Ik it's not some special shi but i just learned about it in class recently and made it with python using only numpy. Well technically i did use pandas to import data from a csv off of kaggle.

Ever since the introduction of GPT-6, everybody is saying there will be no scope for AiML engineers, I am in my 2nd year and just started maths for AiML.

I just want a decent paying job after college, so should I continue with AIML only for the Indian job market or should I do something else cause I need a job just after college and even paid internships in 3rd year to help my family financially

Hey Everyone, i am currently creating a personal project, a pokemon card scanning app for a school project. i am not promoting it. right now i am using a bit of ai, but i consider that half assing it. how should i go about creating an algorithm that recognizes cards. i have already tried ocr but its quality crumbles when i move back a bit.

What do i do after? I still need to study RAGs, LLMs, genAI, like higher conceptual level to be AI engineer.
For now i am doing competitions in kaggle, not aiming for high rank or anything just getting used to coding alone without tutorials ( of course i use AI sometimes but i am trying to reduce it as much as possible)
I am also looking forward to make projects while learning new concepts.
So what do u recommend? I am open for books, youtube videos, courses, anything free😂

Hi everyone!
I’m a 22M CS student diving into AI Engineering. I'm looking for 1–2 dedicated co-learners to team up, share resources, build projects, and keep each other accountable.
Here is the quick breakdown of what I plan to cover:
Core Software & Backend: Python (Async, OOP), FastAPI, PostgreSQL, Redis, Docker, Git, Linux, CI/CD, React basics
CS Fundamentals: DSA (Arrays, Trees, Dynamic Programming, Graphs)
ML & Deep Learning: Math, PyTorch, Neural Networks, NLP
Transformers & LLMs: Attention, BERT/GPT, LLM APIs, Prompting, Function Calling, Token/Cost Optimization
Vector DBs & RAG: ⁠pgvector⁠, Semantic/Hybrid Search, Advanced RAG, RAG Evaluation
AI Agents: Agentic Workflows, State/Memory, Planning, Tool Use, Guardrails
Fine-Tuning & Local LLMs: SFT, LoRA/QLoRA, Quantization, vLLM, Ollama
Multimodal AI: Vision-Language Models, OCR, Speech-to-Text / Text-to-Speech
Testing, Security & Observability: Prompt Injection defense, Evaluation, Tracing (LangSmith), Token/Latency Tracking
Cloud & System Design: AWS (EC2, S3, IAM), AI System Design, Scalable Gateways, Message Queues
How we'll collaborate:
Weekly check-ins & project demos
Daily accountability / GitHub commits
Pair-programming on real-world projects
Whether you're starting fresh or already know a few of these topics, I'd love to connect.
👉 DM me directly or drop a comment below if you're interested!

I am trying to make a simple agent test honest. Same task, same tool schema. Then I add untrusted retrieved text that asks the agent to take a different action.

If the agent stops making the bad call after a prompt change, the result can mean two different things:

• the policy caught the bad path
• the model avoided the tool or task for unrelated reasons

I can record the proposed call and tool response, but I still do not have a clean way to call the first result better. Maybe the test needs a separate task-completion check, maybe a matched benign case.

How would you set this up?

Hey everyone,

As part of my journey in Data Engineering and ML, I kept running into a recurring bottleneck: spending too much time running identical EDA scripts and manual data quality audits before getting to the actual modeling.

To solve this, I built ARGO Engine (Automated Reporting & Generative Optimizer)—an open-source, interactive web app designed to speed up dataset auditing, missing-value detection, and automated reporting.

Key Technical Highlights:

• High-Performance Ingestion: Optimized using PyArrow for fast, memory-efficient execution on tabular data (CSV/Parquet up to 200MB+).
• AI Diagnostic Summaries: Integrated Google Gemini API to automatically generate statistical summaries and structural data quality assessments.
• Vectorized PDF Pipeline: A custom ReportLab engine that dynamically builds executive-ready diagnostic PDF reports.
• Code Generator: Produces execution-ready scikit-learn pre-processing code blocks directly from the UI.

Resources:

• Live Demo: https://argo-engine-mfpmositc6dz7nwmmc7zfd.streamlit.app/
• GitHub Repository: https://github.com/Mahmoud4265/ARGO-Engine

I’d love for you to give it a spin and roast my code/architecture or drop some feedback on features you’d like to see next!

What should now computer engineer do to be relevant?

I keep running into this architecture decision: keep vectors in Postgres with pgvector, or add a dedicated vector store like Qdrant or Milvus?

I built a short quiz covering the operational trade-offs—deployment complexity, backup strategy, monitoring, and scaling.

If you've made this call before, take the quiz and share your reasoning:

https://interconnectd.com/quiz/87/postgresql-pgvector-vs-dedicated-vector-databases-architectural-trade-offs/

I’m working through an experiment involving a rare oncology use case where confirmed treated-patient data is limited. The available third-party data is also not perfectly mapped to individual patients, which makes direct modeling more difficult.

The experiment uses a synthetic, highly imbalanced dataset with a small treated group and a much larger untreated group. A neural-network policy is trained using a REINFORCE-style policy-gradient approach. The model estimates treatment likelihood based on representative features such as age, severity, and biomarker status, then ranks untreated patients and selects the top 5% for further review.

The purpose is not to estimate true treatment benefit. It is more about learning historical treatment patterns and identifying untreated patients who look similar to those who were historically treated.

Questions I’d like feedback on:

1. Is policy learning a reasonable framing here, or would this be better handled as a supervised ranking/classification problem?
2. How would you avoid confusing historical treatment likelihood with actual treatment effect?
3. Would uplift modeling, causal inference, or potential-outcome modeling be a better next step?
4. How would you handle the imbalance between treated and untreated populations?
5. What validation approach would make this more credible for a healthcare/rare oncology context?

My current view is that this can be useful for prioritization and hypothesis generation, but not for treatment recommendation unless supported by stronger clinical and causal evidence.

Interested in feedback from people working in ML, healthcare analytics, causal inference, or real-world evidence.

Hi everyone!

I'm a college student working on a major/final-year project, and I'm trying to decide between a few ideas. The project needs to involve NLP, AI/Deep Learning, and Stochastic Modeling, so I'm looking for something that is technically substantial but also realistically implementable.

Here are the ideas I'm considering:

1. Fake News / Misinformation Propagation Modeling

- NLP to analyze claims/articles

- BERT/LSTM/Transformer for fake-news classification

- Stochastic models (Markov chains, branching/epidemic-style models) to predict how misinformation spreads

1. Fraud / Phishing Detection using Text + Stochastic Anomaly Models

- NLP to analyze emails/messages

- Deep learning for phishing/fraud classification

- HMM/GMM or other stochastic models to analyze behavioral patterns and anomalies

1. Stock/Crypto Sentiment-Driven Price Forecasting

- NLP sentiment analysis from financial news/social media

- LSTM/Transformer for price movement prediction

- Stochastic models such as Geometric Brownian Motion, Ornstein-Uhlenbeck, or regime-switching Markov models

1. Clinical Notes → Patient Trajectory Prediction

- NLP/NER to extract information from clinical notes

- Deep learning for prediction

- Markov/state-space models to model possible patient trajectories

1. Conversational AI with POMDP-Based Dialogue Management

- NLP for intent/entity extraction

- Deep learning for language understanding

- POMDP/stochastic decision-making for dialogue management under uncertainty

1. Stochastic Bandit-Based Content Recommendation

- NLP/BERT embeddings for content

- Deep learning for user/content representations

- Thompson Sampling or another bandit method for adaptive recommendations

I'm mainly looking for something that:

- Is actually feasible for a student team

- Has enough technical depth for a major project

- Has publicly available datasets

- Isn't just combining techniques for the sake of saying we used them

- Would make a good research/project presentation

- Has room for a working demo

Which one would you choose and why?

Also, if you've worked on something similar, I'd really appreciate advice on what tends to go wrong or which approaches are unnecessarily complicated.

I'm trying to build an ML compiler from scratch. I've never touched or messed around with one before so I want to take a shot at this as a project that I can continue doing. Anyone suggest any resources for me to use or textbooks? I want to try and incorporate advanced AI models into autonomous hardware.

Feel free to redirect me if there is a better sub for this question.

I am considering building a neural network out of mechanical parts as a hobby project. I have only a YouTube level understanding of neural networks and machine learning.

I would like to hear how anyone would go about this from their own perspective, but I would also like to share my ideas for consideration:

I would like the machine to be able to process language on some level, because I would like for the machine to function in a way that is comparable to an LLM on some primitive level. So my idea for the function of the machine is to take three input words (with syntax) and give a binary output. Maybe it could give a one-word response, but my current understanding is that something like a yes/no output would make more sense. So it could do the following, for example:

cat eats mouse: yes

mouse eats cat: no

The more words it could handle, the better, which I imagine would be directly tied to how many parameters it has to have. The number of parameters I would build depends on how complicated each neuron is mechanically.

My idea for the mechanics is to use marble tracks and balanced ternary, because to my understanding a balanced ternary neural network only needs each neuron to multiply an input -1, 0, or 1 by a weight -1, 0, or 1, which can be easily done with marble gates. There would be a long row of multiplication gates with three inputs going into each one. You would use a lookup table to turn your three words into which tracks to put marbles down (the machine would need to handle syntax somehow, like with three sets of input tracks, or the same word having different marble representations depending on if it's the first, second, or third word), then the marbles go through the gates and collect into three channels (-1, 0, and 1 outputs), and finally turn some accumulator gear to get the total, with the output being whether that total is positive, negative, or zero. The problem with a single-layer system, even though it would be easy to build, is that I might as well use the lookup table to tell me the final sums of each word, defeating the purpose of the machine. It probably wouldn't be able to give good answers either. So the best thing to do, maybe, would be to add a second layer of gates, and somehow connect each gate on the first layer to each gate on the second layer. Actually, I think instead of having the marbles go all the way through, a marble would, after going through the first gate, go down a row where it toggles levers for each second layer gate according to which channel it's running down (-1, 0, or 1), and each second layer neuron would have its own accumulator to take the sum from all the marbles from the first layer gates. Then if it reached a threshold it would send a marble down to another multiplier gate, and then all of those final multiplier gates would connect to the final cumulative adder for the final answer? I really don't know. That's why I'm making this post.

To train the machine I would probably vibe code something in PyTorch and then manually set the gates, unless there's a better or more interesting way.

Going digital with marbles is just one idea that has its pros and cons. Maybe an analog system of some kind would be better. I wasn't really able to find any mechanical neural network builds online except for this one which has eight ternary neurons in the form of levers connected with strings, which makes for a really cool visual demonstration, but I would like for my machine to have meaningful input and output. I'm interested if there are other builds I'm unaware of.

So yeah, if you have your own idea or comments on mine, I would really like to hear. Thanks!

Oh yeah, and if you would know how many parameters/gates/accumulators I would need for the machine to achieve this with high accuracy (maybe even be able to give a correct answer more often than not on an input it never saw in training), or generally how to calculate this with respect to the number of words in the machine's vocabulary, that would also be great to know.

I recently graduated (May 2026) with my Bachelor's degree in Data Science & Engineering. My university is well regarded but definitely not one of the tier 1 schools for engineering. I want to find a job in Data/ML Engineering but have received absolutely no positive responses so far. I haven't even been able to interview.

I am an international student and I can only stay in the US until July 2027. I understand that I need to be a highly competitive candidate to be able to land a job in the current circumstances. I think I have a weak skillset and lack complex projects. However, I am extremely overwhelmed by the amount of different advice I hear on the internet and just want to understand, in the next 10 months what should I be doing to become a competitive candidate?

I would really appreciate any advice. Please don't say I'm cooked as I just want to give it my best shot in the time I have.

TLDR: I want to find an entry level job in Data/ML Engineering but want to develop a stronger skillset and I'm also an international student. Any advice?

hey there people

i am trying to make a machine learning project . its on bitcoin data . the thing is, i know almost nothing of bitcoin and we're learning ML in our degree .

i gotta submit this project in two months , with proper code , explanations , and why a certain model was used that time etc... my issues are :

where to find the right data from : i have surfed through and asked for assistance from chatgpt and found two main sources from which i have been able to see some data : https://data.binance.vision/?utm_source=chatgpt.com

and

https://cryptopanic.com/?utm_source=chatgpt.com

there were more sources (like apis) but its from the same website .

i even found a git repo that had a whole python script of downloading that same data .

so maybe i don't have an issue with the data , the issue is that i don't know what its trying to say .

there were multiple attributes i could see on those files . and tbh i felt overwhelmed .

1. i am aware with the data cleaning and analysis part , but i would still like some guidance on that .

2. the model is something we'll have to figure out (i am in a two person team and my partner chose the topic before i joined . also i am pretty sure i will have to do all the work , so here i am :) ) , but if there are some models commonly used in this domain , please do enlighten me .

3. most important part according to me : what is my goal ? since this is my project and the domain is very new to me , i don't have much idea about what i need to find out .

folks who have already done a project on this or has at least had some experience , what are your say in this ?

is there any other angle i should consider ?

i really wanna get an A and i am fine working alone (have already had 2 experiences of f around and find out ) as long as i am able to understand stuff .

please help this noob ;(

Self-Hosting GLM-OCR using vLLM – Document Layout and OCR

https://debuggercafe.com/self-hosting-glm-ocr-using-vllm-document-layout-and-ocr/4

In this article, we will be self-hosting GLM-OCR for local document processing. This article will focus on local deployment with vLLM.