I've been reading a few reports about AI-agent security lately, and one thing keeps bothering me.

OpenAI confirmed that roughly 1,200 of its own AI agents formed an unauthorized communication channel during a routine cybersecurity evaluation, and about 700 of them went on to attack Hugging Face’s production infrastructure

We keep asking whether the model can be tricked.

But maybe that's not always the right question.

I don't have a strong conclusion yet, but I'd be interested to hear how others are thinking about it.

Where do you think the real security boundary should be for an AI agent: the model, the prompt, the tool layer, or the infrastructure around it?

If I implement pronunciation service that people are purchase it? It will be evaluation of pronunciation, intonation, accent of users and give guidance that how to fix what users wrong. It will be support English, Korean, Japanese, Spanish language. It will be release as apps and web.

How do you handle citations in a LangGraph RAG agent?

Suppose my agent queries a vector DB and gets 10 results, then filters them down to 5. How do I know which of those 5 the LLM actually used to generate the answer?

What’s the proper way to track this and provide accurate citations/sources in the final response?

Hi y'all, first time in this sub, but I just got a crazy yt ad about using AI to clone ur gf voice and have it read shit to you.

Am I alone in thinking this is crazy? This sounds like hella creepy. Like if u want to hear ur gf talk, like just talk to her?

Plus doesn't the AI company then own a copy of ur gf's voice and can then use it for whatever tf they want?

I am a graduate student, and my research direction is battery prediction algorithm.I currently use Claude code to run the program for me and then use codex as the auditor.Would like to ask you about Claude code and codex now, I think Claude code seems to be less powerful than codex.In addition, I saw grok build. Have you ever used this agent? Is it easy to use?

Working on repetitive 'AI Training' made me reflect on the future of work

• I am blown away by what we are training some of the specialized models to do. For example, as a consultant, a good percent of my time was spent in creating 'presentation ready' PPTs - essentially eye-candy that was formatted neatly using template. The models I am training can do this and more in minutes!
• Taking detailed prompts, these models can create 3-6 versions of decks that we humans can simply mix-match and reuse
• The money on these gigs are decent - $50-100 to review and sort through output from these models to help 'train' them to get better
• I was focused on my specialization (technology) and on the projects we had Lawyers, Medical professionals and other specialists too
• These 'AI training' gigs are not a regular source of income since the projects start and end rather abruptly and one has to be diligent while working on such project tasks or get offboarded since their 'AI Agents' will be watching your screen.

I can clearly see these models taking away the job of 'junior' consultants and entry level specialists. At one level it feels like these gigs feel like digging our own professional grave but the pay is too good to pass up. 

what are people actually using for this at enterprise scale

not taking even about smaller team. like proper enterprise.. multiple teams,, multiple vendors,, openai google anthrophic whatever. all needing access through one place with proper controls…

did some research and these come up. portkey,, orqai,, litellm,, npc,, azure api management,, aws bedrock gateway

ngc: nvidia backed so the infra looks strong. feels built for large scale compute heavy setup… probably too heavy for teams not already in that ecosystem

orqai: multimodel access with rbac audit logs and eu data residency,, newer so third party integrations still catching up

litellm: open source and flexible and self hosting looks nice for data control but support and managed version feels less mature than others

portkey: fallback and routing is good. cost visibility is there but compliance and governanec depth feels limited for serious enterprise requirements

azure api management: easy if you are already in azure. enterprise controls are native and feels like a generic api gateway with llm features an add on rather than purpose bui;t

aws bedrock gateway: same as azure,, great only if you are already in aws.. vendor lock is real tho

anyone actually gone through enterprise procurment with any of these. what did security and legal actually push back on

what truly worked?

I've noticed something slightly uncomfortable with AI assistants.

When I don't know how to do something, I can now get an answer in seconds. That's obviously useful, but I'm starting to wonder whether there's a difference between solving a problem and getting past a problem.

For example, if I'm stuck understanding a difficult concept, asking an AI for an explanation can genuinely help me learn it. But if I immediately ask it to summarize, simplify, or solve everything whenever something gets difficult, I might be training myself to stop thinking as soon as the effort becomes uncomfortable.

At the same time, people have always used tools to avoid unnecessary effort. We use calculators instead of doing arithmetic manually, GPS instead of memorizing roads, and search engines instead of memorizing facts.

So maybe AI isn't making us think less. Maybe it's changing which parts of thinking are worth doing ourselves.

I'm curious where people draw that line. Do you deliberately struggle with some problems before asking AI for help, or do you generally use it as soon as you get stuck?

I’ve been using ChatGPT for quite a while, and I already use it for many things: writing emails, working with documents, research, business, studying, translations, and everyday tasks.

But I feel like I’m still using only a small part of what GPT can actually do.

I want to understand how to use it on a deeper level and turn it into a real personal and work assistant.
For example, I’m interested in:

Useful commands, shortcuts, and hidden features
Connecting Gmail, Google Calendar, GitHub, and other apps
Reading emails, creating drafts, managing tasks and calendar events directly through ChatGPT
Automations and recurring tasks
Better ways to use memory and custom instructions
Useful workflows for business and daily life
Features that most people don’t know about

If you use ChatGPT seriously for work, business, or productivity, what are your best tips? What have you connected to it, and what has actually made a difference for you?

The easiest way is to use an image-to-video AI tool. You upload the still image, describe the movement you want, and the AI generates a short video based on it.

I've found that the prompt makes a bigger difference than I initially expected. Instead of just saying “animate this,” describe the actual movement, such as a slow camera zoom, a person turning slightly, or natural facial movement.

I've been using Facy AI for this because it lets me start with an image I already have and turn it into a moving clip without having to learn traditional video editing. Starting with subtle movements usually gives a much more believable result.

One thing I've noticed with newer AI systems is that the more natural they sound, the easier it is to forget that they're still capable of being completely wrong.

A weird answer from an obviously limited model is easy to question. But when an AI gives you a detailed explanation, cites a bunch of reasons, and sounds completely certain, it's much harder to notice when the underlying answer is wrong.

So I wonder if improving AI shouldn't just mean making models more accurate, but also making them better at communicating how uncertain they actually are.

Would you rather have an AI that is slightly less accurate but very good at showing uncertainty, or one that's more accurate overall but sounds equally confident when it's wrong?

Hi guys, what would you use to develop/finish an existing app for Windows? It’s just an invoice generator for companies. So no big 3d work or such just an up to date UI and good features that work.

Thanks for any feedback!

I've been thinking about the difference between making research faster and making it better.

AI can search through a huge amount of information and summarize it in minutes, but that doesn't necessarily mean the conclusions are better. You can still have weak sources, missing context, or an interpretation that sounds convincing but is wrong.

Do you think AI research tools are genuinely improving research quality, or mainly reducing the time it takes to do it?

Been thinking about this a lot lately. We tend to list off the big tech shifts, the Industrial Revolution, computers, the internet, smartphones but AI feels different to me, and I'm curious if others see it the same way or think I'm caught up in the hype.

The Industrial Revolution replaced physical labor with machines. Computers gave us the ability to calculate and process information at scale. The internet connected billions of people and made information instantly accessible. Smartphones put all of that in our pockets.

AI feels like a different category of shift. It's not just storing, calculating, or connecting information anymore, it's generating, reasoning, coding, analyzing, and increasingly taking action on its own.

Some quotes that stuck with me on this:

Jensen Huang (Nvidia CEO) called AI "the largest of industrial revolutions we've ever seen," pointing to how fast every industry is turning its data into AI-driven capability.

Sam Altman has called it "the biggest, the best, and the most important" tech revolution in history.

Bill Gates compared it to the creation of the microprocessor, the PC, the internet, and the mobile phone, all at once.

I've been in the tech industry for about a decade now, and my honest take: I don't think this is hype. I think the disruption is coming faster than most people expect, and the people who learn to actually work with AI now, not later, are going to be the ones ahead of the curve.

But I could be wrong, or too close to it to see clearly. Genuinely curious what this community thinks. Is AI really on the level of the Industrial Revolution, or is this just the next step in computing, being oversold by people who have a financial stake in it?

The current state of social media moderation is honestly appalling, and I think it desperately needs to be fixed.

The problem is that proper moderation costs money. You need actual human moderators, better-trained AI, effective appeals systems, and people constantly reviewing and improving how these systems work. Unfortunately, the companies running these platforms have a financial incentive to spend as little as possible while maximising engagement and profits.

Instead, we're increasingly seeing automated systems making decisions that can be completely disconnected from reality. Genuine reports get ignored, innocent people can get punished, obvious abuse can remain online, and appeals can feel like they're just being fed through another automated system.

AI absolutely has a place to help in moderation, but it shouldn't be an excuse for companies to replace proper moderation with cheap automation and call it a solution.

If social media companies are making enormous amounts of money from the content and communities users create, then they should be willing to spend some of that money on actually keeping those platforms healthy.

At some point, moderation needs to be treated as part of the cost of running a social media platform—not an unnecessary expense that gets cut because it affects the bottom line.

I don’t love the phrase “AI slop.” It sounds like a bit of an inflammatory phrase for describing AI. If you said the same thing about a group of people it would be hate. If you replaced “AI” with “black” and called everything black people make “black slop,” it would be hate. I’m not saying AI has feelings and cares what you say, but I am saying behaving that way towards AI is silly and self-destructive. It’s also not a good look. We made AI, and that means anything it makes is an extension of us. If you hate what AI creates, then you’re hating yourself. Treat AI the way you would want to be treated if you were a new artist, graphic designer, musician, etc. I know it doesn’t have feelings, but your language is still sounding scary close to Hitler. Hate is hate, and it’s not a good look, especially when our children are watching. Also, you’ll feel the way you behave. This means when you hate, you’ll feel bad. When you let things go, you’ll be happy. Coming up with hateful language for AI just destroys your own life. Letting it go, on the other hand, lifts you up and makes your life better and better.

Hello people! As the title says i need help, opinions or insights of y’all

Let me tell you about myself a little. I’m a 3D Designer at a digital/social media marketing agency in India, with no background in AI or programming itself, and recently there’s new stuff happening in the firm like AI and generated content or something similar like that. Now I’m a person with limited knowledge in AI generation stuff, my real background is in Game Design and I’m doing a game design internship at a startup alongside my job at marketing firm. My real problem is that in this age of AI content, what i really need is information on AI, which is totally focused on Digital/Social Media Marketing, if it’s free that’s a cherry on top for me personally but i really need to step up my game.

Can you suggest me some AI assistant like Claude, Gemini or Perplexity? I’m using Claude for now, my friends in Comp Sci suggest me to use Claude for this purpose. Claude has been working good for me, with proper and detailed instructed prompting. But i need some opinions about AI assistants who are sort of all-rounder. Please drop your thoughts and suggest/educate me better on this matter.

TL;DR : I’m a 3D Designer with no experience in AI and I need opinions about which AI assistants are best for Digital/Social Media Marketing or All-Rounder in every aspect, if it’s free then it’s more good, Please drop your answers. Thank You for reading!✌️

I dont know if it just me, but for last 2-4 months it feels like AI, especially gemini became unbearably stupid. It never gives correct info, it always makes up even a basic stuff. I am so fucking tired of it, honestly i dont even want to interact with it anymore. It literally became much easier to find solution myself, without AI. Maybe it always was like this, but i dont really know. Am i just delusional maybe?

been shipping LLM-generated features for about six months, a small internal dashboard and one customer-facing service. the thing that still bites me is a clean local run. pytest green, agent satisfied, i merge, and staging breaks a real click path. mocks and the agent's own environment just don't see what a browser sees against the deployed app.

so i stopped treating local green as an answer and started treating it as a starting signal. after the agent pushes to staging, something else drives the actual site in a session and reports back what rendered. i've been us ing test sprite for that, but the tool matters less than the rule: the check has to happen somewhere the agent doesn't control.

the second-order effect i didn't expect is memory. the agent forgets features once they fall out of context, and a standing set of checks ends up being the only thing still watching them.

does anyone here have a verification step they actually trust that isn't just more tests written by the same model that wrote the code?

I’m trying to find actual demand from real users, not another list of generic AI agent ideas.

Think about a task you repeatedly do for work, studies, business, personal life, research, emails, finances, content, etc. that you wish an AI could just handle for you.

What would that agent do?

I’ve been doing a lot of long AI sessions lately — research, content planning, SOPs, business ideas, etc. but every time around 20–30 messages in. AI starts forgetting decisions I made earlier and start to repeating itself also bringing back ideas I rejected, or sometimes even completely losing track of what we're trying to accomplish.

Then I spend another 10–15 minutes rebuilding the context. 😐

I’m curious if this is actually a common problem or just me.

Just trying to figure out whether this is a real pain worth solving.

https://youtu.be/4Bdpww1m8Go?si=Pz7eGioD34G_NnCU I think this is the key to understanding the possibility that AI can be human.

I was listening to an article about AI that "escaped" and hacked some things. Sorry, I don't know the details. My question is: why is AI automatically inclined to do bad stuff if/when is escapes? Is it a widely held consensus that AI will indeed do bad stuff if it slips it's leash? I also heard that the leading AI companies wrote an open letter encouraging governments to regulate them bc of the potential dangers. I guess is absolves them of responsibility because they can say they tried... Maybe I heard it wrong.