The Curious Engineer

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Tech Blog on ✰Vicki Boykis✰

vickiboykis.com

★Vicki Boykis★

AI / MLSoftware / engineering practiceEssays / personal

’15’20’25
Posts
150 from 15 Dec 2014 to 29 Oct 2026
Posting
monthly (15 in the last year)
Kind
personal blog
Feed
https://vickiboykis.com/index.xml

2026 13

  1. Slides on retrieval and ranking with ONNX inference
  2. How to Learn ML in 2026 interview
  3. Bad code is kudzu
  4. Write for people
  5. Running local models is good now
  6. We should be more tired than the model
  7. Tagging my blog posts with BERTopic and LLMs
  8. Build yourself flowers
  9. Mechanical sympathy
  10. On Programming Joy and Octocat
  11. NASA Elements of Engineering Excellence
  12. Antidote
  13. Querying 3 billion vectors

2025 12

  1. Favorite Books of 2025
  2. 2025 in review
  3. I want to see the claw
  4. Walking around the app
  5. How big are our embeddings now and why?
  6. Enabling Hugo static site search with Lunr.js
  7. My favorite use-case for AI is writing logs
  8. 20 years of YC
  9. You can just hack on ATProto
  10. How FastAPI path operations work
  11. Everything I did in 2024
  12. Favorite Books of 2024

2024 11

  1. Write code with your Alphabet Radio on
  2. Why are we using LLMs as calculators?
  3. Dead Internet Souls
  4. Don't worry about LLMs
  5. We've been put in the vibe space
  6. How I search in 2024
  7. Redis is forked
  8. Both pyramids are white
  9. GGUF, the long way around
  10. What's new with ML in production
  11. Retro on Viberary

2023 10

  1. Favorite Books of 2023
  2. Why if TYPE_CHECKING?
  3. Build and keep your context window
  4. What we don't talk about when we talk about building AI apps
  5. Naming things
  6. What should you use ChatGPT for?
  7. Welcome to the jungle, we got fun and frames
  8. What I did in 2022
  9. Leggendo Wohpe
  10. Argentina Trip

2022 13

  1. Everything I learned about accidentally running a successful tech conference
  2. The cloudy layers of modern-day programming
  3. Some notes on the Stable Diffusion safety filter
  4. How I learn machine learning
  5. On the team as a system
  6. How to prepare an AWS test image for PyTorch
  7. Looking back at two years at Automattic and Tumblr
  8. On owning a software problem
  9. Sorrentino
  10. Git, SQL, CLI
  11. Migrating to Hugo
  12. My Favorite Books of 2021
  13. 2021 Work Recap, or the conjoined triangles of success

2021 11

  1. Veggie Borscht Recipe
  2. The programmer's brain in the lands of exploration and production
  3. Recsys 2021 Recap
  4. Doing small, fun projects
  5. Reaching MLE (machine learning enlightenment)
  6. The local minima of suckiness
  7. Writing for distributed teams
  8. The ritual of the deploy
  9. The humble hash aggregate
  10. My Favorite Books of 2020
  11. The ghosts in the data

2020 5

  1. What we've lost
  2. Getting machine learning to production
  3. Securely storing configuration credentials in a Jupyter Notebook
  4. Lauching a Jupyter notebook from within a virtual environment
  5. My Favorite Books of 2019

2019 10

  1. How to make sure your question gets answered online
  2. A deep dive on Python type hints
  3. Keeping the world away
  4. IT runs on Java 8
  5. Breaking down Apple's iOS Screen Time report
  6. The luxury of opting out of digital noise
  7. The attic and the basement of apps
  8. Meghan Markle's heels
  9. Data science is different now
  10. Commit to your lock-in

2018 15

  1. My Favorite Books of 2018
  2. How the headphone jack-less iPhone came to be
  3. The Case of the Broken Lambda
  4. On Excel and developer productivity
  5. America Online and off
  6. Good small datasets
  7. One month with the 2017 MacBook Pro
  8. Dear Facebook user 752461218193242
  9. It's still hard for beginners to get started with Python
  10. On competence
  11. Building a Twitter art bot with Python, AWS, and socialist realism art
  12. It didn't taste like Pamplemousse
  13. Strava and the boat in the data lake
  14. Working with AWS
  15. My Favorite Books of 2017

2017 14

  1. Logs populi or, Thanks, Netflix!
  2. Data ex machina
  3. Recent podcast and radio appearences
  4. Alice in Python projectland
  5. The demise of SoundCloud
  6. Should Spark have an API for R?
  7. We know the way
  8. So, you want to data science
  9. Talk at Skeptech - Manuscripts don't burn
  10. Good things don't scale
  11. Setting up SQL for beginners is hard
  12. Should you replace Hadoop with your laptop?
  13. Being a woman in programming in the Soviet Union
  14. What should you think about when using Facebook?

2016 16

  1. My favorite books of 2016
  2. Fix the internet by writing good stuff and being nice to people
  3. Strata 2016 and the rise of New Data
  4. How to parse emails with Python
  5. Walkthrough of Jupyter Lab Alpha
  6. My recent experiences using Azure ML
  7. Why doesn't MySQL have window functions?
  8. Data strategy in 2016
  9. How to keep up with the data industry today?
  10. Russia's window to the West
  11. Me and Markov
  12. Building presentations the hard way with reveal.js and Jupyter
  13. This, too, shall pass
  14. Learning tech by analogy
  15. Content is dead. Long live filler!
  16. Starting to dig inside Spark RDDs

2015 19

  1. JJ Abrams opens the box of my childhood
  2. Diaper by diaper and bottle by bottle
  3. Let's work on the hard stuff
  4. How non-technical teams work together
  5. Generating fake Hacker News headlines with Python
  6. How to think in two languages
  7. How large should your sample size be?
  8. Who is doing this to my internet?
  9. Cumulative totals three different ways in data science
  10. How many people are actually using your stuff?
  11. Reddit was amazing
  12. Really easy data concepts that get abused all the time
  13. What do we do with all these photos?
  14. Creating readable SQL.
  15. How to get into big data
  16. I'm not so sure about Tufte
  17. Man, do static sites suck.
  18. It is hard to talk to my baby in Russian
  19. No, I will not enjoy every moment

2014 1

  1. Pushing to production