I have around 1 year of industry experience as an AI/ML engineer, and I want to seriously level up over the next 1–2 years.
I’m not looking to become someone who just knows how to use APIs, build basic RAG applications, or glue together existing models. I want to develop the kind of depth where I can actually understand what I’m doing, build things from scratch when necessary, read and implement papers, and eventually be capable of working at a strong senior/research-engineering level.
The problem is that there are so many things to learn — ML, deep learning, mathematics, LLMs, systems, distributed training/inference, research, DSA, software engineering, etc. — and I don’t want to spend the next couple of years consuming random courses without actually becoming significantly better.
So I’d really like to hear from people who are already working at a strong senior/research level in AI:
If you were starting again with ~1 year of experience, what would you learn and in what order?
What topics would you go extremely deep into, and what would you only learn practically?
Which courses/books/resources genuinely made you much better?
How much mathematics did you actually learn, and which parts turned out to matter?
How would you balance DSA/interview preparation vs AI/ML depth vs software engineering?
What kinds of projects would actually make you a substantially better engineer rather than just look good on a resume?
How would you approach implementing research papers?
Are there particular papers or repositories you think every serious AI engineer should work through?
How would you approach contributing to open source if your goal is to become a better engineer/researcher?
What skills do you think aspiring AI engineers massively underestimate?
And most importantly: what would you NOT spend time learning?
I’m specifically interested in hearing from people who have already gone through this transition — Senior AI Engineers, Research Engineers, ML Engineers, researchers, etc.
If you could go back to having ~1 year of experience and had 12–24 months to become dramatically better, what would you do?
I’m looking for honest answers, including things you tried that turned out to be a waste of time.
Thanks!

Hello everyone,

I’ve built a repository exploring two novel recurrent neural network architectures (PLUG and ILRM) designed to address the vanishing gradient problem in long-sequence modeling without relying on attention blocks.

The core mechanism feeds the cell a deterministic, normalised inverse-lag history (\(P_{t}\)) of previous inputs. Weighted by harmonic numbers, the hidden state update is modified so that historical information decays at a rate of \(1/k\) rather than scaling exponentially. The direct token-to-history gradient pathway decays polynomially (\(\frac{1}{t\ln t}\)) rather than exponentially, creating a stable gradient horizon evaluated efficiently at \(O(T \log T)\) via zero-padded FFT linear convolutions.

Due to local hardware restrictions, I could only run smaller synthetic tests on CPU, but the preliminary screenings look somewhat promising on these small tests:

Long-Range Extrapolation: When trained only on delays of 16–64 steps, both models extrapolated up to 4,096 steps with 100% accuracy on a one-bit retention task, while the baseline GRU collapsed to chance.

Gradient Horizons: At 1,024 steps, the mean initial token gradient remained stable ( ~ 10⁻⁴ ) while the standard GRU suffered absolute numerical underflow ( ~ 10⁻¹⁴⁵ ).

The repository contains the complete mathematical framework PDFs and minimal, executable PyTorch implementations for both models. I would love to get your feedback on the math and the implementation!

This is a continuation of last week’s post where I had the models compete in a Three.js cup and ball game. This time, I’m using real-world footage, which is even more challenging because of distractors. I might test this out on some of the Anthropic models sometime. 

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

I pulled together every AI-security incident from August. The number that stood out: AI-agent exploits are now the single largest attack-vector category, ahead of credential theft, zero-days, supply chain, phishing, and ransomware — each counted individually.

The month in numbers: 123 incidents, 23 critical and 97 high severity, across 38 named organizations, 331M+ records exposed. 65 incidents involved AI as the weapon or the target. Attack vectors broke down as: AI-agent exploits (37), credential theft/reuse (28), zero-days (23), supply chain (12), phishing (9), data exfiltration (8), ransomware (6).

The stories that stood out:

- McKesson: 284M records, the largest single breach of the month by a wide margin.

- Carhartt (12.9M), Exact Sciences (10.9M), and CareCloud (3.7M) round out the biggest named incidents — three of four sit in or next to healthcare.

- Five confirmed RCEs landed across Microsoft SharePoint, Windows, F5/nginx, and the PyPI package index twice.

- Two separate PyPI supply-chain poisoning campaigns, plus a compromise of n8n, an AI workflow automation platform.

Every one of the breached companies almost certainly runs a modern security stack — CrowdStrike, Okta, Palo Alto Networks, Microsoft Defender, that class of tooling. None of it stopped these incidents, because none of it operates at the point where a credentialed agent actually acts, or where a poisoned dependency resolves at build time.

Full report, with the specific control that maps to each incident: https://runtimeai.io/blog/2026-08-monthly-breach-report.html

Genuinely curious how others are approaching this: is anyone actually testing whether their existing guardrails hold against a real simulated attack, or is it still mostly an assumption that they will?

I’m working on a little project to benchmark how vision-language models do with classifying grappling techniques. These results are the vanilla models without any fine-tuning, so it’s sort of hit or miss. I’m sure with enough data, the guesses can get pretty accurate. If any of you fellow grapplers who are engineers are interested in playing around with this, I’d be happy to open source it. 

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I am currently researching by my own how neural networks work, in this part, I am researching how a shared-weight resiudal neural network's feature map behaves, curently, sharing the stage 3 blocks of ConvNext. It seems that it iteratively refines the feature map instead of computing different ones. If I get all the feature maps of the d_th output and its velocity f(x), since we do x' = x + f(x), we obtain this result.

I don't have much idea about interpretability or Differential equations, but this is clearly a ODE solver.

I ommited 1 channel in the first plot, here that just accelerates and goes a lot further, close to value 600 and then velocity decays. Maybe the network learned in which step it is using that channel?

I know it's a very niche topic... But if anyone knows about this, I'd like to know more. I've readed about the ResNet ODE solver hypotesis and the Neural ODE solvers.

But, I archieved to extrapolate a network of 9 layers to 100 and even 10000 without fine-tunning nor lossing a significan ammount of image ent top-1 accuracy, just 0.5% . I just doing some piping work.

I am just asking if anyone has worked on this or has any idea how this can be applied or if this is just usless. I am kinda of stuck in here.

Hey everyone

I’ve spent quite a good amount of time learning the mathematics behind deep learning, and honestly, it has been a wonderful journey so far. For me, math and philosophy are probably the two subjects that interest me the most, so studying the mathematical foundations of AI has been a really enjoyable experience. I especially like the process of going from an intuitive idea → mathematical formulation → understanding why it works → and finally seeing how it translates into an actual deep-learning algorithm.

I’ve been making my own notes along the way, mainly covering the mathematical foundations that I think are useful for understanding deep learning.

I want to pursue my career in the AI research field, and that’s one of the main reasons I’ve been spending so much time learning the mathematics behind deep learning. I believe having a strong mathematical foundation will help me better understand research papers, derive things myself, and develop a deeper understanding of the ideas and algorithms I’ll be working with.

That said, I'm still learning myself, so I’d really appreciate some honest feedback.

An extortion group called FulcrumSec is claiming it stole more than 80 GB from Manchester Airports Group and is threatening to publish it. Airport infrastructure data — the kind that includes operational systems and customer records — sitting exposed long enough for a bulk extraction nobody caught in time.

The pattern is not new. Sensitive records concentrated in accessible systems, pulled in bulk before any alert fires. What is changing is the speed. As more automated processes and integrations touch operational data, a single compromised access point can move 80 GB faster than any human review cycle can respond.

The blast radius question is no longer just about perimeter security. It is about what happens after an attacker or a compromised service account already has legitimate-looking access. At that point, traditional controls have already lost.

For those working in enterprise security or infrastructure: how are you thinking about limiting bulk data movement once something inside the perimeter is already authenticated? Are you relying on volume thresholds, destination allowlists, behavioral anomaly detection, something else entirely? Curious what has actually worked in practice versus what looked good on paper.

Almost every VLM I’ve put through this test has struggled, but it makes sense because it requires them to count, something that isn’t their strongest trait. In this case, it’s just counting lines and spaces, but if we introduce different key signatures, they would also need to count the sharp and flat symbols. 

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Seeking Feedback from the ML Community 🙏

I recently trained a prototype-based network on Tiny ImageNet (200 classes). It uses learnable prototypes with responsibility scoring and multi-loss training (CE + Pull + Push + Diversity), achieving 51.29% validation accuracy with only 595K parameters.

I'm still learning, so I'd love to hear your thoughts:

Is this a reasonable result for this model size?

What would you suggest to improve it?

This was trained on free Colab with limited resources, so I know there's much room for improvement.

GitHub: https://github.com/jalalnablsi/signature-painter