📚 blogs and books:

• https://rust-unofficial.github.io/patterns/

• https://book.avr-rust.com/001-introduction.html

• https://nnethercote.github.io/perf-book/title-page.html

• https://rust-lang-nursery.github.io/rust-cookbook/intro.html

• https://smallcultfollowing.com/babysteps/

• https://lucumr.pocoo.org/

• https://www.lpalmieri.com/

• https://blog.yoshuawuyts.com/

• https://www.i-programmer.info/programming/theory.html

• https://www.i-programmer.info/babbages-bag/

• https://without.boats/blog/

📡 networking

🛠️ tools:

• tokio
• impl riker actor for each socket connection
• libp2p pub/sub stack based on tokio tcp and udp with borsh and serde
• zmq pub/sub socket for async I/O event streaming like audio and video
• rpc capnp
• hyper
• juniper gql

🎭 actor concepts:

• mailbox to receive async messages from other actors or other functions inside different part of the app
• pub/sub channels for broadcasting, executing and scheduling async tasks using tokio cron scheduler
• tokio worker green threadpool to run task in other threads using tokio::spawn(async move{})
• rpc capnp based communication with outside world actors to call each other methods directly
• tokio message queue channles like mpsc and oneshot for sharing Arc<Mutex>: Send + Sync + 'static between threads and different parts of the app
• tokio event loop using tokio::select!{} to select an async I/O event task

💡 concepts:

• distributed (replication and sharding) and decentralized concepts:
  • search, db and routing engines like elastic, cassandra and p2p kademlia:
  • create best objective function to find the most rewarded (less cost actions) path in the network graph env (route planning) greedily using:
  • hybrid tech algorithms like NN, GA 🧬 and neurofuzzy(ANFIS)
  • gradient optimization methods like stochastic gradient descent
  • none gradient optimization methods like GA and FA
  • graph theory and heuristic search algorithms like DAG, dijkstras, floyd, bellman, DFS, BFS and A*
  • reinforcement learning algorithms like qlearning using mdp and bellman equation with off and on policy methods based on markov decision process and markov chain
  • other algorithms using greedy, dynamic programming, backtracking, divide and conquer, recursive and brute forcing methods
  • hashmap based algos like hash tables (DHT) to find closest peers to a specific range of key inside a replication like cassandra db and p2p nodes
  • rpc capnp for actor method based communication on two different machines like calling between smart contract actors
  • tokio tcp and udp and jobq channels for sharing Arc<Mutex>: Send + Sync + 'static between actor threads in a same machine
  • actor tokio worker green threadpool and message queue for task and method broadcasting and scheduling to the pub/sub channels (tokio jobq, socket or rpc)
  • pub/sub and other zmq socket actors to broadcast from publishers to subscribers (m2m) using sockets
  • in libp2p peers can find each other using either mDNS (over LAN) or kademlia (over WAN)
  • in libp2p peers can communicate with each other based on pub/sub floodsub or gossipsub protocols on top of rpc capnp, tokio udp and tcp, websocket, webrtc, zmq like sockets (req/res, cli/srv or pub/sub) actors
• proxy, firewall, vpns, packet sniffer and load balancer like pingora, HAproxy, v2ray and wireshark for all layers concepts:
  • v2ray and tor protocols
  • decompress encoded packet
  • cpu task scheduling,
  • weighted round robin dns,
  • vector clock,
  • event loop
  • iptables
  • zmq pub/sub
  • simd divide and conquer based vectorization

🏗️ system

💡 concepts:

• compiler, vm using llvm and os
• streaming of async I/O events compression and ram (buffer) algos like deflate, lz4 and snappy
• data serialization codec and protocols like borsh, bson, serde, capnp and gql
• runtime
• video and audio codec, compressor and streamer like ffmpeg
• cryptography
• bridge between blockchains
• language binding like writing rust code on top of apis of the compiled (.so) code in c like rust bindings to libsodium

🤖 AI

💡 SSL (Self Supervised Learning) concepts:

• health improvement based on a pattern mining approach and semantic attributes like
  • predicting missing part of the unseen input based on the exact style of the input
  • reconstructing and labeling the unseen input based on the exact style of the input
• feature or representation learning and extracting from the data itself
• feature or representation selection using dimensionality reduction algorithms like GA, TSNE, PCA & VAE
• extracting semantic attributes from the input for 0/1/few/n-shot learning
• autoregressive based sequence modeling to predict the future based on past events and behaviors
• anomaly detection, 0/1/few/n-shot and meta learning
• solving catastrophic forgetting problem using MemTransformer instead of MANN with NTM architecture LSTM based
• FF algorithm as an alternative to gradient descent

🛠️ models and tools

• Transformers (https://arxiv.org/abs/2101.12037)
• SEER (https://arxiv.org/pdf/2103.01988)
• VAE & GAN
• BART (https://arxiv.org/pdf/1910.13461)
• VISSL (https://vissl.readthedocs.io/en/v0.1.5/)
• GNN (Graph Neural Network)
• MemTransformer (https://arxiv.org/pdf/2006.11527.pdf)
• Decision Transformer (https://arxiv.org/pdf/2106.01345v1.pdf)
• TransZero (https://arxiv.org/abs/2112.08643)
• DALLE-2 (https://arxiv.org/abs/2204.06125)
• CLIP (https://arxiv.org/abs/2103.00020)
• DayDreamer (https://arxiv.org/abs/2206.14176)
• Algorithm Distillation (https://arxiv.org/abs/2210.14215)
• You Only Live Once (https://arxiv.org/abs/2210.08863)
• Forward-Forward Algorithm (https://www.cs.toronto.edu/~hinton/FFA13.pdf)

💡 NLP concepts:

• 🛠️ preprocessing
  • tokenization
  • Lexicon Normalization(stemming & lemmatization)
  • lowercasing
  • stopwwords removal
  • padding
• 📄 vocabulary building, word embedding, vectorization, feature extraction and semantic analysis
  • BOW(ngram based with TF-IDF normalization)
  • Word2Vec(skip-gram & CBOW based)
  • GloVe
  • GPT
  • BERT
  • BART
• 📈 models and statistical results
  • ML models like SVM, Naive Bayesian, Random Forest and Logistic Regression
  • DL models like LSTM(MLP+CNN), Transformers and Attentions
  • classification confusion matrix