Series
Ordered post sequences, grouped by topic.
System Design
1
Backend system design scope
2 Designing RESTful APIs
3 Authentication and session management
4 Database design for backend systems
5 Caching in backend systems
6 Background jobs and task queues
7 File upload and storage
8 Search integration
9 Email and notification delivery
10 Webhooks: design and security
11 Payments integration
12 Multi-tenancy patterns
13 Backend for Frontend (BFF) pattern
14 GraphQL server design
15 gRPC and internal service APIs
1
Design a URL shortener
2 Design a key-value store
3 Design a rate limiter
4 Design a web crawler
5 Design a notification system
6 Design a news feed
7 Design a chat application
8 Design a video streaming platform
9 Design a music streaming service
10 Design a ride-sharing service
11 Design a food delivery platform
12 Design a hotel booking platform
13 Design a search engine
14 Design a distributed message queue
15 Design a code deployment system
16 Design a payments platform
17 Design an ad click aggregation system
18 Design a distributed cache
1
What frontend system design covers
2 Rendering strategies: CSR, SSR, SSG, ISR
3 Performance fundamentals: Core Web Vitals
4 Loading performance and resource optimization
5 State management at scale
6 Component architecture and design systems
7 Client-side caching and offline support
8 Real-time on the frontend
9 Frontend security
10 Scalability for frontend systems
11 Accessibility as a system design concern
12 Monitoring and observability for frontends
1
Monolith vs microservices
2 Microservice communication patterns
3 Service discovery and registration
4 Event-driven architecture
5 Distributed data patterns
6 Caching architecture patterns
7 Search architecture
8 Storage systems at scale
9 Notification systems
10 Real-time systems architecture
11 Batch and stream processing
12 Multi-region and global systems
1
Introduction to low level design
2 SOLID principles
3 Design patterns: Creational
4 Design patterns: Structural
5 Design patterns: Behavioral
6 Designing a parking lot
7 Designing a library management system
8 Designing an elevator system
9 Designing a hotel booking system
10 Designing a ride-sharing model
11 Designing a rate limiter
12 Designing a logging framework
13 Designing a notification system
14 API design and contract-first development
15 Data modeling for system design
1
What is system design? interview framework and real-world fundamentals
2 Estimations and back-of-envelope calculations
3 Scalability: vertical vs horizontal scaling
4 CAP theorem and distributed system tradeoffs
5 Consistency models in distributed system design
6 Load balancing in system design: algorithms and architecture
7 Caching in system design: strategies, patterns, and pitfalls
8 Content Delivery Networks
9 Databases: SQL vs NoSQL and when to use each
10 Database replication
11 Database sharding and partitioning
12 Consistent hashing
13 Message queues and event streaming
14 API design: REST, GraphQL, gRPC
15 Rate limiter system design: algorithms, trade-offs, and Redis
16 Proxies: forward and reverse
17 Networking concepts for system design
18 Reliability patterns: timeouts, retries, circuit breakers
19 Observability: logging, metrics, tracing
20 Security in system design
DevOps
1
Cloud fundamentals and the shared responsibility model
2 Compute: VMs, containers, serverless
3 Networking in the cloud
4 Cloud storage services
5 Managed databases in the cloud
6 Cloud IAM and access control
7 Serverless architecture patterns
8 Cloud cost management
9 Multi-cloud and cloud-agnostic design
10 Cloud Well-Architected Framework
1
What DevOps actually is
2 The software delivery lifecycle
3 Agile, Scrum, and Kanban for DevOps teams
4 Trunk-based development: branching strategy for fast CI/CD
5 Environments and promotion strategies
6 Configuration management
7 Secrets management
8 Deployment strategies
9 On-call culture and incident management
10 DevOps metrics and measuring maturity
1
Docker containers explained: how containers work under the hood
2 Docker fundamentals
3 Dockerfile best practices for production images
4 Docker networking explained: bridge, host, overlay, and DNS
5 Docker volumes and storage
6 Docker Compose guide: services, networks, volumes, and health checks
7 Docker and container image security: CVE scanning and hardening
8 Docker in CI/CD pipelines
1
Why Kubernetes exists
2 Kubernetes architecture
3 Core Kubernetes objects
4 Kubernetes networking
5 Storage in Kubernetes
6 Kubernetes configuration and secrets
7 Resource management and autoscaling
8 Kubernetes workload types
9 Kubernetes observability
10 Kubernetes security
11 Helm and package management
12 GitOps with ArgoCD
13 Kubernetes cluster operations
14 Service mesh concepts
1
What SRE is
2 Reliability fundamentals
3 SLIs, SLOs, and error budgets in practice
4 Toil reduction and automation
5 Capacity planning
6 Performance testing and load testing
7 Chaos engineering
8 Incident response in practice
9 Postmortems and learning from failure
10 Production readiness reviews
11 Reliability patterns for services
Computer Vision
1
Computer Vision Roadmap: From Basics to Real Projects
2 What is Computer Vision? From Pixels to Decisions
3 A Short History of Computer Vision: 1545 to Now
4 Math for CV Beginners: Vectors, Matrices, Convolutions
5 Image Fundamentals: Color, Histograms, Noise, Filtering
6 How Images and Video Are Stored: Colour, JPEG, Frames
7 OpenCV Setup + First 10 Tasks in Python
8 Vision Metrics: Accuracy, Precision, Recall, mAP, IoU
9 CV Project Workflow: Dataset, Baseline, Error Iteration
10 Intensity Transforms and Frequency-Domain Filtering
11 Edge Detection and Thresholding That Actually Work
12 Morphology, Contours, and Shape Analysis for Real Images
13 Feature Matching (SIFT/ORB) and Image Stitching
14 Camera Calibration and Perspective Correction
15 Epipolar Geometry, Stereo Vision, and Depth Estimation
16 Optical Flow and Motion Tracking in Video
17 HOG, HOF and MBH: Descriptors Before Deep Learning
18 Your First Image Classifier (PyTorch + Transfer Learning)
19 Data Pipelines and Augmentation for Vision Models
20 CNN Architectures Explained: From LeNet to ResNet
21 YOLO Detection Pipeline: Data to Inference
22 Semantic and Instance Segmentation: U-Net to Mask R-CNN
23 Vision Transformers (ViT) and When to Use Them
24 CV Explainability: Grad-CAM, Failures, Bias Checks
25 3D Vision Basics: SfM, Point Clouds, and Pose Estimation
26 Multimodal Vision: CLIP, Embeddings, and Retrieval Systems
27 Video Understanding: Flow Networks, Interpolation, Stabilisation
28 Real-Time CV Systems: Tracking, Latency, Streaming
29 Deploying CV Models: ONNX, TensorRT, Edge, and APIs
30 Responsible CV: Privacy, Fairness, Security, Governance
GPU Programming
1
How a CPU actually works: cores, clocks, and execution
2 CPU caches and memory hierarchy: why memory access speed matters
3 CPU pipelines and instruction-level parallelism
4 Memory models and why concurrent CPU code is hard
5 SIMD and vectorization: parallelism on a single CPU core
6 Processes, threads, and context switching
1
GPU vs TPU vs NPU vs FPGA vs ASIC: choosing an AI accelerator
2 GPUs: from pixels to parallel supercomputers
3 Your first CUDA program: kernels, threads, and grids
4 Python GPU programming: CuPy vs Numba vs PyCUDA
5 Thread hierarchy in CUDA: threads, blocks, warps, and grids
6 Inside a modern NVIDIA GPU: SMs, schedulers, CUDA cores, and tensor cores
7 Warp divergence in CUDA: detection and optimization
8 Floating-point performance on GPUs: precision, FLOPs, and numerical error
9 CUDA memory hierarchy: where your data lives matters
10 Memory coalescing: the most important optimization you will learn
11 Shared memory and tiling: the key to fast matrix operations
12 Debugging and profiling CUDA programs
13 Device functions, host functions, and CUDA function qualifiers
14 Building reusable CUDA libraries with CMake and Python bindings
15 CUDA synchronization and atomics: __syncthreads, atomicAdd, and barriers
16 Parallel prefix sum and reduction: the core parallel primitives
17 Concurrent data structures on the GPU
18 CUDA streams and asynchronous execution
19 cudaEventSynchronize and CUDA events: accurate kernel timing
20 Dynamic parallelism: kernels launching kernels
21 Unified virtual memory: one pointer for CPU and GPU
22 Multi GPU CUDA: NCCL, NVLink, and peer access
23 Memory allocation patterns and multi-dimensional arrays in CUDA
24 Texture and constant memory: specialized caches
25 CUDA occupancy and register pressure: performance tuning guide
26 Case study: matrix multiplication from naive to cuBLAS speed
27 Case study: implementing a convolution layer in CUDA
28 Case study: reduction and histogram at scale
29 Heterogeneous computing: CPU and GPU working together
30 Advanced memory patterns: pinned memory, zero-copy, and more
31 Advanced stream patterns and concurrent kernel execution
32 Performance case studies and optimization patterns
33 CUDA Sobel edge detection: from naive kernel to profiled pipeline
34 Where to go from here: CUDA ecosystem and next steps
Deep Learning
1
Neural networks explained: neurons, activations, and layers
2 Backpropagation: forward pass, chain rule, and gradient flow
3 Training neural networks: initialization, learning rates, and schedules
4 CNNs explained: convolution, pooling, and feature maps
5 RNNs and LSTMs: sequence modeling and gating mechanisms
6 Attention and transformers: self-attention, multi-head, and positional encoding
7 Word embeddings: Word2Vec, GloVe, and FastText explained
8 Transfer learning and fine-tuning: from ImageNet to your task
9 DNN optimization: momentum, Adam, and learning rate schedules
10 Deep learning regularization: dropout, batch norm, and data augmentation
11 Encoder-decoder architecture: seq2seq and U-Net explained
12 Generative models overview: autoregressive, flow-based, and latent variable
13 RBMs explained: energy-based models and contrastive divergence
14 Deep Belief Networks: greedy layer-wise pretraining explained
15 VAE explained: ELBO, reparameterization trick, and KL divergence
16 GANs explained: minimax game, mode collapse, and Wasserstein distance
17 DCGAN, conditional GANs, CycleGAN, and StyleGAN explained
18 Representation learning: autoencoders, contrastive learning, and SimCLR
19 Domain adaptation: covariate shift, DANN, and few-shot learning
20 Distributed representations: latent spaces and embedding geometry
21 AutoML: grid search, Bayesian optimization, and Hyperband
22 Neural Architecture Search: reinforcement learning, evolution, and DARTS
23 Model compression: pruning, quantization, and knowledge distillation
24 Graph Neural Networks: GCN, GraphSAGE, and GAT explained
25 Debugging deep learning: loss curves, gradient health, and ablations
26 ResNet architecture explained: skip connections and residual learning
DSA
1
How computers store data
2 Big-O and algorithm analysis
3 Bits, bytes, and bitwise tricks
4 Arrays: fixed-size and indexing
5 Dynamic arrays: how lists grow
6 2D arrays and matrices
7 Strings and character encoding
8 String manipulation patterns
9 Two-pointer technique
10 Sliding window technique
11 Prefix sums and difference arrays
12 Fast and slow pointers
13 Singly linked lists
14 Doubly & circular linked lists
15 Stacks (LIFO)
Linux
1
What is Linux and how it differs from other OSes
2 Installing Linux and setting up your environment
3 The Linux filesystem explained
4 Users, groups, and permissions
5 Essential command line tools
6 Shell scripting fundamentals
7 Processes and job control
8 Standard I/O, pipes, and redirection
9 The Linux networking stack
10 Package management and software installation
11 Disk management and filesystems
12 Logs and system monitoring
13 SSH and remote access
14 Cron jobs and task scheduling
15 Linux security basics for sysadmins
Machine Learning
1
What is Machine Learning? Types, examples, and how it works
2 ML pipeline: data splitting, scaling, and feature preparation
3 Linear regression: normal equations and gradient descent
4 Bias-variance tradeoff: underfitting vs overfitting explained
5 Regularization: Ridge, Lasso, and ElasticNet
6 Logistic regression: sigmoid, cross-entropy, and softmax
7 Classification metrics: precision, recall, F1, and ROC-AUC
8 Naive Bayes classifier: Bayes' theorem for ML
9 K-Nearest Neighbors (KNN) algorithm explained
10 Decision trees: Gini impurity and information gain
11 Bagging and Random Forests: ensemble methods explained
12 Boosting: AdaBoost and Gradient Boosting
13 Support Vector Machines: margins, kernels, and soft margins
14 K-means clustering algorithm: formula, K-means++, elbow method, and Python
15 PCA explained: eigenvalues, variance, and dimensionality reduction
16 Gaussian mixture model (GMM): EM algorithm, clustering, and Python implementation
17 Model selection and cross-validation: K-fold, nested CV, and tuning
18 Feature engineering: polynomial features, encoding, and selection
Maths for ML
1
Why Maths Matters for ML: A Practical Overview
2 Scalars, Vectors, and Vector Spaces
3 Matrices and Matrix Operations
4 Matrix Inverses and Systems of Linear Equations
5 Eigenvalues and Eigenvectors
6 Matrix Decompositions: LU, QR, SVD
7 Norms, Distances, and Similarity
8 Calculus Review: Derivatives and the Chain Rule
9 Partial Derivatives and Gradients
10 The Jacobian and Hessian Matrices
11 Taylor series and local approximations
12 Probability fundamentals
13 Random variables and distributions
14 Bayes theorem and its role in ML
15 Information theory: entropy, KL divergence, cross-entropy
ML Optimization
1
What is optimization and why ML needs it
2 Convex sets and convex functions
3 Optimality conditions: first order
4 Optimality conditions: second order
5 Line search method in optimization: Armijo, Wolfe, and backtracking
6 Least squares: the closed-form solution
7 Steepest descent (gradient descent)
8 Newton's method for optimization
9 Quasi-Newton methods: BFGS and L-BFGS
10 Conjugate gradient methods
11 Constrained optimization and Lagrangian duality
12 KKT conditions
13 Penalty and barrier methods
14 Interior point methods
15 The simplex method
16 Frank-Wolfe method
17 Optimization in dynamic programming and optimal control
18 Stochastic gradient descent and variants
Cybersecurity
1
How attackers think: the attacker mindset
2 Networking fundamentals for security
3 Cryptography fundamentals
4 Public key infrastructure and certificates
5 Authentication and authorization
6 Web application security: OWASP Top 10
7 Network attacks and defenses
8 Linux privilege escalation
9 Windows security fundamentals
10 Malware types and analysis basics
11 Reconnaissance and OSINT
12 Exploitation basics and CVEs
13 Post-exploitation and persistence
14 Defensive security: hardening and monitoring
15 Incident response
16 CTF skills and practice labs