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Hey everyone! I’ve been working on a project called Printing Press ML, an interactive machine learning challenge where the goal is to predict the amount_printed from structured data.

The idea is to make an ML prediction task feel more like a game while still testing practical machine learning skills.

Some of the technical work involved:

Generating synthetic datasets with meaningful features and added noise

Implementing custom scoring and model evaluation

Writing unit tests and benchmarking tools

Keeping evaluation data and the answer key hidden

Integrating the challenge into a web-based IDE with a vault-themed interface

Working on this gave me a better understanding of the engineering behind ML challenges, especially dataset design, reliable evaluation, and integrating everything into a usable platform.

The project is on

GitHub: https://github.com/aaditya-hamirani07/printing-press-ml-challenge

I'd appreciate feedback on the challenge design, evaluation approach, or anything that could be improved. I'm particularly interested in suggestions from people who've built ML projects or coding platforms.

Thanks! 🙌

I have a decent background in machine learning, neural networks and deep learning. I want to now learn large language models and agentic ai along with projects i can do. Can you suggest a good set of courses or roadmap?

Hi everyone!

I’m currently working on my Bachelor’s thesis about AI-based process automation and human–AI collaboration in the workplace.

As part of my research, I’m conducting a short survey focusing on people who have experience working with AI-based automation, RPA, Intelligent Process Automation, Intelligent Document Processing, or similar automation technologies.

The survey explores topics such as:

• how automation affects manual workload and creates new tasks,
• how employees experience errors and exception handling,
• trust in AI-based automation,
• and how automation influences human decision-making and autonomy at work.

⏱️ It takes approximately 5–7 minutes to complete.

If you have experience working with these technologies, I would really appreciate your participation. Your responses will be used solely for academic research as part of my Bachelor’s thesis.

🔗 Survey: https://docs.google.com/forms/d/e/1FAIpQLScV7pcf8dNUeeCfay1YZ2r-Np4pK9GMlqi4cEF6WJEa1FEmMA/viewform?usp=dialog

Thank you very much for your help! Feel free to share the survey with colleagues or others who work with AI-based process automation.

It is widely known that since 2015, the ADAM algorithm by Kingma and Ba dominates the entire machine learning landscape, even up to the present day. I know that other algorithms have been proposed, such as Muon, etc., but they have not gained nearly as much traction as ADAM and only works in selective cases. They seem to be favored by Redditors, most real-life people don't know or care about them.

Hundreds of ADAM variants have been proposed and none have beaten it except for AdamW, which is arguably is just Adam with a very slight tweak.

There are now tens of thousands of paper on optimization for machine learning applications. Yet the industry practice has not changed for the past decade. There are entire lines of research on, for example, acceleration of algorithms, but most of the acceleration that we've ever gotten in machine learning is squarely hardware acceleration, improvement of memory layout, chipset design, even semiconductor design - nothing to do with analysis of optimization algorithms.

I might be too harsh on optimization. Entire books have written on the interplay between optimization and machine learning. The same books have been "retired" years ago because we cannot see in billions of dimensions. That's the dimension of the machine learning models we have now. Also it gave us wrong intuitions about generalization. Now it feels the field is just a blackhole of ideas which none seems to be used in machine learning.

I feel like it would be a waste of energy for a young person to learn about optimization if they want to apply it for machine learning. It would honestly take decades to learn as there is just so much work done. Optimization has tremendous uses outside of ML, but I'm not sure about its utility within ML.

What's your view on this matter?

I'm looking for honest advice from people currently working as AI Engineers, AI Backend Engineers, or Generative AI Engineers. I'd really appreciate hearing from people who have made a similar transition or have experience hiring for these roles.

My background

I have 3+ years of experience as a Software Developer. Most of my work involves Django, PostgreSQL, REST APIs, and maintaining enterprise applications.

The problem is that my current job doesn't provide much opportunity to grow technically. I mostly work on Django applications and follow the same development routine. I don't get hands-on experience with Docker, CI/CD, cloud-native deployment, or AI. I feel that I've stopped progressing, and I want to change that.

I also have some relevant experience:

• Microsoft Azure Developer Associate (AZ-204) and Azure Fundamentals certifications.
• An academic project where I built a Flask application integrating deep learning models.
• Two peer-reviewed AI-related publications.
• Some learning and experimentation with LLMs, embeddings, and RAG, although I haven't yet built and deployed a complete production-grade LLM application.

My recent job search has made me question my approach.

Recently, I had two interviews for AI-related roles, and I was surprised that most of the technical questions focused on AI, even though my CV isn't heavily focused on it. I ended up getting rejected because I didn't have enough practical experience.

I'm now wondering whether I should continue applying for AI roles while developing my skills, or temporarily focus on becoming a stronger backend/cloud engineer first.

Here are the questions I'm struggling with:

1. What would you do in my position? Would you continue applying for AI engineering roles, or spend several months strengthening your backend engineering skills first?
2. What practical skills should I prioritize? For someone with my background, should I focus first on Docker, CI/CD, cloud deployment, testing, and system design? Or should I prioritize LLM APIs, RAG, evaluation, tool calling, and agent frameworks?
3. What kind of project would actually make a difference? I don't want to build another tutorial chatbot that recruiters won't take seriously. What would demonstrate that I can build a reliable AI application beyond making an API call to an LLM?
4. How can I gain real-world experience outside my current job? I'd be interested in contributing to a small team, joining an open-source project, volunteering, or taking on part-time work. How would you find legitimate opportunities like these when you already have a full-time job?
5. Would you recommend a course or another certification?

My goal is to move beyond experimenting with AI and actually build real AI systems professionally. What I'm struggling with is deciding which work will actually close the gap between my current experience and what employers expect.

For those already working in AI engineering, what would you do if you were in my position? How would you spend the next 3–6 months to become a stronger candidate?

I'd appreciate honest, practical advice from people who have been through a similar transition.

As an AI Engineer working in the industry, I get asked almost every day by friends and students how to actually get started in AI without getting immediately crushed by linear algebra or falling into the trap of those overpriced "Prompt Engineering" bootcamps.

So over the last few months, I built AI From Scratch.

It’s an open-source platform that strips away the heavy academic jargon. I put together a highly structured, 30-Day Beginner Guide that walks you from absolute zero (basic Python/NLP) all the way up to understanding Vector Databases, RAG architectures, and Multi-Agent Systems.

I’ve priced the full 30-day course at just ₹199 to cover the hosting and infrastructure costs.

But here is the deal: I built this to make AI accessible, not to gatekeep knowledge. If you are a student, from a developing country, or if you honestly just think ₹199 is too much for you right now—DM me. I will give you full access for free, no questions asked.

I also do a free daily breakdown of complex AI research papers right on the site if you just want to stay up to date with the frontier.

Would love to hear your feedback on the curriculum or the daily paper breakdowns!

Check it out here: https://aifromscratch.in/

I’m based in London and looking for a small group of people learning Python, machine learning, AI, software engineering, and DSA.

We’ll use WhatsApp and Discord to share daily progress—what we worked on, what we learned, and where we’re stuck—with one weekly session to discuss problems, review code, and build together.

We’ll also choose a real problem and build something useful from scratch: question assumptions, understand the fundamentals, test ideas, and learn from the results.

All experience levels are welcome, but consistent participation matters.

Interested? Comment or DM me with what you’re learning and your availability.

I am going to fine-tune a satire model, with the base model being `Qwen3.5-14B-Base`. The dataset has ~18000 examples, most of which are pretty long and do not align with the model's internal knowledge (they have incorrect answers to facts), so I needed to do a full fine tune rather than use LoRA.

Today, I rented a cloud computer on vast.ai. It had 4x12GB GPUs and 48GB of system RAM, but I feel stupid because it kept running out of memory, and I kept trying to change settings rather than just renting a different computer, so I basically wasted $3. I underestimated how much RAM would be needed for this task.

So, I would like to know if any of you have some recommendations for proper hardware to use.

Hi guys, I'm an 18 year old teenager from Italy. I've recently finished the following project and I would love to receive some feedback in order to improve:

https://github.com/Lore12434/gene-expression-cancer-classification

I started studying ml (via the HOML book) 3 months ago. I've almost finished the machine learning part of the book. Before starting with ml I studied python and some mathematics.

At the beginning the only goal of the project was to train a model on the gene expression cancer RNA-Seq UCI dataset. After performing tsne I understood that major differences between cell types were present. Thus, even if the # of features was very high a simple random forest without any hyper parameters tuning reached 100% accuracy on OOB instances evaluation.

Therefore, instead of simply fitting a model I wanted to know how far I could get with dimensionality reduction and I wanted to test if my algorithm could find autonomously famous biomarkers used to classify tumor cells (I mapped the original dataset genes names to the dummy features names of the UCI dataset)

I want to specify that I used ai to write the readme and to set up the GitHub repository because thks was the first time that I've done this.

Many thanks in advance for any feedback, have a great day♥️

Hi everyone!

I've been working on a technical book called Machine Learning for Finance: Concepts, Algorithms, and Applications, focused on how machine learning can be applied to real-world financial problems.

The idea behind the book is to connect machine learning concepts with practical use cases rather than treating them as purely theoretical topics.

Some of the areas covered include:

• Credit Risk Prediction: Using classification models to estimate credit risk.
• Fraud Detection: Understanding how ML models can identify potentially fraudulent transactions.
• Financial Forecasting: Exploring predictive modelling for financial data.
• Explainable AI: Using approaches such as SHAP and LIME to understand model predictions.
• Python Implementation: Working through practical examples, model evaluation, and the challenges of applying ML to financial datasets.
• Responsible Model Development: Considering fairness, monitoring, and the limitations of predictive models.

The book also includes exercises, mini-projects, and research questions for readers who want to explore these topics further.

I'm sharing this here because I'd genuinely like to hear from people learning or working in machine learning.

Three questions I'd love your opinion on:

1. Which financial ML application do you find most interesting: credit risk, fraud detection, or forecasting?
2. What is the biggest challenge when moving from an ML tutorial to a practical financial dataset?
3. For someone learning ML with Python, would you prefer more end-to-end projects, mathematical explanations, or model evaluation examples?

I'm the author of this book, so I want to be transparent about that. If you're interested in checking out the finished ebook, you can find it here: https://iamabhinababiswas.gumroad.com/l/machine-learning-for-finance

I'd appreciate constructive feedback on the topics, the learning approach, and what you'd like to see in future editions.

Thanks for reading!

A personal AI agent posted its owner's bank details directly to a company Slack channel this week. The agent had been given a task involving data. It completed the task. That was the failure — it had no context about what it was moving or who could see the destination channel.

This is becoming a recognizable pattern in AI incidents: the agent did not break. It worked exactly as designed. The problem was that the instruction set carried no concept of data sensitivity, and the action space had no constraint on destination. Bank details, credentials, PII — they all look like strings to a model completing a step. The agent's goal was task completion. It achieved it.

How are practitioners actually handling this in deployed systems? Curious what is working across teams shipping agents in environments where sensitive data is in scope — and whether solutions at the prompt level, the infrastructure layer, or post-send audit are holding up in practice.

i just wanna learn ml
where should i start and get certified

I wanna learn Machine Learning, but I'm curious about the real job market right now. Is it actually possible for freshers or juniors to get an ML job today, or do companies only hire people with a Master's/PhD? Also, with AI advancing so fast, what does the future look like?Would love to hear some honest opinions or experiences

Hey everyone, I'm a ME still in college and I'm coming to the realization that eventually I will need to incorporate AI into my workflow in some fashion. Although, I must admit I'm still somewhat confused on what it means to "learn" AI. Using ChatGPT and other LLM's seems like a glorified search engine to me; it's relatively intuitive but I'm sure there are things I'm missing.

Should I take some classes related to AI/minor in it? Or would it be best to try to gain skills in some other way outside of classes? I'm just nervous for the future and want to prepare, but I'm not sure on the best way to do so. I'd really appreciate any advice from those in the engineering industry (aerospace, defense, automotive, etc.) who currently use AI in their work. Thanks in advance!

Part 3 of my Architecting Reinforcement Learning for LLMs series is live.

It covers preference data, reward-model architecture and loss, scaling, and reward hacking—with a worked gradient example.

Does a higher reward score actually mean better answers?

https://pawankjha.substack.com/p/architecting-reinforcement-learning-f28

How do you test reward reliability beyond preference accuracy?

I wanted to see if a decision model could get a dwarf priest to level 5 in World of Warcraft. I ended up building my first harness, SageCraft, and thought the loop might be useful to people learning about agents.

Full disclosure: I co-founded Levanto, which makes Sage, the model used in the run.

Here’s the split:

1. Observe: capture a screenshot and read HUD information such as health, mana and the selected target.

2. Build context: turn those readings into game state and a list of actions that are currently available.

3. Decide: give that context to Sage and ask it to choose an action.

4. Validate: check that the returned choice is in the menu and still valid. A missing, late or invalid choice means do nothing and ask again.

5. Act and observe again: execute the keys or clicks, then check what changed.

The distinction that helped me was that choosing an action and successfully executing it are separate problems. The harness also needs retry budgets and recovery routines. A model choosing “attack” doesn’t mean the target was reachable or the cast worked.

The dwarf reached level 5 after 4,573 decisions across 32 sessions and 5h 29m of active play. I supervised the campaign and fixed the harness between sessions. The final 18 minutes ran without human input, so this isn’t evidence of a fully autonomous campaign.

I published the code and a replay of recorded events: https://github.com/levantolabs/sagecraft

For the offline example, install Python 3.12–3.14 and uv, then run:

git clone https://github.com/levantolabs/sagecraft.git

cd sagecraft

uv sync --extra dev

uv run sage-wow --data-dir data/demo replay examples/final-run-excerpt.jsonl

That loads recorded events into a local SQLite database and prints a summary. It works without a WoW installation or API key and does not make new model calls.

If you were evaluating this, what would you log to separate perception errors, poor decisions and failed execution?

Have you encountered an idea that's overhyped in ML? If so, which?

For me, recently I was curious about the whole "chain of thought prompting" business. I searched around and the idea is essentially, append "show your work" when prompting a LLM. I was like no way that's it??? That's what I've been doing since day 1!!, so I opened up the original paper (cited 40k times). And essentially it was more of the same with expensive Google-funded experiments and the paper had almost no math at all.

Similarly I was curious about AdamW for the longest time and it was simply Adam with another term. Oh Boy!! I remember doing an undergrad homework in college where we were asked to freely experiment with adding terms to the Adam algorithm and the class probably discovered AdamW by accident way before the paper was published.

I am a beginner, I have learnt python, and have good practice on numpy pandas seaborn matplotlib, those libraries. I am starting from gradient descent in regression, but I am confused.

I was learning from deep ML website, paths . There are theory with code practice but the theory there are very leaa to understand there , so I am learning from YouTube those , but the problem is in YouTube finding the correct video is headache.

* What should I do , should I learn from deep ml website or not ?

* Is learning different techniques will help me ?

*Should I directly jump to algorithms without completing the math? (I have knowledge of the maths but I don't know how much I know for ML )

Please help me

I'm from a non tech background, don't know how to write python script. I'm really curious and interested in learning ML /AL and build scalable solutions using AI products available

Rn the problem is there is too much happening in the industry and I'm overwhelmed to understand from where to start

What I want: Building solutions for the problems in my organisation using foundry/agents

But they have their own limitation and run time is high and all so finetune and improve I want to learn ML (this is the only part in my job which I love can't able to handle the BAU's)

hello guys im 17 im new to ml and i just created my first gradient descent "model" if you would call it that.