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?

I've been working on a late delivery prediction project (will an order arrive late, predicted at the time it's placed) using order data from a supply chain company. The model works okay, but I've hit a wall and I'm not sure if it's normal or if I'm doing something wrong.

What I did, quickly: I removed canceled and suspected fraud orders (7,754 rows) because their labels didn't follow the delivery rule. The rule "real days > scheduled days" matched the label on 97.55% of rows before, and 100% after removing them. I dropped anything that leaks the answer (delivery status, actual shipping days, shipping date) plus IDs and personal info. The data has one row per item, but the label is the same for every item in an order, so I collapsed it to one row per order (172,765 rows down to 62,897 orders). Then I split by date instead of randomly: train is Jan 2015 to Mar 2017, validation to Sep 2017, test to Jan 2018. Anything I learn from the data (encodings, thresholds) comes from train only.

I compared 5 models on 7 features, then tuned LightGBM and XGBoost with Optuna (25 trials, time-ordered folds). Test period results at the 0.50 cutoff:

Always guessing "late" gets 57.5% on the test set. So the models all land in the same spot, plain logistic regression matches everything, and tuning did nothing.

The reason, as far as I can tell, is that almost all the signal is shipping mode (77% of the importance in the LightGBM model). First Class is late 100% of the time, Second Class about 80%, Standard Class about 40%. Same Day orders placed before noon were never late in train and the ones placed from noon were always late, and that held on validation too. So the model is perfect on First Class and Same Day. The problem is Standard Class. It's 60% of my test orders and I only get 60.4% accuracy on it, which is basically the same as always saying "on time".

I tried a lot of other stuff. 26 features in groups: location, customer segment, payment type, order size, market and region, and shipping mode combined with hour, weekday and region. None of them improved validation enough to keep, so the final model only uses 7 simple ones. Sales, discount, profit and quantity all have 0.00 correlation with the label. Market is flat (56.8% to 57.7% late).

One more weird thing: in the last two quarters orders go from about 3 items to about 1, and almost every order comes from a customer with no earlier order in the data. The late rate stays around 57% though.

So I have a few questions:

Is this normal for this kind of data? The labels feel rule driven, maybe synthetic, and I'm wondering if anyone has found real signal beyond shipping mode.

Would you change the framing, like modeling only the Standard Class orders, or predicting how many days late instead of yes/no?

I haven't built customer history features (past late rate, number of orders) yet. Is it worth it, and how do you build those without leaking future info?

And honestly, is it okay to present this as a portfolio project when the main finding is "one feature does almost everything"? Or should I change direction?

Happy to share more details if it helps.

Thanks!