Show HN: TamedTable, AI ETL in Natural Language(tamedtable.com) Hi HN, TamedTable is an LLM harness for data ETL. And yes, it was developed using AI, meaning you can take the entire specification and recreate it to your desires: |
Show HN: TamedTable, AI ETL in Natural Language(tamedtable.com) Hi HN, TamedTable is an LLM harness for data ETL. And yes, it was developed using AI, meaning you can take the entire specification and recreate it to your desires: |
[edit] What might be nice would be if it wrote an execution plan about what exact queries it was going to run. That might be helpful because it could spot outliers. But when you're talking about updating millions of rows of data for something, you'd have to have a death wish. Even if you're just starting off a DB with some bunch of data you're getting from somewhere else, you'd want normalization you could trust.
https://github.com/ZSvedic/TamedTable/blob/main/spec/test-ca...
And if an action can be done deterministically, then JS code will be generated:
https://github.com/ZSvedic/TamedTable/blob/main/spec/test-ca...
If data cleanup needs to be deterministic, why is so much of it done by humans? Two humans also won't normalize the same table in the same way. The main thesis is that AI data cleanup is cheaper and less mistake-prone than the same work done by a bored intern.
If all plan queries are deterministic, the "Save recipe as Python" action saves the entire process as a deterministic script:
https://www.tamedtable.com/#save-py
Honestly, you are probably not the target user, as you seem familiar with data cleanup scripts and AI agents. I think the target users would be:
A) Non-technical people in your org who clean data by hand because they don't know to automate it.
B) Technical people who want to test multiple queries and preview transformations before generating an AI-driven transformation flow (and are OK with it).