Andrew Fairless, Ph.D.

2025-08-07

What I Read: database, scale
2025-04-10

What I Read: ScyllaDB
2025-03-31

What I Read: data engineering
2025-03-19

What I Read: polars, pandas
2025-03-04

What I Read: gpu computing
2025-02-06

What I Read: Multi Objective Optimisation
2024-10-29

What I Read: Generative AI Platform
2024-07-23

What I Read: PostgreSQL
2024-06-17

What I Read: Linear Algebra, Random
2024-06-05

What I Read: speed up code, Numba
2024-06-04

What I Read: binary vector search
2024-05-27

What I Read: Modals web infrastructure
2024-05-14

What I Read: 1-bit LLMs, 1.58 Bits
2024-04-18

What I Read: Scaling ChatGPT, Engineering Challenges
2024-04-09

What I Read: Deploy Model
2024-03-21

What I Read: misleading GPU, CPU benchmarks
2024-01-16

What I Read: vectorize wide PyTorch expressions
2024-01-10

What I Read: Compiling NumPy
2023-10-25

What I Read: Nvidia AI Supremacy Temporary
2023-09-20

Add Columns to Polars DataFrames Quickly

There are straightforward, slow ways to do things, and then there are faster ways. Know how to choose.
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2023-08-24

What I Read: speed up Python with Rust
2023-08-01

What I Read: speed up Numba and NumPy
2023-07-29

How fast is Polars compared to Pandas?

Benchmarks are nice, but how fast are our favorite data tools on realistic data workflows?
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2023-05-31

What I Read: smaller LLMs, more tokens
2023-04-26

What I Read: Bloom filter
2023-03-14

What I Read: Feature Platforms
2022-12-05

What I Read: Key-Value Databases
2022-11-22

What I Read: SQLite
2022-04-18

What I Read: One Voice Detector to Rule Them All
2022-03-15

What I Read: marketplace ranking infrastructure
2021-10-12

What I Learn: Scaling TensorFlow
2021-09-01

What I Read: Against SQL
2021-04-29

What I Read: Understanding Key-Value Databases
2021-03-20

What I Watch: Why Distributed Systems Are Hard
2021-02-25

What I Read: Feature Store vs Data Warehouse
2021-02-18

What I Read: Building a Gigascale ML Feature Store
2020-12-21

What I Read: Developers Databases