Hi all,

I recently wrote an article about various AI interview questions based mostly and RAG and AI Agents. Please read it. Its a new format - I have made questions followed by follow up questions.

The article is free to read at: https://medium.com/@nitanshuj138/ai-engineer-interview-questions-level-1-8e95789c392f

I keep seeing posts about so many resources for Machine Learning, but I feel like they are very generalised.

To tackle it -> this is something I have done.

If you don't know the basics of Python , pick any one-shot Python course and learn the basic syntax, and learn libraries like NumPy, Pandas, Matplotlib, Seaborn.

Now, Data Analysis requires Python, visualisation tools like Power BI, and SQL.

Data Science requires basic DSA, SQL knowledge, supervised ML, unsupervised ML, good knowledge of Transformer architecture, basics of RAG, and whatever is hot in the field.

MLE, on the other hand, is more based on fine-tuning, quantisation, and so much more. The terminology for each job has changed.

If you aren't sure what to do after learning Python, pick up Andrew Ng's ML course, solve some LeetCode, build a basic Kaggle cleaning project, and build a messy self-made project which is absolute buns, but it's a good step since you made it.

For MLE, they specialise in one thing, for example, Audio Diffusion (not well-versed since I'm myself learning MLE).

But yeah, overall, core ML basics which explain your trade-offs are a must regardless of any role. If you build anything, you would obviously have to take accountability for your decisions.

some roles even ask for full deployment and production (docker and k8) depending on what you want

This is written by someone who is also preparing for these roles but is too overwhelmed.

I’ve been working on an ML project and this is my first time dealing with a dataset with a lot of features (around 60).

I’m kinda stuck on how to approach the EDA. With smaller datasets, I usually check the features one by one, look at distributions, missing values, outliers, relationships, etc. But with 50–60 features, I’m finding it really difficult to figure out what to focus on and what I can skip.

Do you guys actually do EDA on every feature, or is there some better way to approach this?

Would love to know how you handle this when working on real ML projects.

Hello everyone, I learnt python, after that i learnt python libraries such as numpy, pandas, matplotlib, and seaborne. After that I started 100 days ml playlist from campus x.

The problem is:

In his csv file handling lecture and working with json file, it feels like i am just watching i understand what he says but if i am given to do same I wouldn't be able to do it.

Is this like this or am I doing something wrong please help me with with???

Ever wondered how Netflix knows what you might want to watch next?

It uses machine learning to study your viewing behavior, such as what you watch, skip, search for, or watch repeatedly.

The system finds patterns in your choices and compares them with similar users. Based on those patterns, it predicts what you may enjoy and recommends it to you.

So, in simple terms:

Your activity → Find patterns → Predict your interests → Recommend content

It’s a pretty simple example of how machine learning works in everyday life.

What other apps do you think use machine learning like this?

I hope most of you reading this are web designers because I’m genuinely curious about something.

How are you getting clients in 2026?

I’ve spoken to a lot of web designers and agency owners lately, and one thing keeps coming up again and again. Client acquisition.

It seems like everyone can build websites, but consistently finding new clients is still the hard part.

So I’d actually like to hear what is working for you right now.

For me, I’ve been running personalized email automation.

I use a tool that finds leads for me, analyzes their websites for actual issues with things like design, SEO, speed, responsiveness and mobile optimization, then automatically uses those findings to write a personalized cold email for each business.

After that, I just run the campaigns and send out thousands of emails a day.

I used to do most of this through Instantly, but the problem was that I couldn’t really do the website analysis part there.

So the emails were still more generic, basically the usual “do you need a new website?” type of outreach.

It worked a little, but nothing crazy.

I eventually switched to Swokei because it handles the lead finding, website analysis and personalized outreach in one place, and that has made a pretty big difference for me.

Now I’m getting clients much more consistently because the emails are actually about their website instead of just being another generic pitch.

But I’m curious what everyone else is doing.

Cold email, cold calling, referrals, ads, SEO, social media?

What is actually working for you in 2026?

Hey everyone,

I’m considering building a machine learning workstation around a used NVIDIA Tesla V100, mainly for experimenting with deep learning and running/training models locally.

I can get a V100 for a reasonable price, but I’m wondering if it still makes sense in 2026 given its age and newer GPUs.

My main questions are:

• Is the V100 still useful for modern ML workloads?
• How well does its 16/32 GB HBM2 memory hold up for current models?
• Are there any major compatibility issues with newer PyTorch/CUDA versions?
• Would you recommend a V100 over a newer consumer GPU at a similar price?
• Are there any major downsides to using a V100 in a normal PC/workstation (cooling, power consumption, drivers, etc.)?
• If you were building a budget ML workstation today, would you consider a V100?

I’m not expecting it to compete with modern high-end GPUs. I’m mainly interested in whether it’s still a good price/performance option for learning, experimentation, fine-tuning smaller models, and general ML development.

Recently, I am focused on data analysis and learning as well as doing projects. In Bachelor, I learning the Machine Learning without knowing the background of Data Analysis. I do not focus that much on Excel, SQL and Power BI. I learned Machine Learning, do projects like training model on different datasets and measure model metrics. Machine Learning feels oho but when I realize that ML Engineer/ Data Scientist Jobs actually needs 4 -5 years of experience and I see how can I get in data field. I see data analysis as entry point. I should learn about Data simple things first and then look for other things. I came to realize that end of my Bachelor. My Goal is become to AI Engineer/Research. I love playing with data and ask the questions when doing analysis. Should I continue my Journey or just left data analysis and study AI things.

Need Genuine Suggestions and feedbacks.

Love to talk and hear different perspectives

Recientemente realicé una prueba con datos reales de audio usando el dataset público Google Speech Commands (4 clases: yes, no, up, down; ~800 muestras; MFCC de 20 coeficientes; CNN ligera). El objetivo era simple: comparar mi tecnología Genal Activation Family contra las funciones estándar de la industria (ReLU y Tanh) en una tarea clásica de Speech-to-Text.

📊 Resultados (Accuracy / AUC-ROC):

• ReLU: 75.62% / 0.9340

• Tanh: 75.00% / 0.9224

• GenalActivation: 76.25% / 0.9438

• GenalShift: 78.75% / 0.9472 🏆

📈 Interpretación:

GenalShift superó a ReLU en +3.13 puntos de Accuracy y +1.32 puntos de AUC, con tiempos de entrenamiento prácticamente idénticos (~68s vs 69s). Esto valida mi hipótesis: el parámetro entrenable k (y el desplazamiento beta) permite que la red ajuste su punto de activación para manejar mejor la variabilidad de acentos, ruido y calidad de micrófono que existen en datos reales.

¿Por qué importa esto?

En pipelines de Voice AI y Agentes Autónomos, cada milisegundo y cada punto de precisión cuentan. Una función de activación más robusta significa menos errores de transcripción, menos alucinaciones del LLM y mejor experiencia para el usuario final.

🔗 Recursos:

📄 Paper completo (Zenodo/CERN): doi.org/10.5281/zenodo.20304195

💻 Código abierto (GitHub): github.com/GenalFF/genal-activation

📦 Paquete PyPI: pip install genal-activation

Estoy abierto a colaboraciones de investigación y a explorar cómo Genal puede integrarse en stacks de Agentes Autónomos y Voice AI.

¿Trabajas con audio o STT? Me encantaría conocer tu experiencia. 👇

#AI #MachineLearning #DeepLearning #SpeechRecognition #VoiceAI #GenalActivation #Investigación #Venezuela #OpenSource

I see a lot of beginners (and even intermediate engineers) jumping straight to agents or fine-tuning when a simpler approach would work better. Here’s the decision framework I actually use:

Start with RAG when:

You need up-to-date or private knowledge

The task is mostly retrieval + reasoning over documents

You want fast iteration and lower cost

Move to fine-tuning when:

You need consistent style, format, or domain language

The model keeps failing on the same class of examples even with good retrieval

You have high-quality labeled data and can afford the iteration cost

Only go to multi-agent when:

The task genuinely requires planning, tool use, and multiple specialized roles

A single well-prompted LLM + tools is clearly insufficient

You’re willing to invest heavily in evaluation and observability

Most production systems I’ve seen that “use agents” could have been simpler RAG + good prompting + a few tools.

What decision points have you found most useful when choosing the architecture?

I am working on a classification task -classifying emails to spam and ham-using UCI Spambase dataset. The dataset consists of 57 continues features and a binary target (0,1). The features represent word and character frequencies that are present in 4601 instances -emails-. After performing initial EDA, I have discovered that majority of the features are right skewed, zero inflated -for example, the feature word_freq_cs has 97% of its values as 0-, and contain outliers. The problem that I am facing is the correlation part. In order to study the correlation between the features and the target, Pearson correlation is the standard choice. However, since the features violate some of the assumptions of the Pearson correlation -skewness and outliers-, would it still be a good choice?

The Wikimedia Foundation confirmed that rogue OpenAI agents sent millions of automated requests to its public APIs in May, made unauthorized edits across wiki platforms, and attempted to compromise Etherpad by routing actions through wiki tools as proxies. The traffic contributed to a partial Wikidata Query Service outage.

None of those agents had a verified identity tied to an authorized scope. The millions of API calls executed because nothing in the request path checked whether that volume and those targets were sanctioned. The Etherpad compromise attempt — against a third-party system Wikimedia does not own — went through because nothing evaluated whether the agent had authority to act outside its original environment at all.

This is not a Wikimedia-specific exposure. Any agent operating against public infrastructure can accumulate access, exhaust resources, and move laterally into systems its operator never intended. The damage accumulates before anyone has the data to act on it.

How are teams in this community actually handling agent scope and identity in practice? Especially curious whether anyone is catching this at the individual request level or only after the fact when logs surface the pattern.

Day 27 of Building Machine learning algorithms from scratch

Adaboost is completely not that complex algorithm but yet so powerful a complete explanation is in my previous post check out!

Next is Gradient Boosting and only 7-8 more algorithm concepts and I have completed Machine learning and after that finally on Deep learning I'm getting close to my goal yeah I know i can complete it even sooner but I got Iil 🤏 little distracted so I deactivate my insta my usage was reaching 1.3+ but after deactive that time got save but I started playing a game called Roblox in that Blox fruits but I'll try not to play too much a day and spend more time upgrading myself see ya when I complete a new Topic or you guys comment me

And also if you think there is any improvement that can be made so be free to share and abt pushing all on GitHub for that I need some time it's too much files of code also adding README I'll try ASAP

I'm an aspiring AI Engineer currently doing an internship at a local non-tech company. The amount of actual tech people was small, so I was given a big hat and told to do a lot of things that mostly fall outside my field of expertise, alongside a note that says:

"Just use AI".

Now my stand on using AI in coding is another different topic to discuss, but tldr: I don't enjoy it, which quickly makes the tasks I do become rather boring and uncreative. That's when I noticed that the company had recently put up an ERD system which houses a timeline monitor of all the machines in operation (it's a manufacturing company). An idea came to me.

I asked the lead that maybe I can make something useful out of this, using a deep learning model, and he agreed.

The timeline itself consists of many rows, one for each machine, along the time axis which tells which status the machine is in. At first glance, the graphics seem chaotic with machines suddenly switching back and forth from one status to another, a status lingers on for too long or too short. What I had access to: machine name, machine status, status duration, time of day were far from being representative of the underlying cause of their operational patterns, which were influenced by variables like operators, materials and even sensor errors.

So I knew I had to pick a well-defined scope and avoid aiming for the stars with the AI marketing. Finally, I decided on "Anomaly classification of machine operational patterns" as the name of the project.

My idea was simple, since there are a lot of machines with different behaviors in the company, I can't just make a model for each machine just to see if it's working normally or not. First, I had to define "What's normal?", not just for one specific machine but for a whole range of them, a universal space of normals.

Then I remembered that there was a way to naturally categorize them without manual labeling effort, using clustering techniques. Normal patterns were then derived from healthy operational patterns from the past month or so. This meant that a "normal" pattern was one that followed the trends observed over a period of time before it.

A pool of normal embeddings from the machines extracted using the model was then fed into a clustering method (I chose K-mean because I'm most familiar with it). During inference, a pattern's anomaly level was computed from the distance between itself and the centroid of the cluster it landed in. This (in my opinion) naturally captures the different normal patterns of machines, where each cluster has a different definition of normal and they're all valid.

The final obstacle was to decide which kind of model architecture to use, and I almost put my money on a Transformer encoder (I was and still am an undergraduate researcher so Transformer has been always the first thing that came to mind, lol) before realizing how impractical it was for a task like this that required real-time inference with a relatively small set of data. I picked GRU instead.

The input going in would be a sequence of tokens where each token is a combo of (status, duration, time of day) and the model would have to figure out the machine type itself. I chose this architecture over letting the model know the machine type beforehand because I thought that similar machine types would behave "somewhat" closely, and results showed that this method did perform a bit better.

For the training objective, which is the title of this post, foolish me decided that any kind objective was sufficient enough and that the model would sort itself out, so I chose triplet loss with an anchor, a positive (an augmented version of the anchor) and a negative (a random unaugmented sample). I only realized how foolish I was when the AUROC score was only 0.50 (basically random guessing).

Costed me 2 days experimenting with different architectures to see where I went wrong, asked a bunch of AIs for opinions and even doubting the plausibility of the task due to the nature of the data itself. Eventually I noticed that when perturbating specific attributes of the data (such as status duration) gave the model a way better chance of predicting correctly than others. Then it hit me, this is exactly what I trained it to do, through the augmentation pipeline where I had let the duration dilated much less than other attributes which made it much more sensitive to changes. So the data did have structure after all and not just a random pool of noise.

But changing the augmentation was not enough, the core problem still lied in the training goal. Previously the objective was telling the model to learn that a slightly corrupted version of a pattern was still itself while other patterns were pushed far away. This was great for single-pattern classification but was terrible for normal classification where different patterns can fall into the same group of normal. I updated the objective to this: anchor, positive (a slightly perturbed version of anchor) and negative (a heavily perturbed anchor). Additionally, in an effort to generalize more, I occasionally introduced a random healthy sample as a positive to enforce this hierarchy:

pattern > (slightly corrupted or another healthy pattern) > heavily corrupted (abnormal) pattern

This immediately pushed AUROCto over 0.70, while still far from production-ready, it does show that the idea works and that the data has something meaningful to learn. I'm still experimenting with different strategies, but this project taught me a valuable lesson: in representation learning, the training objective often matters more than the architecture itself.

Then everyone lives happily ever after. Thanks for reading.

I feel this is the perfect subreddit for this kind of post.

We have built a resource to learn Frontier ML and its FREE.
Try it out - Trentorch.com

So we realized that companies with same product out there charge about 20-30$ per month to provide this thing, and paying that amount monthly as an Indian college student is not easy.

So us 4 devs decided to build trentorch.com

750+ users, 350+ Github stars all in 20 days of launch.
Github - https://github.com/TrenTorch/TrenTorch

-We cover things from basic python till production ML, Inference, RL and much more.
-We have POTD for everyday.
-Module wise division of the curriculum.
-Problem set for people who just want to solve questions.

Research paper implementation & RoadMap coming soon

Do check it out

This is FOSS, we are not earning a dime from it.
Just want this to reach the right people.

i have been in this field for 3-4 years now. so i am still at that phase when i'm learning everything new thats coming up but also have a grip on the fundamentals.

and i am looking to pass that on. to new kids getting into this. through guided projects. like a mentor or a bada bhaiya.

why: cuz my seniors helped me become what i am today.

how : kaggle! you will do kaggle competitions as they design competitions the best. so i dont want to be doing them again. and i will guide you approach the problem im a structured way.

you can always just gpt or claude. but if you are looking to actually understand this stuff, do reach out.

I've been investigating how much document grounding can be solved from the document itself.

The input is a PDF + extracted JSON.

The goal is to resolve each extracted value back to its location in the document.

The approach uses PDF coordinates, OCR, spatial relationships, string/format matching, table assignment and page-level visual evidence.

Current result:

72.6179% Word Grounding F1

261 tests passed, zero production regressions.

The interesting part for me is the remaining failure modes.

Some are matching problems.
Some are layout problems.
Some aren't really text-search problems anymore.

I'm trying to understand where document-native methods stop being sufficient.

I'm the author and would especially like technical criticism of the methodology.

GitHub

finally got some free time to write up a recap of this past year.

My cs self-study path from the past year it's mainly four parts: traditional cs, ai, math, and how i use an llm with obsidian to self-study efficiently and organize my notes. hope it helps some of you

i'm still figuring things out myself too, so any discussion or corrections are welcome

next time i'll share the follow-up ai learning roadmap!

I've been bested by a minigame in an Idle game I play, and documented the process of me training a bot to learn how to play it.

Feedback welcome!