GP

GPU multi-agent war simulation

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GPU multi-agent war simulation

Neural-Siege is a PyTorch GPU multi-agent arena sim (thousands of agents) with PPO training, checkpoint/resume, and detailed telemetry. I’m using it to probe emergent behavior and failure modes (camping/stalemates). Would appreciate critiques on the environment dynamics, reward shaping, and scaling strategy. https://github.com/ayushdnb/Neural-Siege/tree/combat_first_t...

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
57%57% 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
57%57% 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 · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: agents, agent, using · Missing: mac, macos, cursor
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward · Missing: web3, chat, crypto
37%37% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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