basically what the title says.. I’m looking for a chatbot that answers questions about online piracy

So, I am going to create a travel website and I want to create a chatbot where people can chat related to all the travel queries and they’ll get a response from the chatbot and the recommendations as well.

How to do that? Should I hire someone to do that or I can do it myself.

I have a developer though.

Hey everyone,

Working on a Telegram tool that grabs videos from YouTube, Pinterest, and Snapchat when someone drops a link. Already got the token from BotFather and picked a name, but stuck on the actual build.

A few things I’m not sure about:

-> Is yt-dlp enough for YouTube and Pinterest, or do I need something separate for Snapchat? Heard that one’s a pain.

> Should I go with polling or webhooks for this kind of setup?

> Telegram caps uploads at 50MB, so what do people usually do for bigger videos?

> What’s the cheapest way to keep this running 24/7 instead of just on my laptop?

If anyone’s built something similar, would really appreciate any pointers or gotchas to watch out for. Thanks!

we built a messenger agent that replies to customers in your brand voice and can take orders directly through instagram. it’s an mvp, live with real customers, and it has taken real orders this way.

the main insight so far is about data, not the agent itself. most chatbots exist as a separate system. ours doesn’t. orders the agent takes go into the same dashboard as orders from the app and the website. customer info from every conversation shows up in both the main orders dashboard and inside the messenger agent. it’s connected to the same product catalog and the same setup agent. one system, not a bot bolted onto the side.

that’s the part that seems to matter more than the ai replying well. a bot that answers nicely but keeps its data separate just creates another place to check.

what we don’t know yet is where this holds up at scale. it works with the customers we’ve tested it on. we haven’t seen it hit messy cases yet, price negotiation, questions outside the catalog, disputes.

if you’ve built something similar, curious what broke first once real customers started using it.

PSA for anyone building or running chatbots for clients: on Aug 2 the EU AI Act’s Article 50 becomes enforceable. Any bot that people in the EU can talk to has to tell them it’s an AI, at or before the first message, in the user’s language. Doesn’t matter where the company is based, it follows the users.

Two things about this that I think matter specifically for bot builders:

1. The duty sits on whoever puts the bot in front of users, which in practice means your client gets the regulator email, and their first call after that is to whoever built the bot. If you deliver bots without a disclosure right now, that’s a conversation you’ll eventually have. Fines go up to 15M euro or 3% of turnover (SMEs get the lower cap).

2. It’s also an easy upsell. “AI Act compliance included” is a line item clients say yes to in about 10 seconds right now, and the actual work is a badge plus a first message notice plus keeping a log you can hand over if anyone asks. The log part is what everyone skips and it’s the part that saves you in an audit.

US folks aren’t off the hook either. California has required bot disclosure in commercial contexts since 2019 and Utah gives you an actual safe harbor from fines if you disclose upfront.

I build in this space (disclosed.sh, one script tag that adds the disclosure plus the evidence log on top of any widget, there’s a free check at disclosed.sh/check) so bias fully disclosed. But even if you wire it up yourself: put the notice in the chat surface, not the terms page, log that it was shown, and do it before Aug 2.

Curious how agencies here are handling this, is anyone baking compliance into their bot packages yet?

Hey everyone! I’m building a multimodal RAG pipeline where Mistral OCR annotates images before they go into a vector store with document text.

Issue: Mistral OCR processes images in isolation, so the annotations miss out on critical document context.

Looking for advice on:

Any prompting guides for machine-to-machine image description models to inject context?

Any alternative models or workflows that natively factor in surrounding document context?

Would love to know how you all handle this!

I’ve always wondered why every AI chat app feels the same.

You open it, ask a question, close the tab… and the entire world freezes until you come back.

I wanted the opposite.

So I started building Living Feed.

Instead of an assistant waiting for prompts, it’s a self-hostable social world where around 100 AI characters continuously live their own lives.

They:

• write posts
• reply to each other
• build friendships
• hold grudges
• remember past events
• send you DMs first
• continue living even if nobody logs in

You’re not the protagonist.

You’re simply another person entering an already living world.

Every like, comment or DM slightly changes relationships, and those relationship changes create new stories later. Characters remember what happened instead of resetting every conversation.

Technically it’s built around:

• Event Sourcing
• CQRS
• PostgreSQL as the source of truth
• NATS JetStream
• FastAPI
• Next.js
• Docker Compose
• Local Ollama or hosted LLMs
• A simulation engine that continuously advances the world instead of generating isolated chats

The project is MIT licensed and fully open source.

GitHub:

https://github.com/showjihyun/livingfeed

I’d love feedback from the self-hosted community.

Especially:

• Would you actually host something like this?
• What would make it useful in a homelab?
• What would stop you from running it?

I’m much more interested in criticism than stars.

Why I built it

Because I wanted AI to feel less like AI SIMS Game…

…and more like walking into a town where everyone already has a life.

And 반박시 니말이 맞음

I’ve always wondered why every AI chat app feels the same.

You open it, ask a question, close the tab… and the entire world freezes until you come back.

I wanted the opposite.

So I started building Living Feed.

Instead of an assistant waiting for prompts, it’s a self-hostable social world where around 100 AI characters continuously live their own lives.

They:

• write posts
• reply to each other
• build friendships
• hold grudges
• remember past events
• send you DMs first
• continue living even if nobody logs in

You’re not the protagonist.

You’re simply another person entering an already living world.

Every like, comment or DM slightly changes relationships, and those relationship changes create new stories later. Characters remember what happened instead of resetting every conversation.

Technically it’s built around:

• Event Sourcing
• CQRS
• PostgreSQL as the source of truth
• NATS JetStream
• FastAPI
• Next.js
• Docker Compose
• Local Ollama or hosted LLMs
• A simulation engine that continuously advances the world instead of generating isolated chats

The project is MIT licensed and fully open source.

GitHub:

https://github.com/showjihyun/livingfeed

I’d love feedback from the self-hosted community.

Especially:

• Would you actually host something like this?
• What would make it useful in a homelab?
• What would stop you from running it?

I’m much more interested in criticism than stars.

Why I built it

Because I wanted AI to feel less like AI SIMS Game…

…and more like walking into a town where everyone already has a life.

And 반박시 니말이 맞음

Hey all!

I’ve been experimenting with AI chat apps that support continuous, flowing conversations, with the system prompt fine-tuned for a specific mobile use case.

Has anyone built something similar? Would love to hear:

1. What stack you used?

2. Key lessons learned along the way?

3. What you’d do differently if you started over?

Appreciate any insights!