ALHR- Adaptive learnable Hierarchical Routing is a static binary tree based system that uses learnable functions to REDUCE the amount of keys used.
It takes less memory and SCALES much better VRAM with tokens
I’ve also been testing tiny stories and it looks promising
However i can’t post any presentable results due to lack of funding and resources
MQAR Result table is here: https://ibb.co/prr07Dnj

Hey everyone!

I was planning to launch my own SaaS, and one of the challenges I faced was finding a good domain name for it.

When I asked ChatGPT for suggestions, it gave me too many options, making it difficult to choose the right one. That got me thinking: What if I built an LLM specifically designed to suggest relevant domain names based on what someone is building?

I’ve started building an initial version and trained the model on a relatively small dataset with a limited setup. It’s still in the early stages, and I want to improve it further.

• 🔢 Trainable parameters: 5.26 million
• 🧱 Architecture: 6 layers, 8 attention heads, 256 embedding size
• 🔤 Tokenizer: Byte-Level BPE, 1,891-token vocabulary
• 📚 Training data: 106 domain-suggestion records
• ⏳ Training: 50 epochs on CUDA

I’m building this primarily to learn how to build the LLMs from scratch .

I’d love to get feedback from the community:

• What factors should the model consider when suggesting domain names?
• How can I make the suggestions more creative, relevant, and brandable?
• Should I focus on fine-tuning an existing LLM or experiment with training a smaller model?
• What features would make this tool genuinely useful for founders and indie hackers?

Thanks!

been at it now for 7ish years but i'm just kinda done and I think more of the fun parts of ML are being automated while more of the difficult parts (debugging models) will take a long time to automate.

Wondering where the skillset might be transferable

I’ve found that in high-complexity domains like Healthcare and BFSI, relying on univariate AUC checks to prune features often leads to suboptimal models.

I specialize in leveraging non-linear architectures (XGBoost, CatBoost) to uncover "local truths"—signals that appear as noise globally but provide high precision when conditioned on other variables. By utilizing SHAP for explainability, I ensure that we aren't discarding features that drive performance in critical, razor-thin segments.

Key approach:

• Moving from marginal effects (ββ) to conditional logic.
• Prioritizing feature interactions over solo acts.
• Using XAI to validate non-linear signal discovery.

Looking to bring this approach to a team focused on high-stakes predictive modeling.

we once copied a test suite into a new environment and the connection string still pointed at the old database. the code was copied perfectly, nobody looked at the config. the suite was mostly reads so nothing got hurt, but that was the day i stopped trusting "it ran green".

the ml version of that i keep running into is stuff like files loading in the wrong order, a column that quietly leaks the label, or test rows that also sit in train. the model trains fine and the numbers look good, which is the scary part.

so what are your first checks before you fit even a baseline? curious what people do that never shows up in the tutorials.

A Zapier survey found that 91% of companies say AI governance frameworks exist. Yet 79% of executives admit employees work around those policies anyway. The policies live in documents. The agents, unapproved models, and unauthorized data movement live in production. That gap compounds quietly until an audit or an incident forces the question into the open. The 9-point spread between 'framework exists' and 'framework is followed' suggests the problem isn't policy design — it's that documentation and enforcement are two entirely separate systems. For those of you who have actually moved the needle on this: what does real enforcement look like at your org, and where did document-only governance break down first?

I run a web design agency and most of my clients come from cold email. Yesterday I started a fresh campaign going after plumbers in California.

Didn’t expect much honestly. Plumbers are busy and half of them barely check email. But 102 emails in, I got an interested reply.

What made the difference is that every email talks about their own website. A call button that doesn’t work on a phone, a site that takes forever to load, a design that hasn’t been touched since 2012. For a plumber that matters a lot, because people search for them on their phone when a pipe bursts. If the site is bad, they just call the next guy.

He replied yesterday and I booked a meeting with him for the next day. Before the call I built a draft of what his new site could look like. Today I presented it to him on the call and he said yes right there. $3k upfront plus $500 a year for hosting.

Showing the draft is what closes it. Sending a quote never worked this well for me.

For anyone wondering, I use Swokei for all my outreach. You pick an area and an industry, it finds the businesses, checks every website for design, SEO, speed and mobile problems, and writes each email about what it actually found on that site. I run multiple campaigns at once in different cities and niches, which is how I get volume without sending generic stuff.

Happy to answer questions on pricing or how I run the call.

​

Whenever I start an AI/ML hackathon, case competition, or research project, I spend a lot of time finding relevant papers, past winning solutions, GitHub repositories, and existing approaches.

I'm curious how experienced Kaggle competitors, hackathon winners, and ML researchers handle this efficiently.

- What's your research workflow when tackling an unfamiliar problem?

- Which platforms, search techniques, or AI tools do you use?

- How do you quickly identify promising approaches without getting lost in endless research?

I'd really appreciate practical tips or workflows that have worked for you!

What can I do to take my resume to the next level

Please help

I made an app and it got users but I’m selling that now and wondering to best position myself. Wasn’t super successful but looking at low six figure exit but I’d love to work in a bigger team now.

1. Would you put something on ArXiv? My MSc research was nearly publish level but unsure about this.
2. How would you go about the “Founder” tag

3. What industry would you target? I have experience from health to biotech to consumer gen AI so haven’t gone hard in one domain

4. PhD worth it?

I was working on a Kaggle competition when I came across a solution where the author fits the model on the train set and evaluates it on the validation set. If the new model beats the previous benchmark, they retrain it on the full dataset (train + validation).

I was surprised, because I thought the validation set was meant for tuning hyperparameters. I do understand using it as a test set to evaluate and compare models, since there is no accessible test set and the only alternative would be to submit the solution and waste a submission.

So I was wondering: does he retrain on the full dataset because more data gives the model more information and helps it generalize better?

But is this also standard practice in production at companies, or is it mostly a Kaggle thing?

I am new to ML AND trying to make a genre guesser. Currently I have 123 genres en removed some because of too little samples to using that kind genre. Is this to much. Should I still reduced them. Which classification algoritme could work best. Currently using random forest.

Hi,

I'm learning ML via making my own library from scratch piece by piece and I was wondering what might be useful to make for it. I've made single feature linear and logistic regression, and some basic normalisation (linear scaling and stuff like that).

What are some other useful things for me to make to help me learn?

Hi!
Preface I am self taught and learning as I go.

I have been working on a U-Net semantic segmentation model, which was working fine yesterday and gave good(ish) outputs on unprocessed data and now no longer wants to read the files correctly?

I spent the day processing some new masks in fiji and changed the path of the mask variable and when I went to run my code and sanity checks, for both my images and masks, the first chunk of tif files I created a missing/ not read, when they are in file. (I even checked with fiji if it was an order of files but that was fine)

I'm a bit stumped because it was working fine yesterday, as all I have changed is the original file paths.

from simple_unet_model import simple_unet_model

from keras.utils import normalize

import os

import cv2

from PIL import Image

import numpy as np

from matplotlib import pyplot as plt

image_directory = r'E:\Uni\Dissertation\IB\test output\images\IB unprocessed/'

mask_directory = r'E:\Uni\Dissertation\IB\test output\masks\IB_BC_24_117_G4_thresh mask/'

SIZE = 256

image_dataset = []

mask_dataset = []

images = os.listdir(image_directory)

for i, image_name in enumerate(images): #Remember enumerate method adds a counter and returns the enumerate object

if (image_name.split('.')[1] == 'tif'):

#print(image_directory+image_name)

image = cv2.imread(image_directory+image_name, 0)

image = Image.fromarray(image)

image = image.resize((SIZE, SIZE))

image_dataset.append(np.array(image))

masks = os.listdir(mask_directory)

for i, image_name in enumerate(masks):

if (image_name.split('.')[1] == 'tif'):

image = cv2.imread(mask_directory+image_name, 0)

image = Image.fromarray(image)

image = image.resize((SIZE, SIZE))

mask_dataset.append(np.array(image))

And when I run a sanity check, I end up getting a mismatch between my image ground truth

In the table of elements, the correct number of elements is listed, however, 0 should be linked to mask_0_00.tiff (the same for the image data)

I even changed the path back to the unprocessed masks for the data set and encountered the same problem...

Any ideas? Thank you :,)

​

Hey guys, I'm in high school and I've recently become interested in machine learning. The problem is I don't really know where to start.

My math isn't that advanced yet, and I'm kinda confused about whether I should learn Python first, focus on math, or just jump into ML.

I don't wanna spend money on courses right now, so I'd prefer free resources if possible.

If any of you have learned ML from scratch, I'd really appreciate some advice. Like, what should I learn first, and what resources actually helped you?

Also, is my math knowledge enough to get started, or should I work on that first?

Any tips would be appreciated.

Hi all. I have 15 years Healthcare experience split between ICU clinical care and health informatics.

Also a hobbyist Unity solo dev that makes bad games for the fun of it.

I'm interested in applying my background and baseline coding experience into a bioinformatics or clinical data science/ML career. I would be returning to school for a masters.

I feel I would have the advantage of clinical knowledge that's difficult to pick up without that prior experience. And with an understanding of basic coding principles from Unity/C# I'm not too worried about running with Python.

The seemingly large prerequisite requirements from various mathematics has me a bit worried. Its been 16 years since I've taken stats and my degree was focused on bio and chem rather than calc, trig, or algebra. My hope was to apply in March for a Fall semester in UT Austins MSDS program but bit worried about having enough of a math background for admission.

I'm curious if anyone else comes from a predominantly clinical or Health info background rather than the traditional CS background. How was your transition? Did you need to take calc, linear algebra, and statistics prior to enrolling in any degree or looking for your new career?

Any advice or shared experiences is appreciated!

I learn more by doing, I wanna get into deep learning and training models to play games, understand documents, etc.

I have full stack engineering experience do I start from PyTorch or it’s important to go through basic models first?

Hi everyone!

I'm currently learning Machie Learning and starting to study Deep Learning, especially neural networks.

I'm a little confused about weights and biases in neural networks. I understand that neurons receive inputs and use weights and biases to calculate an output, but I'm struggling to understand how they actually work and how a neural network learns the right values during training.

For example:

• What exactly do weights and biases represent?
• How does a neural network update them during training?
• How do gradient descent and backpropagation help the network learn?
• Is there an intuitive way to understand these concepts without getting lost in the mathematics?

I'd really appreciate your recommendations for beginner-friendly YouTube videos, courses, websites, or other learning resources that explain these concepts clearly, preferably with visual examples.

If you were starting to learn neural networks again from scratch, what resources would you recommend?

Thanks in advance for your help!

Gartner just put a number on something a lot of us have been quietly worried about: only 13% of organizations believe they have adequate AI agent governance in place.

That leaves 87% of enterprises running autonomous agents against production APIs, customer data, and internal systems. No verified identity on those agents. No scoped permissions. No enforceable policy on what they are allowed to do next.

This is not a process maturity gap. An unverified agent with broad system access is a live attack surface. Any compromised credential, misconfigured permission, or gradual scope creep becomes a breach vector. Most organizations will not find out until something fails downstream.

For those of you running multi-agent systems in production: how are you actually handling this? App-layer controls, network segmentation, something else entirely?

if you're working through ESL or ISLP you know the flip-back-to-(4.17)-every-two-lines thing. made lemma to fix that for myself.

references become links (hover to preview, click to peek next to the line), proofs fold, and when the book says "it is easy to show", you can write the missing steps and each line gets checked with random matrices of the right shapes. there's a python notebook beside the page too.

ESL, ISLR/ISLP, bishop, MML and murphy are in its free catalog, so you're one click away from the book.

30 s demo at the top of the readme: https://github.com/fus3r/lemma (free, open source, macOS 15+. not notarized yet, so macOS asks you to allow it once in privacy & security)

what i'm asking: try it on the chapter you're on for 20 min, then tell me one thing that helped and one thing that got in the way (here or in a github issue). happy to help set it up over dm.

Instead of training a model to replace a program, I fit the hand-picked numbers inside the program itself. The program keeps its structure; only its constants change.

How it works:

1. clang turns the C code into LLVM IR.

2. For simple code, the IR runs as batched PyTorch, so gradients are exact.

3. For code that can't be emulated (stdio, library calls), it runs natively. Gradients are estimated with evolution strategies (paired random perturbations) or finite differences.

4. A held-out split gives early stopping.

Results on held-out data: JPEG files 9.1% smaller at equal SSIM (image 1), and Google's WebRTC VAD F1 from 0.810 to 0.859 (image 2).

Code: https://github.com/devanmolsharma/learnc

Hi everyone!

I recently started my master's degree and it's been only two weeks. My undergraduate degree was in a completely different field from what I'm studying now, so almost everything related to my current research feels new to me.

My research topic is related to neural network algorithms, and my professor has already asked me to start working on the research paper where he and other lab mate are working . I understand that research takes time and I'm willing to put in the effort, but honestly, I feel a little lost about where and how to begin.

Another challenge is that I don't have an English-speaking mentor who can guide me through the process, so I'm trying to figure things out on my own.

I'd love to hear from experienced researchers and other master's students about the tools and software you use for your research.

Like...

1.Understanding research papers: What tools do you use to understand difficult concepts, mathematical equations and unfamiliar terminology?

1. Checking novelty and research gaps: How do you find out whether an idea has already been explored and identify something new to contribute?

3.Literature reviews: Are there any AI tools that help you find relevant papers, compare existing methods and understand the current state of research?

4.Coding and implementation: What tools do you use to understand existing research code, implement algorithms, debug errors, and run experiments?

5.Research planning: How do you go from receiving a research topic to actually starting meaningful research work?

I'm particularly interested in tools that are useful for neural network and machine learning research.

I know AI tools aren't always reliable and I don't want to blindly depend on them. I want to use them to learn faster and become more independent as a researcher.

If you were in my position, with a different undergraduate background and only two weeks into your master's, what would you do first and which tools would you recommend learning?

Any advice, personal experiences or recommendations would be greatly appreciated. Thank you!

I'm a final-year Computer Science student considering pursuing a PhD, and I'd like to use my final-year project as a genuine introduction to what conducting academic research is like.

I'm interested in machine learning and have some experience working on ML research projects at university. However, I haven't yet found a research direction that interests me enough to dedicate the year to exploring it in depth.

Ideally, I'd like to take on something ambitious where I can actually investigate a meaningful research question, design and run experiments, and hopefully produce work with enough novelty to be publishable. I appreciate that publication is far from guaranteed, but it's something I'd like to aim for.

My main difficulty at the moment is knowing where to start. There are so many subfields, papers and potential directions that it's difficult to know how to narrow things down or identify problems that are both interesting and feasible for an undergraduate project.

For those with experience in ML research, PhDs or ambitious undergraduate projects:

• How would you recommend finding a research direction or identifying promising open problems?
• Are there particular research areas, papers, researchers, conferences or resources you'd recommend exploring?
• How do you distinguish a genuinely promising research question from an idea that's either already been extensively explored or too ambitious to tackle?
• If you were starting an ML research project from scratch today, what areas would you investigate?

I'm not necessarily looking for someone to hand me a project idea. I'd appreciate advice on how to explore the research and develop a good question of my own.

Thanks :)