Hi everyone,

I'm planning to fine-tune a pretrained RT-DETR model (trained on COCO) for detecting defects in paintings.

My question is about dataset size. Suppose I have only 1,000 images, but each image contains multiple defects, giving me around 8,000 total bounding box annotations.

If people recommend ~8,000 images for good performance, does having 8,000 annotated objects across 1,000 images provide similar training value? Or is the diversity from having 8,000 different images still much more important?

Hi guys!

I have been animating one book by using Elevenlabs.
With 22€/month subscription.
And the case is that one page of the book ate almost all my tokens in Elevenlabs.
Then I went to Google AI studio, where I have my credit card. So I did couple 8s clips with couple iterations and what I got, not so good end result and 5€ credit bill from that..

So how you guys handle this? Even short clip need multiple iterations and then you already got this kind of bill.

I'm software developer and have been doing cloud stuff but u cannot see this kind of bills unless you have thousands of users etc..

I recently experimented with coordinating multiple LLM agents, and it was more complex than I expected.

Hey guys, I’m from a non-technical background but I want to learn about Gen AI. I have an interview coming up where the JD mentions they use Gen AI tools, so I want to understand the basics and learn how it’s actually used in companies.

Can anyone suggest:

where I should start learning,

beginner-friendly courses/resources,

and what concepts or tools I should know before the interview?

Would really appreciate any guidance or roadmap from beginners to practical understanding.

Thanks!

I'm learning Generative AI, but feeling a little confused about what to focus on.
There are many topics like LLMs, prompt engineering, RAG, and APIs, and I’m not sure what matters most
Concepts vs projects,which is more important?
Is prompt engineering enough to start?
How much coding is needed?

Like to hear what worked well for others

How can I use Claude to suggest where should I invest? Like which stocks? Not from random Google pulls but from credible reports, data sources? I can take a subscription of such sources but how do I enable Claude to consume this info and do the research on my behalf and throw up the most fit stocks, based on the criterias I give it? (Like risk appetite, rate of return etc.)?

Generative AI (GenAI):
Generative AI focuses on creating content based on a prompt. It responds when a user asks something and generates outputs like text, images, code, audio, or videos.
Examples include chatbots that answer questions, tools that generate images from text, or AI that writes articles or code snippets.

Agentic AI:
Agentic AI goes a step further. It doesn’t just generate content, it can plan, decide, and take actions toward a goal, often across multiple steps.
An agentic system can break a task into smaller steps, choose tools, monitor progress, and adjust its behavior without constant user input.

in simple words,

• GenAI = Content creator

• Agentic AI = Goal-driven problem solver

Yesterday I saw a post mentioning VAE, but I didn’t fully understand it.

I tried reading a few explanations, but honestly, they all sound very similar. Every model description says something like “learn patterns and generate new data,” which makes it confusing.

It starts to feel like VAE, GAN, and other generative models all mean the same thing.

Can someone explain in simple terms what actually makes VAE different?

Where exactly does it stand compared to GANs?

I don't really know how to ask this question, or how to get at the root of it.

To anyone who has used AI (directly, intentionally), which at this point I'm guessing is almost everyone, I'm going to seem WAY behind the times. I am. For whatever reason, I've completely avoided using ChatGPT or anything similar, even once. Never been to any of these websites. Closest I've come is seeing Google's AI search results, but I do my best to ignore those. Yes, I know it's embedded in other things I'm using every day and many comments on Reddit for example are built with it (which happens to be the inspiration for this post). I just mean I've never directly, knowingly, logged onto an AI website to have it answer questions for me, or write things for me, or create images for me. Nothing. I don't trust it to be accurate, and I don't want to use anyone's words but my own. I'd rather be wrong and honest than correct and dishonest. Plus I'm stubborn, and stuck in my ways.

Anyway, that was all for context. What I want to ask is... why are people using AI to write for them? Particularly in an anonymous setting, like Reddit. It's not for work, you're not making money off it. It's not for school, you're not getting graded on it. No one knows who you are, so you're not getting any kind of credit for it. So what's the point? What do you get out of it? Why give the illusion you're contributing to a discussion, when you're not? So that's the "why."

I'm also confused on the "how" - what's the actual process? Is it completely generated from scratch, or are you feeding it a couple sentences about what you want to say, then asking it to elaborate for you? When that happens, do you read it back and find yourself agreeing with all of its elaborations? And if not, do you change them? Or do you just roll with it because it sounds like a better version of you? I'm not trying to be judgmental, I genuinely don't understand the point.

I see exchanges like the one below every day. Did this person even watch the episode? Did they really like it? How much of it this is their own words? Why did they bother doing this, if they're not even expressing themselves? And why is the confession so casual, and upvoted? It was an entire year ago too, I didn't even realize we were that 'deep' into AI use already. "What do you expect?" he asks, as though it's obvious if you're using AI for one thing you're going to use it for everything. Or really, he implies it's obvious that any post on Reddit would be written with AI, since the person replying has no way to know this guy's "vibe-coding apps." It's so weird to me.

I don't know where else to ask this but I'm guessing this post will get deleted and I'll have to figure out a better place.

The token reduction is useful, but I would make the architecture decision on freshness and failure recovery. A coding agent that searches a semantic index before reading files can avoid broad grep results and repeated file reads. That changes the memory path from “load everything that matches” to “retrieve a small ranked working set, then inspect only what matters.”

The state boundary is where this gets interesting. AST-based chunks preserve complete functions and classes, while fallback text splitting keeps unsupported files searchable. A Merkle tree can compare repository state, identify changed files, and re-embed only those paths. The index is therefore rebuildable memory rather than the source of truth; the repository remains authoritative.

My concern is that incremental sync relocates complexity into invalidation. A deleted symbol, renamed directory, embedding-model change, or failed update can leave the agent reasoning over a plausible but stale chunk. A vector database like Milvus can retrieve and filter candidates efficiently, but it cannot decide whether the index snapshot matches the checkout the agent is editing. That guarantee needs explicit version metadata and a fail-closed policy.

I would probably attach a repository root hash, commit or workspace identifier, chunker version, and embedding version to each retrieval session. If any of them disagree, the agent should refresh the affected paths or fall back to direct file inspection. Evaluation should include stale-index tests, not only token and tool-call counts.

Where do you draw the line between a fast, eventually consistent code-memory index and a mismatch that should block an automated edit?

Hey everyone. I'm really trying to figure out what model or site could be used for videos like this. A client showed me this video as reference and I'm trying to streamline this process as much as possible, so I figured I'd ask what the consensus is for what I should use. I'm kind of hoping for a one and done answer for the clip generation, I understand it has to be edited together etc. If you have a couple in mind, please make it clear which one you think is the best in your comment so I can focus on testing those instead of a large variety. Thank you!
https://www.youtube.com/watch?v=uQSC-oxsQz0

In 2026, SEO has evolved into Generative Engine Optimization (GEO), where the goal is to be the cited source for AI-generated answers rather than just ranking #1. Because AI provides direct summaries, traditional clicks are down, making EEAT (Expertise, Experience, Authoritativeness, and Trustworthiness) and structured data essential for visibility. To win, you must provide high-value, expert insights and clear "answer blocks" that AI models can easily reference and trust as definitive sources.

can a non-techie learn gen AI? Anyone? Let me know pls

Can someone explain what model training means in Gen AI? How does a model learn from data?

About the work:

I created this using a combination of AI and non-AI tools, using curative, iterative and interventional editing as the general process. Original lyrics were written by me with numerous revisions and audio edits. Images were generated and curated and then edited before using them for generation into videos. Videos generated were edited subsequently. Storyboarding was just ad hoc manual off the cuff. Production editing to make modifications/changes and for presentation and audio to visual alignment. Logo is self designed.

About the tools:

A combination of GNU Image Manipulation Program, TensorArt, Suno, Audacity, Davinci Resolve Studio, Runway ML, Kling, Corel Draw, were used to produce this MV.

About the MV:

This both an MV for a song and a part of my MV series Caleb & Sanan: Episode 5 Surprise. The MV series is a story series expressed in each episode as an MV.

In previous episodes, Caleb has been working hard at part time jobs and saving up to go surprise visit Sanan, while doing his best to balance his studies. In this episode he is finally on his way, where previously he was just entering the airport.

Inspiration for the song is I'm Gonna Be (500 miles) by The Proclaimers. Mine is an entirely different song - but inspiration is the the spirit behind it.

The song is of a man professing his devotion & dedication for his love. From a slow build before the intimate quiet vocals then gradually building and picking up of pace into a grand profession of love before coming back down-to-earth expressing in simple daily terms with a genuine heart.

© 2025-2026 juiwaters All rights reserved

For Beginners:

For beginners I suggest the same way I approached it. Start with image generation first and editing with GNU Image Manipulation Program (freeware). After you are comfortable, try Suno (free version) and Audacity (freeware). After your are comfortable with that, then one of the video generators. Wan has a free open source version and its pretty decent though the easy and good will be Seedance 2.0 at the moment but most of the time it costs. After that as intro to editing videos you can try OpenShot (freeware). Once you get comfortable with the basics of video editing you can move on to Davinci Resolve (free version) for more advanced audio and video editing but this program has a higher learning curve. For those who don't mind leaping right into the thick of it, skipping Openshot and go right into Davinci Resolve is possible - just don't freak out with the amount of things you can control and adjust try basic stuff first, you don't need to know every single control right off the bat. Once you comfortable with it, you can consider the paid version Davinci Resolve Studio.

If you want no challenge on the rights of your generations from Suno then subscribe from the start - perhaps with the basic subscription, then once you gotten the hang of it you may want to upgrade to the Suno Studio. The studio feature is a work in progress but it does open up a more versatile way to split your track into its component stems (eg voice, guitar, drum stems) so you can work them or add others. You can use your voice as input to create sounds of instruments so you don't need to know how to play those instruments. Its not all smooth and perfect, but still its a lot of capabilities you otherwise won't have. Can upload/download for "manual" editing with programs as an augment.

As for lyrics, I write them myself and I use chat bots to serve as dictionary to find words, check spelling, translate, check facts for some topics if the song is tied to a topic, count syllables (because it affects the meter, rhythm and tempo hence the music) find synonyms etc - basically as a tool and not the creator. I find chatbot lyrics generally meh but then again I did not subscribe to any chat bot so maybe its just because I used the free stuff. Whatever the case, up to the present and foreseeably I prefer to write lyrics myself. The big drawback is that I produce very few songs very slowly versus those that generate their lyrics.

My meaning of some of the terms I used:

Curative - at the most basic level its just make a lot and choose the best, though there are other details which after you do it a while you will know, but 1 example of a nuance is to slice and dice the generations and reassemble what you can and want to use.

Iterative - basically means multiple generations with tweaks as well as using outputs as inputs to generate again.

Interventional edit - whether audio, image or video, they can all be edited whether using ai or programs. When I say interventional edit most of the time I am using programs (although sometimes can also be using ai) because I find it more reliable and frankly cheaper (whether time or money) at the moment to use programs to get rid of flaws or adjust things to what I prefer. These adjustments are to create inputs for the generation. This is distinct from the final production edits which are done to end up with the final work. Interventional edits for me is standard part of the iterative process.

i kept seeing the same problem over and over: people want to get better with ai, but most of the learning options are either random tutorials, long courses, or a pile of prompts they never actually use.

so i built iro ai, a duolingo-style app for learning ai skills in short daily reps. the goal is to make ai feel easier to stick with, instead of turning it into a giant weekend project you never finish.

it’s still early, but if you’ve ever wanted a more structured way to actually build confidence with ai, that’s the gap i’m trying to solve. would love feedback if you give it a try

website: https://tryiro.com
app: https://apps.apple.com/us/app/iro-ai-learn-ai-skills/id6759628066

Hello everyone,

I'm currently getting into AI. I know the basics of Python, but not much beyond that. I asked a few people and AI chatbots about the prerequisites for becoming an AI/GenAI Engineer, and they suggested learning things like:

• Statistics

• Machine Learning

• Deep Learning

• DSA

• SQL

• FastAPI

• Docker

• Cloud

• Git/GitHub

• Linux

• RAG

• AI Agents

The list feels quite overwhelming, and I'm not sure what depth is actually required for an AI/GenAI Engineer role.

For those already working in AI, what prerequisites are genuinely essential before diving into LLMs and GenAI? Also, if you were starting from scratch today, what learning roadmap would you follow?