(Researched) Advice I Like
December 23, 2025
A list of advice I like on scholarship.
As with most things on this site, this is a work in progress.
One great thing about the internet is that advice is ample. While I think directly talking to someone you’ve reached out to is valuable, there is a lot to gain from seeking out expert opinion in the wild. This year, I spent a lot of time gaining exposure to research. Research encompasses a broad spectrum of activities, but in my infancy pursuing it, I would define it as the pursuit of understanding as a tool. And I think this pursuit is very cool.
Journalists seek to understand, and their goal is to communicate that understanding to the public. Researchers seek to understand, and their goal is to communicate that understanding to the public. The engineer seeks to understand, so that they can tinker, fix, and build. The (academic) researcher seeks to understand, so that they can build new tools of thought that others can extend upon.
That said, it’s been hard to decompose what it means to do research, how to do it well, and assume the traits of a good researcher. Furthermore, what does “good” even mean?
I think a good researcher is one who has their audience in mind, but I think tending to said audience can feel paralyzing. I think good research doesn’t have to stem from curiosity or “first-principles” but that being a good researcher means valuing both. I also think the best researchers have very top-down research agendas. Faculty positions will ask candidates to present research statements, which can be taken as an expression of your interests and a cover letter of past experiences but in reality read more as expressions of your research taste. Naively, I think the best of them, articulate a narrative about world order, a theory of change coupled with motivations for why that change is needed, and a set of mediums and modes for executing on that change.
Most concretely, some things I have learnt for myself are:
1) The best information is feedback. Even if you are doing theory, the practice of decomposing ideas into testable tasks is a) an exertion of skill that I’m trying to develop and b) a means of scoping out wishy-washy thoughts into ideas and then contributions.
2) Using AI is a personal choice, and it’s important you define your own criteria for what and when it’s useful (on a project-by-project basis). I am very liberal in how I use it, but a general policy is to offload tasks that are not integral to the skills I’m trying to learn. For one, I think human verification is what makes you able to qualify and claim whatever you’re putting out into the world and it’s easier to do this when you’re capable of performing the task yourself or you’ve done the task yourself and can thus replicate and explain it. AI is a collaborator and in the same way you should have a grasp of everything a human co-author contributes to your work, you should be able to do the same for an LLM.
3) I’m getting used to the fact that things are very fuzzy until a draft is being written. I’m still figuring out how to classify the projects I’ve been working on, but I think a good number have their contributions wrapped up in the presentation of data or concepts. And therefore, the methods developed at this stage are the most novel. This also means that it’s a bigger question of articulating the curiosity than “solving the problem” itself. In hindsight, this is very harmonious with my definition of what research is, but there are other kinds that inject novelty at different stages of the evolution of an exploration.
Anyways, this is just a list of writings I like. So here they are: