lbrl.engineering

What We're Reading

Notes on things worth reading — links and commentary from a performance data engineering lab.

Week of September 8, 2026

Version control's second coming

psantosl.github.io

Pablo talks about the version control history from the perspective of someone that was part of it. I still think version control should not exist and should be something that happens under the hood (especially the local copy) and it might be real with AI. TBH, not sure if version control makes sense as we know it if AI is the one that codes and manages complexity (which software is all about)

Week of September 1, 2026

Diiverge — infinite point-and-click

x.com/charliie · diiverge.co

Aaaaaaand we're live! https://diiverge.co A persistent, infinite, point-and-click adventure. Every picture is a fork. Click something in it, decide what happens, and the world grows in that direction.

I think these guys got it. The future is not to use LLMs to generate things in the old way, that's faster horses, it's to enable exploring new ways to do software. In this case it's a point-and-click game but it can be extrapolated to any software discipline.

ClickHouse 26.8

presentations.clickhouse.com

ClickHouse released 26.8 and I think it's a good release. It fixes major flaws in ClickHouse that have been there forever.

  • Atomic populate is killer, without that you are going to miss or duplicate records if you want to generate a new mat view
  • Adaptive codecs: compressing each block with the compression algorithm that suits it best means you don't need to manually pick one. It uses the one that compresses into less bytes but sometimes you want codecs that decompress/compress faster so be careful. An interesting thing, some codecs don't require to compress the data to know the final size.
  • Parquet improvements (data skipping filters mostly). This makes querying parquet files closer to the native ClickHouse format in speed.
  • Column statistics: before that you had to know the shape of the data beforehand to design your query to be optimal. Now CH does that for you. It'll fail for sure as stats are, well, stats, but most people don't understand the basics of working data so it's hard for them to pick the right query.
  • And then a lot of performance improvements for common use cases people are not aware of. When working with data doing massive uniqs, group bys are part of the day to day