Campfire is an emotional-intelligence companion for gamers. What makes it worth a second look isn’t the app, it’s how it got made: I built a simulated Discord server run entirely by AI personas and used it as a standing focus group through every stage of design. That simulation generated its own journey map, which went straight into Figma Make to produce the interactive prototype.
Fall 2025
AI Systems Researcher & Designer
Eric Lopez, Shreesa Shrestha, Conor Mack
Human-AI Interaction
Gaming is one of the last “third places,” but its chat is usually either toxic or silent. What existed for it was chatbots (transactional) or NPCs (scripted). Nothing behaved like a neighbor: present, ambient, not demanding interaction. Meanwhile the emotions actually driving a session, tilt, flow, frustration, stayed invisible to the player and their team. The question driving this: how might gamers understand their emotional states, individually and with teammates, in order to improve wellbeing, communication, and overall performance?
Recruiting the population this needed (gamers, mid-session, honest about tilt and frustration) is slow and expensive. So instead of recruiting one, I built one. CampfireGPT started as cyber-ethnography: field notes from Twitch streams, Discord servers, and Reddit’s r/gaming and r/GamerPals, coding how players actually talk in games like Destiny 2 and Valorant. That coding became a multi-agent simulation trained on Bartle’s Player Taxonomy, the 4 Keys to Fun framework, and channel-specific tone files built directly from the field notes. I interviewed its personas for exploratory ideation, had each one generate an image of its own gaming room to test where players would tolerate sensors, and returned to the simulation at every design stage for rapid synthetic feedback.
3 versions to get one believable simulated community
“so yeah… where we wandering? #gaming for chaos, #general for memes, #design-talk for brain rot” mira, an AI persona, v2.5 (synthetic, not a human participant)
It took three builds to get there. Version one, a free-for-all chatroom, was too chaotic: there was no way in, no thread to grab onto. The final build (React and the Gemini API) runs a Director, Cast, and Reflexionist loop every turn. A hidden Director tracks tension and group sentiment and injects random events (a 15% chance per turn, a server-lag complaint, a new patch). Four fixed personas respond in voice. A Reflexionist self-corrects any persona that breaks character; it catches one persona, Drax, using an emoji it wouldn’t use, and strikes it.
One of those early sessions named the product. I asked an earlier build to synthesize what it had generated about loneliness and connection, and it came back with a naming table on its own: Campfire (“Feel your friends nearby”), Ember, Pulse, Still Here. Its own one-line takeaway read “presence is the new interface.” I didn’t come up with the name in a branding exercise afterward. It’s the literal output the simulation produced, which is also the clearest evidence I have that the research instrument wasn’t just informing the design, it was doing some of the designing.
“Presence is the new interface.” CampfireGPT’s own session synthesis, output before any human branding pass
Silence reads as disengagement in the product, but it isn’t one, so I built the opposite assumption in. My field observation: gamers routinely sit in voice channels saying nothing, and that “parallel play” is a real form of connection, not a failure to participate. I made never forcing a reply Campfire’s Prime Directive at the input layer: go quiet, and the simulation keeps living without you. Confidence here is moderate. It comes from ethnographic pattern-matching across three platforms, not a controlled study, and the thing to watch is whether real players read Campfire’s own silence as ambient or as broken.
A simulation with no structure is unenterable, so onboarding became the first UI decision, not the last. Version one taught me that a formless chatroom gives a new user no way to know where the flow is. Channel logic (#gaming versus #general) became the cognitive anchor, and a “Cold Open” (the app boots directly into a conversation already in motion) replaced the dead blank-chatbox moment at first load.
Exposing the AI’s own reasoning, not hiding it, is what made this a research instrument instead of a toy. A Research Log panel surfaces the Director’s hidden state (tension level, group sentiment, why an event fired), and commands like findings and final report pause the simulation and export what it observed. That traceability is the difference between a chat demo and something you can actually run a study through: I could ask it why it did something and get an answer, not a guess.
The simulation generated its own journey map, the emotional arc of a player’s session, highs and lows, and each stage on that map became a screen or decision point. Then came the handoff: Shreesa took that AI-generated map and prompted it directly into Figma Make.
“Create app screens based on this journey map.” The documented Figma Make kickoff prompt
I checked the AI’s structure against the team’s design instincts from there. The result is Campfire: an interactive prototype, built in Figma Make, of a companion that reads facial signals to track six emotional states (happy, excited, calm, frustrated, concentrated, annoyed) during games like League of Legends and Valorant. A Live Mood Display warns you when you’re tilting. Team Collective Mood makes teammates’ invisible feelings visible before tilt spreads through a squad. Post-Game Analytics tie emotional peaks back to specific events: a boss fight to frustration, a stretch of exploration to calm. Explore the live prototype.
Everything above came from ethnographic observation and a synthetic focus group, not real gamers. Campfire hasn’t been tested with a single human player, and that’s the limit worth stating plainly rather than glossing: every finding here is directional, corroborated by a simulation I built to be behaviorally accurate, not proven on the product itself. I also flagged Campfire’s own accessibility gap before anyone else could: the emotion display currently leans on color alone, and colorblind-safe palettes, contrast ratios, and scalable text are the named next fixes, not an afterthought.
The next study is the obvious one: the simulation could design the app. Only real players can tell us whether an AI’s presence actually makes a lonely gaming session feel less lonely.
Campfire is an emotional-intelligence companion for gamers. What makes it worth a second look isn’t the app, it’s how it got made: I built a simulated Discord server run entirely by AI personas and used it as a standing focus group through every stage of design. That simulation generated its own journey map, which went straight into Figma Make to produce the interactive prototype.
Fall 2025
AI Systems Researcher & Designer
Eric Lopez, Shreesa Shrestha, Conor Mack
Human-AI Interaction
Gaming is one of the last “third places,” but its chat is usually either toxic or silent. What existed for it was chatbots (transactional) or NPCs (scripted). Nothing behaved like a neighbor: present, ambient, not demanding interaction. Meanwhile the emotions actually driving a session, tilt, flow, frustration, stayed invisible to the player and their team. The question driving this: how might gamers understand their emotional states, individually and with teammates, in order to improve wellbeing, communication, and overall performance?
Recruiting the population this needed (gamers, mid-session, honest about tilt and frustration) is slow and expensive. So instead of recruiting one, I built one. CampfireGPT started as cyber-ethnography: field notes from Twitch streams, Discord servers, and Reddit’s r/gaming and r/GamerPals, coding how players actually talk in games like Destiny 2 and Valorant. That coding became a multi-agent simulation trained on Bartle’s Player Taxonomy, the 4 Keys to Fun framework, and channel-specific tone files built directly from the field notes. I interviewed its personas for exploratory ideation, had each one generate an image of its own gaming room to test where players would tolerate sensors, and returned to the simulation at every design stage for rapid synthetic feedback.
3 versions to get one believable simulated community
“so yeah… where we wandering? #gaming for chaos, #general for memes, #design-talk for brain rot” mira, an AI persona, v2.5 (synthetic, not a human participant)
It took three builds to get there. Version one, a free-for-all chatroom, was too chaotic: there was no way in, no thread to grab onto. The final build (React and the Gemini API) runs a Director, Cast, and Reflexionist loop every turn. A hidden Director tracks tension and group sentiment and injects random events (a 15% chance per turn, a server-lag complaint, a new patch). Four fixed personas respond in voice. A Reflexionist self-corrects any persona that breaks character; it catches one persona, Drax, using an emoji it wouldn’t use, and strikes it.
One of those early sessions named the product. I asked an earlier build to synthesize what it had generated about loneliness and connection, and it came back with a naming table on its own: Campfire (“Feel your friends nearby”), Ember, Pulse, Still Here. Its own one-line takeaway read “presence is the new interface.” I didn’t come up with the name in a branding exercise afterward. It’s the literal output the simulation produced, which is also the clearest evidence I have that the research instrument wasn’t just informing the design, it was doing some of the designing.
“Presence is the new interface.” CampfireGPT’s own session synthesis, output before any human branding pass
Silence reads as disengagement in the product, but it isn’t one, so I built the opposite assumption in. My field observation: gamers routinely sit in voice channels saying nothing, and that “parallel play” is a real form of connection, not a failure to participate. I made never forcing a reply Campfire’s Prime Directive at the input layer: go quiet, and the simulation keeps living without you. Confidence here is moderate. It comes from ethnographic pattern-matching across three platforms, not a controlled study, and the thing to watch is whether real players read Campfire’s own silence as ambient or as broken.
A simulation with no structure is unenterable, so onboarding became the first UI decision, not the last. Version one taught me that a formless chatroom gives a new user no way to know where the flow is. Channel logic (#gaming versus #general) became the cognitive anchor, and a “Cold Open” (the app boots directly into a conversation already in motion) replaced the dead blank-chatbox moment at first load.
Exposing the AI’s own reasoning, not hiding it, is what made this a research instrument instead of a toy. A Research Log panel surfaces the Director’s hidden state (tension level, group sentiment, why an event fired), and commands like findings and final report pause the simulation and export what it observed. That traceability is the difference between a chat demo and something you can actually run a study through: I could ask it why it did something and get an answer, not a guess.
The simulation generated its own journey map, the emotional arc of a player’s session, highs and lows, and each stage on that map became a screen or decision point. Then came the handoff: Shreesa took that AI-generated map and prompted it directly into Figma Make.
“Create app screens based on this journey map.” The documented Figma Make kickoff prompt
I checked the AI’s structure against the team’s design instincts from there. The result is Campfire: an interactive prototype, built in Figma Make, of a companion that reads facial signals to track six emotional states (happy, excited, calm, frustrated, concentrated, annoyed) during games like League of Legends and Valorant. A Live Mood Display warns you when you’re tilting. Team Collective Mood makes teammates’ invisible feelings visible before tilt spreads through a squad. Post-Game Analytics tie emotional peaks back to specific events: a boss fight to frustration, a stretch of exploration to calm. Explore the live prototype.
Everything above came from ethnographic observation and a synthetic focus group, not real gamers. Campfire hasn’t been tested with a single human player, and that’s the limit worth stating plainly rather than glossing: every finding here is directional, corroborated by a simulation I built to be behaviorally accurate, not proven on the product itself. I also flagged Campfire’s own accessibility gap before anyone else could: the emotion display currently leans on color alone, and colorblind-safe palettes, contrast ratios, and scalable text are the named next fixes, not an afterthought.
The next study is the obvious one: the simulation could design the app. Only real players can tell us whether an AI’s presence actually makes a lonely gaming session feel less lonely.
Campfire is an emotional-intelligence companion for gamers. What makes it worth a second look isn’t the app, it’s how it got made: I built a simulated Discord server run entirely by AI personas and used it as a standing focus group through every stage of design. That simulation generated its own journey map, which went straight into Figma Make to produce the interactive prototype.
Fall 2025
AI Systems Researcher & Designer
Eric Lopez, Shreesa Shrestha, Conor Mack
Human-AI Interaction
Gaming is one of the last “third places,” but its chat is usually either toxic or silent. What existed for it was chatbots (transactional) or NPCs (scripted). Nothing behaved like a neighbor: present, ambient, not demanding interaction. Meanwhile the emotions actually driving a session, tilt, flow, frustration, stayed invisible to the player and their team. The question driving this: how might gamers understand their emotional states, individually and with teammates, in order to improve wellbeing, communication, and overall performance?
Recruiting the population this needed (gamers, mid-session, honest about tilt and frustration) is slow and expensive. So instead of recruiting one, I built one. CampfireGPT started as cyber-ethnography: field notes from Twitch streams, Discord servers, and Reddit’s r/gaming and r/GamerPals, coding how players actually talk in games like Destiny 2 and Valorant. That coding became a multi-agent simulation trained on Bartle’s Player Taxonomy, the 4 Keys to Fun framework, and channel-specific tone files built directly from the field notes. I interviewed its personas for exploratory ideation, had each one generate an image of its own gaming room to test where players would tolerate sensors, and returned to the simulation at every design stage for rapid synthetic feedback.
3 versions to get one believable simulated community
“so yeah… where we wandering? #gaming for chaos, #general for memes, #design-talk for brain rot” mira, an AI persona, v2.5 (synthetic, not a human participant)
It took three builds to get there. Version one, a free-for-all chatroom, was too chaotic: there was no way in, no thread to grab onto. The final build (React and the Gemini API) runs a Director, Cast, and Reflexionist loop every turn. A hidden Director tracks tension and group sentiment and injects random events (a 15% chance per turn, a server-lag complaint, a new patch). Four fixed personas respond in voice. A Reflexionist self-corrects any persona that breaks character; it catches one persona, Drax, using an emoji it wouldn’t use, and strikes it.
One of those early sessions named the product. I asked an earlier build to synthesize what it had generated about loneliness and connection, and it came back with a naming table on its own: Campfire (“Feel your friends nearby”), Ember, Pulse, Still Here. Its own one-line takeaway read “presence is the new interface.” I didn’t come up with the name in a branding exercise afterward. It’s the literal output the simulation produced, which is also the clearest evidence I have that the research instrument wasn’t just informing the design, it was doing some of the designing.
“Presence is the new interface.” CampfireGPT’s own session synthesis, output before any human branding pass
Silence reads as disengagement in the product, but it isn’t one, so I built the opposite assumption in. My field observation: gamers routinely sit in voice channels saying nothing, and that “parallel play” is a real form of connection, not a failure to participate. I made never forcing a reply Campfire’s Prime Directive at the input layer: go quiet, and the simulation keeps living without you. Confidence here is moderate. It comes from ethnographic pattern-matching across three platforms, not a controlled study, and the thing to watch is whether real players read Campfire’s own silence as ambient or as broken.
A simulation with no structure is unenterable, so onboarding became the first UI decision, not the last. Version one taught me that a formless chatroom gives a new user no way to know where the flow is. Channel logic (#gaming versus #general) became the cognitive anchor, and a “Cold Open” (the app boots directly into a conversation already in motion) replaced the dead blank-chatbox moment at first load.
Exposing the AI’s own reasoning, not hiding it, is what made this a research instrument instead of a toy. A Research Log panel surfaces the Director’s hidden state (tension level, group sentiment, why an event fired), and commands like findings and final report pause the simulation and export what it observed. That traceability is the difference between a chat demo and something you can actually run a study through: I could ask it why it did something and get an answer, not a guess.
The simulation generated its own journey map, the emotional arc of a player’s session, highs and lows, and each stage on that map became a screen or decision point. Then came the handoff: Shreesa took that AI-generated map and prompted it directly into Figma Make.
“Create app screens based on this journey map.” The documented Figma Make kickoff prompt
I checked the AI’s structure against the team’s design instincts from there. The result is Campfire: an interactive prototype, built in Figma Make, of a companion that reads facial signals to track six emotional states (happy, excited, calm, frustrated, concentrated, annoyed) during games like League of Legends and Valorant. A Live Mood Display warns you when you’re tilting. Team Collective Mood makes teammates’ invisible feelings visible before tilt spreads through a squad. Post-Game Analytics tie emotional peaks back to specific events: a boss fight to frustration, a stretch of exploration to calm. Explore the live prototype.
Everything above came from ethnographic observation and a synthetic focus group, not real gamers. Campfire hasn’t been tested with a single human player, and that’s the limit worth stating plainly rather than glossing: every finding here is directional, corroborated by a simulation I built to be behaviorally accurate, not proven on the product itself. I also flagged Campfire’s own accessibility gap before anyone else could: the emotion display currently leans on color alone, and colorblind-safe palettes, contrast ratios, and scalable text are the named next fixes, not an afterthought.
The next study is the obvious one: the simulation could design the app. Only real players can tell us whether an AI’s presence actually makes a lonely gaming session feel less lonely.