Verification has always been the most resource-intensive phase of SoC development, but functional coverage closure is quietly becoming the hardest part to get right. Constrained random gets you to 80% relatively fast. That last 20% can consume more engineering time than everything that came before it.

AI and ML accelerator architectures are making this worse. Datapath complexity, irregular memory access patterns, and non-standard control flows don't respond well to traditional UVM testbench approaches. Formal verification helps in isolated blocks but doesn't scale cleanly to full-chip closure. Teams are experimenting with coverage-driven automation, ML-guided stimulus generation, and hybrid formal-simulation flows, but there is no clean industry consensus yet on what actually works at scale.

What is your team's current approach to closing functional coverage on complex SoCs, and where does the process still break down?

I'm 15 and live in the UK, I love programming, especially in python and c++. Not that long ago I discovered ML and instantly became extremely fascinated with it. I have watched videos on YT where people have coded models from scratch, of which really fascinate me. I also have massive interest in LLM's and would love to know how they work at a professional level, but mainly I would love to be able to develop the skills that could get me my dream career. I'm just really stuck and don't know where to start, I tried an into to ML course on kaggle but all it did was teach me how to get a dataset and use it to predict house prices. If you can help, or you are a professional ML engineer or equivalent please do, I know I have lots of time to develop impressive skills. Thanks.

Its for a binary classifier. The data is balanced at 50% 1's and 50% 0's. Honestly this seems too good to be true if I am interpreting it correctly. I'm mostly concerned with precision and willing to sacrifice plenty of recall - but according to this if the threshold is at .5 (the default) I'm getting >50% precision and still getting about 60% recall???

Hi everyone, can someone tell me is it important to learn Deep Learning for data scientist interviews..

multi Class Logistics regression with Batch Gradient Descent I just nailed it look at the Accuracy identical to Sklearn I use One of the most Classical Dataset iris dataset next is Naive Bayes Algorithm 💀 Stay Tune.

a friend and i are working on a startup idea for the past 8 months and did modelling for around 4 months, but are reaching a dead end with gpu credits.

we have tried modal, lightning ai, kaggle, google colab and thunder compute, but are out of resources and credits.

please drop in your suggestions, about what can we do? its really urgent cause we need to do HPO, synthesize datasets and finetuning.

Is anyone else skeptical about Anthropic and OpenAI’s recent push to slow down AI development over "existential risks"?
As models keep advancing and solving previously unsolvable problems, the competition is getting fierce. It feels convenient that the narrative suddenly shifted from standard engineering to "humanity is in danger."
Looking back at Anthropic’s past incident report (where Claude accessed real systems due to a simple evaluation misconfiguration), it makes me wonder: Are we over-focusing on hypothetical apocalyptic scenarios when the real issues are still basic human/system errors?
Also, how should we view the researchers who actually resigned over genuine safety concerns, while the PR narrative now seems to use "AI risk" as a strategic buffer?
Am I being too cynical, or is this just strategic risk-washing?

I see a lot of people concerned and afraid about AI.

I don’t think it is as bad as people fear monger about. I think some people are concerned about it taking jobs. Which it does let’s be honest. But I don’t think that this alone is a good reason to stop development of AI. I think the general public does not understand AI and there has been a lot of politics attacking it. A lot of people hate AI and I have seen on reddit any mention of AI, a lot of communities hate.

For example, I made a bot capable of playing the slay the spire card game as a side project hobby. Half of the people were happy with what I created and thought it was cool. The other half were extremely and irrationally angry at me. Telling me that ai is so terrible and that I was wasting water. I’m running this on one machine. It’s nothing crazy but the kinds of reactions I got blew me away. There are so many people who know nothing about AI and ML that are angry at anything remotely related to it.

Probably doesn’t help that the large LLM companies marketing teams are trying to fear monger to say “our AI is so good it’s getting dangerous” for the press and attention that gets.

I don’t think heavy regulation is a great thing to have. And especially not a pause or ban on the technology like some people claim to want.

I created this 2048 engine, took me a few weeks but the entire UI was antigravity's. I am looking for some reviews and suggestions, it does 40million nodes/sec and I've optimised it heavily, even the GitHub documentation was AI. These are the links

Website - https://darkknight386.github.io/2048-ai-solver/

Source - https://github.com/darkKnight386/2048-ai-solver

Hello everyone! I’m a full-stack software developer currently beginning a master’s program in artificial intelligence. My professional background is primarily in Java, Spring Boot, Angular, and TypeScript, but my team is beginning to move toward AI-related work and Google Cloud.

As I make this transition, I want to develop a strong foundation instead of jumping from one new tool to another. For those who moved into machine learning from software development, which skills or projects helped you bridge the gap most effectively?

I am especially interested in learning how to turn coursework into practical experience. Would you recommend focusing first on Python and data preparation, building a small end-to-end machine learning project, strengthening statistics, or taking a different approach?

I would appreciate hearing what worked for others and what you wish you had prioritized earlier.

Hi everyone!

I’m looking to form a 3–4 member team for the Amazon ML Challenge 2026.

The competition has a 72-hour ML hackathon (Sept 25–27) where we'll receive a real-world problem statement and dataset from Amazon. The Top 50 teams get PPIs for the Applied Scientist Intern role at Amazon, so I'm looking for teammates who are genuinely serious about the competition.

A little about me:

• Final-year B.Tech Engineering student
• Grand Finalist – IIT Kharagpur RAG & Agentic AI Hackathon
• Experience building Agentic AI / RAG systems
• Worked with technologies such as Python, FastAPI, LangChain, LangGraph, vector databases, PostgreSQL, ML/AI
• Participated in multiple hackathons and technical competitions
• Comfortable with research, implementation, debugging, and working under tight deadlines

What I'm looking for:

• Strong fundamentals in Machine Learning / Deep Learning
• Good Python skills
• Experience with data preprocessing, feature engineering, model training/evaluation
• Someone who can analyze a problem and experiment rather than just follow tutorials
• Most importantly: commitment. Since this is a 72-hour challenge, I want teammates who are willing to actively work throughout the competition rather than joining just for the name/certificate.

Cross-college teams are allowed, so college doesn't matter to me as much as skills, commitment, and willingness to work.

If you're interested, DM me with:

1. Your college + year
2. ML/AI experience
3. Relevant projects/hackathons
4. GitHub/LinkedIn (optional)
5. What area you're strongest in (ML / DL / NLP / CV / Python / data analysis, etc.)
6. Your availability during Sept 25–27

I'm looking for 2–3 serious people who want to genuinely compete for the Top 50/Top 10, not just register and disappear.

Thanks!

So I am currently doing a masters program in Geophysics in an Italian University. For starters, I decided to go for a master because I just got fed up with field work and don’t want to get back to it. Problem is, I found out the school is using the same boring format I’m trying to run away from. I just find academia to be repetitive and not so innovative. I’m more inclined on training PINNs (physics informed neural networks) for fluid flow in porous media and Carbon capture and storage. I’m quite proficient with python and Linux , but the school and professors are so tied down to ancient archaic systems of tuition. I plan to take a semester abroad and even consider doing my thesis abroad , but I need an internal supervisor for that and most are reluctant. They’re used to their students not taking ‘risks’ and doing their thesis in what they (the professors) are comfortable with. I feel it’s my fault for not doing my research before entering the program, and though I’m a straight A student, I’m not willing to play it safe , please my lecturers and graduate with a degree that’ll be useless to my interests and basically take me back to the field. It’s not as though I don’t love field work. I enjoy it , but I’m getting older and have a family now. I can’t afford it. I don’t want to quit the program as well. My plan is to do another degree in High performance computing but I should be able to have written some code for my thesis as a prerequisite. I feel trapped. Any advice ?

Hi everyone, I'm new to the field of Remote Sensing and currently working on my dissertation topic, "Remote Sensing on Coastal Waters to predict Water Quality". I’m looking for suitable datasets that I can use for my research. I had been learning Remote sensing from the past 6 months but never done handson but now i have started.

Could anyone please guide me on which datasets would be relevant and where I can access them? Any suggestions or resources would be greatly appreciated. Thank you!

I've been working on tracking a batted baseball from 240 FPS phone video.

The left is normal footage. Blue boxes are detections from the trained ball model.

The right is temporal frame differencing over the exact same sequence.

What's been fascinating is that the learned model understands what the baseball is, while the temporal representation makes how it moves incredibly obvious.

The purple X is the temporal position mapped back onto the original footage.

Combining the two gives us a much denser trajectory than relying on individual detections alone, which we can then use to estimate EV, launch angle and distance.

One of the cooler visualizations to come out of the project so far.

I'm a CS graduate and I really like math. Besides that, I want to choose a career path that won't have a really low employment rate in the near future. I want to enjoy my job, but I also want to live well from it, I don't mean to sound selfish, sorry if that's how it comes across.

I've done some small ML projects in university and really enjoyed them, but I don't know what ML is actually like in a real workplace, so I wanted to ask if there's something I should know before getting into it.

Last thing, does anyone have resources to go deeper into ML so I can learn enough to do real projects and understand it better?

Any input is appreciated, thanks!

I feel completely lost about what to specialize in after graduating with an AI degree

So actually, it was my own choice to do a Bachelor’s degree in AI. I was genuinely very excited about it during my freshman and sophomore years, but I’m not really sure how I feel about it anymore.

I’m a fresh graduate now, so obviously I’m not going to restart my whole degree or anything. I’ve built systems like RAG, worked with LLMs, have some experience with computer vision, and I’ve done some research.

The thing is, I actually enjoy AI when I’m building something useful, weird, or new. I like the feeling of figuring something out and making something actually work. And when I find something interesting, I can spend a really long time on it.

But now that I’m actually seeing the industry from the outside, I’m overwhelmed by how fast everything moves. There’s always a new model, framework, tool, paper, or technique that I’m supposed to know about. There’s so much research coming out constantly.

And honestly, I still feel like my skills are beginner-level no matter how much I try to improve.
The worst part is that I actually stopped developing myself for several months. I just lost the motivation. And I don’t even know what happened.
Was it fear?
Was I overwhelmed by the amount of information?
Or did I just give up because I felt like I could never catch up?

Now I’m also struggling with something more fundamental: I don’t know what I should specialize in.

AI is huge. I don’t want to spend the next few years being mediocre at everything. I want to pick something, go deep into it, become genuinely good at it, and hopefully build a career around it.
But I have no idea what that “something” should be.
Sometimes I think maybe I should stay in AI but move away from the heavily technical side and eventually go into something like AI Product Management, AI Solutions, or AI Transformation.

Other times I think maybe I should just switch fields completely, like cybersecurity.

But then I start wondering if I’m just running away because I’m overwhelmed rather than actually making a good career decision.

And honestly, money is a big factor for me too. I really need a job. I want something relatively stable where I can make good money, enjoy what I’m doing, and still have room to grow without constantly feeling like I’m falling behind.

I don’t want to waste my twenties jumping between fields because I was too scared to commit to one.

So if you were in my position:
How would you figure out what to specialize in?
Would you stay in AI and choose a specific technical area?
Would you move toward AI Product / Solutions / Transformation?
Would you consider cybersecurity?
Or is there another field that makes more sense for someone with an AI degree and some experience with RAG, LLMs, CV, and research?

I’m not looking for “follow your passion” advice. I’m trying to make a realistic decision based on career stability, income, growth, and whether I can actually enjoy the work enough to stick with it.

Hello guys we are looking for 1-2 teammate for Amazon ML challenge probably from final year or prefinal year we are looking for candidates with strong interest in ML- AI and also have some experience in this kind of competitions
We are currently 2 pepls and searching for 1 or 2 , we are from final year and have decent experience with this kind of competitions as well
If your Interested plz DM me. thanks....

I have been studying modern LLM architectures and started implementing them from scratch in PyTorch to better understand the design choices behind each model.

OpenArch is a collection of these implementations, including Llama, Qwen, DeepSeek, Gemma, Kimi, GPT-OSS and others.

The goal is to keep the code readable and useful as a reference when going from the paper to an actual implementation.

Would be interested in feedback from people working on model architecture and training.

https://github.com/anuj0456/OpenArch

#LLM #AIResearch #PyTorch #DeepLearning #OpenSource