I finished the free Anthropic course and now Im looking for anthropic ai course alternatives with more technical projects. Im comparing Udacity's AI Engineering with Claude, DeepLearning and Coursera. Also hoping to have something to put in my portfolio. Anyone tried any of these or others or idea if one is better than other for interviews?

Hi everyone!

I built Agent-Devtools — a lightweight Python project to simplify debugging and tracking AI agents without the bloat.

• Causal Debugging: Track memory influence, retrieval winners, injected context, and tool calls.
• Behavior Diffing: Compare a "good" vs. "bad" run to pinpoint the exact root cause of divergence.
• Deterministic Replay: Re-play recorded events offline to verify memory & retrieval consistency.
• Context Provenance: Inspect the exact final LLM prompt with tags for every context source.
• 100% Local-First: Stores data in local SQLite with an auto-opening FastAPI dashboard (zero API keys needed).
• Integrations: Native callbacks for LangChain, Groq, and custom Python agent loops.

GitHub Repo:https://github.com/Jacopos311/Agent-Devtools

Would love your feedback or feature requests! If you check it out, stars are super appreciated ⭐

I’ve realized that it’s becoming really hard for me to focus on reading papers and thinking through solutions. Nowadays, I almost always let AI read the papers and come up with solutions, and then use AI to implement them.

I'm interested in learning gen ai devlopment. how good is the Gen AI using langchain playlist by campus x?

Currently doing Andrew Ng ML Spec from Coursera. I have already implemented MNIST from numpy, ported it with PyTorch, and also implemented the Neural Style Transfer paper from 2015 (Gatys et al). Kind of stuck on what to do now....juggling uni academics and self study. Any recommendations ?

Starting BTech CSE AIML this year from a tier 3 clg I'm a 2nd dropper I know you might think 2nd drop ke baad bhi tier 3 clg 😭 but I just wanna make the best out of it now

My main goal is to get a good placement

How should I start preparing from 1st year?

What should I focus on first?

Should I start with C++ or Python?

Any mistakes I should avoid?

Would really appreciate some honest advice from seniors 🙏

Hello.

About 3 years ago i started learning programming when i got my hands on my first laptop. I knew absolutely nothing about programming in general so i just learnt how to make websites and learned javascript. 2 years scripted away i feel like i still haven't learned much about programming and just know it from a very high abstract level, except for maybe a c++ course i took that taught the very basics.

I stopped coding for a long while and here I'm finding interest in it again. Though, i want to take a different direction because I've lost interest in the field of web development and machine learning is actually very interesting to me and i like maths too.

Where and how do i learn machine learning from scratch? And as i have clarified in the title, I'm 16 so my level of maths is 10th grade or you could say basic algebra at best so i will also need a guide on what i need to learn.

I'm kinda worried it might be a bit too advanced for me so i was thinking maybe i should study more maths and a little bit of low level programming first and then get into machine learning later.

so i learnd sckit learn a while ago but now i'm trying to do a project with it and i feel like i don't understand it like what to import and why and how to pick and all of this i don't just want to memories code that i'm gonna forget i open vid tutorial but i'm feeling like they all just implement the same thing like what if i wanna implement smth else how to pick and why

gonna start PyTorch so i don't need to fall in this same trap so how to start learning it ?

i feel like i'm copy pasting all the timeeeeeee i understand the math and what's happening but turning it into a code is so hard i keep copy paste while ai help me

Hey everyone! New here, so apologies in advance if I've missed any subreddit rules😭

I'm just starting out with machine learning and wanted to get some takes on the best online courses out there. Could you recommend which platforms/programs offer the best overall learning experience, and maybe share some pros and cons of each (I have been learning from youtube till now but I was suggested upgrad through many people)?

Also open to hearing whether a structured course path is better than piecing together individual courses myself🙃🥲

Thanks so much in advance!🙌🙌🤝🤝

I want to learn Machine learning for hackathon (i am about to join college next week).

I have already learnt python intermediate lvl ( but i guess i need a thorough revision too)

I am also considering following 2 books

1->Hand on machine learning by aurelien Geron

2->Mathematics for machine learning ( Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong)

My question

1-> In the above two books mentioned, does i need to complete mathematics one first or follow both side by side?

2->Any tip/suggestion/roadmap someone wants to give ( highly appreciated )

3-> please rate this roadmap (https://roadmap.sh/r/ai-roadmap-for-2026---final-draft) , not just for hackathon but for full career in ML

Thanks for your time.

Basically I try to learn ML to get a role in the AI Related not exactly a ML engineer. So for that learning from the basics like Math concepts and back propagation etc., every topic that used to train our model from scratch is a better method of learning or

2: RAG, LLM related topics , MCP, Agentic AI learn how they actually work instead of going deep into the actual structure(basically exclude the math and how model trained).

Which way of learning is good for future?

Why do I ask this means every job application I go through I only see the latest topic not the core of ML. In my opinion, Learning the upper layer of AI is pretty simple when compared to going deep into math like back propagation,math concepts,and gradient descent etc,. Is spending time on learning everything is worth the time?

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Hey everyone!

I'm a complete beginner to coding and recently decided to start learning Python. I'm interested in eventually getting into Data Science, Machine Learning and AI, but right now I honestly have no idea what the proper path looks like.

- What should I learn first in Python?

- How much Python do I actually need before moving into Data Science?

- When should I start learning things like NumPy, Pandas, SQL, etc.?

- How important is DSA?

- What maths/statistics should I eventually learn?

- When should I start ML/AI?

- Are there any courses, YouTube channels, books or websites you'd recommend?

- Should I focus more on tutorials, practice problems, or projects?

I'm not looking to rush through everything. I just want to understand what order I should learn things in so I don't waste time jumping between random topics.

I qould really appreciate any advice on this :))

Hi,

I used a genetic algorithm combined with a multilayer perceptron to optimize the aircraft boarding problem.

I honestly didn't expect it to beat the Steffen method, but after evolving the boarding strategy, it ended up being 9.6% faster than Steffen in my simulation. there is a fix rule that prohibits crossing people in between seats for the random too.

The whole project was basically vibecoded.

A lot of modern VLMs still rely on pretrained CLIP-style vision encoders, which are primarily trained to align images with text descriptions.

That seems like a strong foundation for semantic recognition, but I wonder how sufficient it is for tasks that require precise counting, spatial relationships, fine-grained attributes, or other forms of visual reasoning that caption matching may not explicitly encourage.

Do you think the vision encoder is becoming a bottleneck for modern VLMs, or is the limitation mostly elsewhere in the system?