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AutoGPT is a remarkable AI technology that utilizes GPT-4 and GPT-3.5 through API to create full-fledged projects by iterating on its own prompts and building upon them in each iteration. It can read and write files, browse the web, review the results of its prompts, and combine them with the prompt history.
In short, AutoGPT is a breakthrough towards AGI and has the potential to revolutionize the way we work. It can be given an AI name such as RecipeBuilder and 5 goals that it has to meet. Once the goals are set, AutoGPT can start working on the project until completion.
It is worth noting that AutoGPT is prone to fall into loops and make pointless requests when given complicated tasks. However, for simple jobs, the outcomes are amazing. AutoGPT uses credits from your OpenAI account, and the free version includes $18. Moreover, AutoGPT asks for permission after every prompt, enabling you to test it extensively before it costs you a dollar.
https://numpy.org/doc/stable/user/absolute_beginners.html
NumPy (Numerical Python) is an open source Python library that’s used in almost every field of science and engineering. It’s the universal standard for working with numerical data in Python, and it’s at the core of the scientific Python and PyData ecosystems. NumPy users include everyone from beginning coders to experienced researchers doing state-of-the-art scientific and industrial research and development. The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image and most other data science and scientific Python packages.
The NumPy library contains multidimensional array and matrix data structures (you’ll find more information about this in later sections). It provides ndarray, a homogeneous n-dimensional array object, with methods to efficiently operate on it. NumPy can be used to perform a wide variety of mathematical operations on arrays. It adds powerful data structures to Python that guarantee efficient calculations with arrays and matrices and it supplies an enormous library of high-level mathematical functions that operate on these arrays and matrices.
Linux Shell Script and Command Line for Beginners
Full course
https://www.freecodecamp.org/news/introduction-to-linux
https://neuralradiancefields.io/history-of-neural-radiance-fields/
“Lightfield and hologram capture started with a big theoretical idea 115 years ago and we have struggled to make them viable ever since. Neural Radiance fields aka NeRF along with gaming computers now for the first time provide a promising easy and low cost way for everybody to capture and display lightfields.”
“Neural Radiance fields (NeRF) recently had its third birthday but the technology is just the latest answer to a question people have been chasing since the 1860s: How do you capture and recreate space (from images)?”
“The plenoptic function measures physical light properties at every point in space and it describes how light transport occurs throughout a 3D volume.”
Google project Starline the latest in real time and compression image to 3D technology
https://pyimagesearch.com/2017/08/28/fast-optimized-for-pixel-loops-with-opencv-and-python/
https://learnopencv.com/exposure-fusion-using-opencv-cpp-python/
Exposure Fusion is a method for combining images taken with different exposure settings into one image that looks like a tone mapped High Dynamic Range (HDR) image.
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