Flying Tulip deal.

Who's offering the deal?

Andre Cronje is one of the key builders behind Fantom and today - Sonic. He’s also the originator of the ve(3,3) model. What fascinates me most is ve(3,3): it blends mathematics, game-theory incentives, and DEX mechanics. In the right environment, it outperforms most alternatives.

Real-world results back this up:

• Aerodrome has emerged as the principal DEX on Base.
• Velodrome is the leading DEX on Optimism.
• PancakeSwap remains dominant on BNB Chain and has adopted ve-style gauge voting (veCAKE).
• On Linea, new ve(3,3) DEXs have quickly risen to the top shortly after launch.

Given this track record, I don’t see much reason to doubt Cronje’s competence*.
It reminds me of a conversation with a colleague from Tokenomist: “What’s the minimum valuation you’d ready to buy with no research if the only thing you knew was that one of the builders was ex-Binance? What if they had a PhD? A Nobel in finance?” Each time, the minimum shot up - purely because of the name. To me, this is one of those cases where it’s rational to accept a higher valuation premium because of the builder’s identity.

* I'm talking only about his expertise in the technical field, here I'm not discussing his public decisions, which could probably lead to the loss of user funds and his social activity.

What does Cronje suggest this time?

In fact, all the public information we have today (08.19.2025) is that tweet
https://x.com/AndreCronjeTech/status/1956047290348548559

from not really public information, we have this website
https://flyingtulip.com
https://flyingtulip.com/ltv.html

As you can see we don’t have much information yet, but it’s enough to grasp the new DEX technologies. So let's look at information about technology:

1) Liquidity engine: synthetic delta-neutral pool (backed by staking yield)

The core liquidity is a synthetic delta-neutral pool: the protocol holds yield-bearing assets (e.g., staked ETH) and opens offsetting short exposure (perps/options) so net price exposure ≈ 0.
Result: the pool can quote tight prices with lower risk to LP capital, and its baseline revenue comes from staking yield (not just fees/emissions).

How the hedge is financed: the platform is an integrated exchange + money market, so the hedge can draw margin from the same pooled liquidity (instead of fragmenting across protocols). That “how much margin is safe to draw?” is exactly where the LTV model comes in.

2) Price engine: Adaptive Curve Technology

The AMM curve auto-tunes between constant-product (xy = k) and near constant-sum (x + y ≈ k) based on real-time volatility

Imagine a slider between two pool modes:

• On the left, the “rigid” Uniswap-style mode (xy = k): price moves more when trades are large.
• On the right, the “soft” stable-AMM mode (x + y = k): price barely shifts near 1:1, so slippage is minimal.

The parameter A is the position of that slider:

• Small A → slider left → behaves like Uniswap (good when prices are jumpy).
• Large A → slider right → near a stable-curve (great for stablecoins or closely priced assets).

Why this helps:

• In calm markets, dial A up (a “flatter” curve) so traders lose less to slippage.
• In storms (high volatility), dial A down (a “steeper” curve) so big trades can’t blow out the pool and LPs take less risk.

How A is chosen:
The contract watches price “noisiness” (volatility) and moves the slider smoothly—no abrupt price jumps.

Example:

• Calm: swap 1,000 USDC → you get almost 1,000 DAI.
• Turbulent: for the same 1,000 USDC you get notably less, because the curve stiffened to protect the pool.

Bottom line: A is an auto-tuner for the curve shape, shifting itself between “minimal slippage” and “more protection” based on current volatility. (This matches the project’s own “Adaptive Curve Technology” description.)

3) Risk engine: AMM-based LTV model (for safe borrowing/hedging)

When the pool borrows stablecoins to post margin for its short hedge, it must not over-borrow relative to what it could recover by liquidating collateral inside the AMM itself.
The protocol’s AMM-based LTV sets that limit by combining:

• Depth/slippage of the pool (selling collateral into xy = k moves price).
• Volatility haircut (prices may drop before/during liquidation).
• Current utilization (if a lot of “B” is already borrowed, new liquidations get worse prices).

A compact form of the cap is:

where ΔX/X is the collateral chunk vs. pool size, and δ\deltaδ is the volatility haircut over the liquidation window. Utilization can scale this further down. Intuition: bigger trades and higher vol ⇒ lower safe LTV.

How it all flows (one loop)

1. LPs deposit staked assets → pool earns staking yield as a baseline.
2. To keep quotes stable, the pool opens a short (perps/options) to offset the long inventory of those assets (delta-neutral). Hedge margin is sourced from the pooled liquidity, bounded by the AMM-based LTV so liquidations would be safe even in-pool.
3. Adaptive Curve monitors volatility and reshapes the pricing curve: flatter in calm times (better prices), steeper in storms (more protection). This reduces slippage for users and constrains liquidation impact if the hedge or borrowers need to unwind.
4. As conditions change (volatility ↑, utilization ↑), the LTV limit tightens automatically, which in turn tempers how much margin can be drawn for hedges/new borrows—keeping the system solvent and LP-friendly.

Why the stack is coherent (each layer supports the next)

• Yield-backed delta-neutral liquidity gives the pool a steady P&L base, so it doesn’t rely solely on fees or token emissions.
• Adaptive Curve improves trade execution and limits damage in stress, which makes the assumptions in the LTV math (about slippage during liquidation) more reliable.
• AMM-based LTV prevents over-extension of the hedge/borrows, making the delta-neutral promise credible under real AMM execution, not just mark prices.

Why is the deal interesting?

Flying Tulip offers an unusually clean capital story: your principal (BTC, ETH, SOL, USDC, USDe, USDS, ftUSD) is placed into hedged, yield-generating strategies; only the yield is spent to bootstrap and grow the exchange (marketing, launchpad incentives, liquidity support, and $FT buybacks). Operationally, the exchange is fully on-chain and powered by a synthetic delta-neutral liquidity pool—a market-making engine that earns staking yield while hedging out price direction, so quotes stay tight and slippage low even in choppy markets.

The protection is explicit: every $FT comes with a perpetual put at the same exchange rate and denomination you invested in. Put in 1 BTC, and you can redeem 1 BTC back from FT at any time. That means your downside looks more like smart-contract/custody risk, not market price risk to principal. Meanwhile, all upside remains open-ended: if $FT appreciates, you can sell in the market; if not, you always have the redemption backstop.

$FT’s design compounds this asymmetry. Supply is fixed (no inflation, no token emissions), ownership is split 50% investors / 50% foundation, and there’s constant buyback pressure funded by strategy yield. As the exchange scales, two flywheels reinforce each other: (1) more users and volume → more yield and fees; (2) more yield → larger, systemic buybacks and stronger liquidity → better execution → more users. Because investor principal is preserved, you also reduce panic-sell dynamics typical of early token distributions.

Compared with a standard crypto raise where your principal is spent the FT model preserves principal, funds growth from profits, and hard-codes an exit via the perpetual put. In practical terms, it behaves like a risk-capped call option on the success of a fully on-chain, high-performance exchange: capped downside to principal, uncapped upside from buybacks, adoption, and fixed supply.