Crypto Market Makers: Liquidity Saviors or Sinners?

Posted on by Werner

In cryptocurrency trading few things matter more than speed and information. Maybe one thing does: market liquidity. Ever traded a shitcoin with a small market cap, and seen its price crater (or moon) in a matter of minutes? In most cases, that is the result of some whale executing a big order, driving the price up or down by supply or demand. 

This is where so-called market makers come in. They play a vital role in ensuring exchanges run smoothly by lubricating them with liquidity. But what exactly does liquidity mean, and why are market makers so crucial to crypto markets? Let’s dive in and explore the essential role they play.

What is a Crypto Market Maker?

A crypto market maker can be defined this way: it’s a person or financial entity who stands by to buy and sell a particular cryptocurrency on a continuous basis at publicly quoted prices. These are the prices found on the order books of centralized and decentralized exchange. Market makers place orders on exchanges to buy and sell cryptocurrencies like Bitcoin, Ethereum, or smaller coins. When they do this, they ensure there is sufficient liquidity – meaning other traders can easily enter or exit positions without causing drastic price movements. 

Market makers profit by capitalizing (some say exploiting) the difference between the bidding price and the asking price. This difference is known as the bid-ask spread.

For instance, a market maker might quote a bid-ask spread of $99/$100 for Bitcoin. This means he is willing to buy Bitcoin at $99 and sell it at $100. If a buyer and seller transact with the market maker as a counterparty, the market maker earns $1, minus their transaction fee. Multiply such small profits by thousands or even millions of transactions, and it becomes a very lucrative business model.

To manage their risk, market makers run (and fine-tune) advanced algorithms and technology to continuously adjust prices based on factors like trading volume, price fluctuations, and market volatility. This high-frequency trading allows them to capitalize on fleeting price discrepancies across different exchanges and currency pairs.

Why are Crypto Market Makers Important?

In the crypto space, market makers serve several important functions. 
First and foremost, by providing continuous liquidity, they make it much easier for buyers and sellers to trade without causing sudden, drastic price movements. Imagine if you had to wait for another trader who wants to take the exact opposite position to yours every time you wanted to buy or sell a coin. The market would move extremely slowly.

Additionally, market makers help to stabilize prices and reduce volatility by stepping in to buy when prices fall, and to sell when prices rise. Without them, crypto price charts would resemble seismographs, with prices jumping all over. By tightening spreads and balancing order flow, market makers ‘smooth out’ price action.

Several types of entities engage in crypto market making. Some are individual traders looking for profits from bid-ask spreads. Others are professional market making firms hired by exchanges or projects themselves to ensure sufficient liquidity. 

Many exchanges also act as market makers, either openly or behind the scenes. High-frequency trading (HFT) firms and arbitrageurs also contribute to liquidity through their rapid, algorithm-driven strategies. Finally, some token issuers and crypto projects provide their own liquidity to facilitate trading of their coin or token.

In employing market-making strategies, firms typically rely on a combination of advanced statistical models and formulas, ultrafast computers, and automated trading systems. 

Popular strategies include:

How Market Makers Make Their Money

So how exactly do market makers profit? The most obvious way is through the bid-ask spread. Every time they complete a round-trip transaction (i.e. buying and selling an equal quantity), they earn the spread between their buy and sell prices. 

Many market makers also receive commissions or rebates from exchanges for the liquidity they provide. Finally, some market makers generate profits by opening their own directional trading positions based on anticipated market movements. 

Example: DWFLabs and $Floki

One of the most well-known crypto market makers are DWF Labs, who usually drive up the price of their client’s assets significantly. Follow their portfolio here on CoinMarketCap. Their most recent success is the $GUMMY token, which peaked at a $250 million market cap. 

By accumulating $FLOKI tokens quietly over several months, DWFLabs was able to establish a strong position. They then orchestrated a massive 772% price pump in just three weeks, showcasing their ability to generate substantial returns quickly.

Credit: Werner V. via CoinMarketCap

However, market makers like DWFLabs often employ controversial tactics to manipulate prices. By artificially creating new lows, they can starve out retail investors and accumulate tokens at discounted prices. This cycle of pumps and dumps creates a challenging environment for average token holders, who may struggle to navigate the volatility created by market maker activity.

Therefore, when analyzing a crypto asset, make sure to take a look at both the price and the market cap. The higher the market cap, the more liquid the coin will be, and the less volatile in price.

Credit: Tesfu Assefa

The Good and Bad of Market Makers

An important question arises – are market makers good or bad for the average crypto investor? As with most things, there are two sides to this debate.

On the positive side, the liquidity market makers provide is undeniably valuable. Without it, crypto markets would be highly illiquid, slow-moving, and volatile. Their activity makes it much easier for investors to enter and exit trades seamlessly. Furthermore, competition between market makers keeps spreads tight, allowing investors to trade at fair prices close to the market price. 

However, some argue that market makers – particularly large, sophisticated firms – can take advantage of their role for manipulative purposes. 

Market makers have a range of ways they can potentially manipulate asset prices. With their large capital reserves and advanced algorithms, they can engage in practices like front-running, where they exploit advance knowledge of pending orders to make trades ahead of other investors. They might also employ quote-stuffing, placing large numbers of buy or sell orders they don’t intend to execute in order to create a false impression of demand or supply.

Another tactic is wash-trading, simultaneously buying and selling the same asset to generate fake trading volume and momentum. Market makers’ ability to set bid-ask spreads also gives them some control over short-term price movements. 

The opacity of some market makers’ operations, and their access to information not available to regular traders raises concerns about asymmetric advantages. While not all market makers act maliciously, they all follow the profit motive. This comes with an inherent potential for price manipulation that necessitates robust oversight and regulation.

Conclusion

Crypto market makers are essential actors in the cryptocurrency ecosystem. While not without controversy, these entities – through their combination of advanced technology and trading expertise – help form the circulatory system of the crypto markets we know today.

While they can manipulate markets with their tactics (especially small illiquid markets), most would agree that the benefits of liquidity and more efficient markets outweigh the potential drawbacks. And in most jurisdictions, there are rules and regulations in place to prevent market abuse. While imperfect, market makers are arguably a necessary presence to ensure crypto markets run smoothly.

By providing continuous liquidity, tightening spreads, and balancing order flow, they serve the crucial role of facilitating efficient trading and more stable prices. For any investor to be profitable long-term, understanding how they operate will help them unlock access to the inner workings of crypto trading.

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The Era of 1.58-bit Large Language Models: A Breakthrough in Efficiency

Posted on by Kedist

As large language models (LLMs) continue to grow in capabilities, their increasing computational demands have raised concerns about efficiency, cost, and environmental impact. In a groundbreaking development, researchers at Microsoft Research have introduced BitNet b1.58, a novel 1.58-bit variant of LLMs that could usher in a new era of high-performance, cost-effective language models.

The Era of 1-bit LLMs

The field of AI has witnessed a rapid expansion in the size and power of LLMs, but this growth has come at a significant computational cost. Post-training quantization techniques have aimed to reduce the precision of weights and activations, but a more optimal solution was needed. Recent work on 1-bit model architectures, such as BitNet, has paved the way for a promising new direction in reducing the cost of LLMs while maintaining their performance.

BitNet b1.58: The 1.58-bit LLM Variant

BitNet b1.58 represents a significant advancement in this area, introducing a unique quantization approach that constrains every parameter (weight) of the LLM to ternary values of {-1, 0, 1}. This innovative technique, combined with efficient computation paradigms and LLaMA-alike components for better open-source integration, enables BitNet b1.58 to achieve remarkable results.

Results: Matching Performance, Reducing Cost

In a comprehensive evaluation, BitNet b1.58 demonstrated its ability to match the perplexity and end-task performance of full-precision (FP16) LLM baselines, starting from a model size of 3 billion parameters. As the model size scales up, the benefits of BitNet b1.58 become even more pronounced, with substantial reductions in memory usage, latency, throughput, and energy consumption compared to FP16 LLMs.

At the 70 billion parameter scale, BitNet b1.58 is up to 4.1 times faster, uses up to 7.2 times less memory, achieves up to 8.9 times higher throughput, and consumes up to 41 times less energy than its FP16 counterparts. These astounding results demonstrate the potential of 1.58-bit LLMs to provide a Pareto improvement over traditional models, delivering both high performance and cost-effectiveness.

Credit: Tesfu Assefa

Discussion and Future Work: Enabling New Possibilities

The development of 1.58-bit LLMs like BitNet b1.58 opens up a world of possibilities and exciting future research directions. One intriguing prospect is the potential for further cost reductions through the integration of efficient Mixture-of-Experts (MoE) architectures. Additionally, the reduced memory footprint of BitNet b1.58 could enable native support for longer sequence lengths, a critical demand in the era of LLMs.

Perhaps most significantly, the exceptional efficiency of 1.58-bit LLMs paves the way for deploying these models on edge and mobile devices, unlocking a wide range of applications in resource-constrained environments. Furthermore, the unique computation paradigm of BitNet b1.58 calls for the design of specialized hardware optimized for 1-bit operations, which could further enhance the performance and efficiency of these models.

Conclusion

In the rapidly evolving landscape of large language models, BitNet b1.58 represents a groundbreaking achievement, introducing a new era of 1.58-bit LLMs that combine state-of-the-art performance with unprecedented efficiency. By addressing the computational challenges associated with traditional LLMs, this research paves the way for more sustainable and cost-effective scaling, enabling the deployment of these powerful models in a wider range of applications and environments. As the field continues to advance, BitNet b1.58 stands as a testament to the innovative potential of quantized LLMs and the exciting possibilities that lie ahead.

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Inside the Mind of Ben Ditto | Mindplex Podcast S2EP17

Posted on by Mindplex

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Revolutionizing Language Models: The Emergence of BitNet b1.58

Posted on by Tensae

In recent years, the field of Artificial Intelligence has witnessed an unprecedented surge in the development of Large Language Models (LLMs), fueled by breakthroughs in deep learning architectures and the availability of vast amounts of text data. These models, equipped with powerful Transformer architectures, have demonstrated remarkable proficiency across a plethora of natural language processing tasks, from language translation to sentiment analysis. However, this rapid growth in the size and complexity of LLMs has brought about a host of challenges, chief among them being the staggering energy consumption and memory requirements during both training and inference phases.

To address these challenges, researchers have ventured into various techniques aimed at optimizing the efficiency of LLMs, with a particular focus on post-training quantization. This approach involves reducing the precision of model parameters, thereby curtailing memory and computational demands. While post-training quantization has proven effective to some extent, it remains suboptimal, especially for large-scale LLMs.

In response to this limitation, recent endeavors have explored the realm of 1-bit model architectures, epitomized by BitNet. These models leverage a novel computation paradigm that drastically reduces energy consumption by eschewing floating-point arithmetic in favor of integer operations, particularly beneficial for the matrix multiplication operations inherent in LLMs. BitNet, in its original form, has demonstrated promising results, offering a glimpse into a more energy-efficient future for LLMs.

Building upon the foundation laid by BitNet, researchers have introduced BitNet b1.58, a significant advancement in the realm of 1-bit LLMs. Unlike its predecessors, BitNet b1.58 adopts a ternary parameterization scheme, with model weights constrained to {-1, 0, 1}, thereby achieving a remarkable compression ratio of 1.58 bits per weight. This innovative approach retains all the advantages of the original BitNet while introducing enhanced modeling capabilities, particularly through explicit support for feature filtering.

Credit: Tesfu Assefa

BitNet b1.58 represents a paradigm shift in LLM architecture, offering a compelling alternative to traditional floating-point models. Notably, it matches the performance of full-precision baselines, even surpassing them in some cases, while simultaneously offering significant reductions in memory footprint and inference latency. Furthermore, its compatibility with popular open-source software ensures seamless integration into existing AI frameworks, facilitating widespread adoption and experimentation within the research community.

Beyond its immediate impact on model performance and efficiency, BitNet b1.58 holds immense promise for a wide range of applications, particularly in resource-constrained environments such as edge and mobile devices. The reduced memory and energy requirements of BitNet b1.58 pave the way for deploying sophisticated language models on devices with limited computational resources, unlocking new possibilities for on-device natural language understanding and generation.

Looking ahead, the development of dedicated hardware optimized for 1-bit LLMs could further accelerate the adoption and proliferation of BitNet b1.58, ushering in a new era of efficient and high-performance AI systems. As the field continues to evolve, BitNet b1.58 stands as a testament to the ingenuity and perseverance of researchers striving to push the boundaries of AI technology.

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Introduction to the Parameter Server Framework for Distributed Machine Learning

Posted on by Tibebe S.

The advancement of machine learning applications in various domains necessitates the development of robust frameworks that can handle large-scale data efficiently. To address this challenge, a paper titled “Implementing and Benchmarking a Fault-Tolerant Parameter Server for Distributed Machine Learning Applications” (which sounds like a mouthful but is a pretty simple concept once you break down the words) introduces a powerful Parameter Server Framework specifically designed for large-scale distributed machine learning. This framework not only enhances efficiency and scalability but also offers user-friendly features for seamless integration into existing workflows. Below, we detail the key aspects of the framework, including its design, efficiency, scalability, theoretical foundations, and real-world applications.

Key Features of the Parameter Server Framework

User-Friendly Interface

The framework allows easy access to globally shared parameters for local operations on client nodes, simplifying the complexities often encountered in distributed environments. A notable attribute of this framework is its focus on user accessibility, achieved through the streamlined implementation of asynchronous communication and the support for flexible consistency models. This design choice facilitates a balance between system responsiveness and rapid algorithm convergence, making it an attractive solution for practitioners and researchers alike.

Enhanced Efficiency

Efficiency is at the core of the framework’s design, leveraging asynchronous communication coupled with advanced consistency models like the “maximal delayed time” model and a “significantly-modified” filter. These features are crucial in enabling the system to converge to a stationary point under predetermined conditions. The framework’s asynchronous nature permits substantial improvements in processing speeds, effectively addressing the latency issues typically associated with large-scale data processing.

Scalability and Fault Tolerance

Designed to be elastically scalable, the framework supports dynamic additions and subtractions of nodes, thereby accommodating varying computational demands effortlessly. It also integrates fault tolerance mechanisms that ensure stable long-term deployment, even in the face of potential hardware failures or network issues. This level of reliability is essential for enterprises that depend on continual data processing and analysis.

Credit: Tesfu Assefa

Applications and Theoretical Foundation

The Parameter Server Framework is not only practical but also grounded in solid theoretical principles. It supports complex optimization problems, including nonconvex and nonsmooth challenges, using proximal gradient methods. This theoretical backing is crucial for tasks such as risk minimization, distributed Gibbs sampling, and deep learning. The structure of the framework is designed around server nodes that manage globally shared parameters and client nodes that perform computations asynchronously, thus optimizing the workload distribution.

Implementation Details

Server Nodes: These nodes are responsible for managing global parameters efficiently.
Client Nodes: Client-side operations are executed asynchronously, enhancing overall system performance.

Experimental Validation

The framework has been tested on real-world datasets, including L1-regularized logistic regression and Reconstruction Independent Component Analysis (RICA), demonstrating its capability to handle complex, data-intensive tasks. The results show linear scalability with the increase in the number of client nodes, indicating a substantial speedup that validates the framework’s effectiveness in large-scale settings.

Conclusion

The Parameter Server Framework offers a sophisticated solution to the challenges of large-scale distributed machine learning. With its user-friendly interface, high efficiency, scalability, fault tolerance, and solid theoretical foundation, the framework is poised to significantly impact the field of machine learning. The experimental results underscore its practicality and effectiveness, making it an invaluable tool for researchers and practitioners aiming to leverage the full potential of distributed computing in machine learning.

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Runes Protocol: Did It Ruin Bitcoin or Save It?

Posted on by Werner

The future of Bitcoin was at stake last week in two ways: with both the Halving upgrade and the launch of the Runes protocol, a new token standard for issuing fungible tokens directly on the Bitcoin blockchain. The Runes Protocol laid a foundation that will determine the fate of the chain in the decades to come. Activated on 19 or 20 April 2024 on block 840,000, coinciding with the next Bitcoin halving, Runes aims to provide a more efficient and responsible way of creating fungible tokens compared to existing options. Let’s dive into what Runes is all about, who created it, how it works, and what impact it could have on the Bitcoin ecosystem.

What is the Runes Protocol?

The Runes protocol is a new token standard that allows issuers to create fungible tokens on the Bitcoin blockchain in a more efficient way. It can offer users a streamlined method for creating tokens that represent various assets, from stablecoins to governance tokens. Runes positions itself as a robust platform for token creation and management with all the security and immutability of Bitcoin. At least, that’s the official line. For Bitcoin maximalists, Runes and its predecessors Ordinals and BRC-20 are cynical money-grabs that clutter and congest the world’s most important blockchain with its flood of transactions. 

Rodarmor: The Mastermind Behind Runes

Bitcoin developer Casey Rodarmor, well-known as the creator of the Ordinals protocol, proposed Runes in September 2023. Building upon his experience with Ordinals, which opened the door to NFTs on Bitcoin, Rodarmor envisioned Runes as an improved token standard that addresses the limitations of existing solutions like the BRC-20 standard, which he felt required too many steps to complete and wasn’t built in accordance with Bitcoin’s ethos.

Rodarmor designed Runes to be a simple protocol with minimal on-chain footprint and responsible UTXO management. UTXOs, or Unspent Transaction Outputs, represent individual units of Bitcoin value that have not yet been spent. Unlike the BRC-20 standard, which is complex and produces junk UTXOs that congest the Bitcoin network, Runes aims to be more efficient and user-friendly.

Other fungible token protocols on Bitcoin, such as RGB and Taproot Assets, rely on off-chain data storage. Runes distinguishes itself by keeping all token information on-chain using OP_RETURN, a Bitcoin script opcode for storing data. In this way, Runes ensures that asset metadata remains tightly integrated with the base layer.

Under the Hood: How Runes Works

Runes adopts a UTXO-based model that aligns seamlessly with Bitcoin’s design. When a Rune token is created (‘etched’), minted, or transferred, a protocol message called a runestone is generated. Runestones contain all the necessary information, including the token ID, output index, and amount, encoded in an OP_RETURN output.

The token supply of a Rune is stored within a single UTXO, with a maximum supply of approximately 340 undecillion (340 followed by 36 zeros). Each Rune has a divisibility parameter that determines the number of decimal places it can have, up to a maximum of 38.

New Runes are created in a process called etching, where the token’s properties, such as its name, divisibility, symbol, pre-mine amount, and minting terms, are defined. Once etched, the Rune can be minted according to the established terms, with the minter specifying the Rune ID and the desired quantity.

Transferring Runes is accomplished through ‘edicts’ – instructions that define how tokens move from inputs to outputs within a transaction. Edicts support batch transfers, airdrops, and a transfer of all remaining units of a specific Rune ID in a single transaction.

Runes vs. BRC-20 and Ordinals

Runes vs BRC-20

While both Runes and BRC-20 are token-standards built on the Bitcoin blockchain, there are several key differences between the two.

BRC-20 is a meta-protocol that relies on the Ordinals protocol. This means that BRC-20 inherits the complexity of Ordinals, and requires multiple transactions for minting and transferring tokens. In contrast, Runes is a standalone protocol that operates independently of Ordinals, allowing it to create and manage tokens more efficiently.

Another significant advantage of Runes over BRC-20 is its simplified transaction structure. With Runes, minting and transferring tokens can be done in a single transaction, reducing the overall on-chain footprint and minimizing the creation of unnecessary UTXOs. This streamlined approach leads to improved scalability and a more user-friendly experience for token issuers and holders.

Runes vs Ordinals

Although both Runes and Ordinals are protocols built on top of the Bitcoin blockchain, they serve different purposes. Ordinals is primarily focused on creating and managing non-fungible tokens (NFTs) by inscribing data onto individual satoshis. These inscriptions are unique and can represent various types of digital assets, such as artworks, collectibles, or even text.

On the other hand, Runes is designed specifically for fungible tokens, which are interchangeable and divisible. 

The Potential Impact of Runes on Bitcoin

The Runes protocol could have far-reaching implications for the Bitcoin ecosystem, both good and bad. Developers can use Runes to create various types of fungible tokens, potentially attracting a wider user base and expanding Bitcoin’s utility beyond its primary function as a digital currency.

As more projects build on top of Runes, the increased transaction volume could generate additional revenue for miners in the form of transaction fees. This is particularly relevant in light of the halving of the Bitcoin block reward: the added revenue from fees would compensate for one incentive for miners being reduced.

Moreover, Runes could spur innovation within the Bitcoin developer community. Projects like RSIC, a metaprotocol that combines Ordinals with yield-farming, have already emerged in anticipation of Runes’ launch. As developers explore new use-cases and build novel applications on top of Runes, the Bitcoin ecosystem could witness a surge in creativity and experimentation.

However, Runes has also in its short history attracted an avalanche of scam or low-quality projects that offer little to no chances of a return on investment. 

Credit: Tesfu Assefa

The Road Ahead for Runes

Casey Rodarmor’s next plan is to introduce direct trading between users, potentially reducing reliance on centralized exchanges and mitigating issues like Replace-By-Fee (RBF). Additionally, the approval of the OP_CAT Bitcoin Improvement Proposal (BIP) could pave the way for bridging Runes tokens to Layer-2 networks, enhancing scalability and interoperability.

As the Bitcoin community prepares for the launch of Runes, excitement is building around the potential for a new era of token innovation on the world’s most secure and decentralized blockchain. With its focus on simplicity, efficiency, and responsible UTXO management, Runes aims to address the limitations of existing token-standards, and to provide a solid foundation for growth of the Bitcoin ecosystem.

Only time will tell how developers and users will receive and adopt Runes. However, one thing is certain: when Runes is activated at block 840,000, it marks a significant milestone in Bitcoin’s ongoing evolution, opening up new possibilities for token-creation, management, and exchange on the original and most secure blockchain.

The Runes protocol has the potential to bring numerous benefits to the Bitcoin ecosystem –

  • Firstly, Runes can attract a wider user-base by enabling various types of tokens, such as utility tokens, governance tokens, or even stablecoins. This increased diversity of use-cases can draw new users to the Bitcoin network, driving adoption and fostering a more vibrant and inclusive ecosystem.
  • Secondly, the increased activity generated by Runes can make the entire Bitcoin network more sustainable. As more users engage with Runes-based tokens, the demand for block space will increase, leading to higher transaction fees. These fees will draw in more miners to continue securing the network, especially as the block rewards diminish.
  • Lastly, Runes can serve as a catalyst for innovation and experimentation within the Bitcoin ecosystem. By providing a standardized and efficient platform for issuing tokens, Runes can lower the barriers to entry for developers and entrepreneurs who want to build new applications and services on top of Bitcoin. This can lead to a proliferation of novel use-cases, and a more dynamic, resilient, and interesting ecosystem.

Runes provides a platform for token-related activities directly on the Bitcoin blockchain, and can help drive transaction fees, nourishing a sustainable mining ecosystem. Even if some of the tokens created through Runes are shitcoins or memecoins, Rodarmor argues that the fees generated from these activities are still valuable for the network’s security.

Moreover, Rodarmor sees Runes as a way to bring more users and activity to the Bitcoin ecosystem. This increased adoption and engagement can further strengthen the Bitcoin network and its position as the world’s leading cryptocurrency.

How Runes Works

  • Etching is the process of creating a new Rune token and defining its properties. This is done through a special transaction that includes an OP_RETURN output containing the token’s metadata, such as its name, symbol, and any additional attributes.
  • Minting refers to the act of creating new units of a Rune token. The minting process involves specifying the token ID, which is derived from the etching transaction’s block height and transaction index. Minting can be done through an open process, allowing anyone to participate, or it can be restricted based on predefined terms set during the etching process.
  • Transferring Runes involves moving tokens from one UTXO to another. This is accomplished through a transaction that consumes the input UTXOs containing the tokens and creates new output UTXOs with the updated token balances. The transfer process is governed by a set of instructions called ‘edicts’. These edicts specify the token ID, amount, and destination UTXO.
  • In the event of an error during the etching, minting, or transferring process, a ‘cenotaph’ is created. Cenotaphs are runestones with invalid or unrecognized data, and they cause the associated tokens to be burnt. This mechanism encourages responsible UTXO management and helps maintain the integrity of the Runes protocol.

Conclusion

Existing token standards, such as BRC-20, have certain limitations. Every time they are minted or transferred, multiple transactions have to pass through the Bitcoin blockchain, and this leads to increased complexity and network congestion.

In contrast, Runes offers a streamlined approach, allowing you to create and transfer tokens with minimal on-chain footprint and responsible UTXO management. Fewer transactions are needed and Bitcoin’s limited block space is used more optimally. It is a more efficient and scalable solution for issuing tokens.

Conversely though, the protocol is still young and has had to deal with some adversity. Proponents of BRC-20 feel that Runes projects are too centralized, while others feel Rodarmor’s design was nothing more than a cynical money grab. Only time will tell if they will survive and even thrive. As Samson Mow told me in an interview last year at Bitcoin Miami, “it’s just noise”. 

It pays to zoom out and see where other chains like Ethereum and Cardano are heading, and what’s possible with new protocols and even Layer-2 chains for Bitcoin. When mining rewards become negligible in the next 10 or 20 years, the network will have to rely on transaction fees to keep the miners from revolting and shutting down their machines. Innovations like Runes are asking the right questions in order to get them to stay. 

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