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BesiegeField – LLM Agents Learn to Build Machines in a Physics Sandbox

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BesiegeField – LLM Agents Learn to Build Machines in a Physics Sandbox

Hi HN! We'd like to share something we built: BesiegeField — an environment where large language models design, test, and refine machines in the physics-based construction game Besiege. Machine design is treated as a code generation problem: LLMs choose standard parts and specify how to connect them, making the task well-suited to language models. The environment includes tasks like throwing a stone far or navigating bumpy terrain, and supports custom goals and environments. We used it to explore both agentic workflows (no finetuning) and reinforcement learning, trying tasks like building cars and catapults. It runs 100+ parallel processes on Linux clusters for scalable RL training. Feedback from the AI, RL, engineering, and game dev communities is welcome. GitHub: https://github.com/Godheritage/BesiegeField HuggingFace Demo (single-agent): https://huggingface.co/spaces/Godheritage/BesiegeField-Machi...

Visit productView on Hacker NewsOct 23, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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