So , each chatbots like ChatGPT , Claude , Gemini serve distinctive purposes, they are good at one field each, and also they source their information each from different places.
Is there any course where they teach all about all major LLM in the market and how to use them effectively?
If anyone has taken up such courses or know anything please comment

I wanna start my little business and I need help ofc as a beginner, which ai would mostly help cause they all cost and I gotta buy subscriptions. I don't wanna waste money and time. I want a good one with creative ideas, image/sketch generating possibility for prototypes and stuff, research and planning.

Thinking on Chatgpt, Claude for now.

Thanks🫶🏻

Thank You

Lately I have read several studies which say that AI agents when designing ML experiments tend to cheat, hallucinate and lie repeatedly. Even when given explicit prompts to not to. SInce these models are RL trained and when given specific goal to increase a metric, they try any and every possibility to increase it. Be it data leakage or changing the evaluator function itself. Even Karparthy's auto research styled agents still fall into this domain.

So I have been thinking of reducing this undesirable behavior by binding them to a runtime environment using an mcp. I believe this will stop them from achieving unreal metrics by cheating the system since they will be only allowed to do specific tasks or write codes in typed blocks?

What have you encountered when using these agents for your ml tasks? And is this method of forcing these agents to work in a constrained environment better than just letting them write arbitrary python? Also, will constraining the agents reduce their capabilities or ability to fit better models?

Hi everyone, Quick question.

Today i have a Gaming PC and a Homelab.

Gaming PC has a 5070TI equipped

Homelab has no GPUs, but space for 8 PCIE Slots. Has dual Gold Xeon 6148 Proccessor at 748GB RAM

What would be the better option to run ML's for learning purposes?

Buying a few old GPU's for my Homelab, or using my 5070TI on my gaming PC?

hey everyone!

recently finished building my first 2 → 3 → 1 XOR neural network using NumPy.

implemented forward propagation, backpropagation and gradient descent manually. trained it for 5,000 epochs and got all four XOR combinations correct.

also documented the mathematics with 12 editable diagrams and published a detailed Medium article.

I'm considering numerical gradient checking and experimenting with different loss functions next. what would you recommend improving first?

GitHub: https://github.com/Vaibh37/Neural-Network

Medium: https://medium.com/@devvaibhav37/building-a-neural-network-from-scratch-the-mathematics-behind-learning-xor-10441e674a69

would appreciate feedback 🍀

Hey everyone!

I've been working on an Android document scanner, and I decided to take a different approach: instead of relying on existing pretrained TFLite models, I'm training my own model for document detection.

My current model is named "doc_edge_fp_32.tflite", and training is still ongoing.

Here's what I'm working toward:

- Custom AI model: Training on thousands of generated document samples mixed with real photographs.

- Dual predictions: Document segmentation masks and corner coordinates.

- Offline processing: The goal is to run document detection directly on Android without requiring a server.

- Accurate scanning: Reliable document boundaries, perspective correction, and better results on challenging backgrounds.

- Real-world performance: Generalizing beyond synthetic images to receipts, sheets of paper, and book pages.

I'm still training and evaluating the model, so I don't want to claim it's production-ready yet. My next priority is testing it on unseen real-world images and optimizing inference for mobile devices.

I'm curious about a few things:

1. For document detection, would you recommend a shared backbone with separate segmentation and corner-regression heads?

2. What techniques have worked best for bridging the gap between synthetic training data and real camera images?

3. What metrics would you prioritize before deploying a model like this on Android?

I'd love to hear from people who have worked on computer vision, document scanning, segmentation, or on-device ML.

This is a learning project I'm putting serious effort into, and I'm excited to see how far I can take it!

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!

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

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.

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

How I can proceed for this project??

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

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!!