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SimFic – A multi-agent narrative simulation for interactive fiction

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SimFic – A multi-agent narrative simulation for interactive fiction

Hello HN, I'd like to share a hobby project I'm working on called SimFic: a multi-agent interactive fiction simulation engine. Many of you like to read. But have you wanted to do more than just follow through someone's story? What if you could step into their shoes, and play it out yourself in a simulated environment? The problem: simply prompting an AI chatbot (e.g. ChatGPT) directly with a world/story-building prompt and expecting a rich, non-linear output is fundamentally flawed; in the real world, information asymmetry, Theory of Mind, non-determinism, etc. affect how people think and act. Asking a single LLM like ChatGPT to mentally simulate these constraints is flawed because of Transformer attention, as it is omniscient by technical design. Furthermore, LLMs are heavily tuned to be helpful and finish things early. If you've tried writing a book with an LLM, you know that the result is laughably short and shallow. If we use an LLM as-is, every action will succeed, every path will be correct, and the narrative degenerates into a dry "happy path". That's why I built SimFic: 1. The Architect: work with it to envision and define your world, characters, and story before hopping in. It'll ask follow up questions as appropriate but not too many so as to be constraining. When the Architect has enough, it starts concretizing the world, however, it doesn't build everything statically at all once. Instead, it scopes a detailed document that is handed to downstream agents for procedurally generating context-aware new environments (i.e. new environments are created on the spot to suit the mood, story plot, etc.). I call it "Schrodinger's Map" since things don't exist until the user observes them :) 2. The Orchestrator: the central backbone running the story loop and law enforcement (e.g. physics). Manages time and clocks, assigning time cost to player actions to make time pressure real, and advances background world clocks (the world is always living, moving, reacting, even if you are stationary). For example, a bartender NPC may see you take a seat. As you talk with another NPC, the world clock ticks, bartender finishes polishing a glass, and chooses to talk with you. The world is active, not just reactive. Another key design is real RNG and affordance: if you try to wrestle a strong security guard, the Orchestrator queries another AI agent for a probability score of succeeding, then rolls a real random number to decide if you succeed or not. 3. The Director: the omniscient mastermind and driver behind the simulation, working in a two-step process: (1) resolution: Director receives player's input, NPC intents, current world state, outputs JSON to decide what exactly happens next, tracking injuries, changing trust levels, handling pacing. (2) narration: after resolution, Director now receives updated info to write high-quality, coherent prose for the player to read. 4. The Characters: (NPCs) this is a major part of what makes SimFic realistic, engaging, and unpredictable. If you used ChatGPT, all "characters" would be just a single ChatGPT trying to pretend to be each character; inherently flawed. In SimFic, every NPC is its own separate LLM agent with its own isolated context and agenda. Each NPC knows only what it would know in real life, so Theory of Mind and other behavioral features are much more realistic. NPCs have their own feelings, memory, motivation, boundaries, and conditions for creating trust. They can misinterpret your facial expression, panic, or act selfishly. The can also play along with you, cooperate, and read between the lines. There are actually many more parts of SimFic that I haven't talked about due to the content length limit on HN, and parts I haven't even found yet, as I am only one pair of eyes, and there are infinite possibilities and emergent discoveries to be made. Which is why I'd love for HN readers to give it a try and provide feedback and opinions! Thanks for reading!

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting
97%97% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, training, active · Missing: mrr, revenue, profit
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, real world, real life · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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