DEV Community: Alex Roor

From ICO fuel to modular infrastructure

Alex Roor — Wed, 30 Jul 2025 07:45:17 +0000

Launched in 2015 at ~$0.30, Ethereum introduced a new model:
– programmable money
– smart contracts
– open dApp platforms

While others collapsed post-ICO, ETH evolved.

📊 Milestones:

• 2016 - DAO hack → chain split → Ethereum Classic
• 2017 - ICO mania → ATH $1.4K
• 2020 - DeFi summer → TVL surges, gas crisis
• 2022 - Merge → PoW to PoS
• 2024–2025 - L2s, restaking, EigenLayer dominance

Today ETH isn’t just a blockchain it’s the foundation of L2 rollups, staking economies, RWA scaling, and modular design.

It’s not perfect. Gas remains an issue. UX still sucks. But structurally, Ethereum is a proven settlement layer.

🔍 Bonus context:
The “Crypto Evolution” campaign by WhiteBIT, Tether & TradingView is live — aimed at building real skills, not just chasing pumps. Trading tools + education + up to 1,000 USDT in rewards.

Worth checking out if you're trying to improve your edge.

ETH went from “altcoin” to base layer.
Its real power isn’t the price - it’s the stack that grew around it.

🔗 I Connected ChatGPT to Smart Contracts. What Could Possibly Go Wrong?

Alex Roor — Thu, 03 Jul 2025 09:35:48 +0000

TL;DR: I wanted to teach GPT how to interact with Ethereum. The result? An agent that can parse ABIs, call contract methods, and almost sent $50 to a random wallet. Almost.

🤖 Why Put LLMs Into Web3?
I work at the intersection of AI and blockchain, and lately I’ve been wondering:
we still write contract.methods.transfer() manually,
while LLMs already build entire backends for us.

So I built a prototype AI assistant that can:

Read ABI files;

Interpret method signatures and data types;

Act as a CLI helper or Telegram bot to interact with contracts for you.

⚙️ Mini Stack
Node.js + ethers.js — to handle contract interactions

OpenAI API — to parse ABI and generate code

Express.js — simple interface to test commands

🧪 Test #1: Reading Contracts
I started with the ABI of USDT (classic ERC20).
Fed it to GPT-4 and asked:

"What does the method transfer(address,uint256) do?"

LLM answered:

“This method sends tokens from the current account to a specified address…”

✅ It worked. But GPT started hallucinating about return values and gas costs — so I tuned the temperature down.

🧨 Test #2: Executing Transactions
Next step: use LLM to generate the full transfer code.
Prompted with something like:

“Send 50 USDT to this address…”
→ await contract.transfer("0xabc...", ethers.utils.parseUnits("50", 6))

Then the chaos began:

Wrong decimal assumption (USDT uses 6, GPT assumed 18)

Address was missing the 0x

At one point it suggested sending the entire balance via signer.getBalance()

🧠 Lesson:
AI is a great co-pilot — but never let it hold the keys. Not yet.

🔐 What's Next?
No, I’m not giving GPT my private key.
Transactions are only executed after explicit user confirmation.

But imagine an AI acting like a DevOps assistant for Web3:

Listing methods;

Fetching logs;

Running gas estimates.

Right now it’s just a CLI toy — but I already see the real-world use cases:

DAO dashboards;

AI interfaces for trackers (integrating with WhiteBIT API maybe? 👀);

Voice control for non-coders: “send 20 tokens to Alex.”

📌 Final Thoughts
Merging LLMs with Web3 is like handling two fireballs —
don’t leave them unattended,
but if you train them right, they could be the future of crypto UX.

How AI & Web3 Developers Can Monetize Trading Activity — Without Charging Users

Alex Roor — Tue, 24 Jun 2025 09:28:19 +0000

As someone who builds at the intersection of blockchain and AI, I’m constantly thinking about sustainable monetization models that align with decentralization. I’m not a fan of paywalls or subscription fatigue — especially when your users are early adopters who value transparency and flexibility.

So here's a thought that’s been growing on me lately: what if we stop charging users for access — and start earning based on value delivered?

Why the Old Models Don’t Work

Many AI-driven crypto tools — like portfolio bots, signal providers, or trading dashboards — rely on charging monthly fees. That works... until it doesn’t. Users churn, competitors undercut, and you’re stuck spending more on user acquisition than you make in MRR.

But if your platform facilitates trading, you’re already creating measurable value. The question is: why not monetize it at the protocol level?

Enter: The Broker Model

Instead of building your own exchange or charging upfront fees, there’s a smarter route — connect to an existing exchange infrastructure and earn from trading volume.

Platforms like WhiteBIT offer exactly this through their Broker Program. You integrate trading into your service, and for every trade placed via your bot, dashboard, or AI assistant — you earn up to 40% of the commission.

Why This Is a Game-Changer for Devs

Let’s say you’ve built a:

Decentralized AI trading assistant

Smart contract-powered strategy layer

Multi-exchange rebalancing tool

Copy trading protocol on-chain

With a broker setup, you don’t need to:

Handle custody or compliance

Build liquidity from scratch

Convince users to pay monthly

You focus on building, while the exchange handles the backend — and shares the revenue with you.

Beyond Revenue: Growth Perks

What caught my attention with WhiteBIT’s program isn’t just the revshare. They also offer:

Co-branded marketing (socials, blogs, AMAs)

Trading tournaments for your users

Paid influencer campaigns and PR support

For a developer or startup, that’s a big deal. You're not just earning — you’re getting help growing.

My Opinion

In my view, the next generation of AI + crypto tools will lean heavily into embedded monetization. Not through fees — but through aligned infrastructure.

You help users trade smarter. Exchanges help you earn from it.

So if you’re building smart contracts, AI agents, or DeFi interfaces that interact with real markets, it might be time to think like a broker.

Start with WhiteBIT or research similar models. But don’t miss the opportunity to let your tech work for you — every time your users click “trade.”

⚙️ Setting Up DCA Investment Strategies via API: A Practical Guide

Alex Roor — Tue, 03 Jun 2025 14:18:40 +0000

Dollar-Cost Averaging (DCA) is one of the most time-tested investment strategies — and for developers, automating it through APIs is a no-brainer.
Why manually chase market entries when you can build a system that invests for you on schedule?

In this article, we’ll walk through building a simple DCA investment bot using API integrations — no magic, just practical scripting.

📌 What Is Dollar-Cost Averaging?
DCA is the process of investing a fixed amount at regular intervals, regardless of asset price.
Its purpose is to reduce the impact of volatility and emotional decision-making.

Instead of trying to time the market, DCA helps you:

Build positions gradually

Buy more when the price is low

Buy less when the price is high

Maintain long-term consistency

🔧 API Setup for a DCA Bot
Let’s assume we’re integrating with a crypto exchange API.

Steps:

Authentication
Set up secure API keys (and secrets) with limited trading permissions.
Use .env files to store credentials.

Price Fetching Endpoint
Use an endpoint like /ticker or /market_price to get real-time prices.

Buy Execution Endpoint
You’ll need a POST endpoint for placing market orders — or limit orders with optional price boundaries.

Scheduler
Use cron jobs or cloud functions (AWS Lambda, Vercel Cron, GitHub Actions) to trigger the bot regularly.

Example snippet (Node.js):

🛡️ Error Handling and Security
Always include:

Retry logic on failed requests

Logging (console + remote)

Alerts for failed executions

API key rotation logic

Secure your API with:

IP whitelisting

HMAC signatures

2FA on exchange accounts

🔁 Going Further
You can evolve this basic bot by adding:

Multi-asset support (rotate between BTC, ETH, etc.)

Dynamic allocation (e.g., based on portfolio weight)

Performance tracking (log cost basis, ROI)

Telegram notifications (for visibility)

🔍 Real-World Parallel: WhiteBIT's Auto-Invest
If you're not ready to code from scratch, some platforms already offer DCA automation natively.
For example, WhiteBIT allows users to create Auto-Invest plans with:

Flexible scheduling and amounts

No minimum investment

Optional price conditions

Wide asset selection (BTC, ETH, SOL, etc.)

Their feature essentially acts as a no-code API abstraction for retail users.

Whether you build it or use a ready tool, DCA is a strategy that rewards consistency — not timing.

🔐 Rebuilding Referral Systems On-Chain Using NFTs and Soulbound Tokens

Alex Roor — Mon, 02 Jun 2025 17:09:13 +0000

Web3 is full of dApps, tokens, and dashboards — yet when it comes to referrals, we’re still stuck in the Web2 playbook. Most crypto projects use centralized links, obscure dashboards, and off-chain logic to manage invites.

But what if we reimagined referral systems using NFTs and SBTs (Soulbound Tokens)?
Let’s bring trust and transparency back — on-chain.

🧱 Why Off-Chain Referrals Don't Scale
The traditional model works like this:

You get a link

Someone signs up

You maybe get paid

But it breaks easily:

No transparency

Easy to game (bots, sybil attacks)

No interoperability across dApps or wallets

In a permissionless world, shouldn’t our growth tools also be permissionless?

🛠️ Blueprint: NFT-Driven Referrals
Imagine this:
Every user who joins via a referral gets minted a unique ERC-721 NFT. That NFT contains:

Who referred them

Timestamp of joining

On-chain activity (trading volume, milestones)

Referral trees become visible and verifiable. You can:

Display leaderboards publicly

Use NFT traits for gamified rewards

Run multi-tier systems with composable smart contracts

solidity

mapping(address => address) public referrerOf;
mapping(address => uint256) public referredVolume;

🧠 Enter Soulbound Tokens (SBTs)
Now take it further. Want to KYC-gate your referrals like real exchanges do?
Soulbound Tokens are your answer.

Minted only if the user completes KYC

Non-transferable = proof of verified identity

Can be burned or updated as their activity grows

This is especially useful in CeFi x DeFi hybrids, where trust matters.

🌐 Real-World Parallel: WhiteBIT Hybrid Model

WhiteBIT, one of the leading centralized exchanges in Europe, offers a strong referral program:

Up to 50% of commissions from invited traders

KYC is required for full rewards

QR codes and shareable APIs help devs integrate it flexibly

It’s still off-chain today — but it’s a perfect test case to build a smart-contract-powered clone.

Imagine: your dApp tracks user referrals, rewards based on volume, and verifies identity — all without a backend.
That’s the future we’re building.

🔄 The Vision
We talk a lot about decentralization.
But it’s time to decentralize how growth works too.

Referral programs shouldn’t be stuck in spreadsheets.
Let’s put them on-chain, make them programmable, and give devs the tools to build trust — with code.

How I Built a DCA Engine with GPT Agents and Web3 APIs

Alex Roor — Wed, 28 May 2025 10:22:28 +0000

Let’s be honest — building a crypto trading bot from scratch isn’t exciting anymore. Everyone’s done it. But building one that’s driven by AI agents and fine-tunes itself based on live market behavior?

That’s where it gets interesting.

💡 The Idea
I wanted to create a simple Dollar-Cost Averaging (DCA) engine…
Except instead of hardcoding rules like “buy $100 of ETH every Monday,” I wanted it to:

Choose the token based on market signals

Determine the interval based on volatility

Use GPT to evaluate the logic at runtime

And adapt.

Essentially: a self-evolving DCA agent.

🧱 The Stack
GPT-4 via API + long-term memory via Supabase

Viem (ethers.js alternative) for wallet + price data

Simple CLI with logging + fallback control

CEX integrations: used WhiteBIT for stable API benchmarks

Off-chain feedback loop (Twitter sentiment via HuggingFace model)

My agent could say:

“Based on current 30-day BTC volatility and ETH funding rates, I suggest reallocating DCA from ETH to LINK for the next 3 cycles.”

🧩 Why I Compared It with WhiteBIT AutoInvest
You can’t optimize what you don’t benchmark.
So I ran side-by-side comparisons:

My adaptive AI agent

Static DCA via WhiteBIT AutoInvest

Guess what?
In stable market conditions, AutoInvest matched or beat my agent.
But in volatile days, the agent performed slightly better — though sometimes overreacted.

“WhiteBIT AutoInvest gave me a reliable floor. My agent could try to beat it — but never ignore it.”

🚀 Final Thoughts
Sometimes the smartest thing isn’t to over-automate — it’s to observe what works, then build from there.
AutoInvest is proof that simplicity at scale is still powerful.

And if you can layer AI intelligence on top of that… well, that’s the next frontier.

🤖 I Asked GPT-4 to Audit My Smart Contract. Here's What Happened

Alex Roor — Tue, 20 May 2025 11:32:14 +0000

Let me preface this: I’m a Web3 dev. I don’t fear Solidity.
But like any honest builder, I’ll admit this — auditing your own code sucks.

You miss your own blind spots. You get tired. You skip stuff. So I did what any curious developer in 2025 would do:

I asked GPT-4 to audit my smart contract.

Spoiler: it didn’t disappoint.

💥 The Setup
I gave it a pretty straightforward ERC-721 implementation with some edge-case logic for minting rules and royalties. My goal? See what it would catch — and if it could explain vulnerabilities better than the average GitHub reply.

Here’s what GPT-4 flagged:

✅ Missing reentrancyGuard on a custom withdraw function

✅ An unused event that bloated gas slightly

✅ Poor randomness source in a “lucky mint” mechanic

✅ Lack of failsafe if royalty receiver was the zero address

Some of this I knew. Some I definitely missed.

📚 But It Wasn’t Just the Errors…
It was how clearly GPT-4 explained why each thing mattered.
And it went a step further — suggesting best practices that most juniors (and frankly, some seniors) overlook.

I ran a side-by-side test:
GPT-4's audit vs ChatGPT-3.5’s — the difference in depth was very real.

💡 Where WhiteBIT Comes In
Here’s the kicker: I’ve worked with projects getting ready to list on WhiteBIT, and this level of audit hygiene is exactly what makes or breaks the trust in early token launches.

Whether it’s a simple staking contract or a more complex DAO flow, being able to run AI-assisted audits before manual code review helps avoid PR disasters and makes listing smoother.

WhiteBIT, as one of the most structured exchanges for onboarding new assets, cares about smart contract quality. This experience made that make even more sense.

🧠 Should You Trust AI With Your Contracts?
Not blindly. But it’s like having a super-fast, opinionated junior dev who doesn’t sleep and constantly reads all the best security blogs.

Would I use it again? Definitely.
Would I rely on it alone? Never.
But as a first pass — it’s an insanely useful tool in your stack.

🔥 Why You Should Ditch Popular Frameworks (And What to Use Instead!) 🔥

Alex Roor — Mon, 19 May 2025 14:19:02 +0000

Ever felt overwhelmed by the endless parade of frameworks like React, Next.js, or Vue.js? Sure, they're popular and powerful, but let’s be real — sometimes they’re just overkill. Today, I'm diving into sleek alternatives that deliver top-notch results without bloating your projects.

🚀 Web Components: Native Power!
Forget heavy libraries. Vanilla Web Components give you modular, reusable elements with zero dependencies. Here’s how simple it can be:

Easy, clean, and supported everywhere. Say goodbye to version nightmares and hello to a lightweight, fast-loading UI.

🌐 AJAX-driven HTML: Dynamic Simplicity!
Modern doesn't mean complex. Fetch data directly into your markup effortlessly:

Using tech like HMPL, you get server-side rendering simplicity with client-side dynamism. Super fast, scalable, and ideal for seamless user experiences.

📚 Content Management Systems (CMS): Old School, Still Cool
You might think CMS platforms like WordPress belong in a museum, but they're still massively effective. With countless plugins and themes, you build functional, secure, SEO-friendly websites effortlessly.

👉 Fact Check: 43% of the web still runs on WordPress. Old doesn’t mean outdated!

⚡ Static Site Generators: Fast and Furious
Need a lightning-fast blog or documentation site? Meet Jekyll, Hugo, or Astro. Minimalistic and blazing-fast, these generators compile Markdown to HTML at build time, ensuring rapid load speeds and top-tier security.

Here's how a typical Jekyll page looks:

Ideal for developers who value simplicity and performance above all.

✨ Wrapping It Up
Frameworks are powerful, but they're not the only way. Depending on your needs—speed, simplicity, maintainability—you might find that simpler methods vastly outperform complex frameworks.

Supply Chain Security in 2025: Why SBOM and SLSA Matter More Than Ever

Alex Roor — Wed, 23 Apr 2025 09:25:05 +0000

In today’s cloud-native, container-heavy, and open-source-driven world, supply chain security is more than just a buzzword — it’s a survival strategy. From build systems to dependencies, developers are under pressure to secure the full lifecycle of their applications.

📦 SBOM: The Software Bill of Materials

An SBOM is like a nutrition label for your software — a detailed list of all components, libraries, and tools in your stack. With increasing regulatory interest (think: U.S. Executive Order 14028), maintaining an SBOM is becoming essential.

Tools like Syft, SPDX, and CycloneDX make it easier to generate SBOMs for your builds.

Modern CI/CD pipelines are beginning to enforce SBOM checks as part of compliance.

✅ What Is SLSA?

SLSA (Supply-chain Levels for Software Artifacts) is a framework for securing software from source to production. It focuses on:

Provenance: where your code and binaries come from

Integrity: making sure builds are tamper-proof

Traceability: auditing every step from commit to deploy

Platforms like WhiteBIT, Coinbase, and OKX are adopting rigorous supply chain standards to ensure the trustworthiness of their backend services and protect user funds.

🧰 How Devs Can Start

Add SBOM generation to your CI pipeline

Use signed commits and container image signatures

Monitor for CVEs in dependencies with tools like Snyk, Trivy, or GitHub Dependabot

If you're touching production code in 2025, supply chain security should be part of your daily routine.

Signals in Modern Frontend: Preact, Solid.js, Qwik

Alex Roor — Tue, 22 Apr 2025 09:44:06 +0000

Say goodbye to the virtual DOM — the new generation of frameworks are all about reactivity at the core. Preact Signals, Solid.js, and Qwik are leading the charge.

⚡ What Are Signals?

Signals are reactive primitives that track and respond to changes in state directly, without needing a full re-render cycle. They:

Eliminate the virtual DOM diffing overhead

Enable fine-grained reactivity

Scale better in complex UIs

🌟 Framework Highlights

Preact Signals – Minimal and fast. Integrates with the Preact ecosystem.

Solid.js – Uses JSX but compiles to pure functions. Super-fast and fine-grained.

Qwik – Focused on resumability. Delivers minimal JS to the browser, and runs instantly on interaction.

🧩 Why It’s a Big Deal

Better performance out of the box

Optimized for edge and mobile environments

Reduces JS payloads dramatically

🔍 Use Cases

Real-time dashboards

Interactive tools on low-power devices

Apps where Time to Interactive (TTI) is mission-critical

If you’re looking to build fast, reactive UIs without bloated bundles — Signals might be your new best friend.

AI Agents for Developers: Automating CI/CD, Docs, and Tests

Alex Roor — Thu, 17 Apr 2025 11:33:18 +0000

Modern development isn’t just about writing code — it’s about maintaining pipelines, writing documentation, and testing constantly. These tasks are essential but time-consuming. That’s where AI developer agents come in.

💡 What Are Dev Agents?

Dev agents are purpose-built AI tools (often powered by LLMs like GPT-4 or Claude) that assist or fully automate parts of the software lifecycle. They can:

Create CI/CD workflows

Write or update documentation

Generate and run unit tests

Think of them as smart assistants that never sleep and never forget syntax.

🔧 Key Capabilities

CI/CD Automation – Agents like OpenDevin and AI-augmented CLI tools can set up GitHub Actions, Docker builds, and deployment scripts just from a project description.

Documentation Generation – Agents analyze your codebase and generate clean, structured docs — from README files to in-code comments.

Testing Agents – Tools like Codegen, GPT Engineer, and internal OpenAI/Anthropic projects help create robust test suites, detect missing cases, and even auto-debug.

⚡ Developer Workflow Example

You write a new feature.

An agent detects changes, updates the docs, and modifies your CI pipeline.

Another agent writes unit tests and runs them.

If a test fails, it suggests fixes — or applies them directly.

🧪 Real Tools in Use

Continue: VSCode plugin that integrates LLMs into your workflow.

Cody (by Sourcegraph): For codebase-wide understanding and documentation.

GitHub Copilot X: Enhanced Copilot with test/gen/docs support.

🚀 Why It Matters

We’re moving toward AI-native development environments. Instead of jumping between tools and tasks, agents handle the glue work. This:

Speeds up shipping

Improves code quality

Frees devs for higher-level design

If you’re still writing your CI from scratch or maintaining test coverage manually — it’s time to give AI agents a spin.

How Coding Has Changed Over the Last 2 Years

Alex Roor — Mon, 07 Apr 2025 09:55:39 +0000

The tech world doesn’t sit still—and neither do developers. Over the last couple of years, it feels like we’ve hit the fast-forward button on how we code, what we code, and even why we code. If you’ve been anywhere near GitHub, Twitter (X?), or Stack Overflow, you’ve probably noticed the shifts too.

Let’s take a casual stroll through the changes, shall we?

✨ AI: From Sidekick to Co-Pilot
Let’s start with the elephant in the IDE: AI-assisted coding.

Two years ago, tools like GitHub Copilot were still making their debut. Today? It’s becoming weird not to have some form of AI helping out. Whether it's autocompleting entire functions or refactoring spaghetti code into angel hair pasta, AI has become a real-time pair programmer for many of us.

It’s not just about productivity—it’s about changing how we think when we code. We're writing fewer lines, but thinking at a higher level.

📦 Frameworks Are Getting... Weirdly Good
Remember when a new JS framework felt like a chaotic reinvention of the wheel? Now, they’re starting to feel more like thoughtful evolutions.

React Server Components are changing how we approach rendering.

SvelteKit and Next.js have matured into serious contenders for production apps.

Even Bun is making us question our loyalty to Node.js.

Frameworks today feel like they’re finally listening to developers: faster builds, smarter defaults, and fewer headaches.

🧪 DevOps Got Friendlier (Sort Of)
Deployments, CI/CD, containers—these were all things devs either loved or feared. Now, tools like Vercel, Netlify, and GitHub Actions have abstracted much of the pain away.

It’s easier than ever to ship code. You still need to know what you’re doing, of course, but the barrier to entry is lower. And that’s kind of beautiful.

💬 Coding Communities Have Fragmented—and That’s Okay
Two years ago, Twitter was the watercooler for devs. Since then, things have shifted. We now see:

More developers moving to Discord and Slack communities.

Reddit resurging for deeper tech convos.

And of course, Dev.to staying cozy and evergreen.

There’s no single place where devs hang out anymore, but that’s okay. More choice means more specialized, meaningful convos.

🌍 The Rise of the Polyglot Developer
Languages like Rust, Go, and even Zig are gaining serious traction. At the same time, classics like Python and JavaScript aren’t going anywhere.

But what’s changed is this: devs are more open to learning multiple languages, and not just because they have to—but because they want to. We’re curious creatures, after all.

So... What's Next?
If the last 2 years taught us anything, it’s that change is the only constant in coding. Whether you’re writing your first hello world or building distributed systems, the way we think, write, and collaborate around code keeps evolving.

And maybe that’s what makes coding so addictive: there’s always something new around the corner.