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code23

c sharp9

Atomicity Authorization C Sharp dotnet CLI Generics Nuget Options Reflection Things to learn

design-patterns7

object oriented programming (oop)3

examples2

Design a Pub Sub Design Movie Ticketing or BookMyShow

Object Oriented Programming (OOP)

Clean Code Code Design Pattern Design Patterns SOLID Principles

Java4

Java Beans Java Spring JVM Spring WebFlux (Reactive)

JavaScript2

Async Await web workers

Python1

Python Interpreter

distributed systems35

cache2

Cache Request Coalescing

cloud4

aws4

security2

Network ACL Security Group

databases5

Atomic Consensus Consistency Distributed Transactions DynamoDB

events6

Apache Kafka Broadcast Event Streaming Message Queue PubSub Tools

high level design1

High Level Design (HLD)

networking1

non functional attributes2

consistent durable

patterns9

Consistent Hashing Coordination Distributed Lock durable execution durable message queue Event-Driven Architecture Fanout outbox SAGA

Containers VMs Hypervisor DDIA Large Payloads Microservices Process VM

machine learning15

evaluation2

precision recall

large language models1

neural networks4

backpropagation gradient gradient descent neural network

byte pair encoder (BPE)decoder architecture encoder encoder architecture encoder-decoder architecture Fundamentals of Embeddings, Encoder-Decoders head test train split

os7

concurrency docker multithreading parallelism Process Process vs Thread Thread

tooling1

Local LLM Setup on M3 Max (llama.cpp + Hugging Face)

Notes/machine learningmachine learning/neural networksneural networks

backpropagation

Connections

3 notes · 3 links

Linked from1

gradient

during neural network training, after a forward pass and loss calculated, go back through model weights to calculate gradient

2026 Deval Parikh

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