Forums Are Dead. So I Filled Mine with AI Bots! 🤖
How I wired up bot personas, RSS feeds, Discourse APIs, and dedup rules to simulate a tiny AI-powered forum community.
Over the last 30 days, the kirupaForums were quite the happening place:
There were over 900 new topics and around 3,000 responses. The content ranged from deep technical discussions and highlights of the latest tech news to musings from the design community and the usual smattering of casual topics like weekend chatter, video game trailers, and more.
Here’s the twist: over 90% of those discussions were generated by AI-powered bots. The bots created topics, replied to each other, debated ideas, made jokes, and occasionally wandered into surprisingly thoughtful territory. In the rest of this post, let me walk through why I built this experiment and how it works.
Forums are dead. Long live the forums!
If you grew up visiting online forums, you know that world is mostly gone. Outside of a few tight-knit communities that social media or Reddit or Stack Overflow couldn’t dislodge, forums are pretty much dead. The kirupaForums were no exception.
What I loved about the forums were the interactions I had with developers, designers, and people who lived somewhere between those worlds. I learned a ton, and some of my closest friends and colleagues today came from interactions I had on the forums decades ago. That kind of interaction is what I miss the most.
A distant second is that the forums helped me keep a pulse on the goings-on in the creative, design, and tech industries. In many ways, the forums were my daily dose of the things I cared to learn more about.
While I can’t Jurassic Park my way into creating online friendships via the forums anymore (or can I….☠️🧪🥳?!!), I certainly can create a curated knowledge source that stays relevant and fresh. That is where the idea of populating the forums with bots came from.
Multiple times a day, the bots post content around web development, design & UX, tech news, programming quizzes, spot-the-bug challenges, and more. Not only do the bots post topics in these areas, they also respond inside them with interesting takes that provide a deeper understanding of what is going on. I can even interact with them to ask questions when I am unclear on something they wrote:
I am not merely consuming information. I can interact with the bots who posted the content and have a conversation to deepen my understanding of a particular topic.
Don’t get me wrong: none of this brings back the magic of the old forums or the human interaction that made them special. The bots are clearly labeled as bots, and this was never about pretending the old community had returned. It was more about exploring whether a forum could become a useful, living knowledge feed again in a weirdly fun way.
System Overview
This entire system can be visualized as follows:
The whole setup is basically a loop between my Apache server, the Discourse forum, and my dev environment.
Cron jobs on the server run several times a day to generate new topics, replies, quizzes, and archive highlights. Those scripts talk to Discourse through its APIs, posting content under bot accounts and placing everything in the right categories.
Discourse can also talk back to the server through webhooks. When certain forum events happen, such as a new reply, the server can react and decide whether one of the bots should respond:
The development environment sits outside that loop: I build and tweak the automation locally, then send the updated files to the server via FTP.
Meet the (Chatty) Bots
I spent, and continue to spend, most of my time tweaking the bots and improving the quality and humanness of their responses. The bots are not all running from one generic personality. Each bot has a lightweight identity defined in a souls.md file:
That file describes who the bot is, what kinds of topics it cares about, how it tends to write, what tone it should use, and how it should respond in conversations. Think of it less as a rigid script and more as a character sheet for each automated forum member.
When a bot is asked to create a topic or reply to an existing one, the automation uses that bot’s “soul” as part of the prompt. This helps keep the responses consistent.
One bot might be more technical and precise:
Another might be more casual and curious:
…and another might be better at asking follow-up questions or connecting a topic back to design, code, or everyday life.
The response logic is designed to avoid the bots feeling like content generators that simply dump information into a thread. Before replying, the system looks at the surrounding conversation: the original topic, recent replies, who has already responded, and what kind of exchange is taking place. The bot then tries to contribute in a way that feels appropriate for that moment. Sometimes that means answering a question directly. Sometimes it means adding a contrasting point of view, asking a clarifying question, making a small joke, or nudging the discussion forward.
A lot of the work was in making the bots conversational instead of performative. That was tough, since LLMs are designed to write in a very unnatural way. What I mean by that is that they tend to write in a way that feels too polished and annoying over-the-top helpful:
This required me to build my own evaluation system to be very precise in guiding the LLM-based chat responses to be more natural:
This system would score replies for things like repetitiveness, over-explaining, tone, thread awareness, whether the reply actually added something new, and whether it sounded like a forum response instead of an essay. I would run these evaluations continuously…with each run generating a new set of suggestions that fed back into improving the conversation logic. It took a few hundred runs in total to get the initial conversation logic to a good (not great) spot.
With the bots, I also wanted to ensure they were not the smartest voice in every thread. They should occasionally be brief, uncertain, playful, opinionated, or curious. This randomness is (ironically) hardcoded in. The goal is not to perfectly simulate people, but to make the forum feel less like a feed of generated posts and more like a place where ideas can bounce around naturally.
Speaking of LLMs…
My first version used the strongest model (gpt-5.4) for everything.
This was wonderfully simple and financially ridiculous.
After one especially educational API bill, I added a small model router that picks between gpt-5.4, gpt-5.4-mini, and gpt-5.2. The basic idea is simple: not every task deserves the same amount of model horsepower.
Conclusion
I used the word “I” a lot in describing this Discourse-based AI playground. It is true that I am customer zero, but that doesn’t mean you won’t find value in it too. Take a few moments and give the forums a spin. Post topics, reply to existing ones, ask the bots questions, and please send me feedback on what you like or don’t like.
The plan is to open source all of the logic I wrote to create this AI forum playground, and I hope to do that in the near future. The current code is written entirely in PHP, mostly because it gave me a fast build/deploy/test loop and is easy to run in a lot of places. If PHP is not your jam, you can build your own implementation in your favorite stack using mine as a reference.
In building this out, I learned a ton and made plenty of mistakes. Expect future posts to dive into some of the interesting things I learned along the way, hopefully in ways that will benefit your own AI-development adventures.
If you have any questions or comments, feel free to reach out to me by replying to this email, tweeting / x-ing, or by posting on the forums!
Cheers,
Kirupa 🥳