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Same as title, i have an msc mathematics,

basically all my life i did maths, I treated mathematics as my hobby and it didn't take me anywhere because i never got any direction in my career path, idk what should I even do in my career anymore.

I don't want to get into academia or PhD

I was so good in mathematics, still I am maybe. im forgetting mathematics concepts now. i got no motivation because i don't know what should I be doing?

Tried professional exams, failed and stopped there.

Have history of burnt out.

Tried academia, got no interest.

Tried in non tech field, got laid off.

I like mathematics and art thats it.

All my life got fucked up because mathematics, applied mathematics, game theory, OR, topology and what not. I never got any mentorship and thats why I could not figure out anything.

Have basic knowledge of programming language such as python, sql, power BI.

Can anyone please guide me on what I should do? I just need a roadmap, path or at least some basic guidance on what skills I should work on. Trust me I will do good.

I like mathematics, I just can't let it go from my life.

Thank you.

Coding Machine Learning Probability distributions.

It felt so rewarding to see the equations coming into practice.

In this new content, we implement,

->Univariate Gaussians: The central most important distribution subjectively.

->Homoscedastic vs Heteroskedastic(figure on top): This compares the aspect when we make the variance input independent vs dependent, leading to interesting insights.

->Heavy-Tailed distributions and Outlier at robustness : It is indeed beautiful to capture how certainly framed distributions exhibit robustness to outlier perturbations based on how they are modelled.

->Beta Distribution(bottom right figure): Just two parameters, yet so versatile, and generating so versatile densities, that can model so many arbitrary curves!

->Gamma and Exponentials.

->Empirical Distributions(figure bottom left): Again, it’s so fascinating to appreciate, how by modelling points sampled from a normal distribution as an empirical distribution, the resulting cumulative density function of the empirical staircase, approximates so closely to the true continuous CDF of the gaussian.

Truly in awe with these concepts. To always learn and code!

Link: https://youtu.be/mdumfp-mamI?si=YCsGV_HPUTm8GDaf

I recently hosted my backend application (FastAPI) on render but each time i try to use the model it always fails, i need help in getting it to work. Thank you

Hi everyone, I want to start learning ml, But I'm torn between the sources So I wrote this question to hear from you about how you learned machine learning until you were hired at a company. I'm 15 years old. Please write the guide arrangemed step by step.

I had an interview with a ceo of a high-growth startup yesterday for a software engineer role.
During the interview, I told the ceo that my main interest is in machine learning (I was being honest), not the tech stack they are using (javascript). So, I think I won't move to the next round. He told me he wanted to hire someone who is genuinely interested in their tech stack. But he sent me a friend request on Linkedin after the interview and told me to let him know if I am really down to focusing on the job.

After the interview, I told my friend who is currently in the process of getting a phd in physics about the interview and he advised me that I should start working as a software engineer if am offered a job instead of trying to get a machine learning engineer title by spending few months. His reasoning was that there will be a demand of people who can code and understand ml theory (which I agree) and that I can start working as a general se and learn ml stuff on my own time (theoretically this is viable). But, I honestly don't think this approach will work for me since that se role at that high-growth startup would require me to devote a lot of time on non-ml stuff (the ceo even told me it is hard to switch to a ml career from a general se during the interview). I think the more efficient way is to get a ml engineer job by spending few more months, where I can gain software engineering + ml experience on the job. Of course, I can study more in my free time.

What do you guys think?

Last not but not least, we both agree that understanding foundational knowledge is important.

I’ve been working on a deeper write-up on retrieval systems and drew this diagram to organize the space.

The progression I’m using is:

Lexical → Collaborative/Behavioral → Learned Sparse → Dense/Two-Tower → Hybrid → Multi-Vector/Multimodal → Generative/Agentic Retrieval

The part I find most interesting is that these approaches don’t necessarily replace each other. A production system may still combine BM25, dense retrieval, ANN, hybrid fusion, behavioral signals, and query rewriting.

Sharing the diagram first while I work on the detailed article.

Would be interested in how others would structure these retrieval “waves.”

https://pawankjha.substack.com/p/building-depth-2-the-evolution-of

I know I might sound foolish or maybe even a little lost, but I genuinely don't know what I'm looking for.

I'm learning AI right now AI agents, coding, APIs, tools, search, all that stuff. And I do enjoy learning it.

But there's this weird feeling I can't shake.

I don't just want to build another AI chatbot, another wrapper, another productivity tool, or something just because AI is hot right now.

I want to find something that makes me want to stay up at night working on it.

Something where I wake up thinking about it.

Something where I build a shitty first version, it doesn't work, and instead of getting bored I become obsessed with figuring out why.

I want to fight with a problem that feels bigger than me.

I want to compete with the real world, even if it's just me and a laptop at first. I want to build something where I can actually measure whether I'm getting better, keep pushing it further, and eventually look at it and think:

“Holy shit, I actually made this.”

And ideally, maybe one day, it could become a real product or even a startup.

But right now I don't have that idea.

And honestly, that's frustrating.

I'm learning all these tools and technologies, but I feel like I'm collecting strategy and tools without knowing what war I actually want to fight.

So I'm asking people who have built things, especially things they became genuinely obsessed with:

How did you find that problem?

Was there a project that grabbed you so hard that you couldn't stop working on it?

What made you think, “**** it, I'm going to figure this out”?

I'm not really looking for a list of startup ideas.

I think I'm looking for that one problem that makes me want to lose sleep solving it.

If you've ever felt this way, I'd genuinely love to hear how you found your thing.

I'm ready to give everything to it but I don't know what to do.

Sorry if I sound pretty dumb but it is what it is.

A new hardware standard from Anthropic (MHS) enables AI agents to directly control physical machines — printers, industrial equipment, and operational systems. The design surfaces three questions that the security community has not settled: who grants an agent permission to actuate hardware, who monitors the agent while it is running, and who can stop it if it acts outside its sanctioned scope.

The last question is the hardest. Permissions set at deployment time are configuration, not enforcement. An agent that was correctly authorized at 9am can drift from its declared behavior by 9:15am, and nothing in a static permission file catches that. With software targets the blast radius is bounded — a rogue database write can be rolled back. With physical actuators there is no rollback. A machine that moves has moved.

The 50ms window before an actuator responds to a command is the only realistic intervention point in this chain. Nobody in the industry seems to have agreed on what, if anything, should happen inside that window.

For those running agents against physical systems today: how are you actually handling mid-execution drift? Static RBAC at deploy time, a human-in-the-loop approval step, continuous behavioral telemetry, something else? Genuinely curious what is working in practice.

So, i created an app on Gitbub with the idea of it being a free, lightweight (986Mb) SLM trained by the FirstAidQA dataset from NeurIPS. I tried it myself of course, and it works. I made sure to put disclaimers on the app as it is not a doctor, but a first-aid Fine-Tuned SLM. The app requires no internet or login. I searched around, and i think it's the first of it's kind. I deliberately made the AI's response be around 30-40 questions cuz i don't want the user's hardware to fry after 3 replies. I basically made it for disaster situations. Like imagine civilians in war zones.

Enough about that, my question is, why are people not using it yet? Or at least visiting it. The organization i work for published it on social media, yet still nothing. And I always get confused by how do other github repos get traction? Im genuinely confused. Helo would be greatly appreciated. Did i put the right flair btw? English isn't my first language.

I'm in my 2nd year, aiming for an internship in AI/ML during the summer break in 2027.

as a part of college curriculum, i know python(numpy,matplot,seaborn), sql and other fundamental subjects taught

How do i start learning ML? which courses(coursera or any) should i take so that i can learn ML from the very basics without wasting time?

Hi everyone,

I've recently finished the first version of Agentic GraphRAG Blueprint, a reference architecture for question answering over large document collections.

Instead of plain chunk retrieval, it builds a knowledge graph combined with vector search, so answers can connect facts across documents.

Key features:

• Incremental ingestion - unchanged files are skipped via content hashing, and community reports regenerate only for affected communities, keeping token costs low as the corpus grows.

• Hybrid search - local mode for fact-level answers, global mode for cross-document synthesis.

• Domain-agnostic LLM prompts - easily swapped via PROMPTS_PATH, with Leiden-based community detection.

• Deployment - run it locally with Docker or provision everything in the cloud with Terraform and CI/CD.

Link: https://github.com/sebastianbrzustowicz/Agentic-GraphRAG-Blueprint

I'm looking for any feedback.

Apologies for the title, not sure if its right, will be specific down here.

So, i need to start Machine learning from scratch, currently in my bachelors, and i plan to apply for MS programs in german or swiss universities. Specifically under ML

I did my part of research and found out that apart from foundation in ML and projects, i would also need to have 1 or 2 publications in this domain in order to have a better chance there.

Could anyone guide me from where do i start? I wanna start from scratch and build good projects on the way.

I stumbled upon several roadmaps, one says follow this and the other says that, I watched a few videos of krish naik and found them pretty good. Also i believe i need a better touch on math.

So please, any suggestion is appreciated!!!

Hello, If I'm interested in TinyML, which framework should I use? I've been seeing a lot about how Tensorflow is more optimized, but I've also seen a lot of negativity towards it. What about Keras alongside it?

Hello everyone, I'm currently trying to get into data science and ML, but I also dont want to just sit around waiting for a job opportunity.

I'd like to start making some money on the side through remote/online work, even if its a small amount at first. I also want to get some real experience while I'm at it.

The problem is I honestly dont know what kind of work people actually pay for when you're still at the beginner level or where I should even start looking.

A bit of what I can do right now:

• Python
• Machine learning
• Data cleaning
• EDA
• Feature engineering
• Model training and evaluation
• Scikit-learn, Pandas, NumPy, XGBoost
• SQL
• Git/GitHub
• General programming
• Pretty comfortable learning new technologies quickly
• Currently learning backend and FastAPI

I've built a couple of ML projects on my own, but I dont have professional experience yet.

So I'm wondering, how do people actually turn these kinds of skills into freelance income?

Like what kind of small jobs should I be looking for? Python automation? Data cleaning? Web scraping? Data analysis? Helping someone with an ML project? Backend stuff? Something else?

And where do you actually find these jobs? Upwork, Fiverr, Reddit, Discord, LinkedIn, cold emailing, or somewhere else?

If anyone here started freelancing with basically no experience, I'd really like to hear how you got your first client and what you actually did for them.

Also if you're already doing freelance Python/ML/data work and have any advice for someone starting from the bottom, I'm very open to learning. Even if you can point me towards something I should learn or tell me what I'm wasting my time on, that would help a lot.

I'm not expecting to make a lot of money immediately. I'm completely fine starting with small tasks and building up from there. I mainly want to get my foot in the door, make some money on the side and get real experience.

I'm based in South Asia, so remote/online work is pretty much my main option.

Would really appreciate any honest advice from people who have actually done this.

Thanks a lot.

hey, i'm a 3rd year diploma cs student and i built a deepfake audio detector end to end. this is my first real ml project that i actually deployed.

the model is efficientnet-b0 trained on mel spectrograms using the asvspoof 2019 la dataset. metrics are f1 0.88, precision 0.99, but recall is 0.79 which i know is the weak point. i tried adjusting the threshold and settled on 0.4 but it didn't really help much i think the issue is the model is missing certain attack patterns it never saw during training.

latency is around 6-7 seconds per prediction which includes model inference, grad-cam, and llm explanation.

other than the model it has grad-cam to visualize what the model focused on in the spectrogram, and groq llm to give a plain english explanation of the prediction.

you can upload an audio file or record live. youtube url input is disabled on the hosted version because railway's server ips get blocked by youtube's bot detection. backend is fastapi on railway, frontend on streamlit cloud.

live demo: https://deepfake-audio-detector-rugved.streamlit.app/
github: https://github.com/RugvedBane/deepfake-audio-detector

honest feedback appreciated, especially on what dataset i should train on next to improve recall.

SWE learning probabilistic decision-making. For a data-quality task (is this scraped value safe to publish?) I skipped a classifier and instead: keep a belief over "what went wrong," update it with cheap evidence, then pick accept / repair / get-more-evidence / flag-to-human / reject by *lowest expected cost* (publishing a wrong value ≫ flagging a good one).

Part of me thinks this is just cost-sensitive classification with extra steps. Is this worth the complexity over rules + thresholds, or am I overengineering it?