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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: “The amount of energy needed to refute bullshit is an order of magnitude bigger than to produce it.”
https://en.wikipedia.org/wiki/Brandolini%27s_law
This is why every time you kill a lie, it feels like nothing changed. It’s why no matter how many facts you post, how many sources you cite, how many receipts you show—the swarm just keeps coming. Because while you’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 you’ve written the tenth, the people you’re trying to reach have already scrolled past.
Every viral deception—the fake quote, the rigged video, the synthetic outrage—takes almost nothing to create. And once it’s out there, you’re not just correcting a fact—you’re prying it out of someone’s identity. Because people don’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 still—you must correct it. Still, you must fight.
Because even if truth doesn’t spread as fast, it roots deeper. Even if it doesn’t go viral, it endures. And eventually, it makes people bulletproof to the next wave of narrative sewage.
You’re not here to win a one-day war. You’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 isn’t about instant wins. It’s about making the cost of lying higher. It’s about being the resistance that doesn’t fold. You don’t fight because it’s easy. You fight because it’s right.
GenUE brings prompt-driven 3D asset creation directly into Unreal Engine using ComfyUI as a flexible backend. • Generate high-quality images from text prompts. • Choose from a catalog of batch-generated images – no style limitations. • Convert the selected image to a fully textured 3D mesh. • 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.
https://edwardurena.gumroad.com/l/ramoo
What it offers:
• Base rigs for multiple character types
• Automatic weight application
• Built-in facial rigging system
• Bone generators with FK and IK options
• Streamlined constraint panel
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
• Prompt GPT-Image-1 directly in ComfyUI using text or image inputs
• Set resolution and quality
• Supports image editing + transparent backgrounds
• Seamlessly mix with local workflows like WAN 2.1, FLUX Tools, and more
What makes it special?
• Massive 10B parameter geometric model with 10x more mesh faces.
• High-quality textures with industry-first multi-view PBR generation.
• Optimized skeletal rigging for streamlined animation workflows.
• Flexible pipeline for text-to-3D and image-to-3D generation.
They’re making it accessible to everyone:
• Open-source code and pre-trained models.
• Easy-to-use API and intuitive web interface.
• Free daily quota doubled to 20 generations!
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.
https://lllyasviel.github.io/frame_pack_gitpage/
Image-to-5-Seconds (30fps, 150 frames)
A Maya script that introduces a node-based graph system for procedural modeling, like Houdini
https://github.com/AnthonySTZ/ProceduralMaya
What do people report as their #1 greatest fear?
It’s not death….
It’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
→ Scan 5 faces; Hold each gaze for 5 seconds.
→ Repeat every 5 minutes.
→ Creates an authentic connection.
2/Power Pause
→ Dead silence for 3 seconds after key points.
→ Let your message land.
3/ The 3-Part Open
→ Hook with a question.
→ Share a story.
→ State your promise.
4/ Palm-Up Principle
→ Open palms when speaking = trustworthy.
→ Pointing fingers = confrontational.
5/ The 90-Second Reset
→ Feel nervous? Excuse yourself.
→ 90 seconds of deep breathing reset your nervous system.
6/ Rule of Three
→ Structure key points in threes.
→ Our brains love patterns.
7/ 2-Minute Story Rule
→ Keep stories under 2 minutes.
→ Any longer, you lose them.
8/ The Lighthouse Method
→ Plant “anchor points” around the room.
→ Rotate eye contact between them.
→ Looks natural, feels structured.
9/ The Power Position
→ Feet shoulder-width apart.
→ Hands relaxed at sides.
→ Projects confidence even when nervous.
10/ The Callback Technique
→ Reference earlier points later in your talk.
→ Creates a narrative thread.
→ Audiences love connections.
11/ The Rehearsal Truth
→ Practice the opening 3x more than the rest.
→ Nail the first 30 seconds; you’ll nail the talk.
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, it’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.
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.
𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝘆 𝗚𝗲𝗻-𝟰 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴:
✨ 𝗨𝗻𝘄𝗮𝘃𝗲𝗿𝗶𝗻𝗴 𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆
• Characters and environments 𝗻𝗼𝘄 𝘀𝘁𝗮𝘆 𝗳𝗹𝗮𝘄𝗹𝗲𝘀𝘀𝗹𝘆 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 across shots—even as lighting shifts or angles pivot—all from one reference image. No more jarring transitions or mismatched details.
✨ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗠𝘂𝗹𝘁𝗶-𝗔𝗻𝗴𝗹𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝘆
• Generate cohesive scenes from any perspective without manual tweaks. Gen-4 intuitively 𝗰𝗿𝗮𝗳𝘁𝘀 𝗺𝘂𝗹𝘁𝗶-𝗮𝗻𝗴𝗹𝗲 𝗰𝗼𝘃𝗲𝗿𝗮𝗴𝗲, 𝗮 𝗹𝗲𝗮𝗽 𝗽𝗮𝘀𝘁 𝗲𝗮𝗿𝗹𝗶𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 that struggled with spatial continuity.
✨ 𝗣𝗵𝘆𝘀𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗙𝗲𝗲𝗹 𝗔𝗹𝗶𝘃𝗲
• Capes ripple, objects collide, and fabrics drape with startling realism. 𝗚𝗲𝗻-𝟰 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝘀 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗽𝗵𝘆𝘀𝗶𝗰𝘀, breathing life into scenes that once demanded painstaking manual animation.
✨ 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗦𝘁𝘂𝗱𝗶𝗼 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻
• Outputs now blend effortlessly with live-action footage or VFX pipelines. 𝗠𝗮𝗷𝗼𝗿 𝘀𝘁𝘂𝗱𝗶𝗼𝘀 𝗮𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗮𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗚𝗲𝗻-𝟰 𝘁𝗼 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝘀𝗰𝗲𝗻𝗲𝘀 𝗳𝗮𝘀𝘁𝗲𝗿 and slash production timelines.
• 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Gen-4 erases the line between AI experiments and professional filmmaking. 𝗗𝗶𝗿𝗲𝗰𝘁𝗼𝗿𝘀 𝗰𝗮𝗻 𝗶𝘁𝗲𝗿𝗮𝘁𝗲 𝗼𝗻 𝗰𝗶𝗻𝗲𝗺𝗮𝘁𝗶𝗰 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗶𝗻 𝗱𝗮𝘆𝘀, 𝗻𝗼𝘁 𝗺𝗼𝗻𝘁𝗵𝘀—democratizing access to tools that once required million-dollar budgets.
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:
1️⃣ 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.
2️⃣ 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.
3️⃣ 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.
4️⃣ 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.
5️⃣ Kaolin (https://lnkd.in/eyj7QzCR)
NVIDIA’s 3D deep learning library. Offers differentiable renderers and accelerated GPU implementations of common point cloud operations.
6️⃣ 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.
7️⃣ 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.
8️⃣ 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.
9️⃣ 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.
🔟 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.
https://github.com/Comfy-Org/comfy-cli
comfy-cli is a command line tool that helps users easily install and manage ComfyUI, a powerful open-source machine learning framework. With comfy-cli, you can quickly set up ComfyUI, install packages, and manage custom nodes, all from the convenience of your terminal.
C:\<PATH_TO>\python.exe -m venv C:\comfyUI_cli_install
cd C:\comfyUI_env
C:\comfyUI_env\Scripts\activate.bat
C:\<PATH_TO>\python.exe -m pip install comfy-cli
comfy --workspace=C:\comfyUI_env\ComfyUI install
# then
comfy launch
# or
comfy launch -- --cpu --listen 0.0.0.0
If you are trying to clone a different install, pip freeze it first. Then run those requirements.
# from the original env
python.exe -m pip freeze > M:\requirements.txt
# under the new venv env
pip install -r M:\requirements.txt
https://www.comfydeploy.com/docs/v2/introduction
1 – Import your workflow
2 – Build a machine configuration to run your workflows on
3 – Download models into your private storage, to be used in your workflows and team.
4 – Run ComfyUI in the cloud to modify and test your workflows on cloud GPUs
5 – Expose workflow inputs with our custom nodes, for API and playground use
6 – Deploy APIs
7 – Let your team use your workflows in playground without using ComfyUI
https://www.anthropic.com/news/anthropic-economic-index-insights-from-claude-sonnet-3-7
As models continue to advance, so too must our measurement of their economic impacts. In our second report, covering data since the launch of Claude 3.7 Sonnet, we find relatively modest increases in coding, education, and scientific use cases, and no change in the balance of augmentation and automation. We find that Claude’s new extended thinking mode is used with the highest frequency in technical domains and tasks, and identify patterns in automation / augmentation patterns across tasks and occupations. We release datasets for both of these analyses.
https://github.com/nnanhuang/SegAnyMo
Overview of Our Pipeline. We take 2D tracks and depth maps generated by off-the-shelf models as input, which are then processed by a motion encoder to capture motion patterns, producing featured tracks. Next, we use tracks decoder that integrates DINO feature to decode the featured tracks by decoupling motion and semantic information and ultimately obtain the dynamic trajectories(a). Finally, using SAM2, we group dynamic tracks belonging to the same object and generate fine-grained moving object masks(b).
https://vast-ai-research.github.io/HoloPart
https://huggingface.co/VAST-AI/HoloPart
https://github.com/VAST-AI-Research/HoloPart
Applications:
– 3d printing segmentation
– texturing segmentation
– animation segmentation
– modeling segmentation
– Why traumatic memories are not like normal memories?
– What it was like working in a mental asylum.
– Does childhood trauma impact us permanently?
– Can yoga reverse deep past trauma?
We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution.
We wrote a scenario that represents our best guess about what that might look like.1 It’s informed by trend extrapolations, wargames, expert feedback, experience at OpenAI, and previous forecasting successes.
https://huggingface.co/learn/agents-course/en/unit0/introduction
In this course, you will:
https://audials.com/en/apps/manage-music?utm_source=chatgpt.com
https://umatechnology.org/the-best-free-music-management-tools-for-organizing-your-mp3s
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