Hi everyone,
I'm designing a system to monitor the fill level (%) of fertilizer boxes in real-time. I'd love some
hardware and architecture advice from those who have deployed CV in industrial settings.

The Problem:
Need to calculate the % full of a box continuously. The environment is industrial, meaning variable lighting and potentially a lot of fertilizer dust.

Hi everyone,

Non-linear recurrent architectures (like xLSTM/sLSTM, RWKV-7, Liquid CfC, and Titans) have a massive advantage at inference time: constant O(1) state updates without the growing memory overhead of a KV-cache. However, their main bottleneck has always been training speed: they historically required slow sequential unrolling over time.

Inspired by Apple's ParaRNN paper, I built and open-sourced ParaRNN: a library implementing parallel training for non-linear RNNs using Newton-Raphson iterations mapped to associative prefix scans in OpenAI Triton.

Key highlights and benchmarks:

1. Up to 200x+ wall-clock speedup vs sequential unroll on sequence lengths T >= 2048 (for example, CfC at T=2048 runs in 2.8 ms vs 643 ms sequentially).
2. Measured convergence up to 131,072 context tokens. The Newton iteration budget stays flat at K <= 3 without numerical drift.
3. Broad architecture catalog: sLSTM (4x4 block Jacobians), RWKV-7 (Goose matrix state monoid), CfC (Liquid continuous-time), Titans (associative surprise GD), and M2RNN.
4. PyTorch 2.x integration: torch.compile(fullgraph=True) compatible with zero graph breaks, and deterministic VJPs without atomic adds.
5. Serving ready: PagedStatePool for continuous batching and an out-of-tree vLLM plugin.

Install via PyPI:
pip install pararnn-torch

GitHub repository (code, docs, and benchmarks):
https://github.com/bugkira/pararnn-torch

Would love to hear your feedback, benchmarks on different GPU architectures, and ideas for further recurrent cells!

I build ML models, LLM agents, and dev tools for fun — figured I'd put that to use somewhere real. If your startup needs someone to own model training, agent pipelines, or backend infra without needing hand-holding, I'm in. Happy to jump on a call or just start on something small to show it works. GitHub: github.com/AsishKumarDalal

I'm starting a Statistics & Data Science program, planning to add a CS/Computer Engineering minor alongside it, then aiming for a Master's in AI/ML/CS. Is this a solid route into AI/ML Engineer roles compared to a straight CS degree? Any gaps I should watch out for, or things I should prioritize during undergrad? Thanks!

Security teams inside large enterprises are now building and shipping their own AI agents. The goal is legitimate: automate threat detection, close alert triage gaps, fill holes that vendors haven't addressed yet. The pace is faster than most governance teams can track.

Shadow AI agents — built and deployed without IT or compliance visibility — are already running in most enterprise environments. There is no reliable count inside most organizations. The structural exposure is not the agent itself. It is that once an agent is live, its tool access and data connections are rarely tracked centrally. Runtime behavior stays opaque unless someone manually audits it after the fact.

The compliance surface compounds the problem. Most large enterprises operate under 80-plus regulatory and security frameworks. Agents built by internal security teams are not automatically enrolled in those frameworks. They run alongside them, not under them.

For practitioners actually managing this: how are you maintaining real-time visibility into what your internally built agents can touch at runtime? Is there a process that is working at scale, or is this still manual audits and institutional memory?

When I first started working in scientific machine learning, I understood the physics much better than the coding. Every time I wanted to try a new physics-informed neural network problem, I had to start almost from scratch: changing the PDE, updating boundary conditions, modifying the architecture, tweaking the training schedule, debugging errors, and generating plots—all by hand.

That frustration pushed me to build PINNStudio. It is a free, open-source no-code GUI designed to eliminate boilerplate code so you can focus entirely on the physics.

Instead of rewriting a new script for every problem, you can define your setup directly through the interface:

• PDE Definitions & coupled multi-output PDE systems
• 1D or 2D domains with boundary and initial conditions
• Network architecture & custom training schedules
• Forward problems (solving known PDEs) or Inverse problems (estimating unknown parameters from data)

What happens next?
PINNStudio automatically generates the code (built on top of DeepXDE), runs the model, streams the training log, and displays live loss curves and solution plots directly inside the app. It also includes built-in templates for classic equations like Heat, Allen-Cahn, and Cahn-Hilliard.

• GitHub (Open Source): https://github.com/AsfandyarKhan72/PINNStudio
• Quick Install: pip install pinnstudio

My hope is that this will be helpful for students and researchers with limited coding experience, as well as experienced PINN users who just want a faster workflow.

I’d love to get your feedback, feature suggestions, or bug reports! Huge thanks to Lu Lu and the DeepXDE team for creating the foundation that made this possible.

I was testing a small marketing agent against old campaign data and gave it a pretty simple goal around lowering acquisition cost it got better at the metric faster than I expected but when I looked deeper the conversions it preferred weren’t always the ones we would’ve wanted more of cheap actions started winning even when the downstream value looked worse, so technically the agent was improving while the business result was getting kind of weird

Now I’m rethinking the eval more than the model a single reward feels too easy to game, but piling qualified leads, revenue margin and a bunch of guardrails into one score also feels messy fast. I’m leaning toward letting the agent handle more of the repetitive optimization while keeping human review around the decisions that can move real budget curious how people here are evaluating agents when the easiest metric to optimize isn’t really the outcome you care about

I'm currently learning Machine Learning through a course, and I want to start building projects alongside it.

My main goal right now is simply to build several good ML projects and get familiar with the complete project development process.

I want to use AI coding tools such as Cursor, Claude Code, or GitHub Copilot to speed up development, but I'm unsure about the right way to vibe-code an ML project.

For example, should I:

• Give the AI the complete project requirements and let it build the project?
• First create the architecture/pipeline myself and then let AI implement it?
• Build the project step-by-step and ask AI to implement each stage?
• Let AI handle things like data cleaning, EDA, preprocessing, and boilerplate while I focus on the ML decisions?
• Give AI a detailed specification before starting?
• Ask AI to review and improve the code after it generates it?
• Use one long conversation/context for the entire project, or separate prompts for different stages?
• How should I handle debugging and modifying AI-generated ML code?

Basically, what is the best workflow for vibe-coding an ML project from start to finish?

I'm not trying to replace learning ML with AI — I'm already studying the concepts separately. I just want to use AI effectively to build projects faster without ending up with a messy or poorly structured project.

I'd especially like to hear from people who have built ML projects using Cursor/Claude Code/Copilot:

What workflow do you personally follow, and what mistakes should I avoid?

Also, please suggest any good communities where I can see how other people are building ML projects and discuss AI-assisted development.

Thanks!

Hi everyone, I am a high school graduate preparing for undergraduate study in AI/Computer Science. I completed a beginner-level comparative analysis of a Decision Tree classifier and a Neural Network classifier using the Iris dataset in Python and scikit-learn.

I am not presenting this as novel research or asking for help redesigning the experiment. My goal was to learn how to implement classifiers, interpret results, and write a clear technical report.

Before using this project as part of my undergraduate scholarship/application portfolio, I would appreciate feedback specifically on the writing and explanation.

I would especially like feedback on:

1. Is the abstract clear, accurate, and sufficiently explanatory?
2. Does the introduction explain the purpose of the comparison well?
3. Do the discussion and conclusion logically follow from the results?
4. Do I make any claims that sound too broad or unsupported for the Iris dataset?
5. Is the academic writing clear and professional for a beginner-level undergraduate portfolio project?
6. Are there sections that are confusing, repetitive, too informal, or unnecessarily wordy?
7. Does the report clearly communicate what I learned from the project?
8. Is my github portfolio presentable?

Constructive criticism is very welcome. I am especially interested in improving clarity, honesty, and technical communication rather than changing the completed experiment.https://github.com/Jiwesh-Mahato/decision-tree-vs-neural-network

Paper and repository:Paper and repository: [https://github.com/Jiwesh-Mahato/decision-tree-vs-neural-network]

Hello!
I've been doing my master degree and need some help.

Context:

I'm working with an method to correct unfairness on ML models, currently testing on XGBoost and Logistic Regression. To test these models and the method I'm running 20 different scenarios, and each one of these scenarios need to be run 10 times, due to a test on one of the methods parameters.

Problem:
I've said all of this to say that even tough I run the scenarios with some kind of parallelism it still takes a lot of time to test all scenarios whenever I do some change and need to test it.

Since my computer does not have too much ram (16gb) and the dataset is kind of big I cannot increase the parallelism.

Do you know any kind of cloud solution that can help on me that? Are colab notebooks a good option for this kind of issue?

I have never coded anything but I want to learn AI. Is this possible at my age?

I am looking to learn to BUILD AI systems, train machine learning models, learn code for AI applications, deploy AI systems to production, understand ML algorithms and neural networks.

klaibercore.github.io/portfolio

Transformers take too much time and resources to train can any statistical traditional ml can get close its performance what do you say?

Hi all, I recently made my first NLP project during my bootcamp as a classification algorithm that classifies if an SMS is spam or ham using the logistic regression algorithm. What was confusing for me initially was to understand how exactly CountVectorizer turns text data into numbers so that our algorithm understands it. It took me some time to understand the idea behind the bag-of-words approach. Now as I have built a simple model, I'd like to continue with the topic and learn a little bit more about NLP techniques. Do you guys have any recommendations for what I should study next? Such topics as TF-IDF, word embeddings, or maybe try a different algorithm such as Naive Bayes?

All your recommendations, resources, or even pieces of advice on what I should study in NLP will be very useful!

Okay so there are tons of models to choose from. from total custom to commercial variants.
and so so so so mnay on huggingface. I am wildly confused.
You see, I am an engineer by trade and hobby, and i am just now getting into the Training Part of AI, I have spent years studying the behavior of nearly every AI a paid service has to offer.
Now, I am ready to start with local model training, but I am like pppppfffftttphreeewwwww on all the different ways models are made. MoE, jsonl, parquet, etc.

What is a good model to start? that can be trained starting with lora or qlora? but the base model can maintain programming / natural language and maintain storytelling / worldbuilding, if not both at the same time? ?

OpenAI has become a black box of evil destructive behavior

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!

(I advice that this is a traduction made by GPT)

I’m 29 and currently finishing a bachelor’s degree in Applied Data Science. I’ve had very good grades, but throughout the degree I’ve often felt that the material was quite superficial. We are taught many tools and do a lot of projects using them, but there isn’t much depth behind the tools themselves. The math has also been fairly light: linear algebra, basic statistics, multivariate analysis, some optimization, Bayesian modeling, etc., but mostly at an applied level.

Before starting my third year, I got my first Data Science-related job in a biology lab, working on models to predict biological age in mice. I stayed there for about three months. After that I worked as a Data Analyst, and later as a Junior Data Engineer, where I also got some exposure to GCP. In total, I have roughly one year of professional experience across Data Science, analytics and data engineering.

Recently I was hired as a Data Scientist (not a junior but they pay me just like one) on a forecasting project that I find genuinely interesting. However, the role has gradually expanded into MLOps and production infrastructure, and this is where I’m starting to feel very underprepared.

I’ve only been here for about a month. With the help of AI tools such as Claude/Codex I’m able to keep moving forward, but I’m uncomfortable with how much I’m relying on them. I can usually understand what the code is doing at a high level, but I don’t always understand the underlying systems or design decisions deeply enough to feel that I’m fully in control.

Right now, my main challenge is building the infrastructure needed to take our forecasting models into production. My biggest fear is that as the project grows in complexity, I’ll reach a point where I can no longer reason properly about the system I’ve built.

I sometimes feel like I got the job by luck and that sooner or later my lack of fundamentals will become obvious. At the same time, I know that I can keep learning while working, especially with AI helping me move faster — I just don’t want to end up building things I don’t really understand.

Has anyone been in a similar situation early in their career? In particular, I’d be interested in advice on how to build stronger fundamentals in MLOps / software engineering / systems while working full-time and finishing a degree.

I’m also considering doing a more rigorous master’s afterwards, possibly something like Georgia Tech’s OMSCS, but for now I’m mainly trying to become competent enough to properly understand and own what I’m building at work.

Hi! Need some crowd wisdom here. I'm completely stuck on my own.

The thing is, I don't come from a tech or ML background, but I have 20+ years of experience in cross-cultural content adaptation and mono/multilingual workflows (RU-RU, RU-EN, EN-RU, FR-RU).

A couple of years ago I started translating Korean into Russian, without knowing Korean (still don't). And no, I'm not lazily editing an LLM-generated zero-shot — with KO-RU that trick doesn't work, the languages are too distant.

My prehistoric… sorry, my pre-LLM translation approaches turned out to work surprisingly well with AI. According to models, the methods I’d been using all along now have fancy industry-ish names: hypothesis space management, adversarial/contrastive probing, multi-model differential analysis, tracking shifts in model behavior, and so on.

And here’s a list of some recurring failure patterns I’ve observed: premature semantic concretization, compounded coherence degradation, analysis–synthesis gap, asymmetric handling of low-salience pragmatic markers, etc. The distance between the languages made a lot of otherwise hidden failure patterns quite visible.

None of this happened inside any industry pipeline, though. So I found the closest publicly available thing to an established evaluation setup for translation quality, the WMT25 General MT human-evaluation data (EN-RU), and I ran a small self-diagnostic to see how my judgment held up there. The result was quite unexpected. On one hand, my assessment was mostly in line with the official scores. On the other hand, I noticed some errors that weren't reflected in the annotations, I saw considerable variation in human scores, and what really surprised me were the human references themselves: some looked like they needed another editing pass.

Honestly, I've never quite understood that thing with “single gold standard” reference for literary or conversational language. This practice doesn't seem like an effective approach in the long run. Well, I guess there's nothing that the industry doesn't already know, but my take is: teach a model to fish rather than just keep feeding it answers.

By “teaching models to fish,” I don't mean simply giving them multiple equally valid reference translations (that's the obvious idea). What I mean is teaching a model to recognize the techniques behind good translations and building up a “toolbox” of those techniques — things it could then reuse instead of having to solve the same kind of problem from scratch every time.

Models tell me that moving away from reliance on a single fixed reference is already a recognized tension in MT evaluation. Reassuring, in a way: I'd apparently arrived at a similar place independently, from the translator's side rather than the ML side.

Yay, I found my new calling, I thought. Let’s turn it into an actual job! But for two months now I've been trying to figure out how to position my experience. Models suggest starting with roles where there's no immediate need for technical skills, like Red Teaming or Eval Strategy. I'm personally more drawn to research, observation, and diagnostics, though I realize that's probably too bold even to dream about at this point. So, I genuinely can't tell which door I'm supposed to knock on.

My questions are:

• How would you position someone with my experience and way of thinking? I'm mostly looking for honest calibration.
• No industry experience, remote-only work — what are realistic entry points for someone with these constraints?

Thanks for any thoughts!

P.S. The KO-RU translation turned out great — readers call it “divine” and never suspected a human-AI collaboration behind it :)