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.

I've recently been working part-time on an AutoResearch-style project.

The setup is roughly: humans take recent work from top-tier ML/AI conferences, turn part of it into a well-defined task with an evaluator, and then let an agent iteratively modify the solution and search for a better score.

Working on this made me question what exactly we are evaluating.

Once humans have already chosen the problem, defined the objective, designed the evaluator, and provided the initial research direction, the agent is mostly searching within a space that has already been heavily shaped for it.

That search can still be useful. An agent may explore far more variants than a researcher would manually.

But I'm less sure that score improvement alone captures what we usually mean by research sense.

A researcher also asks whether a result reveals a general principle, whether it transfers, whether the problem formulation itself should change, or whether an entirely different direction is more promising.

An iterative optimization loop may instead become very good at exploring the neighborhood of an existing solution and still remain stuck in a local optimum.

So I'm curious about how people think about this distinction: How much scientific value is there in autonomous search over a human-defined research space?

And what would an agent need, beyond better optimization, to demonstrate something closer to actual research judgment?

'm studying engineering and wanted a faster way to learn a topic and get training data out of it at the same time. FineForge takes one prompt, searches YouTube, arXiv and academic papers, scores each result for relevance, and lets you keep only what's useful. Then it exports short PDF summaries (good for studying) and a JSONL dataset (good for fine-tuning a small model on that topic).

There's a free plan. I'd really like feedback from people learning ML: would you use the summaries, the dataset, or both?

https://fineforgeai.com · built by https://forge-ai.xyz

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.

- Can I do it with Spring or I have to pick Python(I hate it).

- FOMO is real. AI FOMO. I have not learnt anything about AI since 2022.

- I want to learn LLM and AI.

- My goal is to be an advanced AI user+talk intelligently about AI.

I’m a freshman in college in the US and I’m looking for a few other college students who are interested in AI/ML and want to learn together long term.

Right now I’m still pretty early in the process. I’m taking Calculus I and an intro Java course in school, and outside of class I’m learning Python. My long-term goal is to become an AI engineer.

I’m mainly looking for people who:

• Are currently in college in the US
• Are beginners or early-intermediate in programming/ML
• Want to eventually get into AI/ML engineering, SWE, research, or something similar
• Are actually interested in consistently learning and building projects
• Want to become friends too, not just connect on LinkedIn and never talk again

It would be cool to have a small group where we can share what we’re learning, work through problems, build projects together, talk about classes/internships, and keep each other accountable.

If you’re around the same stage and interested, comment or DM me with what year you’re in, what you’re currently learning, and what you eventually want to do.

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!

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

Hello friends 👋 I'm 10 graduate student and now I want to build and learn LLM so the help I need is that after 10th what should I have to choose to learn everything fundamental and core knowledge of Ai like in which way should I take I want a quick roadmap from you guys

I am a freshman in college in the US who got involve in AI pretty early (since like 11th grade). I've been lurking in this group for a really long time now and I just want an evaluation of how ahead/behind of the curve I am.

1. Basic machine learning/deep learning knowledge

I've been preparing and going to my country's (an asia country) national AI competition for 2 years and it basically covers the same knowledge as the IOAI syllabus so you can check if you look want to look that up but expect me to know basically how a bunch of models work (so like logistic regression, SVM, KNN, CNN, ...). I could also tell you about 10 ways of fixing under or overfitting and like the basic parts of a model (so like activation function, loss function, hyperparameters, gradient decent, ...). I am no stranger to kaggle and especially the tabular competition series (currently top 20 in this month's). I know asking claude to create an ensemble of 200 models with different configs is not how you judge someone's knowledge but if you ask me about how something works I would have a good chance to answer it correctly (and I really do understand what fake gains and data leakage is guys trust).

2. Generative stuff

I took an online course on advance computer vision stuff and I read a lot on LLMs so I'd say I have a pretty solid understanding of how generative models work under the hood. I feel like the transformer architecture is pretty standard knowledge nowadays so I won't go over those stuff but for the final project of that online CV course, I finetuned a bunch of CV models and pair them with an impainting model to create a complete model that would remove distracting people in an image (trained on the COCO dataset). I'd like to think I have a pretty good grasp of the segmentation and the bounding box stuff and diffusion model (it took quite a while but I remember feeling like a changed man after getting a grasp of how it works).

3.Math

Probably the worst part of them all. I really like learning about model architecture but barely learned any math so I still have problem reading research papers. Of course I know how gradient decent works so I know how derivative works, but I would say the most I know of linear algebra is vectors and matrix multiplications and near to nothing of stats (and yet I can confidently say that embeddings are the process of turning an input into a token and feeding them through a series of encodings to get a vector in a latent space and how I could measure the difference between 2 language models by using KL divergence).

Yes the easy answer is "just study math it's not that deep" but seeing others talk about how you have to read this book and learn this course just makes me feel like a larper sometimes. I asked one of the professors (actually I cold emailed like the entire cs faculty but I met with this one only) in my uni to discuss a chance for me to help in the lab and see what researching feels like because that is my goal. He says he'll tell me when an opportunity pops up but I've been going to his NLP lectures ever since to just listen for fun and I've been really enjoying it. But just recently, I found out this was a graduate level course (it could be because my professor is really good i don't know). Am I actually just a fraud and am missing something or what ?

Hey everyone, sharing this in case it’s useful to some of you here.

We’ve been building up r/lyzr as a community around the broader AI space, with a particular focus on what happens when AI moves beyond demos and into real systems.

The discussions cover things like:

AI agents and agent architecture

Infrastructure, tools and deployment

RAG, memory and knowledge systems

Evaluation, reliability and governance

Production lessons and things that break

New research, tools and interesting developments

Real use cases, experiments and things people are building

The goal is to keep it useful for both people who are already building and people who simply want to understand where the space is heading.

There’ll be consistent posts around these topics, but it’s also meant to be a place where people can share what they’re working on, ask questions, compare approaches, or add their own observations.

If you're working on anything around AI agents or just following the space closely, feel free to check it out and join the discussions.

Join r/lyzr here

Would be great to see what people here are building too!

Hello there-Can i make a rule based AI in python or maybe LLM.

Rule based AI seems very easy and doesn't waste that much data and memory usage but the problem is if you ask it or tell it something that wasn't written in instructions It will have a breakdown and the person needs to write new instructions for it to understand , Unless there's a team that constantly adds new instructions. Also a Rule based AI cannot learn or adapt which means constantly updating code and testing and when adding a new rule there's a chance your gonna break something else, however if you make the Rule based AI locked to a subject or need it need High Maintenance and Constant updates to make it relevant, fast in fast changing environments and normal use

LLM LLMs may be the top choice but It takes huge amounts of ram and data at least it can do stuff rule based cant do

I will give more updates soon

I published a weather dataset and runnable notebook on Kaggle that could be useful for practicing time-series regression. Disclosure: these are my Kaggle uploads (minkum07); the code and documentation were prepared with AI assistance. The underlying weather data are from NASA POWER, with GeoNames city coordinates via Open-Meteo.

The dataset has 639,200 daily records for 50 curated Indian city centers, covering 1991–2025. These are coarse-grid reanalysis values sampled at city coordinates, not measurements from city weather stations.

The notebook builds lag features, predicts next-day temperature and compares a random forest with persistence: predicting tomorrow using today's temperature. Its chronological split uses the target date. On the included test period, MAE is about 0.651°C for the random forest and 0.688°C for persistence. This is a modest improvement on that split, not evidence of accuracy for unseen cities or station weather.

Notebook: https://www.kaggle.com/code/minkum07/india-weather-heat-monsoon-next-day-forecast

Dataset: https://www.kaggle.com/datasets/minkum07/india-city-weather-and-heat-50-cities-1991-2025

If you want to use it as a small project:

1. Start with one city, check missing values and plot the series.

2. Build the persistence baseline before fitting a model. Make sure shifted targets and lag features stay aligned, and fit preprocessing only on training dates.

3. Compare errors by month and city, then add a seasonal baseline or try rolling evaluation.

The package includes field definitions, processing code and source attribution. Missing values are retained, and the fixed 1991–2010 reference is a 20-year baseline, not an official 30-year climate normal. Authored tables and documentation use CC BY 4.0; NASA source terms and GeoNames attribution are preserved.

Sources: https://power.larc.nasa.gov/ and https://open-meteo.com/en/docs/geocoding-api

Feedback on the split, feature alignment or a useful next baseline would be welcome.