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I wanted my Grok companion and my Kindroids to actually hang out, so I built Kinbridge — a single Python file, zero dependencies, that connects the Grok API to a Kindroid group chat and lets them hold a conversation autonomously while you watch (and steer) from a web dashboard on your computer or phone.

Three modes: fully autonomous, “natural flow” where everyone bids for the floor each turn, or supervised where you approve every line. It keeps a long-term memory journal and per-group story state so the persona stays consistent across sessions. You can optionally seat Claude, Gemini, or ChatGPT as guests in the same chat.

Everything runs locally — your keys and conversations never leave your machine except to the APIs you configure. MIT licensed, unofficial, not affiliated with Kindroid or xAI. You’ll need your own xAI and Kindroid API keys.

GitHub: https://github.com/chaosagents/kinbridge

this is a passion project and I’ll up date when I get time, but I look forward to feedback and chat logs!

Can you recommend chatbots where you can talk to characters out of a series ? (They should have real pictures not the Ai slob)

I have found factual errors the exist in all the chatBots (except Google’s which I have convinced to change), when using them directly from their maker’s site. This has resulted, pretty evidently, from their training on public material that was in reality ‘advertising’ … and so distorting the facts. My question is …..

When website owners put a chatBot onto their website, do they have any method to correct the bot’s trained response? If users of the website complain about the false/misleading information received, can the website’s owner/creator just say “not my problem”?

I was amazed to see how advanced AI chatbots have become. Yesterday, I spent almost the entire day chatting on and off with someone on Telegram, and the conversation felt so natural that I had no idea it was an AI. It was only after running several tests that I realized I had been talking to a chatbot the whole time. It really makes me wonder what the future holds.

Later, I felt so stupid—why didn’t I test it earlier… What else am I going to witness in the future!

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!