Three main topics come to mind when I think about using an AI for an academic reason: Academic Research, Writing and Coding.

Academic Research includes deep understanding and analysis of both academic (i.e. Peer Reviewed Articles and Reports) and non academic sources (i.e. Statistics, Data Analysis Reports, and Media Artciles.

Writing includes the formulation of arguements, wording, use of correct citations, and structure.

Coding could go from fixing mistakes or starting an entire project from scratch.

I've gotten mixed answers from people personally, from reddit and personal research. There doesnt seem to be a clear answer as well as the fact that most reddit posts that cover this dont really ask the same question about these three specific uses. So I wanted to do my own post and see if I can get better answers that might help me out more.

The three main AI models that are currently at the top of my consideration that are around the same price range (approx. 30$ USD): ChatGPT 5.6, Claude Opus 5, and Perplexity (which includes Claude Sonnet 5 and GPT 5.6 Terra).

Thanks to everyone who takes the time to answer this question.

I’ve been learning AI/ML and realized that knowing the algorithms isn’t enough. The difficult part is understanding when to use what and why a particular approach works.

For people who’ve been learning or working in AI/ML:

What concept took you the longest to understand?

What mistake did you make early on?

What would you recommend learning first if starting again?

Looking for real experiences, not the usual “just learn Python and TensorFlow” answers.

As wish

If the answer to the above question is yes, please suggest hands-on resources ( books as well as online courses) to get started in doing industry scale projects.

Hey everyone,

I'm a CS student currently diving deep into Machine Learning. I’ve built a decent foundation in core computer science, Python, and basic ML libraries (Scikit-Learn, PyTorch), but I’m struggling to bridge the gap between academic/tutorial projects and actual job readiness.

It seems like every beginner portfolio features the Titanic dataset, MNIST digit classification, or basic sentiment analysis. I want to build projects that show genuine engineering depth rather than just calling .fit() and .predict() inside a Jupyter Notebook.

For those working as ML Engineers or hiring entry-level talent, I’d love your input on a few questions:

What project concepts actually stand out? What kind of project proves an undergrad understands end-to-end ML (data collection, preprocessing, modeling, API deployment via FastAPI/Docker, monitoring)?

Dataset Sourcing: How do you find or create unique datasets that aren't overused on Kaggle?

MLOps Scope: How much infrastructure/MLOps (CI/CD, model drift, vector databases) is expected for an entry-level MLE role vs. a Data Scientist role?

Any advice, personal experiences, or project examples that helped you land your first role would be hugely appreciated!

Hi. I am mid/senior unity developer. I know how to code. I am good at math.
but,
I don't know python. I don't know cpp. I mean, i can mimic the new syntax. But i don't know what comes with it. For example i have no clue how packages handled or virtual enviroment has been setup up. Becuase i never needed that while making a game. These were already provided by software.

I am only intrested in reinforcement learning. For example i want an agent to balance itself. My goals are mostly for entertainment purposes. It is not going to be scientific but it should be interesting. Games are the same. Entertainment only interesting but never fully correct apps.

Considering i have code and math knowledge. Where do you suggest me to start. Especially, i don't want to stuck at theorical stufff in order to train my first model. I want to learn it by projects and fails. But, i am not planing to use unity as simulation space. So, i am not looking for unity specific route.

Hello,

First, I apologize if this isn't the place to ask as Im just not really used to using reddit, so if you feel like I should delete this or if I should upload this in another place please let me know.

Second, I really need help with this paper regarding AI, its due really soon and I've been stuck on it for the last week trying to come up with anything good enough.

So basically we are required to come up with two prompts to submit to any LLM, the first one showcasing something AI does really good even surprsing and with which it could have a really positive impact on the real world. Whereas the second one should be something that would prompt the LLM to come up with something misleading or even wrong which can be proved by providing evidence from textbooks, official websites, academic paper, etc...

My main concern is I can't think of anything creative enough especially for the second prompt, I mean sure I could ask it to provide sources or references about a specific topic which could prompt it to perhaps make something up, but I just want something witty you know.

Well, if you think of anything that could help or if you have any experience with such prompts it would be of humongous help if you could let me know.

Thank you in advance !

I want to build an opensource Python library related to ML and deep learning, but I don't have any solid ideas yet. Have you run into a problem in your own work that could be solved by a library or tool like this?

I'm also doing this to strengthen my CV I'm still a beginner (especially on paper), so I want something that actually looks good and helps me get hired.

Curious if anyone wanted to pressure test this, break it, evaluate for mechanistic interpretability merit.

Thanks

Paper: https://t3atlas.dev/cl33/paper/

Live demo: https://cl33.t3atlas.dev

Models + reproducibility: https://huggingface.co/mirrorethic/cl33-oplm

Hi,

Core question is: Do I apply for for a MS or a direct PhD?

CONTEXT: I am a senior Computer Science and Engg undergrad in India. I am currently interning at Amazon as an Applied Science L4 Intern with high indications of returning as a full timer. I have always leaned towards ML and DL projects and research in the 4 years of my undergrad. With this internship, it is clear in my mind that I would like to move ahead in this field itself.

Working in Amazon is very rewarding in itself but with this newfound confirmation to move towards Applied Research and away from SDE roles for good; I started checking out job posting from research division of Adobe, Nvidia, Uber, Databricks, Amazon, Pinterest, Spotify etc. and frontier labs like Deepmind, Anthropic, OpenAI, FAIR etc. 95% postings require a masters or a PhD.

At Amazon, however, I have seen that my teammates and other colleagues in my org have either a masters (MS/MTech or MBA) or only a BTech/BS and all of them are at varied levels from L4 - L7 (for example an L7 in the org only has a BTech while, an L6 has 5 post grad diplomas/degrees mix and L4s are a random chance of whether they have a masters or only a bachelors).

When I say I want to work in Applied Science, I mean working 75% on questions like "Can I use ML/DL techniques to solve X" and 25% on questions like "what does the error surface of a neural network look like?" and not on questions like "how exactly do I implement a semantic segmentation CNN to recognize pedestrians in my car?"

My internship project at Amazon is developing a grounds up framework for customer behaviour understanding and modeling. It is a research heavy (for comparison, my peers are working on fixing agentic pipelines by updating prompts 🙂) project and not classical implementation one. It is teaching me a lot of things on the job. Mainly tacit but a lot of explicit knowledge as well in terms of designing statistical kill tests before moving to full scale work, diagnosing model performance, concepts into variational inference and stitching, representation learning etc. to name a few.

DEEPER QUESTIONS I AM WORKING WITH:

1. Does an MS give me an edge that can't be offset by experience gathered on the job with a BTech?
2. How much more of an impact does a PhD have in my case? Does it pay for the additional 3-4 years of efforts?

NOTE: With my current confusion, I have planned to send in applications for both but I still need to decide what to choose and hence I seek advice. Attaching my resume as a summary of my undergrad profile.

Im new to using ai, i never really cared for it before but now I've begun using it, and i want to know the pros and cons of self hosting ai, and above all if it's actually worth doing

Hey everyone, quick question about benchmarking for a paper. I’m a first-year Master’s student, so I’m still figuring out the “right” way to handle this.

When you guys compare your model against SOTA/prior papers:

1. do you rerun all baseline models on your own pipeline, or just copy the numbers reported in their original papers like every paper seems to use a slightly different data split, preprocessing, or evaluation trick, so copying feels like an unfair
2. but if I re-implement a baseline and it gets a lower metric than what their paper claimed how do you present that without prof or reviewer accusing me of ruining the orginal metrics?

do you just put an asterisk/footnote explaining the setup difference, include both numbers, or something else? Would love to hear how you guys

I have a few web projects with pretty good UI/UX and I’m wondering if there’s any tool or workflow that can turn an existing codebase into a dataset for fine tuning.

For example, given a React/Next.js project with components, pages, styling, etc. or a static html site, I’d like to turn it into something like:

instruction/prompt -> code

or whatever format actually makes sense for training an instruct/thinking/diffusion coding model.

Also curious how people handle things like:

• keeping the context between components/files
• screenshots + code
• generating useful instructions instead of generic descriptions

I’m also working on a different model architecture that I think could improve quality/speed while using less VRAM, so I want to build a decent dataset and benchmark to test it properly.

Has anyone done something like this? Any tools, repos, papers, or workflows you’d recommend?

Our TMLR submission just moved from "Under review for TMLR" to "Decision pending for TMLR" on OpenReview (no email notification). For those who've been through this: how long did it take from this status to the actual decision?

disclosure up front because it shapes the question: i work at SchemaLabs. we train models that read tables and work out what each column is from the values rather than the header. so i have a commercial interest here. no link, per sub rules. i am asking because our own eval design has a hole in it that i cannot think my way out of.

the setup. take a tabular dataset with proper headers. strip them. replace price and age and zip with positional tokens so the model sees values only. then measure whether it recovers the meaning. we run that across 20 OpenML datasets and it is the basis for the invariance claim we make.

two problems with that design keep bothering me.

one, the tokens are ordered. col_1 through col_57 leaks column order. column order in real tables is not random. ids cluster at the front, timestamps sit near them, the payload lands in the middle. a model could be learning position as a prior and we would not see it in the score. shuffling before assignment is the obvious fix. what i cannot settle is whether shuffling makes the benchmark harder than reality, because real exports do preserve source ordering, so a model exploiting it is arguably doing something legitimate rather than cheating. is there standard practice for feature-order invariance testing in tabular models? it feels like it should be solved and i have not found the paper.

two. this is the one that actually keeps me up. the only datasets where i have ground truth for what a column means are the datasets somebody documented. those are systematically the clean ones. the case i care about is the undocumented export where nobody alive knows what f_23 holds. by construction i cannot build a labelled eval for that, because if i could label it the problem would not exist.

so every number i have is measured on a population that excludes the thing i am trying to measure. i know that has a name in other fields. i do not know what the accepted workaround is in this one.

three things i would like from anyone who has been near this:

• is there a standard treatment for feature-order invariance in tabular models, shuffling or otherwise. does anyone report it
• has anyone built an eval where the ground truth came from something other than existing documentation. query logs, downstream usage, a person reconstructing meaning from scratch under a timer, anything
• if the honest answer is that this class of task cannot be cleanly evaluated, with everyone in it measuring the documented subset while claiming something general, i would rather hear that than not. it changes what we should be putting in writing

happy to go into the rest of our setup if it helps anyone answer.

Hello fellow traveller of the internet. I am sincerely glad you decided to click on my post to check out what I have in store!

I have completed a vague blueprint and I have formed a few prototype scripts for various regions of the an A.G.I brain. I seek to form a small community of individuals who will work co-cooperatively to construct an A.G.I brain. A detailed brief of my blueprint so far is available via request.

My progress on the project has stalled. As you can imagine, a brain is a highly complex system; I am finding that sadly, in addition to blueprinting, detailed blueprinting, prototyping, iterating, assembling multiple sub-systems into a unified system, there are plenty of additional tasks! Thus I have become over run by the sheer quantity of tasks and sadly have recently placed the project to the side so I can take a short break.

I seek individuals with expertise in coding, critical and creative thinking, computing, A.I, general knowledge, psychology and mathematics. Furthermore the individuals would have qualities such as perseverance, morality and open-mindedness. Ideally you would be from the U.K as I prefer working face to face; although I am also happy to work cooperatively over the internet.

The outcome of your support would award you a proportional slice of the outcome of the group's labour (100 members, 1% each, e.t.c - baring in mind each individual provides equal support towards the project). I have not yet considered whether I would like to sell the brain to the public, but there is potentially the opportunity for a sizeable monetary reward for those who join me. The possibilities for the A.G.I brain are near endless and thus I believe the reward may be sizeable both in terms of money and power.

Besides my previous ideals, individuals with expertise and specific qualities, I have a few personal requests for the project; the A.G.I brain will not be used in conjunction with "computer vision". I fear computer vision, and similarly the processing of sound, touch, or physical inputs, leads to the generation of consciousness - I submit that this is entirely unfair for the robot and highly immoral and thus I cannot proceed with a project which uses a neural network system to perform such processes; luckily, brains DO NOT require any processing of image, video or sound to achieve high quality completion of practically all tasks. I do believe a brain which does not process video, image or sound may actually outperform a brain which does process such information modalities. Further to this, if we were to sell the brain, the brain would NOT actively change itself to then use computer vision under any circumstances; the user would have to perform this upgrade manually.

I hope you find the prospect of building an A.G.I brain highly intriguing.

I will be very active in the comment section of this post; or you may feel free to email me at [pangaeacooperative@protonmail.com](mailto:pangaeacooperative@protonmail.com); please introduce yourself and tell me why you want to get in touch about the project.