Interesting comp sci material to read

• Things that everyone needs to know - Cheat sheet for class "Hardware Accelerators for Machine Learning".
• Google AI's CO2 Footprint - Article by David Patterson, 2017 ACM A.M Turing Award winner.
• Scalable Dataframe Systems - Modin is a very easy to use library that addresses Pandas' scalability issues.
• KMN - The power of choice in data-aware cluster scheduling.
• BytePS - Effective utilization of both CPU and GPU resources during DL training.
• Omega - Flexible, scalable shared-state schedulers for large compute clusters.
• Ghostor - Secure data-sharing system from decentralized trust.
• OSTEP - Free Operating Systems book from professors at UW Madison.
• Light Node - How to interact with Ethereum network using just 400MB of storage.
• Non-Blocking Concurrent Queue Algorithms - Implementation of concurrent queues without lock.
• Sorting a million 32-bit integers in 2MB of RAM using Python - Efficient implementation of external sorting in Python.
• Visualizing the load balancing problem - Excellent load balancing playground at the end of the post.
• What Color is Your Function? - An interesting take on async functions.
• Understanding the Python GIL - A talk by David Beazley.
• Python Concurrency - When to use threads, when to use processes, and when to use async.
• A Guide to the Go Garbage Collector - Visualization of trade-off between GC CPU and memory.
• Gappy's Page - Rare case of a quant researcher sharing so much cool material, including all his bookmarks.
• Go: 90% Perfect, 100% of the time - Go is a language which is fast for both humans and computers.
• Build Systems - An explanation of not just how Bazel works, but also why it was designed this way.
• A Git Origin Story - A look at Linux kernel developers' various revision control solutions through the years.
• LDOS - Developing next generation OS with ML-based policies.

Newsletters I don't always skip

Side projects

📍Area Reveal - Compilation of various resources to help understand the area, including noise pollution, the ratio of owners to renters, residents' occupations, the percentage of electricity coming from renewable sources, ratings of nearby hospitals, maps of bike paths, and more.