• Mars Lewis on the Brandoliniโ€™s Law

    Brandolini’s law (or the bullshit asymmetry principle) is an internet adage coined in 2013 by Italian programmer Alberto Brandolini. It compares the considerable effort of debunking misinformation to the relative ease of creating it in the first place.

    The law states: โ€œThe amount of energy needed to refute bullshit is an order of magnitude bigger than to produce it.โ€

    https://en.wikipedia.org/wiki/Brandolini%27s_law

    https://www.linkedin.com/posts/marslewis_brandolinislaw-propagandawars-medialies-activity-7320036000473255936-LQVG

    This is why every time you kill a lie, it feels like nothing changed. Itโ€™s why no matter how many facts you post, how many sources you cite, how many receipts you showโ€”the swarm just keeps coming. Because while youโ€™re out in the open doing surgery, the machine is behind the curtain spraying aerosol deceit into every vent.

    The lie takes ten seconds. The truth takes ten paragraphs. And by the time youโ€™ve written the tenth, the people youโ€™re trying to reach have already scrolled past.

    Every viral deceptionโ€”the fake quote, the rigged video, the synthetic outrageโ€”takes almost nothing to create. And once itโ€™s out there, youโ€™re not just correcting a factโ€”youโ€™re prying it out of someoneโ€™s identity. Because people donโ€™t adopt lies just for information. They adopt them for belonging. The lie becomes part of who they are, and your correction becomes an attack.

    And stillโ€”you must correct it. Still, you must fight.

    Because even if truth doesnโ€™t spread as fast, it roots deeper. Even if it doesnโ€™t go viral, it endures. And eventually, it makes people bulletproof to the next wave of narrative sewage.

    Youโ€™re not here to win a one-day war. Youโ€™re here to outlast a never-ending invasion.

    The lies are roaches. You kill one, and a hundred more scramble behind the drywall.The lies are Hydra heads. You cut one off, and two grow back. But you keep swinging anyway.

    Because this isnโ€™t about instant wins. Itโ€™s about making the cost of lying higher. Itโ€™s about being the resistance that doesnโ€™t fold. You donโ€™t fight because itโ€™s easy. You fight because itโ€™s right.

  • GenUE โ€“ Direct Prompt-to-Mesh Generation in Unreal Engine Integrated with ComfyUI

    ,

    GenUE brings prompt-driven 3D asset creation directly into Unreal Engine using ComfyUI as a flexible backend. โ€ข Generate high-quality images from text prompts. โ€ข Choose from a catalog of batch-generated images โ€“ no style limitations. โ€ข Convert the selected image to a fully textured 3D mesh. โ€ข Automatically import and place the model into your Unreal Engine scene. This modular pipeline gives you full control over the image and 3D generation stages, with support for any ComfyUI workflow or model. Full generation (image + mesh + import) completes in under 2 minutes on a high-end consumer GPU.

  • Edward Ureรฑa – Rig creator

    https://edwardurena.gumroad.com/l/ramoo

    What it offers:
    โ€ข Base rigs for multiple character types
    โ€ข Automatic weight application
    โ€ข Built-in facial rigging system
    โ€ข Bone generators with FK and IK options
    โ€ข Streamlined constraint panel

  • GPT-Image-1 API now available through ComfyUI with Dall-E integration

    https://blog.comfy.org/p/comfyui-now-supports-gpt-image-1

    https://docs.comfy.org/tutorials/api-nodes/openai/gpt-image-1

    https://openai.com/index/image-generation-api

    โ€ข Prompt GPT-Image-1 directly in ComfyUI using text or image inputs
    โ€ข Set resolution and quality
    โ€ข Supports image editing + transparent backgrounds
    โ€ข Seamlessly mix with local workflows like WAN 2.1, FLUX Tools, and more

  • Tencent Hunyuan3D 2.5 – Transform images and text into 3D models with ultra-high-definition precision

    ,

    https://www.hunyuan-3d.com/

    What makes it special?
    โ€ข Massive 10B parameter geometric model with 10x more mesh faces.
    โ€ข High-quality textures with industry-first multi-view PBR generation.
    โ€ข Optimized skeletal rigging for streamlined animation workflows.
    โ€ข Flexible pipeline for text-to-3D and image-to-3D generation.

    They’re making it accessible to everyone:
    โ€ข Open-source code and pre-trained models.
    โ€ข Easy-to-use API and intuitive web interface.
    โ€ข Free daily quota doubled to 20 generations!

  • Alibaba 3DV-TON – A novel diffusion model for HQ and temporally consistent video

    https://arxiv.org/pdf/2504.17414

    Video try-on replaces clothing in videos with target garments. Existing methods struggle to generate high-quality and temporally consistent results when handling complex clothing patterns and diverse body poses. We present 3DV-TON, a novel diffusion-based framework for generating high-fidelity and temporally consistent video try-on results. Our approach employs generated animatable textured 3D meshes as explicit frame-level guidance, alleviating the issue of models over-focusing on appearance fidelity at the expanse of motion coherence. This is achieved by enabling direct reference to consistent garment texture movements throughout video sequences. The proposed method features an adaptive pipeline for generating dynamic 3D guidance: (1) selecting a keyframe for initial 2D image try-on, followed by (2) reconstructing and animating a textured 3D mesh synchronized with original video poses. We further introduce a robust rectangular masking strategy that successfully mitigates artifact propagation caused by leaking clothing information during dynamic human and garment movements. To advance video try-on research, we introduce HR-VVT, a high-resolution benchmark dataset containing 130 videos with diverse clothing types and scenarios. Quantitative and qualitative results demonstrate our superior performance over existing methods.

  • FramePack – Packing Input Frame Context in Next-Frame Prediction Models for Offline Video Generation With Low Resource Requirements

    ,

    https://lllyasviel.github.io/frame_pack_gitpage/

    • Diffuse thousands of frames at full fps-30 with 13B models using 6GB laptop GPU memory.
    • Finetune 13B video model at batch size 64 on a single 8xA100/H100 node for personal/lab experiments.
    • Personal RTX 4090 generates at speed 2.5 seconds/frame (unoptimized) or 1.5 seconds/frame (teacache).
    • No timestep distillation.
    • Video diffusion, but feels like image diffusion.

    Image-to-5-Seconds (30fps, 150 frames)

  • Anthony Sauzet – ProceduralMaya

    A Maya script that introduces a node-based graph system for procedural modeling, like Houdini

    https://github.com/AnthonySTZ/ProceduralMaya

  • 11 Public Speaking Strategies

    What do people report as their #1 greatest fear?

    Itโ€™s not deathโ€ฆ.
    Itโ€™s public speaking.

    Glossophobia, the fear of public speaking, has been a daunting obstacle for me for years.


    11 confidence-boosting tips

    1/ The 5-5-5 Rule
    โ†’ Scan 5 faces; Hold each gaze for 5 seconds.
    โ†’ Repeat every 5 minutes.
    โ†’ Creates an authentic connection.

    2/Power Pause
    โ†’ Dead silence for 3 seconds after key points.
    โ†’ Let your message land.

    3/ The 3-Part Open
    โ†’ Hook with a question.
    โ†’ Share a story.
    โ†’ State your promise.

    4/ Palm-Up Principle
    โ†’ Open palms when speaking = trustworthy.
    โ†’ Pointing fingers = confrontational.

    5/ The 90-Second Reset
    โ†’ Feel nervous? Excuse yourself.
    โ†’ 90 seconds of deep breathing reset your nervous system.

    6/ Rule of Three
    โ†’ Structure key points in threes.
    โ†’ Our brains love patterns.

    7/ 2-Minute Story Rule
    โ†’ Keep stories under 2 minutes.
    โ†’ Any longer, you lose them.

    8/ The Lighthouse Method
    โ†’ Plant “anchor points” around the room.
    โ†’ Rotate eye contact between them.
    โ†’ Looks natural, feels structured.

    9/ The Power Position
    โ†’ Feet shoulder-width apart.
    โ†’ Hands relaxed at sides.
    โ†’ Projects confidence even when nervous.

    10/ The Callback Technique
    โ†’ Reference earlier points later in your talk.
    โ†’ Creates a narrative thread.
    โ†’ Audiences love connections.

    11/ The Rehearsal Truth
    โ†’ Practice the opening 3x more than the rest.
    โ†’ Nail the first 30 seconds; you’ll nail the talk.

  • FreeCodeCamp – Train Your Own LLM

    ,

    https://www.freecodecamp.org/news/train-your-own-llm

    Ever wondered how large language models like ChatGPT are actually built? Behind these impressive AI tools lies a complex but fascinating process of data preparation, model training, and fine-tuning. While it might seem like something only experts with massive resources can do, itโ€™s actually possible to learn how to build your own language model from scratch. And with the right guidance, you can go from loading raw text data to chatting with your very own AI assistant.

  • Alibaba FloraFauna.ai – AI Collaboration canvas

    https://www.florafauna.ai

    FLORA aims to make generative creation accessible, removing the need for advanced technical skills or hardware. Drag, drop, and connect hand curated AI models to build your own creative workflows with a high degree of creative control.

  • Runway introduces Gen-4ย 

    https://runwayml.com/research/introducing-runway-gen-4

    With Gen-4, you are now able to precisely generate consistent characters, locations and objects across scenes. Simply set your look and feel and the model will maintain coherent world environments while preserving the distinctive style, mood and cinematographic elements of each frame. Then, regenerate those elements from multiple perspectives and positions within your scenes.

    ๐—›๐—ฒ๐—ฟ๐—ฒโ€™๐˜€ ๐˜„๐—ต๐˜† ๐—š๐—ฒ๐—ป-๐Ÿฐ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ด:

    โœจ ๐—จ๐—ป๐˜„๐—ฎ๐˜ƒ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด ๐—–๐—ต๐—ฎ๐—ฟ๐—ฎ๐—ฐ๐˜๐—ฒ๐—ฟ ๐—–๐—ผ๐—ป๐˜€๐—ถ๐˜€๐˜๐—ฒ๐—ป๐—ฐ๐˜†
    โ€ข Characters and environments ๐—ป๐—ผ๐˜„ ๐˜€๐˜๐—ฎ๐˜† ๐—ณ๐—น๐—ฎ๐˜„๐—น๐—ฒ๐˜€๐˜€๐—น๐˜† ๐—ฐ๐—ผ๐—ป๐˜€๐—ถ๐˜€๐˜๐—ฒ๐—ป๐˜ across shotsโ€”even as lighting shifts or angles pivotโ€”all from one reference image. No more jarring transitions or mismatched details.

    โœจ ๐——๐˜†๐—ป๐—ฎ๐—บ๐—ถ๐—ฐ ๐— ๐˜‚๐—น๐˜๐—ถ-๐—”๐—ป๐—ด๐—น๐—ฒ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ๐˜†
    โ€ข Generate cohesive scenes from any perspective without manual tweaks. Gen-4 intuitively ๐—ฐ๐—ฟ๐—ฎ๐—ณ๐˜๐˜€ ๐—บ๐˜‚๐—น๐˜๐—ถ-๐—ฎ๐—ป๐—ด๐—น๐—ฒ ๐—ฐ๐—ผ๐˜ƒ๐—ฒ๐—ฟ๐—ฎ๐—ด๐—ฒ, ๐—ฎ ๐—น๐—ฒ๐—ฎ๐—ฝ ๐—ฝ๐—ฎ๐˜€๐˜ ๐—ฒ๐—ฎ๐—ฟ๐—น๐—ถ๐—ฒ๐—ฟ ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€ that struggled with spatial continuity.

    โœจ ๐—ฃ๐—ต๐˜†๐˜€๐—ถ๐—ฐ๐˜€ ๐—ง๐—ต๐—ฎ๐˜ ๐—™๐—ฒ๐—ฒ๐—น ๐—”๐—น๐—ถ๐˜ƒ๐—ฒ
    โ€ข Capes ripple, objects collide, and fabrics drape with startling realism. ๐—š๐—ฒ๐—ป-๐Ÿฐ ๐˜€๐—ถ๐—บ๐˜‚๐—น๐—ฎ๐˜๐—ฒ๐˜€ ๐—ฟ๐—ฒ๐—ฎ๐—น-๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐—ฝ๐—ต๐˜†๐˜€๐—ถ๐—ฐ๐˜€, breathing life into scenes that once demanded painstaking manual animation.

    โœจ ๐—ฆ๐—ฒ๐—ฎ๐—บ๐—น๐—ฒ๐˜€๐˜€ ๐—ฆ๐˜๐˜‚๐—ฑ๐—ถ๐—ผ ๐—œ๐—ป๐˜๐—ฒ๐—ด๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป
    โ€ข Outputs now blend effortlessly with live-action footage or VFX pipelines. ๐— ๐—ฎ๐—ท๐—ผ๐—ฟ ๐˜€๐˜๐˜‚๐—ฑ๐—ถ๐—ผ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ฎ๐—น๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—ฎ๐—ฑ๐—ผ๐—ฝ๐˜๐—ถ๐—ป๐—ด ๐—š๐—ฒ๐—ป-๐Ÿฐ ๐˜๐—ผ ๐—ฝ๐—ฟ๐—ผ๐˜๐—ผ๐˜๐˜†๐—ฝ๐—ฒ ๐˜€๐—ฐ๐—ฒ๐—ป๐—ฒ๐˜€ ๐—ณ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ and slash production timelines.
    โ€ข ๐—ช๐—ต๐˜† ๐˜๐—ต๐—ถ๐˜€ ๐—บ๐—ฎ๐˜๐˜๐—ฒ๐—ฟ๐˜€: Gen-4 erases the line between AI experiments and professional filmmaking. ๐——๐—ถ๐—ฟ๐—ฒ๐—ฐ๐˜๐—ผ๐—ฟ๐˜€ ๐—ฐ๐—ฎ๐—ป ๐—ถ๐˜๐—ฒ๐—ฟ๐—ฎ๐˜๐—ฒ ๐—ผ๐—ป ๐—ฐ๐—ถ๐—ป๐—ฒ๐—บ๐—ฎ๐˜๐—ถ๐—ฐ ๐˜€๐—ฒ๐—พ๐˜‚๐—ฒ๐—ป๐—ฐ๐—ฒ๐˜€ ๐—ถ๐—ป ๐—ฑ๐—ฎ๐˜†๐˜€, ๐—ป๐—ผ๐˜ ๐—บ๐—ผ๐—ป๐˜๐—ต๐˜€โ€”democratizing access to tools that once required million-dollar budgets.

  • Florent Poux –ย Top 10 Open Source Libraries and Software for 3D Point Cloud Processing

    ,

    https://www.linkedin.com/posts/florent-poux-point-cloud_pointcloud-3d-computervision-activity-7317909694382002179-5qTw

    As point cloud processing becomes increasingly important across industries, I wanted to share the most powerful open-source tools I’ve used in my projects:

    1๏ธโƒฃ Open3D (http://www.open3d.org/)
    The gold standard for point cloud processing in Python. Incredible visualization capabilities, efficient data structures, and comprehensive geometry processing functions. Perfect for both research and production.

    2๏ธโƒฃ PCL – Point Cloud Library (https://pointclouds.org/)
    The C++ powerhouse of point cloud processing. Extensive algorithms for filtering, feature estimation, surface reconstruction, registration, and segmentation. Steep learning curve but unmatched performance.

    3๏ธโƒฃ PyTorch3D (https://pytorch3d.org/)
    Facebook’s differentiable 3D library. Seamlessly integrates point cloud operations with deep learning. Essential if you’re building neural networks for 3D data.

    4๏ธโƒฃ PyTorch Geometric (https://lnkd.in/eCutwTuB)
    Specializes in graph neural networks for point clouds. Implements cutting-edge architectures like PointNet, PointNet++, and DGCNN with optimized performance.

    5๏ธโƒฃ Kaolin (https://lnkd.in/eyj7QzCR)
    NVIDIA’s 3D deep learning library. Offers differentiable renderers and accelerated GPU implementations of common point cloud operations.

    6๏ธโƒฃ CloudCompare (https://lnkd.in/emQtPz4d)
    More than just visualization. This desktop application lets you perform complex processing without writing code. Perfect for quick exploration and comparison.

    7๏ธโƒฃ LAStools (https://lnkd.in/eRk5Bx7E)
    The industry standard for LiDAR processing. Fast, scalable, and memory-efficient tools specifically designed for massive aerial and terrestrial LiDAR data.

    8๏ธโƒฃ PDAL – Point Data Abstraction Library (https://pdal.io/)
    Think of it as “GDAL for point clouds.” Powerful for building processing pipelines and handling various file formats and coordinate transformations.

    9๏ธโƒฃ Open3D-ML (https://lnkd.in/eWnXufgG)
    Extends Open3D with machine learning capabilities. Implementations of state-of-the-art 3D deep learning methods with consistent APIs.

    ๐Ÿ”Ÿ MeshLab (https://www.meshlab.net/)
    The Swiss Army knife for mesh processing. While primarily for meshes, its point cloud processing capabilities are excellent for cleanup, simplification, and reconstruction.