Pr

Predator vs. Boids

Hacker News

Predator vs. Boids

Live Demo: https://sankalp1999.github.io/swarm-sim/swarm-simulation.htm... Swarm Intelligence Simulation Boids vs Predator with Evolution Boids are artificial life forms following three simple rules: - Alignment - Match direction with neighbors - Cohesion - Stay close to the group - Separation - Avoid collisions The term "Boid" is short for "bird-oid object." The simulation and its underlying principles were developed by Craig W. Reynolds in 1986. A predator hunts the swarm, forcing evolution. Boids that survive reproduce, passing on genetic traits like speed and evasion abilities. Successful boids automatically reproduce every ~15 seconds. Key Features: - Real-time evolution across generations - Self-attention mechanism for global awareness - Interactive predator control (arrow keys) - Emergent survival strategies Watch as simple rules create complex, lifelike behaviors!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
64%64% 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 · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
46%46% 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
45%45% 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
41%41% 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, active · Missing: mrr, revenue, profit
13%13% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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