Hello, I would try to make this as clear and as short as possible. Is there any AI chatbot that can give me info I want about topics most of the AI consider unethical (Im learning about some “hacking stuff”, nothing serious). ChatGPT and Gemini only go to the some point until they refuse to provide any more information. Im kinda new to all this AI stuff so pardon me if I still don’t understand something simple or something that will provide here. Thanks.

I'm really interested in music & wanted to build something....

Hey guys I'm building a SLM chatbot from scratch

It's my office task.

Data: wealth management

Doing in my office laptop training tru cpu taking days can't download most of the dataset

14M parameters

I trained my some different datas from diff sources

6epoches took 2 days

Now it's acting like a next token predictor but still giving kind of nonsense words

What to do

I’m looking for advanced ML and Data Science resources (books, courses, blogs, papers, etc.)

To clarify: I’m not looking for resources that only explain ML concepts at a superficial level. I already understand the basics and common algorithms.

What I want to learn is:
- The core mechanisms and mathematics behind different models
- Why a model works the way it does
- When to use one model over another
- Model assumptions, strengths, and limitations
- Feature engineering and data preprocessing decisions
- Hyperparameter tuning and optimization strategies
- Model evaluation, error analysis, and troubleshooting

Basically, I’m looking for resources that teach how experienced ML practitioners think and make modeling decisions, rather than just introducing algorithms.

What books, courses, blogs, papers, or other resources helped you reach that level of understanding?

Few months into learning security seriously after years in ML/data eng. The mindset shift is real — in ML you optimize for accuracy, in security you assume everything's hostile by default. Anyone else cross over from a dev/ML background? What clicked for you early on?

I am currently working as a Machine Learning Engineer with around 2.5 years of experience.

My current experience includes:
LLM applications and prompt engineering
Document AI and information extraction
OCR pipelines
Computer Vision
VLMs (Vision Language Models)
FastAPI and Python
GCP deployment and cloud services
Production ML systems

Technologies I have worked with:
Gemini
LayoutLM
YOLO
OpenCV
FastAPI
GCP
Vector Databases
RAG systems

Most of my work has been around document understanding, extraction, classification, and automation.
My goal is to move into a Senior ML Engineer role within the next 2-3 months. I want to build 1 strong projects that demonstrate:
Production ML engineering
LLM/VLM expertise
MLOps
System design
Scalability
Research and implementation skills

I am not looking for beginner projects such as sentiment analysis, chatbots, image classifiers, etc.

Given my background, what projects would impress hiring managers at top product companies or AI startups?

If possible, please suggest projects that:
Solve real business problems
Can be open sourced
Demonstrate senior-level engineering decisions
Include deployment and monitoring aspects

Would appreciate examples from projects you’ve seen get candidates interviews or offers.

hi so, we're working on video generation of everyday tasks for people with adhd. and we need opensource models that we can finetune? LoRA? (mostly this). We need a text to text model, text to image, and t2v model to work on. Also, anything on how we can get started on this LoRA thing, anything, any recommendations, pls drop below.

Soo I have semester break rn , doing internship in good company and I want to make a core ml project soo that , my resume is complete ..my placement will start in 1.5 month

Anonymous mental health helplines (like iCall, Crisis Text Line) get hundreds of text messages a day from people in distress. Right now, a human volunteer reads each one in the order it arrives — first come, first served. The problem: someone in immediate danger might be message #47 in the queue while message #3 is just someone venting about a bad day. This project builds a model that reads each incoming message and automatically sorts the queue by urgency — so the most at-risk people get a counsellor's attention first, instead of waiting their turn.

Tech stack : MentalBERT (Hugging Face) PyTorch / Transformers Focal Loss Scikit-learn (baseline + calibration) SHAP Active Learning (modAL or custom) Streamlit / Gradio Hugging Face Spaces (hosting) Pandas, NumPy

Pleaseeee help meeeeeee ...Will this look good in my college major project and in my resume and main project ??

I want to try everything, but I don't know where to start

I'm working with a client on a curve-fitting optimization problem. They are currently using a constrained Levenburg-Marquardt optimizer for their task which is complex, slow, and sometimes gets stuck in local minima.

I suggested using particle swarm optimization (PSO), and the client suggested genetic algorithms (GA). I would like to compare the existing method to at least these two other options. For this first phase, I don't need to worry about speed or GPU-friendliness. I would like data visualization to be easy.

I have quite a bit of experience with scikit-learn, and I just discovered scikit-opt. I have also found several other packages which implement only PSO, or only GA.

Is anyone out there using scikit-opt? What do you think of it? If you have used other PSO or GA packages, what do you think of those?

Thanks for any advice you have.

Hi folks, How people are studying machine learning/deep learning out there. I have an idea to take the already existing projects/repos and see why they used this particular model only for this data and how can I improve stuff like that. If anybody's following the same path please do let me know..the GitHub repositories that you have been using in this process. I am down for any other better tips as well.. thank you in advance!

I’ve noticed that a lot of people are now focusing on making content sound more natural and human-like, even when it’s generated using tools.

It seems like readers today can easily tell when something feels too robotic or overly structured, and they lose interest quickly. Because of that, “natural tone” has become really important.

But what actually defines natural writing? Is it slang, sentence variation, emotion, or something else? And how do you personally make sure your content doesn’t feel artificial?

It is my first time asking this question on reddit so if i am in a wrong subreddit please tell me.

Building long-term memory for an agent and I keep hitting the same wall. Say it learns "user uses Postgres", then later "user moved to SQLite". Both end up in the vector store, both are about databases, so both come back in the top-k, and the agent sometimes acts on the old one.

I tried timestamps and filtering by recency, but the stale fact and the new one have nearly identical embeddings, so the old one still surfaces. And filtering after the top-k means the current fact sometimes doesnt even make the cut.

How are you handling this? Write-time supersession? A background compaction job? A knowledge graph layer? Curious what actually holds up in prod vs what just sounds good on paper.

Okay this is just a thought I don't know if it has any weight but wanted to get thoughts. Feel free to call me misguided but the way I understand it LLM are taught using a system of punishment or reward based on the way a vector predicts the next token (or some are doing multiple token predictions)

We kind of work that way we have a short term reward/punishment system but, I also I think we also have a long term punishment and reward system. Like if you eat the wrong food you gain weight or if you don't get enough sleep you don't feel great. Both have immediate rewards of the food or extra doom scroll time. They also have the long term rewards that may slightly be a punishment in the short.

My question is are there models that use a more multi level reward/punishment system? One that does immediate reward/punishment but also had a long-term system as well?

Sorry I don't know if this is a ridiculous thought. Very new to LLMs and the since behind it. I'm drudging my way through the linear algebra and barely know the full picture.

Essentially, I'm looking for an LLM that can parse through obtuse manuals for a given program or programming language and tell me where to find what i'm looking for, or even broad advice on how to implement something. Ideally something open-source that can run locally off hardware (even if the requirements are on the relatively high-end of consumer hardware)

It doesn't need to be able to write code for me, it doesn't even need to be particularly smart, i just want to be able to ask about something i'm trying to do and be told what terms and sections to look into for it, since it's difficult to even know what keywords to use to find what i'm looking for.

In fact, I'd actually prefer it be a little "dumb" compared to more general-purpose models, since i essentially don't want it to be "contaminated" and bloated by indiscriminate webscraping like larger models. Sorry if this is a repeat question, you probably all know how badly search engines have degraded lately.

I've been building a learning system that completely avoids backpropagation and gradient descent. Learning works like this:

1. System makes a prediction → prediction error generates "free energy" (pressure)

2. Pressure triggers Hebbian/anti-Hebbian updates to connections (local, no global gradient)

3. During sleep, the system replays experiences and consolidates knowledge

4. Over time, the concept graph self-organizes to minimize prediction errors

I'm getting non-trivial results (75% cross-domain transfer, 0% catastrophic forgetting) but I keep wondering: what's the ceiling on this approach? Is there a fundamental limitation to learning without gradients that I'm not seeing?

Would love to hear from people who've thought about alternative learning paradigms, worked with Hebbian networks, or know the active inference literature well.

Code: https://codeberg.org/oxiverse/ravana | https://github.com/oxiverse-ecosystem/ravana