r/FromPipettes_to_Code

Academia vs. Industry: Stay for the "prestige" or leave for the actual money?

Academia has officially become an infinite while loop, and it's crashing our brains.

Looking at the stats lately, it’s wild how much happier PhD holders are in industry compared to academia. Has the ivory tower really gotten that toxic? Because it honestly feels like it.We invariably get stuck in this exhausting cycle, like a buggy Python while loop. The problem is, academia forgot to increment the wellbeing variable, so while stressful == True: just runs forever. There’s no break statement in sight, it's a total infinite loop, and it is actively crashing the brains of PhDs and scientists everywhere. Maybe I’m wrong, maybe I’m right—but it really feels like the system is just completely frying us right now.

For those who already hit Ctrl + C and executed a keyboard interrupt to escape to industry: is the grass actually greener, or does it just have a different set of bugs? And for everyone still stuck inside the loop—what is keeping your script running?

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u/Middle-Box3509 — 4 days ago

▲ 10 r/FromPipettes_to_Code+1 crossposts

2 years into my PhD and still figuring out GitHub etiquette. What scripts do you actually upload?

Hey everyone,

I’m hitting the 2-year mark in my PhD, focusing on bioinformatics, structural workflows, and pipeline optimization. While I'm getting the hang of coding and version control, I’m still a bit new to GitHub and trying to figure out the actual day-to-day work.

I’m hoping to get some perspective on two things:

What counts as "repo-worthy" vs. clutter? I’ve optimized a few automated pipelines for batch running structural prediction tools. Still, I also have smaller, separate scripts for downstream statistics (like Kruskal-Wallis ANOVA test and other non-parametric tests). Do you upload everything associated with your project, or do you keep your repositories strictly focused on the core tool/pipeline? Where do you draw the line? (Also, how do you handle data? I assume keeping heavy structural files off GitHub is the standard).

Can someone give me a human-friendly explanation of Pull Requests vs. Merge Requests? I know they are essential for collaboration and branching, but the actual mechanics of when/why to use them still feel a bit abstract to me.

When should I commit changes to the main branch and when should I create a new branch? Suppose I have to make some changes in the readme.md file, so is it advisable to directly commit to the main branch or create a new branch for this (it's just an example)

# My questions might look dumb, but I am really struggling with these minor concepts

u/Middle-Box3509 — 13 days ago

▲ 3 r/FromPipettes_to_Code

Be honest: How much can we actually trust Claude/Claude pro for scripting when we aren't software engineers?

Hey everyone,

I’ve been noticing a ton of people out here lately relying heavily on Claude for computational stuff, and to be honest, it’s a total lifesaver. When you’re coming from a non-techie domain, having an AI to help automate painful data tasks, parse weirdly formatted text files, or string together pipeline scripts feels like a cheat code.

But I’ve been having a bit of a reality check lately. How much of it can we really trust?

When you don’t have a deep coding background, it’s so easy to look at a script that runs without throwing an error and assume it did exactly what you wanted. But hidden bugs, weird logic flaws, or classic AI hallucinations can completely warp data calculations or statistical analyses without you even realizing it until it's too late.

For those of you who started with zero programming experience but now use LLMs for your day-to-day data and dry-lab workflows:

How do you actually validate the code Claude writes for you?
What are your golden rules for double-checking its logic so you don't accidentally trash your dataset?
Do you ever feel like you're relying on it too much instead of learning the foundational stuff?

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u/Middle-Box3509 — 11 days ago