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 Dec 2025

November

  • Started reading the book Advances in Financial ML by Marcos López de Prado (still on chapter 1’s exercises about using the popular sampling techniques - tick bars, volume bars, dollar bars, CUSUM filters)
  • While helping my partner for her Stat ML class, I realised that Ridge and Lasso can be derived from Bayesian modelling with a Normal and Laplace prior, respectively and decided to derive it for fun (post here)
  • I saw that Stanford Uni updated the Computer Vision course taught by Fei-Fei Li so decided to re-watch that whole thing
  • Started doing some of the introductory cyber security courses on tryhackme
  • In december, I will try to do Advent of Cyber by tryhackme (in addition to the usual advent of code)

October

  • Organised a small Discord community (~30 people) to follow along the scikit-learn MOOC. It’s a great resource and I wanted to help others learn from it as well as it was amazingly helpful during my preparation for the scikit-learn certification exams
  • Read The Vital Question by Nick Lane - it was recommended by A. Karpathy many times so I decided to give it a go. As someone with super basic chemistry/biology knowledge (from high school), the book was hard to follow at times so from my first read I learned how unlikely it was that smart living creatures began to exist in some hot vent 3.5-4 billion years ago
  • Read The Social Contract by Jean-Jacques Rousseau (more books out of my comfort zone :D )

September

  • Attended PyData Paris and talked about skore on probabl’s stand - learning what users like/want more
  • Presented skore at a local ML Club meetup (which kind of prepared me for PyData Paris)
  • Completed my European Summer of Code journey with probabl :pray:

August

  • Now that I am in a country with easy access to English books, I bought some favourites to start building my library
    • Mathemtics for Machine Learning (physical version of http://mml-book.github.io/)
    • Linear algebra: essence & form (physical version of https://www2.math.upenn.edu/~ghrist/preprints/LAEF.pdf)
    • Make it stick: The science of successful learning
  • Of course I re-read them and I felt I knew more compared to ~1 year ago when I read them for the first time
  • Continued working on skore

July

  • started my OSS contributor journey with probabl.ai under the European Summer of Code stipend; I also added a new page to me blog fetching my contributions - ESoC page
  • new youtube video Using keras SKLearnClassifier in a skore report - youtube
  • decided to learn more about OS -> found OS in 1000 lines of code
  • dived into that XOR trick and how python’s sum() works

June

  • [teach] Finished my Stanford University Code in Place 2025 Section Leader responsibilities 🥳
  • [mini-project] Network programming in C during my flight. I was on a ~15hr transfer so I wanted to do something that doesn’t require wifi. I found “Beef’s introduction to Network programmin” and it blew my mind with how easy it describes networking concepts, with great examples and I was able to create my own scripts:

I ended up making 3 things (some of the code overlaps):

  1. Read entire data.txt into memory and send over TCP to a client
  2. Stream large data.txt in fix-sized chunks over TCP
  3. Receive TCP data and write to stdout

Github

  • [book] Read “Make It Stick: The Science of Successful Learning” - amazing
  • [mini-project] Created a simple ssh politics.news terminal app to help introduce people interested in politics to the terminal (inspired by ssh terminal.shop) (using Go, wish, bubblegum, other Charm libs); Github (need to add README and figure out if I can deploy it for free somewhere)
  • [book] Read “Responsible AI: implementing unbiased and ethical algorithms” - amazing; definitely something to re-read, refer to for responsible AI questions, and recommend to beginners

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

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

January