Why Trust Matters More in Web3

The nature of blockchain is irreversible — that’s well known. This means every transaction decision can have serious repercussions.

From a UX perspective, most decentralised products still have a long way to go. For instance, you may feel confused when there’s a lack of feedback or response after you’ve confirmed an action like “Claim.” ( oops, where it went…)

Both CEX and DeFi products carry the risk of being hacked. For users new to Web3, understanding how digital signatures work during transactions can be overwhelming.

Unlike traditional finance, blockchain allows users to verify independently — for example, a digital signature can be verified by anyone who has the sender’s public key.

Take a pause.

We can assume most customers are new to Web3. Therefore, jargon-heavy products destroy trust instantly. Add a UI lacking clarity and real-time feedback, and that’s a recipe for hell disaster.

The Role of Motion in Verify

Motion plays an essential role in all my Web3 projects.
When we talk about motion, we shouldn’t confuse it with UI animation only. Some principles overlap — for example, micro-interactions and movement — but motion design defines how customers perceive your brand and overall UX strategy.

Motion UX is a strategic tool that guides Web3 users.
There are a few key principles for building motion patterns in the Web3 verification process.

A set of key principle

Rather than listing abstract principles, let’s see how to apply them in real use cases.

Intentional — Verify First

Most end users don’t know how to verify the authenticity of a payment link or any sender request from the blockchain.
A guided verification process can use motion purposefully — for example, verifying the sender’s Web3 public profile.
Compared to a wall of text instructions, this provides real-time, continuous feedback.

Movement — permission & authorisation

Oftentimes we overlook how natural movement can guide users. The completion of block should be perceived to be securly united following by the rules of neutral physics.

• Permission — when authorising, motion can indicate security and full access

• Approval — during verification, motion helps users understand when and how a process will be completed

To make Web3 payments more approachable, we first need to reduce cognitive load and improve user understanding. Motion plays a key role here — arguably more so than in Web2 products.

In traditional finance, you simply pay from the balance you have. In Web3, however, users must understand bridging chains, transaction fees, networks, and other technical details. The experience often falls short of expectations. Motion can bridge this gap.

Motion Principles in Web3 UX

1. Communicating state changes

• Users often don’t know which balance is being used across multiple chains or wallets. For example, when Alice sends $10 to Bob or spends $20 in a store:

• UX Motion: Animate the “consolidation” of balances and show how assets are being utilised. This enhances understanding and reduces confusion.

2. Providing feedback for actions

• Instant transfers are ideal, but delays are common in Web3 due to permit flows, which can lead to frustration and uncertainty.

• UX Motion: Animate token movements, including when they are blocked and when they become available again.

3. Guiding attention

• In a unified balance mechanism , users can send tokens across chains, but they may want the option to choose specific tokens or chains for a transaction.

• UX Motion: Use animations like bouncing or pulsing to highlight selected or unselected options, emphasizing the user’s choices.

Branding and Trust

Finally, motion reinforces branding and trust that I have another article for this. A well-crafted animation communicates safety and reliability, particularly when handling assets. Motion is not just about aesthetics — it helps users feel confident and informed in their Web3 interactions.

Wrap up

The Web3 story doesn’t stop here. How UX motion techniques help users and clients navigate risk and uncertainty is a big challenge — but an exciting one.

🧠 Step 1: Intelligence & Design Extraction

Here’s how AI helped compress what normally takes days (or weeks) into minutes.

Instead of inventing colors from scratch, the AI acted like a design engineer.

It began by analysing the light mode variable ( if you had JSON file or screenshots would have worked) 

• Identified core brand colours (Primary Purple: #5F4DBC)

• Classified semantic roles (backgrounds, surfaces, text, accents)

Then it applied logic-based transformations to generate Dark Mode:

• Surfaces: Inverted lightness (White → #121214)

• Brand: Tuned saturation and brightness for dark environments (#5F4DBC → #816EDE)

• Accessibility: Automatically validated contrast against WCAG AA standards

  
  

🔄 Step 2: Design ↔ Code Interoperability

Moving design tokens between engineering and Figma usually means exporting/importing JSON or relying on plugins.

To see how much AI could streamline this workflow, I put it to the test.

In practice, standard token formats didn’t import cleanly into the existing Figma setup. The most reliable solution still came down to manual work—creating variables by hand. That’s the current reality.

However, to bridge the gap between raw token data and visual understanding, you’d ask AI to generate a Design Docs that mirrors the visual utility of Figma documentation. 

📱 Step 3: Rapid Hifi Prototyping

With the tokens in place, the next step was to see how they actually translated into the existing UI—starting from the light theme and moving toward Dark Mode.

This meant building out detailed previews, including:

• Trade inputs

• “Best Price” toggle

• Chart lines

• Layout spacing

Some custom assets—like avatars created in SVG—aren’t always easy for AI to reproduce perfectly on the first pass. That required a few iterations to resolve visual inconsistencies and fine-tune the details.

Still, one especially useful outcome was generating a practical cheat sheet that maps every UI element to its underlying token. This “variable map” can become a clear guide for engineering:

• Trade Card → background surface

• Text styles → semantic typography variables

• Components → design tokens

Having this mapping in place makes implementation far more straightforward—and removes much of the guesswork for developers.

The final UI result covers both light & dark themes. 

✨ Key Takeaway

The AI didn’t just write code; it became a design partner. It participated in the entire product design lifecycle:

• Extracting requirements

• Designing accessible systems

• Solving tooling compatibility ( you’d still need quite a workaround to fix issues) 

• Producing documentation

• Building production-grade prototypes