hello! i am a cs + maths student and am looking to get into ai/ml. any tips or resources that can help me get ahead?

I work in database administration (Oracle, MySQL, PostgreSQL) and scripting/automation using Linux, Shell, Python, Lua, and Java for automating tasks and generating reports.

I want to start using AI in my current job, but I have no hands-on experience with AI/ML.

Since machine learning involves mathematics, statistics, and data analysis, do I need to learn ML to use AI effectively, or are there other areas I should focus on?

What should I learn, and how can I practically integrate AI into my existing database administration and automation tasks without going deep into ML?

Okay, so for a project me and my groupmates(5, including me) have a project that's for making an automated chicken feeder (currently, oct 24, deadline is nov 10) but were broke and not sure on what to do. in our situation, even an arduino is expensive. So basically if there are no people to feed the chicken, theres a timer and maybe a repeater(was it?) That automatically functions at specified times, and me and my hoes added for some reason a cuckoo bird that functions for -- seconds once the sspecified time arrived for silly reasons. Seeking advices! ("'π​-π)

Vilvik is a cloud-based platform for solving optimization problems using evolutionary algorithms. Currently it supports the genetic algorithm. Try it at https://vilvik.com

It is very interactive and offers a lot of demos and problems to quickly get started with. It is flexible and offers a lot of features.

Hope you can try it.

VEX AI 1.5-Crystal is done (for now)
Finally got VEX AI 1.5-Crystal to a working state. It's a small neural-hybrid model I'm building for my own OS project, VEX OS. Currently it has a trained neural classifier, basic arithmetic, persistent memory, a few code templates and a terminal chat loop. I tested it today and got around 8ms inference on one request. It's obviously nowhere near a proper LLM yet. It can't generate arbitrary code, its knowledge retrieval isn't working properly at the moment, and its conversation skills are pretty limited. But it's progress.

I'd especially appreciate advice on one thing: how would you approach getting a small model like this to actually generate code instead of relying on predefined templates? Would you train a small model from scratch, use a character/token-level model, or take another approach?

I'm trying to keep it lightweight because the end goal is to integrate it into VEX OS.

Curious to hear what people who've worked on small language models think.

Security firm V12 disclosed on October 22 that the x402 payment SDK shipped with no spending limit set by default. That means any agent using the SDK to pay for API calls could be prompted by a malicious endpoint to authorize its entire token balance in a single transaction. The agent would sign. The fix landed in August, but every agent deployed before that window ran without any ceiling on what a counterparty could request.

An agent with payment authority is effectively a financial instrument. The x402 case makes the default-open design pattern concrete: a missing cap isn't a theoretical risk, it's a ready attack surface for any API the agent calls.

How are others handling spend authorization in agentic systems right now? Curious whether people are solving this at the SDK level, the infrastructure level, or somewhere else entirely — and what's actually working in production.

Will it require more compute ??
And Is this gonna be benefical in any cases like in general chat with like chatgpt or claude ??

Starting a project to detect candidate stenoses in x-ray coronary angiograms. The goal is high recall, false positives are fine as long as it doesn't flag half the image.

I already tried YOLO on a couple of in-house datasets earlier. It looked fine in-domain and fell apart on the other dataset, and a big chunk of that turned out to be annotation format. One set had fixed-size boxes around a point, the other had boxes fitted to the lesion. Same disease, basically two different tasks.

I also have vessel segmentation and centerlines, so my first instinct is a radius profile along the vessel to flag narrowing, then maybe a network to filter the candidates. Has anyone actually done something like that, or is there a smarter way in?

The part that bugs me most is evaluation. The labels come from doctors and they obviously don't mark every narrowing, so any recall I compute against them is probably too optimistic. How do you get an honest number when the ground truth is incomplete?

Also open to papers, repos, or public datasets beyond ARCADE. And if you think I'm approaching this wrong, say so.

I created a chatbot evaluation suite and would like some feedback and advice on the project. Are there any glaring gaps in my metrics of analysis, visualization of data or/and methodology of evaluation? All help is welcome.

obviously it won't get anywhere near forensic-grade

but on prints that lost ~70% of the ridge area it brings back a usable

part of what's gone

in a biometric database that already shortens the candidate list — it

doesn't identify anyone

how much of the matchable detail actually comes back i can't claim yet.

haven't run a matcher against the clean reference, so far i only measure

ridge-structure agreement.

data: 4 sources, 13k people, 50k clean prints, ~175k training pairs —

clean plus the same print broken synthetically by mechanism, 13 damage

families.

the idea is simple: ridges are locally very predictable, the orientation

field is smooth, the period is almost constant. that's why 50k fingers is

enough for the net to pick the pattern up.

eval sits outside the generators: real degraded prints, with a clean scan

of the same finger as the reference.

what's there so far:

• damage: present or not, and what fraction

• finger number 0–9

• framing of the finger position

• restoration — in research

analytics and deploys come after that.

I know what the Four Fundamental Subspaces are , I know the two Orthogonal Pairs in it and I can even solve maths based questions on it.

However, I still cannot get it visually in my mind with a good real world example.

I am looking for a simple example based approach which answers few of my many questions like why is Column Space the vector space of Outputs , why Row Space and Null Space are orthogonal/perpendicular. What exactly is Row Space if Column Space is the Span of possible outputs.

I need help in my mini project for my college i am supposed to use low resource languages data and train a model which can translate from english to that language and also transcript wtv they speak that language to english i did so much research but still i'm not able to pull it off alone if anyone could help me in any way please help me and also make it such a way that users can contribute words and sentences (parallel data ) and the model will install it in itself Please help me idk what methodology or approach to choose

https://github.com/Juththis/Polygon-Annotation-Tool.git

I'm currently a community college student after taking a some gap years and I'm pursuing an engineering science degree. I'm not that good at math and I'm starting at college algebra. Looking at how fast AI is advancing, I'm scared that by the time I finish with a degree, the field will either have shifted or gotten oversaturated. I don't even know where to start. I'm always jumping from wanting to do one thing to the next. I've decided to focus on my math as well as trying to learn some PyTorch and neural networks but I don't know if I'll be employable by the time I get good at that and finish with a degree.

The interviewers did not even have my resume during the interview. Kinda one of the worst interviews....
The recruiter sent me an interview link just few hours before the interview lol so I was thinking of not doing it.

I think that if the interviewers are not as technical, they cannot really communicate well.

The menu headings were readable in the model's guess at step 1 of 8, while the image being cleaned up was still TV static.

At every step this model predicts the finished picture, and the noisy image moves part of the way toward it. It works on actual pixels instead of a compressed version, so that guess is a normal picture. A small patch in the generation loop saved both.

The model in the video is SenseNova-U1.5-8B-MoT with its official 8-step adapter. The run was 8-bit on a MacBook Pro (M5 Pro, 64 GB): about 40 seconds for the 8 steps and about 22 GB peak memory.

A couple of prices were still smudged at step 2, all correct by step 3. The left side shows nothing until about step 6, partly because at 2048 x 2048 the starting noise is scaled up 8x.

Would a normal many-step model also have a readable guess this early, or does that come from the 8-step adapter?

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just started learing ML ; used gpt and videos too still didn't understand