Monthly 2025 learning blog

I will add bullet points in descending date order of some things I am doing (besides applying for jobs) in 2025

Updated: 1 June 2025

May

• [book] Finished Writing an Interpreter in Go (along with some YT videos) - have a bit better understanding of how interpreters work
• [tool] Explored ICO’s AI and Data protection risk toolkint + Microsoft’s responsible AI toolbox
• [teach] Completed my last official session for Stanford’s Code in Place 2025 - will have a voluntary extra session to answer any student questions
• [teach] Did 9 TeachNow sessions in the Code in Place platform (1-to-1 help sessions around 30mins each)
• [zig] Read the Zig language docs + did Ziglings on youtube
• [zig] Writing Karpathy’s micrograd in Zig for AI + Zig practice (wip)
• [course] Concluded Eroc Riddoch’s Cloud Eng for Python devs course

April

• [course] Started teaching Python in Stanford’s Code in Place 2025
• [course] Started Stanford’s AA228V Validation of Safety Critical Systems I System Modeling link
• [course] Learned about memory management in C and built a basic garbage collector on boot.dev
• [course] Finished teaching the MLOps 101 course at my university club
• [oss] Made my first oss contributions to skore - the scikit-learn sidekick
• [mini-project] Develop an ML model API following the Open Inference Protocol
• [book] The Last Algorithms Course You’ll Need
• [book] Write an interpreter in Go by Throsten Ball + this youtube series about a language parser in Go
• [book] The Alignment Problem by Brian Christian
• [book] Let’s Go by Alex Edwards
• [book] Build Your Own Database From Scratch in Go
• [cartoon-book] How HTTPS Works
• [btw] Started using neovim when writing Go and I ❤️ it

March

• [project] built an API with Go, which is tested, Dockerized, published on Docker Hub, deployed in a local Kubernetes cluster, and monitored with dashboards for Kubernetes and the API’s usage - github
• [project] setup and learned about Platform Engineering and automation with Backstage - video + linkedin post
• [book] started reading Kafka: The Definitive Guide, 2nd Edition (will finish it in April)
• [book] learned about language from linguists’ point of view by reading Course in general linguistics by Ferdinand de Saussure (1916) and Syntactic Structures by Noam Chomsky (1957) - linkedin
• [book] read Generative AI on Kubernetes by Roland Huss, Daniele Zonca (+ waiting for new chapters)
• [book] (Feb - Mar) read Alex Xu’s System Design Interview (Vol. 1)
• [book] (Feb - Mar) read ByteByteGo’s ML System Design book
• [book-ish] read through FastAPI’s full documentation (amazing)
• [book-ish] read through Kubernetes’ documentation related to CKAD (very nice examples)
• [course] covered the study materials for Certified Kubernetes Applications Developer (CKAD)
• [course] (Jan-Mar) watched some of Stanford’s MLSys seminar series
• [course] covered Stanford’s CS149 - Parallel Computing
• [mini-course] built a mini-http server from scratch & learned about nginx
• [course] covered Eric Riddoch’s Taking Python to Production: A Professional Onboarding Guide
• [bootcamp] (Jan - Mar) completed Maria Vechtomova and Başak Eskili’s End-to-end MLOps with Databricks boot camp (got as a Christmas present 😆)
• [bootcamp] (Jan - Mar) Completed Zach Wilson’s Data Engineering boot camp (won my spot by being an active member and helper in Zach’s free DE boot camp) - Combined Certificate of Superbness

February

• [book] read ML for Tabular data by Mark Ryan and Luca Massaron
• [course] covered MIT’s Intro to Algorithms Spring 2020
• [project] Finished an example project for a MLOps 101 mini-course I will teach to my university club
• [project] Finished a full-stack voice-to-voice app that lets users interact with their finances through an AI agent
• [project] Finished my capstone for Zach Wilson’s bootcamp - Using voice data to extract insight from team communication

January

• [book] read Clean Coder, The: A Code of Conduct for Professional Programmers by Robert C. Martin
• [book] read Refactoring Guru’s design patterns
• [book] read Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
• [book] reading Chip Huyen’s ML Interviews book
• [sql-leetcode]finished all SQL questions in Data Lemur (also ML and Stats)
• [course] re-watched CS50’s lectures on Data Structures, Algorithms, C
• [course] covered a practical Kubernetes course (really good!)
• [blog] found podcasts episodes with MarvelousMLOps’ Maria and Başak and read through a lot of MarvelousMLops’ posts
• [mini-project] did a conceptual, logical (+physical in Neo4j) graph data model for EU AI Act data
• [book] read Machine Learning Engineering with Python - Second Edition
• [book] read through Software Engineering for Data Scientists