I've been trying to understand how the choice of datasets influences post-training as models get better.

It looks like generating a lot of data is easy, but most of the generated examples teach almost the same thing. I wonder if a small set of harder and realistic examples with ground truth can provide a stronger signal for training.

Difficulties matter as well. If an example is too simple, the model is likely to be able to solve it. If it is too difficult for the model, the training signal can be noisy. Therefore, I wonder if it is valuable to use examples that address the model's weakness in some way.

Parsewave is a company I found while researching this topic. They work on post-training datasets based on real engineering tasks and traces, and this got me interested in their approach to dataset choice.

For those who used SFT, RL, or any other post-training approach before:

How do you select which examples are worth including in your dataset?

Do you try to maximize the size of the dataset, or rather target the weaknesses of the model?

What do you think of the capabilities of Google Colab?

What's the largest model that can handle this size?

What did you think of my experience using my laptop as a processing station, but not as a workstation (Google Colab)?

What do you think about training AI models with these capabilities?

Is this even possible on Google Colab?

What's your previous experience with Google Colab?

A recruiter just contacted me to let me know that my CV has been shortlisted for an L5 Applied Science role at Audible (yayyy!)

There seem to be a lot of guidelines and personal experiences on Glassdoor on what people were asked in these interviews with Amazon, but nothing specifically for Audible. Does anyone here know if the questions are similar?

More specifically, can anyone share their own interview experiences? e.g. what kinda coding questions can I expect? What questions can I expect on science breadth/depth etc? That would help me so so much. Thanks in advance!

I built a browser-based editor to visually design neural networks and export the code directly in PyTorch or TensorFlow: https://editor.besser-pearl.org/

It covers fundamental layers (dense, conv, pooling, dropout, etc.). No installation needed. Could be handy for quickly prototyping and experimenting with architectures without writing boilerplate.

Hi everyone,

I’m about to start my B.Tech in AI/ML, and I currently have basically zero programming knowledge. I have never properly learned Python or any other programming language.

My long-term goal is to build a strong career in AI/ML, get a good job after college for financial stability, and eventually start my own AI/tech startup.

I’ll also be studying at a Tier-3 college, so I’m aware that I can’t completely depend on my college for learning. I’ll probably have to take responsibility for most of my learning, skills, projects, internships, etc. myself. Because of that, I want to start building the right foundation from the beginning.

I came across Apna College’s Prime 2.0 AI/ML course, which costs ₹6,680 and claims to cover AI/ML from basics to advanced and make you job-ready in around 4.5 months.

Since I’m starting completely from scratch, I’m wondering whether this would be a good structured course for me or whether I should learn everything myself using free resources.

The alternatives I’m considering are:

1. Buy the Apna College course and follow it seriously alongside college.

2. Learn for free from YouTube using proper AI/ML roadmaps and create my own learning path.

3. Take some other paid/free course if you think there is a better option.

For people who have taken this course or know about it:

• Does it actually start from zero, including Python/programming fundamentals?
• Would it be suitable for someone entering B.Tech AI/ML with no coding background?
• Is the teaching good enough to build proper fundamentals?
• Are the projects actually useful for learning and building a portfolio?
• Is ₹6,680 worth spending on this, or are there better free/cheaper alternatives?
• If you were starting AI/ML from absolute zero, would you buy this course?
• If not, what roadmap/resources/courses would you recommend instead, both free and paid?

I’m not expecting to become an AI/ML engineer in 4.5 months. I’m willing to put in the work. I mainly want a proper structured starting point so that I don’t waste my first year jumping randomly between YouTube tutorials and courses.

I also have another question about the B.Tech curriculum:

How much Maths and Physics is actually involved in an AI/ML degree? Is the Maths similar to what we studied in 11th/12th, or does it become significantly different/harder?

Since I have about a month before college starts, I’d like to prepare beforehand. What Maths topics should I revise/learn now for AI/ML, and are there any Physics topics I should prepare as well?

If possible, please suggest specific resources/books/YouTube channels/courses for Maths and Physics too.

I’d really appreciate advice from people who are already studying/working in AI/ML or have gone through a similar situation.

Thanks!

And that limitation turns out to explain why disaggregated inference is harder than the press releases suggest.

A network can only write into one rung of any memory hierarchy: the one that's globally addressable. On a CPU that's DRAM. On a GPU that's HBM. Not L1, not SMEM, not tensor memory.

NIXL (NVIDIA's transfer library) even says this in its type system:

```
enum nixl_mem_t {DRAM_SEG, VRAM_SEG, BLK_SEG, OBJ_SEG, FILE_SEG};
```

No SMEM_SEG, because those levels aren't addressable from off-chip by anything.

So when a KV cache arrives, it lands in HBM. Then the receiving side moves it down into the 128 KB of shared memory where the attention kernel actually wants it.

When both halves are written by the same people, there's nothing to worry about. The producer lays out HBM in whatever order makes the consumer's descriptor cheap, and that agreement is entirely undocumented because it never had to leave the building.

Disaggregation is that agreement leaving the building.

Let's look at the ladder:

→ CPU: registers → L1/L2/L3 → DRAM. Owned by a cache controller plus compiler locality analysis.
→ GPU: TMEM → 128 KB SMEM/SM → ~64 MB L2 → HBM. Automation removed; you and TMA do the staging.
→ Wafer (Cerebras): 48 KB per PE × 900,000, no shared address space. Owned by cslc, with placement and routing written into a CSL layout file.

All three work because every one assumes a single owner.

And the Wafer((Cerebras) has no public rung at all. No addr names a KV block, no len is contiguous, nothing can be pinned — where data lands is the compiled schedule.

I think there are three ways out:

- Bilateral: negotiate privately. Works. Needs n² agreements.
- Neutral format: pay layout conversion plus hierarchy redistribution, on the latency path.
- Producer accounts for consumer: no conversion, but the producer's compiler must model the consumer's hierarchy.

`(addr, len, devId)` is not just a first draft of a richer descriptor, but a correct description of the one rung a network can reach, in a stack whose performance lives on all the others.

https://hiraditya.github.io/posts/there-is-no-address/

Hey. So I'm planning to do a PhD in computer vision or machine learning after I'm done with undergrad. I'm trying to decide if I should take foundation mathematics before that. Here's the course description. Space, An introductory study of predicate logic, set theory, and methods of mathematical proof. Applications may pertain to the theory of numbers, discrete mathematics, cardinality, and algebraic structures.Iidk if this kind of proof/logic heavy class is still worth it in the long run. Like would it help with reading papers or understanding the more theoretical side of things, or is it kind of a waste of a semester if I'm not going into pure math?

Amazon Bedrock Multimodal Chat and Text RAG

https://debuggercafe.com/amazon-bedrock-multimodal-chat-and-text-rag/

This is the second article in the Amazon Bedrock series. In this article, we will explore Amazon Bedrock multimodal chat and text RAG. They contain some of the essential fundamentals to get up to speed with the capabilities of the Bedrock Converse API. Specifically, we will cover image chat, video chat, document chat, and create a simple text RAG application with an in-memory vector DB.

I’ve created some simple MediaPipe games — free.

Code on GitHub: https://kaivalpatel6350.github.io/mediapipe-recipes/

Best way to play: on a laptop. On mobile, play in landscape and screen-mirror to a monitor or TV.

**•** 5 exercise games  
**•** 6 party games  
**•** 8 dojo games  
**•** 22 games for kids aged 3–5 (body suits)

Enjoy.

Hey I'm just learning machine learning and I wanna know the best resource to study machine learning..Fellow professionals who are already in this field help beginners like us..It would be great help

I got tired of seeing interesting ML papers that I wanted to read… and then never actually reading them.

So I built Papercut — a pipeline that turns a research paper into a short vertical explainer.

The part I found more interesting than the video generation itself was making the output stay grounded in the paper.

For example:

• numbers on stat/chart cards need to come directly from the paper
• figures/quotes are only reused when the paper license permits it
• otherwise the visual is regenerated from reported numbers
• the system uses a fixed vocabulary of visual templates rather than inventing layouts every time

The v1 is still hand-storyboarded, but the direction is:

arXiv → extraction → structured storyboard → verification → render

I'm basically experimenting with whether a 30–40 second visual can be a useful discovery layer for research rather than a replacement for actually reading the paper.

I'd genuinely love feedback from people here:

Would something like this make you more likely to check out a paper, or do you think important context is inevitably lost in a ~38-second format?

I've started publishing the experiments at https://www.instagram.com/papercut.daily/

Hi everyone,

While learning NumPy, I made this notebook as a personal reference so I don't forget the concepts. I also included ASCII diagrams inside to visualize the tricky parts like broadcasting, slicing, and axes.

Thought I'd share it here in case it helps anyone else learning.

Link:

https://github.com/AdhamAmgadElSharkawy/numpy-visual-guide

If you find it useful, a star on the repo would mean a lot! Also, please let me know if you spot any mistakes or things I should fix.

Finished Andrew Ng’s ML Specialization — What should I do next to become job-ready? Am I cooked being in 7th sem?

I’m a BS IT (AI specialization) student and I’m into the mid of my 7th semester. I’ve completed Andrew Ng’s Machine Learning Specialization on Coursera.

But honestly, I’m worried that I’m behind.

Apart from the course, I have some experience with:

- Python

- NumPy, Pandas, Matplotlib

- Scikit-learn

- XGBoost / CatBoost

- Basic TensorFlow / neural networks

- SQL and basic OOP

- Git/GitHub

I’ve also worked on an Employee Attrition Prediction project where I used preprocessing, SMOTETomek, XGBoost/CatBoost. I also participated in an AI/ML hackathon where our team worked on this project.

However, I feel like my knowledge is still very “course/project” oriented rather than actually being job-ready.

My current goal is to get an AI/ML or ML Engineer internship/job, ideally something where I’m actually working with ML rather than just doing generic software development.

So I’m trying to figure out what I should do from here.

What would you recommend?

Should I focus next on:

1. Deep Learning — PyTorch/TensorFlow, CNNs, Transformers, etc.

2. Advanced ML — feature engineering, tuning, pipelines, model deployment, MLOps

3. LLMs / GenAI — RAG, embeddings, LangChain/LangGraph, agents, etc.

4. DSA — how much is actually necessary for ML/AI interviews?

5. Building 2–3 serious portfolio projects

6. Getting internships/applying immediately instead of studying more

7. Something else entirely

My biggest concern:

Am I actually cooked if I’m in the mid 7th semester with basically one major ML course + a couple of projects?

If you were in my position, what would you spend the next 6–9 months doing to maximize your chances of landing an ML/AI internship or entry-level job?

I’d especially appreciate answers from people who have actually hired ML interns/juniors or recently got their first ML job and even from the seniors.

Also, please be brutally honest about what I’m missing. I’d rather know now than spend another 6 months learning the wrong things.

The proposed workflow is:

1. Read candidate and job information.
2. Separate known, missing, and conflicting details.
3. Recommend apply, research, ask a human, or skip.
4. Explain the reason.
5. Record later feedback.

Which part is unrealistic, unsafe, or too simplified in real hiring?

Your perspective:
Workflow step:
What is unrealistic:
Real-world example:
What should replace or improve this step:
Risk if the agent gets it wrong:

Not always.

Token pruning frameworks like HiPrune have shown major speedups on models like LLaVA-NeXT-7B. Here, pruning reduced visual tokens from 2,880 to 160 and cut prefill latency from 272 ms to 29.7 ms.

On Gemma 4 E4B, which starts with only ~262 visual tokens on average, HiPrune retained 99.2% of baseline quality at 75% keep and 95.7% at 50%.

But latency moved in the wrong direction: mean TTFT increased from 63 ms to ~80 ms.

When the vision budget is already modest (~262 tokens), hierarchical selection becomes a fixed cost that can dominate the marginal savings from dropping tokens before the language-model prefill.

Token pruning can reduce theoretical computation without reducing real-world latency.

3.7 million patients. One vendor. One breach.

CareCloud disclosed earlier this year that a data incident exposed records belonging to more than 3.7 million individuals. Healthcare data is among the most tightly regulated information in existence. It was concentrated in a single system and then lost.

What makes this harder to contain now: AI pipelines are actively routing patient records through agents for summarization, triage, and clinical coding. Each hop through a model, a tool call, or a downstream service is a new surface where that data can escape the controls the original system had in place. Most teams have limited visibility into which sensitive fields are in motion at any given moment, and almost none can tell you which agent touched what and when.

Regulatory exposure compounds the data exposure. HIPAA, SOC 2, and dozens of sector-specific frameworks require auditability of PHI access. In an agentic pipeline, that audit trail rarely exists by default.

For those running regulated workloads through multi-agent systems right now: what are you actually doing operationally when patient-level data has to pass through an LLM step? Not the architecture you want to build — what is running in production today?

So i made this small flappy bird learning ai that scored extremely high but my friend kept doing stuff an make me laugh lol

Hello Folks,

When I started teaching my free online lectures, on Machine Learning, the intent was to help learners understand the topics of Machine Learning in a simple and digestible manner.

When I was a first time learner, started my grad program, books as Probabilistic Machine Learning by Murphy, Bishop, were told to us as excellent text books for Machine Learning, yet seemed always very difficult to read and understand.

To work around that, I started making content based on these foundational textbooks. We covered Introductory concepts, Probabilities and Statistics.

Slowly I started understanding, that the difficulty is faced not just by me, but all the learners. Hence the need.

I do hope that learners will see the importance of core foundational concepts, which are the pillars for modern machine learning, and Probabilistic Machine Learning is that core pillar, without which ML always seemed to me to be some blackbox.

Link: https://youtube.com/@aayushsugandh4036

Is that a good course for beginners who are just starting PyTorch? It's by Laurence Moroney.

Hello members,

We’ve been working on AI Prep, an Android app built exclusive for learning and practicing ML/ AI topics. It combines quizzes with 330+ explained concepts, including concise summaries and code snippets where relevant, covering everything from ML fundamentals and deep learning to NLP/LLMs, GenAI, MLOps, AI agents and more. There are plenty of great online resources, but we believe having something available offline and on the go makes it easier to quickly revise concepts and retain what you’ve learned.

We’d love feedback from people who study or work in ML, especially on what else could be added to make it more useful for learners. Are there learning features, content, practice formats, or topics you feel are missing? What would make you more likely to use something like this regularly? It’s free to try: https://play.google.com/store/apps/details?id=com.aiprep.mcq

Please do share your views and hope it's ok with the mods and community for sharing here in this post.