I also suspect that there are many "slow-moving", Microsoft heavy enterprises but with in-house devs that can't get anything but Copilot approved, and Microsoft trusts this will remain so. It's not turning consumption…
I like this Tufte quote from https://www.edwardtufte.com/notebook/book-design-advice-and-...: It is also notable that the Feynman lectures (3 volumes) write about all of physics in 1800 pages, using only 2 levels of…
Writing documentation for LLMs is strangely pleasing because you have very linear returns for every bit of effort you spend on improving its quality and the feedback loop is very tight. When writing for humans,…
DuckLake is more comparable to Iceberg and Delta than to raw parquet files. Iceberg requires a catalog layer too, a file system based one at its simplest. For DuckLake any RDBMS will do, including fs-based ones like…
https://www.sirlin.net/articles/designing-defensively-guilty... This is probably it. As the article says, it’s also a cleverly layered mechanic if a player correctly predicts when their opponent will use it.
I also was nerdsniped into trying this and found that after extracting the features array into a newline delimited json file, DuckDB finishes the example query in 500 ms (M1 Mac), querying the 1.3 GB json file directly…
I also suspect that there are many "slow-moving", Microsoft heavy enterprises but with in-house devs that can't get anything but Copilot approved, and Microsoft trusts this will remain so. It's not turning consumption…
I like this Tufte quote from https://www.edwardtufte.com/notebook/book-design-advice-and-...: It is also notable that the Feynman lectures (3 volumes) write about all of physics in 1800 pages, using only 2 levels of…
Writing documentation for LLMs is strangely pleasing because you have very linear returns for every bit of effort you spend on improving its quality and the feedback loop is very tight. When writing for humans,…
DuckLake is more comparable to Iceberg and Delta than to raw parquet files. Iceberg requires a catalog layer too, a file system based one at its simplest. For DuckLake any RDBMS will do, including fs-based ones like…
https://www.sirlin.net/articles/designing-defensively-guilty... This is probably it. As the article says, it’s also a cleverly layered mechanic if a player correctly predicts when their opponent will use it.
I also was nerdsniped into trying this and found that after extracting the features array into a newline delimited json file, DuckDB finishes the example query in 500 ms (M1 Mac), querying the 1.3 GB json file directly…