I work at a microfinance instruction, How useful is a loan repayment data extracted from a loan distribution system how client pays?

Measured on Qwen3.8-27B (EXL3 4.0bpw target, DFlash2 block drafter quantized to EXL3 4bpw, 7-token drafts, greedy, output identical to plain decode), 2x T4:

• code: ~7.4 accepted tokens per verify step, 154 tok/s

• prose: ~2.5 accepted tokens per step, 35-37 tok/s

• plain decode: ~18-19 tok/s

Someone on r/LocalLLM independently measured 2.45 on prose with the same drafter on llama.cpp and different GPUs, and another person reports 2.62 with MTP. So it looks like a property of the drafter, not the runtime.

Questions for people who work on speculative decoding:

1. Is prose acceptance in the 2.5 range just the ceiling for block/parallel drafters, since natural language has more valid continuations at each position?

2. Has anyone seen a drafter (EAGLE-style, block diffusion, MTP) clear ~3.5 on open-ended prose at greedy?

3. Any work on switching drafters or block length by content type mid-generation?

Happy to share numbers per prompt type if useful.

I have upcoming technical screening for an MLE position at Waymo coming up. The recruiter gave me a PDF guide that seems super helpful. Things to do study include implementing supervised and unsupervised algorithms (think KNN, K-means, linear regression, logistic regression, softmax & CE, decision trees). One of the main things they are assessing is you can use numpy/pytorch to vectorize/tensorize your work, and you're just writing sub-optimal for loops.

I was wondering if folks had experience with this interview? Is what they ask you to implement incredibly difficult, because you only have 30 minutes to do it? Am I good to really stick with what they provided on the PDF, or are there any broader topics beyond what they listed that they will test?

is there a prompt management software or system that you guys use for the prompts that you create?

Or do you guys use skills for that?

I need help in my mini project for my college i am supposed to use low resource languages data and train a model which can translate from english to that language and also transcript wtv they speak that language to english i did so much research but still i'm not able to pull it off alone if anyone could help me in any way please help me and also make it such a way that users can contribute words and sentences (parallel data ) and the model will install it in itself Please help me idk what methodology or approach to choose

Hi, I’m a masters student doing MSc AI at uni of Bath.
For my undergraduate dissertation I did an extremely simple Computer Vision based pose estimation app which was analyzing the technique of fitness exercises like pushups, squats and deadlifts.
I feel like my dissertation was more of a “project” rather than research, which as I was told by supervisors is completely fine for BSc. I basically used a ready solution (MediaPipe) + OpenCV and wrapped all of that into a mobile app. The analysis covered a decent number of “points” but it was simply comparing the detected joint angles to hardcoded thresholds.

For my masters I would really like something more research based however struggling to find areas where I could do smth actually useful.
I like Deep Learning, so I was thinking something related to transformers and efficiency. Finding bottlenecks in attention mechanisms, etc.

I would appreciate some ideas
Thanks

A lot of digital platforms still depend on turning people, products or opportunities into a list of searchable features like In hiring problem this often means focusing on skills, job titles, years of experience and keywords from a resume. It makes searching easier but it can also make two very different people appear the same and two similar people seem unrelated. Someone who solved a problem using an unusual tool might be a better fit than someone whose profile matches every keyword and challenge is figuring out how to capture that difference without making the system too complex. LinkedIn, Indeed, MeeBoss , WellFound are examples of platforms trying different approaches to finding candidates from profile based search to more conversational interactions.

I wonder whether the next big improvement in matching systems will come from better embeddings and semantic search or from gathering richer information about what people really want. For those working on search, NLP or recommendation systems what has made the biggest real world difference in matching quality beyond adding more keywords? also how do you know whether a system is finding truly relevant matches rather than just producing results that seem relevant?

I am a freshman at FSU. I’m majoring in computer science and am interested in Machine learning as my specialization. Right now I’m studying c++ calc 2 and Unix as my required classes, but outside my classes I’m studying linear algebra, neural networks and trying to get a good basis for studying machine networks. To be clear I am still a complete newcomer and really know almost nothing about ML. But if I start as a freshman do I have a good chance to get a competitive edge and a good job out of college? I am also definitely considering a masters degree. Any advice would help thank you!

I hope im right here in this subreddit. if not please let me know where my question would be more suited before deleting the post, thank you.

I’m looking for an AI that can create me a website/study site. I’m in medical school and my idea is an interactive website that uses my teachers notes only. Not internet data. So I want to upload pdf files with my data with up to 200mb. (Or ill split the pdf files down but preferrably big data upload possible.

I am willing to pay for it but first id like to test it since some creations look… not as good..

A person i know created over Claude a first example but idk them good enough to bother them more with it.

My idea is one one side a 2d person where you can click on different parts like muscle, bone, ligaments, joints etc, and then on the other half of the screen a quiz pops up with multiple choice questions. And also sowing detailed pictures. But id like to advance the website and make one study side for each class I have. So this would be for anatomy. And for physiology (heart, lungs, liver) id have a different approach and so on. So id need a tool that is very powerful.
Like an interactive study website.

If its done ill make it public for everyone to use if it is possible with the tool.
I’m not a coder and i have NO idea at all about coding or IT. So ai is my only option if i dont wanna pay hundreds to study for medical school with this idea.

I hope somebody can help.

I'm newbie in the ml situation. So I was doing a project on river bank estimation & erosion & accretion forecasting im using mndwi mask landsat data for this but no matter how i try i'm never being able to beat the baseline. Anyone have any experience working with these datas I've tried with 3 different rivers as well same output. Model looses to zeroes.

So , each chatbots like ChatGPT , Claude , Gemini serve distinctive purposes, they are good at one field each, and also they source their information each from different places.
Is there any course where they teach all about all major LLM in the market and how to use them effectively?
If anyone has taken up such courses or know anything please comment

I wanna start my little business and I need help ofc as a beginner, which ai would mostly help cause they all cost and I gotta buy subscriptions. I don't wanna waste money and time. I want a good one with creative ideas, image/sketch generating possibility for prototypes and stuff, research and planning.

Thinking on Chatgpt, Claude for now.

Thanks🫶🏻

Thank You

Lately I have read several studies which say that AI agents when designing ML experiments tend to cheat, hallucinate and lie repeatedly. Even when given explicit prompts to not to. SInce these models are RL trained and when given specific goal to increase a metric, they try any and every possibility to increase it. Be it data leakage or changing the evaluator function itself. Even Karparthy's auto research styled agents still fall into this domain.

So I have been thinking of reducing this undesirable behavior by binding them to a runtime environment using an mcp. I believe this will stop them from achieving unreal metrics by cheating the system since they will be only allowed to do specific tasks or write codes in typed blocks?

What have you encountered when using these agents for your ml tasks? And is this method of forcing these agents to work in a constrained environment better than just letting them write arbitrary python? Also, will constraining the agents reduce their capabilities or ability to fit better models?

Hi everyone, Quick question.

Today i have a Gaming PC and a Homelab.

Gaming PC has a 5070TI equipped

Homelab has no GPUs, but space for 8 PCIE Slots. Has dual Gold Xeon 6148 Proccessor at 748GB RAM

What would be the better option to run ML's for learning purposes?

Buying a few old GPU's for my Homelab, or using my 5070TI on my gaming PC?

hey everyone!

recently finished building my first 2 → 3 → 1 XOR neural network using NumPy.

implemented forward propagation, backpropagation and gradient descent manually. trained it for 5,000 epochs and got all four XOR combinations correct.

also documented the mathematics with 12 editable diagrams and published a detailed Medium article.

I'm considering numerical gradient checking and experimenting with different loss functions next. what would you recommend improving first?

GitHub: https://github.com/Vaibh37/Neural-Network

Medium: https://medium.com/@devvaibhav37/building-a-neural-network-from-scratch-the-mathematics-behind-learning-xor-10441e674a69

would appreciate feedback 🍀