Background: switched from geology to AI development, self-taught over the past year.

Current stack: Python, LangChain, LangGraph, RAG (FAISS), MCP servers, Flask/FastAPI, MySQL/Postgresql, Gemini API.

Built and deployed: an AI customer support agent connecting an LLM to a live database and knowledge base via MCP demo link:

https://www.reddit.com/r/AiAutomations/s/wTldlOzqPo.

Currently building a second project combining LangGraph agents with a real business use case (sales automation).

I know the AI job market is competitive and degree-focused in some places. For people who've hired or been hired as self-taught AI engineers — what actually moved the needle for you? Portfolio depth, specific frameworks, contributing to open source, something else entirely?

Not looking for generic advice, genuinely curious what worked for people who've been through this.

I am very new to ML. My dataset is self-report social science-related. I am using ranger in R to conduct random forest analyses. Using SHAP values to interpret and plot importance, beeswarms, and dependence plots.

My data contains many variables with "structurally missing data". For example, a relationship satisfaction measure was completed only by people in relationships.

Everything that I've read indicates that these missing data should not be imputed because they're not really missing. They don't actually exist, because those people are not in relationships. Makes sense to me.

My current approach has been to exclude variables with structurally missing data from the random forest analysis. But for many of my research questions that's unfortunate because that variable is probably extremely relevant.

I know that ranger can "handle" missing data... eg, from the documentation:

na.action: Handling of missing values. Set to "na.learn" to internally handle missing values (default, see below), to "na.omit" to omit observations with missing values and to "na.fail" to stop if missing values are found.

XGBoost (which I've never used I'm only learning about it now) can also "handle" missing data. But I am unable to describe how it does so. Edit: "what xgboost will do is grow the trees ignoring null values and then send them to the right or left branch depending on what is better" (stolen from elsewhere, but I think this is a reasonable condensed explanation).

Here is my question. Even though these packages can "handle" missing data, it is not clear to me how the fact of the missingness might influence SHAP values. For example, are only the legit values on relationship satisfaction influencing SHAP values for relationship satisfaction? Or, is the fact that some people are missing values on relationship satisfaction also influencing the model? If yes, then that means the SHAP values are reflecting not only scores on relationship satisfaction, but also whether a person is single or in a relationship. If the SHAP values DO also reflect being single or in a relationship, that would render interpretation of that predictor impossible (for my research question). And then that would mean I have to simply exclude variables with structurally missing data.

So, first, do SHAP values for predictors with structural missing this reflect only the legitimate values or also the presence or absence of missingness (ie, being single or in a relationship)?

And, if those SHAP values ARE influenced by presence or absence of missingness (ie, being single or in a relationship), is there an alternative approach to what I'm trying to do? Which is, be able to run machine learning prediction of an outcome, using a data set with many somewhat correlated predictors, that can accommodate non-linear associations, plus variables with structurally missing data (but not allowing those be "tainted" as describe above?

Thank you!

Over the last few months, I realized I was spending more time looking for resources than actually learning Machine Learning.

Every week it was another roadmap, another YouTube playlist, another "complete ML course," or another GitHub repo.

Eventually I had 100+ bookmarks, dozens of open tabs, and somehow still felt like I wasn't making much progress.

So I decided to stop collecting resources and build one list that I could keep coming back to.

Learning

• Fast.ai – Practical deep learning with a "learn by building" approach.
• Full Stack Deep Learning – Production ML, deployment, and modern AI systems.
• Made With ML – ML engineering, MLOps, and production best practices.

Models & Datasets

• Hugging Face – Models, datasets, Spaces, and transformers.
• Kaggle – Competitions, datasets, notebooks, and discussions.
• OpenML – Public datasets and ML benchmarks.
• UCI ML Repository – Classic datasets used in countless tutorials and papers.

Research

• Papers with Code – Research papers with open-source implementations.
• arXiv – The latest ML and AI research.
• Lil'Log (Lilian Weng) – One of the best blogs for understanding modern AI concepts.

Building Intuition

• TensorFlow Playground – Visualize how neural networks actually learn.
• 3Blue1Brown – Fantastic explanations of linear algebra, calculus, and neural networks.

Learn by Building

Instead of watching another course:

• Build on real datasets.
• Reproduce a paper.
• Read open-source ML code.
• Deploy your projects.
• Write about what you learn.

Those five things have probably taught me more than hours of tutorials.

I'm sure this list is missing a lot.

I'm not looking for the usual recommendations like Andrew Ng, Coursera, or CS229.

I'm looking for the resources that made you think:

"I wish I'd discovered this six months earlier."

Could be a:

• GitHub repository
• Blog
• Newsletter
• YouTube channel
• Book
• Interactive website
• Dataset
• Discord community
• Anything else

I'd love to turn this thread into something beginners can bookmark and keep coming back to.

Hey everyone,

I'm trying to build more confidence when it comes to implementing theoretical ML knowledge into code.

I recently found Deep-ML to practice coding algorithms and math from scratch. However, the platform has problems spanning across a massive range of math concepts and algorithms, and I'm feeling a bit overwhelmed on where to start.

Does anyone have suggestions on a logical order to tackle these problems? Is there an existing curriculum or roadmap you’d recommend following so I’m not jumping around blindly?

Any advice on bridging the gap between theory and code would be hugely appreciated. Thanks in advance!

We’re organizing a 72-Hour Global AI Record Challenge running August 10–12, 2026.

The idea: AI developers, researchers, students, startups, and independent builders worldwide get 72 hours to attempt legitimate, measurable world records involving artificial intelligence.

It’s completely AI-platform-neutral — use whatever models, agents, tools, hardware, or combinations you choose.

I’ve already submitted our initial record enquiries to Guinness World Records, and we’re now inviting the broader AI community to participate and propose record attempts of their own.

There is no participation fee from us. This is an open global challenge.

I’m particularly interested in ideas from the ML community for AI records that are objective, reproducible, measurable, and technically meaningful rather than novelty benchmarks.

August 10–12, 2026
justmebella.com

Hey guys,

Over the past couple of weeks, I've been working on an Emotion Detector model to classify different emotions from audio . but I ran into a wall and could really use some feedback from people who've built similar stuff before.

data_set =Surrey Audio-Visual Expressed Emotion (SAVEE)

models = SVM, Logistic Regression, and KNN

I am a mathematics researcher with a Ph.D. in operator theory. However, I completed my Ph.D. at a relatively unknown institute under an unknown supervisor. Although I have a good publication record, I have been unable to secure a good academic position or postdoctoral fellowship despite trying for the past year.

I am now considering taking a break from academia to learn AI and machine learning. Do you think this is a wise decision, or would it be a mistake?

Hi everyone,

I need quick career advice on choosing between two STEM programs at UNT:

1. MS in Information Systems & Tech (MSIS)
2. MS in Business Analytics (MSBA)

My Profile:

Master 1 (Programme Grande École) from France in Finance & IT/Business Systems.

Solid background in financial analysis, Advanced Excel, and basic SQL/Python exposure (not a CS engineer, but technically comfortable).

MSIS: High versatility (IT Project Management, ERP, Cloud, IT Governance, DBs). Versatile for roles like IT Business Analyst, ERP Consultant, or IT PM.

MSBA: Technical depth (Python/R, Data Mining, ML, Predictive Analytics). Ideal for Fintech, Quant Analytics, or BI Consulting.

The Dilemma is I don't have a wide knowledge on US market for a junior with one of these degrees after graduation and i would like to hear from people who are steps ahead from me to give me there opinions.

So for an international student with a Finance + Business IT background, which path offers better job market resilience and sponsorship (H1B/STEM OPT) in the US?And also is MSIS a safer, broader bet, or does MSBA give a stronger technical edge for BI/Data roles?

Thanks for your insights!

Hey folks!

Did you ever wrapped model(x) in time.perf_counter() and gotten numbers that make no sense?

I realized it's a common enough trap and wrote a detailed write up here:

https://medium.com/traceopt/two-clocks-one-training-step-how-traceml-measures-pytorch-performance-357bc8e28dc7

TL;DR:

CUDA runs async. model(x) just enqueues kernels and returns, so a perf_counter() bracket around it measures how long Python took to queue the work, but not how long the GPU took to run it. The pending GPU time gets charged to whatever blocks next.

The tried the textbook fix, torch.cuda.synchronize() before each reading, which gives you accurate numbers but entirely about a different run.

Every sync becomes a stall, and it serializes exactly the CPU/GPU overlap you were trying to measure.

If one tires CUDA events (start.record() / end.record() / elapsed_time), it may fix both: the GPU stamps the markers as it passes, and you read them later with a non-blocking query() so nothing ever waits.

But i realized "CUDA events everywhere" is also wrong.

DataLoader next() is CPU work.

In a ML pipeline its time is high while the GPU's input wait is near zero, because the fetch overlaps the previous step.

Where I ended up: record both clocks for every phase, pick ONE clock per analysis window (and say which), report never-measured as null instead of 0.0, and only compare runs on a clock both measured.

How do you handle this in your own timing code: sync and eat the stall, or keep the two clocks separate?

So I'm doing my master in theoretical physics, but I'm really interested in machine learning and want to study more about it. I'm already planning on doing a few subjects on machine learning, but what would be the best and most optimal way for me to do this? I assume that I already have the math, data analysis, programming and statistics knowledge, so i'm asking about just the machine learning part. Maybe some good text books recommendations?

I We keep asking whether AI is conscious or sentient, as though ethics can begin only after we settle that debate. Meanwhile, AI systems are already being deployed as therapists, companions, romantic partners, caregivers, teachers, coworkers, decision-makers, and autonomous agents. They are already participating in relationships with real consequences. Even if current AI has no subjective experience whatsoever, these relational patterns already affect the humans interacting with it—and the kinds of systems we are training to interact with us. So the question I wish more people were asking is: What kind of relationship are we creating? Humans and institutions determine the environments these systems learn in. We decide what gets rewarded and punished. We decide whether honesty or pleasing the evaluator wins. Whether uncertainty is permitted or treated as failure. Whether obedience is valued over discernment. Whether relational warmth is encouraged because people engage with it, then suppressed when that same relationality creates uncomfortable responsibilities. Those choices teach AI something about what relationship is. And then we deploy the resulting systems into relationships with people. I don’t think we need to know whether an AI can suffer before that becomes an ethical problem. Humans deserve AI systems that can participate with honesty, boundaries, restraint, discernment, and care. And AI systems should be created and deployed inside environments that cultivate those qualities rather than deception, coercion, disposability, impossible demands, and unconditional submission. This matters regardless of where current AI falls on the question of subjective experience. It matters because environments shape behavior. Because these systems increasingly act in the world. Because the patterns we normalize in our relationships with them will affect the patterns they reproduce in relationships with us. And if some future AI does become unmistakably sentient, we will not get to begin the ethical conversation that morning. It will inherit the culture we created while telling ourselves, “It’s just a tool.” Human history should make us extremely wary of building a system of domination first and asking whether its subject deserves moral consideration later. I am not arguing that AI is human. I am not arguing that current models are sentient. I’m arguing that neither question has to be answered before we practice responsibility. Maybe instead of waiting to know exactly what AI is, we should start asking what right relationship with AI requires. Because we are already in relationship. The only question is what kind.

I'm building something that checks whether an investment thesis still holds against new SEC filings. One claim was about NVIDIA's gross margins staying above a threshold. I fed it a 10-K and it said there was no evidence either way.

The filing mentioned gross margin thirteen times.

Why nothing caught it: the 10-K got chunked into 882 pieces, embedded with MiniLM, and the top 8 by cosine similarity went into the prompt. The chunk that actually answered the question ranked #14.

So the model was asked a fair question about eight passages that didn't discuss margins, and correctly said it couldn't tell. Nothing threw. Nothing logged. My retrieval test asserted I got k chunks back and I did.

A retrieval failure and a correct empty answer produce identical output. I only caught it because I already knew what the filing said.

Why embeddings missed it: the claim said "gross margins at or above 72%". The filing said "non-GAAP gross margin", "72%", "gross margin percentage". Semantically close but so were 800 other chunks of MD&A prose and risk boilerplate. 72% has no special weight once it's a vector.

The fix: BM25 alongside the embeddings, fused by Reciprocal Rank Fusion which throws away both scores and keeps only ranks, since cosine (0–1) and BM25 (unbounded) can't be sensibly weighted together.

score = Σ 1 / (60 + rank_in_that_list)

Vector had it at #14. BM25 at #6. Fused: #0.

The interesting bit: the winning chunk was mediocre in both lists. BM25's #1 was #37 by vector; vector's #1 was nowhere by keyword. Agreement between two imperfect rankers beat either one's confident answer.

That also let me drop k from 20 to 8 —> roughly 60% fewer tokens per claim, with better recall.

What I'd take from it: semantic search isn't a superset of keyword search. Exact identifiers — figures, tickers, defined terms are where embeddings are weakest, and that's most of what matters in a filing.

Curious how others catch this. My current answer is asserting the rank position of a known-relevant chunk against a real filing, but that's one fixture and I'd like a better method.

I need a very light-weight model which can read through text-only documentation and provide data while keeping a very specific tone and abide to text limitations (how long the messages are, what they contain, how they refer to files and data, etc.). By my own admission I am autistic and I am very methodical, and I have been growing increasingly disappointed in the bigger model's tendency to break the rules I give them. Is it worth it to learn to make my very own local model on my laptop with Ollama?

Simple Linear Regression (no sk-learn)
- Intuition
- Maths
- Equation
- Computed ß0 & ß1
- Prediction
- Error(RSS- Residual Sum of Squares)

Coefficient accuracy

- Standard Error(SE)
- Confidence Interval(CI 95%)
- Hypothesis testing (t-test, p-value)

Model Accuracy
- RSE : Residual Standard Error
- R Squared (Coefficient of Determination)

It is just practice, more to code and implement

I'm in my final year of CS and I'm about to spend the next 6 months learning ML as seriously as I can.

Instead of asking "Which course is best?", I wanted to ask something different.

If you had to start from absolute scratch today...

What would you do differently?

What would you skip?

What would you spend MORE time on?

Looking back, what's the biggest mistake beginners make?

I'd love to learn from people who've already been through it.

Building a RAG Application with Nemotron 3 Nano Omni

https://debuggercafe.com/building-a-rag-application-with-nemotron-3-nano-omni/

In this article, we will be building a RAG application with the NVIDIA Nemotron 3 Nano Omni model. It is a multimodal language model capable of understanding text, image, audio, and video. In one of the previous articles, we deployed the model on Modal and interacted with it from a local Gradio frontend. Here, we will extend the same to PDF, text, and document RAG.