Top Related Projects
Video discussing this curriculum:
A complete computer science study plan to become a software engineer.
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
😎 Awesome lists about all kinds of interesting topics
:books: Freely available programming books
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Quick Overview
OSSU/computer-science is a comprehensive, open-source curriculum for studying computer science. It provides a complete undergraduate-level education in computer science using online materials and courses, all freely available. The curriculum is designed to be self-paced and covers a wide range of topics from introductory programming to advanced subjects like machine learning and computer graphics.
Pros
- Free and accessible to anyone with an internet connection
- Comprehensive curriculum covering a broad range of computer science topics
- Regularly updated with new courses and materials
- Supported by a large community of learners and contributors
Cons
- Requires significant self-discipline and time management
- Lacks formal accreditation or official degree
- Some courses may become outdated or unavailable over time
- Limited direct interaction with instructors compared to traditional education
Competitor Comparisons
Video discussing this curriculum:
Pros of open-source-cs
- More concise curriculum, easier to follow for beginners
- Includes practical projects and coding exercises
- Focuses on modern, industry-relevant technologies
Cons of open-source-cs
- Less comprehensive coverage of computer science fundamentals
- Fewer advanced topics and specialized courses
- Limited community support and updates compared to computer-science
Code Comparison
While both repositories primarily contain curriculum outlines rather than code, open-source-cs includes some project suggestions. Here's an example from the Web Development section:
# open-source-cs project suggestion
def fibonacci(n):
if n <= 1:
return n
else:
return fibonacci(n-1) + fibonacci(n-2)
computer-science doesn't typically include code snippets, focusing instead on course listings and resources.
Both repositories aim to provide free, self-paced computer science education. computer-science offers a more traditional, academically-oriented curriculum, while open-source-cs takes a more practical, project-based approach. The choice between them depends on individual learning goals and preferences.
A complete computer science study plan to become a software engineer.
Pros of coding-interview-university
- More focused on interview preparation and algorithmic problem-solving
- Includes specific advice for getting a job at large tech companies
- Provides a structured study plan with estimated time commitments
Cons of coding-interview-university
- Less comprehensive coverage of general computer science topics
- May not provide as much depth in theoretical concepts
- Primarily geared towards software engineering roles, potentially limiting for other CS career paths
Code Comparison
While both repositories primarily focus on learning resources rather than code, coding-interview-university does include some code examples for data structures and algorithms. For instance:
# coding-interview-university example (Binary Search Tree)
class Node:
def __init__(self, data):
self.left = None
self.right = None
self.data = data
computer-science, on the other hand, doesn't typically include code snippets directly in the repository. Instead, it links to external resources that may contain code examples.
Summary
coding-interview-university is more tailored for those preparing for software engineering interviews, with a focus on practical skills and problem-solving. computer-science offers a broader, more academic approach to learning computer science fundamentals. The choice between the two depends on your specific goals and learning preferences.
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
Pros of developer-roadmap
- Provides visual roadmaps for different tech roles (frontend, backend, DevOps)
- Regularly updated with current industry trends and technologies
- Offers a broader overview of various tech career paths
Cons of developer-roadmap
- Less structured learning path compared to computer-science
- Focuses more on technologies and tools rather than fundamental CS concepts
- May lack depth in theoretical computer science topics
Code comparison
Not applicable for these repositories, as they primarily contain educational content and roadmaps rather than code samples.
Summary
computer-science offers a more structured, academic approach to learning computer science fundamentals, while developer-roadmap provides a visual guide to various tech career paths and current industry technologies. The former is better suited for those seeking a comprehensive CS education, while the latter is ideal for professionals looking to navigate their career development in specific tech roles.
Both repositories serve different purposes and can be complementary. computer-science provides a solid theoretical foundation, while developer-roadmap offers practical guidance on industry-relevant skills and technologies. Depending on your goals, you might benefit from using both resources in conjunction.
😎 Awesome lists about all kinds of interesting topics
Pros of awesome
- Broader scope covering various tech topics beyond computer science
- More community-driven with contributions from a large number of developers
- Frequently updated with new resources and categories
Cons of awesome
- Less structured learning path compared to computer-science
- May be overwhelming due to the sheer volume of resources
- Quality of resources can vary as it's a community-curated list
Code comparison
While both repositories primarily consist of markdown files, awesome includes a small amount of JavaScript for its website:
// awesome
const awesome = require('awesome-lint');
awesome().then(function (result) {
console.log(result);
});
computer-science doesn't contain any code samples, as it focuses on curriculum structure and course links.
Summary
awesome is a vast collection of curated lists covering various tech topics, offering a wide range of resources but potentially overwhelming for beginners. computer-science provides a more structured approach to learning computer science, with a clear curriculum path but a narrower focus. Both repositories serve different purposes and can be complementary for learners depending on their goals and experience level.
:books: Freely available programming books
Pros of free-programming-books
- Extensive collection of free resources covering a wide range of programming languages and topics
- Regularly updated with community contributions
- Available in multiple languages, making it accessible to a global audience
Cons of free-programming-books
- Lacks a structured curriculum or learning path
- Quality of resources may vary, as it's a curated list rather than a designed course
- May overwhelm beginners due to the sheer volume of resources
Code comparison
Not applicable, as both repositories are primarily resource collections and don't contain significant code samples.
Summary
computer-science offers a structured curriculum for self-taught computer science education, while free-programming-books provides a vast collection of free programming resources. The former is better suited for those seeking a comprehensive, guided learning experience, while the latter is ideal for reference and supplementary materials across various programming topics.
computer-science focuses on depth and structure, whereas free-programming-books emphasizes breadth and accessibility. Both repositories serve different purposes and can be complementary for learners at various stages of their programming journey.
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Pros of system-design-primer
- Focused specifically on system design and scalability concepts
- Includes visual aids and diagrams to illustrate complex topics
- Provides practical examples and case studies of real-world systems
Cons of system-design-primer
- Narrower scope, covering only system design rather than a full CS curriculum
- Less structured learning path compared to computer-science's comprehensive course list
- May require more prior knowledge in computer science fundamentals
Code comparison
While both repositories primarily focus on educational content rather than code, system-design-primer does include some code snippets for illustrative purposes:
# system-design-primer example (Python)
def get_user(request):
user_id = request.GET.get('user_id')
user = User.objects.get(id=user_id)
return JsonResponse({'user': user})
computer-science doesn't typically include code snippets, as it's more of a curriculum guide.
Summary
system-design-primer is an excellent resource for those specifically interested in system design and scalability, offering in-depth coverage with visual aids and practical examples. However, computer-science provides a more comprehensive computer science education covering a broader range of topics in a structured curriculum format. The choice between the two depends on the learner's specific goals and prior knowledge.
Convert 
designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual CopilotREADME
Open Source Society University
Path to a free self-taught education in Computer Science!
Contents
Summary
The OSSU curriculum is a complete education in computer science using online materials. It's not merely for career training or professional development. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines, and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own, but with support from a worldwide community of fellow learners.
It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc., but specifically chosen to meet the following criteria.
Courses must:
- Be open for enrollment
- Run regularly (ideally in self-paced format, otherwise running multiple times per year)
- Be of generally high quality in teaching materials and pedagogical principles
- Match the curricular standards of the CS 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science
When no course meets the above criteria, the coursework is supplemented with a book. When there are courses or books that don't fit into the curriculum but are otherwise of high quality, they belong in extras/courses or extras/readings.
Organization. The curriculum is designed as follows:
- Intro CS: for students to try out CS and see if it's right for them
- Core CS: corresponds roughly to the first three years of a computer science curriculum, taking classes that all majors would be required to take
- Advanced CS: corresponds roughly to the final year of a computer science curriculum, taking electives according to the student's interests
- Final Project: a project for students to validate, consolidate, and display their knowledge, to be evaluated by their peers worldwide
Duration. It is possible to finish within about 2 years if you plan carefully and devote roughly 20 hours/week to your studies. Learners can use this spreadsheet
to estimate their end date. Make a copy and input your start date and expected hours per week in the Timeline sheet. As you work through courses you can enter your actual course completion dates in the Curriculum Data sheet and get updated completion estimates.
Warning: While the spreadsheet is a useful tool to estimate the time you need to complete this curriculum, it may not be up-to-date with the curriculum. Use the spreadsheet just to estimate the time you need. Use the OSSU CS website or the repo to see what courses to do.
Cost. All or nearly all course material is available for free. However, some courses may charge money for assignments/tests/projects to be graded. Note that both Coursera and edX offer financial aid.
Decide how much or how little to spend based on your own time and budget; just remember that you can't purchase success!
Process. Students can work through the curriculum alone or in groups, in order or out of order.
- We recommend doing all courses in Core CS, only skipping a course when you are certain that you've already learned the material previously.
- For simplicity, we recommend working through courses (especially Core CS) in order from top to bottom. Some students choose to study multiple courses at a time in order to vary the material they are working on is a day/week. A popular option is to take the math courses in parallel with the introductory courses. Course prerequisites are listed to help you determine if you are prepared for a given course.
- Courses in Advanced CS are electives. Choose one subject (e.g. Advanced programming) you want to become an expert in and take all the courses under that heading. You can also create your own custom subject; the Discord community may provide feedback on your planned subject.
Content policy. If you plan on showing off some of your coursework publicly, you must share only files that you are allowed to. Respect the code of conduct that you signed in the beginning of each course!
Getting help (Details about our FAQ and chatroom)
Community
- We have a Discord server!
This should be your first stop to talk with other OSSU students. Why don't you introduce yourself right now? Join the OSSU Discord - You can also interact through GitHub issues. If there is a problem with a course, or a change needs to be made to the curriculum, this is the place to start the conversation. Read more here.
- Add Open Source Society University to your Linkedin profile!
Warning: There are a few third-party/deprecated/outdated material that you might find when searching for OSSU. We recommend you to ignore them, and only use the OSSU CS website or OSSU CS Github Repo. Some known outdated materials are:
- An unmaintained and deprecated firebase app. Read more in the FAQ.
- An unmaintained and deprecated trello board
- Third-party notion templates
Curriculum
Curriculum version: 8.0.0 (see CHANGELOG)
Prerequisites
- Core CS assumes the student has already taken high school math, including algebra, geometry, and pre-calculus.
- Advanced CS assumes the student has already taken the entirety of Core CS and is knowledgeable enough now to decide which electives to take.
- Note that Advanced systems assumes the student has taken a basic physics course (e.g. AP Physics in high school).
Intro CS
Introduction to Programming
If you've never written a for-loop, or don't know what a string is in programming, start here. This course is self-paced, allowing you to adjust the number of hours you spend per week to meet your needs.
Topics covered:
simple programs
simple data structures
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| Introduction to programming | 10 weeks | 10 hours/week | none | chat |
Introduction to Computer Science
This course will introduce you to the world of computer science. Students who have been introduced to programming, either from the courses above or through study elsewhere, should take this course for a flavor of the material to come. If you finish the course wanting more, Computer Science is likely for you!
Topics covered:
computation
imperative programming
basic data structures and algorithms
and more
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| Introduction to Computer Science and Programming using Python | 9 weeks | 15 hours/week | high school algebra | chat |
Core CS
All coursework under Core CS is required, unless otherwise indicated.
Core programming
Topics covered:
functional programming
design for testing
program requirements
common design patterns
unit testing
object-oriented design
static typing
dynamic typing
ML-family languages (via Standard ML)
Lisp-family languages (via Racket)
Ruby
and more
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| Systematic Program Design | 13 weeks | 8-10 hours/week | none | chat: part 1 / part 2 |
| Programming Languages, Part A | 5 weeks | 4-8 hours/week | Systematic Program Design (Hear instructor) | chat |
| Programming Languages, Part B | 3 weeks | 4-8 hours/week | Programming Languages, Part A | chat |
| Programming Languages, Part C | 3 weeks | 4-8 hours/week | Programming Languages, Part B | chat |
| Object-Oriented Design | 4 weeks | 4 hours/week | Basic Java | chat |
| Design Patterns | 4 weeks | 4 hours/week | Object-Oriented Design | chat |
| Software Architecture | 4 weeks | 2-5 hours/week | Design Patterns | chat |
Core math
Discrete math (Math for CS) is a prerequisite and closely related to the study of algorithms and data structures. Calculus both prepares students for discrete math and helps students develop mathematical maturity.
Topics covered:
discrete mathematics
mathematical proofs
basic statistics
O-notation
discrete probability
and more
| Courses | Duration | Effort | Notes | Prerequisites | Discussion |
|---|---|---|---|---|---|
| Calculus 1A: Differentiation (alternative) | 13 weeks | 6-10 hours/week | The alternate covers this and the following 2 courses | high school math | chat |
| Calculus 1B: Integration | 13 weeks | 5-10 hours/week | - | Calculus 1A | chat |
| Calculus 1C: Coordinate Systems & Infinite Series | 6 weeks | 5-10 hours/week | - | Calculus 1B | chat |
| Mathematics for Computer Science (alternative) | 13 weeks | 5 hours/week | 2015/2019 solutions 2010 solutions 2005 solutions. | Calculus 1C | chat |
CS Tools
Understanding theory is important, but you will also be expected to create programs. There are a number of tools that are widely used to make that process easier. Learn them now to ease your future work writing programs.
Topics covered:
terminals and shell scripting
vim
command line environments
version control
and more
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| The Missing Semester of Your CS Education | 2 weeks | 12 hours/week | - | chat |
Core systems
Topics covered:
procedural programming
manual memory management
boolean algebra
gate logic
memory
computer architecture
assembly
machine language
virtual machines
high-level languages
compilers
operating systems
network protocols
and more
| Courses | Duration | Effort | Additional Text / Assignments | Prerequisites | Discussion |
|---|---|---|---|---|---|
| Build a Modern Computer from First Principles: From Nand to Tetris (alternative) | 6 weeks | 7-13 hours/week | - | C-like programming language | chat |
| Build a Modern Computer from First Principles: Nand to Tetris Part II | 6 weeks | 12-18 hours/week | - | one of these programming languages, From Nand to Tetris Part I | chat |
| Operating Systems: Three Easy Pieces | 10-12 weeks | 6-10 hours/week | - | Nand to Tetris Part II | chat |
| Computer Networking: a Top-Down Approach | 8 weeks | 4â12 hours/week | Wireshark Labs | algebra, probability, basic CS | chat |
Core theory
Topics covered:
divide and conquer
sorting and searching
randomized algorithms
graph search
shortest paths
data structures
greedy algorithms
minimum spanning trees
dynamic programming
NP-completeness
and more
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| Divide and Conquer, Sorting and Searching, and Randomized Algorithms | 4 weeks | 4-8 hours/week | any programming language, Mathematics for Computer Science | chat |
| Graph Search, Shortest Paths, and Data Structures | 4 weeks | 4-8 hours/week | Divide and Conquer, Sorting and Searching, and Randomized Algorithms | chat |
| Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming | 4 weeks | 4-8 hours/week | Graph Search, Shortest Paths, and Data Structures | chat |
| Shortest Paths Revisited, NP-Complete Problems and What To Do About Them | 4 weeks | 4-8 hours/week | Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming | chat |
Core security
Topics covered
Confidentiality, Integrity, Availability
Secure Design
Defensive Programming
Threats and Attacks
Network Security
Cryptography
and more
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| Cybersecurity Fundamentals | 8 weeks | 10-12 hours/week | - | chat |
| Principles of Secure Coding | 4 weeks | 4 hours/week | - | chat |
| Identifying Security Vulnerabilities | 4 weeks | 4 hours/week | - | chat |
Choose one of the following:
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| Identifying Security Vulnerabilities in C/C++Programming | 4 weeks | 5 hours/week | - | chat |
| Exploiting and Securing Vulnerabilities in Java Applications | 4 weeks | 5 hours/week | - | chat |
Core applications
Topics covered:
Agile methodology
REST
software specifications
refactoring
relational databases
transaction processing
data modeling
neural networks
supervised learning
unsupervised learning
OpenGL
ray tracing
and more
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| Databases: Modeling and Theory | 2 weeks | 10 hours/week | core programming | chat |
| Databases: Relational Databases and SQL | 2 weeks | 10 hours/week | core programming | chat |
| Databases: Semistructured Data | 2 weeks | 10 hours/week | core programming | chat |
| Machine Learning | 11 weeks | 9 hours/week | Basic coding | chat |
| Computer Graphics (alternative) | 6 weeks | 12 hours/week | C++ or Java, linear algebra | chat |
| Software Engineering: Introduction | 4 weeks | 8-10 hours/week | Core Programming, and a sizable project | chat |
Core ethics
Topics covered:
Social Context
Analytical Tools
Professional Ethics
Intellectual Property
Privacy and Civil Liberties
and more
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| Ethics, Technology and Engineering | 9 weeks | 2 hours/week | none | chat |
| Introduction to Intellectual Property | 4 weeks | 2 hours/week | none | chat |
| Data Privacy Fundamentals | 3 weeks | 3 hours/week | none | chat |
Advanced CS
After completing every required course in Core CS, students should choose a subset of courses from Advanced CS based on interest. Not every course from a subcategory needs to be taken. But students should take every course that is relevant to the field they intend to go into.
Advanced programming
Topics covered:
debugging theory and practice
goal-oriented programming
parallel computing
object-oriented analysis and design
UML
large-scale software architecture and design
and more
| Courses | Duration | Effort | Prerequisites |
|---|---|---|---|
| Parallel Programming | 4 weeks | 6-8 hours/week | Scala programming |
| Compilers | 9 weeks | 6-8 hours/week | none |
| Introduction to Haskell | 14 weeks | - | - |
| Learn Prolog Now! (alternative)* | 12 weeks | - | - |
| Software Debugging | 8 weeks | 6 hours/week | Python, object-oriented programming |
| Software Testing | 4 weeks | 6 hours/week | Python, programming experience |
(*) book by Blackburn, Bos, Striegnitz (compiled from source, redistributed under CC license)
Advanced systems
Topics covered:
digital signaling
combinational logic
CMOS technologies
sequential logic
finite state machines
processor instruction sets
caches
pipelining
virtualization
parallel processing
virtual memory
synchronization primitives
system call interface
and more
| Courses | Duration | Effort | Prerequisites | Notes |
|---|---|---|---|---|
| Computation Structures 1: Digital Circuits alternative 1 alternative 2 | 10 weeks | 6 hours/week | Nand2Tetris II | Alternate links contain all 3 courses. |
| Computation Structures 2: Computer Architecture | 10 weeks | 6 hours/week | Computation Structures 1 | |
| Computation Structures 3: Computer Organization | 10 weeks | 6 hours/week | Computation Structures 2 |
Advanced theory
Topics covered:
formal languages
Turing machines
computability
event-driven concurrency
automata
distributed shared memory
consensus algorithms
state machine replication
computational geometry theory
propositional logic
relational logic
Herbrand logic
game trees
and more
| Courses | Duration | Effort | Prerequisites |
|---|---|---|---|
| Theory of Computation (alternative) | 13 weeks | 10 hours/week | Mathematics for Computer Science, logic, algorithms |
| Computational Geometry | 16 weeks | 8 hours/week | algorithms, C++ |
| Game Theory | 8 weeks | 3 hours/week | mathematical thinking, probability, calculus |
Advanced Information Security
| Courses | Duration | Effort | Prerequisites |
|---|---|---|---|
| Web Security Fundamentals | 5 weeks | 4-6 hours/week | understanding basic web technologies |
| Security Governance & Compliance | 3 weeks | 3 hours/week | - |
| Digital Forensics Concepts | 3 weeks | 2-3 hours/week | Core Security |
| Secure Software Development: Requirements, Design, and Reuse | 7 weeks | 1-2 hours/week | Core Programming and Core Security |
| Secure Software Development: Implementation | 7 weeks | 1-2 hours/week | Secure Software Development: Requirements, Design, and Reuse |
| Secure Software Development: Verification and More Specialized Topics | 7 weeks | 1-2 hours/week | Secure Software Development: Implementation |
Advanced math
| Courses | Duration | Effort | Prerequisites | Discussion |
|---|---|---|---|---|
| Essence of Linear Algebra | - | - | high school math | chat |
| Linear Algebra | 14 weeks | 12 hours/week | corequisite: Essence of Linear Algebra | chat |
| Introduction to Numerical Methods | 14 weeks | 12 hours/week | Linear Algebra | chat |
| Introduction to Formal Logic | 10 weeks | 4-8 hours/week | Set Theory | chat |
| Probability | 15 weeks | 5-10 hours/week | Differentiation and Integration | chat |
Final project
Part of learning is doing. The assignments and exams for each course are to prepare you to use your knowledge to solve real-world problems.
After you've completed Core CS and the parts of Advanced CS relevant to you, you should identify a problem that you can solve using the knowledge you've acquired. You can create something entirely new, or you can improve some tool/program that you use and wish were better.
Students who would like more guidance in creating a project may choose to use a series of project oriented courses. Here is a sample of options (many more are available, at this point you should be capable of identifying a series that is interesting and relevant to you):
| Courses | Duration | Effort | Prerequisites |
|---|---|---|---|
| Fullstack Open | 12 weeks | 15 hours/week | programming |
| Modern Robotics (Specialization) | 26 weeks | 2-5 hours/week | freshman-level physics, linear algebra, calculus, linear ordinary differential equations |
| Data Mining (Specialization) | 30 weeks | 2-5 hours/week | machine learning |
| Big Data (Specialization) | 30 weeks | 3-5 hours/week | none |
| Internet of Things (Specialization) | 30 weeks | 1-5 hours/week | strong programming |
| Cloud Computing (Specialization) | 30 weeks | 2-6 hours/week | C++ programming |
| Data Science (Specialization) | 43 weeks | 1-6 hours/week | none |
| Functional Programming in Scala (Specialization) | 29 weeks | 4-5 hours/week | One year programming experience |
| Game Design and Development with Unity 2020 (Specialization) | 6 months | 5 hours/week | programming, interactive design |
Congratulations
After completing the requirements of the curriculum above, you will have completed the equivalent of a full bachelor's degree in Computer Science. Congratulations!
What is next for you? The possibilities are boundless and overlapping:
- Look for a job as a developer!
- Check out the readings for classic books you can read that will sharpen your skills and expand your knowledge.
- Join a local developer meetup (e.g. via meetup.com).
- Pay attention to emerging technologies in the world of software development:
- Explore the actor model through Elixir, a new functional programming language for the web based on the battle-tested Erlang Virtual Machine!
- Explore borrowing and lifetimes through Rust, a systems language which achieves memory- and thread-safety without a garbage collector!
- Explore dependent type systems through Idris, a new Haskell-inspired language with unprecedented support for type-driven development.
Code of conduct
How to show your progress
Fork the GitHub repo into your own GitHub account and put â next to the stuff you've completed as you complete it. This can serve as your kanban board and will be faster to implement than any other solution (giving you time to spend on the courses).
Team
- Eric Douglas: founder of OSSU
- Josh Hanson: lead technical maintainer
- Waciuma Wanjohi: lead academic maintainer
- Contributors
Top Related Projects
Video discussing this curriculum:
A complete computer science study plan to become a software engineer.
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
😎 Awesome lists about all kinds of interesting topics
:books: Freely available programming books
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual Copilot