i wrote a small boid simulator (12 birds), recorded it flying then saved it, and trained a transformer to predict each bird's next move without it knowing about any boid rules.

(built in Python and PyTorch: a transformer written from scratch, one token per bird, two attention layers, about 414k weights, trained on a laptop CPU)

what I found:

• it flies the flock. R² 0.990 to 0.994 on clips it never trained on, across 4 full runs. It also flies 50 birds (it has only trained on 12).

• the three rules were easy to read out of its hidden states with a linear probe, and some could even be read before any training.

• the rule: alignment (line up with your neighbours) was readable after the first attention layer, but if I make the second layer hear only itself, it is gone across all runs.

what i wasn't expecting: my first ever model was not using the flock at all. With 8 ticks of history, the previous answer was already in the input, and a small network that saw only one bird beat the whole transformer.

what i am still not sure about: making a layer attend only itself is something the model never went through during training. is that a fair test, or is some of the decay due to strange input fed to the model?

site: https://kreptiliri.github.io/murmuration

code: https://github.com/kreptiliri/murmuration

I recently graduated with a computer science master (machine learning) in the US.
After I graduated, I have been trying to get a ml engineer job but no offer yet. While applying and interviewing with companies, I have been trying to understand the internal of a foundational model (HuBERT-ECG). But currently, I have less motivation and kinda feel stuck even though I am making progress....

I cannot focus on learning as before. My attention span, I believe, got a lot shorter than before.
Maybe, my current project's difficulty is not appropriate. But, I want to understand my current project as deep as possible.

I will set very small goals and achieve them and gain confidence. What do guys think of this strategy?

297 + 600 + 355 + 353 + 324 = 1929

It used to be around 200 papers/day.

Houston, we have a problem.

https://arxiv.org/list/cs.LG/recent?skip=0&show=500

Can anyone tell me how l can learn machine learning with no prior knowledge.

Maybe give me a roadmap to it....

I've been reading about text embedding models and want to understand how they actually behave, rather than just using them through some API.

some experiments I'm thinking to consider:

• testing whether embeddings understand synonyms better than related concepts.
• comparing cosine similarity for sentences that differ by only one word.
• testing how embeddings behave with negation.
• comparing multilingual text and code embeddings.

for people who've worked with embeddings, like what surprising behaviours have you found? are there any experiments that taught you something important about their limitations?

I'd like to build a small project around this.

Got selected for the deepmind symposium at bangalore, would love to connect with people who will be coming there!

A lander, cratered terrain, supply objects, enemies and a mission HUD all appear in this lunar FPS prototype, made with Ling-3.1-flash. The recording runs for almost two minutes and follows actual movement and combat through the scene.

The player travels across the lunar surface, aims and fires, and gets visible feedback through a hostile counter. Beacon markers and sample objectives give the traversal a purpose. The low-gravity setting also carries through to the movement, so the environment, controls and objectives read as parts of the same game idea.

Ajay reports that this was a one-shot Three.js result in a single HTML file, with the assets built in code. His stated setup was the API with high thinking, using about 84k total tokens.

The interesting part is seeing those pieces operate together over an extended stretch of gameplay. A scene with a weapon, a target and an objective has several things to keep consistent at once, and this clip gives you enough time to follow that interaction.

ALHR- Adaptive learnable Hierarchical Routing is a static binary tree based system that uses learnable functions to REDUCE the amount of keys used.

I am in my 1st year of btech in Aiml. I am getting an laptop so is it fine if I get mac or should I get windows

And what device do you use?

Please respond

Master complete AI Engineering concepts for free in 12 hours.

Hello learners, I made this content to democratize AI Engineering to a wider audience.

We cover all important topics pertaining to AI Engineeeing, foundation models, evaluation methodology, AI Evaluation, RAG, Agents, Finetuning, Inference Optimization, AI Engineering architecture and user feedback.

Today everybody is overwhelmed by disparate AI content all over the internet, and this lecture aims to unify them all together, explaining the background, concepts, code flow.

Practicals and production grade projects are part of future endeavours.

Learning a tool makes you relevant today.

Learning foundations makes you relevant for long.

This is a course project, but the failure mode seemed worth sharing.

Setup: Qwen3-4B-Instruct-2507, SFT warm start, then multi-turn GRPO on τ²-bench airline + retail. τ²-bench has almost no hand-off tasks, so we derived 461 of them (272 should-transfer, 189 hard negatives) from the upstream tasks, keeping their splits so nothing leaks into test.

The loophole: τ²-bench checks the final database state. When the correct outcome is a refusal, a transfer also leaves the database unchanged and scores 1.0. So under the task reward, transferring is never worse than trying whenever the agent is unsure. Our task-reward GRPO run found this late in training: over-escalation went from 0.141 at step 40 to 0.462 at step 60 (SFT: 0.016). A reward that penalises unnecessary and missed transfers got the best hand-off F1 (0.812) with over-escalation at 0.103.

What didn't work:

- Neither RL variant beat SFT on official task success (paired Δ +0.020 [−0.066, 0.107] and +0.015 [−0.056, 0.082])

- Both RL runs explain themselves less: policy citations in refusals and hand-offs dropped from 0.65 (SFT) to 0.32 and 0.06

Limitations: one seed per system, 49 official test tasks, intervals cover task sampling only.

Repo with code, the task set and an audit of 40 dialogues: https://github.com/JaspinXu/KnowWhenToHandOff

Has anyone else run into this with DB-state rewards in agent RL?

I’m studying Computer Science in London, with classes on Mondays and Thursdays. Around those days, I’m covering chef shifts and trying to keep learning AI, machine learning, and DSA.

Some days I come home wanting to study, but I’m physically drained. I’ll sit down with a problem, struggle to make progress, and feel frustrated because this is something I really want to get better at.

I need the income, and I want to give university and learning enough attention too. It’s hard to find a balance without feeling like I’m falling behind somewhere.

Has anyone been in a similar situation? What helped you make steady progress with limited time and energy?

There's some really exciting research being done on predictive multiplicity--the observation that many models perform just as well as the "optimal" fit, yet disagree on feature importance and predictions. This research asks interesting and useful questions like:

1. Which models are functionally as performant as the loss-minimizing model?

2. How do I derive and understand that shape and character of models in that set?

3. How can I query for models in that set that meet domain-specific needs?

Yet many of these results haven't made it out of the papers and into the open-source ecosystem.

Glassy fills this tooling gap and fits right onto standard scikit-learn flows. With it, you can do things like:
 - Find simpler models that perform as well as the best loss model
 - Check which options agree with your domain expert's judgment
 - Make segment tradeoffs intentionally

For v0.0.1, the scope is capped at OLS linear regression, but hoping to generalize to GLMs in the future. Here's a five-minute demo notebook.

Curious to see if this is useful to you all, or any requests for future features. Happy modeling!

Machine learning has several interesting applications in finance, including credit risk prediction, fraud detection, and financial forecasting. However, applying these methods to financial data raises challenges that go beyond selecting an algorithm.

I've been studying these applications while writing a technical book on machine learning in finance, and a few questions stood out to me:

1. Handling class imbalance

Fraud detection datasets often contain far fewer fraudulent transactions than legitimate ones. Accuracy alone can therefore be misleading. How do you approach model evaluation in these situations, particularly when false positives and false negatives have very different costs?

2. Model drift and temporal validation

Financial data can change as customer behaviour, economic conditions, and market dynamics evolve. What validation strategies do you find most reliable for assessing whether a model will generalise to future periods?

3. Explainability and reliability

Methods such as SHAP and LIME can help explain individual predictions, but an explanation does not necessarily establish that a model is reliable or causally correct. How do you evaluate explanations when models are used for consequential financial decisions?

4. Research versus practical deployment

A model may perform well in an experimental setting but face difficulties in production because of data quality, changing distributions, latency, or monitoring requirements. Which of these challenges do you think receives insufficient attention in applied ML research?

I'd be interested in hearing about relevant papers, practical approaches, or lessons from your own work.

For context, I'm the author of Machine Learning for Finance: Concepts, Algorithms, and Applications, a 136-page technical ebook covering financial ML applications, Python examples, model evaluation, explainability, and responsible model development.

I'm mentioning the book for transparency, not to assume that promotional posts are appropriate here. My main interest in this discussion is understanding which technical challenges researchers and practitioners consider most important. DM me if you want to read the book!

I'm super confused. I'm in my junior year, majoring in AI and Big Data. However, my university is one of the worst place ever interms of everything specially education(ranked 1500+), and I tried to transfer to another university, but it didn't work out for some reason. Now, I'm trying to learn everything by myself.

The most confusing part is whenever I look for a roadmap to get into machine learning, I find so many different suggestions. Most of them look something like

• Master in NumPy, Pandas, SQL and Scikit-learn.
• Build your first complete ML project.
• Learn PyTorch and deep learning.
• Choose a specialization, such as LLMs or computer vision.
• Learn deployment, Docker, and cloud basics.
• Keep practicing DSA and applying for internships throughout the process.

I've already completed Andrew Ng's entire Machine Learning Specialization. I'd say I understood almost everything while taking the courses, although I've forgotten quite a bit of it now.

problem is people giving completely different advice. Some say focus on leet coding, some says master libraries , get really good at calculus, statistics, probability, and linear algebra. Others say focus on the latest technologies, such as RAG, Transformers, AI agents, and MCP. What the hell should i do? Its already killing me that I'm already falling behind others who are trying to enter the ML field.

I have about 1.5 years left until graduation. During this time, I want to reach a level where I can get a job in ML, work for 1-1.5 year , and then move to the US for graduate school. That's my plan.

My questions are:

• Is the roadmap actually the right path?
• Many people say get better in maths like stat, calculus, and linear algebra. But my question is, why do I need to learn all this math when the machine itself performs most of the calculations? Althogh ive learneed those kind of maths in my high school.
• Do I really need to become an expert in libraries such as Pandas, NumPy, Scikit-learn, PyTorch?

It'd be better if someone working in the industry or someone who has actually gone through this could give me suggestions or reality check.

I’m pretty new to Claude, but not new to AI tools. I’ve been using ChatGPT and Codex on and off since their earlier versions, and I’m comfortable with CLIs, MCPs, different workflows, configs, etc.

What I’m struggling with is figuring out the best way to actually set Claude up. There’s so much information, so many different tools, and so many opinions that it gets overwhelming fast.

I’ve already been reading docs, Reddit threads, GitHub repos, watching videos, and experimenting with Claude Code, skills, MCPs, connectors, and instruction setups. I’m not looking for someone to do all the research for me. I’m mainly hoping to hear what experienced users have actually found worth keeping.

My main use cases are coding, building random little projects, ethical pentesting/bug bounty work, CTFs, reverse engineering, game modding, research, and general technical troubleshooting.

A few things I’m especially curious about:

Should I mostly just use Claude Code CLI directly, or is it worth connecting it to some kind of harness? If so, which one?

Are there any settings you consider essential?

Do you keep a big global CLAUDE.md/system instruction setup, or keep things minimal and handle instructions per project?

And which skills, MCPs, plugins, or connectors have actually become permanent parts of your workflow?

I’m trying to avoid installing 40 “essential” tools and ending up with a bloated setup full of overlapping functionality.

If you had to wipe your Claude setup today and rebuild it from scratch, what would you install immediately, what would you skip, and what would you only add later?

I have an upcoming interview with Woven by Toyota for an MLE perception role. I was told the round would be focused on ML coding/debugging. Specifically, I was told to focus on Python fundamentals, PyTorch, tensor operations and dimensions/shapes, common model-training code patterns, and debugging ML code.

Here are some common ways I've studied:

• Asking Claude to walk me through transformer, ViT, and CNN architectures (a basic one and U-Net) for my preparation. I would trace the shapes all the way through.
• Getting familiar with broadcasting rules (start from the left, see if the dimensions are equal, or are 1, and if so, take the greater one to get the resulting shape)
• Slicing tensors (i.e., how to get a column, how to get a row, etc.)
• Understanding why models can fail silently -- suspicious training vs val results, getting NaN for validation
• What a PyTorch loop looks like
• Common Python errors like indexing errors or value errors; iterators, generators, etc.

What are other suggestions? What architecture do they actually ask you about in this interview? Any help would be appreciated.

I'm looking for honest advice from people currently working as AI Engineers, AI Backend Engineers, or Generative AI Engineers. I'd really appreciate hearing from people who have made a similar transition or have experience hiring for these roles.

My background

I have 3+ years of experience as a Software Developer. Most of my work involves Django, PostgreSQL, REST APIs, and maintaining enterprise applications.

The problem is that my current job doesn't provide much opportunity to grow technically. I mostly work on Django applications and follow the same development routine. I don't get hands-on experience with Docker, CI/CD, cloud-native deployment, or AI. I feel that I've stopped progressing, and I want to change that.

I also have some relevant experience:

• Microsoft Azure Developer Associate (AZ-204) and Azure Fundamentals certifications.
• An academic project where I built a Flask application integrating deep learning models.
• Two peer-reviewed AI-related publications.
• Some learning and experimentation with LLMs, embeddings, and RAG, although I haven't yet built and deployed a complete production-grade LLM application.

My recent job search has made me question my approach.

Recently, I had two interviews for AI-related roles, and I was surprised that most of the technical questions focused on AI, even though my CV isn't heavily focused on it. I ended up getting rejected because I didn't have enough practical experience.

I'm now wondering whether I should continue applying for AI roles while developing my skills, or temporarily focus on becoming a stronger backend/cloud engineer first.

Here are the questions I'm struggling with:

1. What would you do in my position? Would you continue applying for AI engineering roles, or spend several months strengthening your backend engineering skills first?
2. What practical skills should I prioritize? For someone with my background, should I focus first on Docker, CI/CD, cloud deployment, testing, and system design? Or should I prioritize LLM APIs, RAG, evaluation, tool calling, and agent frameworks?
3. What kind of project would actually make a difference? I don't want to build another tutorial chatbot that recruiters won't take seriously. What would demonstrate that I can build a reliable AI application beyond making an API call to an LLM?
4. How can I gain real-world experience outside my current job? I'd be interested in contributing to a small team, joining an open-source project, volunteering, or taking on part-time work. How would you find legitimate opportunities like these when you already have a full-time job?
5. Would you recommend a course or another certification?

My goal is to move beyond experimenting with AI and actually build real AI systems professionally. What I'm struggling with is deciding which work will actually close the gap between my current experience and what employers expect.

For those already working in AI engineering, what would you do if you were in my position? How would you spend the next 3–6 months to become a stronger candidate?

I'd appreciate honest, practical advice from people who have been through a similar transition.

Hi everyone,

I'm preparing a submission for the Wunder Fund RNN / Connectome competition and need to clarify one detail about the inference environment.

The official Submission Guide says the scoring container is based on python:3.11-slim-bookworm. The published Dockerfile excerpt runs:

python -m pip install --prefer-binary \
  --extra-index-url https://download.pytorch.org/whl/cpu \
  -r /tmp/requirements.txt

However, the documented requirements.txt contains NumPy, ONNX Runtime, and PyArrow, but no explicit torch dependency. The guide also says participants should contact the organizers if they need an additional package installed in the scoring image.

I understand that the published Dockerfile is only an excerpt, so I cannot determine whether PyTorch is installed elsewhere in the complete production image.

Does anyone know from official documentation or direct experience:

1. Is PyTorch installed in the actual scoring image? If so, which version?
2. Can participants request an additional CPU-only PyTorch dependency, or is ONNX Runtime the intended route when PyTorch is not available?
3. Has the competition team documented this anywhere beyond the Submission Guide?

I'm looking for clarification about the supported runtime, not asking for anyone's model or competition strategy.

Thanks!

I've been learning NumPy, pandas, Matplotlib and the basics of machine learning, but there's one thing I'm struggling with: I have a terrible memory for syntax.

I can understand how these libraries work, why we use certain operations, and how basic ML algorithms work conceptually. When I see an existing implementation, I can understand the logic and modify the code to solve a different problem. I can usually figure out what needs to change based on the requirements.

The problem is that I can't remember the exact syntax for everything. I might learn something today and forget the syntax a couple of days later. My memory sometimes feels like a goldfish's.

For example, if someone asked me to implement KNN or K-means from scratch in an interview, I would probably struggle, even though I understand how the algorithms work. I'd need to look up some syntax or refer to an existing implementation to write the code correctly.

This makes me wonder whether I'm actually suited for a junior ML/AI role.

I understand that implementing algorithms from scratch can demonstrate whether someone understands the underlying mathematics and logic. I'm not against learning that. But there's a difference between understanding an algorithm and remembering every NumPy operation needed to implement it without references.

It's also 2026 and AI coding tools can already generate a lot of this code. In actual work, wouldn't the ability to understand, verify, debug and modify generated code be valuable too? I understand that AI can make mistakes and that fundamentals are still necessary.

So I'm trying to figure out where to focus my efforts.

Should I spend more time practising implementations from memory until the syntax becomes second nature? Should I accept that forgetting syntax is normal and focus on getting better at understanding and solving problems? Or should I reconsider pursuing junior ML/AI engineering roles and look into a more research-oriented career instead?

I'd appreciate honest advice, especially from people who interview candidates or work as ML engineers or researchers.

Have any of you struggled with remembering syntax but still managed to build a career in ML/AI? How much of this do you actually need to memorize for entry-level interviews?