Stephanie revisits the concept of "spiking"—a phase of exploration to determine the feasibility of a technical implementation or to address unknowns in feature requests—sharing her recent experiences with a legacy Rails application. Joël brings a different perspective by discussing his involvement with a client project that heavily utilizes the dry-rb suite of gems, highlighting the learning curve associated with adapting to new patterns and libraries. Joël used to be much more idealistic and has moved to be more pragmatic. Stephanie has moved the other way. So together, Stephanie and Joël engage in a philosophical discussion on being an idealistic versus a pragmatic programmer. They explore the concept of programming as a blend of science and art, where technical decisions are not only about solving problems but also about expressing ideas and building shared understandings within a team. Spike tasks episode dry-rb Working with Maybe talk Problem solving with maybe Programming as Theory Building The Pragmatic Programmer Transcript:  JOËL: Hello and welcome to another episode of the Bike Shed, a weekly podcast from your friends at thoughtbot about developing great software. I'm Joël Quenneville. STEPHANIE: And I'm Stephanie Minn, and together, we're here to share a bit of what we've learned along the way. JOËL: So, Stephanie, what's new in your world? STEPHANIE: So, a few weeks ago, we did an episode on spiking in response to a listener question. And I wanted to kind of revisit that topic for a little bit because I've been doing a lot of spiking on my client project. And for those who are not familiar, the way that I understand or define spikes is kind of as an exploration phase to figure out if a technical implementation might work. Or if you have a feature request with some unknowns, you can spend some time-boxed spiking to figure out what those unknowns might be. And I'm working on your typical legacy Rails application [laughs]. And I think one thing that we talked about last time was this idea of, at what point does spiking end up being just working on the feature [laughs]? And I think that's especially true in an older codebase, where you kind of have to go down a few rabbit holes, maybe, just to even find out if something will trip you up down the line. And the way I approached that this time around was just, like, identifying the constraints and putting a little flag there for myself. Like, these were rabbit holes that I could go down, but, you know, towards the initial beginning phase of doing the spiking, I decided not to. I just kind of bookmarked it for later. And once I had identified the main constraints, that was when I was like, okay, like, what kind of solutions can I come up with for these constraints? And that actually then helped me kind of decide which ones we're pursuing a little bit more to get, like, the information I needed to ultimately make a decision about whether this was worth doing, right? It kind of kept me...I'm thinking about, you know, when you are bowling with those safety guards [laughs], it keeps your ball from just rolling into the gutter. I think it helped with not going too deep into places that I may or may not be super fruitful while also, I think, giving me enough information to have a more realistic understanding of, like, what this work would entail. JOËL: Would you say that this approach that you're taking is inspired or maybe informed by the conversation we had on the episode? STEPHANIE: I was especially interested in avoiding the kind of binary of like, no, we can't do this because the system just, you know, isn't able to support it, or it's just too...it would be too much work. That was something I was really, like you said, kind of inspired by after that conversation because I wanted to avoid that trap a little bit. And I think another really helpful framing was the idea of, like, okay, what would need to be done in order to