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Quickstart

Get up and running in under five minutes.

Installation

pip install optimumai                  # core — numpy + rich + click, no GPU needed
pip install "optimumai[viz]"           # add matplotlib plots and GIF export
pip install "optimumai[llm]"           # add LLM tutor and token generation clients
pip install "optimumai[notebooks]"     # add JupyterLab launcher
pip install "optimumai[dashboard]"     # add Streamlit progress dashboard
pip install "optimumai[all]"           # everything at once

Verify the install:

python -c "import optimumai; print(optimumai.__version__)"
optimumai --version

First steps in Python

Every operation returns the numeric result and can explain itself. Add explain=True to see the step-by-step trace:

from optimumai import Vector, Matrix, softmax, Attention

# Linear algebra
Vector([1, 2, 3]).dot(Vector([4, 5, 6]), explain=True)          # 32
Vector([1, 2, 3]).cosine_similarity(Vector([2, 4, 6]), explain=True)  # 1.0
Matrix([[1, 2], [3, 4]]).matmul(Matrix([[5, 6], [7, 8]]), explain=True)

# Probability
softmax([2.0, 1.0, 0.1], temperature=0.5, explain=True)

# Transformers
Attention.demo().render("engineer")

Prefer structured data over printed output? Use the *_trace variants:

from optimumai import Vector

trace = Vector([1, 2, 3]).dot_trace(Vector([4, 5, 6]))
trace.result    # 32.0
trace.steps     # [Step(label=..., computation=...), ...]
trace.why_ai    # ['Similarity between two embedding vectors', ...]
trace.render("beginner")   # print at any level later

First steps in the CLI

optimumai start                        # guided 30-second tour (start here)
optimumai course                       # full learning path with progress bars
optimumai learn dot                    # run any lesson (auto-marks complete)
optimumai learn attention --level researcher

optimumai algebra dot "[1,2,3]" "[4,5,6]"
optimumai softmax "[2,1,0.1]" --temperature 0.5
optimumai attention --demo
optimumai backprop
optimumai train --steps 150 --lr 0.05

optimumai quiz softmax                 # active-recall quiz
optimumai review                       # spaced-repetition review (SM-2)
optimumai exercise backprop            # compute-the-answer exercise

optimumai kernel matmul                # GPU kernel on the pure-Python simulator
optimumai generate "The key insight behind attention is"
optimumai playground softmax           # interactive drag-the-inputs circuit
optimumai dashboard                    # Streamlit progress dashboard

Explain levels

The same math at four levels of detail. Pass --level on the CLI or level= in Python:

Level What you see
beginner steps + plain-English "why AI uses this"
intermediate per-step detail notes (CLI default)
engineer intermediate values + algorithmic complexity
researcher everything: formulas, proofs, source references 
optimumai algebra dot "[1,2,3]" "[4,5,6]" --level beginner
optimumai algebra dot "[1,2,3]" "[4,5,6]" --level researcher

from optimumai import softmax
softmax([2.0, 1.0, 0.1], level="researcher", explain=True)

Next steps