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The financial industry has experienced rapid changes in recent years, largely fueled by digital assets, blockchain technology, and advanced automation. Among the most talked-about developments is the rise of crypto algorithmic trading bots. These software-driven programs are designed to analyze market data, identify trading opportunities, and execute trades automatically. While trading bots are not new — traditional financial markets have relied on them for decades — their application in cryptocurrencies introduces unique features and challenges.

Businesses exploring opportunities in this space often interact with blockchain development services to create custom trading applications that meet the distinct requirements of digital asset markets. Understanding how crypto bots differ from traditional ones is essential for companies, investors, and even blockchain developers who want to offer practical solutions.

This comprehensive blog explores the core differences between crypto algorithmic trading bots and their traditional counterparts. We will cover their structures, functions, benefits, limitations, and most importantly, what businesses should consider before integrating or developing them.

What Are Trading Bots?

A trading bot is essentially a piece of software that executes trades based on pre-defined rules. These rules can be as simple as buying when prices drop below a certain point or as complex as using advanced statistical models and AI-driven predictions.

Traditional Trading Bots

Traditional bots are primarily used in stock markets, forex, and commodities trading. They are built to work with centralized exchanges, where rules are strictly defined and market hours are limited.

Crypto Algorithmic Bots

In contrast, crypto trading bots operate in 24/7 markets without central oversight. This makes them more dynamic but also more complex. They need to accommodate irregular volatility, sudden liquidity changes, and risks specific to digital assets, such as exchange outages or wallet security.

Algorithmic trading, sometimes called algo-trading, became mainstream in traditional markets in the early 2000s. By relying on automation, algorithmic systems could process vast amounts of data in milliseconds — something no human trader could achieve consistently.

In cryptocurrencies, algorithmic trading became popular much faster. This is because crypto exchanges operate globally at all times, creating continuous opportunities for arbitrage, momentum strategies, and pattern recognition. Trading bots, therefore, became indispensable tools for both institutional and retail traders.

Whether traditional or crypto-based, bots typically consist of three layers:

1. Market Data Analysis — Collecting and interpreting real-time price, volume, and order book data.
2. Signal Generation — Using predefined strategies to determine when to buy or sell.
3. Execution — Placing orders quickly and efficiently with an exchange.

While the structure is similar, the underlying data sources, execution methods, and regulatory frameworks vary dramatically between traditional and crypto environments.

Here’s where the distinctions become clear:

1. Market Hours

• Traditional markets operate on fixed schedules (e.g., 9:30 AM — 4:00 PM EST for the stock market).
• Crypto markets never sleep, which means crypto bots must be capable of non-stop monitoring and quick decision-making.

2. Market Volatility

• Stocks and forex markets experience fluctuations but are more stable compared to crypto.
• Crypto markets are infamous for extreme volatility, demanding bots that can handle sudden and sharp movements.

3. Liquidity Structures

• In traditional markets, liquidity is deep and highly synchronized across exchanges.
• Crypto liquidity is fragmented, with hundreds of exchanges offering different prices for the same assets.

4. Regulation

• Traditional markets are heavily regulated, requiring strict compliance.
• Cryptocurrency regulations vary drastically across jurisdictions, often leaving grey areas for developers.

5. Execution Speed and Infrastructure

• Traditional bots rely on co-location and high-frequency infrastructures in well-established data centers.
• Crypto bots often interact with APIs provided by exchanges, making them more dependent on third-party performance quality.

Traditional Bot Strategies

• Mean Reversion
• Statistical Arbitrage
• Basket Trading
• Latency Arbitrage

Crypto Bot Strategies

• Arbitrage across multiple exchanges
• Trend-following strategies in highly volatile markets
• Market-making in tokens with growing interest
• Exploiting liquidity mining or decentralized finance (DeFi) yield opportunities

Each environment has unique strategies that adapt to volatility, liquidity, and regulatory conditions.

Creating trading bots is not as simple as writing a script. Businesses must consider:

• Data feed reliability
• Exchange API integration
• Latency management
• Cloud or dedicated hosting solutions
• Wallet integrations and transaction costs
• Security against attacks, hacks, or price manipulation

This makes the role of professional blockchain development companies critical when designing crypto bots, compared to firms working in conventional equities or forex development spaces.

Traditional trading bots operate in secure systems where brokers and exchanges already provide high-level security safeguards.

In contrast, crypto trading bots must address risks such as:

• Hacking of exchange APIs
• Vulnerabilities in wallet integrations
• Risks from decentralized platforms where code may contain bugs
• Phishing and malicious bot impersonation

Managing these risks requires sophisticated coding standards and resilient safeguards during the development phase.

For financial firms, the decision to build or use bots often comes down to:

• Market opportunity: Crypto offers higher volatility, and therefore higher potential gains.
• Cost efficiency: Traditional market entry requires brokers, clearing firms, and licenses; crypto only needs access to exchanges.
• Innovation scope: Blockchain-based bots enable integration with DeFi protocols, NFTs, and token staking — something traditional bots don’t cover.

• Hedge Funds use bots for risk management and systematic trading.
• Retail Traders rely on bots to stay active in the market without manual supervision.
• Businesses and Exchanges implement automated bots for liquidity provision.
• Blockchain Startups integrate custom bots with services like arbitrage engines or decentralized trading solutions.

Despite the growth of crypto bots, challenges remain:

• Exchange reliability issues
• Regulatory uncertainty
• Rapidly evolving attack vectors in blockchain
• Technological barriers for non-technical businesses

Organizations need expert guidance to navigate these challenges effectively.

Crypto algorithmic trading bots and traditional bots share a fundamental principle: automating decisions for faster and more efficient trading. Yet, the differences between them highlight why businesses must approach crypto bot development with fresh perspectives. Continuous markets, volatility, fragmented liquidity, and regulatory uncertainty make crypto bots distinct from traditional ones — not just in operations but in the very way they’re conceived and maintained.

For businesses looking to create or integrate algorithmic bots in the crypto ecosystem, expertise in blockchain is critical. Partnering with skilled developers allows companies to build reliable, secure, and efficient trading systems that align with market realities.

At Codezeros, we help businesses build the future of trading with advanced blockchain development solutions. Whether you are a financial institution, a startup, or an enterprise exploring digital asset automation, our blockchain development services can help you conceptualize, design, and launch reliable trading bots that perform in today’s evolving markets.

Get in touch with Codezeros today to discuss your next blockchain development project.

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The ongoing evolution of decentralized finance (DeFi) is changing the crypto industry in unprecedented ways. Among the many innovations reshaping digital finance, DeFi trading bots stand out as one of the most impactful developments in recent years. These automated tools are helping crypto businesses and individual traders manage trades, monitor prices, and react to market events faster than ever before. In this comprehensive guide, we explore how DeFi trading bots work, their influence on business models, their benefits and risks, and why working with a DeFi Development Company can be pivotal in harnessing their full potential.

DeFi trading bots are automated software programs that interact directly with decentralized finance protocols on blockchain networks, executing trades according to predefined criteria. Unlike manual trading, where users must actively monitor markets and place orders, bots follow programmed instructions and can operate across multiple decentralized exchanges (DEXs) and protocols without any human supervision.

These bots connect with user wallets using secure integrations and interact with smart contracts on blockchains like Ethereum, Binance Smart Chain, and Polygon. Once deployed, they continuously scan market data, assess opportunities, and execute trades at optimal moments based on their configuration and strategy.

DeFi bots have become indispensable to various crypto businesses, from hedge funds and market makers to decentralized autonomous organizations (DAOs) and individual traders. The need for round-the-clock trading, greater efficiency, and adaptability to fast-moving markets has made automation a central pillar of modern crypto operations.

A DeFi Development Company builds, customizes, and maintains these bots, helping clients integrate advanced trading features such as portfolio rebalancing, yield optimization, risk management, and liquidity provision into their business models.

• Arbitrage: Identifying price discrepancies across multiple DEXs and executing near-simultaneous trades to lock in profits.
• Market Making: Providing liquidity to pools and earning fees by automating buy and sell offers.
• Yield Optimization: Maximizing returns from liquidity pools, farming, and staking by auto-allocating assets where yields are highest.
• Risk Management: Using stop-loss orders, rebalancing portfolios, and diversifying holdings automatically.
• Sniping and Quick Trades: Reacting within seconds to new token launches or sudden market movements, often ahead of manual traders.

DeFi bots operate by following a standardized process:

1. Market Data Collection: Bots extract real-time data from DEXs and blockchain oracles, assessing metrics such as price, liquidity, and volume.
2. Signal Analysis: Through built-in algorithms, bots determine buy or sell signals based on technical indicators (e.g., RSI, moving averages) or custom logic.
3. Automated Trading: When an opportunity aligns with programmed criteria, the bot automatically executes trades.
4. Monitoring and Adjustments: Bots review positions and shift strategies based on updated market conditions, ensuring continuous optimization.
5. Unlike centralized bot operations, DeFi bots interact directly with smart contracts, offering more transparency and usually reducing the risks tied to centralized exchanges.

Efficiency and Productivity

DeFi trading bots can process vast amounts of data and execute trades at a speed that manual traders cannot match. This efficiency leads to higher trading volumes, reduced latency, and improved market liquidity. For crypto businesses, this means the ability to serve more clients, offer competitive trading fees, and take advantage of fleeting market opportunities around the clock.

New Revenue Streams and Cost Optimization

Through automated liquidity provision, arbitrage, and market-making strategies, businesses can generate consistent revenues from trading fees, yield farming, and protocol incentives. As reported, companies using AI-driven bots have reduced transaction costs and accessed faster execution, leading to improved profitability.

Emotional Discipline and Error Reduction

Unlike human traders, bots operate strictly on programmed parameters. This removes the risks associated with impulsive or emotional trading decisions, leading to more consistent and reliable outcomes.

Accessibility and Inclusivity

DeFi bots make sophisticated trading strategies accessible to a wider audience — even those without advanced technical knowledge. With user-friendly interfaces and customizable settings, more businesses and individuals can enter and compete in the DeFi market.

Each type serves a specific purpose, and businesses will often deploy a mix of bot strategies to diversify operations and manage risks.

• Continuous Trading: Bots operate 24/7, responding instantly to market changes — a defining feature in crypto markets that never sleep.
• Speed and Scalability: Handle thousands of trades and manage complex strategies across multiple assets or protocols at once.
• Liquidity and Volumes: Automated participation in DEXs boosts overall liquidity, fostering healthier trading environments.
• Risk Management: Automation includes risk controls such as stop-losses and dynamic allocation to help reduce the chances of major losses.
• Cost Savings: Lower transaction costs and human resource requirements free up capital for strategic growth.
• Data-Driven Decisions: Bots analyze market data in real time, leading to more informed and profitable trading.

While the benefits are significant, DeFi trading bots come with important challenges:

• Smart Contract Risks: Bugs or vulnerabilities in bots or DeFi protocols may expose funds to security threats.
• Volatility: Unexpected market swings can cause losses, especially if bots are not configured with proper risk controls.
• Regulatory Uncertainty: The regulatory framework for DeFi and trading automation remains unclear in many jurisdictions, posing compliance risks to businesses.
• Over-Competition: As more bots operate in the market, profits from strategies like arbitrage can decrease due to increased competition and shrinking price gaps.
• Technical Complexity: Developing, deploying, and maintaining advanced bots requires expertise — a critical reason for businesses to work with experienced DeFi developers.

A professional DeFi Development Company provides critical support in designing and deploying customized trading bots tailored to the unique needs of crypto businesses. Their services include:

• Smart Contract Development: Crafting secure, audited contracts for bot interactions.
• Strategy Engineering: Integrating advanced algorithms, machine learning models, and risk controls for automated strategies.
• User Interface Design: Building simple web or app interfaces for easy configuration and monitoring of bots.
• API Integration: Ensuring seamless connections with wallets, exchanges, and oracles.
• Security and Compliance: Conducting regular audits and updates to guard against vulnerabilities and comply with evolving regulations.

By collaborating with an expert development partner, crypto businesses can turn complex, resource-intensive processes into streamlined, automated trading solutions.

• AI Integration: Many trading bots are now incorporating machine learning, enabling more adaptive algorithms that learn from historical and real-time data, improving accuracy and profitability.
• Multi-Chain Strategies: Advanced bots are extending their reach across chains for broader market opportunities and risk spreading, thanks to improved blockchain interoperability.
• User Experience: Intuitive interfaces and automated portfolio management tools are making DeFi participation accessible for both retail and institutional participants.
• Greater Market Maturity: As more institutional players enter DeFi, robust, compliant, and transparent trading bots will be a vital backbone for protocol governance, liquidity provision, and risk management.

To embrace the full advantages of DeFi trading bots while maintaining safety, efficiency, and compliance, working with a trusted DeFi Development Company is essential. Whether you need a custom strategy for arbitrage, advanced risk controls, or user-friendly interfaces for your clients, the right development partner will deliver purpose-built solutions that align with your business vision.

Ready to accelerate your crypto business with cutting-edge DeFi trading technology? codezeros specializes in building robust, scalable, and secure DeFi trading bots tailored to your business needs. Contact us today to discover how our DeFi development services can unlock new growth opportunities for your venture!

Crypto Journalist

Anas Hassan

Crypto Journalist

Anas Hassan

About Author

Anas is a crypto native journalist and SEO writer with over five years of writing experience covering blockchain, crypto, DeFi, and emerging tech.

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Last updated: 

July 24, 2025

Two MIT-educated brothers accused of orchestrating the largest MEV bot exploitation in cryptocurrency history will face trial after a federal judge rejected their attempts to dismiss fraud and money laundering charges.

Anton Peraire-Bueno, 24, and James Peraire-Bueno, 28, allegedly stole $25 million in cryptocurrency within 12 seconds by manipulating Ethereum’s MEV-Boost protocol in April 2023.

Technical Error or Deliberate Exploit?

The brothers meticulously planned their operation over several months, studying trading patterns of Ethereum bots and establishing shell companies.

They created 16 Ethereum validators using approximately $880,000 in cryptocurrency, then executed what prosecutors called a “bait, block, search, and propagation” scheme targeting three victim traders operating MEV bots.

Their exploit involved proposing “lure transactions” to induce victim traders’ bots to purchase illiquid cryptocurrencies worth $25 million.

The brothers then sent a false signature to the relay system, gaining premature access to private transaction data.

They replaced the lure transactions with their own trades, selling the illiquid tokens and rendering the victims’ holdings worthless.

Following the theft, the brothers laundered the stolen funds through complex transactions across multiple addresses and foreign exchanges with limited KYC requirements.

They converted the cryptocurrency to DAI stablecoin, then to USDC, before transferring $20 million to U.S. dollar accounts. Foreign law enforcement froze $3 million of the stolen funds.

The case comes amid rising concerns about MEV exploitation across blockchain networks.

Recent incidents include a $2 million insider attack on Bedrock’s UniBTC protocol by a former Fuzzland employee and a notorious Solana MEV bot named “arsc” that accumulated $30 million in two months through sandwich attacks.

Brothers’ Legal Battle Reaches Critical Juncture

Federal prosecutors arrested the Peraire-Bueno brothers on May 15, 2024, with Anton taken into custody in Boston and James in New York.

U.S. Attorney Damian Williams described the scheme as meticulously planned, noting how the brothers “used their specialized skills and education to tamper with and manipulate the protocols relied upon by millions of Ethereum users.“

The brothers face charges of conspiracy to commit wire fraud, wire fraud, and conspiracy to commit money laundering.

Each charge carries a potential 20-year prison sentence. A federal judge scheduled their trial for October 14, 2025, after denying their motions to dismiss the indictment.

The court found the wire fraud charges legally sufficient, determining that the brothers’ lure transactions and false signatures constituted material misrepresentations.

The judge ruled that the $25 million in stolen cryptocurrency represented a traditionally recognized property interest, not merely contingent profits.

IRS Criminal Investigation’s New York Cyber Unit traced the stolen funds back to the brothers despite their sophisticated laundering efforts.

Special Agent Thomas Fattorusso noted that investigators “simply followed the money” using cutting-edge technology and traditional investigative methods.

Growing MEV Threat Challenges Blockchain Scalability

MEV exploitation has emerged as a dominant threat to blockchain scalability, according to recent research from Flashbots.

According to a report covered by Cryptonews in June, MEV bots now consume 40% of all blockspace on Solana and over half of the gas usage on Ethereum rollups, such as Base and OP Mainnet.

The Peraire-Bueno case represents the first criminal prosecution of MEV manipulation; however, similar exploits continue to occur across various networks.

A Ronin Network breach in August 2024 initially appeared malicious but was later revealed to be a white-hat operation, with the hacker returning $9.8 million after discovering a vulnerability in the bridge.

Recent data from EigenPhi shows more than 81,000 users fell victim to sandwich attacks in the last 30 days alone.

These attacks now account for nearly $1 billion in weekly trading volume on Ethereum-based decentralized exchanges.

Flashbots has proposed new frameworks to address MEV abuse, including explicit MEV auctions and programmable privacy using Trusted Execution Environments.

The organization argues that current spam from MEV bots creates artificial fee floors, undermining the promise of near-zero transaction costs on scaled networks.

The brothers’ trial, scheduled for October, is likely to set precedents for future MEV-related prosecutions, as it isn’t technically precise whether it can be attributed to an exploit of a technical oversight.

Imagine a world where digital characters move and act just like real people. Meta’s new AI model, called Meta Motivo, aims to make that happen. It’s designed to give virtual agents more natural movements and responses, allowing them to fit smoothly into Metaverse experiences. With Meta Motivo, digital characters feel more alive, making virtual worlds richer, more inviting, and a lot more fun.

The main idea behind the Meta AI Model is to help virtual characters feel more genuine. In the past, making AI characters move or behave naturally often required lots of careful planning and fine-tuning. Meta Motivo changes that.

It learns on its own how to carry out a wide range of tasks—like walking, standing, or responding to a sudden change—without constant human input. As a result, these digital figures look and feel more like real people.

Source Meta

Full-Body Control Made Simple

One of Meta Motivo’s greatest strengths is its ability to control an entire digital body. It can track motion, achieve certain poses, and find its way around different places, all with minimal extra training.

Because it understands how bodies are supposed to move, it can jump into new situations and still behave naturally. This realistic movement makes it easier for us to connect with these virtual characters, almost as if they were right there with us.

Meta put the model to the test using datasets from all kinds of scenarios and languages. They also let human reviewers judge how well it performed. The results were impressive. Compared to other AI models, Meta Motivo handled a broad variety of tasks smoothly and didn’t need special instructions or massive rewrites of its code. This kind of testing shows that the Meta AI Model is ready to bring its realistic behaviors to the real world.

While Meta Motivo focuses on making characters feel more human, Meta is also working on tools to keep online content trustworthy. One such tool is Meta Video Seal, which helps confirm the origin of a video.

It does this by placing hidden marks in the video, acting like a signature that proves where it came from. By doing this, Meta aims to reduce misinformation and help people trust what they watch and share online.

Learning Without Labels

An important part of Meta Motivo’s learning process is something called unsupervised reinforcement learning. Instead of relying on carefully labeled examples, the model learns from raw data—like recordings of movement—and figures out what to do on its own.

By storing all this information in a shared space and understanding the rewards for certain actions, the model quickly picks up a wide range of skills. Whether it’s handling whole-body tasks or adjusting to sudden changes in its virtual world (like a gust of wind), Meta Motivo becomes more flexible and realistic simply by learning as it goes.

Editor’s note: Written with the assistance of AI – Edited and fact-checked by Jason Newey.

I’ve been interacting with OpenSim bots — or NPCs — for nearly as long as I’ve been covering OpenSim. Which is about 15 years. (Oh my God, has it really been that long?)

I’ve been hoping that OpenSim writing would become by day job, but, unfortunately, OpenSim never really took off. Instead, I covered cybersecurity and, more recently, generative AI.

But then I saw some reporting about a new studies about AI, and immediately thought — this could really be something in OpenSim.

The study was published this past April in the journal Neuroscience of Consciousness, and it showed that a majority of people – 67% to be exact – attribute some degree of consciousness to ChatGPT. And the more people use these AI systems, the more likely they are to see them as conscious entities.

Then, in May, another study showed that 54% of people, after a conversation with ChatGPT, thought it was a real person.

Now, I’m not saying that OpenSim grid owners should run out and install a bunch of bots on their grids that pretend to be real people, in order to lure in more users. That would be dumb, expensive, a waste of resources, possibly illegal and definitely unethical.

But if users knew that these bots were powered by AI and understood that they’re not real people, they might still enjoy interacting with them and develop attachments to them — just like we get attached to brands, or cartoon animals, or characters in a novel. Or, yes, virtual girlfriends or boyfriends.

In the video below, you can see OpenAI’s recent GPT-4o presentation. Yup, the one where ChatGPT sounds suspiciously like Scarlett Johansson in “Her.” I’ve set it to start at the point in the video where they’re talking to her.

I can see why ScarJo got upset — and why that particular voice is no longer available as an option.

Now, as I write this, the voice chatbot they’re demonstrating isn’t widely available yet. But the text version is — and its the text interface that’s most common in OpenSim anyway.

GPT-4o does cost money. It costs money to send it a question and to get a response. A million tokens worth of questions — or 750,000 words — costs $5, and a million token’s worth of response costs $15.

A page of text is roughly 250 words, so a million tokens is about 3,000 pages. So, for $20, you can get a lot of back-and-forth. But there are also cheaper platforms.

Anthropic’s Claude, for example, which has tested better than ChatGPT in some benchmarks, costs a bit less — $3 for a million input tokens, and $15 for a million output tokens.

But there are also free, open-source platforms that you run on your own servers with comparable performance levels. For example, on the LMSYS Chatbot Arena Leaderboard, OpenAI’s GPT-4o in in first place with a score of 1287, Claude 3.5 Sonnet is close behind with 1272, and the (mostly) open source Llama 3 from Meta is not too far distant, with a score of 1207 — and there are several other open source AI platforms at the top of the charts, including Google’s Gemma, NVIDIA’s Nemotron, Cohere’s Command R+, Alibaba’s Qwen2, and Mistral.

I can easily see an OpenSim hosting provider adding an AI service to their package deals.

Imagine the potential for creating truly immersive experiences in OpenSim and other virtual environments. If users are predisposed to see AI entities as conscious, we could create non-player characters that feel incredibly real and responsive.

This could revolutionize storytelling, education, and social interactions in virtual spaces.

We could have bots that users can form meaningful relationships with, AI-driven characters that can adapt to individual user preferences, and virtual environments that feel alive and dynamic.

And then there’s the potential for interactive storytelling and games, with quests and narratives that are more engaging than ever before, create virtual assistants that feel like true companions, or even build communities that blur the lines between AI and human participants.

For those using OpenSim for work, there are also applications here for business and education, in the form of AI tutors, AI executive assistants, AI sales agents, and more.

However, as much as I’m thrilled by these possibilities, I can’t help but feel a twinge of concern.

As the study authors point out, there are some risks to AIs that feel real.

First, there’s the risk of emotional attachment. If users start to view AI entities as conscious beings, they might form deep, potentially unhealthy bonds with these virtual characters. This could lead to a range of issues, from social isolation in the real world to emotional distress if these AI entities are altered or removed.

We’re already seeing that, with people feeling real distress when their virtual girlfriends are turned off.

Then there’s the question of blurred reality. As the line between AI and human interactions becomes less clear, users might struggle to distinguish between the two.

Personally, I’m not too concerned about this one. We’ve had people complaining that other people couldn’t tell fantasy from reality since the days of Don Quixote. Probably even earlier. There were probably cave people sitting around, saying, “Look at the young people with all their cave paintings. They could be out actually hunting, and instead they sit around the cave looking at the paintings.”

Or even earlier, when language was invented. “Look at those young people, sitting around talking about hunting, instead of going out there into the jungle and catching something.”

When movies were first invented, when people started getting “addicted” to television, or video games… we’ve always had moral panics about new media.

The thing is, those moral panics were also, to some extent, justified. Maybe the pulp novels that the printing press gave us didn’t rot our brains. But Mao’s Little Red Book, the Communist Manifesto, that thing that Hitler wrote that I don’t even was aided and abetted by the books they wrote.

So that’s what I’m most worried about — the potential for exploitation. Bad actors could misuse our tendency to anthropomorphize AI, creating deceptive or manipulative experiences that take advantage of users’ emotional connections and lead them to be more tolerant of evil.

But I don’t think that’s something that we, in OpenSim, have to worry about. Our platform doesn’t have the kind of reach it would take to create a new dictator!

I think the worst that would happen is that people might get so engaged that they spend a few dollars more than they planned to spend.

Hypergrid Business editor and publisher Maria Korolov is a science fiction novelist. During the day, Maria Korolov is an award-winning freelance technology journalist who covers artificial intelligence, cybersecurity and enterprise virtual reality. See her Amazon author page here and follow her on Twitter, Facebook, or LinkedIn, and check out her latest videos on the Maria Korolov YouTube channel. Email her at maria@hypergridbusiness.com. Her first virtual world novella, Krim Times, made the Amazon best-seller list in its category. Her second novella, The Lost King of Krim, is out now. She is also the publisher of MetaStellar, a new online magazine of speculative fiction.

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