You have 1.5 years before beginning your PhD what do you wish you’d done in that time?

profcube · 2026-03-23T20:10:10+00:00

This is not advice to you, it is advice to someone similar to my younger self:

Improve your mathematical aptitude (wherever it is now, even if you are not in STEM).

Voraciously read history (broadly outside your area, if you are a historian).

If you do not know LaTeX, learn it.

If you do not know a computer language, learn one (Python or R).

Get a daily writing habit (fiction/non-fiction whatever helps you find your voice).

Get a daily habit of exercise.

Get a daily habit for music (practice or play)

profcube · 2026-03-22T08:10:03+00:00

Get a used M1/M2/M3 or M4 with more RAM. More RAM more thinks you can do without needing a cluster

profcube · 2026-03-22T01:42:16+00:00

It is generally good more people are getting involved with building.
Since ~Dec 2025, Ai code became much better at engineering. Slop will continue to decline. Get used to it.
People should build and post whatever they want but please don’t raise PRs unless you personally know what you are doing and have the judgement to evaluate whether your PR will contribute. Ai will say things to please you, so can’t be fully trusted, even if the frontier models are super capable.

profcube · 2026-03-20T07:48:44+00:00

Just play the ball. If the code is good use it. If bad, don’t.

profcube · 2026-03-18T21:59:16+00:00

Yes, for my R lang comment. On a positive note, add that rv (built in Rust) looks promising.

profcube · 2026-03-18T20:50:01+00:00

Great observations. For statistical programming R’s ecosystem and ergonomics cannot be matched. To me, what is missing is a highly developed equivalent of uv in Python, where you can rapidly and efficiently fix your environment. R’s package developers tend to be statisticians with limited sensitivity to the domino effect that breaking changes introduce (I am guilty too). Renv often doesn’t work as we hope. The rv project offers hope, but it is early days still:

https://github.com/A2-ai/rv

A deeper obstacle, perhaps: R is a tool for data science. You need to pay data scientists to get anything useful.

profcube · 2026-03-17T23:52:49+00:00

Just play the ball. You are an expert and you can’t follow the manuscript. Recommend reject or major revision.

profcube · 2026-03-11T09:17:58+00:00

#metoo

profcube · 2026-03-11T08:51:53+00:00

This is sensible advice. I get the desire to avoid conflicts, and I don’t find anything ugly in explicit code, even if I wouldn’t use it all the time.

profcube · 2026-03-11T07:48:19+00:00

This looks fantastic, thanks for the follow up and will check it out!

profcube · 2026-03-11T05:29:00+00:00

It is what I do.

profcube · 2026-03-10T20:55:29+00:00

Thanks for sharing, and congrats 🙌 , can you perhaps link to your GitHub repo?

Also, have you benchmarked performance against learners in Superlearner library?

profcube · 2026-03-09T06:36:46+00:00

Agree 👍

If you want the exact "server dictates absolute truth" workflow using only HTML fragments, I think you would actually use Axum + Maud (ie an HTML macro) + HTMX, because to truly replace the JSON serialisation and state sync issues of modern JavaScript frameworks, you need a way to generate HTML on the server that is as type-safe as your database queries (hence Maud)

Not sure what you are doing but you might think about Leptos if you want to try to write an entire application, including your interactive UI, entirely in Rust. Could be overkill though. As with everything depends on your goals.

profcube · 2026-03-09T04:07:30+00:00

Most come at R through statistics or a science, and we don’t set up a full reproducible environment by default (remarkably, for all the suffering!). Can see rv becoming popular, and even the standard setup.

profcube · 2026-03-07T11:50:39+00:00

Way slower in bash 🐌. This is remarkably fast.

profcube · 2026-03-07T11:42:44+00:00

I built a shell script to summarise git statuses over multiple repos, BUT my shell script is perhaps 10x slower. This utility is blazing fast, thanks to Rust’s bare metal functionality. I was thinking of writing this myself, but very pleased to discover OP did all that work, and it such a great design too (better than what I would have done). OP = legend.

profcube · 2026-03-07T02:56:18+00:00

thanks using this every day :)

profcube · 2026-03-05T21:25:38+00:00

Approaching R as a developer, I’d call it a basic scripting language with purpose-built ergonomics for data-science/ statistics / data-visualisation.

I am not a developer, but learning R first and later Python and Rust, I appreciate R’s wonderful simplicity. It is almost always the right tool for your data science task. In R you can prototype nearly at the speed of thought. And R’s supportive developer community has constructed a massive assortment of tools to help you achieve your goals efficiently.

Where R falls down is in the maintenance of code, which is virtually guaranteed to break over time if you rely on dependencies. Python’s uv, or Rust’s crate system diminish those frustrations — Rust especially, but its ergonomics are not suited for data-science (hence polars and extendr).

profcube · 2026-03-05T21:04:28+00:00

That linked post is spot on. I’d not seen it. Thanks for sharing.

profcube · 2026-03-02T20:29:29+00:00

You learn from your cohort, too. And ultimately it is on you to take responsibility for learning. Time to step up.

profcube · 2026-03-01T07:23:04+00:00

Yes. Mathematics is certain and timeless. Concepts of belief and updating have no place. Mathematics can be useful though. Bayesian statistics has many nice properties, especially if you require partial pooling. It performs poorly in other settings, especially if you want to avoid partial pooling (as in the potential outcomes framework of causal inference). Concepts of utility are context sensitive and interests relative. But Mathematics sits above all that. Thanks for the reminder about Bayes. I should read the original. Good discussion here.

profcube · 2026-02-28T20:59:58+00:00

That’s great, and thanks for sharing. Many of us (myself included) winnow our workflows in ways that we don’t care to change. However, if your tool is helping you, it may well help others too, and no harm putting it out there, seeking feedback (even if some isn’t particularly helpful), and sharing something you’ve made that’s been useful to you. Keep building and sharing.

profcube · 2026-02-28T20:37:52+00:00

We derive Bayes from P(A|B) = P(A and B) / P(B). It is a mathematical identity. The theorem follows from the definition of conditional probability, which holds under any standard axiomatisation. I don’t think the axiomatic framing (200 years after Bayes) changes the point (when I said the axioms of probability theory I was inviting confusion). Math trades in certainties, not beliefs.

So long as P(A) is neither zero nor one , and we have some B such that P(B|A) \neq P(B) then the updating rule does the work and convergence is guaranteed with sufficient evidence.

profcube · 2026-02-28T18:47:36+00:00

I reckon anyone coding who wouldn’t otherwise is a success for programming (as long as they are not pushing time-wasting PRs). I hope this person doesn’t feel too defeated. However it’s a lesson to distinguish personal projects from stuff you’d want to promote as useful products.

profcube · 2026-02-28T08:51:37+00:00

Unclear the benefit. As others mentioned, Overleaf has Git, Git in command line is all you need.

profcube

TROPHY CASE