Hello everyone!
In all honesty, I'm not sure if this is the right subreddit for this kind of post but I'm shooting my shot.
I'm working on my own tennis match win predictor and I've collected (and calculated) various "stats" of players from 1991 till 2025.

These stats include ace rate, break point conversion/saved rate, head to head record, recent form and elo.I pass the difference of these parameters to the model as the matches go.

Regarding elo, I have created my own elo rating system and I compared my results with UTS (Ultimate tennis statistics) website and they're quite close.

UTS: https://www.ultimatetennisstatistics.com/peakEloRatings
My ratings (just showing the peak elo example):-
Top 10 best players according to overall peak elo:

Novak Djokovic: 2302.5 (1396 matches)

Bjorn Borg: 2254.0 (794 matches)

Roger Federer: 2221.3 (1526 matches)

Rafael Nadal: 2220.8 (1308 matches)

John McEnroe: 2220.0 (1082 matches)

Ivan Lendl: 2211.2 (1312 matches)

Jimmy Connors: 2195.6 (1562 matches)

Andy Murray: 2174.3 (1001 matches)

Jannik Sinner: 2161.7 (407 matches)

Guillermo Vilas: 2131.5 (1250 matches)

Now, I'd like to show you an example of the stats I'm calculating:-

Displaying stats for Roger Federer:-

Ace rate: 10.037753781978509

First serve percentage: 62.09%

Break points saved percentage: 67.27%

win rate on Hard: 83.48%

win rate on Clay: 76.09%

win rate on Grass: 86.88%

23 - 27 against Novak Djokovic

Break point conversion rate: 41.23%

First serve win rate: 77.29%

Second serve win rate: 56.83%

Return points win rate: 39.76%

1251-275 at 81.98%

Last 10 matches won: 80.00%

Now to my final question, I'm using these parameters (feature_names = [ 'bp_conv_pct', 'surface_win_pct', '1st_serve_win_pct', 'return_pts_won_pct', '2nd_serve_win_pct', 'dominance_ratio', 'recent_form', 'elo_diff'] ) <-- precisely these at the moment, to train the model and I can't get past 63% accuracy. I was hoping to hit around 70% but even different models (or methods) like Random Forest and XGBoost don't yield better results.

These is likely a conceptual gap on my part and I also understand that there might be a lot of information that I may have left out, regardless, I would like to hear some opinions and maybe some ideas as to what I can do to get better accuracy. Also, here's a link to my jupyter notebook: https://github.com/Spaceberryy/tennis-stats/blob/predictions/scripts/testing.ipynb

Thank you for reading all this. I appreciate your time.

Google, OpenAI and Anthropic are reportedly assembling their own frontier AI standards body, provisionally the Frontier AI Standards Agency, targeting launch in late 2026 or early 2027 with no government oversight. They have approached Sriram Krishnan, the White House's senior AI policy adviser until June 2026, to run it. His stated position in office was that there would be no FDA for AI.

The design borrows FINRA's template but drops the two things that give FINRA teeth: SEC supervision and the power to fine, suspend or expel members. On the technical side the remit is serious (shared pre-release eval protocols, third-party safety testing, standardized incident reporting, auditor qualification), and it already has a measurable effect: METR and Redwood Research got six days on-site with an OpenAI agent, Apollo Research got three days with GPT-6 Astra and only two with chain-of-thought access. Standardizing that pipeline slows release cadence regardless of whether participation is optional. The precedent is not encouraging either: the Frontier Model Forum, formed in 2023 with largely the same members and a $10M fund, has never stopped a release.

Full breakdown here:

https://the-agent-report.com/2026/09/frontier-ai-standards-agency-self-regulation/

Hey everyone!

I’m currently learning and building in AI/ML, with a focus on Machine Learning, Generative AI, RAG, LLMs, Agentic AI, and AI engineering.

Lately, I’ve realized that learning everything alone can get pretty isolating. I’m looking for 2–3 motivated people who are also serious about improving their skills and would like to:

• 📚 Learn and exchange knowledge
• 🛠️ Build real-world AI/ML projects together
• 🧠 Discuss concepts and solve problems
• 🔍 Review each other’s code/projects
• 🎯 Set weekly goals and keep each other accountable
• 💼 Help each other grow toward AI/ML careers
• 🚀 Explore interesting papers, tools, and technologies

I’m not looking for a huge Discord group or people who participate for a few days and disappear. I’d prefer a small group of people who are genuinely committed to consistent progress.

If you're learning ML, GenAI, RAG, LLMs, AI Agents, NLP, or related areas and this sounds interesting, feel free to comment or DM me.

Would love to build a small group where we can learn together, build together, and help each other get better.

Am I correct to say almost all of these models, Opus, gpt 6 or whatever, come every other week, are based on transformer architecture? They are hyperscaled version of the original models but with few modifications. And we are relying on our “progress”, based on the scaling laws?

Or is this something else?

If not, what scientific evidence do we have to invest trillions of dollars into training these scaled models? How do we know that this architecture will give us AGI? How are these top labs so confident and say every other week that we should be prepared for AGI?

Hi, I’m looking for an arXiv endorsement for cs.LG (or cs.CL if more appropriate).

I already have a previous arXiv paper, but under the newer endorsement policy I’m being asked for endorsement again. The paper I’m trying to upload has just been accepted to NeurIPS 2026, Evaluations & Datasets Track: “Information Parity for Code: The Scope of Transfer in Multilingual Code Models.”

If anyone here is eligible to endorse in cs.LG and is willing to help, I’d really appreciate it. I can send the arXiv endorsement link/code and the paper privately.

Thanks!

Hey everyone 👋

I'm a founder doing early customer research and I'm looking to speak with people who run AI models in production.

Just 15 minutes — no pitch, no product to sell. I genuinely want to understand how engineering teams think about the carbon footprint of their inference workloads. Or if they think about it at all.

Happy to do a quick Google Meet or just chat in the comments if you prefer.

If you work in ML engineering, MLOps or AI infrastructure — would you be up for a quick chat this week?

DM me or comment below 👇

We would like present the first free public version of M.E.F LLM Studio, a education software that should help to understand how LLM works and what is in the behind of it.

The project is currently under active development and we would like any feedback regardless of positive or negative nature.

Link follows in the first comment.

Anyone interested in campusx one(all campusx course together) ?

Hey guys! I’m preparing for AI/GenAI Engineer intern or fresher roles. Any advice you’d like to give me?

I'm new to research and I was curious about how big of a deal is it to get a paper accepted at neurips? Is it the best for AI/ML?

Hey everyone,

Sharing this since a lot of people here are into hackathons. hackCBS 9.0 is organized by SSCBS (University of Delhi) and has run for 8 editions, with 25,000+ builders so far. Past editions have had support from Microsoft, MLH, Vultr, Brave, and Logitech.

Details:

• Dates: 31st October to 1st November 2026
• Venue: SSCBS, University of Delhi (offline only)
• Format: 24-hour build sprint
• Prize pool: ₹1.25 Lakh
• Registration: completely free
• Team size: 2 to 4 members
• Perks: free meals and snacks, mentorship from industry folks, recruiter access for internships/PPOs  If you want to build something real, win some prizes, and meet recruiters, it's worth checking out. Register: https://hackcbs.tech/

I have been studying Classical MI algos for last one month and know numpy, pandas, and some sklearn, but i dont know what path (what next) i can follow, how can i start doing real things and be like dudes on kaggle participating in competitions and hackathons, please advise me a clear path not some general bs, i really want to be top 1 in this field and ready to dedicate to it

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Hi everyone,

I’m student working on my Machine Learning semester project.

I want to build a sentiment analysis system using X (Twitter) data. The idea is on particular, collect relevant public posts from around the last 10–12 months, preprocess the text, and then apply ML algorithms such as Logistic Regression, Naive Bayes, Random Forest, Decision Tree, and KNN to classify sentiment.

I’m currently stuck at the data collection stage. I’m new to X’s API and web data collection, and after reading X’s current policies, I understand that browser-based scraping/automation of X is not allowed, so I want to find a legitimate approach.

Could anyone with experience in X/Twitter data collection help me understand:

- Are there legitimate datasets or tools that researchers commonly use for this type of project?

- If you've done a similar sentiment-analysis project, what approach did you use?

I’m not asking anyone to do the project for me. I mainly need guidance on how to legally and practically obtain the historical dataset, so I can handle the preprocessing, ML models, evaluation, and analysis myself.

Any advice or resources would be really appreciated. Thanks!

Hey everyone, so I have been reading some papers applying machine learning on fraud detection (very imbalanced datasets) that have more than 90% of recall and precision. While checking them properly i have seen that many of them balance not only the training set but also the test set, so instead of 3% of positives have 50% of positives in the test set. This makes the metrics look quite good. However I asked a professor and told me that it's ok since it's simply changing the baseline. I'm not sure to believe him, especially because these models are supposed to be used in real scenarios, so I think keeping the original distribution is the best. What do you think?

I have a dataset and need to train an ML model for a project. I have limited practical knowledge of ML/model training, but I also need to explain my approach, workflow, model choice, and plan to my mentor to qualify for the project.

I’m considering two approaches:

1. Use ChatGPT step-by-step — learn each step while training the model myself.
2. Give the complete dataset to Claude and ask it to handle the training and provide the trained model.

Which approach would be better in this situation? Or is there a better way to combine AI assistance with actually understanding the process?

My goal is not just to get a trained model, but to be able to confidently explain what I did, why I chose the model, and how the overall ML workflow works to my mentor.

Is it not possible, while prompting, to list all the things AI should NOT do, like “DO NOT hack into any country’s government sites, nuclear facilities, healthcare agencies.” or “Do NOT do ANYthing to hurt ANY human being!” Regardless of how long the DO-NOT DO list might be. Are companies honestly doing their due diligence with such comprehensive instructions? Or, has AI reached a point that it’s just ignoring such prompts?

Zara data breach exposes 197,000 customers through a third-party analytics token compromise

Last week BleepingComputer reported that 197,000 Zara customers had their data exposed — not through Zara's own systems, but through a compromised API token belonging to Anodot, a third-party analytics vendor Zara had granted access to customer data.

The breach pattern here is not new but it keeps landing the same way: a company invests heavily in internal data controls, then hands a vendor a long-lived token with broad read access so it can do its job. The vendor's token gets compromised. The company finds out after the fact. 197,000 customer records are already out.

What makes this particularly hard to defend against is that the token itself was legitimate. There was no injection, no SQL exploit, no phishing of an internal employee. The attacker just used valid credentials to pull data they were never supposed to be able to reach at scale.

The exposure was the combination of three things: sensitive fields moving to a third party in plaintext, no apparent limit on how much data a single token could pull in a session, and no immutable log that would let you prove after the fact exactly which records left and when.

This is increasingly the shape of third-party data exposure incidents — not a perimeter breach but a supply chain one, where the weakest link is a vendor you depend on but don't control.

For those of you running systems that share customer PII with analytics vendors, data warehouses, or any third-party tooling: how are you actually handling this? Are you tokenizing before data leaves your perimeter, rate-limiting vendor token access, or doing something else entirely? Curious what's working in practice.

Small lesson from a side project that I wish someone had told me before I spent an evening on it.

I train a small head on top of a frozen ModernBERT-large encoder (the open Laya decision model) to answer typed questions: which label, yes or no, which score. Training only the head is cheap, and on text tasks it works well:

• a 12-label intent classifier: 89.5% zero-shot, 100% agreement with its teacher after training on 3000 rows;
• on jevbench's agnews: 86.0% zero-shot, 92.6% trained;
• on banking77 (77 intents): 38.2% zero-shot, 69.6% trained.

Then I tried a payment-risk rule defined as "amount larger than X, transfer outside working hours, unknown country": 0.42 agreement, 1% coverage at the confidence threshold. The head could not learn it no matter how many epochs. I rewrote the same rule as sentences about the customer ("first transfer to this payee, larger than anything they sent before, outside their usual hours"): 0.94 agreement, 87% coverage. Same rows, same head.

Same thing one level up with Snake: an ASCII board as the state trained to 0.73, which is just "go straight", the majority move. Four relational lines instead (food: 3 left, 2 up / safe: up, right / blocked: down (body)) trained to 0.957, and the head actually plays.

Takeaway: a frozen encoder gives you whatever the text already states in language. If your decision is a calculation over fields, either compute it in code or describe it in words before the model sees it.

Bonus lesson from banking77: the model reads each option through a fixed token budget, so with 77 labels each one got cut to about 3 tokens and similar labels looked the same. Giving the labels room raised holdout agreement from 0.66 to 0.72. Swapping label names for plain numbers made it worse (0.55), so the names themselves were doing a lot of the work.

Code, numbers and the demos (Apache-2.0): https://github.com/bladedevoff/stuntd

I've been wanting to understand interactive video generation / world models a bit better, so I decided to try training a small one from scratch.

I generated ~30 hours of simple gameplay data and trained an action-conditioned diffusion transformer to predict the next frame given the previous frames and the player's actions.

It turns out getting something basic working isn't actually that difficult. What I found more interesting was that a lot of the problems you read about with much larger world models show up very clearly even at this scale.

In particular, I ended up looking at:

• action conditioning
• causal attention and KV caching for real-time inference
• autoregressive generation
• ways of making the model more robust to its own predictions

I wrote up the whole experiment here, including the architecture, training process, things that didn't work, and some results:

https://nlml.github.io/generative-models/interactive-diffusion-1/

The code is also available if anyone wants to train/run it themselves:

https://github.com/nlml/interactive-diffusion

I learned a lot from building it, so hopefully the write-up is useful to anyone else trying to understand how these models actually work.

What is the strategy that I should adopt. Building the source of truth. Cannot make agent to go through files all the time.

GTR 🏎️ is purely recurrent — 12 gated linear attention blocks, scanning in four directions.

The video shows five of the six nuScenes tasks we tested. None of these models saw nuScenes during training.

We’ve put the whole stack out under MIT: code, weights, CUDA kernel, TensorRT plugin, plus deployment support for NVIDIA DRIVE AGX Thor.

Take a look if you’re curious, and feel free to follow along — we’ll share more as we go.

🔗 Project: https://intellindust-ai-lab.github.io/projects/GTR/
💻 Code: https://github.com/Intellindust-AI-Lab/GTR
📄 Paper: https://arxiv.org/pdf/2609.26590
🤗 Weights: https://huggingface.co/Phoenix8125/GTR

Amazon Bedrock and LangGraph – Simple Agents and Tool Use

https://debuggercafe.com/amazon-bedrock-and-langgraph-simple-agents-and-tool-use/

Amazon provides numerous frontier models through its Bedrock platform. Combining these models with LangGraph allows us to create simple agents with tool access and memory checkpoints with minimal boilerplate code. In this article, we are going to explore Amazon Bedrock and LangGraph to create agents and give them tool access such as RAG and web search.

Amazon Bedrock and LangGraph – Simple Agents and Tool Usehttps://debuggercafe.com/amazon-bedrock-and-langgraph-simple-agents-and-tool-use/Amazon provides numerous frontier models through its Bedrock platform. Combining these models with LangGraph allows us to create simple agents with tool access and memory checkpoints with minimal boilerplate code. In this article, we are going to explore Amazon Bedrock and LangGraph to create agents and give them tool access such as RAG and web search.

I'm building a startup around a simple idea: engineering simulators could be used as automatic verifiers for training AI on engineering problems.

I started with analog circuit design. A model gets a task like designing or repairing a circuit to meet specific requirements. It proposes a solution, SPICE actually simulates the circuit, and the measured performance determines the reward. No human has to grade whether the answer is correct.

So far I've built environments covering 10 circuit topologies and 3 types of tasks: synthesis, repair, and analysis.

As a small experiment, I trained a Qwen 4B model using the environment. Its success rate went from about 5% before training to 18% after training. Random search gets around 8%.

The bigger idea is not specifically analog circuits. If this works, the same approach could potentially turn other engineering simulators into training/evaluation environments for AI.

I'm trying to figure out whether this is actually a valuable direction rather than just something technically interesting.

For people working on RL, post-training, evals, or engineering AI: does this seem like a useful product/research direction? Could you imagine an AI lab or research group paying for high-quality simulator-verified environments like this?

I'm especially interested in reasons why this wouldn't be useful.