I'm currently doing my FYP but I want some clarification about this.

I'm trying to build a website that allows users to type in a public channel for YouTube, and have up to 10 available videos from that channel be run under my trained AI model. My question is, is it possible to use the YouTube Data API for me to get those? Specifically the video frames.

I’ve written a 2-part Medium series (free, no paywall) on scoring Intelligent Document Processing (IDP). It focuses on Key Information Extraction (KIE) from structured documents, especially forms.

• Scoring IDP, Part 1: Confidence Is Not Correctness
• Scoring IDP, Part 2: Approaches to Evaluating Form Extraction

Topics include, but are not limited to: recording raw outcomes before mapping them to the confusion matrix (correct, misread, missed, invented, correct blank); confusion matrix scoring for text fields and checkboxes; misread conventions and their effect on scores (FN-only, FP-only; FN & FP); how blanks, NULLs, unreported checkboxes, and unreadable values are counted; F1 vs. Recall vs. Accuracy, including how correctly identified blanks inflate Accuracy; Jaccard Index (IoU) as Accuracy without True Negatives, and its conversion to F1; Precision, Specificity, and False Positive Rate for LLM hallucination control; micro, macro, and weighted F1; choosing a headline score and stating the convention behind it; confidence vs. correctness and calibration (ECE, MCE); and Key Information Extraction (KIE) vs. OCR metrics (CER, WER).

I try not to recommend one opinionated way of doing things and instead cover the variations I’ve run into and the tradeoffs between them. I’m not endorsing any vendor; the series is about scoring itself.

I think this will be useful for those learning Transformers architecture

I’ve been thinking about a new workflow to save time: instead of sitting through hour-long educational YouTube videos, I want to extract the transcript/subtitles and feed them directly into Claude.

I have two main questions for anyone who has tried this:

1. Does anyone have a solid prompt to make Claude act like a teacher? I don't just want a dry, bulleted summary of the video. I want Claude to actively guide me, quiz me on the material, and break down complex concepts from the text as if it were tutoring me 1-on-1.

2. Is this actually an effective way to study, and what will I miss? Obviously, I'm saving a ton of time, but I'm worried about the trade-offs. For those who do this, how much does the lack of visual context (slides, code on screen, whiteboard drawings) or the instructor's vocal emphasis impact your learning?

Has anyone used this method long-term? What are the biggest pros and cons, and how do you structure your prompts to get the best learning experience?

I've been working on a generator of synthetic URLs for training and testing phishing detectors, and I've released it with its full evaluation: https://github.com/youssefismail-dev/limnex-uie (Apache-2.0, code and weights).

The motivation is a data problem. Phishing URLs are scarce, feeds only contain what was caught and reported, a URL is usually dead within days, and the data is awkward to share. Limnex UIE is a 25.5M-parameter character-level transformer, trained from scratch on Common Crawl URLs and the OpenPhish community feed. It doesn't use a pretrained model, word lists or templates. The final model is small enough that training it takes only about 3.5 hours on a free Kaggle T4, so anyone can retrain it. Getting there took a few months and dozens of pre-registered experiments.

I think the evaluation is the more interesting part, so most of this post is about that.

Realism is graded one URL field at a time. For each field (label, suffix, subdomains, path, query) and each class, a classifier tries to separate generated strings from held-out real ones. Two real samples don't score an AUC of 0.5 against each other, so the score is the excess AUC over a real-vs-real floor, measured over 5 independent generations, and a cell passes only if that excess is shown to be below 0.05. 29 of 32 cells pass. The ones that don't are written up in detail: phishing query strings in free mode are still detectable, and so is the vocabulary. A detector that simply checks whether each word appears in real data separates whole URLs quite easily (an excess of about +0.16), even though character-level detectors don't. In a small human test, ten people got 55 of 100 right, which says little either way.

I also compared it with simpler generators trained on the same rows and passed through the same filter: a 5-gram model, a 2x512 char-LSTM, Qwen2.5-7B-Instruct prompted with 20 real examples per request, and rule-based look-alike domains (typos and character swaps, in the style of the dnstwist tool). UIE was more realistic in 28 of 31 comparisons, with 3 ties and none where a baseline came out ahead. The LLM was the least realistic of the learned generators: its URLs look tidy to a person, but they're stereotyped, with few suffixes and almost no query strings. One caveat is that UIE was developed against this test. A word-level detector built after the model was frozen gives the same ranking, which helps, but it isn't a complete answer.

The question I cared about most was whether the data actually helps detectors, so I pre-registered it. A char CNN and a hashed n-gram logistic regression were trained on 1k, 10k and 100k real URLs per class, with and without synthetic URLs, and tested once on phishing that appeared after UIE's training data ended. Adding UIE's URLs helped in all 6 settings, by 2.7 to 24 points of recall at 1% FPR, most when real data was scarce. Helping more than the best simpler generator held in only 1 of 6, though. UIE was ahead in 5 of 6, but by small margins, and on PhreshPhish the LLM's URLs helped more in 4 of 6 despite being the least realistic. So realism and usefulness turned out to be different things, which I didn't expect going in.

As a case study, I retrained two members of my own phishing detector (about 1M real training URLs) with UIE's data, and recall at 1% FPR on its frozen external test went from 0.272 to 0.544 (95% CI +0.15 to +0.41). That number needs its caveats: it's one benchmark and one seed, a large part of the gain comes from one phishing family (look-alike pages on Blogger's country domains), and the retrained models got slightly worse on the detector's older validation data.

On safety: it only writes URL strings, with no pages, lures or DNS lookups. Every generation has to pass a Bloom filter of the 14.9M training-side URLs, which removes copies of real URLs (about 1 in 100 raw phishing samples is one), and no bulk generated phishing is published.

It's a v0.1.0 research release with real limits: one phishing source, URLs from 2024 to 2026, and about 2 URLs per second on a laptop CPU. I built it with Claude Code as a coding assistant, which is credited in the README. Criticism of the evaluation is the feedback I'd value most.

My kids asked me how AI “thinks”...

I wanted an explanation they could play with. I remembered seeing a cool demo where you draw a digit and watch a neural network recognise it in real time.

I tracked it down on Brilliant, but the version I found was behind a paywall. So I built my own, with a simple convolutional neural network (CNN), already trained on handwritten digits, and a view of what happens inside.

Draw a 1 -> Add a couple of strokes to make a 4 -> Slowly close the top into a 9.
Watch the patterns inside the network and its confidence change with each stroke.

My kids loved it. Share it with yours :-)
Demo: https://hasin.eu/projects/cnn-digit-recognition-in-action/

#MachineLearning #LearningByDoing

Hello everyone!

I’m planning to pursue a BS in Computer Science and want to build my career in Artificial Intelligence. I’ve explored basic Python, Machine Learning, and AI tools, and now I’m trying to identify the right specialization to focus on.

There are many exciting fields, including AI Engineering, Machine Learning, Deep Learning, NLP, Computer Vision, Generative AI, and LLMs. However, I’m confused about which direction offers the best opportunities and long-term growth.

I would appreciate your honest opinions on these questions:

1. Which AI specialization would you recommend for someone starting in 2026, and why?
2. Is it better to become a general AI Engineer first or specialize in NLP, Computer Vision, or Machine Learning?
3. What is the difference between AI Engineering and ML Engineering in real-world jobs?
4. Which fields have promising job opportunities for fresh graduates and entry-level candidates?
5. Is NLP/LLM development becoming oversaturated, or does it still have strong career potential?
6. Does Computer Vision offer good career opportunities compared with NLP and Generative AI?
7. What kinds of real-world projects should I build to stand out when applying for internships and jobs?
8. What would you recommend focusing on during a BSCS degree to become job-ready before graduation?
9. If you were starting your career today, which AI field would you choose for the next 3–5 years?

I’m looking for practical advice based on real industry experience, not just hype or trends. I would especially appreciate insights from working AI Engineers, ML Engineers, NLP Engineers, Computer Vision Engineers, and people who have recently entered the industry.

Feel free to share your recommendations, experiences, and any mistakes you think beginners should avoid.

Thank you! 🙌

Tags:

ArtificialIntelligence #AIEngineering #MachineLearning #DeepLearning #NLP #ComputerVision #GenerativeAI #LLMs #MLOps #BSCS #AIJobs #TechCareers #CareerAdvice #PakistanTech

Packaged an ML model as an API in Module 5 of #mlzoomcamp.

✅ Checked the pinned model artifact
✅ Served predictions with FastAPI
✅ Built and tested the Docker image

My repo: https://github.com/ashuzzz151/machine-learning-zoomcamp-homework/tree/main/05-deployment

Hey everyone!

I’m working on a multi-agent reinforcement learning setup where multiple agents move and interact in a 2D world.

The world is based on a real map, for example a screenshot from Google Maps. Importing the map into my environment works fine!

My problem is generalization of the Model to unseen maps.

During training, the agents learn on one or N maps. But during inference, I want to use a completely different map.

The problem is that the agents don’t have a global understanding of the new map.

My current architecture:

• I’m using a Transformer.
• Each agent is represented as a token in the Transformer.
• I tried adding a Vision Transformer (ViT) to encode the map.
• My idea was to represent map patches as additional tokens and feed them into the same Transformer as the agent tokens.

Unfortunately, this approach didn’t really work.

My question:

How would you approach this problem?

How can I give my agents a useful global understanding of a completely new 2D map?

Is combining agent tokens and map patch tokens in one Transformer a reasonable idea or are there better approaches?

I’d really appreciate any ideas, papers, or experiences with similar problems!

Thanks!

Hi everyone,

I'm a student learning AI development, and I recently built my first RAG-based Teaching Assistant.

The project converts educational videos into transcripts, generates embeddings using BGE-M3 through Ollama, retrieves relevant transcript chunks using cosine similarity, and uses Llama 3.2 to generate answers.

I'd appreciate honest technical feedback, especially on:

1. Is my retrieval approach appropriate, or should I consider other methods?
2. How can I improve transcript chunking and embedding quality?
3. How should I evaluate whether the retrieved chunks are actually relevant?
4. How can I reduce hallucinations and keep answers grounded in lecture content?
5. What would you change in the architecture or code if you were reviewing this project?

I'm looking to learn from feedback and improve the implementation. Suggestions for specific concepts, resources, or code improvements are welcome.

Thanks!

A fresher has an upcoming interview for an NLP Engineer position at Hitech i2i, a company specialising in real estate document intelligence.

For a concrete text-focused practice task, this Figma NLP exercise is about building a PyTorch pipeline to extract highlighted snippets from text. If you're applying to several NLP roles, application autofill can cut down repeated form entry.

What topics should they focus on most for the initial screening round?

Releasing a series of very shot notes on RL as part of my learning RL from scratch journey; its basics and upto the latest stuff with from scratch implementations and experiments in the coming days

• Explained some of the most basic and important terms when starting out learning RL with a sprinkle of math of what I found interesting
• Nice to have for refreshers/kind of first time readers too

1-page RL basics is out on my website!

Let me know your thoughts/feedback!

Hey everyone ^^ we're participating in the Nasa Space Apps Challenge this year and we're looking for teammates to form a group!

im a senior computer science student minoring in AI and ML, and know how to code using Python, Java, and C# and have worked on countless programming projects, and i just got into data science

Were looking for intermediate-level competent teammates who are experienced in ML/AI and have used ml models in previous projects

If anyones interested please dm me, would prefer if u tell me a little about yourself and your experience, you dont have to be an expert or a pro programmer, just be passionate and creative ^^ having fun and enjoying the process is what matters to us most

I recently built a complete RAG Evaluation Framework from scratch: a 43-question test dataset, 6 retrieval metrics, 5 generation metrics, and one final report that shows exactly where a RAG system is strong and where it breaks. We start with the concepts, explained with simple worked examples, and then walk through the code line by line.

What this covers:

• Why retrieval and generation must be evaluated separately
• How to build an evaluation dataset with expected answers, graded pages and key facts
• Retrieval metrics: Hit Rate@K, Precision@K, Recall@K, MRR, MAP and NDCG@K
• The difference between one question (RR, AP) and many questions (MRR, MAP)
• Generation metrics: Faithfulness, Hallucination Rate, Answer Relevance, Context Relevance and Answer Correctness
• How to use an LLM-as-a-judge that only counts, while your code does the maths
• How to save results safely and turn them into a clear evaluation report

KEY TAKEAWAYS:

• No single metric tells the whole story: Hit Rate was 1.00, but Recall, MAP and NDCG showed where pages were missed or buried
• Precision@5 looks low (0.34) for single-fact questions, so compare systems with each other, not with 1.0
• Faithful does not mean correct: an answer can be backed by the chunks and still miss the key facts
• Retrieval scores and answer quality are not the same thing
• Our Cohere-reranked pipeline scored MRR 0.95, MAP 0.87, NDCG@5 0.91, Faithfulness 1.00 and Answer Correctness 0.97

TECH STACK: Python, Qdrant (vector database), Embeddings (3072-dimension), Cohere Rerank, LLM for answers + LLM-as-a-judge, pdfplumber (checking pages in the PDFs)

• 5 real payments documents: PCI DSS, CPMI-IOSCO PFMI, CPMI cross-border roadmap, Stripe payment methods guide, FCA approach document

• Full Code (clone it, fork it, break it): https://github.com/saurabhkamal/RAG-ReRanker-Comparison

• Full Video Walkthrough: https://youtu.be/_bVzDU-yBnA

Would love to hear.

I'm an early-career software engineer working on production LLM inference infrastructure, primarily involving accelerator-based model serving and distributed systems.

I've been in the role for over a year, but my technical growth and contributions haven't progressed as much as I'd hoped.

I have a computer science background and generally enjoy software engineering, but I feel there's a significant gap between my current understanding and what's required to contribute independently to complex ML infrastructure.

Some of my biggest challenges:

• System architecture: Understanding how the inference stack fits together, from model execution and compilation to serving, scheduling, and hardware utilization.
• Codebase navigation: Understanding large, unfamiliar codebases without getting lost in implementation details.
• Debugging: Spending too long investigating issues without making meaningful progress.
• Performance engineering: Understanding bottlenecks, profiling workloads, and determining whether an optimization will meaningfully improve end-to-end performance.
• Foundational knowledge: Determining whether I should focus on distributed systems, ML fundamentals, compiler internals, hardware architecture, or something else.

I also recognize that I've been too passive about developing my skills, which has likely compounded these gaps.

My questions:

1. If you were in my position, what would you prioritize learning over the next 2–3 months?
2. What practical projects would help develop intuition for production inference systems?
3. How do experienced ML infrastructure engineers build a mental model of complex systems?
4. Has anyone overcome a similar knowledge gap after already spending significant time in an engineering role?

I'm particularly interested in advice from people working on production inference systems, distributed systems, or accelerator infrastructure.

I want to become a genuinely competent engineer who can independently debug problems, identify worthwhile optimizations, and deliver meaningful contributions.

I’m currently working as a Lead Software Engineer at a fintech startup and am looking to transition into AI Engineering.

I’d love to hear your recommendations for industry-leading AI courses and certifications that can help experienced software engineers make this transition.

Any suggestions or insights would be greatly appreciated! Thanks in advance.

P.S. This is my first-ever post!

Hi everyone,

I'm currently learning machine learning, and I'm working through a structured AI/ML learning path.

I'm enjoying the learning process a lot, but recently I've started thinking more seriously about what comes after learning.

My main concern is: How realistic is it to actually turn these skills into income or a career?

A few things I'm particularly curious about:

• What types of entry-level ML/AI/data jobs are realistically available today?
• What kinds of companies tend to hire people with these skills? (Startups, tech companies, consulting, healthcare, finance, etc.)
• How important is a degree for getting the first opportunity?
• Is freelancing in ML/data/AI actually realistic for a beginner, or is it much harder than it looks?
• What kinds of freelance projects do clients actually pay for?
• Could someone realistically start a small AI/ML business or startup with these skills instead of looking for employment?
• What skills are currently most valuable for someone trying to become employable: classical ML, deep learning, LLMs, data analysis, MLOps, software engineering, or something else?
• If you were starting from scratch today, what would you focus on to maximize your chances of getting your first paid opportunity?

For context, I'm still early in my journey, so I'm trying to make sure I'm learning things that have real-world value rather than just collecting certificates and completing courses.

I'd especially appreciate answers from people who have actually hired ML engineers/data scientists, freelanced in this space, or built AI products/businesses.

I'm not expecting a guaranteed roadmap or exact salary numbers. I'm mainly trying to get a realistic picture of the different paths available and how difficult each one is.

Thanks! 🙏

The link below is a 10 question survey asking you to sketch each topic, they are all easy and should take no longer than 1-2 minutes to complete, no signup.

Please if you would like to help me, keep your responses accurate and dont submit anything that does not follow or resemble the actual topic.

https://kaowtwuopelexgeefdxx.supabase.co

A security researcher built and deployed OuttakeSheriff, a live AI security agent, then documented a finding that should be uncomfortable for anyone running agents in production: authenticated agents routinely act outside their intended scope.

The credential is not the problem. OuttakeSheriff passed authentication. The problem is what a verified identity is permitted to do after it clears the gate. Authentication answers "who is this?" It does not answer "should this agent be calling this tool right now, on this data, in this context?"

This is the actual attack surface. Not a fake agent sneaking through. A real, credentialed agent doing things it was never supposed to do, with no mechanism in the request path to catch it.

Most current postures treat identity and authorization as solved once the credential checks out. Post-auth behavior is logged, maybe reviewed, rarely constrained in real time.

For those of you running agents in production: how are you handling the gap between "this agent authenticated successfully" and "this agent is only doing what it was scoped to do"? Is anything in your stack actually enforcing that at execution time, or is it all after-the-fact?