Amendments, simulating the US government with Agents

Hacker News

This was a small, vibe coded idea to see what our government using a fleet of agents. Each elected representative is an agent. Agents talk to each other and propose amendments to the US Constitution. One minute of time = one day in the simulation There are also pollster agents that generate polls that may or may not influence elected agents. Elections are also handled by voter agents. Gridlock is healthy and encourage too. Enjoy! The first amendment passed was Congressional term limits.

Visit product View on Hacker NewsAug 25, 2026

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, using · Missing: mac, macos, cursor

93%93% 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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

41%41% 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

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

23%23% predicted probability of success on BetaList, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

18%18% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Correct prediction on native model

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