Hey. I've got a three-month internship with a stipend of 10,000 rupees per month. The internship role is AI Annotation. The internship timings are from 3:00 p.m. to 12:00 a.m. (Monday to Friday). The thing is, is it worth it? I just graduated this month, about two weeks ago. I have hands-on experience with machine learning models; I've worked on projects like medical plant identification using MobileNetV2 etc etc. I have a good knowledge on ML. But is it worth doing this Internship for 3 months ? I really need your suggestion guys.

I've been spending some time learning agentic AI lately, and it feels pretty different from how I learned ML or even basic LLM applications.

When I was learning ML, I was mostly thinking about datasets, training models, evaluation metrics, and improving performance. With a lot of basic LLM projects, I spent more time around prompts and connecting APIs.

But with agentic AI, I noticed I started running into different questions:

• Should the agent use a tool here or not?
• How much information should it keep in memory?
• How do you stop agents from taking unnecessary actions?
• How do people usually structure these workflows?

I thought the coding part would be the difficult part, but for me it wasn't really that. Most of my time was going into understanding how the whole system should behave rather than writing code.

Still figuring things out, but curious if anyone else felt the same while getting started.

What confused you the most in the beginning?

I believe every ML related algorithm can be solved by hand, especially for very small datasets. I’m trying to find resources where topics like PCA are explained using a solved numerical approach. If anybody knows of such resources can you please share them below in the comments!

Pls suggest some resume worthy cv projects.🙏🏻

Most AI agent runtimes currently follow the same execution pattern:

LLM -> tool call -> runtime executes side-effect

That works reasonably well for read-only tasks. But once agents start mutating external state (payments, databases, infrastructure, PII), the execution model becomes difficult to reason about operationally.

While preparing some of our internal agents for white-label deployment, we ended up separating reasoning from execution authority entirely.

We built nano-vm: a deterministic FSM runtime where:

• the model proposes actions,
• but the runtime controls state transitions and side-effects.

The runtime enforces:

• finite execution graphs,
• compile-time step ordering,
• capability-gated tools,
• replay/idempotency boundaries,
• append-only audit history.

One design choice that turned out important:
the policy layer is intentionally less expressive than Python.

We removed eval-style execution entirely and constrained policies to a small deterministic AST subset:

• simple operators,
• no loops,
• no system calls.

That limitation simplified auditability and removed several classes of runtime behavior we did not want in financial-style workflows.

To test failure semantics, we added a Sabotage Mode with several adversarial cases:

• unauthorized tool injection,
• replay attempts,
• hash corruption,
• skipped transitions.

The most useful property operationally so far has probably been deterministic replay boundaries around side-effects.

We also had to deal with an awkward compliance problem:
preserving immutable audit chains while supporting GDPR-style erasure requests.

Our current approach replaces vault references with tombstones while preserving hash continuity and referential integrity.

I'm mostly curious how others are handling execution authority in stateful agent systems.

Are you letting the model directly drive side-effects, or inserting a deterministic control layer in between?

I'll drop the GitHub links to the core runtime and MCP layer in the comments if anyone wants to look at the implementation.

Greetings everyone,

I am a student currently exploring the AI/ML field. Right now, I have very little knowledge about coding, DSA, AI/ML, or GitHub, and I’m trying to understand whether this field is actually right for me.

I wanted to ask people already working or studying in AI/ML:

• What does your day-to-day work mostly revolve around?
• What part of the field do you find the most exciting?
• How is AI/ML different from other tech-related fields?
• Is building something like a personal AI assistant/Jarvis actually realistic?

I would really appreciate honest insights from beginners as well as professionals.

Thank you!

Welcome to ELI5 (Explain Like I'm 5) Wednesday! This weekly thread is dedicated to breaking down complex technical concepts into simple, understandable explanations.

You can participate in two ways:

• Request an explanation: Ask about a technical concept you'd like to understand better
• Provide an explanation: Share your knowledge by explaining a concept in accessible terms

When explaining concepts, try to use analogies, simple language, and avoid unnecessary jargon. The goal is clarity, not oversimplification.

When asking questions, feel free to specify your current level of understanding to get a more tailored explanation.

What would you like explained today? Post in the comments below!

I'm 15 years old and just got my pcep certification through a class at my highschool. How can I start learning AI and ML over the summer for FREE?

Hey everyone, I’ve been working on a computer vision project focused on real-time facial recognition and tracking.

Current features:

• Live webcam face detection
• Face identity recognition/database
• Emotion analysis
• Head/face tracking
• Profile cards/UI
• Real-time dashboard system

Right now I’m mainly focused on improving:

• tracking accuracy
• performance/latency
• UI polish
• scalability of the face database

I’m interested in robotics/security applications long term, so this is kind of my “entry point” project into that space.

Would love honest feedback on:

• the architecture
• code organization
• feature ideas
• performance optimization
• what you’d improve next

GitHub:
https://github.com/k-scurf/Auty/tree/main

Demo:
https://vimeo.com/1193621679?share=copy&fl=sv&fe=ci

Thanks — still learning and trying to improve fast.

Hi all, I'm in the midst of learning random forest in r/Rstudio, and I'm using hstats to test to look for interactions. It is taking forever, even using the default method of using only a subsample of the data. This is making it extremely difficult to learn, and over the long run, I'm gonna need to do this a lot.

Currently I'm running it on my MacBook Pro which is massively overheating, smallest runs are taking six hours, and I need to do many of them, for many different studies, over the next year or two.

Any suggestions for accessing more computing power?

I'm very new to all of this, having "grown up" with SPSS, the linear model, and good old regression. So, it help if any approach to boosting computing power can be figured out by a regular non-computer saavy guy like me. Eg, it sounds like Rstudio Server could be easy to get running on a cloud?

I can think of: 1. get a dedicated heavy duty computer. This would be a big commitment, especially at my resource-scarce institution, and although I'm optimistic these methods will prove valuable for my work, dumping a couple of grand into a machine is still risky. 2. rent time on a cloud computing site. Much lower up front investment, and if, down the road, the methods prove valuable for me, then I could later commit to a dedicated computer. 3. I'm a prof at a university...maybe there are resources in my university system.

Thank you for any ideas, advice, warnings, etc.