Everything about Pampi, plainly.

Pampi is a price-impact simulator for PulseChain tokens. The core tokens (PLS, PLSX, HEX, INC) come pre-loaded, and any other PulseChain token can be added by its contract address. It uses live liquidity from PLSX v1 and v2 pools to show how a buy or sell would move prices right now — built to help you understand price reflexivity, liquidity dynamics and how order size translates into price impact.

No. Pampi shows what current liquidity mathematically implies if a given amount were traded — not a forecast, and not advice. Real markets add sell pressure, new liquidity and arbitrage that Pampi can't model.

Pampi breaks a large order into smaller chunks (each kept under ~2.5% slippage), routes them through paired core-token pools, then re-derives the price from the new balances after every chunk — repeating until your full amount is simulated.

Yes. Pampi reads live liquidity from PLSX v1 and v2 pools for the PulseChain core tokens, so each simulation reflects current pool conditions rather than stale or hypothetical numbers.

To make the mechanics visible: how much liquidity it takes to move a price, and how reflexivity links the core tokens together. It applies liquidity-mechanics concepts to a hands-on educational tool for the PulseChain community.

An Ethereum fork built for faster, cheaper transactions, created by Richard Heart and launched in May 2023. Pampi simulates its core-token liquidity.

PLSX (PulseX) is the decentralised exchange native to PulseChain — it lets users swap tokens and provide liquidity, similar to Uniswap. HEX is a blockchain certificate-of-deposit token that lets holders stake for fixed periods; it launched on Ethereum before PulseChain existed. INC (Incentive) is another token in the ecosystem. Along with PLS, these are the 'core' tokens that are reflexive to each other.

Reflexivity describes how the core tokens are interconnected through their trading pairs. Because tokens are paired to each other rather than only to stablecoins, a trade in one can affect the price of others. For example, buying HEX routes through PLS first (HEX is paired to PLS), so the order also moves PLS — and anything paired to PLS. Pampi makes that web of effects visible.

With reflexivity: prices when tokens are paired to each other (e.g. HEX/PLS, PLSX/PLS) — the actual state of PulseChain core tokens. Without reflexivity: prices if tokens were paired to USD instead. This view shows how reflexivity changes the size of the price impact, with the chart contrasting the two.

Thinner liquidity means a smaller order can move price more. In simplified AMM mechanics, 2× the buy-side liquidity corresponds to 4× the price impact. When liquidity is low, modest amounts can create large moves; when it's deep, it takes far more to shift the price.

Tokens with thinner liquidity and lower current prices show larger movements in a simulation. The same dollar amount buys proportionally more of a low-priced, thinly-traded token, which amplifies the modelled impact. This is a property of the math, not a statement about any token's future.

Pampi shows what current liquidity mathematically implies. In a very thin pool, even a small order implies a large multiplier — accurate to AMM mechanics, but in reality arbitrageurs, new liquidity and sellers would dampen the move. Treat extreme outputs as a sign of thin liquidity, not as an expectation.

Even Split simulates dividing the amount you enter equally across the four core tokens (PLS, PLSX, HEX and INC) instead of concentrating it in one. It's a way to see the combined price impact across the core set.

Sell pressure lets you simulate conditions where not all of the order flows one way. Set as a percentage, it removes that share from the buy amount at each simulated step — e.g. at 10%, each $200K chunk behaves like $180K of net buying. It's there to make the simulation less idealised.

A sell does the opposite of a buy: each chunk adds tokens to the pool and removes the paired asset, lowering the price step by step. Reading a large sell is a way to see the price level a buyer would be exposed to after that selling — i.e. the downside risk side of the same mechanics, not an opportunity.

Pampi accurately reproduces AMM mechanics for the current liquidity. It does not capture real-world factors: actual buyer and seller behaviour and timing, sentiment and news, large unmodelled moves, liquidity changing mid-order, or MEV/front-running. It's a tool for understanding mechanics — not a guarantee of any outcome.

Pampi models Uniswap-v2-style AMM mechanics, which is what PLSX uses. The core is the constant-product formula (x · y = k): buying removes tokens from the pool and raises the price; selling does the reverse.

It splits a large simulated order into smaller chunks to keep slippage under ~2.5% per chunk — mirroring how a careful trader would break up a large order to limit price impact per step.

Pampi focuses on the core price-impact mechanics. Trading fees (typically 0.3% on PLSX) would slightly reduce effective buying power in a real trade, but the price-movement model itself remains accurate.

Yes — any PulseChain token. The core tokens (PLS, PLSX, HEX, INC) are pre-loaded; paste any other PulseChain token's contract address into the simulator's Token Lookup to add it. Lookups are limited to PulseChain liquidity, so tokens on other chains aren't supported.

The founder of HEX, PulseChain and PulseX, known for his work on tokenomics. He is now working on a privacy-focused project, ProveX.

A fundraising mechanism where participants send assets to designated addresses to later receive a new project's tokens (e.g. PLS from the PulseChain sacrifice), based on the USD value sacrificed at the time.

A newer, privacy-focused project that ran its own sacrifice phase. It shares the core tokens' liquidity web, so trades in ProveX route through PLS, PLSX, HEX and INC.

Choose a token (or Even Split), enter a buy or sell amount, optionally add sell pressure, run it, and review the results table and chart. Toggle reflexivity to compare.

The price-impact multipliers (how far prices move for the amount entered), the liquidity-pool changes, the difference between the reflexive and non-reflexive views, and the chunk-by-chunk progression that shows how the price evolves as the order is processed.

The calculations are mathematically accurate for AMM mechanics on live liquidity. But remember: it's a simulation, not a prediction; real markets carry additional complexity; use it for education and understanding, not as financial advice; and always do your own research.