January 9, 2025

code2prompt (via)

This is a really cool tool. I use LLMs extensively in my side projects with great success (my workplace is still investigating whether there are ways we can use them that will align with their privacy needs) and often simulate what this tool is purpose-built to do. I think it's going to save me even more time.

When I first started playing with AI-assistant editors (like Cursor, Windsurf, Co-pilot, etc.) I assumed they would be way better than chatting back-and-forth with an LLM, but it turns out, counterintuitively (to me anyway), that I much prefer chatting over the in-editor suggestions. I find these mostly intrusive and wrong, and they just create more work for me. In a chat interface, though, I can guide the conversation in constructive directions.

It might have something to do with the types of questions I ask them, or maybe just my personality. In any case, I frequently copy-paste dozens of files into a project, describe in great details a project's structure and data model, and then ask questions about it to an LLM. It sounds like this tool can automate and improve that process, which is great news for people like me who frequently engage in this kind of workflow.

link/#llms#software#software-engineering#tools

January 6, 2025

dltHub (via)

I had a super interesting conversation with Ananth Packkildurai of Data Engineering Weekly today and a couple of interesting tools came up. One was this: dltHub. There are a lot of people trying to solve some of the main pain points in data engineering, and I'm convinced (like Ananth) that functional languages and approaches to software engineering have a lot to offer in solving them. This tool in particular appears to attempt to automate away some of the painful parts of the process, and I'm super curious to explore their approach. It seems like a really hard thing to do well. The problem of extracting data from traditional sources (DBs, files, APIs) and moving it elsewhere is well solved, but it's the increasingly complex transformations, large volumes, and disparate end use cases that are causing the strain with the current standard data stack.

link/#data-engineering#tools