Bookmarks
A running list of links I keep coming back to, loosely organized.
Current
Courses & Lectures
- Northeastern DS4420: Machine Learning 2
- Northeastern CS 4410/6410: Compilers
- Northeastern CS3650: Computer Systems
- Professor Joe Gibbs Politz Classes
- How to Design Programs, Second Edition
- Northeastern CS4400: Programming Languages (Fall 2024)
- Algorithms by Jeff Erickson
- Northeastern CS7670: Computer Systems Seminar
- Northeastern CS4400: Programming Languages (Spring 2025 Notes)
- Northeastern DS 4300: Large Scale Information Storage and Retrieval
- CS 61B: Data Structures (Spring 2020) — Berkeley
- Data Structures and Algorithms: Deep Dive Using Java
- CS 184/284A Spring 2026 | Computer Graphics at Berkeley
- CS 188 Spring 2026 | Introduction to Artificial Intelligence at Berkeley
- Stanford CS 144: Introduction to Computer Networking
- Project #0 - C++ Primer | CMU 15-445/645 :: Intro to Database Systems (Spring 2026)
- Learn Database Internals - Mini-LSM — Build a Database Storage Engine in Rust
- CMU 15-445/645 :: Intro to Database Systems (Spring 2026)
- Berkeley CS 186
- Berkeley CS 186 — YouTube Lectures
- StanfordOnline: Compilers | edX
- AI Engineering from Scratch
- Teach Yourself Computer Science
- Algorithm Design - Jon Kleinberg and Eva Tardos, Tsinghua University Press (2005).pdf - Google Drive
- Northeastern CS 4973: Introduction to Software Development Tooling
CS Fundamentals & Self-Study
- Data Structures and Algorithms with Visualizations – Full Course (Java) - YouTube
- Data Structures Easy to Advanced Course - Full Tutorial from a Google Engineer - YouTube
- Python Pandas For Your Grandpa - YouTube
- GitHub - brandon-rhodes/pycon-pandas-tutorial: PyCon 2015 Pandas tutorial materials · GitHub
- Skiena's Algorithms
- Fundamentals of Physics II | Open Yale Courses
- Fundamentals of Physics I | Open Yale Courses
- Computer Engineering Curriculum Map Fall 2022 | The Grainger College of Engineering | Illinois
- My 55-Step Self-Taught CS Curriculum (r/learnprogramming)
- GitHub - ossu/computer-science: 🎓 Path to a free self-taught education in Computer Science! · GitHub
- MIT OpenCourseWare | Free Online Course Materials
- Head First Design Patterns — O'Reilly
- Stanford CS231n: Deep Learning for Computer Vision
- Stanford CS231n (2016) — YouTube Lectures
- An introduction to Shader Art Coding - YouTube
- A survivor's guide to Artificial Intelligence courses at Stanford (Updated Feb 2020)
- Linear Algebra and Optimization for Machine Learning — Amazon
- Linear Algebra with Applications
- Node.js Express in Action
- Design Patterns
- The Missing Semester of Your CS Education
Systems, HPC & Languages
Recommender Systems & Search
- Neural Collaborative Filtering
- ContextGNN: Beyond Two-Tower Recommendation Systems
- Monolith: Real Time Recommendation System With Collisionless Embedding Table
- Scaling the Instagram Explore recommendations system - Engineering at Meta
- Two Tower Model Architecture: Current State and Promising Extensions - Sumit's Diary
- Recommender Systems Paper — ACM Digital Library
- Collaborative Filtering for Implicit Feedback Datasets
ML Systems & Inference
- A Survey of Distributed Graph Algorithms on Massive Graphs
- Challenges and Research Directions for Large Language Model Inference Hardware
- Understanding Task Transfer in Vision-Language Models - Microsoft Research
- EgoMemory: Memory-Augmented Personalized Retrieval for Long-Context Egocentric Video - Microsoft Research
- Serving Models, Fast and Slow: Optimizing Heterogeneous LLM Inferencing Workloads at Scale
Prompt Injection & Jailbreaking
- MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks
- PromptLocate: Localizing Prompt Injection Attacks
- WebCloak: Characterizing and Mitigating Threats from LLM-Driven Web Agents as Intelligent Scrapers
- AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs
- Learning to Inject: Automated Prompt Injection via Reinforcement Learning
- Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning
- PISmith: Reinforcement Learning-based Red Teaming for Prompt Injection Defenses
- Automatic and Universal Prompt Injection Attacks against Large Language Models
- GAAPO: Genetic Algorithmic Applied to Prompt Optimization
- Beyond the Benchmark: Innovative Defenses Against Prompt Injection Attacks
Agentic AI & Red-Teaming Security
- Towards Automating Data Access Permissions in AI Agents
- Investigating the Impact of Dark Patterns on LLM-Based Web Agents
- deepSURF: Detecting Memory Safety Vulnerabilities in Rust Through Fuzzing LLM-Augmented Harnesses
- DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling
- Parasites in the Toolchain: A Large-Scale Analysis of Attacks on the MCP Ecosystem
- R1-Fuzz: Specializing Language Models for Textual Fuzzing via Reinforcement Learning
- TrojVLM: Backdoor Attack Against Vision Language Models
- Trusted AI Agents in the Cloud
- Multi-Agent Penetration Testing AI for the Web
- Multi-Agent Risks from Advanced AI
- Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use - Microsoft Research
- Position: Web Agents Should Use Typed Actions Instead of Click-Based Browsing - Microsoft Research
- Source Models Leak What They Shouldn’t: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization - Microsoft Research
- Zoo (MADWeb Workshop Paper) — PDF
- USENIX Security '26 Preprint — Kim & Juhee: Agentic Systems (PDF)
- Why Genetic Algorithms Are the Only Answer to the AI Security Crisis — Medium