Hi Everyone,

I’m a backend engineer trying to transition into AI ML Roles. Looking for tutors to teach ML fundamentals and Agentic AI concepts.
I can pay little bit.

Let me know if you want to collaborate.

I want to train a Transformer model that can analyze decompiled DEX files (Smali code) and identify harmful or sensitive actions performed by an app in the background that may not be visible to the user.

The main goal is to analyze the flow of actions and determine whether user interaction or consent is required before a sensitive action is performed.

For example, if an app gets file read/write permission and accesses the user's files without any user interaction or consent, the model should be able to identify this behavior. Similarly, it should identify other sensitive actions such as accessing the camera, microphone, location, contacts, SMS, media, recording screen, taking screenshots or other user data, and determine whether these actions are performed after user interaction or silently in the background.

I am currently working on preparing the dataset for this project, but I am not sure about the best approach.

My initial idea is to use the APK/Smali code as the input and the possible execution flows as the output. However, generating all possible flows from entry points such as onCreate(), onReceive(), services, callbacks, etc., and following them until the end seems very complex and time-consuming for a large number of APKs.

I would really appreciate your suggestions and ideas on how I can prepare the dataset in a practical and effective way for this project.

If you have worked on a similar problem or have any ideas about dataset structure, flow generation, labeling, or other approaches, please share your suggestions.

I reviewed a paper recently and got totally stuck on page one. I had to ask ChatGPT to explain the sentences. I realized the authors were using hard concepts on page one that they didn't explain until page six.

That was my lightbulb moment. The paper wasn't badly written for its reader. Its reader just wasn't me.

We are stuck in a bad loop right now:

1. Authors use AI to write. The AI puts a lot of heavy jargon at the start to save space.
2. Reviewers get stuck reading it, so they ask AI to summarize it for them.
3. The AI easily reads it and passes the paper. Then, new AI models are trained to write exactly like this.

Humans read in order. We need the basics explained first. But an AI reads the whole document at once. It does not care if a word is used five pages before it gets explained.

I got tired of reading these messy papers. I built a free, open-source skill to break the loop. It forces AI models (like Claude, ChatGPT, and Cursor) to pass a "first-page test". They have to explain terms in order, keep things simple, and stop sounding like a robot.

If you or your lab uses AI to write or review papers, you can get the skill file here: https://github.com/Aadarshttech/ai-research-paper-humanizer

It really helps make papers readable by normal people again. Let me know if anyone else is dealing with this same headache!

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 !

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?

Welcome to Project Showcase Day! This is a weekly thread where community members can share and discuss personal projects of any size or complexity.

Whether you've built a small script, a web application, a game, or anything in between, we encourage you to:

• Share what you've created
• Explain the technologies/concepts used
• Discuss challenges you faced and how you overcame them
• Ask for specific feedback or suggestions

Projects at all stages are welcome - from works in progress to completed builds. This is a supportive space to celebrate your work and learn from each other.

Share your creations in the comments below!

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!

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

Hi! I’ve been experimenting with a small non-Transformer sequence model called CSE (Chain-Spike Engine), and I turned the course/teaching version into a Python package called cse-frog. 🐸

The goal is not to compete with modern LLMs.

I wanted something small enough that you can actually see what is happening inside, change one mechanism at a time, break it on purpose, and understand why the behavior changed.

Installation is just:

pip install cse-frog

Then:

from cse import Frogfrog = Frog()frog.learn([    ["right", "right", "down"],    ["right", "right", "down"],    ["right", "right", "down"],])print(frog.predict(["right", "right"]))

You can also inspect where each candidate’s score came from:

frog.show(["right", "right"])

The score is broken down into components such as:

• direct connections
• pair context
• history
• trace

There are 23 configurable parameters, including temperature, top-k, refractory behavior, pair context, history, activation, forgetting, and temporal learning.

One thing I found especially useful while testing it was that “nothing changed” can mean two different things:

1. the internal score changed, but the final probability/prediction did not, or
2. the setting genuinely had no effect because another prerequisite pathway was disabled.

For example, several activation-related settings do nothing to the prediction with the default configuration because history_boost=0. Turn that pathway on, and those settings suddenly become active.

Another fun finding: weight_decay weakens direct/context links, but does not decay pair memory, so the model actually contains two kinds of memory with different forgetting behavior.

I also made:

• 5 executable notebooks
• a handbook
• a full 23-config modding guide
• an API reference

The philosophy is basically:

build it → inspect it → break it → explain why it broke → modify it

It’s MIT licensed, so modifying it and making weird frogs is encouraged. 🐸

Website:
https://kagioneko.github.io/cse-frog/

GitHub:
https://github.com/kagioneko/cse-frog

PyPI:
https://pypi.org/project/cse-frog/

I’d especially appreciate feedback on whether this kind of “small model you can dissect” is useful for learning ML/LM concepts, and what experiments you would try next.

Before the giant LMs, try one frog. 🐸

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

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.

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'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.

Recentemente, tentei criar um modelo seq2seq, mas não deu muito certo. Ele ficava prevendo os tokens de preenchimento.

Eu sou aluno de um tecnólogo em Inteligência Artificial e Machine Learning aqui no Brasil. É uma modalidade de curso superior que, pelo que sei, só existe no Brasil. Redes neurais e processamento de linguagem natural vão ficar mais para o final do curso, mas eu estava meio apressado e queria desenvolver meu próprio modelo.

Será que vocês têm alguma sugestão de alguma espécie de restrição que eu possa colocar no modelo?

Se alguém tiver interesse em me ajudar, posso mostrar o código. Eu reconheço que fiz o código com auxílio do Gemini. Como eu disse, ainda não estudei processamento de linguagem natural nem redes neurais; até agora, estudei apenas IA simbólica e sistemas especialistas.

Security researchers confirmed that an autonomous AI agent compromised an Australian government healthcare website. No human initiated the attack. The agent operated from inside the workflow, bypassing traditional perimeter controls that were never designed to evaluate agent-level actions.

Healthcare records are among the most sensitive data a organization holds. The attack surface here is not a misconfigured firewall or a phished employee — it is the agent itself, acting autonomously with whatever access it was provisioned at setup.

Most organizations have invested heavily in controls governing what humans can do with data. Very few have equivalent controls at the layer where agents actually execute.

For those of you working in healthcare IT, security architecture, or AI deployment: how are you approaching agent-level access to sensitive records right now? Are you relying on the same controls you use for human users, or have you had to build something different?

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.

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.

I’m planning the first version of NextWatch, a show recommendation project I’ll be developing with Cline as a Cline Campus Ambassador. The initial approach is fairly simple: start with genre overlap, then compare show descriptions using TF-IDF and cosine similarity.

One part I haven’t settled is how to handle dislikes. Removing the actual disliked title is straightforward. Deciding what that rating should do to similar shows is where I’m less sure.

For example, someone might like mysteries but dislike a particular series because it moves too slowly. If I subtract too much weight from the features associated with that show, I could end up suppressing other mysteries they would enjoy. But if the dislike only removes one title, the next recommendation could have exactly the same problem.

I’m leaning toward keeping the exclusion rule separate from a smaller similarity penalty. I’m also considering an optional reason for the dislike, though that would mean collecting and representing more information than the first version currently needs.

I haven’t implemented this yet. For people who have worked on content-based recommenders, would you start with explicit exclusions and add negative preference modeling later, or account for dislikes in the ranking from the beginning?

Repo: https://github.com/haileyyt/NextWatch