https://preview.redd.it/y3wqyw43de2h1.png?width=2300&format=png&auto=webp&s=dd299b5f600939c828c0fc687400b0c1a9d8329f

https://preview.redd.it/fqulakn3de2h1.png?width=3184&format=png&auto=webp&s=04f5fe7aa19fe4d0aa7e63f80d36f1a907bd3378

I finally made a prompt that gets AI to generate clean, modern academic presentation slides instead of ugly corporate PPT templates.

If you're doing:

• research presentations
• thesis defenses
• lab meetings
• conference talks
• paper reports

this prompt works surprisingly well.

Prompt

You are a professional academic presentation designer.

Generate a modern academic-style PPT suitable for:

• research presentations
• conference talks
• thesis defenses
• lab meetings
• paper presentations

Overall Visual Style

• Style keywords: minimalist, modern academic, clean, scientific, technology-oriented, international conference style.
• The presentation should feel like: NeurIPS / CVPR / Nature / top university lab presentations.
• Use a white or extremely light blue-gray background.
• Main color palette: white + blue.
• Blue should be used for: titles, key metrics, diagrams, arrows, highlights, and important concepts.
• Small amounts of light gray and cyan can be used as secondary colors.
• Maintain strong whitespace and breathing room.
• Avoid:
  • heavy shadows
  • excessive gradients
  • glowing effects
  • cyberpunk aesthetics
  • overdecorated slides
  • dense layouts
  • giant text walls

Layout Rules

• One core idea per slide.
• Titles should be conclusion-oriented and concise.
• Keep large safe margins around all content.
• Never fill the entire slide with content.
• Prioritize:
  • whitespace
  • diagrams
  • architecture figures
  • charts
  • comparison tables
  • concise explanations
• Avoid paragraph-heavy slides.
• Use clean visual hierarchy: title → conclusion → figure/result → short explanation.
• Use very few cards/components per slide.
• No nested cards.
• Cards should have:
  • 6–8px rounded corners
  • thin borders
  • minimal/no shadows

Academic Visualization Style

• Use clean flat vector-style diagrams.
• Maintain a unified visual language:
  • thin lines
  • light blue grids
  • arrows
  • data flow lines
  • database cylinders
  • lightweight scientific graphics
• Diagrams should look modern and editable.
• Use horizontal process flows whenever possible.
• Keep charts clean and publication-like.

Tables & Charts

• Tables should use:
  • light gray separators
  • blue headers
  • generous row spacing
• Avoid dense tables.
• Highlight best results in blue.
• Use lightweight comparison layouts.

Formula & Algorithm Slides

• Equations should be centered with sufficient whitespace.
• Highlight important symbols in blue.
• Code blocks can use:
  • light gray background
  • or deep blue background
• Code blocks should resemble modern code editors with minimal UI.
• Avoid oversized code sections.

Typography

• Use modern sans-serif fonts:
  • PingFang SC
  • Microsoft YaHei
  • Source Han Sans
  • Inter
  • Helvetica
• Titles should have slightly heavier weight.
• Body text must remain readable on projectors.
• Use relaxed line spacing.

Animation

• Use subtle transitions only.
• Prefer fade-in or appear animations.
• Avoid flashy effects.

Output Requirements

• Export editable PPTX.
• Keep all text, diagrams, charts, and shapes editable whenever possible.
• Include speaker notes for every slide.
• Before exporting, verify:
  • no text overflow
  • no blocked content
  • readable tables
  • sufficient whitespace
  • visual consistency
  • modern academic aesthetics
  • not resembling a traditional dense lecture slide deck

Target Aesthetic

The final presentation should resemble:

• NeurIPS / CVPR presentations
• modern ML research talks
• top-tier university lab slides
• Apple Keynote-style scientific minimalism
• high-end academic visual storytelling

Lately I’ve been thinking a lot about where the “healthy middle ground” is with AI tools during research work.

On one side, some people avoid using AI completely because they feel it weakens critical thinking. On the other side, some workflows are becoming so AI-dependent that it almost feels impossible to work without constant assistance.

Personally, I’ve found AI most useful for things like organizing scattered thoughts, summarizing long material, or helping me get unstuck mentally when writing, not really for replacing the actual thinking process itself.

I’ve actually been building a small project called CentAI around this general idea of making AI feel more supportive and less overwhelming for students and research-focused users, which is probably why I’ve been thinking about this topic more deeply lately.

I’m curious how people here are approaching it at the PhD/research level because the conversation around AI in academia still feels very divided.

Do you feel AI has genuinely improved your productivity as a researcher, or do you think it’s slowly making deep focus harder over time?

I honestly don’t know if I’m overreacting or if this situation is genuinely unfair, so I wanted outside opinions from other PhD students/researchers because right now I feel mentally exhausted and honestly very lost.

I just finished my second year of my PhD, and I recently received an below expectation research grade from my PI (who is a relatively new PI, if that matters). The scary part is that in my program, getting two below expectations research grades can essentially get you removed from the PhD program. This is my first one, but now I’m heading into qualifying exams already stressed, anxious, and honestly terrified about my future.

What makes this difficult for me is that I genuinely don’t feel like I deserved that evaluation. I was in the lab almost every day, regularly attending meetings, discussing experiments with my PI, and presenting progress every two weeks in group meetings. On top of that, I had one of the hardest TA assignments this semester, and despite the workload, I actually performed very well as a TA according to both students and faculty.

Research-wise, yes, not everything worked. I somewhat agree that I probably failed to communicate progress in the exact way my PI expects. But at the same time, I consistently showed data including failed experiments, troubleshooting attempts, and new directions. I was still working every day and trying to move the project forward. It’s not like I disappeared for days or stopped caring.

What hurts is that when I asked my PI why I received the unsatisfactory grade, the explanation was basically that I “haven’t done enough work” and that he expected more productivity. But from my perspective, I genuinely did put in significant effort this semester. He basically wants me to micromanage now, taking stamps of what I do every hour every day. During that discussion, I even mentioned that I felt like I had contributed more work and data than some other PhD students in the lab, but the response I got was something along the lines of “X is doing good work, they’re just not focused.” That honestly confused and hurt me because, objectively, I feel like I was putting in at least as much effort if not more than some of my colleagues.

On top of that, the lab environment itself has become really toxic for me mentally. We have a collaborative project involving three PhD students, but instead of feeling like teamwork, it often feels like everyone is competing to make themselves look the best in front of the PI. Communication is poor, people move ahead without coordinating properly, and I constantly feel pressured to prove myself rather than feeling supported. I often end up chasing people for updates, data, or information related to the project.

The worst part is how much this has affected me mentally. I genuinely feel depressed right now. I’ve been crying constantly for the past few days and questioning whether I even belong in academia anymore. I know PhDs are difficult, but right now it feels like no matter how much effort I put in, it is never enough. I’ve even started thinking about quitting because I feel emotionally burned out and defeated.

What also scares me is that this happened so early in my career without much warning or discussion beforehand. It’s making me question how supported I’ll actually be moving forward not just for qualifying exams, but also later for publications, recommendations, and career development.

Has anyone else dealt with a PI or lab culture like this? Did things improve in later stages of PhD? How do you recover mentally from something like this and move forward without constantly feeling like you’re failing? I genuinely need advice because right now I feel completely lost.

TL;DR: Received a below expectation research grade during my second year PhD despite being consistently present, working hard, TA’ing heavily, and regularly presenting data. PI says I “didn’t do enough,” but I feel I did good in this semester. Now I’m burned out, depressed, and questioning whether I should continue in academia.

Research tool discussions are basically impossible on Reddit right now. Either the sub is flooded with AI-generated garbage, or the mods are so aggressive about filtering it that any real conversation gets killed too.

There are people here who have thought seriously about what's missing in their workflow. And there are researchers who are actually building things to fill those gaps. Those two groups should be talking but there's no space for it.

I'm one of them, I've been building something for the past year, I won't get into it. But I've noticed that the moment you've made something, any question you ask gets read as market research, even when it's genuine.

r/MachineLearning has a weekly self-promotion thread. It's not perfect but it does something smart: it puts all the noise in one place and leaves the rest of the sub free for actual discussion. Would something like that work here, or is there a better way to handle it?

lately ive noticed that the hardest part of writing at PhD level is not always the research itself but recognizing when my own writing stops being clear

after spending days inside the same draft, everything starts sounding reasonable to me even when the structure is probably too dense or repetitive for someone reading it fresh

the strange part is that feedback usually isnt about the actual argument anymore

its more things like:
“this paragraph is harder to follow than it needs to be”
or
“the wording feels heavier here”

and honestly those comments are frustrating because theyre difficult to catch on your own while editing

recently ive been experimenting with more structured revision passes instead of endlessly rereading manually

one thing that helped more than expected was using analysis tools that break writing down line by line and highlight patterns that feel overly artificial, repetitive, or unclear under closer inspection

it made me realize how often clarity problems come from sentence habits you stop noticing after staring at the same chapter for too long

still working on improving it, but it feels more useful than just doing another proofreading round while mentally exhausted

curious if other PhD students run into this point where familiarity with your own draft actually makes editing less effective

Not sure if the same time limits apply everywhere, so just to be safe: in Czechia you get 3 years of full-time PhD study, extendable to 4. After that you can add up to 3 more years in distance study mode.

My 4 years were up.

I had submitted all the required publications, some still conditionally accepted, completed all the teaching and coursework, and had most of the literature review done.

I was mostly waiting on the last couple of acceptances. Then I planned to finish writing.

Since my stipend was up, I started a job, thinking I would finish the thesis over weekends. I was determined to wrap it up during the first year of distance study.

It did not go well.

I hoped to write 10 pages every weekend. Not only did I almost never hit that goal, but some weekends the page count actually went down because I kept re-editing previous sections.

The mood kept souring.

Finally, as the first year was closing in and I hated every minute of writing, I decided I wanted it done on schedule.

So I took a 2-week vacation from work for the sole purpose of finishing the blasted thing.

The plan was simple: 50 pages in 10 days. No editing. Just pile the slop until all the meat and bones were there. This was before 2022, so even slop took effort. Then I would spend 4 days turning it into something I wouldn’t hate.

I hated every single day of it.

With passion.

And with increasing distaste for my own publications, which until that moment I had considered decent. In the moment, I hated every bit of them, because I couldn’t just copy-paste. I had to rewrite everything.

Every formula, every premise, every claim. I hate-typed it all. I chased the end of each chapter, then the end of each section, then the end of each paragraph.

And I made it.

Ten days. Exactly as planned.

I didn’t even need the full 4 days for edits, because I was so resentful of the whole thing that after two days I thought: good enough.

I remember sending it to my supervisor with a message, paraphrasing but not by much:

I am sending the penultimate version, pending one more grammar pass. If it is more likely to pass than not, please just tell me I can submit it, because I don’t think I have the willpower to work on it anymore.

At that point we were very friendly, and he knew how much I was struggling.

He replied:

It’s good. Submit it. Congratulations.

Only years later could I look at it properly. Some parts are clunky, but overall it was not bad considering half of it was written over 4 years and the other half essentially in two weeks.

I’m not sure if this helps anyone, but I wanted to share it because I think “productivity” sometimes sounds too clean.

This was not clean.

It was containment. I took the task, put a fence around it, lowered the standard from “good” to “submittable,” and hatewrote until it existed.

Like I said, not really a motivational story, but still … something?

I’ve been thinking about this recently because a lot of modern research seems to involve collaboration across multiple fields now, especially in areas connected to AI, complex systems, data science, and computational research.

In theory, interdisciplinary environments sound great because you’re exposed to different perspectives, methods, and ways of thinking. But I’m also wondering whether they sometimes create more friction than productivity:

more meetings

more coordination

broader project scope

difficulty staying focused deeply in one area

For people who’ve worked in highly interdisciplinary labs or institutes during their PhD:

Did it actually help your productivity and research quality, or did it mostly make research more complicated?

Genuinely curious , what problems do you guys face while reading research papers?

Could be anything:

• understanding the math
• too much jargon
• bad explanations
• papers being unnecessarily dense
• figuring out whether the paper is even good
• reproducing results
• attention span dying halfway through

Basically anything that makes the process painful or inefficient for you.

Would love to hear both student & researcher perspectives.

Hello from Germany! I am currently writing my master thesis and it seems like there is a lot of information and hence i struggle with filtering it out. Summarisers and other tools are there but so many , can’t actually figure out, which one to use and rely upon and whether free version is sufficient or paid version should be opted. Reading papers is time consuming too and when you don’t find the relevant information thats another level disappointment. Somedays its productive and sometimes its not while i also struggle with my supervisor who seems concerned that i may not finish up on time while he is mostly not available or just tells me to figure out. Thats your work. What tools can be used to enhance productivity and effectiveness? Tips are appreciated. Submission in 3 months.

I originally wanted to use Obsidian for everything, but I’ve realized it may not be the best fit for calendar and task management. However, I prefer that everything to be synced.

For those who use Obsidian regularly, what app do you pair it with for scheduling, reminders, and tasks? Or are you using only Obsidian for everything? I’d love to hear your workflow.

Hi Everyone,

I'll be starting my PhD in July on quantum optics. It will be very helpful if you can provide some advice. You can suggest me about - time management, social skills, hobby, subject matter best book, writing, what actually matters, what beginners usually do wrong, how to stay productive without burning out, dealing with slow progress, paper reading/writing, advisor relationships, networking, publishing, imposter syndrome(as an international), or literally anything you think a first-year student should know.

I couldn't stand using Grammarly anymore. They're shoving their AI down my throat. I built the 2019 version of Grammarly.

Follow-up to my post a month ago: link to original

Last time I asked "is it worth my time" and the answers mostly landed on the podcast side of what I'm building. That was on me. I buried the part I actually care about. Coming back to that now.

The way I see it, lit review breaks into two problems with very different deficiencies.

First problem: finding things.

Most of how you find papers right now is lexical. Aggregators, journal search bars, Google Scholar. The algorithm is keyword-match. Unless you already know the exact terminology a paper uses, you end up iterating: try one phrase, refine, try synonyms, prune. A lot of false starts before you get hits that actually relate to your question.

Some questions:

• What's your strategy for keyword discovery when you're searching unfamiliar terrain?
• How many search iterations do you usually run before the corpus stabilizes?
• Any semantic-search-layered tools you actually use, or do you stick with the journals' built-in lexical search?

Second problem: extracting what's in the papers.

Once you've got a corpus, abstracts only get you so far. You've got heuristics you've built up over years. Figure 1 first, methods last, etc. But if you've got 20 papers in your pile and a deadline, reading them cover to cover isn't realistic. Same situation if you've uploaded your own draft and you want to interrogate it.

NotebookLM is what most people reach for here. Conceptually it's the right shape, RAG over a paper set. But it has known flaws for specific use cases, especially anything with dense technical claims. Part of that is context rot. If you haven't run into the term, LLMs degrade at recall as their context window fills up, and they're worse at retrieving from the middle of the context than from the beginning and end (the "lost in the middle" effect). So when you ask a detailed question about page 17 of a 40-page paper, you often get answers that look right but quote the wrong figure.

The approach I've been building doesn't put the whole document in context. Each question pulls only the relevant body of work, answers from that, and gives you a clickable reference. If you don't trust the answer, you click through to the exact source span.

Some questions:

• For people using NotebookLM (or similar) for academic work, where does it actually break for you?
• How many papers can you realistically interrogate in a single session before quality drops?
• What do you wish existed that would make this less painful?

The thing exists and is live, on my profile under SOTA Institute. I'm not here to pitch. I want to know what the right solution actually looks like from people who do lit reviews for a living.

I was recently accepted into a summer research opportunity at a well-known university, only to later have the offer withdrawn with a vague explanation about “interactions” and “professional fit.”

What makes it confusing is that I never disrespected anyone, I was genuinely excited about the research, and I had been looking forward to learning from the mentors involved. The explanation was broad enough to feel serious, but not specific enough to actually learn from.

I think academia needs to talk more honestly about how subjective “fit” language can become — especially for undergraduates and early-career students who are still learning how to navigate professional spaces.

I’m trying to handle it professionally, but I’m also wondering: has anyone else experienced a sudden reversal, vague feedback, or a “fit” decision in academia that left them with more questions than answers?

Hi Everyone,

I am not here to promote. I am a PhD student in life sciences. I found myself talking with ChatGPT a lot on research ideas and experiments that did not work. Sometimes, I would ask "why didn't this work?" Now that I think of it, I wasn't really trying to get chatgpt's input, but somewhere to vent. As I get frustrated often with failed experiments, I would ask chatGPT just to get some ideas. But, a couple of things I noticed was that Chatgpt would make up sources sometimes. Also chatgpt trains its AI model on the data I input. So, for mine, I can ask if I have any research ideas that need evaluation or if I need help with troubleshooting.

Now, I know some of you will say "you shouldn't rely on chatgpt for thinking, you are doing a phd, blah blah blah..." It's also not 1990 when you got your PhD, okay? The world is becoming more AI dependent and you still have to do your own experiments. The tool is basically a chatgpt wrapper and I admit it, but I am using gemini vertex API so it won't use your data to train the model. I also added a strict internal rubric so that it closes any gaps in logic when it it is coming up with experimental designs.

2nd year PhD here (social sciences). My ADHD has been brutal this semester and I've been testing tools to keep myself afloat. Already paying for ChatGPT but it's too general for actual research work. Wanted to share what's working and hear what others swear by.
What I've tried so far:

Zotero: non-negotiable at this point. Free, browser connector saves me hours, group libraries are great for advisor collab. UI is dated but I've made peace with it.

Elicit: best for early scoping. Pulls relevant papers and extracts methods/findings into a table so I can triage fast. Leans life sciences though, so YMMV.

Consensus: solid for quick "what does the literature actually say about X" checks. Good for sanity testing claims before I go deeper.

BeFreed: recent find. Turns PDFs, YouTube, articles into podcast-style lessons where you control length, depth, voice, and narration style. Makes dense material digestible on a commute. If you upload your own sources it builds a personalized learning plan across them, which has helped me organize scattered readings into something coherent. Wouldn't use it for papers I need to cite closely.

Obsidian: where my literature notes live. Backlinks genuinely change how I connect ideas. Easy to spend more time configuring than writing though.

Has anyone tried Research Rabbit or Scite? Keep hearing about them but haven't pulled the trigger. Also open to any writing-stage recs, that's where I'm still drowning.
edit: thanks all, didn’t expect this many replies 🙏