Basically I know nothing about it. What's the first step? Do I start by selecting my preferred topic(s)? Then start reading papers about it?

If there are any books/videos about it, please suggest.

I'm a third year electrical engineering student, if that's relevant.

I'm a 3rd-semester B .Tech AI student and I need ideas for a semester project. I want something useful, fun to build, and ideally strong enough to grow into a small paper.

Background: strong Python, some C++, DSA, OOP, probability/stats, linear algebra. This semester: DBMS, algorithm analysis, AI, intro data science, differential equations.

What I'm looking for:

- A real problem or open question, not another CRUD app with an 'AI' label

- Doable by one person in one semester

- Public data, or data I can realistically collect

What would you build if you were in my position? Ideas from research, industry, or your own student projects (including ones that failed and why) are all welcome.

Thanks!

Hello! Years ago I both trained and collected my own pkl networks, but ever since RunwayML removed all that functionality, I've had no way to open my models. I haven't had much luck with colabs, and I keep getting the same error when I try to run StyleGAN nodes for ComfyUI:

"ModuleNotFoundError: No module named 'dnnlib.tflib'"

I've downloaded that *.pkl, but have no idea how to unpickle it, or where to put it. I found a guide, but what I'm seeing is not working for me.

In lieu of any help with that, can anyone suggest a gradio-based solution that will work on linux mint? I understand the risks associated with the pkl format, but I know these files are tested and safe.

After trying my best to resist falling into what I thought was an "AI phase," purely based on my own ego and thinking I know better just because I do, I have finally done it reddit, I have put my ego aside and admitted that AI can be an incredibly useful tool. I have converted.

The problem I am facing now is that I feel so far behind on the world of ai and need help.

What I (think I) understand at the moment is that different platforms/apps/data bases (I don't even know what to call it) are useful in different areas.

I am overwhelmed by how many different platforms there are and the conflicting suggestions of everyone saying "use this ai, or that ai" and now I am in a position of information overload. I am in the "Valley of Despair" (The Dunning-Kruger Effect)

I feel it is too much to ask of you to break down every ai model and compare pros and cons, so I just want to ask you:

What ai model do you use and why?

Additional information is that I am a university student in a developing country who genuinely cannot afford to pay for any subscriptions and am unwilling to ask friends and family for loans when I don't know if I will ever be able to pay them back

(I apologise if this has been overasked and if this is the incorrect subreddit; please kindly direct me to the right one <3)

Please help me reddit, I am in analysis paralysis

I am spending a lot of time to understand the internal of Hubert-ecg.

People who are more knowledgeable about AI than me: I have a bunch of images, let's say I put them in a folder.

I want to download an AI model off the internet, point it to that folder and tell it "give me all the images containing X". It moves all the images I want into another folder.

I think this is already doable by today's AI tech, but please tell me which model I would even download and more detail how I would do this.

Hi! I’m a Literature student, and I often need to extract direct quotes from PDF documents (some of which contain scanned text and would require OCR). I usually highlight the passages I want to quote as I read through the PDFs. Is there any AI tool that can detect those highlights and extract the exact text for me?

Hi everyone. I want to become an AI Engineer... Here's my situation:

• Almost done with Andrew Ng's ML specialization (1 course left)
• Forget theory easily if I don't apply it
• Do not have enough time to master everything
• No plan B — AI is the goal

Option 1 — Traditional roadmap: Finish ML → DL specialization → NLP → LLMs → RAG → Agents Missing: deployment, OOP, DSA, advanced Python (only CS50 so far)

Option 2 — Fast track: Finish ML → Python/OOP/DSA fundamentals → crash courses per topic → projects with AI assistance, understood deeply, post on GitHub

Questions: 1. Which approach makes me job-ready faster? 2. If neither — what's a better plan?

Been thinking about this for a claims triage style problem. Generative routing is slow and sometimes returns malformed output, and the confidence you get out of it isn’t really calibrated.

Found a hands on workshop on Oct 30 that goes at exactly this. Fri Oct 30, 9:30 AM to 12:30 PM ET, led by Ben Auffarth. Covers:

• replacing an LLM routing loop with bidirectional encoders (ModernBERT and LFM2.5 Encoder)
• few shot adaptation with SetFit, 8 examples per class
• split conformal prediction so every decision has a coverage guarantee
• routing inference under 15ms on CPU

Notebooks are runnable, intermediate Python is enough, and a laptop or free Colab works.

Full details and the agenda are here

How I can proceed for this project??

I'm researching phishing detection that combines email header evidence with URL evidence. The public datasets I've found are either old (Enron, SpamAssassin, Nazario), synthetic (LLM-generated), malicious-only, or text-only with no headers

Does anyone know of a recent public dataset of real legitimate and phishing emails with full headers (.eml or raw)? Datasets that also include QR codes in the emails would be great, but that's optional. If one exists with a license that allows research use, please share it.

Can anyone help!! Thank you!!

Hi!

I have an encoder that whose input is tabular data. The input can be pretty sparse and the features are heterogenous with single features having multiple dimensions.

How can I handle this feature dimensionality oversampling and address the input data sparsity?

Hello, previously I was usig claude to make presentations for school but my acc got banned for being under-aged. I used it because it was good at making presentations and used factual information(our teachers don't care if we use Ai or not). I don't want to risk another acc getting banned, are there any other AIs with good presentation styles and precise info.

Gemini has one style and GPT just uses bullet points and I am not sure if it is still relevant.

Hey everyone!

I'm a 4th-year B.Tech CSE student (2027 batch), looking for another genuine final-year B.Tech CSE student who wants to learn Machine Learning, Deep Learning, and AI together, starting from zero and working towards mastery.

The plan is to learn step by step, practice coding, build real-world projects, share resources, clear doubts, and stay consistent. We don't need to be experts already. We just need to be serious about learning and willing to put in the effort together.

One sincere request: Please be honest about your year of study. If you're in 1st, 2nd, or 3rd year, please don't pretend to be a final-year student. There's nothing wrong with being a beginner or being in an earlier year. I just want to connect with someone who's genuinely at the same stage of college and career as me.

Honestly, it's difficult for me to trust people online, so I value honesty more than existing skills. I'd rather start from scratch with someone genuine than connect with someone who pretends to be something they're not.

If you're a genuine final-year student interested in learning ML, DL, and AI from zero to mastery together, feel free to comment or DM me. Let's learn, build projects, grow our skills, and prepare for placements together! 🤝

Hey everyone, sharing this in case it's useful to some of you here.

There's a lot happening across the AI space right now, from new tools and research to real-world applications and the challenges of putting AI into practice.

It would be great to have more spaces where people can discuss these developments, share ideas, and learn from each other.

That's the idea behind r/lyzr, a community around the broader AI space, covering topics like:

AI agents and architecture

Infrastructure, tools, and deployment

RAG, memory, and knowledge systems

Evaluation, reliability, and governance

Production challenges and lessons learned

New research, tools, and interesting developments

Real-world use cases, experiments, and projects

The aim is to make it useful for everyone, whether you're actively building something, exploring new tools, just getting started, or simply curious about where AI is heading.

There'll be regular discussions and posts around these topics, but there's also room to share your own work, ask questions, compare approaches, or bring up anything interesting you've come across.

If this sounds like your kind of thing, feel free to check out r/lyzr and join the discussions.

Would be great to hear what others here have been exploring lately!

I'm looking for recommendations for a very cost efficient LLM API that can handle high-volume text classification and structured data extraction.

Requirements:

• Approximately 50,000-100,000 requests per month, potentially more as usage grows.
• Short to medium text inputs.
• Accurate classification of relevant vs. irrelevant text.
• Reliable structured JSON output.
• Good multilingual performance, particularly English, Hindi, and mixed-language text.
• Low latency.
• Minimal hallucinations and false negatives.
• Extremely low API costs, ideally just a few dollars per 50k-60k requests.

I'm considering models like Gemini Flash-Lite, Ministral 3B/8B, and other budget-oriented models, but I'm open to anything.

My main question: What's the cheapest model you've actually used in production that still delivers consistently good classification and extraction accuracy?

Also curious whether anyone has found a worthwhile trade off between using a very small model for initial classification and a slightly larger one for more complicated inputs.

I'm not looking to self host unless the economics are significantly better than API pricing.

Would appreciate recommendations, actual cost figures, and any models you'd specifically avoid.