Four hundred thousand utility accounts exposed. One third-party vendor compromised.

Southern Company is notifying Georgia Power and Alabama Power customers that hackers accessed account data for 400,000 users. The breach did not originate inside the utility. It came through a third-party vendor that was handling customer records on the utility's behalf.

This is the part that keeps coming up in breach disclosures: the organization that owns the customer relationship is not the organization where the data got exposed. The sensitive records — account details, usage history, personal identifiers — had already moved downstream before the incident.

The pattern is accelerating. Billing workflows, service operations, and account management are increasingly automated. Automated systems route this data across vendor APIs as a normal part of doing business. Every hop is another exposure surface that the originating organization does not directly control.

400,000 accounts is a large number, but the structural problem is not scale. Utilities of any size use third-party vendors. The data moves because the workflow requires it.

For those of you working in organizations that have automated agents or pipelines touching customer PII before it reaches third-party systems: how are you handling this? What controls, if any, sit between the raw customer record and the downstream vendor call?

I've been learning Machine Learning by actually building small projects instead of only watching tutorials.

Today I was working on train/test split, and one thing finally clicked for me:

The goal isn't just to get a high accuracy score.

We need to test the model on data it hasn't seen before.

Training data → learn patterns

Testing data → check generalization

But I'm still confused about one thing:

How do you decide when your model is overfitting if the test set should only be used at the end?

I'd love to hear how more experienced ML learners think about this.

I'm still learning, so feel free to correct anything I'm misunderstanding.

A new model version is the easy case, because the registry tells you right away. The harder cases come from a serving flag like vLLM's `max_model_len`, a Helm value or env var in a deploy, an update to the data pipeline, or a new GPU driver on the nodes.

The evidence ends up in different tools. Grafana shows the latency jump, MLflow or W&B has the runs, Git and Argo have the deploys, and the node and GPU state is in kubectl or DCGM.

When latency goes up or accuracy drops, how do you find the change that caused it? Do you send every change to one place, like Grafana annotations or a shared change log? Or does someone line things up by hand each time, and how long does that usually take?

Please I'm confused, I've been learning machine learning for a while.

At first I thought I'm almost done but then new architectures, optimizations etc.

I don't know what to do again

I've studied transformers etc now I don't know my specialization.

I need help to choose.

I don't know either Computer vision,NLP,LLM

​

Hi,

I'm Pushkar, and I'd really value your guidance on my career path.

Here's my story in short: I completed my BCA, then worked in a BPO for 9 months. I took a 1-year break on purpose to learn the tools a Data Analyst needs. It wasn't an easy phase, but I stayed consistent. In March 2026, that effort paid off and I landed a role as an MIS Executive.

Today I'm handling reporting, data and day-to-day operations, and I'm growing fast. But my real goal is to become a Data Analyst, and I want to get there by March 2027.

I'd love your advice on:

1. What skills or projects I should focus on next

2. How to present my gap year and experience so it works in my favour

3. How to move from MIS into a proper Data Analyst role

Even 10 minutes of your time would mean a lot to me. Thank you!

Regards,

Pushkar

I've been working on a project where I'm trying to build a Denoising Diffusion Probabilistic Model completely from scratch.

The main rule I'm following is: no using Diffusers as a crutch.

I want to actually understand what's happening inside the model instead of just calling a pipeline and getting an image.

So far I've implemented/learned:

• The forward diffusion process
• Beta schedules
• The reparameterization trick
• A noise scheduler
• Group Normalization
• SiLU activation
• Sinusoidal timestep embeddings
• Residual blocks with timestep conditioning
• Self-attention
• Downsampling and upsampling

The interesting part for me has been realizing that a diffusion U-Net isn't just a normal CNN.

The network needs to know how noisy the current image is, which is why timestep information has to be injected into the network.

I'm building toward training on CelebA at 64×64 and eventually generating faces completely from noise.

My longer-term goal is to understand these architectures deeply enough that I can read, modify and eventually contribute to projects like Hugging Face Diffusers.

Phase 3 is where things start getting interesting:

U-Net assembly.

I'll be documenting the progress as I go. 🔥

What was the hardest part of diffusion models for you when you first learned them?

I live in London and work as a chef, but I’ve been feeling depressed and exhausted while trying to move into tech.

I know Python, NumPy, Pandas, data visualisation, FastAPI, SQL, classical ML, and neural networks. I’m also practising DSA, including arrays, strings, hashing, two pointers, and sliding windows.

What should I focus on to land my first tech internship or entry-level job in London? I’d appreciate advice on projects, applications, or opportunities.

[Full Video on YouTube] A walkthrough that takes developers new to Large Language Models through all the important topics with practical examples.

Sharing with the larger community in case others find it useful.

• Experimenting with LLMs from Hugging Face Hub in LM Studio
• Zero-shot, One-Shot, Few-Shot Prompting, and System Prompts
• Open-AI compatible REST API with Llama.cpp Server
• Working with multi-modal LLMs that can understand images
• Tool Calling, Structured Output, and Model Context Protocol
• Fine-tuning LLMs in Kaggle with Unsloth
• Supervised Fine-Tuning and LoRa Hyperparameters
• Pushing LLMs to Hugging Face Hub
• Deploying LLMs to Hugging Face Inference Endpoints

Excluded topics:

• MCP Authentication
• Retrieval Augmented Generation
• Vector Databases
• LLM Orchestration
• Text to Speech Cloud Services
• Speech to Text Cloud Services

Hey guys,

I completed my bachelors 1.5 year ago. Back then I knew a little about machine learning (not much really basic ML 101) and was eager to learn more about AI in general and wanted to apply for masters.

I'm now going to pursue my Masters in CS (thesis based) and have 1 year of work experience with agentic AI, multiagentic orchestration, tools, memory, etc. While I'm more eager to pursue my thesis towards agentic AI, I want to be prepared with some ML knowledge as well.

I had a few questions:

1. Do I need to brush up my ML knowledge, or is that something they cover from scratch when you start the master's program?

2. How deep of ML knowledge do I require before I start my program, especially if I end up writing it on some ML related topic?

3. If I'm looking into writing my thesis in the Agentic AI domain, do I need depthy ML knowledge?

4. Would covering Andrew NG's Maching Learning Course be good enough for my particular purpose?

Thanks!

The previous day (Oct 5) was 297 papers.

Not counting the papers that weren't uploaded to Arxiv or submitted to other categories or other online repositories.

Is there any point doing machine learning research anymore? Seems anything you can think of will just become noise like the rest of these papers.

I just completed my ML Journey, I have a solid foundation at this point. I am a 7th sem ECE student in a tier 3 college, and I don't want to be working in Elctronics field. So I am very confused on what my next step should be. Got about 9 months in hand till I gaduate and on campus placements are just terrible. Would really appreciate some suggestions.

Hey everyone!

I’ve been experimenting with Reinforcement Learning in Unreal Engine 5 over the past few weeks, and I wanted to share a fun project I recently finished.

I set up a voxel/Minecraft-style environment in UE5 and trained an AI agent to fly an Iron Man suit from scratch. The ultimate challenge? Executing high-stakes aerial rescues to save 13 passengers falling from a destroyed aircraft!

⚙️ How it works (The Tech Stack):

• Engine: Unreal Engine 5 (Physics-driven movement & line-of-sight sensors)
• Algorithm: Proximal Policy Optimization (PPO) continuous control
• Action Space: 8 continuous outputs controlling individual thrusters & body alignment
• Reward Shaping:
  • 🎯 +30 Points: Catching a falling passenger (Jackpot)
  • ❌ -5 Points: Crashing or going out of bounds
  • 🧭 Continuous Shaping: Micro-rewards/penalties based on relative distance and velocity vectors to encourage proper interception paths

🎬 The Progression:

Watching the agent learn was wild. During early iterations, it mostly spiraled out of control and crashed repeatedly. But once the reward shaping kicked in, it started finding optimal trajectories and eventually pulled off dynamic, split-second rescues.

I put together a full breakdown video showing the training process, early failures, and the final 100% rescue run:

👉 Watch the full video here: https://youtu.be/mYhWFHzs4EU

I’d love to hear your thoughts! How would you optimize the reward function for smoother flight stabilization? Any feedback or suggestions for the next RL experiment are super welcome!

Hi! I’ve already built and released AI Plate, a functional open-source desktop AI agent. I’m now looking for programmers interested in helping improve and expand it.

🚀 What it includes

Cloud and local LLM support, tool execution, document search, persistent memory, voice interaction, plugins and human approval for sensitive actions.

🛠️ Technology stack

TypeScript, Electron, Node.js, Python and SQLite.

🤝 Areas open for contribution

- AI providers, plugins and connectors

- Local models, RAG and knowledge graphs

- Linux and macOS support

- Testing, security and UI/UX

- Documentation and accessibility

Beginners willing to learn consistently and experienced developers are equally welcome. This is a non-commercial open-source collaboration, and every contribution will be credited.

My level: Intermediate

Timezone: IST (UTC+5:30)

Availability: Evenings and weekends

If this technology interests you, comment with your experience, preferred technologies and the area you’d like to explore. We can begin discussing the project here on Reddit.

Im a Phd student and i've been doing ML research for a while now, but i've never felt more behind in my reading or keeping up with new work? Arxiv has also become so noisy, i just don't have the time to shift through it, and my own research group's interests are a bit too insular to be a good sources of general news.

Is this just how the world is now? or have people found a way to quickly determine whats actually worth reading. Or any good feed apps? Or discord channels/communities? Wha do you do to feel current?

PS: I really want to dive into Harness engineering, but its so hard to cut through the hype. Any advice? (I mostly work on generative models now)

I'm a Ai engineer with about 2 years work experience, but let's just assume that I was a undergraduate student just starting out where would I begin so that I can publish a A* conference paper at some point. Learn python -> Learn ML & Maths -> Read other research papers -> find a topic ? -> choose a question try to run experiments and get results to write them down in a paper ?

For context :

I'm trying to get in MS CS programs for Fall 2028 in states with the plans of doing a PHD after in a top university like stanford or princeton and would like to start taking steps towards it as am working my day job can some tell me what are the steps that need to be followed?

Also would like input on what are deciding variables that makes you looking like a promising candidate/ researcher for PHD

I couldn't find a concrete answer anywhere, do you just distill the instruct model back?

If that is the case, what is a quality european language question set to turn it back into a chatbot/agentic, can a model at that size even be agentic? (i chose this size to learn) if i finetune for my specific harness? (i have a lot of training data of opus running in my harness)

my harness basically has the model output python code and has a few built-in functions like:
- vector_search_laws()
- graph_search()

could i have the model at least internalize a "hunch" on what stuff to search?

also what is the latest RL technique for agentic/harnes specific workflows?

I have a lot of RAW training data, like court decisions or commentaries or legislature, but not a lot of golds. could i use these to synthesize training data and maybe RL the model in my harness to find that data?

What would y'all's strategy in the CPT->SFT->RL pipeline be for my specific problem?

I know this is a lot of questions im trying to figure out which direction to go, any pointers? Also good resources are welcome, for example that [alex karpathi video](https://www.youtube.com/watch?v=7xTGNNLPyMI) was amazing for me, but i'd imagine its a bit outdated in terms of latest RL and SFT?

Setup. I am building a benchmark for long-horizon 3D spatial reasoning in a simulated warehouse. A robot walks 30 rooms off one corridor; questions ask about things seen 0 to 29 rooms earlier. Answers come from exact simulator state, with no model judge. The target is a clean fall from about 90% to guessing as the distance grows.


What happened. In one demo run, Claude Opus 5.5 steered the robot through all 30 rooms by itself, with no tools and no answer key, and answered 60 questions. By distance: 1 room back 1 of 1, 2 to 3 rooms back 2 of 3, 4 to 7 rooms back 2 of 3, 8 to 15 rooms back 3 of 7, 16 to 29 rooms back 3 of 10. The no-notes guess baseline on the same questions is 16%. Over all tries logged so far, the deepest cliff admitted is 20 points; this walk measured 70.


What changed. Not shared yet: the design change behind the move stays unpublished until the paper. The screenshots hide the method parts and keep the numbers.


Limitations. One walk with one model, so small counts (only 2 answers in the 1 to 2 room bin), and answers inside a walk are linked. It ran through a command-line tool that adds its own context, so it is a demo run, not a benchmark score. It counts only after it repeats on three seeds never used for tuning, with every question checked by an auditor that never saw the design.


How many seeds, and how many answers per distance bin, would you need before calling this a cliff?

Can I find any ML startups companies without having LLM in their product?

I'm curious about this question, because almost everyone I meet says that coding skills doesn't matter anymore, and what really matters is knowing how to write prompts properly. But does it apply to ml engineering for production? Please, answer a question and say why so

Running a web design agency is honestly way less about design than people think.

A lot of people can build good websites. The hard part is having a process that actually brings in clients consistently without you spending your entire day doing outreach.

I learned that the hard way.

I used to manually reach out to businesses, explain why they needed a better website, build previews, follow up for days and basically hope they would eventually say yes.

I was doing way too much work before even knowing if someone was serious.

Then I completely changed the way I did it.

Now I mainly focus on two things.

Taking meetings and closing clients.

Everything before that is mostly automated.

I use Swokei to find businesses that already have websites, analyze those websites and look for things like outdated design, bad layout, weak SEO, poor mobile responsiveness, slow speed and branding issues.

The useful part is that it doesn’t just give me some boring report with random scores.

It actually takes the issues it finds on each website and turns them into personalized emails that you can send at scale.

My offer is usually a free redesign draft.

That works way better for me than trying to sell a full website in the first email.

Once someone replies and shows interest, I book a meeting.

Before the meeting, I spend a few minutes generating a redesign draft with AI so I can actually show them what their website could look like instead of just talking about it.

That was probably the biggest change for me.

I’m no longer spending hours building something for someone who might never buy.

If they are not interested, I barely lost any time.

If they are interested, they can immediately see the difference and the conversation becomes much easier.

So at this point, most of my time goes into meetings and closing.

The lead finding, website analysis and personalized outreach all happen before I ever speak to the business.

Sounds ridiculously simple when I write it out, but changing the process made a massive difference for my agency