Okay, the Beat & Bench was a kind of a little short portfolio project that I started and then it kind of developed and grew and grew and grew. And then I decided to archive it and grab some lessons learned, so I thought I'd walk through those real quick here. So I've always used Postgres alongside QGIS and Power BI, but as agentic AI, agentic AI, as these capabilities grow and I'm learning fast, this was kind of my first time really handing over a lot to AI because I've never been a super expert Python coder.
And during the project closeout, I thought it was a great time to grab some lessons learned and we'll go through it here real quick. So I use Postgres and publicly available data, built out the website, used some mapping standards and jinx to bring some consistency to the visuals. The visuals are available here in the website, and also down here is a audio of bot presenting back to me.
What did I do the hard way? So I need to do a better job importing requirements on each bot and I use too many of them. I'm so familiar with the datasets. I've used them so many times over the years, so I think that I could definitely do better, 100% agree.
Speccing the data, I did spec some of the data, but I know I can always do a better job there. And as ideas and concepts go from ideas and start growing, you got to manage that framework as it changes and builds out. And it wasn't until I decided to go from a simple portfolio project for the website to maybe bolting on a subscriber functionality that I turned on GitHub. So I definitely should have done that sooner, but I've used the data so much that I'm familiar with it, but I can definitely do better.
During this process, I migrated away from Microsoft to more freeware, so this move from OneDrive to pCloud broke some paths, but that was pretty minor. What I did the hard way, the scope inside the data, authenticating it to make sure that it's repeatable. It's consistent across four or five, ten different sources.
It didn't become clear until after looking at all the different data sources that only certain years were really reliable. So deciding the scope of work before is important, but somewhat impossible because until you really see the data, until you really see it come together, can you only really determine how many data sets overlap, data sources overlap.
The download links, I did store those, but cataloging them came later. The data portals, GIS data portals are typically pretty consistent, so I know where to go grab it because I've used it so much. Daily reminders, this is kind of how I do daily workflow, so less procrastinating, Robert, and I could definitely do better on that.
Having bots review themselves was good. Was Postgres the right tool in some levels? Yes, for sure. And that's what I'm familiar with. There's a lot of other options out there, obviously. I didn't use DuckDB this time. I definitely should have used that, and only using Postgres and PostGIS for the finished product. But I partially agree with that because you can work tables and coordinate reference systems inside those tables, so it's kind of like how my mind works. But yes, I could definitely use DuckDB on the next round, the next project, to help sort those 1 .8 million rows where it should have lived because I was going back and forth. Should it really sit in a server in the cloud?
In some ways, I'm really going to seriously think about not only having Postgres local, but just automatically put it into the cloud. I've used Railway in the past. I haven't used Hostinger's VPS yet, but I think I could try that. It looks like it's pretty cheap, so for $10 a month or whatnot, I could try that. From day one, that GitHub repo, so experimenting with datasets and data sourcing and public repositories, what a weekend project turns into something more, I need to quickly set up the repo.
Yeah, the data source catalog is important. I had very few sources, but I could definitely create that catalog sooner. For the case cards, the FastAPI, maybe, Leaflet, and HTML and Leaflet together work really well. But what went right? Keeping everything read -only for sure agreed.
Map standards, that was definitely good. Setting limits on different sources, so that was good. Top lessons for the project, the one hour of planning first. I disagree with that. A lot of ideas start as a pilot in an idea, weekend hobby, or something like that. So yes and no on that.
Setting the rules and scopes before loading, for sure. It's always good. The date ranges. What's available and what's not is hard to know before you actually see it, before you see the sourcing and the data and stuff like that. But doing my best I can to set the rules and scopes in advance is great. I can always do better. Fewer bots, as that idea, pilot idea moves into a website stage and the public starts seeing it.
There's a lot of background work that happens and my mind works with this bot does this and this other bot does that, but I can definitely do better. Scale back the counts. You have the GitHub. Even when I'm doing idea work, just set up the repo and use DuckDB.
For the next checklist, for the next project, when an idea goes from a portfolio example for the website to a more structured outcome, it's kind of hard. But audience is obviously very important. Inventory, how I inventory it, how I roster the bots, keep minimum three, that's probably good advice.
I was automatically doing those backups. It was really good, but I thought I'd just do a recording real quick for lessons learned for the website. Thank you for your time.