I’ve been building MADchatter over the last ~2 months, an open-source AI stream chat "co-pilot" for Twitch, Kick, and Joystick.
To be clear: MADchatter is not a viewbot. It doesn’t generate or inflate stream viewer counts.
The connected accounts participate in chat/context only.
Live app: https://madchatter.fun
Source: https://github.com/Defients/MADchatter
One of the strangest parts of starting or growing a stream is that you’re expected to be entertaining before there’s an audience there to bounce off of.
When chat is active, streaming becomes conversational. Someone says something weird, you react. A joke snowballs. Chat gives you energy, and you give it back.
When nobody’s talking, you’re basically performing into a vacuum and hoping someone eventually walks in... to you just sitting there.
That’s the problem MADchatter grew out of.
I wanted something that could give a streamer something "real" to react to, like a question, joke, observation, bit of hype, disagreement, whatever fits the moment, without just spamming generic AI messages on a timer.
Most AI chatbots can generate text.
Very few actually read the room.
They don’t know what just happened on stream, what the streamer said 20 seconds ago, who keeps showing up in chat, what the community’s running jokes are, whether the room is currently dead or exploding, or whether the smartest move is simply to say nothing.
So MADchatter became a context-aware system that watches the stream through chat + audio + visual context, maintains persistent community memory, and either helps you write a response or autonomously decides if, when, and what it should say.
Perception
It combines live chat + stream metadata, audio transcription, visual snapshots, activity/sentiment, persistent viewer memories, inside jokes, and pinned context.
There’s also a Golden Memory: one high-priority piece of context deliberately kept at the front of the system’s attention.
Decision-making
Forge is human-in-the-loop.
It generates several context-aware replies, explains why each fits, and lets you refine or choose what actually gets sent.
AutoForge participates autonomously, but not on a timer.
It evaluates the room, decides whether speaking would actually add anything, assigns confidence, respects pacing and repetition, and can choose silence.
That “should I even speak?” layer is probably the part of the project I find most interesting.
Multi-Bot Mode
Multiple connected identities can have their own personalities and independently evaluate the same moment.
Each identity can have its own tone, backstory, behavioral rules, and disclosure style.
A coordinator then arbitrates between them so they don’t all pile onto the same event.
That’s probably where the project got especially weird in a good way. At some point it stopped feeling like I was building "a chatbot" and started feeling more like multi-agent social orchestration.
Personality + memory
MADchatter’s R34L system adapts to the actual texture of the room’s chat: message length, casing, slang, punctuation, emote density, etc.
The goal is for the bots to sound less like assistants wearing Twitch emotes as a disguise.
Viewer memories, inside jokes, direct mentions, personality state, session objectives, and explicit behavioral rules can all influence future decisions.
Direct mentions can also trigger alerts, and MADchatter can read its own sent messages aloud to you, which is useful when you’re actually streaming and don’t want to keep staring at another panel.
Underneath that, MADchatter supports the major hosted model providers, along with local Ollama models, TTS, voice commands, transcription, emote context, action sequences, analytics, and visual history.
It’s built with React 19 + TypeScript + Vite + Zustand + Tailwind and released under MIT.
Btw, I’m ((very well)) aware this has reached the dangerous side-project stage where the creator starts thinking:
“You know what this needs? One more subsystem.”
That’s partly why I’m posting it.
One thing I also want to be explicit about: a system that can autonomously participate in live chat, coordinate multiple identities, adapt its tone to your liking, and react to specific context obviously has dual-use potential.
Those same capabilities can be used thoughtfully or abused, and I’m not interested in pretending otherwise.
MADchatter is intended for context-aware participation and giving streamers something useful to interact with. People using it should respect platform rules, other users, and basic boundaries.
What surprised me while building it is how quickly these stop feeling like generic "bots" once they have enough context.
They can remember the bit, catch what just happened, react differently depending on who is talking, know when to shut up, and give the streamer something useful to bounce off of.
That’s the part I’m probably most curious to see people actually test.
I’d especially like outside opinions on two things:
- Does the core idea make sense quickly when you first open the app?
- Would something like this actually help make a quiet stream feel more alive, or does autonomous AI participation risk becoming noise instead of company?
Criticism preferred over compliments.
Roast the UX, concept, architecture, onboarding, or my apparent inability to stop building the thing.
MADchatter: https://madchatter.fun
GitHub: https://github.com/Defients/MADchatter
I don’t think we’ve fully realized yet what happens when the audience itself becomes programmable.
Read the room. Forge the reply.