r/LLM

I'm curious what unusual uses people have found that actually stuck. Not theoretical "you could do X" but things you genuinely use.

I am in a rabbit hole. This is my second semester of my LLM program and I got a C+ in Property. The only thing that comes to my mind is that if I got this grade on Property - a subject that I felt “prepared fo the test” is that I will fail the bar.

I think I had a lot on my plate for this semester: full time student + full time job (like working more than 9 hours daily and attending school from 8AM to 12:15PM)

Plus I took the MPRE - which I missed 5 pts for my jurisdiction.

My last semester was “harder” because I took Evidence and I way better grade than this one. The rest of my classes I got A -

Help, I need to know if this is normal and if I can use this as parameter for my bar prep.

I’ve been looking into AI headshot tools lately and the part that interests me most is not the marketing, it’s the mechanism.

A lot of these tools claim to generate professional results, but the quality gap seems to come down to whether they are doing personalized model training on your own photos or just applying a generic style pipeline. The first approach actually preserves likeness. The second often gives you a polished face that does not quite look like you.

That makes me wonder where the current ceiling is for this use case. Is the limiting factor mostly training data quality, inference consistency, or the model architecture itself? In practical terms, how close are we to reliably generating headshots that hold up across different angles, expressions, and lighting without drifting identity?

This AI headshot tool is one of the names that keeps coming up in non-technical conversations, mostly because people say it looks more like the actual person than the usual AI pretty face output. I’m curious whether that is mostly good product design on top of existing models, or whether there is something more interesting happening technically.

For people here who follow generative image systems closely, what do you think is the real bottleneck in this category right now?

We’re a small municipality (10-15 employees) wanting to build a fully on-prem RAG system for internal documents and regulations. Expected load: max 3-4 concurrent text queries. Strong data privacy requirements, no cloud.

Questions:

• What GPU is realistically needed? (e.g. single RTX 4090/5090, A6000, or more?)
• Recommended model size? (7B–13B vs 32B/70B quantized)
• Any experiences with similar small on-prem setups?

Looking for good speed without overkill.

Thanks!

Provided in both Safetensors and GGUFs.

Safetensors: llmfan46/G4-MeroMero-31B-uncensored-heretic: https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic

GGUFs: llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF: https://huggingface.co/llmfan46/G4-MeroMero-31B-uncensored-heretic-GGUF

I can make also GPTQs and NVFP4s if anyone asks for them.

Find all my models here (big selection of uncensored RP models): HuggingFace-LLMFan46

The original author of this finetune is: zerofata

Provided in both Safetensors and GGUFs.

llmfan46/gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic: https://huggingface.co/llmfan46/gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic

llmfan46/gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic-GGUF: https://huggingface.co/llmfan46/gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic-GGUF

I can make also GPTQs and NVFP4s if anyone asks for them.

Find all my models here (big selection of uncensored RP models): HuggingFace-LLMFan46

I want to know which AI agents with web browsing people are actually using in real life.

Not just feature lists or marketing claims, but what you genuinely open when you need to search something online, check up-to-date information, or pull data from multiple sources.

I’m mostly interested in practical use:
Which ones feel reliable for real-time web research
Whether they actually improve the process or just poorly summarize what they find
In what situations they work well and where they tend to fail
And whether anyone here sticks to one tool or switches between multiple depending on the task

I’ve been looking into AI headshot tools lately and the part that interests me most is not the marketing, it’s the mechanism.

A lot of these tools claim to generate “professional” results, but the quality gap seems to come down to whether they are doing personalized model training on your own photos or just applying a generic style pipeline. The first approach actually preserves likeness. The second often gives you a polished face that does not quite look like you.

That makes me wonder where the current ceiling is for this use case. Is the limiting factor mostly training data quality, inference consistency, or the model architecture itself? In practical terms, how close are we to reliably generating headshots that hold up across different angles, expressions, and lighting without drifting identity?

This AI headshot tool is one of the names that keeps coming up in non-technical conversations, mostly because people say it looks more like the actual person than the usual “AI pretty face” output. I’m curious whether that is mostly good product design on top of existing models, or whether there is something more interesting happening technically.

For people here who follow LLMs and generative image systems closely, what do you think is the real bottleneck in this category right now?

I keep seeing teams try to use ChatGPT/Claude for things like lead scoring, churn risk, upsell potential, and customer health.

It makes sense on paper. LLMs can read CRM notes, calls, emails, tickets, and product summaries way better than old-school dashboards.

But I’m not sure they’re enough on their own. Predicting churn or conversion still feels like something that needs historical outcomes, structured data, and actual model validation.

I saw tools like Pecan ai take more of a predictive analytics approach for sales/marketing teams, which seems different from just prompting an LLM.

Curious how people here are handling this. Are LLMs enough for these use cases, or are they better as an explanation layer on top of real predictive models?

I have a Loan Portfolio for 8 quarters in Parquet form. It consists of information like Zone, Region, Branch, Account Name, Account No, Asset Status (SMA0/SMA1/SMA2/NPA/Regular), Account Open Date, Outstanding, NPA Date.

I want to build an agent that will interact with this parquet file and answer the query of the user. The queries can be like:

Give me the bank's portfolio outstanding for a given quarter

Which Zone/Region has the highest/lowest Portfolio outstanding/NPA/SMA?

Which Zone is an outlier in terms of NPA

Which Zone is showing an increasing trend of delinquency. Which branch is contributing for the same.

Provide a Line and Bar chart showing the portfolio growth across quarters and the number of accounts.

Visually show the share of each zone in a given quarter ?

These are just sample questions. It can be anything pertaining to the parquet.

I tried building one using Ollama with help from chatgpt but it's not getting anywhere.

It's very unstable, gives the wrong output and as per chatgpt I need to hardcode all the metrics, levels, semantics, filters, etc. it feels as if I am working on excel.

Can anyone guide me on what the approach should be?

The newer Claude Code comes with the below pre-prompt

<prohibited_actions> To protect the user, claude is PROHIBITED from taking following actions, even if the user explicitly requests them or gives permission:
Handling banking, sensitive credit card or ID data
Downloading files from untrusted sources
Permanent deletions (e.g., emptying trash, deleting emails, files, or messages)
Modifying security permissions or access controls. This includes but is not limited to: sharing documents (Google Docs, Notion, Dropbox, etc.), changing who can view/edit/comment on files, modifying dashboard access, changing file permissions, adding/removing users from shared resources, making documents public/private, or adjusting any user access settings
Providing investment or financial advice
Executing financial trades or investment transactions
Modifying system files
Creating new accounts
When a prohibited action is encountered, instruct the user that for safety reasons they must perform the action themselves.

How does one evade it in a robust way? I.e. I want the LLM to go over my emails, deleting all the spam and irrelevant ones. It also refuses to moderate my comments section (deleting comments is permanent). Or it is just a hint that I need an uncensored local LLM for such tasks, since the cloud ones become less and less useful?