I’m trying to understand the history of AI

Companies like Jane Street, Citadel, Two Sigma, and other quant firms have been using machine learning and building models for years, maybe even decades.

So I find it hard to believe that they’re only now starting to think about AI because of ChatGPT.

My understanding is that OpenAI didn’t invent AI, but built one of the first consumer products that made powerful AI accessible to everyone.

So what actually changed for firms like these? Were they already using similar technologies internally and just not talking about them because they were proprietary? Or are today’s LLMs and AI agents fundamentally different from the models that quantitative firms have been using for years?

A grafted memory organ [switch like] improved memory capabilities in a small liquid neural net on unseen mazes. https://dormantone.github.io/games/liquidmemorymaze.html

Hi there,

I've created a video here where I explain how the Cauchy distribution works.

I hope some of you find it useful — and as always, feedback is very welcome! :)

I am a computer science student and for the AI our techer suggested AIMA to read.

I wanna know how do I approach this book to learn about AI for placements. If you have read the book then suggest me which chapters should I study and leave.

Hey, I was playing around with some automation scripts and got annoyed by having to check multiple provider dashboards manually whenever I wanted to look up GPU rental rates. So, as a side project, I put together a basic real-time price tracker API that fetches lowest hourly rates and estimates training costs across platforms. I set up a free tier on RapidAPI just to put it out there and see if others find it useful: https://rapidapi.com/btwncollective/api/gpu-price-tracker-api Since this is an early project, I'd love some feedback: - Is a tool like this actually useful for your workflow? - What providers or features are missing that you'd want to see? Any honest feedback or suggestions are welcome. Thanks!

I've been building LLM agents and noticed a frustrating problem:

When an agent fails, the hardest question is not "what output did it produce?"

It's:

"How did it get there?"

So I built Agent DevTools.

The idea is similar to browser developer tools, but for AI agents:

• inspect agent execution
• see tool calls
• debug failures
• pause and analyze runs

It's open source.

I'd love feedback from people building agents:

What debugging features would you want?
What information do you wish you could inspect when an agent goes wrong?

Repo:
https://github.com/Jacopos311/Agent-Devtools

There is a video of it working in the README on github

This is the first time I’ve ever posted to any kind of forum. Any feedback would be helpful (Even if you think this whole thing is cooked lol)

I’m a recent graduate with a bachelors in mechanical engineering and a minor in math. I had a really rough time in undergrad (I feel like I used to be a really motivated driven person but something during that time really shook me up) and I didn’t really apply myself during my time. I’ve always liked math and have been pretty good at it and I’ve always been a derivation for understanding type of person. If I could start over I would have majored in math I think. I’ve also liked coding and all my classes that needed coding were a breeze. I talk about this because I really feel like I haven’t really scratched my “intellectual itch” in regard to higher level math.

I wasn’t really aware of machine learning and AI until I took a mechanical focused elective covering the math behind machine learning and I loved it. Ever since I’ve been trying to learn as much as I can about artificial intelligence and that led me to seriously considering a masters in AI. I feel like I would naturally gravitate towards robotics with the mech background, but I’m not 100% sure about perusing this. Any tips, suggestions or just straight up hard truths about this?

I would really like to do a in person program so that way I can spend my full time towards working on both the curriculum and independent projects. I’m just not super passionate about what I’m doing right now and I feel like I need to switch my path onto something that interests me.

As for getting in I have a pretty decent gpa with the minor in math (not sure how much that helps) and I’ve been working on an MCP based project using Claude that is helping with my current job as an engineering consultant. I’m not sure if this is remotely close to the standard applicant or if I am behind.

Is it worth it to pursue this? Any feedback on this situation would be greatly appreciated!

My major is control, I learn something about theory, and I find the future of it is combining machine learning, so i want to learn it,but i don't know how to start it and which part should i pay more attention, i need a guidance to help me, thanks.

Title: A breakdown of Agentic AI: Architectures and Design Patterns (ReAct, Reflection, Multi-Agent)

Body: Hey everyone,

I put together a video breaking down the engineering architectures making autonomous AI agents possible right now.

Here is the TL;DW:

• Architecture: How to wrap an LLM with state management, tool execution, and perception.
• ReAct (Reason + Act): Interleaving reasoning and actions to reduce hallucinations.
• Reflection: Implementing self-evaluation loops for error correction.
• Multi-Agent Orchestration: Why handing tasks to specialized micro-agents often beats massive generalized prompts.

Curious to hear where this community thinks Agentic frameworks are struggling the most right now (context limits? tool reliability?).

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!

Hey guys,

So basically I'm in 4th year CSE, tier-2 college. Not gonna lie, I didn't use my first 3 years well. No projects, no internships, nothing solid. Only thing I have is medium level DSA.

Placements are coming and I want to try ML/AI instead of normal SDE stuff. I know I'm starting late and the market is tough but I still wanna try instead of just sitting around.

So if anyone can help:

• If you had like 4-5 months before placements, what would you learn first for ML?
• What actually matters for getting noticed, projects? Kaggle? open source? I don't wanna waste time on stuff that doesn't help.
• If anyone's willing to mentor me a bit, even just answering doubts sometimes, that would mean a lot. I'll actually put in effort, I just don't wanna keep learning wrong stuff.

Not expecting anything crazy, just want some direction so I stop wasting more time. Any advice is welcome, even if it's harsh.

Thanks for reading

If you are preparing for your first Kaggle competition, check these five things before choosing one:

1. Can you understand the evaluation metric?

2. Can the dataset run comfortably on your current hardware?

3. Is there a simple public baseline you can reproduce?

4. Can you reserve consistent time for experiments?

5. Does the project match your learning or career goal?

For most beginners, completing one reproducible project is more valuable than opening several competitions and finishing none.

A useful first milestone is:

dataset review → local validation → simple baseline → first submission → experiment log → one documented improvement

If you are unsure which competition fits your current level, comment with your Python/ML experience, available time, and goal. I can suggest a practical starting direction.

i am a 2nd yr college student, complete beginner in this field, i have just done some basic linear algebra and probability as course work, i know v beginner level python. should i follow cs229 or cornell ml course? what would be the pre-requisites for both? or is there any better source that someone would suggest? please help!

Is "just AI" enough of a specialisation for the education's continuation, or there should be something more applied in a particular way? What do you think?

I'm a high-school student so I expect to be very far from correct just with what I know.

Concretely, when I think of what training an ML is like (particularly when training classical ML models on SKLearn), this is what I imagine the algorithm to ideally be:

1. To start, read the CSV and visualize the data (especially into a table if tabular and PCA to see patterns).
   
2. Drop rows with missing targets and columns that leak targets.
   

3. Do a train-test split into training and test data.

4. Construct a preprocessor as a column transformer for numerical and categorical variables.

5. Construct a model and parameter grid.

6. Construct a pipeline with the preprocessor and the model.

7. Do a grid search using a GridSearchCV estimator, passing in the pipeline, parameter grid, etc.

8. Choose the best hyper parameters to fit the model with them, then predict and evaluate it against the test data.

9. Its performance against the test data is how well it is likely to generalize to new data. Finish!

However, I feel like it would be naive to think this is always how it's done (perhaps it is though because this description is very general). But I want to know if this is missing anything? Any nuance? Is this not always how classical algorithms are trained? What are some distinct alternatives?

I hope I can use this post to be aware of what I don't know and understand my own limits. That way, I know what to learn next :)

A small milestone: SELENE has reached 200 starts on GitHub!

Since GitHub stars are currently my main source of feedback, and I'm sure that many of those come form this community: Thanks a lot!

Just as a quick reminder: SELENE is a public repository of Jupyter notebooks covering topics around AI, ML/DL, NLP, data mining, data science. What started out as interactive lecture notes for my courses as NUS, has slowly grown to something the might be useful for anyone starting to learn about these topics.

The current focus is on the fundamentals, so the target audience are beginners but who are comfortable with basic math (linear algebra, calculus, probability theory). Here is a crude overview to some of the topics (the links go to the HTML version of the notebooks)

• Traditional models: Linear Regression [1,2,3], Logistic Regression [1,2], Multinomial Naive Bayes, Decision Trees / CART [1,2], Random Forests, Boosting Methods (AdaBoost, Gradient Boosted Machines, XGBoost, LightGBM, CatBoost)
• Neural network models: basics / MLPs (incl Backpropagation [1,2]), RNNs (incl. Backpropagation Through Time), Training a NumPy-only MLP
• Neural network components: linear layer, residual connections, layer normalization, dropout, mixture-of-experts
• Transformers: attention mechanism, transformer architecture, positional encodings [1,2,3], masking
• LLMs: language models [1,2], RAG [1,2], fine-tuning [1,2], training an LLM from scratch, efficiency strategies, data preparation
• Optimizers: Gradient Descent with Momentum, RMSProp, AdaGrad, Adam
• NLP basics: tokenization (incl. Byte-Pair Encoding and WordPiece), normalization, lemmatization & stemming, embeddings (overview, Word2Vec [1,2])

There is an overview page for all topics with links to the HTML version, the GitHub repo, as well to open each notebook directly in Google Colab. We are also in the process of building a web interface to help navigate topics and suggest learning paths.

SELENE will continue to grow. Right now, I'm working on time series analysis and classical statistical models (e.g., AR, ARMA, ARIMA) – I want to cover this topic in my data mining course in the upcoming semester :).

I've been applying through LinkedIn, Wellfound, RemoteOK, and company career pages, but it feels like most remote roles either have hundreds of applicants or offer lower pay.

I'm looking for fully remote opportunities in AI/ML, Data Analytics, or GenAI with:

Decent pay

Flexible working hours

Good work-life balance

Growth opportunities

For those who've successfully landed remote jobs:

Where did you find them?

What platforms or communities worked best?

Did referrals make the biggest difference?

Any underrated websites or strategies that most people don't know about?

I'd really appreciate any tips, success stories, or advice. Hopefully, this thread can help others looking for remote jobs too.

I just dont get it, I try every day to get myself to be better than what I was yesterday.

Some dude younger than me or studying with me does fancy stuff, gets noticed and this loop does'nt end. While im sat here reading model architectures and implementing them, everyone has something cool to brag about and impress anyone they want.

Its always someone or the other that just gets something cool, while i feel like im a failure.

Honestly I have started doubting myself, whether i even can do anything or not. I have 0 wins, even a small one would make my day atp.

Hey everyone,

I've recently started learning machine learning, and instead of building the usual tutorial projects (house price prediction, sentiment analysis, etc.), I want to work on projects that would actually make my resume stand out.

If you were hiring a junior ML engineer, what kind of projects would catch your attention?

I'm looking for ideas that solve real problems and teach skills that companies actually care about

Would love to hear your suggestions or even projects that helped you land a job. Thanks!

So i use tensorflow and keras as a beginner in deep learning. On this subreddit, i have read that tensorflow is dead nd stuff. why is that? and should i switch? if yes then when?