Most builders give up after their first failed attempt.
@enoch_danijel built 4 different perp DEX models before founding @LemonMarkets - the one that worked.
Here's how he iterated his way to success 🧵
The problem seemed simple: he wanted to long/short meme coins.
But every existing perp DEX model he studied (GMX, Hyperliquid, AAVE-based) was either:
• Too expensive to bootstrap
• Required massive liquidity upfront
• Wasn't scalable for new tokens
So he did what most builders don't:
He rebuilt it from scratch. Multiple times. Each iteration taught him what didn't work. Each failure revealed constraints he hadn't seen before.
His process for each iteration:
1. Research existing models
2. Build for weeks
3. Test with real use cases
4. Discover it doesn't scale
Most people would quit at step 4. He went back to step 1.
His toolkit for rapid iteration:
• AI for breaking down complex white papers and docs
• Twitter search for finding others who've solved similar problems
• Testing assumptions quickly before spending too much time on the idea
The turning point was changing his perspective:
Instead of asking "how do other perp DEXs work?"
He asked "what's the simplest model that solves MY specific problem?"
That led to the P2P matching approach.
His advice for builders stuck on hard problems:
"Don't double down on your failures. Learn from them and keep iterating."
"Do not make the same mistake twice."
"It's not work. It's fun. We're building things people will use."
Innovation often comes from builders who are willing to:
• Throw away weeks of work
• Start over with new assumptions
• Treat each failure as data, not defeat
4 iterations might seem like a lot. But it's what it took to find the right solution.
Watch the full showcase here:

1,915
10
本頁面內容由第三方提供。除非另有說明,OKX 不是所引用文章的作者,也不對此類材料主張任何版權。該內容僅供參考,並不代表 OKX 觀點,不作為任何形式的認可,也不應被視為投資建議或購買或出售數字資產的招攬。在使用生成式人工智能提供摘要或其他信息的情況下,此類人工智能生成的內容可能不準確或不一致。請閱讀鏈接文章,瞭解更多詳情和信息。OKX 不對第三方網站上的內容負責。包含穩定幣、NFTs 等在內的數字資產涉及較高程度的風險,其價值可能會產生較大波動。請根據自身財務狀況,仔細考慮交易或持有數字資產是否適合您。

