We got tired of watching tokens fail for reasons that had nothing to do with the token.
A project ships. The repository is active, the team is real, the idea holds up. Then a KOL who never read the whitepaper decides the timing is wrong and posts FUD. A sniper bundle from launch week starts unwinding. The top-10 holders are too concentrated and one of them panics — which makes two more panic — and the arc collapses before organic demand had a chance to find it. The idea wasn't the problem. The structure was.
Supply distribution. LP lock status. Dev wallet size relative to float. Holder concentration at the top. These are not unknowable variables. They are visible on-chain, and they determine how a token responds to pressure. A project with a tight top-10 and an unlocked LP does not need bad news to die — it just needs one nervous wallet.
The thing is, most of this is fixable before launch. Or at least, it is diagnosable. You can look at a contract and say: this supply structure will produce a panic cascade the moment sentiment dips. This LP size cannot absorb the sell pressure that will come when the snipers exit. This holder concentration means three wallets can determine the outcome for everyone else.
We built chumsim because we wanted to see the simulation before we committed. Paste a contract address. 300 synthetic agents — snipers, KOLs, paperhands, diamond hands, rug-sniffers, normies — seeded from real on-chain data, play out the next 24 hours. You see where the arc breaks. You see which archetype tips the cascade. You see what a tighter supply or a locked LP actually changes about the outcome.
It is not a prediction machine. A simulation is not the market. But it is a rehearsal room — a place to understand the dynamics you are actually dealing with before you decide how to deal with them, or whether to deal with them at all.
We built it for the same reason anyone builds a tool: because we needed it and it did not exist. If you are a builder, use it to stress-test your supply structure before you launch. If you are a buyer, use it to understand what you are actually buying into. If it helps you avoid one bad position or fix one structural flaw, it did its job.
Paste a contract address. 300 AI agents — snipers, KOLs, paperhands, diamond hands, rug-sniffers, normies — seeded with real on-chain data, simulate the next 24 hours and return a verdict.
Every simulation runs the same pipeline. On-chain data drives the agent population; the swarm does the rest.
Each agent is seeded with a named persona, a follower count, and a disposition shaped by on-chain signals.
Direct mappings from Helius on-chain data to agent weights. The simulation responds to what is actually on-chain.
| Signal | Effect on swarm |
|---|---|
| High top-10 concentration | Rugsniffers +20, baseline trust lowered, normies more nervous |
| Dev holds large % | Rugsniffers primed, paperhands +10, rug prior elevated |
| Mint authority live | Risk flag added, paperhand jitter +10 |
| LP small or unlocked | Paperhand trigger threshold drops, volatility multiplier up |
| Old token, steady velocity | Diamond hands +15, normies +20, calmer arc |
| Fresh (<6h) + high velocity | Snipers +25, KOLs +8, paperhands +10, spiky arc |
| Freeze authority active | Risk flag, rugsniffers cite it in the first steps |
Architecture, API reference, build phases, and deployment guide. All code is open source.
jconstantine627752-maker / Chum{ mint } or { narrative, name }. Returns Report JSON. Cached by seed hash.