#tools/5 entries

I was recently fixing up the feeds on my website and wanted to make sure a) that they render probably and b) that the filtered ones contain the correct entries, so I built this little tool to preview atom or rss feeds.

I've been hearing more and more things about jujutsu and want to try it out. It is so hard to learn new things that replace something I've been using almost every day for over 10 years though. The muscle memory is so ingrained and my mental model of what version control even is is so rigid, it's hard to wrap my head around the idea that it could be anything other than git's. My main motivation is that I've "heard" it's better for a) working with stacked branches and b) working on multiple worktrees (called workspaces) at once. These are both things I do way more now because of all the agent-driven-development stuff I'm learning than I ever did before and I feel like I am spending a ridiculous amount of time fighting git. But learning new things is so hard. Wish me luck.

One of the main ways I work with AI since I had a baby 5 months ago is prompting genies from my phone while I'm naptrapped or breastfeeding (did you know newborns feed for ~5 hours per day?! I did not). It’s very sweet and cozy napping with a baby but eventually also boring.

Anyway it’s very cumbersome to review code when building things this way, but I decided that’s mostly fine since for a lot of little personal utilities I have it build I don't really care whether the code is particularly good or not (although I do find lately it writes perfectly passable Clojure, still my language of choice in this age of AI).

I always have it set up CI from the start so the project ships somewhere I can try it out from my phone and give feedback, but sometimes the tools are CLIs or other utilities I want for when I'm back at my desk. In those cases I have Claude write a book about how the project works for me, then I give feedback based on that about what needs to change (either in the project itself or just the prose). The result is that I understand the project and end with an edited book I can reference as documentation. This has been working great for me and has been resulting in many useful personal tools that I'm really enjoying building and using.

To make the process of giving feedback on books (or any other text) easier, I had the AI build itself this little annotation tool. It lets me paste a link in (to e.g. one of the deployed book chapters), then I can highlight particular passages and copy the feedback in a way that is easy for a genie to act on. You could use it for anything that requires taking notes on specific passages of a webpage though.

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.

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.