How I Designed World's Fastest Onchain Execution Bot by Rethinking the Destination

🛒 Imagine Buying Shoes Like This:

You walk into a store.

Before you can say anything, they ask:

"Which warehouse would you like to ship from?"

"What delivery van brand do you prefer?"

"What's the SKU code of the shoe you don't want?"

That's how most DeFi apps work.

They ask for chain, token, address, amount, gas fees, and confirmation — before they even understand what you want to do.

🧭 The Real World Doesn't Work Like That

In real life, we say:

• "I want a coffee."

• "Book me a cab to the airport."

• "Buy these shoes in size 10."

We express intent.

The system figures out the rest.

🧠 IGRIS: Built for Human Intuition, Not Blockchain Bureaucracy

IGRIS flips the traditional crypto UX.

It starts from your destination — and reverse-engineers everything to get you there.

You say: "Buy $10 aero for me on base with usdc in arbitrum."

IGRIS handles:

– Wallet sourcing

– Best route across 5 chains

– Gas provisioning

– Execution + fallback

– Confirmation in under a second

1. Hick's Law

The time it takes to make a decision increases with the number and complexity of choices.

Most dapps overwhelm users with 8–10 sequential decisions (wallet, chain, gas, approve, sign…).

IGRIS minimizes choices to 1: your intent.

→ Result: Decision latency drops by 3–5x.

2. Information Foraging Theory

Users act like digital predators — they follow "information scent" to get what they want, as fast as possible.

• IGRIS exposes only relevant info.

• Keeps UI shallow and actionable (chat-first, real-time suggestions).

• Every click has predictive utility.

→ Reduces user drop-off by minimizing cognitive detours.

3. Constraint Theory in UX

Constraints aren't limitations — they're clarifiers.

IGRIS defines 6 constraint variables:

Max permutations = 7

Avg. decisions by user = 2–3

→ This creates a bounded decision space, which AI can optimize in < 2.5s.

4. Optimization Theory (Combinatorics)

We modeled every user intent as a graph traversal problem:

• Nodes = tokens × networks × wallets

• Edges = bridges, routes, liquidity paths

• Goal = minimize cost + time + steps

IGRIS uses an intent-graph optimizer to select the lowest-friction path in real time — similar to Dijkstra's shortest path, but domain-weighted for gas fees, bridge risks, and token volatility.

🎯 Key Results

The writing is still in progress — more to come.