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Layer-2 Networks are 'Consolidating' Around Tech Stacks as Pectra Upgrade Looms

Layer-2 (L2) networks are coalescing around dominant tech stacks rather than working toward universal standards, research from onchain explorer Blockscout reveals.

Speaking with Decrypt in a video interview, Ulyana Skladchikova, head of product; and Kirill Fedoseev, head of research at the open-source explorer, revealed how a shift toward technical "clustering" is happening, despite overall transaction growth.

"We see chains kind of get together around some big players and establish interop within those groups of chains," Skladchikova said.

The OP stack, for instance, has emerged as a dominant player in the L2 sector, with most networks using its tooling, real-time data from Rollup.wtf cited by Blockscout shows. This stack is a set of open-source standards used primarily on the Optimism network and its "Superchains."

Ethereum Isn't 'Unified' Enough Amid Layer-2 Expansion, Says Vitalik Buterin

Such a trend points to a "consolidation" around L2 networks, Skladchikova told Decrypt.

This also shows how "bridge abstraction," which refers to the process of simplifying how users could move assets between different chains, could become a challenge for L2 players in the long run.

Instead of contributing to a universal standard, major L2 players are developing proprietary interoperability solutions that may cause "UX friction for users," making it difficult for them to "navigate and transfer funds between networks," Blockscout said in a statement, responding to follow-up questions.

Data paradox

But the data presents a paradox: while transaction volumes on L2 networks like Base have increased nearly threefold to 80 million monthly transactions, native bridging between L1 and L2 has declined by approximately 80% since early 2024, data from Blockscout's internal research shared with Decrypt shows.

This points to an emerging challenge around "chain clustering," a phenomenon where related networks try to form shared standards for communicating with each other to help ease interoperability.

Ethereum Layer-2 Network Linea Reveals Plans to Launch Token

"There will be chain clusters around every large ecosystem player," Fedoseev told Decrypt.

Monthly active users per L2 chain spiked 250% in autumn 2024, reaching over 14,000 before stabilizing around 11,000.

Such a pattern signals sustained adoption rather than temporary interest driven by airdrops or speculation, Blockscout noted in a separate research document shared with Decrypt.

"Early hype doesn't always translate into sustained use." Blockscout wrote. "When users migrate from a larger established platform to a new chain, it signals better future sustainability."

With Ethereum's Pectra upgrade approaching, these consolidation patterns suggest L2s are optimizing for specific capabilities that work across chains, rather than attempting to be general-purpose scaling solutions.

If this becomes the case, "we'll have this one homogeneous interop solution that everyone will just use," Skladchikova said.

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Layer2OptimismInteroperability
Mar 18, 2025 - 31天前
Trump Pro-Crypto Policy Could Trigger Global Financial Crisis: ECB Official

The Trump administration’s pro-crypto agenda risks "sowing the seeds" of a financial crisis, according to a top European Central Bank official.

In an interview with French weekly La Tribune Dimanchei, François Villeroy de Galhau, Governor of the Bank of France and member of the European Central Bank's Governing Council, argued that the U.S. "risks sinning through negligence."

Villeroy de Galhau argued that, "by encouraging crypto assets and non-bank finance," the Trump administration "is sowing the seeds of future upheavals," adding that financial crises "often originate in the United States and spread to the rest of the world.”

His admonitions echo a previous warning from sixteen Nobel Economists who claimed in June last year that Trump's "fiscally irresponsible budgets" could "reignite" inflation and broader economic instability. Villeroy de Galhau argued that Trump pursues a “false vision" in which the global economy functions as a "zero-sum game," calling on Europe to "strengthen" its negotiating position.

European Central Bank Takes Step Toward Blockchain-Based Payments System

Earlier this year, the ECB announced a two-phase digital payments infrastructure initiative, under which it plans to  explore “a more integrated, long-term solution” for settlements of central bank money-denominated transactions on a blockchain. The initiative would lay the groundwork for a central bank digital currency (CBDC).

Trump’s crypto agenda

In the lead-up to and after the U.S. elections, President Donald Trump has vowed, acted, and worked on his administration's promises to embrace and bolster the country's leadership in crypto and digital assets.

President Trump Signs Executive Order to Establish Bitcoin Reserve, Crypto Stockpile

In its first couple of months, the Trump administration has established a crypto council, a Presidential Working Group on digital assets, worked to pass crucial crypto bills, signed an executive order to establish a Bitcoin Reserve, hosted the inaugural crypto summit at the White House, and promised to end Biden-era crypto banking rules, among other moves that have boosted the profile of the crypto industry.

Trump’s pro-crypto push has failed to shore up the crypto market, though, with both Bitcoin and U.S. equities rattled by market volatility sparked by his economic agenda. Following Trump's threat to impose 200% tariffs on European spirits on Thursday last week, the S&P 500 plummeted more than 10% from its February high, reporting from Reuters indicates.

Bitcoin, at the time, tumbled to $81,600—down 25% from a January peak of $109,000. On the same day, crypto markets saw liquidations of over $1 billion, with analysts pointing to risk aversion across global markets and escalating U.S. tariff disputes as key drivers.

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TrumpCryptoAgendaECBBlockchainInitiativeBitcoinReserve
Mar 18, 2025 - 31天前
Strategy’s Latest Purchase Adds 130 Bitcoin to Its Massive Reserve

Michael Saylor’s Strategy has resumed its Bitcoin buying.

 

After a brief pause, the business intelligence firm has snapped up another $10.7 million worth of BTC, adding 130 coins to its already massive stash, bringing its total holdings to nearly half a million. 

 

The purchase, made at an average price of $82,981 per Bitcoin, represents the smallest acquisition by the company since it first started buying Bitcoin in 2020.

 

https://x.com/saylor/status/1901606324447646170 

 

With this latest buy, Strategy's total Bitcoin holdings have now reached 499,226 BTC, valued at around $41.4 billion, or approximately 2.4% of the total Bitcoin supply, per the latest U.S. Securities and Exchange (SEC) Commission filing.

 

The acquisition was funded using proceeds from the “STRK ATM,” a new program Strategy launched to raise up to $21 billion in fresh capital. 

MicroStrategy to Redeem $1 Billion in Debt as Bitcoin Stash Nears $50 Billion

An At-The-Market (ATM) offering allows a company to sell shares directly into the secondary trading market at prevailing market prices over time, rather than through a traditional public offering.

 

The Bitcoin giant's long-term plan involves raising $42 billion over the next three years to expand its holdings significantly as part of its “strategy” to acquire Bitcoin as a key asset despite market fluctuations.

 

Following the announcement, Bitcoin’s price jumped slightly, but has since fallen by 0.3%, trading at $82, 921.51, CoinGecko data shows.

 

Amid the recent price fluctuations of the asset, Strategy’s Bitcoin yield stands at 6.9% year-to-date, though it still falls short of the firm’s target of 15% for 2025.

 

While the latest buy is among its smallest, the company had its largest acquisition of 2025 on Feb. 24, purchasing 20,365 BTC for nearly $2 billion. Starting in early November, it had purchased Bitcoin on a near-weekly basis. 

 

Alongside its corporate acquisitions, Michael Saylor, co-founder of Strategy, has always advocated for Bitcoin as a strategic reserve asset for the U.S. government. 

 

Last month, Saylor suggested the U.S. could purchase up to 20% of Bitcoin’s total supply, potentially using the asset to eliminate the national debt—just before President Donald Trump unveiled plans for a strategic “crypto reserve.”

 

Even with the smaller size of the latest acquisition, Strategy continues as the largest corporate Bitcoin holder, followed by MARA Holdings (MARA) and Riot Platforms (RIOT), Bitcoin Treasuries data shows

 

Strategy stock (NASDAQ: MSTR) was most recently trading at about $293.60—a 13.41% increase at the close, per Yahoo Finance.

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BTCMicroStrategyMichael Saylor
Mar 18, 2025 - 31天前
Salesforce Developer Creates LLM Assistant That Runs Locally On Your MachineI've been experimenting with the local LLMs inside Salesforce and would like to tell you about the component I developed as a result. It has the already familiar chat interface which uses Salesforce records for context. It works locally on your computer, so processed data is not being sent to any third-party service. The introduction of Agentforce was what influenced me to develop the component. Agentforce uses agents  —  systems that can make decisions and perform various actions. Assistants, in contrast, only process information reactively. Even though I believe it's possible to build a local agent using Pico LLM, it would take enormous effort. Thus, I decided to develop an assistant instead. As you would expect an LLM to work, it generates responses on any topic, as it's pretrained on a vast set of data. Moreover, it's able to use Salesforce records for extra context. The features of the component are: From an end user’s perspective, the process is straightforward. You upload a model, select a system prompt, select records, write a user prompt, and look at the result being generated. Running LLMs in a browser is a resource-consuming task because of the model’s size, bandwidth requirements and RAM needs. Therefore, the Pico team developed their picoLLM Compression technique, which makes usage of LLMs locally much more efficient for computers. They provided the picoLLM Inference Engine, as a JavaScript SDK, to allow front-end developers to run LLMs locally across browsers. It supports all modern browsers including Chrome, Safari, Edge, Firefox, and Opera. To know more about how the picoLLM Inference Engine works, you can read their article . The component serves as a bridge between a user and PicoLLM interface. At the core of the component is a Visualforce page embedded as an iframe. The page loads the PicoLLM SDK and communicates with the LWC allowing the last to use SDK via post messages. The whole combination of elements handles the following: On the back-end side of things there is nothing fancy. The Apex code does all the heavy lifting related to detecting the relationships between the objects using a record Id from the record page. Also, it performs a couple of SOQL queries, and thereby its duty is done here. Previously, I used the unpkg tool to execute code from the node module in LWC component. This approach led to additional configuration steps, and was a less secure way to make it work. This time, I wanted to execute the PicoLLM module directly from Salesforce and not only from the Experience Cloud site, which I had done previously, but the Lightning Experience interface. Under the hood, PicoLLM uses web workers for parallel processing, and it was the main problem because it’s not allowed to run them from LWC. Luckily, no one refused to let us run web workers from a visualforce page, and it was the approach I used. I downloaded the raw PicoLLM code and added it as a static resource to the visualforce page. In LWC I used an iframe which contained the visualforce page. The communication between the LWC and the page inside the iframe allowed me to use web workers. The page triggers the PicoLLM-related code from the lightning web component. Copy and paste Salesforce records in a JSON or CSV format, throw it into any online LLM and watch. It will consume the records, use them for extra context and generate a response. It turned out that it is not that easy when using compressed models for local processing. At first, I was simply putting the records, in JSON format, right into user prompt. Then I expected the thing to be smart enough to distinguish the prompt itself from the additional context I provided. I used different models of various sizes and didn’t understand why it wasn’t using the JSON to generate responses. It was mostly refusals to respond to my prompt or generation of fictional data not related to what I asked it to do. I started to experiment with different formats of the context data: using CSV, using JSON, using prompt dividers to strictly differentiate prompt from context — nothing helped. I nearly abandoned the idea because the key feature wasn’t functioning. After a couple of months, I suddenly got a stupidly simple brainwave. What if I just reversed the order of prompt parts? From user prompt coming first and context coming second, to context coming first and prompt second. To my surprise it worked, and any model I used immediately started to understand Salesforce records as context. The component’s functionality was tested on these machines: The most time-consuming part of using the component is the initial model loading. You might expect the 9900X to easily outperform the Snapdragon X-Elite, but you'd be wrong. To my surprise, the latter is faster. Since it has faster memory, I presume that the faster your RAM, the faster the model loads. Here’s a model loading speed comparison table for reference: The same story with the response generation speed. As I understand, you need to have a fast combination of CPU and RAM to get the fastest generation possible. Because response generation varies with the same prompt, I did not conduct precise speed tests. Nevertheless, the generation speed is extremely fast, almost as fast as the online alternatives. Indeed, using a GPU to generate responses would be much more efficient. While it's possible to use a GPU with PicoLLM, I haven't tested that configuration myself. There are a couple of reasons for this. First, I believe it uses the WebGPU feature, which isn't enabled by default in most browsers (except Edge). Second, it likely requires several gigabytes of VRAM to load the model which I don’t have. Developing this assistant has been a fascinating journey of exploration. From grappling with web worker limitations to discovering the crucial role of prompt order in providing context, the challenges have been both stimulating and rewarding. The result is a Lightning Web Component that offers a unique approach to leveraging the power of Large Language Models within the Salesforce ecosystem. While the initial model loading time can be a consideration, especially for larger models, the ability to process data locally offers significant advantages in terms of data security, responsiveness, and cost-effectiveness. The potential use cases, from automating content generation to providing intelligent assistance, are vast and waiting to be explored. Check out the GitHub repo .
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SalesforcePicoLLMLocalLLM
Mar 18, 2025 - 31天前
Yield-Bearing Stablecoins Will Underpin New Financial Architecture: Coinshift CEO

The lines between stablecoins, lending and banking are blurring, leading to the emergence of new financial primitives, according to Tarun Gupta, CEO of payments and accounting solution Coinshift.

In a video interview with Decrypt, Gupta elaborated on his thesis, explaining that this emerging financial architecture would be underpinned by yield-bearing stablecoins like Coinshift’s own csUSDL.

“For the past six or seven years, stablecoins have solved one simple use case, which is money,” Gupta explained. “Today, you can basically make the movement of money faster, cheaper and better with stablecoins. However, they're not solving any kind of yield use case or lending use case.”

Stablecoins' "architecture of promises"

Yield-bearing stablecoins, which enable holders to receive passive income while maintaining the stability of fiat-pegged tokens, build on the “architecture of promises” underpinning verifiable on-chain assets, Gupta said.

“How I see this is, you need a trust layer, and then you need a transparent layer on top,” he said. “Once you combine these two layers with the power of smart contracts and blockchain, you end up having a lot of convergence between banking, lending and yield mechanisms.”

Based on that thesis, Coinshift launched csUSDL. “Can you combine a USDC-like stablecoin from the real world, which is Paxos’ USDL, and then use it to lend on a platform like Aave?” Gupta said. Coinshift’s csUSDL leverages “permissionless market creation system” Morpho Protocol, which enables the creation of verifiable markets, alongside the provably collateralized USDL.

The end user, he explained, is “trusting this entire promise of, once you hold csUSDL, you will get one USDL against that, and you don't need to trust Coinshift for that,” he said. Instead, users place their trust in a protocol, a smart contract layer and ultimately the provably verifiable blockchain. “This financial architecture is way better than what we have in TradFi,” Gupta said. “Once we have this architecture, the thesis is that every other instrument will move on-chain,” he explained, bringing trillions of dollars in value with it.

Regulatory clarity

For that to happen, as well as the technology being in place, regulations have to catch up. Crypto-friendly jurisdictions like that of Abu Dhabi are “setting examples for other governments on how you can innovate with new technology,” Gupta said. In the U.S. the proposed GENIUS Act is putting the nation on “the right path,” he added, with institutions able to “use a specific stablecoin issuer because it’s regulated and supervised.”

Crypto Markets Structure, Stablecoin Bills Will Pass Within Trump's First 100 Days: Senator Scott

The proposed legislation “brings lots of clarity,” while its rules on reserve management “ensures more trust” to end users like institutions, he said, adding that he is “100% confident” that institutions and neobanks will adopt stablecoins.

As a result, there’s now “no reason to use the old banking infrastructure to move money,” he said, adding that other fintechs like payroll providers will also migrate to stablecoins. In the future, he explained, “all fintechs will actually outcompete banks with stablecoins like USDC.”

SHIFT work

In the increasingly crowded stablecoin field, Gupta said, those with “the largest liquidity, deep integrations, and distribution” are poised to win.

For its part, Coinshift is focusing on two key areas. First, the launch of its SHIFT reward and governance token, which has two purposes: to reward the TVL of Coinshift’s cs-focused assets, and to regulate its ecosystem.

Second, the company aims to grow csUSDL adoption by playing on its yield-bearing proposition. In the short term, that means driving the stablecoin’s market cap to $100 million through “majorly large institutions,” Gupta said. “If you’re already holding USDC, you should think about holding csUSDL, because the risk is on the lowest side, and the secondary liquidity is also very high,” he explained.

As well as increasing secondary liquidity “so that people always stay in csUSDL,” the next stage of Coinshift’s plan is to fully integrate csUSDL into its platform, “to offer it to all our clients, especially b2b organizations,” Gupta said. In addition, Coinshift plans to work with DeFi protocols to “have it as collateral or become a reserve for other stablecoins.”

Already csUSDL has “15-plus DeFi integrations on day one,” Gupta said, explaining that, “At the end of the day, we’re building this underlying blueprint for making these yield-bearing instruments more liquid in DeFi, and it should be deeply integrated.”

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stablecoinsyieldbearingDeFi
Mar 18, 2025 - 31天前
Will Musk Colonize Mars In His Lifetime? The Answer Won't Surprise YouAs part of the Spacecoin Writing Contest , the fine folks at Spacecoin and HackerNoon have asked: Will Elon Musk colonize Mars in his lifetime ? It depends, I suppose, on what you mean by colonize . If you mean creating a vibrant, large-scale settlement in the grandiose Kim Stanley Robinson sense of the word, the answer, unsurprisingly, is no. That’s not to say there won’t be some kind of Martian foothold in the not-too-distant future. We’ll likely see boots on the ground before Musk shuffles off this mortal coil for a questionable afterlife on a Neuralink server . And when that moment comes, the media spectacle alone will be worth the wait. The first selfie, taken against a rust-colored sky and beamed across the void by The First Human On Mars, will break the internet, triggering profound audience capture and giving Earth’s perpetually outraged masses something to argue for instead of against. At least for a few news cycles. Of course, the reality lurking behind the euphoric headlines will be far less cinematic. A functional, self-sustaining colony in Musk’s lifetime—as in a place where people live, love, and build lives that don’t hinge on recurring supply drops—just isn’t in the cards. The problem isn’t lack of ambition. It’s physics, biology, and the Red Planet’s fundamental indifference toward our delusions of Martian grandeur. What will we see? Probably a mishmash spectacle of 20-minute time delayed live streams and gritty DITL updates on X peppered with ironic branding opportunities. And of course, a few extremely lonely astronauts transmitting increasingly bleak vlogs to the good folks back home. Think a glorified Antarctic research outpost but with worse WiFi, or The Martian , but less funny and with more product placements. Which is to say, we won’t see anything resembling the glassine Martian arcologies or gerontological life-extension clinics of Red Mars . Here is a passage taken from SpaceX’s Mars & Beyond Roadmap To Making Humanity Multiplanetary . And I quote: Why Mars? At an average distance of 140 million miles, Mars is one of Earth's closest habitable neighbors. Mars is about half again as far from the Sun as Earth is, so it still has decent sunlight. It is a little cold, but we can warm it up. Its atmosphere is primarily CO2 with some nitrogen and argon and a few other trace elements, which means that we can grow plants on Mars just by compressing the atmosphere. Gravity on Mars is about 38% of that of Earth, so you would be able to lift heavy things and bound around. Furthermore, the day is remarkably close to that of Earth. I have to tip my hat to the copy editors at SpaceX. This is a really effective piece of writing. One carefully engineered understatement after another designed to coax the reader into a quasi-hypnotic, carefree state. Martian life, evidently, is bliss. Spin of this caliber is remarkably persuasive. The language and economy of expression is so gentle and unassuming it would lull a caffeinated squirrel to sleep. Not so much sterile corporate-speak, but a form of lowkey verbal jujitsu that by comparison makes the summit of Mt. Everest sound like a Sandals resort. For example, calling Mars “one of Earth’s closest habitable neighbors” is like a realtor describing a condemned building as “full of potential” or an airline referring to a mid-air explosion as an “unscheduled rapid deboarding event.” It’s the same rhetorical sleight of hand that could rebrand suffocating in a barren, radiation-blasted wasteland as “adapting to novel atmospheric conditions.” Mars is “habitable” in the same way the bottom of the Marianas Trench is. Habitable, sure, technically speaking, but only if you stick the landing, bring your own highly complex life support systems, and make peace with the constant, looming specter of instant death. Mars is about half again as far from the Sun as Earth is, so it still has decent sunlight. Translation: Mars is so far away, the Sun will look like a small anemic disk in the sky. And thanks to the planet’s pathetically thin atmosphere and no global magnetic field, hazardous energy particles from said anemic disc, along with a constant bombardment of cosmic radiation, will scramble your DNA and pose significant long-term health risks like cancer and acute radiation sickness. It is a little cold, but we can warm it up. Translation: It’s really cold. Colder than Antarctica at its absolute worst. For reference, the median surface temperate is –85°F and can dip to around -225°F. The kind of cold that will induce frostbite faster than you can say “terraforming.” Speaking of, we’re looking further into radically transforming the entire planet’s climate via nukes, moholes, and orbital mirrors. Heads-up, it could take a while, so pack extra sweaters. We can grow plants on Mars just by compressing the atmosphere. Translation: I don’t even know where to begin. #JustCompressIt Gravity on Mars is about 38% of that of Earth, so you would be able to lift heavy things and bound around. Translation: You can lift heavy objects and do sick backflips. But your newfound super-power comes at a cost. Prolonged exposure to low gravity will lead to back pain, muscle atrophy, and bone density loss. Oh, and Martian gravity might, among other things, mess with your organs and eyesight, too. Make no mistake, your body is evolved for life on Earth, not Mars. Given the absence of artificial gravity, bring a gym bag and prepare for an intense regimen of frequent exercise. Furthermore, the day is remarkably close to that of Earth. Translation: Mars has a 24-hour and 37-minute day. Your circadian rhythm might not be too off-kilter, but that’s assuming you can even sleep at night. You know, Claustrophobic Existential Dread (CED) of living in a glorified tin can for the rest of your life where a single system failure means instant death. (Let’s also note the roadmap’s strategic omission of the fine dust situation on Mars. Dust like orange talcum powder that will get into everything. EVERYTHING. Computers, living modules, rovers, your bloodstream. Often leading to headaches, sinus trouble, sore throat, bronchitis, lung distress, and the occasional hardware malfunction that spells no uncertain doom.) I really am. There’s something undeniably seductive about the notion. Back in the 2010s, before the Texas Exodus, I toured SpaceX HQ in Hawthorne, CA. I rubbed elbows with brilliant aerospace engineers and inspected Merlin engines up close. The sheer audacity of such precision-controlled hellfire. It was a genuinely inspiring experience. There’s poetry in the idea of some future descendant of the first Martian standing atop a wind-sculpted dune and pointing at a pale blue dot in the sky and thinking, That’s where great-great-grandpapa grew up. But a colony in Musk’s lifetime? No. Not in any meaningful, civilization-altering sense of the word. For now, that’s a fantasy best left to sci-fi novelists and TED Talk enthusiasts. And yet—perhaps absurdly, naively—it’s a fantasy worth having. Because even if the SpaceX roadmap is more branding exercise than blueprint, there’s something deeply human about looking up and wanting more. About staring at a barren red rock 140 million miles away and thinking, we should try anyway. The reckless belief that maybe we could has a gravity of its own. In a world dominated by cynicism, doomscrolling, and algorithmically optimized despair, maybe it’s one force we shouldn’t try to escape. Product placements and all.
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MarsColonizationSpaceXElonMusk
Mar 18, 2025 - 31天前
Canary Capital Files Application With SEC for SUI-Based ETF

Canary Capital filed Monday with the Securities and Exchange Commission to launch a SUI exchange-traded fund, roughly a week after a Trump-linked decentralized finance project added the token to its reserves. 

“We’ve seen a significant migration of developers into the SUI ecosystem, and given the chain’s speed and efficiency, we believe it is primed for substantial future adoption,” Canary Capital CEO Steven McClurg told Decrypt in an email.

The filing comes roughly two weeks after Canary Capital registered a Delaware Trust entity for a SUI ETF—the first step to creating an investment vehicle tracking the altcoin's price. In its application, the firm did not name the exchange on which its SUI ETF, if approved, would trade. 

Canary Capital's push to launch a SUI ETF comes 10 days after the Trump-affiliated World Liberty Fi collective announced its deal with the token's issuer, Sui Network. Under the agreement, WLFI will add SUI to its reserves and explore product development opportunities with the layer-1 blockchain. 

The Nashville-based firm's bid to offer a SUI-based investment vehicle comes amid a broad push by issuers to secure approvals for a variety of digital asset-based investment products from securities regulators that have softened their stances on cryptocurrencies under newly elected President Donald Trump. Amid that increasingly crypto-friendly regulatory environment, asset managers such as VanEck, 21 Shares and Franklin Templeton have filed to offer Solana and XRP ETFs, among other digital asset-based products. 

Canary Capital Files for Litecoin ETF Just Days After XRP Move

Joining the fray, Canary Capital has filed to launch several ETFs based on blue-chip cryptocurrencies and altcoins over the past few months. Last fall, the investment firm submitted S-1 forms for Litecoin and HBAR ETFs to the SEC. And, more recently, the firm has made progress on its petitions to roll out funds tracking the prices of Solana and XRP.

SUI was recently trading at $2.36 on Monday, up 5.1% in the past 24 hours, according to digital asset data provider CoinGecko. The token is the 23rd largest cryptocurrency with a market value of more than $7.4 billion, the same data shows.

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SUICryptoETFSolana
Mar 18, 2025 - 31天前
Trump, Associates Net $390 Million Payday From World Liberty Token Sale

President Donald Trump, and other principals of DT Marks DEFI LLC, have already netted roughly $390 million in revenue from their Ethereum-based decentralized finance project—and that’s just for promoting it. 

On Monday, World Liberty Financial announced it had completed its second round of token sales. All in all, the project has brought in $550 million from two rounds of sales of WLFI, its native governance token, according to the company’s own accounting. 

Though Trump himself is technically a promoter of World Liberty, and not an active member of the project, the president is still entitled to the lion’s share of revenues the project reaps. According to World Liberty’s gold paper, Trump and his business partners are set to receive 75% of net revenues earned by the project, including WLFI token sales, after operating costs are taken into consideration.

Of the sum raised from sales of WLFI, $30 million has been earmarked to cover company expenses, indemnities, and obligations. Trump and his partners in DT Marks DEFI LLC then get 75% of the remaining amount—a full $390 million—as payment for Trump promoting the project “from time to time” and allowing it to use his name and likeness, according to the project’s gold paper.

It’s unclear who, besides the president himself, will receive funds from the LLC. According to SEC filings, DT Marks DEFI is based in Jupiter, Florida, at the address of the Trump Organization’s executive offices. 

When World Liberty first launched token sales in October, the project struggled to attract investors. On the eve of the 2024 election, it had sold less than $15 million worth of WLFI tokens, according to Dune—a fraction of the project’s stated goal of $300 million in sales. 

But after Trump recaptured the White House, the project enjoyed a sharp spike in interest. Tron blockchain founder Justin Sun, for instance, said he purchased tens of millions of dollars worth of WLFI, and shortly thereafter joined the project as an advisor. 

In late February, the SEC asked a judge to pause its years-long fraud lawsuit against Sun, in order to explore a “potential resolution.”

SEC Softens Crypto Stance as Justin Sun Eyes Settlement, Gemini Cleared of Probe

Since returning to office, Trump has faced questions about potential conflicts of interest, given his personal investment in various crypto ventures at a crucial juncture when his administration is set to effectively write the book on crypto regulation.

One such project, the TRUMP meme coin that launched on the eve of the president’s inauguration, entitles his companies to eventually hold over $9 billion worth of the token at current prices.

Earlier this month, the president’s AI and crypto czar, David Sacks, dismissed the president’s crypto projects as “irrelevant” to industry regulation.

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EthereumWLFITRUMP meme coin
Mar 18, 2025 - 31天前
AI Honeypots Are the Future of CybersecurityPhoto by  Carla Quario  on  Unsplash Responding to cybersecurity threats means a company must be five steps ahead of the hackers. Clone traps, a vanguard in deception technology, automate threat response by deceiving the deceivers and fighting AI with AI. By the end of 2024, there were 240,830 live cybersecurity vulnerabilities and exposures (CVE): These are only the publicly disclosed vulnerabilities, and there may be many more. In the first half of 2024  new common CVEs identification represented a 30% increase , potentially leaving doors wide open for cybercriminal exploitation. The advent of AI technologies is only adding to this list, with cybercriminals using Generative AI to empower their attack chains. As a result of security gaps and advanced attacks, a staggering 95% of bot attacks go undetected. There is also a 19% increase in manual/human attacks; manual attacks are often complicated and multi-part, making detection challenging. Organizations around the world stand at a precipice. Dealing with this unparalleled complex cyber-attack level has led to an intelligence gap. As such, the tsunami of zero-day and complex exploits requires a sophisticated approach. Here, we look at how a vanguard in AI-powered deception technology, clone traps, will help firms of all sizes persistently protect and strengthen their systems and reduce the risk of a successful cyber-attack. A new security kid is on the block: Clone traps are next-gen honeypots that are about to turn the table on cybercriminals. This is less a new type of honeypot, and more a quantum leap in deception technology to catch even the most persistent and evasive attackers. Clone traps are deeply integrated with a firewall and provide AI-driven intelligence to super-target protection, fighting AI with AI. Clone traps also provide crucial data to the entire cybersecurity system and enhance a customer’s cyber resilience. One of the most powerful features of a clone trap is the dynamic and real time use of data. This dynamism allows firewall data to be put to immediate use, the clone trap’s AI engine learning from firewall data to identify an attack instantly, and protect the firewall – stopping an attack before it becomes an incident. Future clone trap developments include AI-driven "modelling,” used to generate attacks to identify weaknesses in firewalls and to train defensive AI. The continuous innovation in cyberattack methods requires a similar innovative approach to detection and prevention. The central pivot upon which the digital world turns is data. Therefore, the next generation of deception technologies must be able to optimize the use of data. This is precisely what clone traps do. Clone traps are part of a broad cybersecurity ecosystem: the traps, firewalls, data, and the cybersecurity/ SOC (Security Operational Center) team. This ecosystem approach provides exceptional results, rates of detection improved by up to ten times the market average. Clone traps serve as the entry gates for valuable data, creating up-to-date feeds of malicious attack sources, strange URL patterns, abnormal frequency request signatures, client geo triggers, and behavioural changes in a system. All this information is delivered to the core cybersecurity platform, which matches the data from honeypots with the usual requests and intelligence from over 100 sources: open source data, proprietary sources such as known attacker databases, and even the darknet, which can provide vital attack intelligence. With all this data collated, the core system is able to decide what constitutes an attack and what does not, and packages these decisions into a threat feed for firewalls. Data from hundreds, even thousands of traps is used to form a complex mesh of intelligent insights that are used to identify emerging threats, zero-days, and complex multi-part attacks. The integration of clone traps with a firewall is designed to provide an automated response to all types of threats by leveraging the power of AI and real-time data. Data unification is core to the clone trap's success in attack detection. However, the security team is another essential part of the success mix. Once the decoy is set using the most enticing data and the system hardened, the security team can wait for the attack to begin. Once detected, the cybersecurity platform shares the data with the firewall and the rest of the company's infrastructure, and the firewall automatically blocks the hacker. Your internal security team or SOC uses these alerts to respond to the attack, closing down the pathways that can lead to ransomware infection, data breaches, and other cybersecurity events. Meanwhile, the trap lets the hacker in, revealing all its depths so that you can study his behavior. Ongoing threat intelligence generated by clone traps provides the data needed to create a robust cyber security strategy and to update and adapt the policies based on clone trap feedback. Clone traps take the decoy technique to new levels of response, handling the aftermath of the attack through auto-remediation and auto-healing. Using automation, detection and resolution of cyber threats require no direct human intervention, which removes human error and reduces the time to threat resolution. Also, the intelligence generated by clone traps provides the documentation auditors need to demonstrate that a company is using robust security measures. No one should be able to figure out the trap; however, clone traps must be discoverable, as an undetectable honeypot may lead to hackers finding out that it's a trap.  On the contrary, they invite the hacker in. Of course, clone traps have to be close to reality and sufficiently complex, so that the whole concept works by providing information about the hacker's techniques, rather than just distracting them. In a scenario where a hacker has managed to penetrate the clone system, the internal security team or SOC will receive an alert. The report provides complete details on the attack, providing the team with insights into the attack method to allow reverse engineering of the attack. The intelligence gathered will be used to further harden the system against future attacks. The clone trap itself, after being hacked, may either remain unchanged - waiting for the next "victim" - or be protected by a firewall if desired. A question that may come to mind is, “What if a legitimate user, like an employee, falls for a clone trap?” Employees and legitimate users are almost unable to interact with a clone trap. In other words, even though clone traps camouflage themselves as standard services, their unique positioning means that usual clients will rarely stumble upon them without prior knowledge. Hackers, however, are likely to encounter them while searching for vulnerabilities. False positive alerts are a serious problem for the security team. False positives don’t just waste time, but block regular users and create false alert fatigue, which can cause real attack signals to be missed. The following impact can also result in lowered employee morale. Clone traps prevent false positive alerts as legitimate users cannot normally navigate to the cloned instance – the net result is that the intelligence derived from the clone trap is from genuine attackers engaging the clone traps; this data is therefore rich with threat actor IoCs (Indicators of Compromise). As a result, by combining clone trap intelligence with known threat data sources, false positives are effectively eliminated. Clone traps provide the security team with a powerful tool to automate the detection and resolution of cyber threats. They, however, are augmented by a security professional. Administrators of clone traps provide configuration guidance, and each time a clone trap generates an incident report, they interpret the results, log into systems, set stricter policies, use different configuration approaches, and ensure that config scripts reflect the current challenges. The automated response to cyber threats by clone traps allows security teams to use their industry knowledge to work on strategic system protection. Clone traps are a result of ongoing research and development. Cybersecurity scientists use their deep knowledge of the threat surface to mimic hackers and understand their deviant tactics. This threat intelligence has allowed researchers to create clone traps perfectly designed to entrap their hacker prey and extract their tactics. By using a mix of AI and human experience, clone traps can stop the most persistent and complex cyber-attacks and stay five steps ahead of cybercriminals.
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CybersecurityDeceptionTechnologyCloneTraps
Mar 18, 2025 - 31天前
Robinhood Debuts March Madness Prediction Markets as Event Derivatives Gain Traction in US

Robinhood has launched a prediction market that will allow users to bet on this year's March Madness basketball tournament—the latest sign event derivatives are gaining traction in the U.S. despite ongoing regulatory challenges.  

Beginning Monday, Robinhood users can place wagers on the National Collegiate Athletic Association's March Madness tournament, which runs from mid-March to early April. 

The prediction markets are powered by Kalshi, a startup that fought and won its legal battle against regulators to operate event contracts during the U.S. presidential election last fall.

US Election Results a Validation on Prediction Markets, Crypto Experts Say

It is unclear if the trading platform supports wagers denominated in crypto, which both startups support, or if users may only place bets in US dollars. 

Neither Robinhood nor Kalshi immediately responded to Decrypt's request for comment on the matter. 

Robinhood's rollout of money-line markets for sporting events comes as prediction market operators show signs of clearing at least some of the regulatory hurdles that have long prevented them from operating stateside.

Late last year, Kalshi won its lawsuit against the Commodities and Futures Trading Commission, enabling the startup to reaunch its betting pools on U.S. election outcomes. 

In the weeks following its legal victory, the trading platform had notched more than $275 million in inflows to its electoral markets—a sign of Americans’ appetites for prediction markets. 

Prediction Market Kalshi's Long-Game Play to Court Crypto Traders

However, market operators still face some scrutiny from regulators, complicating their efforts to increase access to prediction markets in the U.S. 

Last month, Robinhood shuttered its prediction market service for the Super Bowl just a few hours after its launch, Fortune Magazine reported. The decision came after the trading platform received considerable pushback from the CFTC, according to the publication. 

Meanwhile, Kalshi received last week an order from gambling regulators in Nevada to halt its trading offerings for sporting events and elections, Kalshi co-founder Tarek Mansour said in an X post. The cease-and-desist order accused the startup of offering contracts in a similar manner to unlicensed sports betting services.  

Robinhood shares are trading at $42.02, up roughly 3% in the past 24 hours.

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PredictionMarketsRobinhoodKalshi
Mar 18, 2025 - 31天前
The Best Interview Question to AskFinding out whether a candidate can do the job is incredibly challenging. The usual slate of techniques – from so-called “experiential questions” (“Tell me about a time when you didn’t agree with a coworker”) 1 to Google-style brain teasers (“How would you go about finding out the weight of the moon using nothing but croutons”) 2 to supposed leet code questions (“You have two linked lists and you need to combine them.”) 3 – fall short in so many ways, but primarily they fail in the most critical way: None tell you if the candidate can DO the JOB – the work of the work, day after day, showing up and pitching in and getting things done. Add to this the growing trend of candidates using AI during interviews by quickly typing questions into a separate window and then scanning for the “right” answer. And before you cry foul, just remember that every single company is using AI to recruit candidates, so fair is fair. Also, in what world would a real employee be chastised for using a tool (including AI) to solve a problem? Why would you handcuff them during the interview by prohibiting those tools, and how does this reveal anything about their day-to-day on-the-job performance? MEANWHILE… on the other side of the Zoom call, sits the candidate. Someone who is being asked to go through 8 rounds of interviews with each company, telling variations of the same stories in the hopes that the message of “I can do this job” lands and distinguishes them from the 9,634 other people vying for the same role. And they are getting… Those same lame-ass techniques that are either trick questions, invitations to invent an answer out of whole cloth, or irrelevant to this (or any other) job. In both of those scenarios, I’d like to offer up my favorite interview question. “Tell me how to get to your favorite restaurant.” Why do I like this question so much? First, it can’t be gamed. There’s no empirically right or wrong answer. Any answer is fine (even if the candidate makes it up) because the critical information you gain doesn’t come from the answer, but from the WAY in which it’s answered. Even in the variety of options for execution, there’s no ONE right way to approach it. I might be interviewing for an SRE whose job is to quickly fix things, and send a link to a Google map (or more appropriately a knowledge base article), who doesn’t burn a lot of time explaining why manhole covers are round or how they learned all this is what I need. There’s also the chance to see how they respond if I change the scope halfway: “Oh, I’m trying to be more climate-friendly. Can you tell me how to get there by public transportation?” No, this will not tell me if they can program a fizz-buzz in Perl. But it goes a long way to telling me how they approach a task and interact with a requester. It opens the conversation up to asking procedural and even technical questions along the way. “What tools do you normally use for this task?” “If that doesn’t work, what would you do next?” And so on. Also, if it’s not obvious, “tell me how to get to your favorite restaurant” can be replaced with any process that focuses on the experiential rather than a simple set of instructions or a single-answer question. Because the focus is on “your favorite”, not “restaurant.” Meanwhile, back on the other side of the Zoom call, whenever the conversation strays into those standard techniques I share this technique with interviewers (I’ve got chutzpa. I know.), but with a twist. “You know,” I start out. “I understand where that question is coming from, and I’m going to answer it. But I’ve sat on your side of the desk, and I know it’s hard to find ways to really assess a person’s ability to do the job. If you don’t mind, I’d like to share MY favorite interview question. “ And then I share it. And then I finish by saying: “And more than the question itself, I’d like you to consider that this is what I do. I quickly shared that question with you and a little of the idea behind it, and yet you can now execute it perfectly. Not only that, but you can go to your team, tell them, and THEY will be able to execute it perfectly as well. Because what pride myself in is finding solutions that scale. Solutions that not only work for me, but which can be quickly and easily shared with the team and allow everyone to have the same results I do. That’s what you get if you hire me.” Interviews are hard. Hopefully, this idea catches on and makes it easier for everyone. If you’ve got your own favorite interview technique, let me know in the comments below. Footnotes: I’m sorry to all the HR folks who swear by these, but for a theater major, all you are doing is asking me to spontaneously improvise a plausible-sounding fiction where I come out sounding wise, insightful, and reasonably level-headed. Which we all know I am not. ↩︎ My son had the all-time best answer: “I would find an astronomer, ask them the weight of the moon, and throw croutons at them until they told me.” Chip off the old block, that one. ↩︎ Alberta.tech has the best answer ever . ↩︎
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AI in RecruitmentInterview TechniquesCandidate Evaluation
Mar 18, 2025 - 31天前
Here's What US Stock Futures Say About Bitcoin's Next Move

Bitcoin and Ethereum extended losses on Sunday as traders braced for another volatile week, with U.S. stock futures pointing lower ahead of the Federal Reserve’s upcoming policy meeting.

Bitcoin slipped nearly 1.8% to trade around $82,700, while Ethereum dropped 2.5% to $1,889, as investors assessed the impact of macroeconomic uncertainty and shifting regulatory developments.

As of Sunday evening, Dow Jones Industrial Average futures declined 0.37%, while S&P 500 and Nasdaq Composite futures fell 0.46% and 0.55%, respectively.

Why Are Bitcoin, Ethereum Prices Falling?

Markets are increasingly focused on the Federal Reserve’s rate outlook, with futures traders pricing in a high probability that the central bank will hold interest rates steady this week. 

While expectations for rate cuts later this year remain intact, recent inflation data and strong labor market numbers have raised concerns that the Fed may delay easing monetary policy on Wednesday.

A more hawkish stance could weigh on risk assets, including crypto, which have traded in lockstep with equities in recent months.

Geopolitical tensions are also adding pressure.

President Trump’s recent announcement of new tariffs and potential retaliatory measures from the European Union have injected fresh uncertainty into global markets. 

In addition, his executive order to establish a Strategic Bitcoin Reserve briefly fueled speculation about U.S. government involvement in crypto markets before investors realized no immediate budget had been allocated for purchases.

Bitcoin Price Slides as Crypto Market Reacts to Trump’s Strategic Reserve Order

Bitcoin's initial spike following the announcement was short-lived, with prices reversing once traders recognized the lack of immediate action from Washington. 

Meanwhile, in the derivatives market, leverage remains high. 

Coinglass data shows crypto futures open interest remains elevated, despite over $253 million in liquidations over the past 24 hours. 

Funding rates, which briefly turned negative during last week’s sell-off, have returned to neutral, suggesting uncertainty in market positioning.

With macroeconomic risks mounting and regulatory developments unfolding, traders are looking for a catalyst to break the current downtrend. 

The Federal Reserve’s policy decision, coupled with any new signals from institutional investors or regulatory bodies, could determine whether crypto markets regain momentum or face further downside pressure in the coming weeks.

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BTCETHFederalReserve
Mar 17, 2025 - 32天前
FalconX Carries Out ‘First’ CME Group Solana Futures Block Trade

Digital asset prime broker FalconX said Saturday it has completed the “first-ever” block trade for CME Group's Solana futures with StoneX as counterparty, a day ahead of the SOL futures launch of new contracts expected on March 17.

The San Mateo, California headquartered broker executed the transaction with the goal of providing a way "to manage risk and price exposure on a regulated venue," Josh Barkhordar, head of U.S. sales at the firm said in a statement.

A block trade in this context is a large-volume, privately negotiated transaction of the futures contracts, done outside the open market to avoid disrupting the asset’s price.

Solana May Soon Get a Major Change—Here’s Why Builders Are Butting Heads Over SIMD-0228

CME Group debuted the Solana futures contract in late February to meet "increasing client demand," as it vies to position the offering as a precursor and "primary pre-requisite" to a SOL ETF.

​Several asset management firms have filed applications with the U.S. Securities and Exchange Commission to launch Solana ETFs.

Notably, Franklin Templeton, managing over $1.5 trillion in assets, submitted a filing in late February 2025. Other firms, including Grayscale, 21Shares, Bitwise, VanEck, and Canary Capital, have also filed for spot Solana ETFs.

Solana futures follow the pattern established with Bitcoin and Ethereum, where futures trading preceded ETF authorization and approval from a regulatory body.

The new contracts come in two sizes: standard contracts representing 500 SOL and micro contracts representing 25 SOL.

The futures are then cash-settled based on the CME CF Solana-Dollar Reference Rate, calculated daily at 4:00 p.m. London time, providing a standardized benchmark for SOL's U.S. dollar price.

FalconX operates as a key liquidity provider for CME Group's crypto derivatives suite. The company reports executing over $1.5 trillion in trading volume across over 400 tokens for approximately 600 institutions.

Solana Hits Yearly Low as ‘Perfect Storm’ Batters Price

The firm has pursued strategic expansion in institutional crypto markets, acquiring derivatives platform Arbelos Markets in January 2025 and partnering with liquidity and data solutions provider TP ICAP's Fusion Digital Assets in February last year.

Meanwhile, CME Group claims that its crypto derivatives market has demonstrated substantial growth, with average daily volume reaching 202,000 contracts in early 2025, representing a 73% increase year-over-year. 

The exchange reports average open interest of 243,600 contracts, up 55% year-over-year, with more than 11,300 unique accounts trading its crypto products.

On centralized crypto exchanges, Solana derivatives show a 66% volume increase to $7.24 billion, covering a bullish bias with multiple long/short ratios above 2, despite some $12.29 million in 24-hour liquidations, data from Coinglass shows.

Solana is down 6.4% to $127, and remains under pressure following its January all-time high near $293.31, CoinGecko data shows.

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SolanaCME Groupcrypto futures
Mar 17, 2025 - 32天前
Filmmakers Bet on Web3 to Fix Hollywood Film Financing

Cutter Hodierne knew the odds were against him. As an independent filmmaker trying to secure funding for "Cold Wallet," a crypto thriller about a heist gone wrong, he faced the usual hurdles—hesitant investors, an unpredictable industry, and a financing system that favored big studios over fresh voices.

“In Hollywood’s centralized model, breaking in is difficult,” he said. “You never know if you met the right person, if your script was overlooked, or if your work is truly considered.”

So instead of taking the traditional route, he turned to the decentralized film industry. Often referred to as Film3, it leverages blockchain technology, community voting, and cryptocurrency to fund movies, and television series. Unlike the traditional Hollywood system, which relies on centralized studios, agents, and intermediaries, Film3 lets filmmakers connect directly with their audiences and financing.

Hodierne put 10 minutes of his film up on the Decentralized Pictures website for review, where a community of producers, writers, investors, and film buffs got a look at his sizzle reel. In exchange for reviewing the clip, they earned $FILM, Decentralized Pictures' token, which the studio says is “fuel for the platform. Users can stake them on their favorite projects, use them to pay others to review their own submissions (as a rewards pool), or simply purchase entry vouchers to pay for application fees for various creative financing rewards.”

One reviewer in particular was especially struck by the clip: “Steven Soderbergh, the king of the heist genre, gave us his blessing,” said Hodierne.

Soderbergh invested in the film, and Decentralized Pictures followed on with a grant, giving "Cold Wallet" enough money to make Hodierne’s movie. Now screening as a “Steven Soderbergh Presents” project, the movie is at select theaters and available for rent or purchase on Apple and Amazon Prime Video, and has garnered respectable reviews.

“Hopefully, it connects with viewers,” Hodierne said. “What excites me most is that you can rent and buy it on-chain with crypto—it’s highly appropriate.”

It’s another big step in the journey of the dominant studios in the Film3 movement, Decentralized Pictures and Gala Films, which have more than 60 movies and TV series in the works.

“We're building the studio of the future,” said Decentralized Pictures co-founder Roman Coppola, a member of the Coppola filmmaking family. “At our company, American Zoetrope, and in my dad's work, we value community and a cafe culture where people come together, share ideas, and compare notes.”

Coppola and others pointed out that just as important as community participation is the decentralized funding model that can support filmmakers, particularly those with distinctive voices and meaningful stories to tell, by allowing them to bypass industry hierarchies.

“The term we've been using—DeFiFi, decentralized film finance—represents a shift in film funding,” Stacy Spikes, co-founder of movie subscription platform MoviePass said during an interview with Decrypt at ETH Denver. “Distribution companies will still be needed to push films into the marketplace, but until now, back-end participation wasn’t possible. With smart contracts, it is.”

The idea is already gaining traction. In 2024, Film3 made history when actress Mena Suvari earned a Primetime Emmy nomination for her role in "RZR," a sci-fi series created by David Bianchi’s Exertion3 Films in collaboration with the blockchain-powered streaming platform Gala Film.

Spikes likened the potential of decentralized filmmaking to past independent and genre film movements.

“If you go with the community—particularly black and brown communities or genre films—Web3 is a great place to tap into,” Spikes said. “I feel that people who were hesitant to invest will now be more likely to do so because they know they'll get their money back.”

Hodierne also noted that by eliminating distributors and sales agents, all proceeds go directly to the filmmakers and Decentralized Pictures, allowing them to reinvest in independent artists and future projects.

“As a filmmaker, I’ve seen how centralized and fickle the industry is. This shift is a big deal, especially as streaming platforms pay artists less while struggling themselves,” he said. “It’s an exciting convergence for films, and for 'Cold Wallet,' a crypto thriller, this feels like the perfect first step in opening that door.”

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Film3Decentralized FinanceCrypto Cinema
Mar 17, 2025 - 32天前
The Algebra of EverythingAuthor: (1) Thierry Boy de la Tour, Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG 38000 Grenoble, France. Abstract and 1 Introduction 2 Basic Definitions and Notations 2.1 Sets 2.2 Sequences 2.3 Signatures and Algebras and 2.4 Categories 3 Monographs and their Morphisms 4 Limits and Colimits 5 Drawing Monographs 6 Graph Structures and Typed Monographs 7 Submonographs and Partial Morphisms 8 Algebraic Transformations of Monographs 9 Attributed Typed Monographs 10 Conclusion and References Monographs are graph-like structures with directed edges of unlimited length that are freely adjacent to each other. The standard nodes are represented as edges of length zero. They can be drawn in a way consistent with standard graphs and many others, like E-graphs or 8-graphs. The category of monographs share many properties with the categories of graph structures (algebras of monadic many-sorted signatures), except that there is no terminal monograph. It is universal in the sense that its slice categories (or categories of typed monographs) are equivalent to the categories of graph structures. Type monographs thus emerge as a natural way of specifying graph structures. A detailed analysis of single and double pushout transformations of monographs is provided, and a notion of attributed typed monographs generalizing typed attributed E-graphs is analyzed w.r.t. attribute-preserving transformations. Keywords : Algebraic Graph Transformation, Graph Structures, Typed Graphs Many different notions of graphs are used in mathematics and computer science: simple graphs, directed graphs, multigraphs, hypergraphs, etc. One favourite notion in the context of logic and rewriting is that also known as quivers, i.e., structures of the form pN, E, s, tq where N, E are sets and s, t are functions from E (edges) to N (nodes), identifying the source and target tips of every edge (or arrow). One reason for this is that the category of quivers is isomorphic to the category of algebras of the many-sorted signature with two sorts nodes and edges and two operator names src and tgt of type edges Ñ nodes. In conformity with this tradition, by graph we mean quiver throughout this paper. In order to conveniently represent elaborate data structures it is often necessary to enrich the structure of graphs with attributes: nodes or edges may be labelled with elements from a fixed set, or with values taken in some algebra, or with sets of values as in [1], etc. An interesting example can be found in [2] with the notion of E-graphs, since the attributes are also considered as nodes. More precisely, an E-graph is an algebra whose signature can be represented by the following graph: The names given to the sorts and operators help to understand the structure of E-graphs: the edges relate the nodes among themselves, the nv-edges relate the nodes to the values, and the ev-edges relate the edges to the values. Hence the sort values holds attributes that are also nodes. But then we see that in E-graphs the ev-edges are adjacent to edges. This is non standard, but we may still accept such structures as some form of graph, if only because we understand how they can be drawn. Hence the way of generalizing the notion of graphs seems to involve a generalization of the signature of graphs considered as algebras. This path has been followed by Michael L¨owe in [3], where a graph structure is defined as a monadic many-sorted signature. Indeed in the examples above, and in many examples provided in [3], all operators have arity 1 and can therefore be considered as edges from their domain to their range sort. Is this the reason why they are called graph structures? But the example above shows that E-graphs are very different from the graph that represent their signature. Besides, it is not convenient that our understanding of such structures should be based on syntax, i.e., on the particular names given to sorts and operators in the signature. Furthermore, it is difficult to see how the algebras of some very simple monadic signatures can be interpreted as graphs of any form. Take for instance the signature of graphs and reverse the target function to tgt : nodes Ñ edges. Then there is a symmetry between the sorts nodes and edges, which means that in an algebra of this signature nodes and edges would be objects of the same nature. Is this still a graph? Can we draw it? Worse still, if the two sorts are collapsed into one, does it mean that a node/edge can be adjacent to itself? We may address these problems by restricting graph structures to some class of monadic signatures whose algebras are guaranteed to behave in an orthodox way, say by exhibiting clearly separated edges and nodes. But this could be prone to arbitrariness, and it would still present another drawback: that the notion of graph structure does not easily give rise to a category. Indeed, it is difficult to define morphisms between algebras of different signatures, if only because they can have any number of carrier sets. The approach adopted here is rather to reject any structural distinction between nodes and edges, hence to adopt a unified view of nodes as edges of length 0, and standard edges as edges of length 2 since they are adjacent to two nodes. This unified view logically allows edges to be adjacent to any edges and not just to nodes, thus generalizing the ev-edges of E-graphs, and even to edges that are adjacent to themselves. Finally, there is no reason to restrict the length of edges to 0 or 2, and we will find good reasons (in Section 6) for allowing edges of infinite, ordinal length. The necessary notions and notations are introduced in Section 2. The structure of monograph (together with morphisms) is defined in Section 3, yielding a bestiary of categories of monographs according to some of their characteristics. The properties of these categories w.r.t. the existence of limits and co-limits are analyzed in Section 4. We then see in Section 5 how monographs can be accurately represented by drawings, provided of course that they have finitely many edges and that these have finite length. In particular, such drawings correspond to the standard way of drawing a graph for those monographs that can be identified with standard graphs, and similarly for E-graphs. Section 6 is devoted to the comparison between monographs and graph structures, and the corresponding algebras (that we may call graph structured algebras). We show a property of universality of monographs, in the sense that all graph structured algebras can be represented (though usually not in a canonical way) as typed monographs, i.e., as morphisms of monographs. The notion of graph structure has been introduced in [3] in order to obtain categories of partial homomorphisms in which techniques of algebraic graph rewriting could be carried out. The correspondence with monographs established in Section 6 calls for a similar development of partial morphisms of monographs in Section 7. The single and double pushout methods of rewriting monographs can then be defined, analyzed and compared in Section 8. The notion of E-graph has been introduced in [2] in order to obtain wellbehaved categories (w.r.t. graph rewriting) of attributed graphs, and hence to propose suitable representations of real-life data structures. This is achieved by enriching E-graphs with a data type algebra, and by identifying nodes of sort value with the elements of this algebra. We pursue a similar approach in Section 9 with the notion of attributed typed monograph by identifying elements of an algebra with edges, and obtain similarly well-behaved categories. Due to the universality of monographs we see that any Σ-algebra can be represented as an attributed typed monograph. We conclude in Section 10. Note that parts of Sections 4 to 6 have been published in [4]. This paper is available on arxiv under CC BY 4.0 DEED license.
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GraphTheoryAlgebraicGraphTransformationTypedGraphs
Mar 17, 2025 - 32天前
Charting The Impact of Trump's Tariffs on Commodity Investing Tariffs have punctuated the age of Trump 2.0, and their impact on the diverse commodities landscape could shake investor portfolios for years to come. The President was quick to place 25% tariffs on imports from Canada and Mexico, and a 10% levy on China, citing factors like illegal immigration and the drug trade as catalysts for the measure. Canada and Mexico promised retaliatory measures, while China suggested that it would challenge the levies at the World Trade Organization as well as adopting other countermeasures. This souring of trade relations points to a possible protracted trade war which could threaten to drive inflation rates higher. The tariffs have prompted volatility through various commodity markets, but what will the impact be on commodities once the dust settles? Tariffs have long been a core component of Trump’s economic strategy, and the measures formed a strong part of the President’s campaign trail. While Trump has been vocal about the reasons behind the tariffs having been prompted by illegal immigration and the flow of fentanyl into the United States, he has also called the word ‘tariff’ the “most beautiful word in the dictionary.” Although tariffs don’t always cause a stock market downturn, and Trump’s use of tariffs during his first term didn’t prompt sustained panic, investors have been caught off guard by the President’s tactics, having anticipated a more measured approach. Given Trump has secured Republican control of the Senate, House of Representatives, and Congress in a clean sweep in the election, it appears that the President is more eager to deliver on his promises or threats than in his previous term. The relative strength of the S&P 500 also appears to have given Trump more confidence to pressure markets before easing off as prices fall. However, the approach may lead to sustained corrections from the high valuations we’ve seen of late. Even in an environment where the enforcement of the sweeping tariffs promised is far from assured, the mere threat of levies has unsettled commodities markets. Data shows that traders on COMEX locked in pre-tariff prices, while skepticism brings a wider sense of stability. However, the LME, which operates on physical deliveries, has experienced a series of disruptions as fears over global tariff implications have led to more anticipatory purchasing, triggering price movements even in copper, which is more susceptible to inflationary impacts. Is this trend of uncertainty here to stay for commodities trading in Trump 2.0? It appears that many different forms of commodity could be impacted by tariffs in the months and years ahead. Seemingly the best place to start would be metals. With Trump promising a 25% tariff on aluminum and steel coming into effect on March 12 with no exceptions, we’re set to see widespread volatility throughout metals markets. With Canada responsible for 50% of the aluminum imported into the US in 2024, Trump’s decision to implement the tariff to encourage domestic production will see considerable initial strain that’s likely to create price inflation. Canada has also promised to retaliate to the tariff, which could trigger more of a skirmish in what may be a protracted trade war. \ Fortunately, we can look to history for indicators as to what could happen for steel and aluminum prices. When Trump announced tariffs of 25% on steel and 15% on aluminum during his first term as President in 2018, we saw price volatility move higher. Tariffs raised the average price of steel and aluminum in the US by 2.4% and 1.6% respectively . However, in 2018, Trump added a series of exemptions for countries like Canada, Australia, and Mexico. Without these concessions, we could see costs range far higher. Analysts have been uncertain about other commodities. Barclays suggested that higher costs of oil may be driven by all three parties in the supply chain, from Canadian producers, refiners largely in the Midwest, and end-consumers all paying more for costs. The analysts concluded that tariffs are generally bad news for oil because they weigh on demand and boost the value of the US dollar, complicating trade further. Likewise, CITI analysts suggested that precious metals like gold and silver could grow in value as a safe haven against market uncertainty. However, they concluded that the outlook for copper is altogether more bearish. Because of the unpredictability of Trump , investments in commodities are likely to be more volatile than those in more traditional securities, particularly if leverage is involved. The value of commodity-based derivatives could also be impacted by wider market movements , commodity index volatility, interest rate changes, and other industry factors resulting from adverse weather events and climate change. Tariffs could add to widespread uncertainty surrounding commodities that could be a concern for the prospects of short and long term investment opportunities but may allow some joy for traders hoping to react fast to volatility moving forward. With the right access to a prime services provider, institutional investors can use low-latency trading opportunities to their advantage, and use tariffs as an added incentive to trade volatility. Much has been made of the impact of tariffs, but it’s important to note that a prospective trade war is far from a black swan event like the 2008 financial crisis or COVID crash, and instead, the fallout from tariffs is more likely to signal a multi-faceted, long-term economic shift. Institutional investors can take advantage of tariffs by adapting their portfolios early and utilizing analytics to create a more holistic overview of shifting market sentiment. When it comes to the convoluted world of commodities, tariffs can shake up short-term outlooks for a range of different metals, fuels, and soft commodities, and there can be some significant opportunities to be taken in trading the uncertainty of Trump’s actions in a proactive manner.
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TradeWarImpactCommodityVolatilityTariffsOnMetals
Mar 17, 2025 - 32天前
Earth's Climate Is Being Hurt By AI in Non-Obvious WaysThis article is copublished with Grist Read on our collaborator's site This story was published in partnership with Grist , a nonprofit media organization covering climate, justice, and solutions. Subscribe to its weekly newsletter here . “Something’s fishy,” declared a March newsletter from the rightwing, fossil-fuel-funded think tank Texas Public Policy Foundation. The caption looms under an imposing image of a stranded whale on a beach , with three huge offshore wind turbines in the background. Something truly was fishy about that image. It’s not because offshore wind causes whale deaths, a groundless conspiracy pushed by fossil fuel interests that the image attempts to bolster. It’s because, as Gizmodo writer Molly Taft reported, the photo was fabricated using artificial intelligence. Along with eerily pixelated sand, oddly curved beach debris, and mistakenly fused together wind turbine blades, the picture also retains a tell-tale rainbow watermark from the artificially intelligent image generator DALL-E. DALL-E is one of countless AI models that have risen to otherworldly levels of popularity in the last few years. But as hundreds of millions of users marvel at AI’s ability to produce novel images and believable text, a wave of hype has concealed how AI could be hindering our ability to make progress on climate change. Advocates argue that these impacts—which include vast carbon emissions associated with the electricity needed to run the models, a pervasive use of AI in the oil and gas industry to boost fossil fuel extraction, and a worrying uptick in the output of misinformation—are flying under the radar. While many prominent researchers and investors have stoked fears around AI’s “ godlike ” technological force or potential to end civilization, a slew of real-world consequences aren’t getting the attention they deserve. Many of these harms extend far beyond climate issues, including algorithmic racism , copyright infringement , and exploitative working conditions for data workers who help develop AI models . “We see technology as an inevitability and don’t think about shaping it with societal impacts in mind,” David Rolnick, a computer science professor at McGill University and a co-founder of the nonprofit Climate Change AI, told Grist. But the effects of AI, including its impact on our climate and efforts to curtail climate change, are anything but inevitable. Experts say we can and should confront these harms—but first, we need to understand them. At its core, AI is essentially “a marketing term,” the Federal Trade Commission stated back in February. There is no absolute definition for what an AI technology is. But usually, as Amba Kak, the executive director of the AI Now Institute, describes , AI refers to algorithms that process large amounts of data to perform tasks like generating text or images, making predictions, or calculating scores and rankings. That higher computational capacity means large AI models gobble up large quantities of computing power in its development and use. Take ChatGPT, for instance, the OpenAI chatbot that has gone viral for producing convincing, humanlike text. Researchers estimated that the training of ChatGPT-3, the predecessor to this year’s GPT-4, emitted 552 tons of carbon dioxide equivalent —equal to more than three round-trip flights between San Francisco and New York. Total emissions likely are much higher, since that number only accounts for training ChatGPT-3 one time through. In practice, models can be retrained thousands of times while they are being built. The estimate also does not include energy consumed when ChatGPT is used by approximately 13 million people each day . Researchers highlight that actually using a trained model can make up 90 percent of energy use associated with an AI machine learning model. And the newest version of ChatGPT, GPT-4, likely requires far more computing power because it is a much larger model. No clear data exists on exactly how many emissions result from the use of large AI models by billions of users. But researchers at Google found that total energy use from machine learning AI models accounts for about 15 percent of the company’s total energy use. Bloomberg reports that amount would equal 2.3 terawatt-hours annually—roughly as much electricity used by homes in a city the size of Atlanta in a year. The lack of transparency from companies behind AI products like Microsoft, Google, and OpenAI means that the total amount of power and emissions involved in AI technology is unknown. For instance, OpenAI has not disclosed what data was fed into this year’s ChatGPT‑4 model, how much computing power was used, or how the chatbot was changed. “We’re talking about ChatGPT and we know nothing about it,” Sasha Luccioni, a researcher who has studied AI models’ carbon footprints, told Bloomberg . “It could be three raccoons in a trench coat.” AI could also fundamentally shift the way we consume—and trust — information online. The U.K. nonprofit Center for Countering Digital Hate tested Google’s Bard chatbot and found it capable of producing harmful and false narratives around topics like COVID-19, racism, and climate change. For instance, Bard told one user, “There is nothing we can do to stop climate change, so there is no point in worrying about it.” The ability of chatbots to spout misinformation is baked into their design, according to Rolnick. “Large language models are designed to create text that looks good rather than being actually true,” he said. “The goal is to match the style of human language rather than being grounded in facts”—a tendency that “lends itself perfectly to the creation of misinformation.” Google, OpenAI, and other large tech companies usually try to address content issues as these models are deployed live. But these efforts often amount to “papered over” solutions, Rolnick says. “Testing their content more deeply, one finds these biases deeply encoded in much or in much more insidious and subtle ways that haven’t been patched by the companies deploying the algorithms,” he said. Giulio Corsi, a researcher at the U.K.-based Leverhulme Centre for the Future of Intelligence, who studies climate misinformation, says an even bigger concern is AI-generated images. Unlike text produced on an individual scale through a chatbot, images can “spread very quickly and break the sense of trust in what we see,” he said. “If people start doubting what they see in a consistent way, I think that’s pretty concerning behavior.” Climate misinformation existed long before AI tools. But now, groups like the Texas Public Policy Foundation have a new weapon in their arsenal to launch attacks against renewable energy and climate policies—and the fishy whale image indicates that they’re already using it. Researchers emphasize that AI’s real-world effects aren’t predetermined—they depend on the intentions, and actions, of the people developing and using it. As Corsi puts it, AI can be used “as both a positive and negative force” when it comes to climate change. For example, AI is already used by climate scientists to further their research . By combing through huge amounts of data, AI can help create climate models, analyze satellite imagery to target deforestation, and forecast weather more accurately. AI systems can also help improve the performance of solar panels, monitor emissions from energy production, and optimize cooling and heating systems, among other applications . At the same time, AI is also used extensively by the oil and gas sector to boost the production of fossil fuels. Despite touting net-zero climate targets, Microsoft, Google, and Amazon have all come under fire for their lucrative cloud computing and AI software contracts with oil and gas companies including ExxonMobil, Schlumberger, Shell, and Chevron. A 2020 report by Greenpeace found that these contracts exist at every phase of oil and gas operations. Fossil fuel companies use AI technologies to ingest massive amounts of data to locate oil and gas deposits and create efficiencies across the entire supply chain, from drilling to shipping to storing to refining. AI analytics and modeling could generate up to $425 billion in added revenue for the oil and gas sector between 2016 and 2025, according to the consulting firm Accenture. AI’s application in the oil and gas sector is “quite unambiguously serving to increase global greenhouse gas emissions by outcompeting low-carbon energy sources,” said Rolnick. Google spokesperson Ted Ladd told Grist that while the company still holds active cloud computing contracts with oil and gas companies, Google does not currently build custom AI algorithms to facilitate oil and gas extraction. Amazon spokesperson Scott LaBelle emphasized that Amazon’s AI software contracts with oil and gas companies focus on making “their legacy businesses less carbon intensive,” while Microsoft representative Emma Detwiler told Grist that Microsoft provides advanced software technologies to oil and gas companies that have committed to net-zero emissions targets. When it comes to how AI can be used, it’s “the Wild West,” as Corsi puts it. The lack of regulation is particularly alarming when you consider the scale at which AI is deployed, he added. Facebook, which uses AI to recommend posts and products, boasts nearly three billion users. “There’s nothing that you could do at that scale without any oversight,” Corsi said—except AI. In response, advocacy groups such as Public Citizen and the AI Now Institute have called for the tech companies responsible for these AI products to be held accountable for AI’s harms. Rather than relying on the public and policymakers to investigate and find solutions for AI’s harms after the fact, AI Now’s 2023 Landscape report calls for governments to “place the burden on companies to affirmatively demonstrate that they are not doing harm.” Advocates and AI researchers also call for greater transparency and reporting requirements on the design, data use, energy usage, and emissions footprint of AI models. Meanwhile, policymakers are gradually coming up to speed on AI governance. In mid-June, the European Parliament approved draft rules for the world’s first law to regulate the technology. The upcoming AI Act, which likely won’t be implemented for another two years, will regulate AI technologies according to their level of perceived risk to society. The draft text bans facial recognition technology in public spaces, prohibits generative language models like ChatGPT from using any copyrighted material, and requires AI models to label their content as AI-generated. Advocates hope that the upcoming law is only the first step to holding companies accountable for AI’s harms. “These things are causing problems now,” said Rick Claypool, research director for Public Citizen. “And why they’re causing problems now is because of the way they are being used by humans to further human agendas.” Credits: Akielly Hu  for Grist Also published here Photo by S. on Unsplash
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AI EthicsClimate Change ImpactMisinformation Technology
Mar 17, 2025 - 32天前
Sneaky UX Tricks That'll Make Your Users Fall in LoveEver wonder why some interfaces have you hooked from the first click, while others send you searching for the escape key? After 8 years in UX design — working on complex systems like ERP, CRM, and EAM software — I’ve learned that if you truly want to seduce your users, you must first uncover what they value most. And for most people, time is the ultimate currency. Respect it, and they’ll embrace your product; waste it, and they’ll abandon it without a second thought. Imagine your product as a person trying to win someone’s heart. Seduction in UX is a form of manipulation — but a benevolent one. It’s not about flashy visuals or empty promises; it’s about making life easier. By crafting interfaces so intuitive that every second feels well-spent, you’ll win their hearts. When users feel their time is valued, they’ll return willingly, eager for more. But how do you create such enchanting experiences? It starts with understanding how people think and what drives their decisions. That’s where foundational psychological principles come into play. In this article, I’ll reveal how key UX principles are rooted in basic psychological insights, and I’ll offer simple examples you can try right away. Give them a go, and watch your users become truly smitten ;) Note: I don’t have a degree in psychology. Everything I share is based on the knowledge I’ve gathered through designing interfaces and exploring human behavior out of sheer curiosity. People can process only a limited amount of information at once due to cognitive load . Our brains are like computers with limited RAM; too much information slows everything down. Simplifying interfaces help users accomplish tasks without feeling overwhelmed, rather than scaring them away with a lot of information. Think of it like dating — you don’t share your entire life story on the first date. Similarly, let users discover features as they need them. Step-by-step processes: Divide large tasks into smaller, manageable steps. On-demand details: Display relevant information based on the user’s current context and allow them to access additional information when they choose. Eg. 1: Use expandable sections to hide advanced settings until the user opts to view them. Eg. 2: Provide “Learn More” links that reveal contextual explanations when clicked. Eg.3: Use tooltips and hover states to provide explanations for complex features. People rely on past experiences to understand new ones — a concept known as mental models . Familiarity reduces the mental effort needed to learn a new system, making it easier to navigate and use the product. By using design patterns that users already know and trust, they can intuitively understand your interface based on their past experiences. It’s like driving a car — no matter the brand, the brake and accelerator are always in the same place. Imagine the chaos if they weren’t! Standard icons and terminology: Use commonly recognized symbols and language for common actions and specifications. Eg. 1: Use a trash can icon for “delete”. Eg. 2: If you’re letting users save or keep something for later, use familiar terms like “Save” or “Add to Favorites”. Eg. 3: Use a heart Icon for “Favorites” or “Like”. Recognisable layouts: Stick to standard layouts that allow users to navigate interfaces intuitively by aligning with their expectations. Eg. 1: Place the main menu on the left side, as is common in enterprise applications. Eg. 2: Use tabs for navigating between sections of the same page. Eg. 3: Use breadcrumbs at the top of pages to show navigation paths. Predictable interactions: Ensure that interface elements behave as users expect. When a button looks clickable and acts clickable, users feel in control. Eg. 1: Clicking on a row in a table opens detailed information about that item. Eg. 2: Utilize standard gestures on mobile devices that users are familiar with, such as swiping to navigate. Eg. 3: Right-clicking opens context-specific menus as expected. People need to know the outcome of their actions to feel confident and in control — this is rooted in psychological principles like operant conditioning and feedback loops . Immediate feedback reinforces learning and helps correct errors by showing users the results of their actions right away. This reduces anxiety and builds trust in the system. Just as sending a message — when you hit “send”, you expect to see it move from your draft to the conversation with a “sent” or “delivered” notification. If nothing happens, you’d wonder if it was sent or if you need to resend it. Success messages: Confirm when actions are completed successfully with messages. Eg. 1: Use a toast notification like “Settings updated successfully”. Eg. 2: Show a confirmation such as “This nickname is available to use”. Eg. 3: Notify users with “New contact added to your contact list”. Error notifications: Alert users to issues promptly and guide them to correct mistakes. Eg. 1: Highlight incorrect form entries with messages like “Password must include at least 8 characters”. Eg. 2: Display an alert if a required field is missing “This field cannot be left blank”. Eg. 3: Use a banner notification for critical errors like “Payment failed, please try again”. Visual cues: Use animations or color changes to indicate activity. Eg. 1: Show a loading spinner while data is being processed. Eg. 2: Use checkmarks or icons to indicate successfully completed steps in a process. Eg. 3: Change button color on hover or click to indicate interactivity. Visual hierarchy influences perception and behavior through principles like Gestalt psychology and selective attention . People instinctively focus on visually prominent elements first, enabling faster decision-making and reducing frustration. By using size, color, and placement, you can draw the user’s eye to key elements and prioritize information effectively. It’s like a restaurant menu — its design guides your choices. The most profitable or chef-recommended dishes are often placed at the top-right corner or highlighted with a box or a different background. Descriptive headings, enticing images, and varied font sizes draw your attention to specific items. Without this hierarchy, you might feel overwhelmed by too many options. Emphasis on primary actions: Make main actions more prominent. Eg. 1: Use a bold, contrasting color for the “Save” button to make it stand out. Eg. 2: Place the most critical action buttons in the bottom-right corner on mobile interfaces, where the thumb naturally rests. Eg. 3: Highlight critical alerts with bright colors or icons to draw immediate attention. Typography and spacing: Use font size and whitespace to organize information. Eg. 1: Larger headings for section titles, with smaller text for details. Eg. 2: Increase spacing between sections to separate content areas visually. Eg. 3: Add numbered or bulleted lists to break up dense content and improve scannability. Color coding: Assign colors to categorize information. Eg. 1: Use red for error messages or overdue tasks, signaling urgency. Eg. 2: Green for confirmations or completed tasks, indicating success. Eg. 3: Implement consistent color schemes for different modules (e.g., blue for sales, green for finance). People are better at recognizing familiar patterns than recalling information from scratch — this is rooted in the psychological concept of recognition memory . By keeping layouts consistent, you help users remember how to use your interface, improving recall and efficiency. Imagine walking into your favorite coffee shop — you know exactly where the counter is, where to pick up your order, and where the sugar packets are kept. If they rearranged the layout every week, you’d spend more time figuring out where everything is than enjoying your coffee. Standardized components: Keep interface elements consistent across the application. Eg. 1: Maintain consistent iconography for similar functions. Eg. 2: Place navigation menus in the same location on every page. Eg. 3: Ensure form layouts follow a predictable structure, such as labels always appearing above fields. Template usage: Provide templates for common tasks to ensure a uniform experience. Eg. 1: Offer predefined templates for creating emails or reports. Eg. 2: Use consistent page layouts for similar types of content (e.g., dashboards, profiles, or settings). Eg. 3: Provide a phone number input mask, such as “(123) 456-7890”. Consistent Terminology: Use the same terms for features and actions throughout the interface. Eg. 1: If you use “Client” instead of “Customer,” do so universally. Eg. 2: Refer to actions consistently, such as always using “Edit” instead of sometimes using “Modify”. Eg. 3: Label categories and sections consistently in menus and submenus. People prefer the path of least resistance — a concept known as the Principle of Least Effort . By automating repetitive actions, you reduce users’ effort and streamline the experience. Automation minimizes the workload on users by handling routine tasks behind the scenes, saving time and reducing the likelihood of errors. Think about setting up automatic bill payments — instead of manually paying each bill every month, you set up autopay and free yourself from the repetitive task. Auto-fill forms: Populate fields with known information. Eg. 1: Automatically enter user contact details in support tickets. Eg. 2: Suggest addresses based on geolocation. Eg. 3: Pre-populate date fields with the current date. Predictive actions: Anticipate user needs based on behavior. Eg. 1: Suggest the next step after completing a task. Eg. 2: Auto-complete search queries based on typing history. Eg. 3: Recommend frequently used actions in a contextual menu. Workflow automation: Set up triggers for common sequences. Eg. 1: Automatically send a follow-up email after a meeting is scheduled. Eg. 2: Trigger notifications when a task is assigned to a team member. Eg. 3: Auto-generate reports at the end of each week. Too many options can overwhelm users — a phenomenon known as the Paradox of Choice . And according to Hick’s Law , the time it takes to make a decision increases with the number and complexity of choices. Simplifying choices helps prevent analysis paralysis, allowing users to make decisions more quickly and confidently. Provide only the options they need and consider pre-selecting the best one — either for their benefit or yours ;) Picture browsing a streaming service with thousands of movies — you might spend more time scrolling than actually watching something. A curated list of recommendations simplifies your choices and helps you start enjoying content faster. Pre-selected options: Set optimal default settings. Eg. 1: Pre-fill form fields with likely selections, such as the user’s country or language based on location. Eg. 2: Enable email notifications by default for critical events, such as account changes or payments. Eg. 3: Auto-apply common filters (e.g., “Available Now”) when loading a product search page. Highlighted recommendations: Highlight suggested actions or settings. Eg. 1: Mark certain fields as “Recommended” during setup. Eg. 2: Highlight the “Standard Plan” in pricing tables as the most popular choice. Eg. 3: Emphasize frequently used settings or options by placing them at the top of menus. Option limitation: Reduce the number of choices presented at once. Eg. 1: Display only the top five filter options, with a button to expand for more. Eg. 2: Provide a streamlined settings panel, with advanced options hidden under an “Advanced Settings” toggle. Eg. 3: Offer a shortlist of frequently used templates, with an option to browse the entire library if needed. People make mistakes, and fear of errors can cause hesitation — based on psychological phenomenons known as operant conditioning , cognitive load , and learned helplessness . By designing forgiving interfaces, you help users feel more confident in their actions, reducing anxiety and encouraging exploration. Providing ways to prevent mistakes and recover from them when they occur enhances the overall user experience. It’s like playing a video game with unlimited lives — when you make a mistake, you can quickly respawn and try again without starting over from the very beginning. This encourages you to explore and take risks, knowing that errors aren’t catastrophic. Confirmation dialogs: Prompt users to confirm critical or destructive actions. Eg. 1: Ask for confirmation before deleting all contacts “Are you sure? This action cannot be undone”. Eg. 2: Confirm bulk actions, like sending an email to hundreds of recipients “You’re about to email 500 recipients. Proceed?”. Eg. 3: Warn users when exiting a page with unsaved changes “You have unsaved changes. Leave without saving?”. Undo options: Allow users to easily reverse their actions. Eg. 1: Offer an “Undo” button after deleting an item. Eg. 2: Provide version history in documents so users can revert to previous versions. Eg. 3: Give users the ability to cancel orders within a brief grace period. Error prevention and recovery: Design to prevent errors and help users recover when they occur. Eg. 1: Disable the “Submit” button until all required fields are filled out correctly. Eg. 2: Highlight form errors with clear messages indicating how to fix them. Eg. 3: Use dropdowns or date pickers to prevent invalid entries. People are naturally motivated by rewards, achievements, and a sense of progress — psychological principles rooted in behaviorism and the dopamine reward pathway . By incorporating game-like elements into your interface, you stimulate dopamine release, enhancing motivation and encouraging continued interaction. Think of it like a fitness app that rewards you with badges for reaching step goals — each badge feels like a small victory, motivating you to keep moving. Progress tracking: Show users their progress toward goals to encourage completion. Eg. 1: Show percentage completion in long tasks (e.g., “80% done” during onboarding). Eg. 2: Use visual indicators like checkmarks or streaks to represent daily task completion. Eg. 3: Offer levels or badges for reaching milestones, such as completing a certain number of tasks. Rewards and incentives: Offer tangible or intangible benefits for engagement. Eg. 1: Unlock advanced features or premium content after completing onboarding steps. Eg. 2: Offer discounts, coupons, or perks for frequent usage of the app. Eg. 3: Award points that can be redeemed for app benefits, such as exclusive features or virtual goods. Challenges and competitions: Introduce friendly competition to motivate users. Eg. 1: Use leaderboards to show top performers, fostering healthy competition among users. Eg. 2: Add time-limited challenges that encourage users to complete tasks faster or more efficiently. Eg. 3: Introduce achievement tiers, where users can compete to reach higher ranks. People have an innate need for autonomy — a key component of Self-Determination Theory . Autonomy refers to the feeling of being in control of one’s actions and decisions, which enhances motivation and commitment. By allowing users to tailor the interface to their preferences, you not only fulfill this need but also increase engagement and overall contentment of your product. Imagine arranging your workspace just the way you like it — adjusting your chair, organizing your desk, and setting up your tools within easy reach. This personal setup makes you more comfortable and productive. Customizable dashboards: Let users choose which widgets or information panels to display. Eg. 1: Allow users to hide or collapse widgets they don’t use frequently. Eg. 2: Save custom views or layouts for quick access in future sessions. Eg. 3: Enable multiple dashboard setups for different contexts, such as work and personal projects. Flexible settings: Provide options to adjust how features work to suit individual preferences. Eg. 1: Allow users to toggle between different views, such as list view or grid view, for content. Eg. 2: Enable theme customization, offering options like dark mode, light mode, or custom color schemes. Eg. 3: Allow notifications to be personalized, enabling users to choose the type and frequency of alerts they receive. Work accelerators: Include features like keyboard shortcuts and touch gestures. Eg. 1: Allow users to customize keyboard shortcuts for frequently used actions. Eg. 2: Support swipe gestures for quick navigation or actions on touch devices. Eg. 3: Enable voice commands for hands-free interaction and accessibility. As we’ve seen, creating intuitive and engaging interfaces isn’t just about neat layouts or quick load times—it’s about understanding human nature. You’re tapping into what people value most (their time) and using psychological insights to guide their decisions. In other words, you’re seducing them by making every second count. But remember, with this influence comes responsibility. Use these principles to simplify, delight, and inspire — not to frustrate. When users leave your product smiling, you’ve done your job right. They’ll return not because they’re trapped, but because they genuinely enjoy the experience. That’s the art of seduction in UX design.
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UXDesignUserExperiencePsychologyInDesign
Mar 17, 2025 - 32天前
I Told My Team to Complain Every Week—It Transformed Our ProductivityPhoto by Dylan Gillis on Unsplash More than a decade ago, I had the opportunity to take a Total Quality Management (TQM) course by none other than Andrzej Blikle at ASBIRO , a unique Polish educational institution where only entrepreneurs teach entrepreneurship. Since then, I’ve been testing and refining various TQM techniques with my teams, especially in the startup world. I’m sharing some of my observations and ideas I’ve implemented, with a focus on weekly Kaizen-style sessions, which have proven effective in a variety of real-life scenarios within our teams. Andrzej Blikle is a prominent Polish entrepreneur, well-known for his work in quality management and as the leader of the Blikle family confectionery business, which is famous for creating iconic and irresistible Polish “ A.Blikle donuts ” (pączki!). He has expanded the company while preserving its legacy, transforming it into a modern, quality-driven organization. Total Quality Management (TQM) is a comprehensive, organization-wide approach focused on continuous improvement and long-term success, involving all members of the organization. Kaizen, on the other hand, is a specific technique within TQM that emphasizes making small, incremental improvements on a daily basis to enhance processes and eliminate inefficiencies. The key takeaway was that for any organization to thrive, it needs to evolve continuously with the contributions of all its members (management included). In the fast-paced world of tech startups, where ideas flood in and deadlines loom, it’s easy to lose sight of the bigger picture. When you’re buried in daily tasks like coding and designing, managing large-scale projects can feel overwhelming. With product teams often numbering in the dozens, it’s essential to have methods in place to maintain both product quality and an efficient software production process. It wasn’t until about 10 years ago that I introduced weekly Kaizen-format meetings to all my teams. I wanted something simple but impactful. These meetings became a cornerstone of our process improvement journey. Why? Because they allow for consistent feedback and provide a platform for team members to raise issues that, if left unchecked, might snowball into larger problems. Here’s how it works: every week, I bring together all members of my product and tech teams. No silos. The last day of the week usually works best. Developers, designers, product managers – everyone. The goal is to give them a space to vent, to share their frustrations, and to point out obstacles that hinder progress. And no, it’s not a “let’s complain about the boss” session. We focus on real, actionable issues. A simple question like “What are you complaining about this week?” opens the door for all kinds of insights. It’s the best way to start a conversation because it allows everyone to express their concerns, whether it’s about the process, communication issues, or even something as trivial as a lack of coffee in the office. I literally used to remind people every week: “We have a kaizen session on Friday, each of you – bring your complaints please!”. Why not just ask for improvement ideas first, right? Well, here’s where it gets a bit counterintuitive. Asking people to propose improvements can often lead to more question marks than solutions. From my experience, the ideas you get from this question tend to be more “nice-to-haves” rather than actual problems. It’s only after you’ve had a few months of these sessions with your team, and everyone is on the same page, that you can start throwing this question around. By then, people are already coming up with improvement ideas on their own – you don’t even need to ask. At first, convincing people to participate in candid discussions can be challenging, especially in cultures where openly pointing out problems, particularly with management, may feel uncomfortable. The key is to emphasize that these discussions aren’t about criticizing individuals but about improving processes. By focusing on solutions, the atmosphere becomes one of constructive feedback rather than blame. The “ why ” behind these sessions needs to be clearly explained from the start. Once we’ve discussed the issues, we assign tasks to be resolved before the next meeting. If a developer struggles with a tool, we’ll make sure they have the resources to get it right. If communication within the team is lacking, we’ll work on new strategies. The important thing is to keep the meetings action-oriented, with clear follow-up on the solutions. In the beginning, it was tough to convince people to share openly, but now, it’s an essential part of our culture. These meetings provide an opportunity for the team to breathe, to voice concerns, and, most importantly, to take ownership of the problems and their solutions. This simple but effective approach has led to long-term benefits, not only in productivity but also in team morale and cohesion. So, why do I swear by Kaizen meetings? They keep the momentum going. In a world full of deadlines and constant pressure, it’s easy to lose sight of the bigger picture. These small, regular adjustments keep us aligned, help us solve problems before they become roadblocks, and ensure we’re constantly improving. And let’s face it, a little bit of complaining now and then is the perfect way to stay connected and keep things moving forward. One unexpected benefit of these meetings is that they help raise awareness and address technical debt over time. By encouraging regular feedback and tackling issues as they arise, we can break down and resolve accumulated tech debt into smaller, more manageable pieces. As a CTO, I’ve found that Kaizen meetings are the best way to foster true ownership within each team member. When people feel they have a direct impact on the business, product and production process, they start to recognize that everyone faces challenges and that there’s always room to improve. These meetings send a clear message: as a team, we have the power to make our work-life less miserable every week. What steps are you taking to improve your team’s production process? I’d love to hear your approach.
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TQMKaizenTechStartups
Mar 17, 2025 - 32天前
This Week in Bitcoin: Strategy Stalls, But White House Plans to Buy More BTC

One week after U.S. President Trump announced a strategic Bitcoin reserve, the asset is trading down—mostly thanks to wider macroeconomic uncertainties stemming from the new commander in chief's dramatic and unpredictable policies.

Bitcoin was priced at a little over $84,000 per coin as of late Friday evening after dipping nearly 4% over a seven-day period, CoinGecko shows.

But despite dipping more than 20% from its record high in January, the slump could be brief, analysts told Decrypt.

Thanks to...well, Trump—again.

A White House official told a room of crypto big wigs on Thursday that the new administration wants to acquire as much Bitcoin as possible.

This week had no shortage of Bitcoin news.

ETF action

American crypto investors continued to cash out of Bitcoin ETFs this week, with nearly $900 million leaving the investment vehicles as of Thursday, according to the latest data from Farside Investors.

Now, Bitcoin ETFs are lagging behind their gold counterparts, after having briefly overtaken them back in December.

Public Keys: Coinbase IRL and Gemini Wants Bitcoin Believers to Look Up

Still, not to worry: Experts told Decrypt that the products have room to run this year, with Bloomberg's ETF analyst Eric Balchunas adding that he thought Bitcoin was likely to win the ETF war over the long-term.

Bitwise launches another BTC-related ETF

Speaking of ETFs, asset managers still don't think the market's crowded: Bitwise on Tuesday launched a new fund giving investors exposure to publicly traded companies with the biggest Bitcoin stashes.

The new Bitwise Bitcoin Standard Corporations ETF—OWNB—tracks 21 firms that hold 1,000 Bitcoins or more, including Strategy (formerly MicroStrategy), Bitcoin miner MARA, America's biggest crypto exchange, Coinbase, and even electric car company Tesla.

Rumble buys more Bitcoin

YouTube rival Rumble wasn't included in Bitwise's index, but the company is a good example of a smaller firm stacking sats: The media firm last year said it would allocate $20 million of its excess cash reserves to Bitcoin.

And on Wednesday, the Nasdaq-listed platform announced it had bought roughly 188 orange coins for its treasury at an average price of $91,000 per token.

Is Strategy done buying?

Bitcoin treasury Strategy, which came up with the blueprint Rumble is now following, has slowed down its BTC buys after a manic shopping spree.

Decrypt spoke to experts who said it was unlikely the company—previously known as MicroStrategy—was giving up its long-term plan, and rather focusing on its new stock offering, STRK.

White House going orange

Perhaps most dramatically for Bitcoiners this week, news dropped that the White House does indeed want to buy more Bitcoin.

Attendees at a closed-door roundtable hosted by the Bitcoin Policy Institute on Tuesday confirmed to Decrypt that the new administration is planning to buy as much of the cryptocurrency as possible. That's at least what Bo Hines, the executive director of the Presidential Working Group on Digital Assets, reportedly said.

Trump Admin Wants to Acquire as Much Bitcoin as Possible: White House

The news comes after President Trump last week followed through with his campaign promise and signed an order to establish a Bitcoin strategic reserve.

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BitcoinBitcoin ETFTrump Administration
Mar 16, 2025 - 33天前
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