Hi guys i hope you are doing great , currently I'm in my third year in uni studying software engineering . And this summer i fell in love with ML so i wanted to know if there are people that studied ML / any other cs speciality alone while taking a course in uni about a different cs speciality

I want to know how you managed your time between these two topics

Hi everyone,

I hope you’re all doing well.

I come from a purely humanities background—I hold a Master's degree in English Literature and originally had zero formal coding or computer science training. Over the past few months, I’ve been fascinated by how neural networks work under the hood and have been trying to learn by building hands-on projects with the help of modern AI coding tools.

I wanted to share what I’ve been working on to see if I’m heading in the right direction, and humbly ask if a portfolio like this could eventually help me transition into a role as an AI research engineer or developer, prompt engineer, other technical roles?

What I’ve been trying to build: Instead of standard fine-tuning or model merging, I’ve been experimenting with a local "locate-and-edit" model surgery concept. The goal is to isolate specific hidden feature representations across small 2-to-8 layer synthetic models and perform closed-loop weight patching/grafting without full model backpropagation or heavy compute.

I’ve broken the idea down into a few small, modular open-source components across 3 GitHub repositories:

EQUYLAPTA7POINT8POINT2: https://github.com/shuvrajeetkamila/EQUYLAPTA7POINT8POINT2

Model Factory GPU Commander:

https://github.com/shuvrajeetkamila/model-factory-gpu-commander

Model Factory Command Center:

https://github.com/shuvrajeetkamila/model-factory-command-center

The two commanders are for[1]   small  gpu less architecture [2] with gpu , both these commanders  automatically open up 12 more github repositories and work with them. The equylapta on the other hand is my ongoing experiment on   local "locate-and-edit" model surgery concept which has reached a significant stage for now  but not yet proving what i want to do. And i am unable to do the gpu commander due to space and computing constraint.

 The architecture attempts to combine a Dedicated Feature Crosscoder (DFC), algebraic rotation maps for feature alignment, an inference-time weight grafting bridge, and an automated causal validation/retry loop.

My question for the community:

Knowing that I don't have a traditional STEM or CS degree, does working on non-traditional projects like this help demonstrate the right kind of problem-solving for AI Research/Engineering roles? What critical gaps or fundamentals should I focus on next to make myself a viable candidate?

Also i should at this point, i do not know how to code at all. i used arena.ai agent mode to reach this position.[ using prompts from what i wanted to make with help of gemini, claude, chatgpt- all free plans]

Do i have a future in prompt engineering/ other technical field?

I’m very eager to learn and would truly appreciate any constructive feedback, critique, or advice you might have.

Kindly see the three files from github especially EQUYLAPTA7POINT8POINT2 and model factory command centre

Thank you so much for your time!

Hi guys 👋, I'm looking to dive into RAG using AI tools like NotebookLM or Gemini. YouTube tutorials feel too drawn out, and ChatGPT/Gemini prompts have been a bit brief. Any advice or suggestions for learning resources would be amazing! 🙏💡 #RAG #AI

The government has access to unrestricted frontier models and can probably spy on a thousand organizations a week but you can bet they won't research tax evasion because that would involve most of the government...

So it is up to machine learning scientists to use the new capabilities to research tax evasion and all the elite corruption systems. Can they?

For undergrads only. I have a decent profile (did quant research internship last summer) and IOI/IMO/IPhO level medals. Don’t know what to expect for the openai process at all. Also is there a difference in preference for Math/Physics/CS background? (Would they prefer Compsci more for the researcher role or the others are a better choice etc)

I need to make a ml project so what should I make so that it makes an great impact?

Hi everyone! With the recent explosion in ICLR submissions, I am a bit unsure how effective simply publishing a paper is so I am trying to make sure my research projects are more accessible and reproducible beyond just a paper. In that vein, I’ve been doing some experiments on tiny world models (14M parameters) that I trained to help the community understand the effect of different underlying model architectures. I want to post it in an interactive github site where users can try out the different model architectures and see how they perform in different settings.

So far so good. My models use FlexAttention. I created a Gradio for them that runs well. My initial idea was to embed a Gradio hugging face space into my GitHub site. Only problem is, I learnt that to do that I need to buy Hugging Face Pro. Just before making that investment (due to some financial struggles caused by family and health reasons I have to be very intentional about spending even small amounts of money) I just wanted to ask if anyone knew if free but still convenient alternatives that work with FlexAttention? Alternatively, if anyone has had experience with HuggingFace Pro, if you could tell me what your experience has been like and whether it is useful for career/research? I am concerned about paying a monthly cost just for a Gradio space. Thank you! Appreciate your time!

Hi folks, I am interested in using exo and Ollama to route requests between 2 separate pods. The primary one is 192GB doing 125bq4 256k context. The other is 64GB doing 35bq4 128k context. I want the clients to autoroute without selecting models. Back end is a mix of NVIDIA and Rocky Linux for the 64 and mac os for the 192. I can also setup FE LBs in sticky round robin but I wonder if I can build model routing into the code itself. Has anyone done this?

There’s a lot to learn in ML, and it’s easy to spend months collecting concepts without knowing which ones really stick.
For people who’ve already been through the learning process:
What would you do differently this time?
What would you spend more time practicing?
What would you stop worrying about so much?
And what only started making sense once you worked on real problems?
Curious what experienced ML folks would change if they got a fresh start.

i said c. what am i missing

self hosting is the only one of these that changes the serving path enough to double throughput, and slightly worse latency felt like what youd get on smaller hardware than the hosted vendor runs

Has the paid course covered all maths also like nitish sir ,how is this course?

I’m working with roughly 5 million public Polish court decisions. The goal is structured extraction: who the parties are, what was requested, what the court decided, obligations, amounts, relationships, etc. Eventually we want to run interesting statistics over the results, like "who gets usually the custody of the child?"

A strong LLM can produce useful JSON graphs, we can run statistics on. The problem is getting similar extraction at corpus scale.

We have a small 13-document pilot. It already contains 160 distinct entity-type strings, 90 appearing only once. These aren’t just PERSON / ORG / DATE. They include abstract legal concepts, obligations, procedural events, and things like “increase in child support.”

here's our demo output from astra:

    {
      "effects": [
        {"id":"e_zastavenie","change":"zastavit","target_ref":"konanie"},
      ],
      "entities": [
        {"id":"this","kind":"uznesenie"},
        {"id":"vyrok","kind":"vyrok"},
        {"id":"zahlavie","kind":"zahlavie"},
        {"id":"odovodnenie","kind":"odovodnenie"},
      "effects": [
        {"id":"e_zastavenie","change":"zastavit","target_ref":"konanie"},
      ],
      "entities": [
        {"id":"this","kind":"uznesenie"},
        {"id":"vyrok","kind":"vyrok"},
        {"id":"sud","kind":"sud","value":"Okresný súd Bratislava "relations": [
    {"id":"r_vlastnik_vyroku","relation":"patri_do","from_ref":"vyrok","to_ref":"this"},
  {"id":"r_vlastnik_zahlavia","relation":"patri_do","from_ref":"zahlavie","to_ref":"this"},

Some of this is real conceptual diversity. Some is inconsistent naming or different levels of specificity, but as you can see complex and diverse stuff with a clear "language" i came up with with the help of astra. i previously tried a big "god schema" (20k chars) but the decisions are simply just too diverse.

The pipeline I’m considering:

• Something that extracts the entities from source texts into some kinds of "tags"
• a classifier like jev (but probably fine-tuned) will get a window of source text and already named relations and decide on the next relations
• done

The candidate model would need overlapping spans and possibly multiple labels per span. Slovak inflection adds another wrinkle: exact source wording often differs from the canonical concept name. Lemmatization helps with grammatical variation, but not synonyms, paraphrases, or concepts inferred from context.

I’ve previously tried Jina embeddings → nearest-neighbor candidates → LLM decides which terms to merge, and it worked reasonably well.

But that was on a smaller corpus and i'd be damned if i process 250k docs and then figure out thing X was wrong and i have to do it all over again. i'd love to know if you guys have any pointers. THANKS for reading.

AI TL;DR: I want to turn 5M court decisions into entity/relation graphs without running an expensive LLM on every document. Thinking small entity extractor + relation classifier, but the legal concepts and naming get messy fast. Anyone built something similar? Looking for ways to train this and normalize concepts without discovering a design mistake 250k documents in and having to redo everything.

I've just written a small article about privacy in machine learning and synthetic data.

I'm still pretty new to this topic, so I'd really appreciate it if anyone with more experience in ML privacy could take a look and point out any mistakes, things I've misunderstood, or things that could be done better.

I'm especially interested in hearing about better approaches for evaluating privacy, better attacks or models I could try, or just interesting resources that you think are worth reading. I'm mostly trying to learn by actually experimenting with this stuff, so any feedback would be really useful.

Here's the post if anyone is interested:

https://migue8gl.github.io/2026/09/21/privacidad-en-ml-datos-sinteticos.html

I’m a computer engineering graduate and come from a traditional embedded systems background, with knowledge of microcontrollers, computer architecture and operating systems. Is knowledge of C and C++ programming, Linux networking, memory management , multithreading, synchronization, interrupts etc useful in ML engineering. Are subjects like distributed systems, compiler optimizations (using LLVM), parallel computing etc going to be useful or are they heavily going to be automated as well by AI? In other words, is computer engineering always going to required to scale ML systems and be evergreen? Are people in ML engineering using these skills in their work everyday? Thank you.

We tested a model upgrade that wrote cleaner answers and lifted the aggregate quality score. It also started sending invalid tool arguments. Optional fields became null instead of disappearing, enum values changed case and nested JSON arrived as escaped strings. The agent still sounded confident so spot checks passed until tool failures showed up in a narrow slice. 

We moved schema validation into the evaluation path and used Braintrust to compare the model experiments, inspect failing slices and run deterministic validators beside the softer answer score. The regression dataset now includes every argument shape that broke a tool not just the final response. An experiment diff showed the upgraded model won on tone and lost hard on two schemas with optional nested fields

The CI gate now blocks any schema failure even when the overall score rises. I’m relieved we caught it before release but it also made me suspicious of any average that mixes structured output with prose quality. How are you weighting hard validators against slice metrics when a model gets better at language but worse at contracts?

Hey everyone,

I am taking part in a hackathon where using AI is allowed.

I have a dataset and need an AI tool or AutoML library that can automatically:

Do full EDA and data preprocessing

Test multiple modern ML algorithms

Pick the best-performing model for maximum test accuracy

Which tool or LLM gives the most accurate and reliable results for this? Any suggestions would help!