Engineer Readings

[platform team] https://martinfowler.com/articles/platform-teams-stuff-done.html

[build your own] https://build-your-own.org/

[langchain][course] https://learn.activeloop.ai/courses/langchain

[microservice][monolith] These days there are blog posts appear from the major players in tech how they migrate back to monolith. Here is the short video about the current state as well as it covers a way to go with deploying distributed apps. https://www.youtube.com/watch?v=tkLSqHiTXHk

[reverse engineering] https://malwareunicorn.org/workshops/re101.html#0

[crdt][dynamic documents] The previous article is based on 2019 knowledge. Since that time some things have changed. https://www.inkandswitch.com/potluck/ Here is an example you can play with: https://www.inkandswitch.com/potluck/demo/?openDocument=aeropress

[crdt][local-first] Research around Collaborative applications and how they behave offline and with merge conflicts. https://www.inkandswitch.com/local-first/

[scaling][cadence] https://www.uber.com/en-NL/blog/announcing-cadence/

[lectures][data science] CS109A Data Science course materials @Harvard are free and open to everyone! 1. Lecture notes 2. R code, Python notebooks 3. Lab material 4. Advanced sections https://harvard-iacs.github.io/2019-CS109A/pages/materials.html

[leadership] I found that quite crucial to leave here along with the technical articles. https://youtu.be/ljqra3BcqWM

[architecture][uber clone] Juraj Majerik dedicated about 7 months (a total of ~300 hours) to create a simulated version of a ride-sharing app (akin to Uber) as a side project. He described each step in his blog: https://rides.jurajmajerik.com/system-design

[hdfs][caching] https://www.uber.com/en-NL/blog/optimizing-hdfs-with-datanode-local-cache/

[llm] TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks “While LLMs have shown great success in understanding and generating text in traditional conversational settings, their potential for performing ill-defined complex tasks is largely under-studied. Indeed, we are yet to conduct comprehensive benchmarking studies with multiple LLMs that are exclusively focused on a complex task. However, conducting such benchmarking studies is challenging because of the large variations in LLMs' performance when different prompt types/styles are used and different degrees of detail are provided in the prompts. To address this issue, the paper proposes a general taxonomy that can be used to design prompts with specific properties in order to perform a wide range of complex tasks.” https://arxiv.org/abs/2305.11430

[googleio][recap] https://developers.googleblog.com/2023/05/io23-developer-keynote-recap.html

[llm][document index demo] https://gpt-index.readthedocs.io/en/latest/examples/index_structs/doc_summary/DocSummary.html

[distributed systems][websocket][messaging] https://slack.engineering/real-time-messaging/

[distributed systems][migration] https://netflixtechblog.com/migrating-critical-traffic-at-scale-with-no-downtime-part-1-ba1c7a1c7835

[llm][benchmarking] https://lmsys.org/blog/2023-05-03-arena/

[platform engineering] https://medium.com/hashicorp-engineering/platform-engineering-on-the-hashicorp-ecosystem-part-1-84fb314e833e

[twitter][algorithm] https://tweethunter.io/blog/twitter-algorithm-full-analysis