RANDOM POSTs
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Tiling ZoeDepth – High resolution depth map generator
Read more: Tiling ZoeDepth – High resolution depth map generatorThis is an adapted version of
Corresponding paper :
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
https://github.com/BillFSmith/TilingZoeDepth
https://colab.research.google.com/drive/1Wi-1Ji_fhcoGpK-drT4dVrl5AjfVUQ5M
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Enhancing 3D Design: Ai texturing with Stable Projectorz – Image to 3D and Text to 3D Ai tools
Read more: Enhancing 3D Design: Ai texturing with Stable Projectorz – Image to 3D and Text to 3D Ai toolshttps://stableprojectorz.com/lessons-and-videos/
https://lumalabs.ai/genie text to 3D
https://www.tripo3d.ai/app Image to 3D
https://www.vizcom.ai/ Web Sketching and design app
https://github.com/lllyasviel/Fooocus
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Conda – an open source management system for installing multiple versions of software packages and their dependencies into a virtual environment
https://anaconda.org/anaconda/conda
https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html
NOTE The company recently changed their TOS and this service now incurs into costs for teams above a threshold.
Use MicroMamba instead. -
Human cell model
Read more: Human cell modelThis is the most detailed model of a human cell to date. Taken using X-ray, nuclear magnetic resonance and cryonelectron microscopy datasets. c/o Ingerson and McGill
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Andreas Horn – ย The 9 algorithms
The illustration below highlights the algorithms most frequently utilized in our everyday activities: They play a key role in everything we do from online shopping recommendations, navigation apps, social media, email spam filters and even smart home devices.
๐น ๐ฆ๐ผ๐ฟ๐๐ถ๐ป๐ด ๐๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ
– Organize data for efficiency.
โ Example: Sorting email threads or search results.
๐น ๐๐ถ๐ท๐ธ๐๐๐ฟ๐ฎโ๐ ๐๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ
– Finds the shortest path in networks.
โ Example: Google Maps driving routes.
๐น ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ๐
– AI models that understand context and meaning.
โ Example: ChatGPT, Claude and other LLMs.
๐น ๐๐ถ๐ป๐ธ ๐๐ป๐ฎ๐น๐๐๐ถ๐
– Ranks pages and builds connections.
โ Example: TikTok PageRank, LinkedIn recommendations.
๐น ๐ฅ๐ฆ๐ ๐๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ
– Encrypts and secures data communication.
โ Example: WhatsApp encryption or online banking.
๐น ๐๐ป๐๐ฒ๐ด๐ฒ๐ฟ ๐๐ฎ๐ฐ๐๐ผ๐ฟ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป
– Secures cryptographic systems.
โ Example: Protecting sensitive data in blockchain.
๐น ๐๐ผ๐ป๐๐ผ๐น๐๐๐ถ๐ผ๐ป๐ฎ๐น ๐ก๐ฒ๐๐ฟ๐ฎ๐น ๐ก๐ฒ๐๐๐ผ๐ฟ๐ธ๐ (๐๐ก๐ก๐)
– Recognizes patterns in images and videos.
โ Example: Facial recognition, object detection in self-driving cars.
๐น ๐๐๐ณ๐ณ๐บ๐ฎ๐ป ๐๐ผ๐ฑ๐ถ๐ป๐ด
– Compresses data efficiently.
โ Example: JPEG and MP3 file compression.
๐น ๐ฆ๐ฒ๐ฐ๐๐ฟ๐ฒ ๐๐ฎ๐๐ต ๐๐น๐ด๐ผ๐ฟ๐ถ๐๐ต๐บ (๐ฆ๐๐)
– Ensures data integrity.
โ Example: Password encryption, digital signatures. -
The Dunning-Kruger effect – Incompetent people fail to see the magnitude of their incompetence
Read more: The Dunning-Kruger effect – Incompetent people fail to see the magnitude of their incompetencehttp://petapixel.com/2014/10/13/dunning-kruger-peak-photography/
The name of the peak refers to the DunningโKruger effect, coined by a pair of researchers at Cornell University in 1999.
Through their study, the scientists discovered that people who are unskilled at something โ photography for example โ are often unable to see how bad they are. Incompetent people will (1) fail to recognize that they are bad, (2) fail to recognize how good competent people are, and (3) fail to see the magnitude of their incompetence.
However, if given more training in what theyโre bad at, those same people will recognize how incompetent they were (this is where people fall from the โDunning-Kruger Peakโ).
Is this the antithesis of the Impostor Syndrome?
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FXGuide – ACES 2.0 with ILMโs Alex Fry
https://draftdocs.acescentral.com/background/whats-new/
ACES 2.0 is the second major release of the components that make up the ACES system. The most significant change is a new suite of rendering transforms whose design was informed by collected feedback and requests from users of ACES 1. The changes aim to improve the appearance of perceived artifacts and to complete previously unfinished components of the system, resulting in a more complete, robust, and consistent product.
Highlights of the key changes in ACES 2.0 are as follows:
- New output transforms, including:
- A less aggressive tone scale
- More intuitive controls to create custom outputs to non-standard displays
- Robust gamut mapping to improve perceptual uniformity
- Improved performance of the inverse transforms
- Enhancedย AMFย specification
- An updated specification forย ACESย Transform IDs
- OpenEXR compression recommendations
- Enhanced tools for generating Input Transforms and recommended procedures for characterizing prosumer cameras
- Look Transform Library
- Expanded documentation
Rendering Transform
The most substantial change in ACES 2.0 is a complete redesign of the rendering transform.
ACES 2.0 was built as a unified system, rather than through piecemeal additions. Different deliverable outputs “match” better and making outputs to display setups other than the provided presets is intended to be user-driven. The rendering transforms are less likely to produce undesirable artifacts “out of the box”, which means less time can be spent fixing problematic images and more time making pictures look the way you want.
Key design goals
- Improve consistency of tone scale and provide an easy to use parameter to allow for outputs between preset dynamic ranges
- Minimize hue skews across exposure range in a region of same hue
- Unify for structural consistency across transform type
- Easy to use parameters to create outputs other than the presets
- Robust gamut mapping to improve harsh clipping artifacts
- Fill extents of output code value cube (where appropriate and expected)
- Invertible – not necessarily reversible, but Output >ย ACESย > Output round-trip should be possible
- Accomplish all of the above while maintaining an acceptable โout-of-the boxโ rendering
- New output transforms, including:
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