rohitg00/ai-engineering-from-scratch: A comprehensive, code-first

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What ai-engineering-from-scratch does

This repository provides a highly structured, open-source curriculum dedicated to taking developers from basic programming to advanced AI engineering. It focuses heavily on practical implementation, guiding learners through the process of building scalable, production-ready applications powered by Large Language Models. The course covers foundational concepts like prompt engineering and RAG, before progressing to complex topics such as agentic workflows, model evaluation, and deployment strategies. By emphasizing hands-on projects and real-world architectural patterns over pure theory, it equips developers with the concrete skills needed to construct robust AI systems.

This curriculum is ideal for software engineers, data scientists, and technical product builders who possess basic programming skills (mainly Python).

Code-First Pedagogy: Centers learning around functional, reproducible code examples and practical projects rather than abstract theory.
Progressive Curriculum: Structured logically from basic API usage to advanced agent orchestration and custom RAG implementations.
Production-Oriented Focus: Covers essential engineering practices for AI, including evaluation metrics, guardrails, and deployment CI/CD.
Comprehensive RAG Patterns: Details various Retrieval-Augmented Generation strategies including naive, advanced, and agentic RAG architectures.
Open Source Tooling Integration: Teaches the integration of popular open-source frameworks like LangChain and LlamaIndex natively.

Where teams use it

Upskilling Software Engineers

For traditional developers wanting to transition into the field of AI engineering by learning how to effectively integrate LLMs into software products.

Building RAG Applications

For teams needing to construct robust, enterprise-grade knowledge retrieval systems using advanced RAG techniques and vector databases.

Developing AI Agents

For builders looking to design autonomous agent systems capable of reasoning, using tools, and executing complex multi-step workflows.

Implementing AI Guardrails

For security-conscious engineers needing to learn how to evaluate and constrain model outputs in production environments safely.

Getting started: Clone the repository and start with the introductory notebooks in the '01-foundations' directory.

README

main branch

From the creator of Agent Memory - #1 Persistent memory ⭐ which naturally works with any agents or chat assistants.

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84% of students already use AI tools. Only 18% feel prepared to use them professionally. This curriculum closes that gap.

503 lessons. 20 phases. ~320 hours. Python, TypeScript, Rust, Julia. Every lesson ships a reusable artifact: a prompt, a skill, an agent, an MCP server. Free, open source, MIT.

You don't just learn AI. You build it. End-to-end. By hand.

_{150,639 readers · 241,669 page views in the last 30 days · as of 2026-06-07}

How this works

Most AI material teaches in scattered pieces. A paper here, a fine-tuning post there, a flashy agent demo somewhere else. The pieces rarely line up. You ship a chatbot but can't explain its loss curve. You hook a function to an agent but can't say what attention does inside the model that's calling it.

This curriculum is the spine. 20 phases, 503 lessons, four languages: Python, TypeScript, Rust, Julia. Linear algebra at one end, autonomous swarms at the other. Every algorithm gets built from raw math first. Backprop. Tokenizer. Attention. Agent loop. By the time PyTorch shows up, you already know what it's doing under the hood.

Each lesson runs the same loop: read the problem, derive the math, write the code, run the test, keep the artifact. No five-minute videos, no copy-paste deploys, no hand-holding. Free, open source, and built to run on your own laptop.

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The shape of the curriculum

Twenty phases stack on top of each other. Math is the floor. Agents and production are the roof. Skip ahead if you already know the lower layers, but don't skip and then wonder why something at the top is breaking.

%%{init: {'theme':'base','themeVariables':{'primaryColor':'#fafaf5','primaryTextColor':'#1a1a1a','primaryBorderColor':'#3553ff','lineColor':'#3553ff','fontFamily':'JetBrains Mono','fontSize':'12px'}}}%%
flowchart TB
  P0["Phase 0 — Setup &amp; Tooling"] --> P1["Phase 1 — Math Foundations"]
  P1 --> P2["Phase 2 — ML Fundamentals"]
  P2 --> P3["Phase 3 — Deep Learning Core"]
  P3 --> P4["Phase 4 — Vision"]
  P3 --> P5["Phase 5 — NLP"]
  P3 --> P6["Phase 6 — Speech &amp; Audio"]
  P3 --> P9["Phase 9 — RL"]
  P5 --> P7["Phase 7 — Transformers"]
  P7 --> P8["Phase 8 — GenAI"]
  P7 --> P10["Phase 10 — LLMs from Scratch"]
  P10 --> P11["Phase 11 — LLM Engineering"]
  P10 --> P12["Phase 12 — Multimodal"]
  P11 --> P13["Phase 13 — Tools &amp; Protocols"]
  P13 --> P14["Phase 14 — Agent Engineering"]
  P14 --> P15["Phase 15 — Autonomous Systems"]
  P15 --> P16["Phase 16 — Multi-Agent &amp; Swarms"]
  P14 --> P17["Phase 17 — Infrastructure &amp; Production"]
  P15 --> P18["Phase 18 — Ethics &amp; Alignment"]
  P16 --> P19["Phase 19 — Capstone Projects"]
  P17 --> P19
  P18 --> P19

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The shape of a lesson

Each lesson lives in its own folder, with the same structure across the entire curriculum:

phases/<NN>-<phase-name>/<NN>-<lesson-name>/
├── code/      runnable implementations (Python, TypeScript, Rust, Julia)
├── docs/
│   └── en.md  lesson narrative
└── outputs/   prompts, skills, agents, or MCP servers this lesson produces

Every lesson follows six beats. The Build It / Use It split is the spine — you implement the algorithm from scratch first, then run the same thing through the production library. You understand what the framework is doing because you wrote the smaller version yourself.

%%{init: {'theme':'base','themeVariables':{'primaryColor':'#fafaf5','primaryTextColor':'#1a1a1a','primaryBorderColor':'#3553ff','lineColor':'#3553ff','fontFamily':'JetBrains Mono','fontSize':'13px'}}}%%
flowchart LR
  M["MOTTO<br/><sub>one-line core idea</sub>"] --> Pr["PROBLEM<br/><sub>concrete pain</sub>"]
  Pr --> C["CONCEPT<br/><sub>diagrams &amp; intuition</sub>"]
  C --> B["BUILD IT<br/><sub>raw math, no frameworks</sub>"]
  B --> U["USE IT<br/><sub>same thing in PyTorch / sklearn</sub>"]
  U --> S["SHIP IT<br/><sub>prompt · skill · agent · MCP</sub>"]

Getting started

Three ways in. Pick one.

Option A — read. Open any completed lesson on aiengineeringfromscratch.com or expand a phase under Contents. No setup, no cloning.

Option B — clone and run.

git clone https://github.com/rohitg00/ai-engineering-from-scratch.git
cd ai-engineering-from-scratch
python phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py

Option C — find your level (recommended). Skip ahead intelligently. Inside Claude, Cursor, Codex, OpenClaw, Hermes, or any agent with the curriculum skills installed:

/find-your-level

Ten questions. Maps your knowledge to a starting phase, builds a personalized path with hour estimates. After each phase:

/check-understanding 3        # quiz yourself on phase 3
ls phases/03-deep-learning-core/05-loss-functions/outputs/
# ├── prompt-loss-function-selector.md
# └── prompt-loss-debugger.md

Prerequisites

You can write code (any language; Python helps).
You want to understand how AI actually works, not just call APIs.

Built-in agent skills (Claude, Cursor, Codex, OpenClaw, Hermes)

Skill	What it does
`/find-your-level`	Ten-question placement quiz. Maps your knowledge to a starting phase and produces a personalized path with hour estimates.
`/check-understanding <phase>`	Per-phase quiz, eight questions, with feedback and specific lessons to review.

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Read it as a book

The whole course compiles into a six-volume book series. EPUB and PDF are built by CI from the same lesson sources and attached to every GitHub release; the links below always resolve to the newest release. Volume numbers index the series, not versions: each copy carries a dated edition stamp, and older editions stay downloadable from their release.

Vol	Title	Phases	Download
1	Foundations · Math, Tooling, and Classical Machine Learning	00-02	EPUB · PDF
2	Deep Learning · Networks, Vision, and Speech	03, 04, 06	EPUB · PDF
3	Language · NLP Foundations and the Transformer	05, 07	EPUB · PDF
4	Large Language Models · Generation, Reinforcement, Pretraining, and Engineering	08-11	EPUB · PDF
5	Agents · Multimodality, Protocols, Autonomy, and Swarms	12-16	EPUB · PDF
6	Production · Infrastructure, Safety, and Capstones	17-19	EPUB · PDF

The book is the snapshot; this repository is the living edition. Every chapter ends with links back to the lesson's animated figures, quiz, and runnable code. Build locally with python3 scripts/build_book.py (pandoc required); pipeline details in book/README.md.

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Every lesson ships something

Other curricula end with "congratulations, you learned X." Each lesson here ends with a reusable tool you can install or paste into your daily workflow.

_{FIG_001 · A} PROMPTS	_{FIG_001 · B} SKILLS	_{FIG_001 · C} AGENTS	_{FIG_001 · D} MCP SERVERS
Paste into any AI assistant for expert-level help on a narrow task.	Drop into Claude, Cursor, Codex, OpenClaw, Hermes, or any agent that reads `SKILL.md`.	Deploy as autonomous workers — you wrote the loop yourself in Phase 14.	Plug into any MCP-compatible client. Built end-to-end in Phase 13.

Install the lot with python3 scripts/install_skills.py. Real tools, not homework. By the end of the curriculum, you have a portfolio of 503 artifacts you actually understand because you built them.

FIG_002 · A worked sample

Phase 14, lesson 1: the agent loop. ~120 lines of pure Python, no dependencies.

code/agent_loop.py _{build it}

def run(query, tools):
    history = [user(query)]
    for step in range(MAX_STEPS):
        msg = llm(history)
        if msg.tool_calls:
            for call in msg.tool_calls:
                result = tools[call.name](**call.args)
                history.append(tool_result(call.id, result))
            continue
        return msg.content
    raise StepLimitExceeded

outputs/skill-agent-loop.md _{ship it}

---
name: agent-loop
description: ReAct-style loop for any tool list
phase: 14
lesson: 01
---

Implement a minimal agent loop that...

outputs/prompt-debug-agent.md

You are an agent debugger. Given the trace
of an agent run, identify the step where
the agent went wrong and explain why...

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#	Lesson	Type	Lang
01	Dev Environment	Build	Python
02	Git & Collaboration	Learn	—
03	GPU Setup & Cloud	Build	Python
04	APIs & Keys	Build	Python
05	Jupyter Notebooks	Build	Python
06	Python Environments	Build	Shell
07	Docker for AI	Build	Docker
08	Editor Setup	Build	—
09	Data Management	Build	Python
10	Terminal & Shell	Learn	—
11	Linux for AI	Learn	—
12	Debugging & Profiling	Build	Python

Phase 1 — Math Foundations 22 lessons The intuition behind every AI algorithm, through code.

#	Lesson	Type	Lang
01	Linear Algebra Intuition	Learn	Python, Julia
02	Vectors, Matrices & Operations	Build	Python, Julia
03	Matrix Transformations & Eigenvalues	Build	Python, Julia
04	Calculus for ML: Derivatives & Gradients	Learn	Python
05	Chain Rule & Automatic Differentiation	Build	Python
06	Probability & Distributions	Learn	Python
07	Bayes' Theorem & Statistical Thinking	Build	Python
08	Optimization: Gradient Descent Family	Build	Python
09	Information Theory: Entropy, KL Divergence	Learn	Python
10	Dimensionality Reduction: PCA, t-SNE, UMAP	Build	Python
11	Singular Value Decomposition	Build	Python, Julia
12	Tensor Operations	Build	Python
13	Numerical Stability	Build	Python
14	Norms & Distances	Build	Python
15	Statistics for ML	Build	Python
16	Sampling Methods	Build	Python
17	Linear Systems	Build	Python
18	Convex Optimization	Build	Python
19	Complex Numbers for AI	Learn	Python
20	The Fourier Transform	Build	Python
21	Graph Theory for ML	Build	Python
22	Stochastic Processes	Learn	Python

Phase 2 — ML Fundamentals 18 lessons Classical ML — still the backbone of most production AI.

#	Lesson	Type	Lang
01	What Is Machine Learning	Learn	Python
02	Linear Regression from Scratch	Build	Python
03	Logistic Regression & Classification	Build	Python
04	Decision Trees & Random Forests	Build	Python
05	Support Vector Machines	Build	Python
06	KNN & Distance Metrics	Build	Python
07	Unsupervised Learning: K-Means, DBSCAN	Build	Python
08	Feature Engineering & Selection	Build	Python
09	Model Evaluation: Metrics, Cross-Validation	Build	Python
10	Bias, Variance & the Learning Curve	Learn	Python
11	Ensemble Methods: Boosting, Bagging, Stacking	Build	Python
12	Hyperparameter Tuning	Build	Python
13	ML Pipelines & Experiment Tracking	Build	Python
14	Naive Bayes	Build	Python
15	Time Series Fundamentals	Build	Python
16	Anomaly Detection	Build	Python
17	Handling Imbalanced Data	Build	Python
18	Feature Selection	Build	Python

Phase 3 — Deep Learning Core 13 lessons Neural networks from first principles. No frameworks until you build one.

#	Lesson	Type	Lang
01	The Perceptron: Where It All Started	Build	Python
02	Multi-Layer Networks & Forward Pass	Build	Python
03	Backpropagation from Scratch	Build	Python
04	Activation Functions: ReLU, Sigmoid, GELU & Why	Build	Python
05	Loss Functions: MSE, Cross-Entropy, Contrastive	Build	Python
06	Optimizers: SGD, Momentum, Adam, AdamW	Build	Python
07	Regularization: Dropout, Weight Decay, BatchNorm	Build	Python
08	Weight Initialization & Training Stability	Build	Python
09	Learning Rate Schedules & Warmup	Build	Python
10	Build Your Own Mini Framework	Build	Python
11	Introduction to PyTorch	Build	Python
12	Introduction to JAX	Build	Python
13	Debugging Neural Networks	Build	Python

Phase 4 — Computer Vision 28 lessons From pixels to understanding — image, video, 3D, VLMs, and world models.

#	Lesson	Type	Lang
01	Image Fundamentals: Pixels, Channels, Color Spaces	Learn	Python
02	Convolutions from Scratch	Build	Python
03	CNNs: LeNet to ResNet	Build	Python
04	Image Classification	Build	Python
05	Transfer Learning & Fine-Tuning	Build	Python
06	Object Detection — YOLO from Scratch	Build	Python
07	Semantic Segmentation — U-Net	Build	Python
08	Instance Segmentation — Mask R-CNN	Build	Python
09	Image Generation — GANs	Build	Python
10	Image Generation — Diffusion Models	Build	Python
11	Stable Diffusion — Architecture & Fine-Tuning	Build	Python
12	Video Understanding — Temporal Modeling	Build	Python
13	3D Vision: Point Clouds, NeRFs	Build	Python
14	Vision Transformers (ViT)	Build	Python
15	Real-Time Vision: Edge Deployment	Build	Python
16	Build a Complete Vision Pipeline	Build	Python
17	Self-Supervised Vision — SimCLR, DINO, MAE	Build	Python
18	Open-Vocabulary Vision — CLIP	Build	Python
19	OCR & Document Understanding	Build	Python
20	Image Retrieval & Metric Learning	Build	Python
21	Keypoint Detection & Pose Estimation	Build	Python
22	3D Gaussian Splatting from Scratch	Build	Python
23	Diffusion Transformers & Rectified Flow	Build	Python
24	SAM 3 & Open-Vocabulary Segmentation	Build	Python
25	Vision-Language Models (ViT-MLP-LLM)	Build	Python
26	Monocular Depth & Geometry Estimation	Build	Python
27	Multi-Object Tracking & Video Memory	Build	Python
28	World Models & Video Diffusion	Build	Python

Phase 5 — NLP: Foundations to Advanced 29 lessons Language is the interface to intelligence.

#	Lesson	Type	Lang
01	Text Processing: Tokenization, Stemming, Lemmatization	Build	Python
02	Bag of Words, TF-IDF & Text Representation	Build	Python
03	Word Embeddings: Word2Vec from Scratch	Build	Python
04	GloVe, FastText & Subword Embeddings	Build	Python
05	Sentiment Analysis	Build	Python
06	Named Entity Recognition (NER)	Build	Python
07	POS Tagging & Syntactic Parsing	Build	Python
08	Text Classification — CNNs & RNNs for Text	Build	Python
09	Sequence-to-Sequence Models	Build	Python
10	Attention Mechanism — The Breakthrough	Build	Python
11	Machine Translation	Build	Python
12	Text Summarization	Build	Python
13	Question Answering Systems	Build	Python
14	Information Retrieval & Search	Build	Python
15	Topic Modeling: LDA, BERTopic	Build	Python
16	Text Generation	Build	Python
17	Chatbots: Rule-Based to Neural	Build	Python
18	Multilingual NLP	Build	Python
19	Subword Tokenization: BPE, WordPiece, Unigram, SentencePiece	Learn	Python
20	Structured Outputs & Constrained Decoding	Build	Python
21	NLI & Textual Entailment	Learn	Python
22	Embedding Models Deep Dive	Learn	Python
23	Chunking Strategies for RAG	Build	Python
24	Coreference Resolution	Learn	Python
25	Entity Linking & Disambiguation	Build	Python
26	Relation Extraction & Knowledge Graph Construction	Build	Python
27	LLM Evaluation: RAGAS, DeepEval, G-Eval	Build	Python
28	Long-Context Evaluation: NIAH, RULER, LongBench, MRCR	Learn	Python
29	Dialogue State Tracking	Build	Python

Phase 6 — Speech & Audio 17 lessons Hear, understand, speak.

#	Lesson	Type	Lang
01	Audio Fundamentals: Waveforms, Sampling, FFT	Learn	Python
02	Spectrograms, Mel Scale & Audio Features	Build	Python
03	Audio Classification	Build	Python
04	Speech Recognition (ASR)	Build	Python
05	Whisper: Architecture & Fine-Tuning	Build	Python
06	Speaker Recognition & Verification	Build	Python
07	Text-to-Speech (TTS)	Build	Python
08	Voice Cloning & Voice Conversion	Build	Python
09	Music Generation	Build	Python
10	Audio-Language Models	Build	Python
11	Real-Time Audio Processing	Build	Python
12	Build a Voice Assistant Pipeline	Build	Python
13	Neural Audio Codecs — EnCodec, SNAC, Mimi, DAC	Learn	Python
14	Voice Activity Detection & Turn-Taking	Build	Python
15	Streaming Speech-to-Speech — Moshi, Hibiki	Learn	Python
16	Voice Anti-Spoofing & Audio Watermarking	Build	Python
17	Audio Evaluation — WER, MOS, MMAU, Leaderboards	Learn	Python

Phase 7 — Transformers Deep Dive 14 lessons The architecture that changed everything.

#	Lesson	Type	Lang
01	Why Transformers: The Problems with RNNs	Learn	Python
02	Self-Attention from Scratch	Build	Python
03	Multi-Head Attention	Build	Python
04	Positional Encoding: Sinusoidal, RoPE, ALiBi	Build	Python
05	The Full Transformer: Encoder + Decoder	Build	Python
06	BERT — Masked Language Modeling	Build	Python
07	GPT — Causal Language Modeling	Build	Python
08	T5, BART — Encoder-Decoder Models	Learn	Python
09	Vision Transformers (ViT)	Build	Python
10	Audio Transformers — Whisper Architecture	Learn	Python
11	Mixture of Experts (MoE)	Build	Python
12	KV Cache, Flash Attention & Inference Optimization	Build	Python
13	Scaling Laws	Learn	Python
14	Build a Transformer from Scratch	Build	Python
15	Attention Variants — Sliding Window, Sparse, Differential	Build	Python
16	Speculative Decoding — Draft, Verify, Repeat	Build	Python

Phase 8 — Generative AI 14 lessons Create images, video, audio, 3D, and more.

#	Lesson	Type	Lang
01	Generative Models: Taxonomy & History	Learn	Python
02	Autoencoders & VAE	Build	Python
03	GANs: Generator vs Discriminator	Build	Python
04	Conditional GANs & Pix2Pix	Build	Python
05	StyleGAN	Build	Python
06	Diffusion Models — DDPM from Scratch	Build	Python
07	Latent Diffusion & Stable Diffusion	Build	Python
08	ControlNet, LoRA & Conditioning	Build	Python
09	Inpainting, Outpainting & Editing	Build	Python
10	Video Generation	Build	Python
11	Audio Generation	Build	Python
12	3D Generation	Build	Python
13	Flow Matching & Rectified Flows	Build	Python
14	Evaluation: FID, CLIP Score	Build	Python
19	Visual Autoregressive Modeling (VAR): Next-Scale Prediction	Build	Python

Phase 9 — Reinforcement Learning 12 lessons The foundation of RLHF and game-playing AI.

#	Lesson	Type	Lang
01	MDPs, States, Actions & Rewards	Learn	Python
02	Dynamic Programming	Build	Python
03	Monte Carlo Methods	Build	Python
04	Q-Learning, SARSA	Build	Python
05	Deep Q-Networks (DQN)	Build	Python
06	Policy Gradients — REINFORCE	Build	Python
07	Actor-Critic — A2C, A3C	Build	Python
08	PPO	Build	Python
09	Reward Modeling & RLHF	Build	Python
10	Multi-Agent RL	Build	Python
11	Sim-to-Real Transfer	Build	Python
12	RL for Games	Build	Python

Phase 10 — LLMs from Scratch 22 lessons Build, train, and understand large language models.

#

Lesson

Type

Lang

01

Tokenizers: BPE, WordPiece, SentencePiece

Build

Python, Rust

02

Building a Tokenizer from Scratch

Build

Python

03

Data Pipelines for Pre-Training

Build

Python

04

Pre-Training a Mini GPT (124M)

Build

Python

05

Distributed Training, FSDP, DeepSpeed

Build

Python

06

Instruction Tuning — SFT

Build

Python

07

RLHF — Reward Model + PPO

Build

Python

08

DPO — Direct Preference Optimization

Build

Python

09

Constitutional AI & Self-Improvement

Build

Python

10

Evaluation — Benchmarks, Evals

Build

Python

11

Quantization: INT8, GPTQ, AWQ, GGUF

Build

Python

12

Inference Optimization

Build

Python

13

Building a Complete LLM Pipeline

Build

Python

14

Open Models: Architecture Walkthroughs

Learn

Python

15

Speculative Decoding and EAGLE-3

Build

Python

16

Differential Attention (V2)

Build

Python

17

Native Sparse Attention (DeepSeek NSA)

Build

Python

18

Multi-Token Prediction (MTP)

Build

Python

19

DualPipe Parallelism

Learn

Python

20

DeepSeek-V3 Architecture Walkthrough

Learn

Python

21

Jamba — Hybrid SSM-Transformer

Learn

Python

22

Async and Hogwild! Inference

Build

Python

25

Speculative Decoding and EAGLE

Build

Python

View on GitHub

Recent activity

commits and pull requests

feat(site): read articles aloud with the browser speech engineOpen
#369 by sivanaikk · updated 8d ago
chore: remove unused subprocess importOpen
#370 by mrbadri · updated 8d ago
docs: compare ClawBench with controlled agent benchmarksOpen
#366 by reacher-z · updated 10d ago
feat(localization): add Turkish curriculum pipelineOpen
#367 by ademiru · updated 10d ago
feat: add Chinese translations for all 503 lessons and 568 auxiliary …Open
#365 by topbat · updated 11d ago
fix(phase-02/01): sharpen ML diagram labelsOpen
#364 by ppandit12 · updated 11d ago
Feature/experimentsOpen
#362 by blackkhawk07 · updated 11d ago
imp(phase-00/01): centralize Check defaults in const fn constructorOpen
#361 by rushil-b-patel · updated 11d ago
chore(site): rebuild data.js
github-actions[bot] committed 7157ca7 · 12d ago
fix(phase-02/15): forecast fed the lag vector in reverse order (#351)
thejesh23 committed d89b4bf · 12d ago
chore(site): rebuild data.js
github-actions[bot] committed ff1c5ae · 12d ago
fix(phase-02/10): total_error omitted the noise term (#352)
thejesh23 committed c77783e · 12d ago

Recent open issues

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[bug] Quiz correct answer is always in the same position
#368 · 10d ago · by DavidReque
bug
Diagram image is low resolution and unreadable after expanding
#363 · 11d ago · by ppandit12
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What Compliance Requirements Should Businesses Consider Before Building an AI Mobile App?
Q&A · 18d ago · 1 comments

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Edition 2026.07v2026.07Jul 25, 20264.5K downloads
First book edition. EPUB and PDF for all six volumes are built by CI from the lesson sources and attached to this release automatically; allow the build a few minutes to upload them. The download links on the site and README always point at the newest release.

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+690.3K lines added, -323.9K removed over the last year.

Commits per week

last 52 weeks

1.5K commits in the last 52 weeks.

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Commit volume by weekday and hour (UTC). Larger dots mean more commits.

Who is committing

last 52 weeks

Maintainer commits1,551 (94%)

Community commits106 (6%)

1,657 commits in total over the last year.

Date	List	Rank	Stars gained
Jul 27, 2026	daily	#22	+3
Jul 23, 2026	daily	#18	+4
Jul 21, 2026	daily	#15	+4
Jul 20, 2026	daily	#7	+4
Jun 16, 2026	daily	#22	+14
Jun 6, 2026	daily	#22	+52
Jun 1, 2026	daily	#12	+46
May 30, 2026	daily	#20	+65
May 29, 2026	daily	#23	+36
May 28, 2026	daily	#9	+74
May 27, 2026	daily	#6	+110
May 26, 2026	daily	#3	+146
May 25, 2026	daily	#4	+220
May 24, 2026	daily	#4	+310
May 23, 2026	daily	#5	+274

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About

License: MIT
First commit: Mar 18, 2026
Age: 4 months
Default branch: main
Tags: 1
Closed issues: 35
Merged PRs: 174
Owner type: Individual

Community health85%

✓Contributing guide
✓Code of conduct
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Continuous integration

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Source

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Languages

Python 66.1%
JavaScript 18.3%
TypeScript 7.3%
HTML 3.7%
Julia 2.5%
Rust 1.4%
CSS 0.5%
Shell 0.2%

Direct dependencies

requirements.txt

numpymatplotlibjupytertorchtorchvisiontorchaudiotransformersdatasetstokenizersacceleratescikit-learnpandaspillowlibrosasoundfiletiktokenanthropicopenai

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