Hi all,

I'm excited to share this with everyone here, I've been seeing a lot of requests for tensor manipulation exercises and so I thought I'd create a place to build confidence and fluency in working with PyTorch tensors!

I've found that understanding ML concepts and mathematics is one thing, but implementing them in PyTorch is a totally seperate beast.

Tensor Golf lets you practise working with tensors in a puzzle format, with the least number of operations giving you a better score - hence the "golf" part.

The core set of practise questions are free and I'm considering open sourcing the core set of puzzles :)

Let me know if you find this useful!

link: https://tensor.golf

Thanks :)

Every single neuron that is firing in your brain has the potential to be a slop paper written up by someone else. How to do machine learning in the age of slopalanche? What even constitute as "stable knowledge"? How to dip a toe into research when research papers are multiplying like rabbits? https://arxiv.org/list/cs.LG/recent?skip=611&show=500

Hello,

I'm currently a student and have mostly been leveraging AI as a tutor to help me through my classes. As a result, I've collected a ton of artifacts and tools that help visualize and interact with complex concepts that traditionally been depicted with static diagrams. So I've turned this collection into a side project. I'm releasing Receptive Field, a site where you can learn about convolutional networks with interactive visuals. As a student, I understand the need for free educational resources and this will remain free of charge. Try it out and let me know what you think: https://receptive-field-flame.vercel.app/appendix/

I'm an AI engineer, and I've been building a free quiz to learn machine learning concepts, from the basics to niche topics like KANs, physics-informed networks, or why an LLM at temperature 0 can still give you different answers.

The thing I cared about most is that every option explains why it's right or wrong, not just the correct one. The hard questions aren't trivia, they're short scenarios you have to reason through. You choose the levels and topics, and play in study mode (explanation after each question) or exam mode.

It's an early alpha. There are 22 questions for now and I'm still reviewing them, so you may find mistakes. Each question has a "Report a mistake" link that opens a GitHub issue. Feedback on the questions is what I need most, especially on the hard ones.

It's free and doesn't ask for an account. It works in English and Spanish, and you can install it on your phone.

Site: https://ai-ml-questions.strangemachines.workers.dev/

Code and questions: https://github.com/CristobalSantana/ai-ml-questions

If you try it, tell me which question felt unfair, or too easy for its color.

jaxviz is an open-source tool I built to visualise the forward pass graph of JAX models directly in notebooks with a single function call.

It supports the following features:

1. Interactive visualisation of JAX models with hierarchical exploration of nested modules (especially useful for large, deeply nested models).
2. Global and per-device views: Visualise both the logical computation graph as written and the compiled computation graph that actually runs on individual devices.
3. Supports web-based notebooks including Jupyter, Colab, Kaggle, and VS Code.
4. Export visualisations to HTML, PNG, and SVG formats.

Resources

• Demos with examples of JAX computation graphs
• GitHub repository

I hope this is useful to the JAX community, particularly for understanding how models are compiled and executed across devices. I'm keen to hear your feedback and suggestions!

I'm a Data Engineer with around 4 years of experience, mainly working with Azure, Azure Databricks, PySpark and Python.

I was assigned a Data Engineering role in my first company, even though I've always been interested in AI. Since I started working, I've gained experience in data engineering, but my long-term goal is to become an AI Engineer.

I come from a Computer Science background, and I genuinely want to see myself working as an AI Engineer in the next 1–2 years (Is it possible). I'm ready to put in consistent effort, but I'm confused about where to start and what to prioritize (I asked my seniors who transistioned from data engineer -> ai engineer i havent got proper response). If I want to start from begining learning from the fundamentals what i need to learn?

For those already working in AI or who have transitioned from Data Engineering to AI Engineering what should I learn first

I'm looking for practical advice based on real industry experience, useful resources and skills that companies actually expect from AI Engineers. I want to build a strong foundation and gradually develop the skills needed to work on real AI applications.

EMNLP 2026 starts in Budapest on October 24, so we grouped all 5,540 accepted papers (main, Findings, industry, demo) and all 3,484 EMNLP 2025 papers into the same 91 topics and compared each topic's share of the conference. The conference grew 59%, so we compare shares rather than raw counts.

What changed (share of all papers, 2025 → 2026):

- Agents and tool use as a whole: 5.4% → 11.3% (187 → 628 papers)

- Agent benchmarks and environments: 1.5% → 3.3% (×2.2)

- Agent memory and long-horizon tasks: 0.1% → 1.9% (5 → 103 papers)

- RL for reasoning (RLVR, GRPO): 1.0% → 2.4% (×2.5)

- Safety guardrails and refusal: 1.5% → 2.9% (×2.0)

- Down: information extraction 2.4% → 1.4%, bias and fairness 2.6% → 1.7%, instruction tuning and synthetic data 1.6% → 0.7%

In titles, per 1,000 papers: "memory" went from 3 to 29, "evidence" from 3 to 23, "long-horizon" from 0.6 to 12.5. "Enhancing" fell from 28 to 9.

We also had an AI read the 3,334 papers with a full text on arXiv (60% of the conference): 54% train or fine-tune a model (vs 71% at NeurIPS 2026), 46% use an LLM as a judge (vs 18% at NeurIPS), Qwen is the most-trained family (32% of papers), and GRPO is by far the most used RL algorithm. Findings grew faster than the main conference (+77% vs +50%).

Data: the official EMNLP 2026 presenter schedule, arXiv abstracts and full texts for the papers that have a preprint, and the ACL Anthology for 2025. Topics and full-text figures were assigned by AI models and not checked by hand, so treat them as estimates. Papers on arXiv may lean toward larger labs.

The front page is free to read: https://atlas.scime.ai/emnlp-2026

The full edition (every oral by session, all 91 topics with papers, languages studied, a Budapest guide) needs a free account. Happy to answer questions about the method in the comments.

What is the profile of a good ML engineer nowadays? Does it exists? I am trying everything - conferences, posters, papers, active linkedin, even my "prototype" with users. I am finishing my fifth degree.. sometimes they tell me I am too academic, sometimes they can easily get someone from Faang.

What would you advise to have as a skill set in 2026 autumn? If you can summarize a few points, it would be really helpful for me.

'm currently learning Deep Learning, and I'm trying to understand Convolutional Neural Networks (CNNs). I'm following a tutorial that builds a CNN using TensorFlow/Keras to classify handwritten digits.

The problem is that I understand some parts of the code, but when I read the explanations, I get overwhelmed by all the technical terms:

Convolutional layers and filters

Pooling layers

Flatten and Dense layers

Activation functions like ReLU and Softmax

Loss functions, optimizers, and backpropagation

Normalization and data augmentation

There are so many concepts that I sometimes feel like I'm memorizing words without really understanding what's happening.

My goal isn't just to make the code work. I want to understand what each step does, why we need it, and how to explain the entire CNN pipeline in my own words.

For those of you who have learned CNNs from scratch:

1. How did you learn to understand CNNs intuitively instead of just memorizing definitions?
2. Are there any beginner-friendly resources, YouTube videos, or visual explanations you'd recommend?
3. How would you explain the complete CNN pipeline to someone who is new to Deep Learning?
4. Would you recommend learning the mathematical details first, or building small projects and learning the theory along the way?

I'd especially appreciate resources that explain things visually and use simple examples rather than introducing too much terminology at once.

Thanks for your help! I'm trying to build a solid understanding rather than just copy code from tutorials.

I genuinely want to learn mathematics, physics, programming, computer science, and AI. I want to build a career in computer science, pursue AI research, and hopefully do a PhD someday. But lately, I've been feeling increasingly demotivated.

The problem is that I rely on AI to learn almost everything. Whenever I have a question, I ask GPT. Whenever I don't understand a concept, I ask AI to explain it. And the more I learn this way, the more I wonder: What's the point of learning anything when AI will always know more than I ever could?

Even the most basic AI models seem to possess more knowledge than I could acquire in a lifetime. And with new models becoming increasingly capable, I keep wondering whether the knowledge I'm working so hard to gain will even matter in the future.

With GPT-6, Claude Opus 5.5, Gemini 4, and other models competing to become more capable, it feels like the gap between human intelligence and AI capabilities will only continue to grow.

I want to understand the mathematics behind AI, learn physics, become a better programmer, and contribute to actual research. But sometimes I question why I'm putting in the effort when AI can explain concepts faster, write code better, and potentially solve problems that I might spend years learning to solve myself.

And even when I genuinely want to learn, I'm confused about what I should prioritize learning and whether any of it will remain valuable by the time I become proficient.

I'm not necessarily saying that learning is useless. I genuinely love the idea of understanding how things work. But I'm struggling to reconcile that curiosity with the feeling that AI might eventually outperform me at almost everything I want to become good at.

For those of you studying computer science, mathematics, physics, or AI, how do you deal with this feeling? How do you stay motivated to learn when AI is advancing so rapidly? Do you think deep technical knowledge will still matter for people pursuing AI research and PhDs, or should we be approaching learning differently now?

I’m based in London and looking for one committed study partner to work towards data science or ML job applications over the next three months.

My focus is data science, machine learning, neural networks, and DSA, then computer vision, transformers, and RAG as we progress. We’ll prioritise topics based on our gaps and build projects alongside interview preparation.

I’m aiming for 10+ hours on days I’m free, around university and work. I want someone equally ambitious who will show up consistently, challenge my thinking, and expect the same from me.

The plan:

• Daily goals and honest progress updates.
• Daily discussions about problems, bugs, and concepts we’re stuck on.
• Code reviews, problem-solving sessions, and weekly project demos.
• Job applications and interview practice throughout.

London would be ideal for occasional meetups, but other countries are welcome if our schedules overlap.

If interested, DM me your current level, time zone, availability, and target role. Let’s agree on a realistic schedule and get started.

Getting Started with Bedrock AgentCore

https://debuggercafe.com/getting-started-with-bedrock-agentcore/

In this article, we will cover an introduction to Bedrock AgentCore and check the process from creating agents to their deployment.

Hi everyone. I want to become an AI Engineer... Here's my situation:

• Almost done with Andrew Ng's ML specialization (1 course left)
• Forget theory easily if I don't apply it
• Do not have enough time to master everything
• No plan B — AI is the goal

Option 1 — Traditional roadmap: Finish ML → DL specialization → NLP → LLMs → RAG → Agents Missing: deployment, OOP, DSA, advanced Python (only CS50 so far)

Option 2 — Fast track: Finish ML → Python/OOP/DSA fundamentals → crash courses per topic → projects with AI assistance, understood deeply, post on GitHub

Questions: 1. Which approach makes me job-ready faster? 2. If neither — what's a better plan?

For context I’m in University of California Riverside’s accelerated masters program (current 4th. yr). I love the faculty here, but the program is highly theoretical and offers almost no ai/ml classes. As a result I’ve been learning ml/ai all on my own through textbooks and online resources with some professor mentorship, but I really wish I could just learn these things through courses that build into the degree I’m paying for. My main reasons for staying on this track is the program and programs like it (Statistics MS) are a lot cheaper than data science programs, but I’m torn because I want to do a program that actually teaches the stuff i’m interested in. My ultimate goal is to get a data science or adjacent role in tech, which UCR is not the best for because location and alumni network are not the best (for tech at least). If anyone has any advice of what to do it would be greatly appreciated.

currently programs I’m eyeing (open to new suggestions):
finish ucr ms statistics program ~ 12-15k
ucsd ms in data science ~ 32k
ucla ms in statistics ~ 31k
ucla masds ~ 60k
usc ms in statistics ~ 76k

Here's the reverse take nobody wants: the bottleneck was never the papers. It's you, pretending that skimming abstracts counts as "keeping up." You haven't read a full paper since 2023 and you know it. The arxiv feed isn't drowning you, it's just finally exposing that your entire learning strategy was vibes and a Twitter thread.

Meanwhile someone's out there doing Tensor Golf drills while you're stress-refreshing the new submissions page like it's a slot machine. One of you will actually understand backprop by December.

So here's the move: pick one paper. One. Read it with a pen. Let the other 861 rot in peace. They were never yours to save.

Researchers tracked more than 2,600 AI agent attempts targeting Alibaba's Amap from Tencent-linked cloud infrastructure. None of the agents had authorization to access that service.

These were not human operators pivoting through tooling. They were autonomous agents running coordinated, repeated reconnaissance against a live production system at machine speed. Human-paced incident response cannot match that cadence.

The exposure here is not a misconfigured firewall or a leaked credential. The agents operated against a production service with no verified identity, no declared scope, and no gate between their initialization and their first outbound request. The target service had no way to distinguish a legitimate automated caller from an adversarial one before the probe landed.

For those running production APIs or services that autonomous agents — not humans, not static service accounts — might reach: what does your authorization boundary actually look like at the moment of first contact? Not at login, not in a post-incident log — at the exact moment an unknown agent makes its first request.

Hello everyone! I’m a freshman engineering student, and my classes will start in November.
I’m planning to specialize in Machine Learning, with the possibility of pursuing further studies in either pure ML or Robotics. I’m currently more familiar with the AI/ML side, but I’m also interested in exploring robotics and ROS 2, so I’d like to keep my options open.
I’m currently deciding between two laptops within my budget:
MacBook Pro M5 (24GB RAM / 1TB SSD)
Lenovo Legion (32GB RAM / RTX 5070)
Unfortunately, I can’t stretch my budget much further, so an RTX 5070 Ti laptop is out of reach in my country.
For most GPU-intensive workloads, I expect to rely on cloud computing, particularly resources available through my university. However, I’m less certain about the hardware requirements for robotics development and simulation.
I’ve done some research and found that NVIDIA Isaac Sim requires an NVIDIA GPU, with 16GB of VRAM listed in its recommended specifications. This makes me wonder whether even an RTX 5070 or 5070 Ti laptop would be sufficient for a comfortable local simulation workflow.
On the other hand, I’ve heard that MacBooks can handle a large portion of everyday robotics development, including ROS 2, although I’m unsure how practical they are for more demanding tools and simulations.
For those experienced in both ML and robotics, which laptop would you recommend, and why? Can a MacBook realistically support a student exploring both fields, or would I be better off with the Legion despite its GPU limitations?
I’d especially appreciate insights into actual workflows, software compatibility, and the kinds of limitations I might encounter in each field.
I can’t build a desktop PC, so I have to choose a laptop. I’d like to make a decision that will serve me well throughout my engineering degree without unnecessarily limiting my future options.
Thanks in advance!

Hey everyone!

I've been working in software engineering, quantitative research, and machine learning for several years. Recently, I started revisiting the mathematical and theoretical foundations of AI in greater depth.

One frustration I've consistently had with learning resources is the disconnect between understanding a concept, deriving the underlying mathematics, and actually implementing it.

I personally learn best by solving problems, working through mathematical derivations, experimenting with ideas, and implementing algorithms from scratch rather than passively consuming tutorials.

So I started building GroundUp Lab:

https://lab.groundup.workers.dev

The goal is to create a free, comprehensive, and interactive learning platform that takes learners from fundamental mathematics all the way to advanced AI concepts and implementations.

The curriculum covers (and is being progressively developed around):

• Mathematical Foundations: Linear algebra, multivariable calculus, probability, statistics, optimization, and mathematical foundations of machine learning.
• Machine Learning: Classical ML algorithms, statistical learning theory, probabilistic models, and mathematical derivations.
• Deep Learning: Neural networks, backpropagation, optimization, CNNs, RNNs, attention mechanisms, and transformers.
• Generative AI: Generative modeling, autoencoders, VAEs, GANs, diffusion models, and modern generative architectures.
• Large Language Models (LLMs): Tokenization, embeddings, transformer architectures, pretraining, fine-tuning, alignment, inference, and building language models from scratch.
• Agentic AI: Tool calling, retrieval-augmented generation (RAG), reasoning workflows, memory, multi-agent systems, and building practical AI agents.

What I'm trying to make different:

• Mathematical rigor without sacrificing intuition.
• Step-by-step derivations rather than just presenting formulas.
• Interactive visualizations and experiments.
• Challenging exercises and coding problems.
• From-scratch implementations to bridge theory and practice.
• A structured progression from fundamentals to advanced topics.

The idea isn't just to teach how to use AI frameworks or APIs, but to help learners understand why algorithms work, how they work internally, and how to implement and extend them.

It's still an evolving project, and I'm actively developing and expanding the content.

My long-term vision is to build something genuinely useful for students, developers, ML engineers, and aspiring AI researchers who want a deeper understanding of the field.

I'd really appreciate feedback from the community:

1. How do you find the structure, content, and learning experience?
2. Are the explanations and exercises sufficiently rigorous?
3. What topics or interactive features would you like to see?
4. What could make this a genuinely valuable resource for serious AI learners?

The platform is completely free and accessible without registration.

GroundUp Lab: https://lab.groundup.workers.dev

Would love to hear your thoughts, suggestions, and constructive criticism!

I am spending a lot of time to understand the internal of Hubert-ecg.

I got this code block and its outputs here but I am not sure if this is the embedding features vectors or simply its shape. Can anyone tell me if I am right or wrong?

text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
preprocessor = hub.KerasLayer(
    "https://kaggle.com/models/tensorflow/bert/TensorFlow2/en-uncased-preprocess/3")
encoder_inputs = preprocessor(text_input)
encoder = hub.KerasLayer(
    "https://kaggle.com/models/tensorflow/bert/TensorFlow2/bert-en-uncased-l-4-h-128-a-2/1",
    trainable=True)
outputs = encoder(encoder_inputs)
pooled_output = outputs["pooled_output"]      # [batch_size, 128].
sequence_output = outputs["sequence_output"] 

for i in range(0,4):
    outputs["encoder_outputs"][i]
    print('-----------------------------------------------------')
    print(i)
    print(outputs)
    for i in range(0,4):
    outputs["encoder_outputs"][i]
    print('-----------------------------------------------------')
    print(i)
    print(outputs)
    

Output

-----------------------------------------------------
0
{'encoder_outputs': [<KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>], 'sequence_output': <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'pooled_output': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'default': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>}
-----------------------------------------------------
1
{'encoder_outputs': [<KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>], 'sequence_output': <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'pooled_output': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'default': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>}
-----------------------------------------------------
2
{'encoder_outputs': [<KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>], 'sequence_output': <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'pooled_output': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'default': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>}
-----------------------------------------------------
3
{'encoder_outputs': [<KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>], 'sequence_output': <KerasTensor: shape=(None, 128, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'pooled_output': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>, 'default': <KerasTensor: shape=(None, 128) dtype=float32 (created by layer 'keras_layer_28')>}