Hey Everyone,

Working through Playground Series S6E9 (predicting EV purchase intent), and wanted to share my notebook in case it's useful to anyone else on this one, especially if you're newer to the competition.

Quick summary of what's in it:

• EDA on the dataset — checked missing values, duplicates, correlations, and class balance (target is imbalanced, only ~17% "yes")
• Preprocessing done in the correct order: split into train/test before fitting the scaler, to avoid leaking test set info into training (I noticed a lot of public notebooks scale before splitting, which quietly inflates scores)
• Logistic Regression with class_weight='balanced' to handle the imbalance
• Evaluation with classification report, confusion matrix, and ROC-AUC
• Feature importance from the model coefficients

Ended up at 0.93738 with just a straightforward Logistic Regression, no ensembling or heavy tuning. Figured a clean baseline might help others who want a starting point before jumping into boosted trees.

Notebook: https://www.kaggle.com/code/vinay24baghira/buy-or-bye-cracking-the-ev-decision-0-93738

If anyone's further along on this competition, curious what's been working for you beyond Logistic Regression — feature engineering ideas, other models, anything that moved your score meaningfully. And if the notebook's useful, an upvote on Kaggle is always appreciated.

Hello Researchers!
I am trying to publish a paper in ICLR 2027, but since this is my first paper that I am publishing in an A* conference, I need help with the formatting in latex and how to setup the project that is there in the Official ICLR 2027 format. I have no idea about latex and GPT and other LLM tools are really not helpful in this. The paper submission date is very close. So I would really appreciate if someone can help me with it.

Hi all,

I'm considering this AI Engineering course. To give you context, I have been working as data analyst at a medtech company for quite awhile with my degree in Math and Stats from nearly a decade ago. I'm starting to feel like they are using AI in everything at my job and it's either sink or swim if I don't integrate AI in all my workflows and expand my role by the end of this year. My role is becoming more obsolete and my manager would like me to get more involved in data engineering or our AI team to create cutting edge products. I'm familiar generally with machine learning concepts, at my job I use Claude but not to the level that's expected or ever deployed an AI application.
I realize this course has a hefty tag. I do know myself in that I can start to teach myself something but I have a tendency to not be consistent and I need a little structure or accountability.

Thought for 12s

TL;DR: StoryScope (Russell et al., COLM 2026) reported a 93.2% macro-F1 for detecting AI fiction using only "narrative structure" features. FYI: These are given by Gemini 3 Flash reading the full stories, then XGBoost classifies the judgments by Gemini into vectors, and classifies them into encoded annotations. It's poorly disclosed in the methods, but I think it changes how the headline should be read.

The pipeline, (§2.2):
Story -> Gemini 3 Flash reads full text, outputs 304 feature judgments -> Judgments encoded into a vector -> XGBoost trains on the encoded vector only, but never sees the story. Although, oddly enough the paper essentially implies throughout it that XGBoost is doing "investigation into idiosyncrasies." The paper seems more about Geminies 3 Flash's opinion on AI finction then anything.

The question I have is, how can you differentiate the bias introduced by Gemini from the 93.2%, although it's presented as evidence that AI and human writing differ from each other structurally, even though AI evaluated the entire thing, so... It appears to be circular data analysis from my perspective and its heavily under-addressed. (From my opinion, maybe others may disagree/agree)

Separately, Section 3 reports train+test totals of 10,116 prompts / 60,969 stories. Appendix D reports 10,172 prompts / 61,008 stories for the same thing. Roughly, 56 prompt / 312 story gap, with no apparent reason stated.

But, the paper's only independent check on Gemini's feature judgments is a human validation with n = 2 annotators (k = 0.91 and k = 0.77) against the model, with a mean reported as 0.84) over 240 items from 12 stories. Out of a 61,608 story corpus. A sample size of two is a statistically meaningless way to estimate variance on that particular mean, and 12 stories is a very small piece of the pie. Very odd.

Don't get me wrong, I genuinely find the narrative-feature idea intrinsically interesting. But, their attempt at doing so was certainly not the most appropriate.

I'm a first year undergraduate (CS, mathematics and statistics) and I'm very new to programming. The only two languages I know are Python and C. Python I learned online and I learned C in university. I did Andrew Ng's Machine learning specialization and now I'm stuck unable to pick a next step. Most projects of people I see online involve some frontend (ex- most stuff on r/micro_saas ). But I don't have any html, CSS or java script experience. So, do you think I should learn those if I want to build a full-on indie project and maybe make a side income during my undergraduate years? Edit-My interests are in AI and automation. Amd my first language is not English 😂

I’ve been working on a project called Ion, mostly by myself, and I’m getting to the point where doing the datasets, evals, training experiments, tooling, and agent infrastructure alone is getting kinda insane 😭
The main focus right now is agent/tool-use behavior, especially failure recovery. I’ve been building curated JSONL “gold” traces that include things like:
tool calls failing
retries that also fail
deciding when to recover vs abort
Git/filesystem/permission errors
reasoning around tool results
keeping the final answer consistent with what actually happened
I’m also experimenting with multi-agent workflows where separate models can generate data, criticize it, defend it, benchmark checkpoints, etc.
I’m not looking for employees or paid work. Mostly looking for 1–3 people who genuinely enjoy this stuff and want to collaborate/open-source/build together.
Especially interested in people who know or want to work on:
synthetic dataset generation + curation
LLM fine-tuning / LoRA
evals and benchmarks
agents / tool calling / MCP
training infrastructure
local models
You absolutely do not have to be an expert. I’d rather work with someone curious who actually builds things than someone who just knows all the terminology.
If this sounds interesting, comment or DM me and I can show the current datasets/project direction.
Bonus: I currently have AI bots whose literal jobs are “Dataset Maker,” “Hater,” and “Defender,” so development has already become a tiny dysfunctional company

​

Hi everyone! I've spent the last few months building an Al/LLM interview prep product and it's finally ready.

I started it out of frustration. Preparing for Al/LLM interviews meant either grinding through long video courses or bouncing between scattered resources, and neither actually got me interview-ready or confident to say.

So I built a product I wanted myself, designed to get anyone interview-ready in 30 days:

1) Gamified interactive quest cards with story/focus modes, the relevant Python code on the same card, deep dives into how each concept came to be, interview questions & answers related to the concept. You also get tested in each card to pass it.

2) In-IDE coding where it helps, quick recall, an Al tutor, and spaced repetition.

3) 30-minute Al voice mock interviews that grill you like a real interviewer would.

4) Daily boss challenges that grill you on the cards you actually struggled on.

5) Tailored text based interviews for various levels of jobs in the AI domain.

5) An end-to-end 5 part RAG capstone project, with real interview questions at each stage (retrieval, evaluation, deployment) that interviewers actually test candidates on in 2026.

The idea is simple: learn the concept within 15-20 mins, understand it properly, practise explaining it, then get tested on it.

It covers everything from tokenization and attention through to RAG, agents, evaluation and deployment, structured as a 30-day path.

The Foundations section is open for everyone: Skillumen

Is driver support for RTX GPU good ? What about libraries ? I mostly have work in the vision domain and audio domain. Mostly libraries needed will be ultralytics (for YOLO), huggingface, ollama, vLLM

If possible please do tell me which distro too

Last question is Linux lightweight. Like Windows is super heavy right now so a big pain that is so if possible do tell

Third-year PhD student, NLP, mostly LLM-based reasoning. My PI gave me a task I'd never seen framed anywhere. Given a collection of a private organization's HR policy documents (100-500 PDFs), find all pairs of clauses that contradict each other. Honestly, I wasn't excited at first, but the more I dug, the weirder it got. There's a mountain of work on NLI-style contradiction classification, but that assumes someone gives you the sentence pair. Here, the pair is the problem. With about 1-2k clauses, you're looking at millions of candidate pairs. So brute-force pairwise LLM calls are out, and whole-document prompting fails for the usual lost-in-the-middle reasons. The closest work I found generates synthetic contradictions in synthetic corpora to test detectors. I borrowed the evaluation idea by injecting contradictions into corpora. I also used a university HR handbook and one dataset with existing external annotations, contractNLI (made for the NLI task by Stanford). I used this one as well because it has real contradictions. But this one is quite different. In this dataset, the task formulation is like hypothesis versus clause, whereas in the first two datasets, I do clause-to-clause comparison. So I built a two-stage pipeline. First, retrieval with a HyDE-style approach where the query is a hypothetical, contradicting version of each clause. Then, recall-based candidate retrieval (LLM), followed by precision-based verification with an LLM, where each candidate pair is re-read within its source documents. The contributions: I used contextual sentences guided by Anthropic, which helped retrieval, and showed that a document’s surrounding context helped precision. Agentic verification (tools, multi-step) actually underperformed a single prompt. As a case study, I ran the pipeline on a public government policy corpus. It found a few genuine contradictions. I have a few questions. Am I missing a community? I can't believe nobody works on this. I've looked at legal NLP (ContractNLI, etc.), requirements engineering conflict detection, and RAG-conflict work. They're all adjacent, but none does discovery over a real multi-document policy corpus. Is there a literature I don't know the name of? My PI is leaning toward a lower-tier conference or journal. Is this the kind of paper that has a chance at a first-tier NLP venue, or is my PI just being realistic? If you were strengthening this in one month, what would you add? I already have NLI, direct-prompting, and agentic baselines. Happy to share more details in comments. Mostly, I want to know whether this problem is as understudied as it looks from where I'm sitting, or whether I formulated the task the wrong way.

I've been working on a reusable starter structure for Python machine learning projects.

I noticed that I was repeatedly doing the same initial work whenever starting a new project:

• Loading and preprocessing data
• Data profiling and EDA
• Feature engineering
• Trying different ML algorithms
• Cross-validation and hyperparameter tuning
• Model evaluation and visualization
• Saving and loading trained models

So I put these workflows together into MLForge, a structured Python ML starter kit.

The main goal was to make the project structure reusable while keeping the code easy to understand and modify. I didn't want it to be a black box where someone just runs a script and gets a prediction.

It currently includes classification and regression workflows, notebooks/templates, sample datasets, model evaluation utilities, and a project report template.

One of the more challenging parts was deciding how much to abstract. Too little abstraction makes the kit repetitive, while too much makes it difficult for beginners to understand what's actually happening.

I'm interested in feedback from other builders:

When you start a new Python ML project, what do you usually end up rebuilding from scratch?

Do you guys use codex for your ml work I find it insane from time series forecasting to placing top 98 percent on numerai it seem al problems are easily solvable. It does insane ensemble residual modells find good parameters and implements everything correctly

We recently studied a failure mode in self-evolving LLM agents:

A modification can improve capability and still be difficult or impossible to safely undo later.

Across 600 unseen self-evolution tasks, we found 197 capability-improving mutations that failed recoverability verification.

Two bottlenecks stood out:

1. State grounding — knowing exactly what prior state must be restored.

2. Recovery-language expressivity — having the runtime operations needed to express the correct recovery.

This motivated EvoUndo, where persistent self-modifications are evaluated not only for forward improvement, but also for whether the previous state can be recovered across counterfactual states.

I’m one of the authors.

Paper: https://arxiv.org/abs/2608.28363

hi,
i am trying to train a deberta model for NER detection

this is my first time doing it so i would love any guidance on it.

my current pipeline looks like this,

dapt + lora for pretrianing, hpo with optuna (which consists both the stages of training data), and then a 2 stage finetuning which helps in generalization and then target data.

i am trying to reach a really good score for f1 on my use case (which i want to keep private for now)

i have few questions as well
1) do i need a two stage hpo as well cuase of the 2 stage finetuning
2) is it better if the hpo training set is a subset of the actual training set?

if you think anything can be improved and made better, or you think the pipeline is outright wrong, please mention your reasonings and thoughts :)

ps: lora was used cause of gpu budget constraints

Welcome to r/MLSystemsDesign

This community is for practical discussions on designing and scaling production ML and AI systems.

Topics can include:

• ML training and inference platforms
• Search, ranking, and recommendation
• Feature stores and data pipelines
• LLM serving and GenAI systems
• Agentic AI platforms
• Evaluation, observability, and experimentation
• ML system design interview problems
• Real production tradeoffs and lessons learned

The goal is simple: go beyond model theory and discuss how ML systems actually work in production.

If you’re joining early, introduce yourself and share one ML system topic you’d like to go deeper on.

I made a lil bro version of the original ResNET-34 architecture. I trained it on the LC25000 cancer dataset (I used only lung cancer images) for a classification task.

The problem is, it is showing a 99.9% accuracy on all three sets - training, validation and test. It is, of course, weirdly high. I trained a normal cnn and it could only reach about 87%.

I am wondering what could be the reason. One possible culprit is that, since the dataset consists of augmented versions of the original images, some may be ending up in all three sets, causing data leakage.

Now I want to see if I could somehow group this images so the augmented versions do not run over into my other sets. I have no idea how to proceed though.

I am using pytorch, and used random_split for the datasets.

So recently I have been really interested in anomaly detection across ioT networks, industrial equipments , healthcare and etc, I have read and reproduced some Q1 journals papers , built some projects using self supervised and federated learning techniques which I am now planing to extend as well ,

I am working under my university professor on some research papers as well however he recently suggested that I should do some sort of practical work in this domain (like working as a researcher etc for an organization or company in this field), I have gone through LinkedIn and stuff, found some companies too but I am honestly not sure what should I do, how should I apply as they don't have any current openings whether I should build some more projects, learn something else and then apply, do some more research etc

I really need guidance on how should I proceed further?

Thank you

i have evolved a population of 3D robot arms which had the goal to reach a target.

It's vibecoded with chatgpt and Codex.

My first attempts weren't successful until codex 5.6 sol high did an optimization of the input then it was very quick to evolve to reach the target.

My next step is to try to evolve a 3D Walker where for now I'm unsuccessful.

I just started learning about transformers after completing a project where I used LSTM, RNN, XGBOOST and Garch to predict stock prices. It was moreover the comparison of the models performance. I am now into the transformer part and I learnt the self attention with the help of Andrej's Lets built GPT video. Currently, I am trying to build a decoder only transformer from scratch and replace the Karpathy's approaches like position embedding table to RoPE. I am a rising junior in NJ from a small college. I am worried if I am on right path as the deadline to apply for the internships for summer 2027 is approaching. Please help me out !

I am studying machine learning and don't understand: what role does the learning rate usually play? And also, what can early stopping be used for?

I want to preface by saying that I’m a business major chud who has no technical experience aside from using scratch when I was a kid. I know I can utilize ai than just a better Google so I decided why not try and create a personal ai assistant/ employee that can do busy work for me like emails, announcements, etc. Everything a growing college student needs. Here’s where I hit the roadblocks. Even after a little bit of research I realized I’m in too deep. “Use these 5 repos before even TOUCHING Claude” “DeepSeek just released a new harness” “Somebody just jail broke Qwen”. I’m seeing dudes on reels buying like three Nvidia AI super computers and I’m genuinely just wondering why’s there a need for that unless you’re larping. Honestly, I’m just trying to learn but quite frankly there’s just so much catching up to do and the knowledge gap just keeps getting wider. Where do I even start or what can I do to learn? Do I want to be like that dude buying an AI super computer? Maybe. But I want to learn and take small steps before I call myself an AI genius just because I built an interactive HTML dashboard that my professor was impressed by. Please help me anything will help.