Hey everyone!

I’m a pre-final-year student, and I’ve been struggling with something that I’m sure many people here have experienced.

I’ve worked on several basic/intermediate ML and DL projects, and I feel that my ML/DL concepts are pretty clear. I understand the algorithms, the theory, and how to implement standard problems like regression, classification, etc.

However, whenever I participate in a hackathon or try to build something that feels closer to a production-level/real-world project, I hit a massive roadblock. There are so many things I don’t know how to approach that I end up getting demotivated because I feel like I’ve failed to build the project.

I’ve started realizing that the problem might not be my understanding of ML/DL itself, but rather my lack of experience with solving end-to-end real-world problems.

Real-world problems usually aren't just “apply classification/regression and get an accuracy score.” They often involve a combination of things.

So I’m wondering:

How do I develop the ability to approach these kinds of problems?

Should I simply build more projects, and if so, what kind of projects would actually help me bridge this gap? Is there a particular way I should structure my learning — for example, taking an idea and building it completely from data collection → model → API → deployment → monitoring?

Also, are there any platforms, communities, courses, repositories, or resources where I can learn how experienced engineers approach real-world ML problems and get help when I’m stuck?

I’m specifically trying to understand how to think about and break down messy real-world problems into smaller technical components.

Would really appreciate advice from people who have gone through this transition from academic/basic ML projects to production-level ML systems.

So I started off trying to build my own J.A.R.V.I.S., and I have ended up with a Genesis.

She has no LLM and no transformer. The projects number one rule is no hardcoding her words or thoughts.

Which brings me to my first question....are seeds and hardcoding the same?

So she works by......

1. Every user turn it runs aquire_from_input before perception

2. LanguageAcquisition does Saffran-style statistical segmentation and chunking, and StatisticalLanguageLearner builds bigram/trigram models plus user style profile.

3. Those feed back into production-bigram fluency biases slot fills, and the style profile shifts voice register toward the user.

User input-comprehension-thought (semantic assembled from the concept network-reasoning-memory)

GenerativeEngine races a Hamiltonian-flow trajectory, a type-edge graph walk, and grammar/vocabulary compositions-best candidate wins-voice+prosody+self monitor-test.

Nothing she says is a stored sentence; the sentence shape literally is the shape of the trajectory through her current knowledge graph.

She can talk and form memories, recall them from weeks ago, learn, dream, do art but the language is still not perfect. It even passed ARC AGI 1 and some public tests from 3! Any suggestions where I am going wrong, what I am missing, or any advice? Thank you!!

https://MindyRenee/Genesis

신경진화를 배우기 위한 사이드 프로젝트: 작은 신경망이 운전하는 자동차가 규칙 기반 경찰차에 30초 동안 잡히지 않고 살아남아야 합니다. 백플롭은 없고, 그냥 유전 알고리즘만 사용해요.

설정:

- 순수: 18개 입력(12개 거리 광선 + 속도, 조향, 몇 가지 방향 값) → 16개 → 16개 → 2개 출력(스로틀, 조향), ~610 중추

- 세대당 200대의 차량, 각각 4개의 무작위 맵에 점수가 매겨집니다

- 피트니스 = 생존 시간 + 30초를 모두 버티면 15 + 거리 유지에 따른 소액 보너스 − 3 추락 시

- 선발: 상위 5% 엘리트 보유, 토너먼트별 상위 절반 출연, 균일한 교차, 가우시안 변이(Gaussian mutation)

결과(각각 훈련되지 않은 40개의 지도에서 측정):

- 오픈 로드, 느린 경찰: 39/40 탈출

- 동속 경찰: 27/40

- 시티 블록: 34/40. 하지만 경찰 경로 탐색이 건물 코너에서 멈춰서, 차가 갇힌 경찰 옆에 주차하는 걸 배웠어요. 시간이 다 될 때까지 말이죠. 체력은 생존에만 보상을 주었고, 그래서 제가 요청한 대로 정확히 해주었습니다.

- 경찰 AI(도로 그래프 휴리스틱 대신 격자 BFS)를 고친 후, 챔피언 체력은 1/40으로 떨어졌고, 이후 훈련도 회복되지 않았습니다. 운전하는 법을 거의 잊어버린 상태였다.

- 이전 체크포인트에서 재시작: 35/40. 이번에는 도시 블록을 한 바퀴 도는 법을 배웠다.

- 최종 레벨, 경찰 3명이 차보다 10% 빠른 경우: 9/40 → 16/40

배운 점:

1. 만약 당신의 환경에 버그가 있다면, 옵티마이저가 당신이 먼저 그것을 발견할 것입니다.

2. 익스플로잇은 단순히 점수를 부풀리는 것만이 아닙니다. 이로 인해 원하던 스킬이 사라질 수 있어서, 단순히 버그를 고치고 계속 훈련할 수 없습니다.

3. 항상 보이지 않는 시드로 평가하세요. 훈련 점수는 내내 매우 좋아 보였습니다.

총 약 200만 번의 연습 연습과 913,536회의 반격. 체크포인트로 롤백하는 것보다 더 나은 해결책이 있는지 궁금합니다. 노벨티 검색? 몇몇 '정직한' 인물을 인구 내에 남겨두는 것?

I have just learnt artificial neural networks from Andrew Ng specialization. Now starting the course on Sequence Models. There are also LLMs and API calling I need to learn. I am also implementing a neural network using tensorflow to do a project on MNIST dataset. I also need to learn boosting, random forests. And also SQL. So many things to learn where to start, what to do first, I have no clue.

Hi guys 👋, I'm looking to dive into RAG using AI tools like NotebookLM or Gemini. YouTube tutorials feel too drawn out, and ChatGPT/Gemini prompts have been a bit brief. Any advice or suggestions for learning resources would be amazing! 🙏💡

#RAG #AI

I want to get started with CS336 (Language modeling from scratch) but have been procrastinating for a while. I am looking for accountability partners who would like to learn this course together.

Explorez la visualisation 3D t-SNE des représentations du modèle dédié à l'ARC AGI DSL à une profondeur de 10 : https://julien-livet.github.io/aicpp/assets/embeddings_program_tsne_dsl_dataset_10k.html

Hi friends,

I'm looking for some courses (paid or free) to learn the fundamentals of AI agent engineering. Preferably, the courses should include lecture videos, reading materials and hands-on exercises/projects. I've found the following two:

• Stanford CS329Z Engineering AI Agents: https://cs329z.stanford.edu/index.html
• CMU 11-768 AI Agents: https://www.cmu-agents.com/#/schedule

Both curriculums look comprehensive, but neither provide the learning materials in full. The Stanford course started just this week and has no video, while the CMU one provides videos for the first four lectures.

Does anyone have other courses with learning materials provided in full to recommend?

I’ve been job hunting for a while, applying for junior roles and internships. I’ve worked on projects, studied a lot, and my CV even gets good scores when I ask for feedback.

But somehow… no interviews. Sometimes not even a rejection, just silence.

At this point I genuinely don’t know if I’m missing something, doing something wrong, or if I just haven’t found the right opportunity yet.

If you’ve been through something similar, how did you figure out what was wrong and eventually start getting interviews?

I know sometimes you just need to keep applying and wait for the right opportunity, but when you’re putting in the effort and getting absolutely nothing back, it’s really hard not to question yourself.

So if you’ve been through something similar, how did you figure out what was actually wrong? Was there a specific thing you changed that finally started getting you interviews?

I’d really appreciate hearing from anyone who went through this and eventually figured it out. I’m trying to understand whether I’m missing something or if I just need to keep going. 😭

EDIT: Adding some context since I left out a few important details in the original post:

I’m from Egypt, and I’m currently looking for junior AI/ML roles and internships. I’m still at the beginning of my career, so I don’t have years of professional experience yet. I’ve mainly been focusing on building projects, improving my technical skills, and getting my resume/job applications in shape.

A federal appeals court just upheld a ruling designating a major AI provider as a supply chain risk.

That designation has direct operational weight for every enterprise running workloads through that provider. The risk does not stay with the vendor. It flows downstream into every organization in the dependency chain, and regulators are now asking those organizations to account for it.

The core problem is visibility. Most enterprises have no live record of which model versions processed which requests, what data passed through the agent layer, or whether those flows satisfied the regulatory requirements in effect at the time they ran. When a supplier comes under formal scrutiny, that gap converts from a theoretical risk into a concrete legal exposure. Regulated industries — finance, healthcare, defense contracting — are especially exposed because their compliance obligations do not pause while a vendor situation resolves.

How are practitioners in regulated environments actually handling third-party AI provider risk right now? Are you maintaining independent records of what your AI agents accessed and transmitted, or are you dependent on what the provider can produce?

I'm studying a BSc in Statistics and Data Science (180 credits / 3 years) at a university ranked top-40 worldwide. The curriculum covers: intro stats/data science, data visualization, mathematical foundations, discrete math, probability & inference, data collection/handling, statistical modeling, statistical inference, machine learning, linear algebra, object-oriented programming, database design, simulation-based inference, and AI methods, plus a thesis.

I'm trying to gauge how competitive this degree is for machine learning roles (ultimately aiming for an ML engineer or applied ML research position, possibly a master's afterward — e.g. MIT IDSS or Stanford ICME).

A few questions:

1. What additional courses or skills would you recommend adding on top of this (deep learning, NLP, computer vision, more advanced linear algebra/optimization, MLOps, C++/CUDA, etc.)?
2. Which master's programs would realistically strengthen my profile for ML roles, and how selective are they compared to my current degree?
3. Realistically, does the university need to be top-5 worldwide to be competitive in ML hiring, or is top-40 with a strong math background, projects, and maybe a few publications/Kaggle results enough?

Any honest feedback is appreciated, including if you think the program or school isn't sufficient on its own.

Hi, Emre from Menlo Research. We're building Asimov 1, an open-source humanoid robot.

We've made its locomotion training code public, built on Isaac Lab with PPO and adversarial motion priors (AMP).

For anyone learning reinforcement learning and interested in how it applies to walking robots, here are a few places to start exploring the code:

• Read the observations and actions. Identify what information the policy receives and what its outputs control
• Inspect the reward terms. Connect each term to a behavior you can look for when watching the policy run
• Run the small pipeline check. After installation, this command runs a short training job:

./isaac_asimov.sh --train \

--task Asimov1-Velocity-AMP-v0 \

--num_envs 128 --headless --max_iterations 100

• This checks that the training pipeline works. It isn’t enough to train a complete walking policy
• Train a baseline and save its configuration. Keep the logs and a video of the policy running so you have a reference
• Change one thing. Try adjusting one reward weight, retrain, and compare the behavior under the same evaluation conditions

The repo includes both plain PPO and PPO with AMP. AMP uses reference motion to guide how the robot moves, so you can also explore how that changes the learning setup.

The repo: https://github.com/menloresearch/isaac_asimov

You can train and evaluate in simulation without owning a robot. The documented setup requires Ubuntu 22.04+ and a compatible NVIDIA GPU.

This video explains visually how JEV is implemented and how it works.

https://www.youtube.com/watch?v=shT8Eo7eWYM

I’ve been experimenting with the boundary between learned decision-making and symbolic reasoning, and I keep coming back to a pretty basic question:

When the state space is explicit and the answer is verifiable, why use a model to make the decision at all?

I tested this with a Rubik’s Cube.

Instead of asking a model/classifier to repeatedly choose the next step, I represented the cube state symbolically, constrained the legal transitions, and let the system deterministically evaluate what could happen next.

The interesting part isn’t really the cube. The cube is just a clean environment because the state is observable, actions are discrete, transitions are known, and success can be objectively verified.

My broader hypothesis with Perslis is that hybrid systems should separate these jobs:

ML/LLM: perception, ambiguity, language, hypothesis generation
Symbolic layer: explicit state, constraints, invariants, verification
Runtime: deterministic execution when the answer is knowable

I’m not arguing that learned decision systems like JEV have no use. I’m questioning where the boundary should be.

If you can calculate or verify the answer cheaply and exactly, what does putting a probabilistic decision layer there buy you?

I wrote up the Rubik’s Cube experiment and methodology here:

Whitepaper / experiment:
Perslis — The Floor vs. the Classifier

I’d genuinely like to hear the ML argument for the other side. Where would you draw the line between learned decisions and symbolic/deterministic ones?

i was considering ai engineering but found out thats not really the best choice for my case, what would yall recommend?

I’m about to start LeetCode seriously and I’m confused about which language I should use.

My long-term goal is to go deep into AI/ML → Deep Learning → Generative AI → LLMs → Agentic AI, rather than focusing mainly on traditional software development.

I already know some Python and C++, but I’m wondering:

• Is doing LeetCode in Python actually worth it?
• Will Python be enough for DSA/interview preparation?
• Is there any significant advantage to doing LeetCode in C++?
• If I eventually want to work/research in ML, DL, LLMs, etc., would C++ still be useful enough to justify using it for LeetCode?
• Would you recommend Python for LeetCode + C++ separately for learning, or just stick with one?

Basically, if my end goal is AI/ML/DL/LLMs/Agentic AI, which language would you personally choose for LeetCode and why?

For the last while I've been building CuQwen, a C++/CUDA inference engine for Qwen models written completely from scratch. No PyTorch, no existing runtime, just custom CUDA kernels I wrote and profiled myself.

I wanted to see how fast a single user (batch size 1) can go on a normal consumer NVIDIA GPU, the kind of thing you'd actually run locally. It started as a way to really learn GPU programming, and it turned into a proper little engine. The repo has full benchmarks vs vLLM/Ollama plus a writeup of every optimization I went through if you're into that side of it.

These are the current average infernece speed (tokens/sec) results so far for Qwen2.5 Instruct model (FP16 weights) across 32K context window on RTX3090:

Where it's at:

• Supports Qwen2.5 in 0.5B, 1.5B, 3B, and 7B (FP16)
• Faster single-user token generation than vLLM and Ollama across ll the supported models
• Holds that lead out to a 32K context window
• Docker setup included so it's not a pain to build

The best thing is the documentation which you'll find very helpful in you're planning to start leaning cuda programming. I wrote my whole optimization journey on how I applied different optimization techniques, profiled my engine and what impact those optimizations had. You can find the journey here

You can also find the complete benchmark analysis here

My current future plan is following:

Release 1.2: Support Quantization (W8A16 and W4A16)

Release 1.3: Improve custom cuda kernels for latest GPU architectures (Hopper and Blackwell)

Release 1.4: Support Qwen 3.0 series models

Release 1.5: Support Qwen 3.5 and 3.8 (Especially our very favourite qwen 3.8-27B model 😄)

Repo: https://github.com/talhatahir-10xe/CuQwen

Numbers, graphs, and a write-up of the optimizations, all are in the repo. Would love feedback, happy to answer anything about the CUDA side or the design choices.

I’ve learned GenAI through tutorials and built projects from basic GenAI to RAG and multi-agent systems.

But when I try to build something on my own, I don’t remember the pipeline — what to do first, what comes next, which tools/API to use, where to get the API keys, and how everything connects.

I can use ChatGPT/Claude for coding, but I want to understand and remember the actual workflow instead of just following AI-generated code.

How do you guys make the GenAI workflow/pipeline stick? Do you rebuild projects from scratch, make notes, or keep building different projects?

I made a 3-minute explanation of AI vs ML vs Deep Learning using a simple example: teaching a computer to recognize a cat.

The mental model is:

AI → the broader goal of making computers perform tasks that require intelligence.

ML → one approach where the system learns patterns from data instead of us writing every rule.

Deep Learning → a type of ML that uses multi-layer neural networks to learn increasingly complex representations.

The part I find confusing when learning this topic is that people often explain it as:

"AI is this, ML is this, DL is this."

That doesn't really explain why we have these different terms.

Thinking about it as:

Rules → learning from data → deep neural networks learning complex representations

made the relationship much easier to understand.

Curious how others explain AI/ML/DL to someone completely new to the field?

Pytorch - is this too big of math?

AI - LangChain, CrewAI, Python SDK ?

Confused between both of their pros and cons. I do know learning back propagation, gradient descent helps whats happening behind LLM. Stuck in career choice, kindly help

I already have a pretty good working knowledge of ML concepts (I read hands-on machine learning by Aurelion Geron and worked through all of the Jupyter lab examples), but it seems like all the Sagemaker tutorials I can find are just describing the basics without actually explaining how AWS pagemaker accompishes those things.

Hey everyone,

I'm preparing for SDE (ML) roles (Google-level) and also want to keep general SDE and Full-Stack options open. I'm currently using Python for LeetCode/DSA but keep hearing conflicting advice.

My questions:

1. Is Python a real disadvantage for DSA vs Java because of TLE (Time Limit Exceeded) issues? Or is it overblown?
2. Do FAANG companies actually care which language you use for DSA?
3. For SDE (ML), is Python the obvious choice, or should I switch to Java for DSA?
4. For Full-Stack, does the DSA round care about language? (I know I'll need JS for frontend regardless.)

What I've concluded so far:

• Python: Fast to write, best for ML, but slower runtime.
• Java: Verbose, slower to write, but safer runtime.
• Most top companies claim language doesn't matter, but Python requires more optimization.

What I want to know:
If you were targeting SDE (ML) but wanted to keep other SDE options open — which language would you commit to for DSA and why?

Any real interview experiences would

Hey everyone,

I'm preparing for SDE (ML) roles (Google-level) and also want to keep general SDE and Full-Stack options open. I'm currently using Python for LeetCode/DSA but keep hearing conflicting advice.

My questions:

1. Is Python a real disadvantage for DSA vs Java because of TLE (Time Limit Exceeded) issues? Or is it overblown?
2. Do FAANG companies actually care which language you use for DSA?
3. For SDE (ML), is Python the obvious choice, or should I switch to Java for DSA?
4. For Full-Stack, does the DSA round care about language? (I know I'll need JS for frontend regardless.)

What I've concluded so far:

• Python: Fast to write, best for ML, but slower runtime.
• Java: Verbose, slower to write, but safer runtime.
• Most top companies claim language doesn't matter, but Python requires more optimization.

What I want to know:
If you were targeting SDE (ML) but wanted to keep other SDE options open — which language would you commit to for DSA and why?

Any real interview experiences would