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Web3 Has a Memory Problem — And We Finally Have a Fix

Web3 has a memory problem. Not in the “we forgot something” sense, but in the core architectural sense. It doesn’t have a real memory layer.
Blockchains today don’t look completely alien compared to traditional computers, but a core foundational aspect of legacy computing is still missing: A memory layer built for decentralization that will support the next iteration of the internet.
Muriel Médard is a speaker at Consensus 2025 May 14-16. Register to get your ticket here.
After World War II, John von Neumann laid out the architecture for modern computers. Every computer needs input and output, a CPU for control and arithmetic, and memory to store the latest version data, along with a “bus” to retrieve and update that data in the memory. Commonly known as RAM, this architecture has been the foundation of computing for decades.
At its core, Web3 is a decentralized computer — a “world computer.” At the higher layers, it’s fairly recognizable: operating systems (EVM, SVM) running on thousands of decentralized nodes, powering decentralized applications and protocols.
But, when you dig deeper, something’s missing. The memory layer essential for storing, accessing and updating short-term and long term data, doesn’t look like the memory bus or memory unit von Neumann envisioned.
Instead, it’s a mashup of different best-effort approaches to achieve this purpose, and the results are overall messy, inefficient and hard to navigate.
Here’s the problem: if we’re going to build a world computer that’s fundamentally different from the von Neumann model, there better be a really good reason to do so. As of right now, Web3’s memory layer isn’t just different, it’s convoluted and inefficient. Transactions are slow. Storage is sluggish and costly. Scaling for mass adoption with this current approach is nigh impossible. And, that’s not what decentralization was supposed to be about.
But there is another way.
A lot of people in this space are trying their best to work around this limitation and we’re at a point now where the current workaround solutions just cannot keep up. This is where using algebraic coding, which makes use of equations to represent data for efficiency, resilience and flexibility, comes in.
The core problem is this: how do we implement decentralized code for Web3?
A new memory infrastructure
This is why I took the leap from academia where I held the role of MIT NEC Chair and Professor of Software Science and Engineering to dedicate myself and a team of experts in advancing high-performance memory for Web3.
I saw something bigger: the potential to redefine how we think about computing in a decentralized world.
My team at Optimum is creating decentralized memory that works like a dedicated computer. Our approach is powered by Random Linear Network Coding (RLNC), a technology developed in my MIT lab over nearly two decades. It’s a proven data coding method that maximizes throughput and resilience in high-reliability networks from industrial systems to the internet.
Data coding is the process of converting information from one format to another for efficient storage, transmission or processing. Data coding has been around for decades and there are many iterations of it in use in networks today. RLNC is the modern approach to data coding built specifically for decentralized computing. This scheme transforms data into packets for transmission across a network of nodes, ensuring high speed and efficiency.
With multiple engineering awards from top global institutions, more than 80 patents, and numerous real-world deployments, RLNC is no longer just a theory. RLNC has garnered significant recognition, including the 2009 IEEE Communications Society and Information Theory Society Joint Paper Award for the work «A Random Linear Network Coding Approach to Multicast.» RLNC’s impact was acknowledged with the IEEE Koji Kobayashi Computers and Communications Award in 2022.
RLNC is now ready for decentralized systems, enabling faster data propagation, efficient storage, and real-time access, making it a key solution for Web3’s scalability and efficiency challenges.
Why this matters
Let’s take a step back. Why does all of this matter? Because we need memory for the world computer that’s not just decentralized but also efficient, scalable and reliable.
Currently, blockchains rely on best-effort, ad hoc solutions that achieve partially what memory in high-performance computing does. What they lack is a unified memory layer that encompasses both the memory bus for data propagation and the RAM for data storage and access.
The bus part of the computer should not become the bottleneck, as it does now. Let me explain.
“Gossip” is the common method for data propagation in blockchain networks. It is a peer-to-peer communication protocol in which nodes exchange information with random peers to spread data across the network. In its current implementation, it struggles at scale.
Imagine you need 10 pieces of information from neighbors who repeat what they’ve heard. As you speak to them, at first you get new information. But as you approach nine out of 10, the chance of hearing something new from a neighbor drops, making the final piece of information the hardest to get. Chances are 90% that the next thing you hear is something you already know.
This is how blockchain gossip works today — efficient early on, but redundant and slow when trying to complete the information sharing. You would have to be extremely lucky to get something new every time.
With RLNC, we get around the core scalability issue in current gossip. RLNC works as though you managed to get extremely lucky, so every time you hear info, it just happens to be info that is new to you. That means much greater throughput and much lower latency. This RLNC-powered gossip is our first product, which validators can implement through a simple API call to optimize data propagation for their nodes.
Let us now examine the memory part. It helps to think of memory as dynamic storage, like RAM in a computer or, for that matter, our closet. Decentralized RAM should mimic a closet; it should be structured, reliable, and consistent. A piece of data is either there or not, no half-bits, no missing sleeves. That’s atomicity. Items stay in the order they were placed — you might see an older version, but never a wrong one. That’s consistency. And, unless moved, everything stays put; data doesn’t disappear. That’s durability.
Instead of the closet, what do we have? Mempools are not something we keep around in computers, so why do we do that in Web3? The main reason is that there is not a proper memory layer. If we think of data management in blockchains as managing clothes in our closet, a mempool is like having a pile of laundry on the floor, where you are not sure what is in there and you need to rummage.
Current delays in transaction processing can be extremely high for any single chain. Citing Ethereum as an example, it takes two epochs or 12.8 minutes to finalize any single transaction. Without decentralized RAM, Web3 relies on mempools, where transactions sit until they’re processed, resulting in delays, congestion and unpredictability.
Full nodes store everything, bloating the system and making retrieval complex and costly. In computers, the RAM keeps what is currently needed, while less-used data moves to cold storage, maybe in the cloud or on disk. Full nodes are like a closet with all the clothes you ever wore (from everything you’ve ever worn as a baby until now).
This is not something we do on our computers, but they exist in Web3 because storage and read/write access aren’t optimized. With RLNC, we create decentralized RAM (deRAM) for timely, updateable state in a way that is economical, resilient and scalable.
DeRAM and data propagation powered by RLNC can solve Web3’s biggest bottlenecks by making memory faster, more efficient, and more scalable. It optimizes data propagation, reduces storage bloat, and enables real-time access without compromising decentralization. It’s long been a key missing piece in the world computer, but not for long.
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XLM Sees Heavy Volatility as Institutional Selling Weighs on Price

Stellar’s XLM token endured sharp swings over the past 24 hours, tumbling 3% as institutional selling pressure dominated order books. The asset declined from $0.39 to $0.38 between September 14 at 15:00 and September 15 at 14:00, with trading volumes peaking at 101.32 million—nearly triple its 24-hour average. The heaviest liquidation struck during the morning hours of September 15, when XLM collapsed from $0.395 to $0.376 within two hours, establishing $0.395 as firm resistance while tentative support formed near $0.375.
Despite the broader downtrend, intraday action highlighted moments of resilience. From 13:15 to 14:14 on September 15, XLM staged a brief recovery, jumping from $0.378 to a session high of $0.383 before closing the hour at $0.380. Trading volume surged above 10 million units during this window, with 3.45 million changing hands in a single minute as bulls attempted to push past resistance. While sellers capped momentum, the consolidation zone around $0.380–$0.381 now represents a potential support base.
Market dynamics suggest distribution patterns consistent with institutional profit-taking. The persistent supply overhead has reinforced resistance at $0.395, where repeated rally attempts have failed, while the emergence of support near $0.375 reflects opportunistic buying during liquidation waves. For traders, the $0.375–$0.395 band has become the key battleground that will define near-term direction.
Technical Indicators
- XLM retreated 3% from $0.39 to $0.38 during the previous 24-hours from 14 September 15:00 to 15 September 14:00.
- Trading volume peaked at 101.32 million during the 08:00 hour, nearly triple the 24-hour average of 24.47 million.
- Strong resistance established around $0.395 level during morning selloff.
- Key support emerged near $0.375 where buying interest materialized.
- Price range of $0.019 representing 5% volatility between peak and trough.
- Recovery attempts reached $0.383 by 13:00 before encountering selling pressure.
- Consolidation pattern formed around $0.380-$0.381 zone suggesting new support level.
Disclaimer: Parts of this article were generated with the assistance from AI tools and reviewed by our editorial team to ensure accuracy and adherence to our standards. For more information, see CoinDesk’s full AI Policy.
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HBAR Tumbles 5% as Institutional Investors Trigger Mass Selloff

Hedera Hashgraph’s HBAR token endured steep losses over a volatile 24-hour window between September 14 and 15, falling 5% from $0.24 to $0.23. The token’s trading range expanded by $0.01 — a move often linked to outsized institutional activity — as heavy corporate selling overwhelmed support levels. The sharpest move came between 07:00 and 08:00 UTC on September 15, when concentrated liquidation drove prices lower after days of resistance around $0.24.
Institutional trading volumes surged during the session, with more than 126 million tokens changing hands on the morning of September 15 — nearly three times the norm for corporate flows. Market participants attributed the spike to portfolio rebalancing by large stakeholders, with enterprise adoption jitters and mounting regulatory scrutiny providing the backdrop for the selloff.
Recovery efforts briefly emerged during the final hour of trading, when corporate buyers tested the $0.24 level before retreating. Between 13:32 and 13:35 UTC, one accumulation push saw 2.47 million tokens deployed in an effort to establish a price floor. Still, buying momentum ultimately faltered, with HBAR settling back into support at $0.23.
The turbulence underscores the token’s vulnerability to institutional distribution events. Analysts point to the failed breakout above $0.24 as confirmation of fresh resistance, with $0.23 now serving as the critical support zone. The surge in volume suggests major corporate participants are repositioning ahead of regulatory shifts, leaving HBAR’s near-term outlook dependent on whether enterprise buyers can mount sustained defenses above key support.
Technical Indicators Summary
- Corporate resistance levels crystallized at $0.24 where institutional selling pressure consistently overwhelmed enterprise buying interest across multiple trading sessions.
- Institutional support structures emerged around $0.23 levels where corporate buying programs have systematically absorbed selling pressure from retail and smaller institutional participants.
- The unprecedented trading volume surge to 126.38 million tokens during the 08:00 morning session reflects enterprise-scale distribution strategies that overwhelmed corporate demand across major trading platforms.
- Subsequent institutional momentum proved unsustainable as systematic selling pressure resumed between 13:37-13:44, driving corporate participants back toward $0.23 support zones with sustained volumes exceeding 1 million tokens, indicating ongoing institutional distribution.
- Final trading periods exhibited diminishing corporate activity with zero recorded volume between 13:13-14:14, suggesting institutional participants adopted defensive positioning strategies as HBAR consolidated at $0.23 amid enterprise uncertainty.
Disclaimer: Parts of this article were generated with the assistance from AI tools and reviewed by our editorial team to ensure accuracy and adherence to our standards. For more information, see CoinDesk’s full AI Policy.
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Dogecoin Inches Closer to Wall Street With First Meme Coin ETF

The first exchange-traded fund (ETF) built around a meme coin could hit the market this week, after multiple delays and much speculation.
The DOGE ETF — formally called the Rex Shares-Osprey Dogecoin ETF (DOJE) — was originally slated to debut last week, alongside a handful of politically themed and crypto-related ETFs. Those included funds tied to Bonk (BONK), XRP, Bitcoin (BTC) and even a Trump-themed fund. But DOJE’s debut never materialized.
Now, Bloomberg ETF analysts Eric Balchunas and James Seyffart believe Wednesday is the most likely launch date, though they caution nothing is certain.
“It’s more likely than not,” Seyffart said. “That seems like the base case.”
Ahead of the introduction of the ETF, DOGE has been among the top performers over the past month, ahead 15% even including a decline of 3.5% over the past 24 horus.
If launched, DOJE would mark a milestone as the first U.S. ETF to focus on a meme coin — cryptocurrencies that generally lack utility or a clear economic purpose. These include tokens like Dogecoin, Shiba Inu (SHIB) and Bonk, which often surge in popularity thanks to internet culture, celebrity endorsements and speculative trading.
Balchunas described DOJE’s significance in a post on X: “First-ever US ETF to hold something that has no utility on purpose.”
DOJE is not a spot ETF. That means it won’t hold DOGE directly. Instead, the fund will use a Cayman Islands-based subsidiary to gain exposure through futures and other derivatives. This approach sidesteps the need for physical custody of the coin while still offering traders a way to bet on its performance within a traditional brokerage account.
The ETF was approved earlier this month under the Investment Company Act of 1940, which is typically used for mutual funds and diversified ETFs. That sets it apart from the wave of bitcoin ETFs that received green lights under the Securities Act of 1933, a framework used for commodity-based and asset-backed products. In short, DOJE is structured more like a mutual fund than a commodity trust.
More direct exposure may be coming soon. Several firms have filed applications to launch spot DOGE ETFs, which would hold the meme coin itself rather than derivatives. These applications are still under review by the U.S. Securities and Exchange Commission (SEC), which has grown more comfortable with crypto ETFs since approving a slate of bitcoin products in early 2024.
The broader crypto market has shown that investor demand can outweigh fundamental critiques. Meme coins have long drawn skepticism for having no underlying value or use case, but that hasn’t kept them from drawing billions in speculative capital.
Seyffart said the ETF market is likely to follow the same path. “There’s going to be a bunch of products like this, whether you love it or need it, they’re going to be coming to market,” he said.
He added that many existing financial products serve no deeper purpose than providing a vehicle for short-term bets. “There’s plenty of products out there that are just being used as gambling or short-term trading,” he said. “So if there’s an audience for this in the crypto world, I wouldn’t be surprised at all if this finds an audience in the ETF and TradFi world.”
Whether the DOJE ETF opens the door to more meme coin funds — or just proves the concept is viable — may depend on how the market responds this week. Either way, it signals a new phase in the merging of internet culture and traditional finance.
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