Coding Probability Transformations.

In this content, we do the code implementations for the topics:

•Transformations of Random Variables
•Moments of Affine Transformation
•Convolution Theorem
•Moment Generating Functions
•Central Limit Theorem
•Monte Carlo approximation

Having code implementations makes the learning of concepts even more rewarding.

Link: https://youtu.be/SJTZK55MgB8?si=EYqy3h4v-bU2FrCJ

Hey guys, I am a fresher preparing for AI engineering / startup interviews. I just built my first project using an LLM agent setup called "Research Copilot" and wanted some advice on how to present it.

What it does: It is a research assistant that fetches new papers from arXiv, checks a SQLite cache to filter out papers I already saw, embeds abstracts, and uses an LLM agent to decide which tools to call depending on what I ask it.

Stack & Tech Choices:

• Used Groq (qwen 27b) for the agent tool calling loop.
• Telegram bot interface + CLI script for live interview demos.
• SQLite for exact ID deduplication (instead of using vector DB for exact matches).
• Used numpy for cosine similarity over local embeddings (all-MiniLM-L6-v2) instead of heavy vector databases like FAISS since dataset size is small.
• Optimized tool outputs so full abstracts are kept in session memory while lightweight metadata is sent to the LLM to avoid hitting token limits.

Need advice on:

1. In interviews, will interviewers ask me to code the agent from scratch or ask about architecture/tradeoffs?
2. I wanted to share how I built it and get feedback on how to position this during technical interviews, as well as what features to add next to make my profile stand out to startups.This is my first time using agent and I really need to know if i do this then in interview they will ask code or what and also like I am using codex/antigravity free tier so if anyone with experience please suggest how to use it better and efficient way !
3. Any tips for freshers applying to AI startup roles?

Thanks in advance for any feedback!

I am still a novice at this, but when I was working on this credit card fraud detection project, I did not know which columns, could be added as features, so I prompted ChatGPT and it suggested a few, but that got me thinking there has to be a better way to this, How do you select feature columns from your dataset, do you research the domain, is there a course I am missing, This was not covered in my Internship classes, and want to know a generalized solution.

Hello! I have independently developed an experimental approach for the ARC AGI 2 benchmark (see my GitHub repository `aicpp`: https://github.com/Julien-Livet/aicpp/tree/dsl_engine).

My current leaderboard score is zero, but I believe there is an interesting approach worth exploring. Despite limited training, the model is already able to generate and execute non-trivial symbolic programs that improve substantially over the identity baseline on some tasks, although it does not yet reliably find the exact solutions.

I have identified a bottleneck in the model's learning/search process that I have not been able to fully understand or resolve on my own. I am therefore looking to form a small team around this approach, particularly with people interested in neural-guided program synthesis, search, ML, or ARC.

The goal would be to understand and break this bottleneck, improve the system, and see how far the approach can go on ARC AGI 2.

If this sounds interesting to you, feel free to reach out or take a look at the repository!

Hi! Hopefully this question hasn't been asked to death already, but I couldn't find quite the discussion I'm looking for.

I'm currently a Staff Engineer with a strong backend background (15 YOE). I work closely with a team that builds recommendation systems, and I'd like to get much deeper into the ML side of things — actually understanding and training models rather than just working on the engineering around them.

I'm particularly interested in things like training embedding models, ranking models, bandits, candidate generation, evaluation, etc.

I also happen to have a yearly training budget that I can spend, so I'm trying to figure out the best way to use it.

I'm wondering whether I should first invest in the fundamentals (ML/statistics/math) or jump straight into something more hands-on and learn by building things.

I'm not a huge fan of online courses like Coursera, Udemy, etc., but I'm not opposed to them if people think they're genuinely the best way to build the foundations.

I'd also be very interested in in-person courses, bootcamps, summer schools, or similar programs anywhere in Europe.

For people who have made a similar transition from software/backend engineering into ML: what would you recommend? What courses/programs/resources were actually worth your time and money?

What happens after AGI? Maybe ASI isn't the endgame.

A lot of discussions about post-AGI assume we'll eventually build a single, extremely capable ASI — essentially one "God-like" model.

But there's a problem with that idea:

A single superintelligent system is also a single point of failure.

What if intelligence at civilization scale looks less like one giant brain and more like an evolving ecosystem of specialized intelligences?

We're Team Auralis, and we've been working on an open-source framework around this idea: ACI (Artificial Civilization Intelligence).

The basic concept is to treat intelligence more like an operating system for a civilization than a single neural network.

The framework currently has three main components:

• OMNIS — a continuous causal world model intended to maintain an evolving representation of the world rather than relying solely on static training data.
• NEXUS — a fabric of specialized agents across areas like science, engineering, economics, etc., which can disagree, debate, and resolve conflicts.
• ASCEND — a long-horizon planning layer designed to reason about and execute plans over decades while continuously correcting course.

We're also exploring OCI (Open-ended Civilizational Intelligence) — an extension that introduces structural plasticity, meaning the system could potentially create new governance mechanisms, agent structures, and even new forms of intelligence as it evolves.

We've open-sourced the framework, including:

• Architecture documentation
• Mermaid diagrams
• Mathematical formulations
• Benchmark methodology (ACI-001)
• Implementation/research directions

📚 Docs: https://team-auralis.github.io/ACI-Architecture-Framework/

💻 GitHub: https://github.com/Team-Auralis/ACI-Architecture-Framework

We're especially interested in criticism here.

Is a distributed, civilization-scale intelligence actually safer than a single superintelligent model? Or does adding more agents, governance, and coordination layers simply create new failure modes?

If you're interested in multi-agent systems, AI alignment, governance, long-horizon planning, world models, or open-ended intelligence, we'd love feedback — especially on the mathematical assumptions and the agent architecture.

Curious to hear what Reddit thinks.

[ Removed by Reddit on account of violating the content policy. ]

I’m building an open-source benchmark for hospital bed-demand forecasting using synthetic data.

Current baseline ideas:

• Seasonal naive / moving average
• ARIMA
• XGBoost
• LSTM

Metrics:

• MAE / RMSE
• sMAPE / WAPE
• Peak-demand accuracy

If you were evaluating this benchmark, what baseline or metric would you immediately expect to see?

I’ve been thinking a lot about what it actually means to build useful AI agents.

The more I learn about agentic systems, the more I realize that an agent isn’t just an LLM connected to a few tools.

Lately, I’ve been learning about what I’m starting to think of as an “agentic discipline,” and one idea has really changed how I think about LLM applications.

The traditional mental model is:

Input → Model → Output / Action

But real-world problems rarely work that way.

You make an initial decision with incomplete information.

Then you take an action.

You observe new evidence.

You update your understanding.

And then you make a better decision.

So I’ve been exploring whether we can think about agentic systems through a probabilistic / Bayesian lens:

Initial belief (Prior)

↓

Choose an action

↓

Observe new evidence

↓

Evaluate the likelihood of that evidence

↓

Update belief (Posterior)

↓

Choose the next action

↓

Repeat

Instead of only asking an LLM:

“Give me the answer.”

What if we design the system to continuously ask:

- What do I currently believe?

- What evidence would change my belief?

- What action should I take next?

- Which action would reduce my uncertainty the most?

- Did the last action actually improve my understanding?

This feels like a much more powerful way to think about agents.

The interesting part isn’t simply adding more tools or more LLM calls.

It’s designing a system that can reason under uncertainty, actively gather information, update its state, and make better decisions over multiple steps.

I’m still exploring this idea and trying to understand where the Bayesian framing is genuinely useful versus where it’s simply a useful analogy.

I’d love to hear from people working on agents, reasoning, or probabilistic AI

How do you think about belief updating and uncertainty in agentic systems?

Hey everyone,

Hope all of you are doing great.

I am looking for feedback from people who have actually purchased the AI for Data Engineers course by Prashant Kumar Pandey (Scholarnest/ Learning Journal).

Is it worth the money? I have almost all his courses on Udemy and found them really good for learning the basics and his way of teaching is something that have always resonated with me. Based, on that I'm thinking about buying the AI course and would really like some feedback.

Things I'm looking for are:
1. Does it cover enough detail as compared to other courses on Udemy/ Youtube (Krish Naik for example)?
2. Is it Databricks heavy/ Databricks focused? Or the topics are explained well in a platform agnostic way with examples given on Databricks.
3. Did you get enough support when you got stuck on any topic?
4. Does it have the following topics explained well enough?
AI guardrails
Deployment
Tuning

Thanks in advance.

Hey!

I'm applying for an AI Bachelor's at the University of Salzburg and I'm spiraling a bit. Would love some honest opinions from people who've actually been through it.

The good:I'm extremely hardworking and enjoy topics once I *get* them.

The scary:I'm average at school math. Slow with mental arithmetic. I forget things if I don't review regularly. And the program is in German(not my native language).

My fear:Is AI only for math naturals who "just see" the solution? I'm the person who has to sit with a problem, fail a few times, and eventually understand it. But once I do - I love it.

My questions:

1. Can hard work actually compensate for not being a math genius?
   
2. How much is abstract theory vs. applied programming?
   Any experiences would mean a lot. Thanks 💜

Fine-Tuning GLM-OCR

https://debuggercafe.com/fine-tuning-glm-ocr/

With specific prompts, along with text recognition, GLM-OCR can also carry out formula recognition. However, it falters in complex mathematical formulas. In this article, we will be fine-tuning GLM-OCR and observe to what extent we can improve the performance of the model on a task-specific dataset.

Fine-Tuning GLM-OCRhttps://debuggercafe.com/fine-tuning-glm-ocr/With specific prompts, along with text recognition, GLM-OCR can also carry out formula recognition. However, it falters in complex mathematical formulas. In this article, we will be fine-tuning GLM-OCR and observe to what extent we can improve the performance of the model on a task-specific dataset.

I just shipped an open-source pull request comment auto-fix system. Today, pull requests are swarmed with comments from AI code reviewers. Most of the comments are real, ranging from small nitpicks to actual issues. Most teams are spending time either manually reviewing and validating these, or have created skill to have an AI agent read and fix them.

With OpenInspect, the system will now automatically do this for the user. Each comment is read and determined if valid. If valid it is fixed and auto resolved, otherwise pushed back on. Huge time savings from having to babysit the pull request to a stable state.

https://github.com/ColeMurray/background-agents

One thing I find increasingly interesting about AI agents is that benchmark scores can hide a major difference in actual behavior.

A model might solve a difficult coding problem when given a clean task, but an autonomous agent has to do much more:

- decide what to do next

- inspect its own work

- recover when something fails

- use tools correctly

- maintain state across many steps

- know when the task is actually finished

That makes me wonder whether we're measuring the wrong unit of progress.

Instead of asking only:

«“How difficult a problem can the model solve?”»

Should we also be asking:

«“How much useful work can the model reliably complete without human intervention?”»

I think that distinction could become much more important as AI systems move from chatbots toward autonomous agents.

What metrics would you use to measure this?

I am originally from South Korea but I finished my master in computer science (machine learning) in the US. In the US, I applied for so many ml engineer roles but my resume was passed only at 2 companies out of hundreds. While, my resume was passed at the rate of approximately 50% at Korean companies...

I am now in my final year of engineering in AI/DS. I haven't been actively building projects, just worked on some academic projects. I want to know how do the engineers with more knowledge in this domain know which project they should work on? How do they find it?

Is it from GitHub, research papers, paperwithcode or hugging face ?

I need a project idea for my academic major project!

I'm a final-year CS student and a fresher trying to build my career in AI/ML.

I've been learning AI/ML for around 8–9 months, but my learning hasn't been completely structured. I've explored different areas rather than following one complete ML curriculum from start to finish.

So far I've studied and worked with things like traditional ML, transformers, LLM concepts, RAG, etc.

I've also built around 4–5 ML models/projects, as well as basic and hybrid RAG systems.

However, I still have this feeling that my knowledge is fragmented and my ML fundamentals aren't as strong as they should be.

For example, I can understand individual concepts when I'm studying them and build things using them, but when I look at ML as a whole, I feel like there are still many gaps in what I actually understand.

I recently came across Microsoft's ML for Beginners repository. It provides a structured curriculum covering:

• Regression
• Classification
• Clustering
• NLP
• Time Series
• Reinforcement Learning
• Real-world ML applications

I'm considering going through it systematically—not because I necessarily need another beginner tutorial, but because I want to identify and fill gaps in my fundamentals.

So I'd like to ask people who have more experience in ML:

Would you recommend this curriculum for someone in my situation?

Should I:

1. Go through the entire curriculum
2. Skim the beginner sections and focus only on areas where I have gaps
3. Skip it and move toward more advanced ML, papers, and projects

I'm particularly interested in hearing from people who have gone through the beginner → intermediate ML stage.

Also, is it normal to feel like "I know nothing" even after spending 8–9 months learning AI/ML and building things like ML models and RAG systems?

I'd really appreciate some honest advice about what you would do in my position.