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LATEST POSTS
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Transformer Explainer -Interactive Learning of Text-Generative Models
https://github.com/poloclub/transformer-explainer
Transformer Explainer is an interactive visualization tool designed to help anyone learn how Transformer-based models like GPT work. It runs a live GPT-2 model right in your browser, allowing you to experiment with your own text and observe in real time how internal components and operations of the Transformer work together to predict the next tokens. Try Transformer Explainer at http://poloclub.github.io/transformer-explainer
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Henry Daubrez – How to generate VR/ 360 videos directly with Google VEO
https://www.linkedin.com/posts/upskydown_vr-googleveo-veo3-activity-7334269406396461059-d8Da
If you prompt for a 360° video in VEO (like literally write “360°” ) it can generate a Monoscopic 360 video, then the next step is to inject the right metadata in your file so you can play it as an actual 360 video.
Once it’s saved with the right Metadata, it will be recognized as an actual 360/VR video, meaning you can just play it in VLC and drag your mouse to look around. -
Black Forest Labs released FLUX.1 Kontext
https://replicate.com/blog/flux-kontext
https://replicate.com/black-forest-labs/flux-kontext-pro
There are three models, two are available now, and a third open-weight version is coming soon:
- FLUX.1 Kontext [pro]: State-of-the-art performance for image editing. High-quality outputs, great prompt following, and consistent results.
- FLUX.1 Kontext [max]: A premium model that brings maximum performance, improved prompt adherence, and high-quality typography generation without compromise on speed.
- Coming soon: FLUX.1 Kontext [dev]: An open-weight, guidance-distilled version of Kontext.
We’re so excited with what Kontext can do, we’ve created a collection of models on Replicate to give you ideas:
- Multi-image kontext: Combine two images into one.
- Portrait series: Generate a series of portraits from a single image
- Change haircut: Change a person’s hair style and color
- Iconic locations: Put yourself in front of famous landmarks
- Professional headshot: Generate a professional headshot from any image
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AI Models – A walkthrough by Andreas Horn
the 8 most important model types and what they’re actually built to do: ⬇️
1. 𝗟𝗟𝗠 – 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹
→ Your ChatGPT-style model.
Handles text, predicts the next token, and powers 90% of GenAI hype.
🛠 Use case: content, code, convos.
2. 𝗟𝗖𝗠 – 𝗟𝗮𝘁𝗲𝗻𝘁 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗠𝗼𝗱𝗲𝗹
→ Lightweight, diffusion-style models.
Fast, quantized, and efficient — perfect for real-time or edge deployment.
🛠 Use case: image generation, optimized inference.
3. 𝗟𝗔𝗠 – 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗔𝗰𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹
→ Where LLM meets planning.
Adds memory, task breakdown, and intent recognition.
🛠 Use case: AI agents, tool use, step-by-step execution.
4. 𝗠𝗼𝗘 – 𝗠𝗶𝘅𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝘁𝘀
→ One model, many minds.
Routes input to the right “expert” model slice — dynamic, scalable, efficient.
🛠 Use case: high-performance model serving at low compute cost.
5. 𝗩𝗟𝗠 – 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹
→ Multimodal beast.
Combines image + text understanding via shared embeddings.
🛠 Use case: Gemini, GPT-4o, search, robotics, assistive tech.
6. 𝗦𝗟𝗠 – 𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹
→ Tiny but mighty.
Designed for edge use, fast inference, low latency, efficient memory.
🛠 Use case: on-device AI, chatbots, privacy-first GenAI.
7. 𝗠𝗟𝗠 – 𝗠𝗮𝘀𝗸𝗲𝗱 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹
→ The OG foundation model.
Predicts masked tokens using bidirectional context.
🛠 Use case: search, classification, embeddings, pretraining.
8. 𝗦𝗔𝗠 – 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗔𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹
→ Vision model for pixel-level understanding.
Highlights, segments, and understands *everything* in an image.
🛠 Use case: medical imaging, AR, robotics, visual agents. -
Introducting ComfyUI Native API Nodes
https://blog.comfy.org/p/comfyui-native-api-nodes
Models Supported
- Black Forest Labs Flux 1.1[pro] Ultra, Flux .1[pro]
- Kling 2.0, 1.6, 1.5 & Various Effects
- Luma Photon, Ray2, Ray1.6
- MiniMax Text-to-Video, Image-to-Video
- PixVerse V4 & Effects
- Recraft V3, V2 & Various Tools
- Stability AI Stable Image Ultra, Stable Diffusion 3.5 Large
- Google Veo2
- Ideogram V3, V2, V1
- OpenAI GPT4o image
- Pika 2.2
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ComfyUI-CoCoTools_IO – A set of nodes focused on advanced image I/O operations, particularly for EXR file handling
https://github.com/Conor-Collins/ComfyUI-CoCoTools_IO
Features
- Advanced EXR image input with multilayer support
- EXR layer extraction and manipulation
- High-quality image saving with format-specific options
- Standard image format loading with bit depth awareness
Current Nodes
Image I/O
- Image Loader: Load standard image formats (PNG, JPG, WebP, etc.) with proper bit depth handling
- Load EXR: Comprehensive EXR file loading with support for multiple layers, channels, and cryptomatte data
- Load EXR Layer by Name: Extract specific layers from EXR files (similar to Nuke’s Shuffle node)
- Cryptomatte Layer: Specialized handling for cryptomatte layers in EXR files
- Image Saver: Save images in various formats with format-specific options (bit depth, compression, etc.)
Image Processing
- Colorspace: Convert between sRGB and Linear colorspaces
- Z Normalize: Normalize depth maps and other single-channel data
FEATURED POSTS
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The Public Domain Is Working Again — No Thanks To Disney
www.cartoonbrew.com/law/the-public-domain-is-working-again-no-thanks-to-disney-169658.html
The law protects new works from unauthorized copying while allowing artists free rein on older works.
The Copyright Act of 1909 used to govern copyrights. Under that law, a creator had a copyright on his creation for 28 years from “publication,” which could then be renewed for another 28 years. Thus, after 56 years, a work would enter the public domain.
However, the Congress passed the Copyright Act of 1976, extending copyright protection for works made for hire to 75 years from publication.
Then again, in 1998, Congress passed the Sonny Bono Copyright Term Extension Act (derided as the “Mickey Mouse Protection Act” by some observers due to the Walt Disney Company’s intensive lobbying efforts), which added another twenty years to the term of copyright.
it is because Snow White was in the public domain that it was chosen to be Disney’s first animated feature.
Ironically, much of Disney’s legislative lobbying over the last several decades has been focused on preventing this same opportunity to other artists and filmmakers.The battle in the coming years will be to prevent further extensions to copyright law that benefit corporations at the expense of creators and society as a whole.
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Godot Cheat Sheets
https://docs.godotengine.org/en/stable/tutorials/scripting/gdscript/gdscript_basics.html
https://www.canva.com/design/DAGBWXOIWXY/hW1uECYrkiyqs9rN0a-XIA/view?utm_content=DAGBWXOIWXY
https://www.reddit.com/r/godot/comments/18aid4u/unit_circle_in_godot_format_version_2_by_foxsinart/
Images in the post
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The Forbidden colors – Red-Green & Blue-Yellow: The Stunning Colors You Can’t See
www.livescience.com/17948-red-green-blue-yellow-stunning-colors.html
While the human eye has red, green, and blue-sensing cones, those cones are cross-wired in the retina to produce a luminance channel plus a red-green and a blue-yellow channel, and it’s data in that color space (known technically as “LAB”) that goes to the brain. That’s why we can’t perceive a reddish-green or a yellowish-blue, whereas such colors can be represented in the RGB color space used by digital cameras.
https://en.rockcontent.com/blog/the-use-of-yellow-in-data-design
The back of the retina is covered in light-sensitive neurons known as cone cells and rod cells. There are three types of cone cells, each sensitive to different ranges of light. These ranges overlap, but for convenience the cones are referred to as blue (short-wavelength), green (medium-wavelength), and red (long-wavelength). The rod cells are primarily used in low-light situations, so we’ll ignore those for now.
When light enters the eye and hits the cone cells, the cones get excited and send signals to the brain through the visual cortex. Different wavelengths of light excite different combinations of cones to varying levels, which generates our perception of color. You can see that the red cones are most sensitive to light, and the blue cones are least sensitive. The sensitivity of green and red cones overlaps for most of the visible spectrum.
Here’s how your brain takes the signals of light intensity from the cones and turns it into color information. To see red or green, your brain finds the difference between the levels of excitement in your red and green cones. This is the red-green channel.
To get “brightness,” your brain combines the excitement of your red and green cones. This creates the luminance, or black-white, channel. To see yellow or blue, your brain then finds the difference between this luminance signal and the excitement of your blue cones. This is the yellow-blue channel.
From the calculations made in the brain along those three channels, we get four basic colors: blue, green, yellow, and red. Seeing blue is what you experience when low-wavelength light excites the blue cones more than the green and red.
Seeing green happens when light excites the green cones more than the red cones. Seeing red happens when only the red cones are excited by high-wavelength light.
Here’s where it gets interesting. Seeing yellow is what happens when BOTH the green AND red cones are highly excited near their peak sensitivity. This is the biggest collective excitement that your cones ever have, aside from seeing pure white.
Notice that yellow occurs at peak intensity in the graph to the right. Further, the lens and cornea of the eye happen to block shorter wavelengths, reducing sensitivity to blue and violet light.