far more HBM can be spread out laterally and packed into the board—several times more than today—without having to push stack heights to extremes. This means the total memory capacity and data bandwidth of the AI accelerator system would expand dramatically, on a scale incomparable to current systems. "Discussing Placing HBM Beneath the GPU"… Form Factor Could Change The industry is now producing a range of architectural design proposals over where exactly to place the HBM within the GPU board. The same memory researcher said, "Options under discussion range from broadly utilizing the space immediately around the GPU to isolating the HBM beneath the GPU board." He added, "In the latter case—isolating it beneath the GPU board—the motherboard would have to be extended lengthwise, so we're discussing even an overall form-factor change with the GPU maker." Specifically, the HBM might surround the GPU from several centimeters away, or a separate HBM zone might be created in the center of the board. "We're keeping every possibility open as we discuss the optimal layout," he said. "Nothing has been confirmed as an official roadmap yet, but as part of preliminary research toward next-generation AI accelerators, we're in talks with our partners." The outsourced semiconductor assembly and test (OSAT) industry is also watching this trend closely. An executive at a global OSAT firm said, "Optical interconnects are a clear trajectory. The only question is timing," predicting that "rack-to-rack and server-to-server links will go optical first, and then chip-to-chip connections within the board will follow." He added, "The larger units will be connected by light first, but optical research is moving so fast that it may not be that far off." Technically, the optical-interconnect technology linking GPU and HBM shares the same underlying principle as the technology connecting server to server inside a data center. The difference is the high technical barrier of shrinking optical-conversion technology—once used for communication between large pieces of equipment—down to the microscopic scale of a single board and chipset. An executive at a domestic developer of co-packaged optics (CPO) components explained, "As HBM stack heights approach their limit, the industry is discussing spreading the memory out laterally to maximize how much can physically be mounted." He added, "The principle is the same as conventional data-center optical interconnects, but HBM optical links that have to operate within a confined board space require optical components to be miniaturized to far smaller sizes and far higher integration density—so the technical difficulty is greater."

SOMEONE JUST OPEN-SOURCED THE SCRIPT BEHIND EVERY FACELESS YOUTUBE CHANNEL a developer built a Python script that turns ANY Reddit thread into a finished, ready-to-upload YouTube Short or TikTok video every faceless YouTube creator making $2k-$10k/month is running this exact workflow now anyone with Python installed can run it for FREE [ the numbers are insane ]: - GitHub stars: 9,000+ - cost to install: $0 - time to first generated video: under 10 minutes - output format: 1080x1920 MP4 ready for YouTube Shorts or TikTok - script length: ~300 lines of Python - typical revenue for a working faceless channel: $500-$10,000/month - price of the "build a faceless YouTube empire" course this replaces: $497-$2,000 [ how the workflow actually works ]: > fetch top Reddit threads via the official Reddit API (no scraping, no Selenium) > screenshot the post title and the top-rated comments > AI voiceover narration (free Google TTS, or premium voices via API) > background footage looped from Minecraft, Subway Surfers, or Trackmania > auto-overlay each comment card synced to the narration timing > export 1080x1920 MP4 ready to upload no Premiere Pro, no DaVinci Resolve, no voice acting, no manual editing press run, get a finished video [ the grift opportunity is wide open ]: > run a faceless niche channel (AITA, Reddit drama, trivia, scary stories, relationship advice) > stack YouTube Creator Fund + TikTok Creator Fund revenue: $500-$10,000/month per channel at scale > spin up 5-10 channels at once with different niches and let them compound > white-label it as a service for small businesses: $300-$1,000 per finished video > wrap a clean UI around it and sell as a $29/month SaaS (multiple people are already doing this) > sell the channel once it grows past 50k subs: typical flip prices $5,000-$100,000 on every "faceless YouTube empire" course you've seen on X is teaching this exact pipeline REPO: 100% OPEN SOURCE, FREE