gwen-shapira

Congrats on the launch!

You may be under-selling LittleHorse a bit (here and even in the README). Few things I find cool about it:

- How easy it makes writing human-in-loop workflows (or these days, agent-in-loop). Everyone wants human-in-loop, but very few workflow frameworks make it simple.

- Using LittleHorse to introduce stateful guardrails to agentic flows. This helps with context compaction and other safety issues.

- The new (experimental?) generation of workflows from JSON. This simplifies things quite a bit.

When you run ALTER TABLE test DROP COLUMN c Postgres doesn't actually go and remove the column from every row in the table. This can lead to counter intuitive behaviors like running into the 1600 column limit with a table that appears to have only 2 columns.

I explored a bit what dropping columns actually does (mark the column as dropped in the catalog), what VACUUM FULL cleans up, and why we are still (probably) compliant with the GDPR.

This is a great question.

At this point, all transactions go through a single transaction coordinator that uses 2PC to coordinate all transactions. This makes many aspects of coordination fast, reliable and simple. It works well because OLTP systems typically have few DDLs an hour, if that (although we tested with hundreds of DDLs per second, just to be sure).

When we look at other types of transactions, OLTP systems need to handle many orders of magnitude more. Even small apps typically have hundreds of transactions an hour, while large systems require millions of TPS.

Getting from the current architecture to something that handles all transactional load of an OLTP system will be a journey. And arguably one better served with a different architecture, one not based on 2PC.

We are planning to open source it soon. Last few cleanups :)

We are still solving problems for all multi-tenant applications.

But AI companies, that tend to scale faster, also seem to benefit the most. So we wanted to make this clear.

I wrote a short guide to popular AI concepts, because I got a bit tired that reading a tweet requires a PhD. This is also the first part of a mini blog series about my adventures building AI Code Assistant with OpenAI, LangChain and pg_vector. Hopefully it will help you read every other AI thing that you run across...