BREAKING NEWS
LATEST POSTS
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Skill Foundry – ARTIFICIAL INTELLIGENCE WITH PYTHON
INTRODUCTION………………………………………………………………………………………….. 3
Setting Up AI Development Environment with Python……………………………….… 7
Understanding Machine Learning — The Heart of AI…………………………………… 11
Supervised Learning Deep Dive — Regression and Classification Models………. 16
Unsupervised Learning Deep Dive — Discovering Hidden Patterns………………. 21
Neural Networks Fundamentals — Building Brains for AI ……………………………. 26
Project — Build a Neural Network to Classify Handwritten Digits ………………. 30
Deep Learning for Image Classification — CNNs Explained………………………… 33
Advanced Image Classification — Transfer Learning………………………………….. 37
Natural Language Processing (NLP) Basics with Python…………………………….. 41
Spam Detection Using Machine Learning …………………………………………………. 45
Deep Learning for Text Classification (with NLP) …………………………………….. 48
Computer Vision Basics and Image Classification ……………………………………. 51
AI for Automation: Files, Web, and Emails ………………………………………………. 56
AI Chatbots and Virtual Assistants …………………………………………………………… 61 -
Eyeline Labs VChain – Chain-of-Visual-Thought for Reasoning in Video Generation for better AI physics
https://eyeline-labs.github.io/VChain/
https://github.com/Eyeline-Labs/VChain
Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time tuning of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.
FEATURED POSTS
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Ian Curtis – AI world models generated these 3D environments from a single image in minutes, and I turned them into a custom spaceship game level in an afternoon
Here’s how I created it:
Design: I started by generating cohesive concept images in Midjourney, with sleek white interiors with yellow accents to define the overall vibe.
Generate: Using World Labs, I transformed those images into fully explorable and persistent 3D environments in minutes.
Assemble: I cropped out doorways inside the Gaussian splats, then aligned and stitched multiple rooms together using PlayCanvas Supersplat, creating a connected spaceship layout.
Experience: Just a few hours later, I was walking through a custom interactive game level that started as a simple idea earlier that day.
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Eyeline Labs VChain – Chain-of-Visual-Thought for Reasoning in Video Generation for better AI physics
https://eyeline-labs.github.io/VChain/
https://github.com/Eyeline-Labs/VChain
Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time tuning of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.
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Photography basics: Solid Angle measures
http://www.calculator.org/property.aspx?name=solid+angle
A measure of how large the object appears to an observer looking from that point. Thus. A measure for objects in the sky. Useful to retuen the size of the sun and moon… and in perspective, how much of their contribution to lighting. Solid angle can be represented in ‘angular diameter’ as well.
http://en.wikipedia.org/wiki/Solid_angle
http://www.mathsisfun.com/geometry/steradian.html
A solid angle is expressed in a dimensionless unit called a steradian (symbol: sr). By default in terms of the total celestial sphere and before atmospheric’s scattering, the Sun and the Moon subtend fractional areas of 0.000546% (Sun) and 0.000531% (Moon).
http://en.wikipedia.org/wiki/Solid_angle#Sun_and_Moon
On earth the sun is likely closer to 0.00011 solid angle after athmospheric scattering. The sun as perceived from earth has a diameter of 0.53 degrees. This is about 0.000064 solid angle.
http://www.numericana.com/answer/angles.htm
The mean angular diameter of the full moon is 2q = 0.52° (it varies with time around that average, by about 0.009°). This translates into a solid angle of 0.0000647 sr, which means that the whole night sky covers a solid angle roughly one hundred thousand times greater than the full moon.
More info
http://lcogt.net/spacebook/using-angles-describe-positions-and-apparent-sizes-objects
http://amazing-space.stsci.edu/glossary/def.php.s=topic_astronomy
Angular Size
The apparent size of an object as seen by an observer; expressed in units of degrees (of arc), arc minutes, or arc seconds. The moon, as viewed from the Earth, has an angular diameter of one-half a degree.
The angle covered by the diameter of the full moon is about 31 arcmin or 1/2°, so astronomers would say the Moon’s angular diameter is 31 arcmin, or the Moon subtends an angle of 31 arcmin.
