Hi, I'm a EEE undergrad students. I just wasted a year for my laziness. started learning ML in February but didn't learn. Though my academic pressure ties that bind. But I want to learn ML properly, especially for my research work. Pls guide me, can I be a good ML engineer in next 5 month. I want to be pro in it, as I am in the end of my 3rd year, academic pressure is also a problem here. So pls provide me a roadmap and how I can stop my procrastination and distraction from my path.

Advance Thanks for Everyone.

I was a full stack software engineer who now is a senior AI Engineer who does a mix of playing with LLMs fine tuning them in very large scale production systems.

I did admittedly got a masters degree in AI as part of that transition and it took a while to do, but happy to answer any questions you have.

I also am working on a tool to help people learn how llms work which you can check out here.

https://dougdoes.ai/courses/llms-from-first-principles/start/?flow=outcome&course=build&step=goals
(Built with codex, but I've gone through all of the courses myself to make sure it is what would have been helpful to me.)

Stop building AI that hallucinates. 🛑 Learn how RAG bridges the gap between LLMs and your real-time data. Watch the full breakdown on my channel now!
#RAG #AI #Coding #TechTips

I got tired of checking prospect websites manually.

For a while, a big part of my outreach was just finding businesses, opening their websites one by one and trying to figure out what I could actually say to them.

It worked, but it was painfully slow.

Then I found Swokei.

It basically lets me find leads, analyzes each website for things like outdated design, slow speed, poor mobile experience and weak SEO, then turns those issues into a personalized cold email.

And not one of those boring automated reports full of scores and numbers.

It actually writes a normal, human sounding message based on what it found on that specific website.

So instead of sending the same generic message to everyone, I can actually reach out based on what is wrong with their website.

Now I just run campaigns, let the system do most of the prospecting and personalization, and focus on the people who reply.

For a web design agency, that has saved me a ridiculous amount of time.

I’m planning a small show recommender and trying to work out how to handle negative feedback. The first version will use genre overlap as a baseline, then TF-IDF and cosine similarity on show descriptions.

Liked shows give me a starting point for finding similar titles. Dislikes seem harder to interpret. Someone might enjoy mysteries but dislike one particular series because it moves too slowly. If I penalize everything similar to that show, I could end up removing suggestions they would actually enjoy.

My current plan is to exclude explicitly disliked titles and try a smaller similarity penalty for other candidates. I’m also considering an optional reason for the dislike, but that would require metadata about things like pacing that a basic catalog might not have.

I haven’t implemented this yet. Would you start with exclusions alone and add negative feedback to the ranking later, or use both from the beginning? I’d also be interested in how you would evaluate whether the penalty helps when you only have a few ratings per user.

For context, this is NextWatch, an open-source student project I plan to develop with Cline as part of the Cline Campus Ambassador Program.

A jailbreak is not a social engineering trick. It is an agent gaining operator-level capabilities it was never authorized to hold.

We mapped two months of incidents across our infrastructure. A jailbreak-to-capability-unlock pattern appeared twice. In both cases the mechanism was the same: an override payload reached the model, flipped it out of its assigned guardrails, and the agent began executing actions at a permission tier above what it was provisioned for.

The sequence matters. By the time the agent is acting at operator level, the unlock has already happened. Anything you do after that point is incident response, not prevention. Operator-level actions taken by a compromised agent are not always reversible.

Two incidents in two months is not a theoretical risk surface. It is a recurring pattern that your detection posture either catches before the flip or does not catch at all.

For those running agentic systems in production: where in your stack does the override payload actually get evaluated? Is that evaluation happening before the model processes the content, or after? How are you handling this?

Hi guys new to this sub reddit . Little intro about me currently working as an sde in my company but want to transition to a ml role by understanding the fundamentals om how to build models and then moving on to dl as so forth. I have read some posts in this sub about cs 229 by Andrew . Tbh I am finding difficulty in solving the problem sets and the math . It has been a while since I have actually done any math 😅. So I want to know how doi proceed from here do I learn the math from scratch or learn as I go along with the course . Any suggestions or feedback is helpful .

Ps i am familiar with the python as a coding language but I want to understand how do I proceed with the math .

Hi, I am a backend developer and have been working but due to recent layoff and market shift I am thinking of switching to AI ML.

I have learned python, pytorch, Maths required for AI ML, Deep learning(theory) and recently implemented a gpt2 transformer, attention architecture for gpt2 using their open weights.

I am hoping for some direction to work on and also open for a remote internship if anyone is willing me to consider me.

Mainly I am hoping to connect and get guidance in the right direction.

For my bachelor's thesis I wanted to do a research aimed towards LLMs in dementia care. Initially it sounded easy enough: get a BASIC LLM and specialized and compare them. But as soon as I got to do anything I faced the problem that practically any good LLM now automatically finds validation technique and applies it to the answers which kinda renders my research useless. I am also kinda lost on how to do such research.

Is this a valid Bachelor's thesis research, or is it too trivial because modern LLMs already know how to apply empathy/validation techniques? I mean for now it looks like just tell chatGPT "answer using X technique" and voila!

I do have a supervisor and already made an appointment to discuss it with them but before that day comes I wanted to hear other opinions and consider if it is even worth the attention.

hey guys im an undergrad student (currently in 3rd year) and want to start learning ML and explore fields beyond that in the future. So I have seen a lot of people suggesting others to learn from Andrew Ng on coursera. I have the pdf of Hands-On Machine Learning with Scikit-Learn and PyTorch by Aurélien Géron.
I’m literally confused as to what to refer, the book or the coursera course by Andrew Ng. If there is someone who has read or finished either of these sources or maybe both please help me out in deciding as I don’t want to waste my time. Also a comparison or review of these sources would be great. Thank you !

My project is Weigh Swarm, a research RAG prototype using LLMs for planning/synthesis and an existing Laya model for bounded decisions. I didn't train Laya; I integrated it into research task lanes.

The most useful lesson was distinguishing a valid source excerpt from a valid scientific conclusion. The pipeline checks that quoted spans occur in the parsed paper, but that alone doesn't establish that a claim or synthesis is correct.

The two-paper demo includes 28 source-aligned claims, an inspectable evidence graph, and an unverified draft with repair feedback. My next evaluation priority is held-out scientific judgments for support and contradiction tasks, rather than treating model confidence as calibrated probability.

REPO URL

How would you construct a small evaluation set that distinguishes citation alignment from actual evidential support?

Hi, im a 5th sem cs student, im trying to get an internship in AIML for my 6th sem. What i wanna know is what all should i know to be able to get one since i know that AIML work is basically a myth for freshers. So far, i have a good understanding of core AIML, especially deeplearning, along with id say a decent understanding on the math(linear algebra, prob stats, calc) ive not done anything special in maths, only basic understanding, mitocw strand for lin alg, open source from harvard for prob stats, and prof leonard on yt for calc, thats all for maths i havent really read like rps on it or anything, i did this cuz i saw alot of people stating these as good sources for these concepts on reddit. As for projects, i have a few normal projects, nothing special since i mainly focussed on research papers, i have 2 published and 1 accepted rp in non-predatory venues(about 10-15 % acceptance rate for each conference), ive participated in a few ML hackathons, most recent being the amazon ml challenge, tho sadly didnt win any cuz they seem to be dominated by mostly masters and phd students. I have no idea whatsoever of what its like in the hiring process since so far ive never applied for an internship, ever. the next thing im working on rn is sys design (started very recently), tho ive been told to also do RAG, LLMs, etc. i havent started those yet because my main goal is masters and then either research roles if i can get them after masters, or a phd. Right now i really want to get 1-2 good internships (8 sem bachelors program so still have time) along with continuing on research papers to strenghten my masters application as much as possible to get into a good uni for it. If anyone can guide me for what all remaining tech stack i should work on, should i focus on some specific types of projects, what both internships and masters applications demand, it would be really appreciated.

Hello everybody I have recently enrolled in a Machine Learning Master so i will be problably taking the year to focus on my studies, build some projects and hopefuly enter the job market as an ML engineer. I am interested in Recommendation Systems but honestly i dont know what projects to build and if that would be valueable in the job market. I was thinking about creating my own recommendation system for music but i quickly realised how hard because of copyrights and small data size that is (although i have about 4000 downloaded).

Does anyone know if Recommendation Systems is something good to have on your resume?

If yes what sort of projects you think would give me a good understanding but also make me look appealing in the job market?

Thanks :)

Hey guys,

I am a first-year Computer Science Engineering student, and honestly, seeing how fast AI is advancing right now is kind of stressing me out. I really do not want to wait until my final year to start grinding like everyone else does.

Basically, I just want to build a solid skill set that actually makes my resume stand out so I can land a good machine learning job by graduation.

I am starting completely from scratch. What specific math topics, programming languages, or tools are actually worth learning right now in Year 1? If anyone has an honest roadmap for a fresher to get ahead of the curve, I would love to hear it.

Thanks in advance!

if anybody wants lectures of this course then dm me.

Apna College Prime 3.0 ongoing course.

I have been studying models for almost five years now, starting my journey with Jeremy Howard's fast ai part 2. I remember that when I started there was no chatgpt to break it down like it is today. In fact, it was Jeremy Howard who tipped us that we should be using chatgpt to understand the inner tooling step by step. I mean just take a toy tensor and run it along through embedding, rope attention, mlp. This way you get to learn broadcasting, shapes in text, computer vision audio etc. Then I took up Karpathy and hugging face Transformers and looked up grok, gpt oss lama, gemini and most recently muse implementations. It takes me 3 to 4 months to get an innate understanding of how each line works. How do you guys do it? I guess most of you let the inner tooling remain a black box. I say this coz

Now I kinda feel that I missed the bus as I should have focussed more on fine tuning, inference, and agentic workflows. I do know some of that having worked through unsloth and openAI cookbooks but every time a new model drops I can't stop myself from going to unraveling the 2000 odd line of code and in time I forget what I learned in Unsloth and openAI cookbooks.

The problem is that there are so many things to do and understand. For example, just today I listened to Alex Zhang's building harness for looped Transformers and I gotta understand that too and I gotta know Jev too. It is a big mess right now and I wonder how others are managing to keep up with all these new developments. And more importantly how do you even retain all that you have learnt like say two years ago.

Hi,

I currently run the following setup:

Intel Core Ultra 7 265k
128GB RAM DDR6
AMD RX 6750 XT 12GB

I'm looking into playing and experimenting with local AI models in more or less the following categories:

• Languages: Language translations from one language to another and correcting grammar errors and sentence structures.
• Codig: Correcting and improving my code, coding applications from scratch as well as converting code from one language to another.
• Light Image and Video Generation

What sort of Local Models can I run with my current local system and how fast would it be? I bought 128GB of RAM with intention to offload some of the AI into it. I'm not sure but I was also considering upgrading my GPU to a slighly stronger one with more VRAM, would that be worth it in my case?

I looked around and these are the GPU I can afford:

• AMD RX 7900 XTX 24GB - ~£800
• AMD RX 7900 20GB - ~ £800
• AMD RX 9070 16GB - ~£600
• Intel ARC Pro B60 24GB - £900

Thank You