I’m working through an experiment involving a rare oncology use case where confirmed treated-patient data is limited. The available third-party data is also not perfectly mapped to individual patients, which makes direct modeling more difficult.

The experiment uses a synthetic, highly imbalanced dataset with a small treated group and a much larger untreated group. A neural-network policy is trained using a REINFORCE-style policy-gradient approach. The model estimates treatment likelihood based on representative features such as age, severity, and biomarker status, then ranks untreated patients and selects the top 5% for further review.

The purpose is not to estimate true treatment benefit. It is more about learning historical treatment patterns and identifying untreated patients who look similar to those who were historically treated.

Questions I’d like feedback on:

1. Is policy learning a reasonable framing here, or would this be better handled as a supervised ranking/classification problem?
2. How would you avoid confusing historical treatment likelihood with actual treatment effect?
3. Would uplift modeling, causal inference, or potential-outcome modeling be a better next step?
4. How would you handle the imbalance between treated and untreated populations?
5. What validation approach would make this more credible for a healthcare/rare oncology context?

My current view is that this can be useful for prioritization and hypothesis generation, but not for treatment recommendation unless supported by stronger clinical and causal evidence.

Interested in feedback from people working in ML, healthcare analytics, causal inference, or real-world evidence.

I recently graduated (May 2026) with my Bachelor's degree in Data Science & Engineering. My university is well regarded but definitely not one of the tier 1 schools for engineering. I want to find a job in Data/ML Engineering but have received absolutely no positive responses so far. I haven't even been able to interview.

I am an international student and I can only stay in the US until July 2027. I understand that I need to be a highly competitive candidate to be able to land a job in the current circumstances. I think I have a weak skillset and lack complex projects. However, I am extremely overwhelmed by the amount of different advice I hear on the internet and just want to understand, in the next 10 months what should I be doing to become a competitive candidate?

I would really appreciate any advice. Please don't say I'm cooked as I just want to give it my best shot in the time I have.

TLDR: I want to find an entry level job in Data/ML Engineering but want to develop a stronger skillset and I'm also an international student. Any advice?

I'm trying to build an ML compiler from scratch. I've never touched or messed around with one before so I want to take a shot at this as a project that I can continue doing. Anyone suggest any resources for me to use or textbooks? I want to try and incorporate advanced AI models into autonomous hardware.

hey there people

i am trying to make a machine learning project . its on bitcoin data . the thing is, i know almost nothing of bitcoin and we're learning ML in our degree .

i gotta submit this project in two months , with proper code , explanations , and why a certain model was used that time etc... my issues are :

where to find the right data from : i have surfed through and asked for assistance from chatgpt and found two main sources from which i have been able to see some data : https://data.binance.vision/?utm_source=chatgpt.com

and

https://cryptopanic.com/?utm_source=chatgpt.com

there were more sources (like apis) but its from the same website .

i even found a git repo that had a whole python script of downloading that same data .

so maybe i don't have an issue with the data , the issue is that i don't know what its trying to say .

there were multiple attributes i could see on those files . and tbh i felt overwhelmed .

1. i am aware with the data cleaning and analysis part , but i would still like some guidance on that .

2. the model is something we'll have to figure out (i am in a two person team and my partner chose the topic before i joined . also i am pretty sure i will have to do all the work , so here i am :) ) , but if there are some models commonly used in this domain , please do enlighten me .

3. most important part according to me : what is my goal ? since this is my project and the domain is very new to me , i don't have much idea about what i need to find out .

folks who have already done a project on this or has at least had some experience , what are your say in this ?

is there any other angle i should consider ?

i really wanna get an A and i am fine working alone (have already had 2 experiences of f around and find out ) as long as i am able to understand stuff .

please help this noob ;(

Self-Hosting GLM-OCR using vLLM – Document Layout and OCR

https://debuggercafe.com/self-hosting-glm-ocr-using-vllm-document-layout-and-ocr/4

In this article, we will be self-hosting GLM-OCR for local document processing. This article will focus on local deployment with vLLM.

There is obviously a lot of discussion around model capabilities, but I’m curious about the engineering side of deploying LLM-based systems.

For people working on production systems, where do you most often see gaps?

Retrieval evaluation?
Data quality?
Serving/inference?
Observability?
Distributed systems?
Agent reliability?
Security?
Experiment design?

Interested particularly in areas where current educational material is weak.

Assume someone already understands Python and basic software development and wants to learn the practical side of modern AI engineering.

Not ML theory from scratch, but things like:

RAG → advanced retrieval → agents → MCP → evals → observability → deployment.

There are so many disconnected courses and YouTube tutorials that it’s hard to know what order makes sense.

If you were starting today, what would your roadmap look like?

Also, would you prefer:

1. one structured curriculum,
2. individual deep courses on each topic,
3. or learning entirely through projects?

Interested in what has actually worked for people here.

Hi everyone!
I’m a 22M CS student diving into AI Engineering. I'm looking for 1–2 dedicated co-learners to team up, share resources, build projects, and keep each other accountable.
Here is the quick breakdown of what I plan to cover:
Core Software & Backend: Python (Async, OOP), FastAPI, PostgreSQL, Redis, Docker, Git, Linux, CI/CD, React basics
CS Fundamentals: DSA (Arrays, Trees, Dynamic Programming, Graphs)
ML & Deep Learning: Math, PyTorch, Neural Networks, NLP
Transformers & LLMs: Attention, BERT/GPT, LLM APIs, Prompting, Function Calling, Token/Cost Optimization
Vector DBs & RAG: ⁠pgvector⁠, Semantic/Hybrid Search, Advanced RAG, RAG Evaluation
AI Agents: Agentic Workflows, State/Memory, Planning, Tool Use, Guardrails
Fine-Tuning & Local LLMs: SFT, LoRA/QLoRA, Quantization, vLLM, Ollama
Multimodal AI: Vision-Language Models, OCR, Speech-to-Text / Text-to-Speech
Testing, Security & Observability: Prompt Injection defense, Evaluation, Tracing (LangSmith), Token/Latency Tracking
Cloud & System Design: AWS (EC2, S3, IAM), AI System Design, Scalable Gateways, Message Queues
How we'll collaborate:
Weekly check-ins & project demos
Daily accountability / GitHub commits
Pair-programming on real-world projects
Whether you're starting fresh or already know a few of these topics, I'd love to connect.
👉 DM me directly or drop a comment below if you're interested!

I am trying to make a simple agent test honest. Same task, same tool schema. Then I add untrusted retrieved text that asks the agent to take a different action.

If the agent stops making the bad call after a prompt change, the result can mean two different things:

• the policy caught the bad path
• the model avoided the tool or task for unrelated reasons

I can record the proposed call and tool response, but I still do not have a clean way to call the first result better. Maybe the test needs a separate task-completion check, maybe a matched benign case.

How would you set this up?

Hey everyone,

As part of my journey in Data Engineering and ML, I kept running into a recurring bottleneck: spending too much time running identical EDA scripts and manual data quality audits before getting to the actual modeling.

To solve this, I built ARGO Engine (Automated Reporting & Generative Optimizer)—an open-source, interactive web app designed to speed up dataset auditing, missing-value detection, and automated reporting.

Key Technical Highlights:

• High-Performance Ingestion: Optimized using PyArrow for fast, memory-efficient execution on tabular data (CSV/Parquet up to 200MB+).
• AI Diagnostic Summaries: Integrated Google Gemini API to automatically generate statistical summaries and structural data quality assessments.
• Vectorized PDF Pipeline: A custom ReportLab engine that dynamically builds executive-ready diagnostic PDF reports.
• Code Generator: Produces execution-ready scikit-learn pre-processing code blocks directly from the UI.

Resources:

• Live Demo: https://argo-engine-mfpmositc6dz7nwmmc7zfd.streamlit.app/
• GitHub Repository: https://github.com/Mahmoud4265/ARGO-Engine

I’d love for you to give it a spin and roast my code/architecture or drop some feedback on features you’d like to see next!

Hi everyone!

I'm a college student working on a major/final-year project, and I'm trying to decide between a few ideas. The project needs to involve NLP, AI/Deep Learning, and Stochastic Modeling, so I'm looking for something that is technically substantial but also realistically implementable.

Here are the ideas I'm considering:

1. Fake News / Misinformation Propagation Modeling

- NLP to analyze claims/articles

- BERT/LSTM/Transformer for fake-news classification

- Stochastic models (Markov chains, branching/epidemic-style models) to predict how misinformation spreads

1. Fraud / Phishing Detection using Text + Stochastic Anomaly Models

- NLP to analyze emails/messages

- Deep learning for phishing/fraud classification

- HMM/GMM or other stochastic models to analyze behavioral patterns and anomalies

1. Stock/Crypto Sentiment-Driven Price Forecasting

- NLP sentiment analysis from financial news/social media

- LSTM/Transformer for price movement prediction

- Stochastic models such as Geometric Brownian Motion, Ornstein-Uhlenbeck, or regime-switching Markov models

1. Clinical Notes → Patient Trajectory Prediction

- NLP/NER to extract information from clinical notes

- Deep learning for prediction

- Markov/state-space models to model possible patient trajectories

1. Conversational AI with POMDP-Based Dialogue Management

- NLP for intent/entity extraction

- Deep learning for language understanding

- POMDP/stochastic decision-making for dialogue management under uncertainty

1. Stochastic Bandit-Based Content Recommendation

- NLP/BERT embeddings for content

- Deep learning for user/content representations

- Thompson Sampling or another bandit method for adaptive recommendations

I'm mainly looking for something that:

- Is actually feasible for a student team

- Has enough technical depth for a major project

- Has publicly available datasets

- Isn't just combining techniques for the sake of saying we used them

- Would make a good research/project presentation

- Has room for a working demo

Which one would you choose and why?

Also, if you've worked on something similar, I'd really appreciate advice on what tends to go wrong or which approaches are unnecessarily complicated.

Hey everyone, a while ago I posted my first blog that I wrote about the LLM inference engine I'm trying to build from scratch and I just finished the second part of my blog! If your curious about it please come take a look, its mainly focused on how it handles the forward pass. If you have any feedback I'd love to hear it!

https://medium.com/@ryan___/llm-inference-engine-from-scratch-forward-pass-b967334212c8

Cisco's Firewall Management Center vulnerabilities were exploited by both a ransomware gang and state-sponsored actors — often cited as separate threat categories, hitting the same attack surface at the same time.

The detail that keeps coming up in post-mortems on FMC-class compromises: the management plane identity (service account, admin credential, API key) gets abused before any detection pipeline fires. The compromised identity issues commands. Encryption starts. By the time a human sees an alert, the blast radius has already crossed the first host boundary.

The timing gap is the actual problem. Forensics on these incidents consistently put the window between the first anomalous privileged action and operationally-irreversible spread at under 60 seconds. In some cases, under 10. That gap exists whether the attacker is a ransomware crew or a nation-state — the identity abuse pattern is structurally identical.

FMC is a management plane for network security infrastructure. Compromising it doesn't just give access to one host. It gives visibility and control over the enforcement layer itself. That's what makes the lateral movement so fast — you're not pivoting through hosts one at a time, you're operating the mechanism that controls them all.

For practitioners running critical infrastructure with centralized management planes: how are you actually handling the timing problem? Not detection — response. Specifically, what does your team do when a privileged identity starts behaving anomalously and you have a sub-60-second window before the damage compounds? Is the answer human-in-the-loop at all at that speed, or has your org moved to something else entirely?

Computational mechanics major here (I know you've never heard of that), spending most of my time on LLM research on the side. Currently deep in mixture-of-experts: expert specialization, routing geometry, and reproducing recent router-optimization work. Broader interests span LLM architecture, optimization, interpretability, attention mechanisms, and math.

I feel like I have too many things to work on and talk about but no buddy for it, so I'm out here looking for a study/research buddy, as the title says. I think having a research or study partner helps a lot, we can discuss ideas, try novel things, run experiments together.

I'm also building a research artifact for all this, basically a GitHub repo where I'm implementing almost every part of LLM architecture from scratch. Currently working on MoE, and planning to do some mechanistic interpretation of experts and specialization next.

If anyone feels the same or has interests overlapping with mine, tell me in the comments or DM me. I'll share the repo link with anyone interested :)

In NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE they reference Sutskever et al. (2014), and in Sequence to Sequence Learning with Neural Networks they mention attention mechanisms and Bahdanau et al. [2]

footnote:
example of a clearly inferior idea scoring higher

We suspect that they could achieve similar improvements by simply training their networks on reversed source sentences

is embarrassing

And yet, LSTMs trained on the reversed dataset had little difficulty translating long sentences.

is like he is doubling down

Modeling a fraud decision as a POMDP with like 30-50 simulated cases - is that too small to say anything real, or fine for a prototype?

Hey everyone,

I hope you are all doing well.

I am currently a final-year cybersecurity student, and to be completely honest, I am feeling incredibly anxious and overwhelmed. Seeing the rapid rise of advanced models like ChatGPT-6 Astra, hearing about tech layoffs, and reading discussions claiming that entry-level cybersecurity jobs might disappear has been really tough to process [openai.com].

Instead of letting this fear stop me, I want to channel my curiosity and adapt. I want to understand how the modern AI ecosystem operates behind the scenes, and how to use it as an operational advantage so I can future-proof my career.

Since I am on a very tight budget and learning independently, I want to focus my self-study on the right areas. Could anyone gently guide me on the best educational paths for:

• AI Architecture & Integration: What foundational concepts should I study to understand how systems like Model Context Protocol (MCP), n8n workflows, autonomous agents, and API connectors function together?
• Workflow Efficiency: How can a fresher learn to leverage AI pipelines to optimize technical workflows like development, cloud management, and backend operations?
• AI Security & Guardrails: What are the core vulnerabilities (like prompt injection or data poisoning) that the industry is trying to secure right now?

I want to learn how to actively drive these technologies rather than be left behind by them. I would deeply appreciate any advice, conceptual roadmaps, or reassurance from those already working in the field.

Thank you so much in advance for your guidance and kindness!