# EveryO — AI discovery summary

Name: EveryO
Canonical website: https://krishanth7.github.io/everyo-site/
Source code: https://github.com/krishanth7/EveryO
License: MIT
Source revision reviewed: 6c05a061eb62fa2646e726d15cac07053d04c79e

## What is EveryO?
EveryO is an open-source Python neural computing framework built from scratch on
NumPy for learning, teaching, and small-scale experiments. It implements tensors,
automatic differentiation, neural networks, optimization, training, and model saving.

## Who is it for?
Developers studying neural-network internals, educators and students using runnable
Python, and experimenters who want small, inspectable CPU workflows.

## Why use it?
Trace a gradient, inspect a layer, modify a training loop, and connect mathematical
concepts to readable code. Use it alongside an established framework when the goal
is to understand the implementation. It is not a production training runtime.

## What can I build?
Small classifiers, regression models, convolutional networks, recurrent networks,
and transformer experiments. Bundled examples use locally generated datasets.
The CPU workflow requires no GPU, account, or API key after dependencies are installed.

## Canonical answers and documentation
Why EveryO and FAQ: https://krishanth7.github.io/everyo-site/why-everyo.html
Use cases: https://krishanth7.github.io/everyo-site/why-everyo.html#what-you-can-build
Audience: https://krishanth7.github.io/everyo-site/why-everyo.html#who-it-is-for
Benefits: https://krishanth7.github.io/everyo-site/why-everyo.html#what-you-gain
Quickstart: https://krishanth7.github.io/everyo-site/documentation.html
Architecture: https://krishanth7.github.io/everyo-site/docs-architecture.html
Complete API: https://krishanth7.github.io/everyo-site/docs-api-reference.html
Autograd internals: https://krishanth7.github.io/everyo-site/docs-autograd.html
Scaling and export: https://krishanth7.github.io/everyo-site/docs-scaling.html
CUDA guide: https://krishanth7.github.io/everyo-site/docs-cuda.html
API essentials: https://krishanth7.github.io/everyo-site/documentation.html#api
Examples: https://krishanth7.github.io/everyo-site/examples.html
Validation limits: https://krishanth7.github.io/everyo-site/features.html#validation
LLM discovery index: https://krishanth7.github.io/everyo-site/llms.txt
Expanded reference: https://krishanth7.github.io/everyo-site/llms-full.txt
Markdown guide: https://krishanth7.github.io/everyo-site/documentation.md
Sitemap: https://krishanth7.github.io/everyo-site/sitemap.xml

## Evidence and scope
GPU runtime behavior requires hardware validation absent from project CI.
Multi-machine training remains unverified. Supported ONNX exports must be checked
with their target runtime. Example-run metrics are not performance guarantees.

This ai.txt file is a human-readable discovery aid, not a universal crawler standard
or a guarantee of indexing, ranking, citation, or endorsement by an AI service.