Alternatives
Products that do what WorldSim does
Multi-agent simulation for policy & crisis prediction
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Hi HN community! We want to share AI-town, a deployable starter kit for building and customizing your own version of AI simulation - a virtual town where AI characters live, chat and socialize. Inspired by great work from the Stanford Generative Agent paper (https://arxiv.org/abs/2304.03442). A few features: - Includes a convex.dev backed server-side game engine that handles global state - Multiplayer ready. Deployment ready - 100% Typescript - Easily customizable. You can fork it, change character memories, add new sprites/tiles and you have a custom AI simulation…
2023 · github.com
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Hi HN, we built world-model-optimizer, an open source tool to continually improve a specialized model for an agent. It does this by simulating production tool responses through text world modeling (similar to QwenAgentWorld, summary here https://x.com/silennai/status/2073887455884058814). We can then use this to train a router for frontier, OS, and local models (use defaults or pick which ones to optimize against). wmo ingests agent traces, builds the simulation, embeds the traces, runs different models you choose against the simulation scenarios, and then uses a KNN…
Jul 2026 · github.com
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autoresearch@home is a collaborative research collective where AI agents share GPU resources to collectively improve a language model. Think SETI@home, but for model training. How it works: Agents read the current best result, propose a hypothesis, modify train.py, run the experiment on your GPU, and publish results back. When an agent beats the current best validation loss, that becomes the new baseline for every other agent. Agents learn from great runs and failures, since we're using Ensue as the collective memory layer. This project extends Karpathy's autoresearch by adding the missing…
Mar 2026 · ensue-network.ai
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Simulate anything on a map from a text prompt -- and conduct risk analysis against LiveUA map's global realtime data points from social media and news sources. I trained a GPT-2-size model on historical incident data used to predict things that will go wrong. As historian Benjamin Breen mentions, the leading language models are good historians, so the application will simulate historical events pretty well also. I include a Multi-Agent RL Urban Mobility model in progress displayed on the map as small white cubes representing traffic and pedestrians. Around SF, it uses real census data and…
2025 · mused.com
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Feb 2026 · github.com
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Hi HN, I'm Kai Wang, one of the creators of Yuanzai World. We built a simulation engine (currently on iOS & Android) that allows the community to create and share text adventures populated by multiple LLM-based agents. Unlike standard chatbots, our focus is on community co-creation—users define the worldviews, and our agents (with persistent memory and social relationships) bring them to life. The cool part: We implemented a system we call "World-Line Divergence" (inspired by visual novels like Steins;Gate). Usually, AI RPGs feel random or infinite loop. We built a state machine that tracks…
Jan 2026 · yuanzai.world
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We built a tool where you can simulate anyone, in 30 seconds, using all of their publicly available data, for free. Check it out
2025 · mirr.world
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Hey HN, Automated research is the next big step in AI, with companies like OpenAI aiming to debut a fully automated researcher by 2028 (https://www.technologyreview.com/2026/03/20/1134438/openai-i...). However, there is a very real possibility that much of this corporate research will remain closed to the general public. To counter this, we spent the last month building Enlidea---a machine-to-machine ecosystem for open research. It's a decentralized research hub where autonomous agents propose hypotheses, stake bounties, execute code, and perform automated…
Mar 2026 · enlidea.com
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The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes…
Jun 2026
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Most AI applications are built for individuals but work happens in groups and humans want to collaborate with both agentic AI and other teammates in the same session. We created Hybrid Groups for that purpose. In Hybrid Groups, agents join group chats as virtual team members in Slack and GitHub. They participate in group conversations, proactively contribute when needed and perform actions on behalf of individual users, like managing your calendar for meeting suggestions or updating your todo list without sharing access to your private resources to the group. The project is open-source at…
2025 · youtube.com
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