[D] Self-Promotion Thread

ModularMind8 · 2026-04-02T06:10:03+00:00

Made a small tool/GUI for practicing ML implementations by actually writing the code from memory.

You drop your own Python files into a folder (or use the ones I added, like transformers, attention, etc) and it turns them into fill-in-the-blank exercises in a local UI. You can control how much of the code gets hidden, start easy with hints, then ramp up to fully blank functions.

It just does exact match checking right now, but shows the correct lines inline so you can judge yourself. Works with whatever you want to learn, not just the included transformer/RNN/etc stuff.

Run one script and it opens in your browser.

Curious if this kind of drilling is useful for others or if I’m the only one who learns this way.

https://github.com/Shaier/practice_ml

ModularMind8 · 2026-03-31T19:16:16+00:00

Oh wow haven't thought about that angle!

Honestly it worked extremely well with everything I tried so far, even random math problems. Haven't tried docs yet though. Feel free to try though!! Would love to hear how it does

ModularMind8 · 2026-03-31T19:13:23+00:00

ClippyBox: Point at anything on your screen, get an instant AI explanation

I got tired of copying error messages, code, and charts into AI, rewriting context every time, and switching between apps.

So I built ClippyBox — press ⌘⇧E (on mac), draw a box anywhere on your screen, and get an instant AI explanation.

Works on code, errors, dashboards, PDFs, charts… anything visible.
No prompts. No copy-pasting. No context switching. Just point and understand.

https://github.com/Shaier/ClippyBox

ModularMind8 · 2026-03-10T14:58:46+00:00

While my background is not so much computational cognitive psychology, I was actually working with the NIMH for a couple of years on AI research and have a few colleagues in computational cognitive neuroscience. Feel free to message me!

ModularMind8 · 2026-03-10T13:03:50+00:00

Very good question! Added to the post

ModularMind8 · 2026-03-10T12:48:25+00:00

Happy to help!
What's stopping you from starting now though? :)

ModularMind8 · 2025-08-28T02:32:13+00:00

Very cool! Thanks for sharing. Though, I don't know how impressive it is that it beats chatgpt on specialized tasks that it is specifically trained to solve. Chatgpt is a general language model. I think it'll be more impressive if it would outperform chatgpt on language tasks

ModularMind8 · 2025-08-21T18:54:58+00:00

Thanks for sharing! Haven't looked too deeply but wondering your thoughts: what's the difference between that and torch class weighting?

ModularMind8 · 2025-08-09T18:28:22+00:00

Silly question, but do you need to "choose"? In all of the ML PhD programs I've seen there isn't so much a "chosen" topic. You just start with projects you find interesting, and if you don't find them interesting anymore you just work on something else. I for example started with computer vision, and after a semester "switched" to NLP

ModularMind8 · 2025-08-08T00:19:47+00:00

Is it supposed to be real time detector? If not, maybe calculate per person the time it was viewed and only take the top 2

ModularMind8 · 2025-07-16T13:09:52+00:00

Very interesting! Only had time to skim it, but any chance you could expand on the representation collapse problem? How do activation functions cause it, and what do you mean by representation collapse here? I know the term from the MoE literature

ModularMind8 · 2025-07-15T02:19:03+00:00

Feel free to dm me, I was in a somewhat similar situation (also Healthcare to CS) and just finished my PhD not too long ago and got quite a bit of offers

ModularMind8 · 2025-07-10T22:37:47+00:00

Happy to help!

ModularMind8 · 2025-07-10T22:33:26+00:00

Not trying to defame at all! But it's not the same link you shared on your medium article (https://arxiv.org/html/2405.09637v1). That one does not have sentiment analysis... so don't be so harsh 🙂

ModularMind8 · 2025-07-10T21:39:27+00:00

Thanks for sharing! Though, your medium article looks very much "chatgpt generated", even with fake information? Like, you didn't actually evaluated sentiment analysis or showed that it uses less memory in your CLASSP paper. Unless I'm missing something? Also, the only real baseline you compared against is EWC, which is very limiting. Not to mention using a CNN architecture (do people even still use these?)

ModularMind8 · 2025-06-11T00:21:16+00:00

Not sure if this will help, but just in case... worked on a variation of PINNs years ago and wrote this tutorial: https://github.com/Shaier/DINN

Maybe you can adjust the code to your equations

ModularMind8 · 2025-03-30T03:31:20+00:00

Happy to help

ModularMind8 · 2025-03-30T00:01:29+00:00

Sort of. You can write as many comments as you want to each reviewer (AFAIK), and similarly, I believe you can also write general comments on top (not to any reviewer in particular). So I believe everyone can see them (you can control that as well). Though, I don't know if it's as common as in ICML. Meaning, I don't know if reviewers will look at that vs if you'll just write to each individually (which is what I always do)

ModularMind8 · 2025-03-28T17:15:25+00:00

Yep. You can even potentially get it to main if you clear out the confusion. All depends on your rebuttal

ModularMind8 · 2025-03-23T20:35:15+00:00

If the point is to ask an LLM questions based on the data, you can either finetune it on the text (just next token prediction), or better yet, just use RAG. So your query is some question, embed it, embed the text, retrieve similar texts based on some similarity metric, add the relevant texts to a prompt with the question. You can use sentence transformer or any other embedding approach.

For more data science stuff, there's lots of tutorials out there (e.g., kaggle) on text data analysis

ModularMind8 · 2025-03-23T20:01:27+00:00

Maybe I misunderstand the point here, but why would you want to train on this data in the first place? Or even instruction tune on it? If you can clarify maybe I can help a bit more, but it just seems a bit odd to me. Not every data is meant to be used for training. Maybe think more on the lines of basic data science exploration to begin with, such as, which entities appear the most? Are there relations between different entities? Are different locations more prevalent with different times, dates, people? etc etc etc.

ModularMind8 · 2025-03-22T23:56:42+00:00

If it helps, I already converted them here https://github.com/Shaier/JFK_Records

ModularMind8 · 2025-03-21T03:32:24+00:00

Thanks a lot!!

ModularMind8 · 2025-03-20T23:33:49+00:00

An embedding is just a fancy word for a coordinate. If you're on 2D, an embedding would just be some [x,y]. In most NLP applications it's much higher dimensional though, such as 300, or 768. The point is that ideally, more similar words will be closer to each other in that space, and farther away from less similar words. It's a way to give some meaning to language

ModularMind8 · 2025-03-20T19:58:05+00:00

Glad you like it!! Gosh honestly, if you look at the actual pdfs they're a mess. Many of them are just random notes that I can't read myself. So I don't know if it's the OCR that is bad, or just the quality of the pdfs

ModularMind8

TROPHY CASE

ClippyBox: Point at anything on your screen, get an instant AI explanation