TRUE

true

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background
2.1 Layer-2 Blockchains
2.2 Project Mariana


Related Work and Contribution


System Architecture


Model
5.1 Automated Market Makers
5.2 Price Impact


Simulation
6.1 Data and Parameters
6.2 Results


Discussion


Conclusions, Acknowledgements, and References Background
2.1 Layer-2 Blockchains
2.2 Project Mariana Background 2.1 Layer-2 Blockchains 2.1 Layer-2 Blockchains 2.2 Project Mariana 2.2 Project Mariana Related Work and Contribution Related Work and Contribution Related Work and Contribution System Architecture System Architecture System Architecture Model
5.1 Automated Market Makers
5.2 Price Impact Model Model 5.1 Automated Market Makers 5.1 Automated Market Makers 5.2 Price Impact 5.2 Price Impact Simulation
6.1 Data and Parameters
6.2 Results Simulation 6.1 Data and Parameters 6.1 Data and Parameters 6.2 Results 6.2 Results Discussion Discussion Discussion Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References Graphs and Tables Graphs and Tables 3 Related Work and Contribution There is an abundance of literature discussing the implementation of CBDCs in various countries [23], [13], [12], [21], [5], but little research on the exchange between CBDCs in AMMs, except for Project Mariana [4]. Lipton and Sepp[20] study the implicit costs (price impact) for G-10 CBDCs exchange at AMM with Concentrated Liquidity (Uniswap v3). This work is the first research that takes the holistic approach to analyze the total costs of CBDCs exchange at various AMMs, including Stableswap Invariant (Curve v2), applied in Project Mariana. This study found that a multi-AMM set-up for CBDCs exchange on L2 is more cost-efficient than a single AMM of Project Mariana, despite the fragmentation of liquidity among various AMMs. Such a multi-AMM system on private L2 is further presented, and its behavior is evaluated via simulation based on the historical FX rates, Mariana Project set up as a benchmark. This work makes the following contributions. – Demonstrating that multi-AMMs set-up for CBDCs exchange is more costefficient than a single AMM. – Designing such multi-AMMs set-up for CBDCs exchange using L2 and L3 blockchains and demonstrating its superior performance for small, medium, and largest transaction volumes and any transaction in case of spikes of gas fees. – Conducting first detailed analysis of the total costs of CBDCs swaps on AMM-DEX—gas fees, swap fees (explicit fees), and price impact (explicit fees), among various AMMs and L1 and L2-based DEXes. – Providing quantitative metrics to evaluate the efficiency of cross-border CBDCs on DEXes for major AMMs. Their implementation is available in the public GitHub repository [17] – Building a framework for simulation based on historical FX rate that can be replicated in other cross-border CBDCs projects or in permission DeFi in the Ethereum ecosystem. For all metrics, the mathematical background and numerical methods with the implementation are provided, such as the Newthon-Raphon method for price impact in Cryptoswap Invariant AMM applied in Project Mariana. Authors:
(1) Krzysztof Gogol, University of Zurich (gogol@ifi.uzh.ch);
(2) Johnnatan Messias, Matter Labs;
(3) Malte Schlosser, University of Zurich;
(4) Benjamin Kraner, University of Zurich;
(5) Claudio Tessone, University of Zurich. Authors: Authors: (1) Krzysztof Gogol, University of Zurich (gogol@ifi.uzh.ch); (2) Johnnatan Messias, Matter Labs; (3) Malte Schlosser, University of Zurich; (4) Benjamin Kraner, University of Zurich; (5) Claudio Tessone, University of Zurich. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

This Study Breaks Down the True Costs of CBDC Exchange

Coding Without the Sweat…Yikes!!!! Coding Without the Sweat…Yikes!!!! Just picture this: you’re sipping your daily doze of caffeine and casually telling your computer, “Design and build an app that tracks my team’s weekly tasks,” and, boom, it’s done. No semicolons, no syntax errors, just pure vibes. Welcome to “vibe coding”, the hottest trend in 2025’s tech scene and the place where AI turns your ideas into code faster than you can say “Stack Overflow.” vibe coding” This cool term has been coined by the AI guru Andrej Karpathy earlier this year, where vibe coding is less about grinding through lines of code and more about flowing with your creative instincts. And guess what???? It’s slowly sneaking into enterprise platforms like ServiceNow, making even the suits jump on the bandwagon. It’s time to dig in to the ‘vibe’ of this revolution and see how it’s shaking up ServiceNow’s world. What is this Vibe Coding, Anyway? What is this Vibe Coding, Anyway? Vibe coding has been recognized as the ultimate “go with the flow” approach to programming. Instead of wrestling with Python loops or debugging JavaScript promises, you chat with an AI, let’s say with Grok 3 or GitHub Copilot, and then you describe what you want in simple, plain English.For instance, “Make a website that shows cat memes” or “Fix this bug” or maybe, “Automate my inbox.” The AI just spits out the code, you run it, and if it flops, you tweak it with another casual prompt. It’s experimental, it’s messy(for sure) but it’s fun. Mr. Karpathy calls it “forgetting the code even exists”, a mindset where your ideas take the center stage whereas the tech fades into the background. A rare sight, for sure! fun And woah, look at these! The stats actually back up the hype. Y Combinator reported in the March of 2025 that twenty five percent of its Winter 2025 startups had codebases that were ninety five percent AI-generated.Multiple tools like Cursor and Replit Agent are powering this shift and turning non-coders or low-coders into creators overnight. But it’s not just for startups.Even the big players like ServiceNow are catching the wave, blending vibe coding into their enterprise-grade ecosystem. How? Let’s explore. ServiceNow: The Unexpected Vibe Coding Playground ServiceNow: The Unexpected Vibe Coding Playground ServiceNow won’t be the first name that you’d associate with a boiling coding trend. It’s the platform for IT workflows, HR automation, and corporate efficiency and not exactly a hacker’s garage. But dig into the platform a little deeper, and you will find a vibe-coding paradise hiding in plain sight. With its low-code or the no-code roots and a hefty dose of AI capabilities , ServiceNow lets you build apps and workflows by vibing your way through it. vibing Here’s where the magic happens: ServiceNow’s App Engine and Now Assist (its AI sidekick) let you talk to the platform like it’s your very own buddy. Let’s assume that you’re an HR manager who wants to automate the onboarding process.You will tell Now Assist, “When a new hire starts, send them a welcome email and order their laptop.” The AI doesn’t think, doesn’t blink.All it does it that it spins up a workflow in Flow Designer, complete with triggers, email templates, and IT catalog requests. You test it(ofcourse), you tweak it (“Add their start date to the email”), and then deploy it, and all this without touching a line of JavaScript. ofcourse That’s vibe coding in a corporate suit! Let’s give you another example here - A Real-World Vibe: Dunder Mifflin’s Onboarding Glow-Up A Real-World Vibe: Dunder Mifflin’s Onboarding Glow-Up Let’s get concrete with Dunder Mifflin,our fictional paper supply chain. Their HR lead, Toby, vibes out an onboarding process in ServiceNow.He isn’t a coder(definitely) but knows the drill: new hires need gear, approvals, and a warm welcome. Armed with ServiceNow, Toby vibes it out: definitely Step 1: Toby tells Now Assist, “Build me an onboarding flow - email the new hire , request a laptop and ping the manager.” The AI capabilities map it to ServiceNow’s data model, pulling employee details and IT catalog items like it’s reading Toby’s mind.
Step 2: The first test run works, but Toby wants more. “Add a monitor to the request.” The Flow Designer adjusts it on the fly.
Step 3: Deployed. Dunder Mifflin’s onboarding is now a slick, automated machine. Toby never sees the backend Glide API or wrestles with database schemas, but just pure, creative flow. Step 1: Toby tells Now Assist, “Build me an onboarding flow - email the new hire , request a laptop and ping the manager.” The AI capabilities map it to ServiceNow’s data model, pulling employee details and IT catalog items like it’s reading Toby’s mind. Step 1 Step 2: The first test run works, but Toby wants more. “Add a monitor to the request.” The Flow Designer adjusts it on the fly. Step 2 Step 3: Deployed. Dunder Mifflin’s onboarding is now a slick, automated machine. Toby never sees the backend Glide API or wrestles with database schemas, but just pure, creative flow. Step 3 Here’s what the Flow Designer workflow looks like: Flow Designer workflow Trigger: “New Employee Record Created” (Table: sys_user, Condition: employee_number is not empty).
Action 1: “Send Email”
To: ${new_employee.email}
Subject: “Welcome to Dunder Mifflin!”
Body: “Hey ${new_employee.first_name}, your start date is ${new_employee.start_date}. Welcome to the company!”
Action 2: “Create Catalog Request”
Item: “Standard Laptop” + “Monitor” (bundled via AI suggestion).
Requested For: ${new_employee}.
Action 3: “Approval”
Approver: ${new_employee.manager}.
Details: “Approve ${new_employee.first_name}’s gear.” Trigger: “New Employee Record Created” (Table: sys_user, Condition: employee_number is not empty). Trigger Action 1: “Send Email” Action 1 To: ${new_employee.email} Subject: “Welcome to Dunder Mifflin!” Body: “Hey ${new_employee.first_name}, your start date is ${new_employee.start_date}. Welcome to the company!” Action 2: “Create Catalog Request” Action 2 Item: “Standard Laptop” + “Monitor” (bundled via AI suggestion). Requested For: ${new_employee}. Action 3: “Approval” Action 3 Approver: ${new_employee.manager}. Details: “Approve ${new_employee.first_name}’s gear.” Toby tests it, adds the monitor with a quick “vibe tweak,” and deploys it .No code, just flow. What’s Under the Hood? A Peek at the Code What’s Under the Hood? A Peek at the Code While Toby didn’t write it, here’s a snippet of what ServiceNow’s AI might generate in the background (e.g., a Business Rule to enhance the flow): (function executeRule(current, previous /* null when async */) {
    var empName = current.first_name;
    var startDate = current.start_date;

    var email = new GlideEmailOutbound();
    email.setSubject("Welcome to Dunder Mifflin!");
    email.setBody("Hey " + empName + ", your start date is " + startDate + ". Get ready!");
    email.addRecipient(current.email);
    email.send();

})(current, previous); (function executeRule(current, previous /* null when async */) {
    var empName = current.first_name;
    var startDate = current.start_date;

    var email = new GlideEmailOutbound();
    email.setSubject("Welcome to Dunder Mifflin!");
    email.setBody("Hey " + empName + ", your start date is " + startDate + ". Get ready!");
    email.addRecipient(current.email);
    email.send();

})(current, previous); This runs when a new employee is added. Toby didn’t touch it, but a developer sure can tweak it later for any supported edge cases. This isn’t hypothetical fluff. Companies like Bayer have actually used ServiceNow’s low-code tools to automate 80% of their legal processes, and with further AI enhancements rolling out in 2025, the vibe-coding potential is skyrocketing. Why It Works on ServiceNow? Why It Works on ServiceNow? ServiceNow’s secret lies in its mix of structure and freedom. Prebuilt components (like email actions, approvals and workflows) give you guardrails, while AI capabilities help you to fill in the blanks. Generative AI in Flow Generation or even the text-to-code capabilities take your vague prompts and turns them into functional logic. It’s not pure chaos like some vibe-coding experiments, you’re still in an enterprise sandbox, but it’s close enough to feel liberating. Additionally,ServiceNow’s wide suite of offered integrations means that your vibes can stretch across systems without breaking a sweat. The Catch: Vibes vs. Reality The Catch: Vibes vs. Reality Before you ditch your IDE, a word of caution: vibe coding isn’t flawless. Even on ServiceNow, simple automations are like a breeze, but complex apps still need a developer to tweak the JavaScript under the hood. Security is often another vibe-killer.AI-generated code can eave vulnerabilities or miss edge cases if you don’t double-check. Simon Willison, nails it, “It’s perfect for prototypes, but production? You’d better know what’s running.” a word of caution The Future: Vibe Coding Goes Corporate The Future: Vibe Coding Goes Corporate As of April 2025, vibe coding’s footprint is definitely growing. ServiceNow’s latest releases double down on AI Agents, with smarter flow suggestions and voice-driven development. Imagine telling your laptop, “Fix my ticket system,” and watching it happen—that’s the vibe horizon. Infact, apart from ServiceNow, the trend is crawling into everything from indie apps to trillion-dollar firms. It is not just coding,it’s a mindset shift- less gatekeeping and more creating. Conclusion Conclusion Vibe coding isn’t just a trend, it’s a power move. It’s gives the HR rep the power to building apps just like a DJ dropping beats or the IT admin automating workflows just like it’s a freestyle rap. Forget the old rules of coding(but not all of them, please) this is creation unchained. So refill your coffee, tell ServiceNow your wildest idea, and let the AI capabilities spin it into gold. but not all of them, please

This 2025 Coding Trend Claims to Solve Your IT Nightmares—But Is It Too Good to Be True?

Scrolling through X, you’ve probably seen the posts: “Testnets will make you rich!” Promises of $100,000 in earnings, no cost, no skills required. Just a few minutes a day, and you’re on your way to financial freedom. But how much of this is real, and how much is just hype? I decided to find out. The Claims Recent posts on X suggest that by participating in free blockchain testnets, users can earn significant rewards. Testnets are essentially trial versions of blockchain networks, where developers test functionality before launching on the mainnet. To incentivize participation, some projects offer rewards like tokens or airdrops. The numbers are eye-catching: $849 daily
$941 daily
$1,700 in profits
Claims of $3,000 to $6,000 per account $849 daily $941 daily $1,700 in profits Claims of $3,000 to $6,000 per account The Plan To separate fact from fiction, I’ll be participating in four of the most mentioned testnets: OG
Humanity
Somnia
Monad OG OG Humanity Humanity Somnia Somnia Monad Monad Over the next several weeks, I’ll document my experience, track the time spent, and calculate any actual earnings. I’ll also assess the legitimacy of these platforms, the complexity of participation, and whether they deliver on their promises. Key Factors I’ll Evaluate Time Commitment: How long does it really take to complete tasks?
Technical Requirements: Are they truly beginner-friendly?
Earnings: Are rewards guaranteed, or is it more like a lottery?
Security and Risk: Is there any personal data or wallet compromise involved? Time Commitment: How long does it really take to complete tasks? Time Commitment: Technical Requirements: Are they truly beginner-friendly? Technical Requirements: Earnings: Are rewards guaranteed, or is it more like a lottery? Earnings: Security and Risk: Is there any personal data or wallet compromise involved? Security and Risk: Stay Tuned Follow along as I navigate these testnets, share detailed reports, and ultimately answer the question: Can testnets really make you rich, or is it just another overhyped crypto scheme? Expect honest insights, real data, and maybe a few surprises along the way. Let’s see if the claims hold up.

Are Web3 Testnet Earnings Claims on X Too Good to Be True?

A Game-Changer for Scalable Storage on Solana:Xandeum Introduces pNodes How can a blockchain handle massive data without slowing down? Xandeum Labs has introduced pNodes (Provider Nodes) on its devnet, marking a major advancement in decentralized storage for Solana smart contracts. Designed to integrate a scalable, random-access storage layer, pNodes address one of Solana’s key challenges: enabling efficient and scalable data storage. Solana processes transactions at high speed with low fees, supporting over 4 billion transactions on its devnet to date. However, its validators struggle to store large datasets, capping the potential of decentralized applications (dApps). Xandeum’s pNodes create a random-access storage layer, separate from Solana’s core system, to address this. The network uses Provider Nodes (pNodes) to store data across a decentralized file system, managed by Validator Nodes (vNodes) running Xandeum-enabled software. Data stays secure through configurable redundancy and erasure coding, verified cryptographically. New Solana primitives, “peek” and “poke,” enable smooth data transfers between accounts and this storage layer. With blockchain adoption surging and decentralized applications (dApps) becoming increasingly data-intensive, storage bottlenecks threaten to stifle innovation. Solana, known for its high throughput and low transaction costs, has long faced the challenge of maintaining a decentralized, scalable, and cost-effective data infrastructure. Xandeum’s pNodes introduce a decentralized file system abstraction, empowering developers to build richer, more sophisticated Web3 applications without compromising speed or efficiency. How pNodes Reshape Smart Contract Storage Unlike traditional blockchain storage, which relies heavily on validators to store every piece of data, pNodes offload storage responsibilities to an independent network. This design not only reduces the burden on Solana validators but also ensures data availability and integrity through cryptographic verification. The Xandeum network employs Validator Nodes (vNodes) to oversee and manage data distribution across multiple pNodes. Using configurable redundancy levels and erasure coding, the system ensures that stored data remains secure, tamper-proof, and always accessible. Furthermore, extended Solana primitives such as “peek” and “poke” facilitate seamless interactions between Solana accounts and Xandeum’s decentralized file system, making pNodes an intuitive extension of Solana’s computing framework. If Solana is the "world computer," pNodes serve as its decentralized "hard drive." While Solana's design prioritizes speed and execution, Xandeum’s innovation provides the scalable storage solution it has been missing—filling a crucial gap in the blockchain ecosystem. Unlocking New Opportunities for Developers and Participants Xandeum’s vision for pNodes goes beyond developers, aiming to serve a broad audience, including crypto enthusiasts, passive income seekers, early investors, bloggers, and node operators. To simplify participation, Xandeum has introduced XandMiner, a user-friendly pNode management application that enables users to set up and manage pNodes, generate key pairs, and create file systems—ensuring a seamless onboarding experience for early adopters. To drive early adoption, Xandeum will launch 300 incentivized devnet pNodes as part of the “Constance” release, marking the start of the Deep South Era—the first of six planned innovation phases in 2025. Each wallet can acquire up to three pNodes, and once the mainnet is fully deployed, the system will transition to a completely permissionless model. A Transformational Moment for Solana and Web3 The introduction of pNodes has the potential to redefine how Solana-based dApps handle storage, opening doors to entirely new use cases for DeFi, NFTs, gaming, AI, and large-scale data applications. Bernie Blume, CEO of Xandeum Labs, emphasized the far-reaching impact of this development: “A low-cost, decentralized scaling solution will drastically expand the global dApp landscape. It can unlock a new revenue stream for pNode operators as well as Solana validators and stakers. The Xandeum solution will have an enormous impact on the growth of the Solana ecosystem.” Final Thoughts Xandeum’s pNodes mark a shift for Solana, potentially unlocking a new wave of Web3 innovation. If successful, this could position Solana as a leader in scalable blockchain solutions by year-end 2025. The real test lies in mainnet performance and adoption rates, which remain to be seen. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO A Game-Changer for Scalable Storage on Solana:Xandeum Introduces pNodes How can a blockchain handle massive data without slowing down? Xandeum Labs has introduced pNodes (Provider Nodes) on its devnet, marking a major advancement in decentralized storage for Solana smart contracts. Designed to integrate a scalable, random-access storage layer, pNodes address one of Solana’s key challenges: enabling efficient and scalable data storage. Xandeum Labs Xandeum Labs Solana processes transactions at high speed with low fees, supporting over 4 billion transactions on its devnet to date. However, its validators struggle to store large datasets, capping the potential of decentralized applications (dApps). Xandeum’s pNodes create a random-access storage layer, separate from Solana’s core system, to address this. The network uses Provider Nodes (pNodes) to store data across a decentralized file system, managed by Validator Nodes (vNodes) running Xandeum-enabled software. Data stays secure through configurable redundancy and erasure coding, verified cryptographically. New Solana primitives, “peek” and “poke,” enable smooth data transfers between accounts and this storage layer. With blockchain adoption surging and decentralized applications (dApps) becoming increasingly data-intensive, storage bottlenecks threaten to stifle innovation. Solana, known for its high throughput and low transaction costs, has long faced the challenge of maintaining a decentralized, scalable, and cost-effective data infrastructure. Xandeum’s pNodes introduce a decentralized file system abstraction, empowering developers to build richer, more sophisticated Web3 applications without compromising speed or efficiency. How pNodes Reshape Smart Contract Storage Unlike traditional blockchain storage, which relies heavily on validators to store every piece of data, pNodes offload storage responsibilities to an independent network. This design not only reduces the burden on Solana validators but also ensures data availability and integrity through cryptographic verification. The Xandeum network employs Validator Nodes (vNodes) to oversee and manage data distribution across multiple pNodes. Using configurable redundancy levels and erasure coding, the system ensures that stored data remains secure, tamper-proof, and always accessible. Furthermore, extended Solana primitives such as “peek” and “poke” facilitate seamless interactions between Solana accounts and Xandeum’s decentralized file system, making pNodes an intuitive extension of Solana’s computing framework. If Solana is the "world computer," pNodes serve as its decentralized "hard drive." While Solana's design prioritizes speed and execution, Xandeum’s innovation provides the scalable storage solution it has been missing—filling a crucial gap in the blockchain ecosystem. Unlocking New Opportunities for Developers and Participants Xandeum’s vision for pNodes goes beyond developers, aiming to serve a broad audience, including crypto enthusiasts, passive income seekers, early investors, bloggers, and node operators. To simplify participation, Xandeum has introduced XandMiner, a user-friendly pNode management application that enables users to set up and manage pNodes, generate key pairs, and create file systems—ensuring a seamless onboarding experience for early adopters. To drive early adoption, Xandeum will launch 300 incentivized devnet pNodes as part of the “Constance” release, marking the start of the Deep South Era—the first of six planned innovation phases in 2025. Each wallet can acquire up to three pNodes, and once the mainnet is fully deployed, the system will transition to a completely permissionless model. A Transformational Moment for Solana and Web3 The introduction of pNodes has the potential to redefine how Solana-based dApps handle storage, opening doors to entirely new use cases for DeFi, NFTs, gaming, AI, and large-scale data applications. Bernie Blume, CEO of Xandeum Labs, emphasized the far-reaching impact of this development: “A low-cost, decentralized scaling solution will drastically expand the global dApp landscape. It can unlock a new revenue stream for pNode operators as well as Solana validators and stakers. The Xandeum solution will have an enormous impact on the growth of the Solana ecosystem.” Final Thoughts Xandeum’s pNodes mark a shift for Solana, potentially unlocking a new wave of Web3 innovation. If successful, this could position Solana as a leader in scalable blockchain solutions by year-end 2025. The real test lies in mainnet performance and adoption rates, which remain to be seen. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: business blogging program business blogging program

Will Xandeum’s pNodes Unlock Solana’s True Potential?

Sal Xandeum se pNodes Solana se ware potensiaal ontsluit?

هل ستطلق pNodes من Xandeum العنان لإمكانات Solana الحقيقية؟

Ці раскрыюць pNodes Xandeum сапраўдны патэнцыял Solana?

Werden die pNodes von Xandeum das wahre Potenzial von Solana freisetzen?

¿Los pNodes de Xandeum desbloquearán el verdadero potencial de Solana?

Avaavatko Xandeumin pNodesit Solanan todellisen potentiaalin?

A Xandeum pNode-jai felszabadítják Solana valódi potenciálját?

Xandeum の pNode は Solana の真の可能性を引き出すでしょうか?

តើ pNodes របស់ Xandeum នឹងដោះសោសក្តានុពលពិតរបស់ Solana ដែរឬទេ?

Дали pNodes на Xandeum ќе го отклучат вистинскиот потенцијал на Солана?

Xandeum-ийн pNodes нь Соланагийн жинхэнэ боломжийг нээх үү?

Adakah pNodes Xandeum Membuka Potensi Sebenar Solana?

Philosophers Challenge the Idea That AI Lacks True Understanding

Have you heard about Elliott Waves? It's a way to understand the movements of prices and anticipate shifts in trends. A “Wave 5” for the ages? According to the Elliott Wave Theory, markets move in five waves. The first wave is small but noticeable, followed by a drop in the second wave. The third wave is the longest and strongest, followed by a pull-back fourth wave and ending with a fifth wave that pushes the price higher but does not give a return as large as wave three. Investopedia has an example: We can see this pattern playing out on the monthly chart from 2022 to today. Let’s not get ahead of ourselves Don’t stress! We don’t know how high wave 5 will go. This is a very long-term chart so it could take 2–3 more months to figure this out. For all we know, December’s high could have completed wave 5. Once we see a pullback of any great size, we can talk about a “second chance” relief rally or complacency bounce before a larger, cyclical correction (like a bear market). At that point, we will reach the A-B-C portion of the image from Investopedia. For now, we’ll just have to see how it goes. Did you like this analysis? It’s a snippet from my the January 14, 2025, market update of my newsletter, Crypto is Easy. You can watch a video of that snippet on YouTube. https://youtu.be/Z8EARP9pyjw?embedable=true Mark Helfman publishes the Crypto is Easy newsletter. He is also the author of three books and a top Bitcoin writer on Medium and Hacker Noon. Learn more about him in his bio and connect with him on Tealfeed. Have you heard about Elliott Waves? It's a way to understand the movements of prices and anticipate shifts in trends. A “Wave 5” for the ages? According to the Elliott Wave Theory, markets move in five waves. The first wave is small but noticeable, followed by a drop in the second wave. The third wave is the longest and strongest, followed by a pull-back fourth wave and ending with a fifth wave that pushes the price higher but does not give a return as large as wave three. Investopedia has an example: We can see this pattern playing out on the monthly chart from 2022 to today. Let’s not get ahead of ourselves Don’t stress! We don’t know how high wave 5 will go. This is a very long-term chart so it could take 2–3 more months to figure this out. For all we know, December’s high could have completed wave 5. Once we see a pullback of any great size, we can talk about a “second chance” relief rally or complacency bounce before a larger, cyclical correction (like a bear market). At that point, we will reach the A-B-C portion of the image from Investopedia. For now, we’ll just have to see how it goes. Did you like this analysis? It’s a snippet from my the January 14, 2025, market update of my newsletter, Crypto is Easy . the January 14, 2025, market update the January 14, 2025, market update Crypto is Easy You can watch a video of that snippet on YouTube . YouTube https://youtu.be/Z8EARP9pyjw?embedable=true https://youtu.be/Z8EARP9pyjw?embedable=true Mark Helfman publishes the Crypto is Easy newsletter. He is also the author of three books and a top Bitcoin writer on Medium and Hacker Noon . Learn more about him in his bio and connect with him on Tealfeed . Crypto is Easy Crypto is Easy Crypto is Easy Medium Medium Medium Hacker Noon Hacker Noon Hacker Noon bio bio Tealfeed Tealfeed

Crypto Supercycle 2025? Elliott Waves Tell the True Story

Every AI Prediction That Came True in 2024—and the Ones That Didn't

Todas las predicciones de IA que se cumplieron en 2024 (y las que no)

Toutes les prédictions de l'IA qui se sont réalisées en 2024, et celles qui ne se sont pas réalisées

Todas as predicións da IA que se fixeron realidade en 2024 e as que non o fixeron

2024-ին իրականություն դարձած յուրաքանչյուր AI կանխատեսում, և նրանք, որոնք չեն իրականացել

כל תחזית AI שהתגשמה בשנת 2024 - ואלה שלא

2024 年に実現したすべての AI 予測と実現しなかった予測

ხელოვნური ინტელექტის ყველა პროგნოზი, რომელიც ახდა 2024 წელს და ის, რაც არ შესრულდა

រាល់ការទស្សន៍ទាយ AI ដែលបានក្លាយជាការពិតនៅឆ្នាំ 2024 ហើយអ្វីដែលមិនបានកើតឡើង

2024년에 실현된 모든 AI 예측과 실현되지 않은 예측

Wszystkie prognozy dotyczące sztucznej inteligencji, które spełniły się w 2024 r. — i te, które się nie spełniły

Todas as previsões de IA que se concretizaram em 2024 — e as que não se concretizaram

2024 දී සැබෑ වූ සෑම AI අනාවැකියක්ම - සහ නොකළ ඒවා

A unified ecosystem with convenient asset transfer between networks is an ideal the crypto space has been chasing for a long time. However, because blockchains have varying token standards and consensus mechanisms, this feat is (currently) only achievable via bridges. The path to Bitcoin (BTC) on Ethereum, Ether (ETH) on the Binance Smart Chain (BSC), Solana (SOL) on Ethereum, and so on runs through a bridge. At the time of writing, DeFiLlama estimated the total value locked (TVL) across all bridges at around $27.79 billion. These are good numbers. But when put in context, they highlight that perhaps, despite the widespread desire for an interoperable crypto ecosystem, bridges are not as widely utilized. Out of the $2.5 trillion of value in the crypto market, only about 0.01% is utilized across chains. This is an indication that not many crypto users are utilizing bridges. And according to Sher, CEO of Zecrey Labs (the team behind Kontos Protocol), a big part of this is down to the user experience. The solution, he says, lies in bridge abstraction. The ABCs of Bridging Crypto Assets The crypto space is not known for its user experience. Blockchain-based applications are typically more complicated to set up and use than regular apps, something that the average crypto bridge takes to the extreme. “The blockchain industry is often perceived as complex and difficult to navigate, especially for newcomers. Managing multiple wallets, handling private keys, and understanding gas fees can be daunting for users,” says Sher. It's not known exactly how many people use crypto bridges. What is known, however, is that for most users, the experience is anything but convenient. Crypto bridges tend to have unintuitive interfaces, with users often required to go through several steps to complete a bridge operation, which can take anywhere from a few minutes to several hours and attract significant fees. At the same time, the bridging scene is itself fragmented. Many bridging protocols only support a limited set of assets and blockchain pairs. “This means that currently, there are numerous bridges for different blockchain pairs, each with its own protocols and interface. Thus, users are regularly forced to use multiple bridges to get their assets on the target chain,” says Sher. When someone wants to interact with a chain like Avalanche, they typically need to address two aspects: Gas fees: They need to bridge AVAX tokens to Avalanche to pay for transaction fees (gas) on the network.


Other assets: They may also want to bridge other assets, such as stablecoins like USDT, to use within the Avalanche ecosystem. This dual requirement often means users must perform multiple bridging operations or use a service that can handle both aspects in a streamlined manner. The complexity of this process can be a barrier for some users, highlighting the need for more user-friendly bridging solutions. This is where bridge abstraction comes into play. What Is Bridge Abstraction? “The Bridge Abstraction Layer (BAL) is a structural design pattern that separates the abstraction (interface) from the implementation (underlying mechanics),” says Sher. “This decoupling allows for greater flexibility and independent development of both components.” Bridge abstraction builds on the concept of chain abstraction, where the aim is to hide the complexities of interacting with blockchains from the end user. When the user isn’t dealing with the specific details of underlying protocols, traditionally complex operations like cross-chain trading and asset transfer become straightforward. Abstraction essentially lets developers build cross-chain solutions without being tied to specific implementation details. According to Sher,  this translates to lesser involvement from the user during a bridging operation, as seen in Kontos’ design, which allows users to bridge across multiple chains via a single interface. Kontos is designed around the user’s intent. If someone intends to get tokens on a particular chain, they can do so using any assets they have on any chain(s). This is possible because the protocol lets users combine assets from various chains and move them to the target chain. It uses smart contracts and a network of participants to handle the complexities of gas estimation and payment behind the scenes. “Kontos' approach to gas abstraction represents a significant innovation in improving blockchain usability,” says Sher. ”By tackling this complex problem, Kontos is addressing one of the major pain points in blockchain interaction, potentially setting a new standard for user experience in the multi-chain ecosystem.” A New Standard for On-chain User Experience Bridge abstraction is a significant leap forward in simplifying interactions across blockchain networks. Users typically had to navigate complex processes when bridging assets, ensuring they had enough gas tokens on the source chain, selecting both the source and target chains, and managing specific assets across networks. With bridge abstraction, these barriers are minimized. Bridging no longer requires holding gas tokens on the source chain or keeping track of which chain assets are located. Instead, utilizing assets from multiple chains in a single task is possible. The system will calculate and optimize costs behind the scenes. This evolution promises a smoother, more intuitive on-chain experience that allows users to focus on their transactions rather than the intricacies of blockchain mechanics. It sets a new standard for blockchain accessibility and usability, paving the way for a more inclusive and user-friendly web3 ecosystem. A unified ecosystem with convenient asset transfer between networks is an ideal the crypto space has been chasing for a long time. However, because blockchains have varying token standards and consensus mechanisms, this feat is (currently) only achievable via bridges. A unified ecosystem with convenient asset transfer between networks is an ideal the crypto space has been chasing for a long time. However, because blockchains have varying token standards and consensus mechanisms, this feat is (currently) only achievable via bridges. The path to Bitcoin (BTC) on Ethereum, Ether (ETH) on the Binance Smart Chain (BSC), Solana (SOL) on Ethereum, and so on runs through a bridge. At the time of writing, DeFiLlama estimated the total value locked (TVL) across all bridges at around $27.79 billion. DeFiLlama DeFiLlama These are good numbers. But when put in context, they highlight that perhaps, despite the widespread desire for an interoperable crypto ecosystem, bridges are not as widely utilized. Out of the $2.5 trillion of value in the crypto market, only about 0.01% is utilized across chains. $2.5 trillion of value $2.5 trillion of value This is an indication that not many crypto users are utilizing bridges. And according to Sher, CEO of Zecrey Labs (the team behind Kontos Protocol ) , a big part of this is down to the user experience. The solution, he says, lies in bridge abstraction. Zecrey Labs Zecrey Labs Kontos Protoc ol Kontos Protoc , The ABCs of Bridging Crypto Assets The crypto space is not known for its user experience. Blockchain-based applications are typically more complicated to set up and use than regular apps, something that the average crypto bridge takes to the extreme. “The blockchain industry is often perceived as complex and difficult to navigate, especially for newcomers. Managing multiple wallets, handling private keys, and understanding gas fees can be daunting for users,” says Sher. says Sher. It's not known exactly how many people use crypto bridges. What is known, however, is that for most users, the experience is anything but convenient. Crypto bridges tend to have unintuitive interfaces, with users often required to go through several steps to complete a bridge operation, which can take anywhere from a few minutes to several hours and attract significant fees. At the same time, the bridging scene is itself fragmented. Many bridging protocols only support a limited set of assets and blockchain pairs. “This means that currently, there are numerous bridges for different blockchain pairs, each with its own protocols and interface. Thus, users are regularly forced to use multiple bridges to get their assets on the target chain,” says Sher. says Sher. When someone wants to interact with a chain like Avalanche, they typically need to address two aspects: Gas fees: They need to bridge AVAX tokens to Avalanche to pay for transaction fees (gas) on the network. Other assets: They may also want to bridge other assets, such as stablecoins like USDT, to use within the Avalanche ecosystem. Gas fees: They need to bridge AVAX tokens to Avalanche to pay for transaction fees (gas) on the network. Gas fees: They need to bridge AVAX tokens to Avalanche to pay for transaction fees (gas) on the network. Other assets: They may also want to bridge other assets, such as stablecoins like USDT, to use within the Avalanche ecosystem. Other assets: They may also want to bridge other assets, such as stablecoins like USDT, to use within the Avalanche ecosystem. This dual requirement often means users must perform multiple bridging operations or use a service that can handle both aspects in a streamlined manner. The complexity of this process can be a barrier for some users, highlighting the need for more user-friendly bridging solutions. This is where bridge abstraction comes into play. What Is Bridge Abstraction? “The Bridge Abstraction Layer (BAL) is a structural design pattern that separates the abstraction (interface) from the implementation (underlying mechanics),” says Sher. “This decoupling allows for greater flexibility and independent development of both components.” says Sher. Bridge abstraction builds on the concept of chain abstraction, where the aim is to hide the complexities of interacting with blockchains from the end user. When the user isn’t dealing with the specific details of underlying protocols, traditionally complex operations like cross-chain trading and asset transfer become straightforward. Abstraction essentially lets developers build cross-chain solutions without being tied to specific implementation details. According to Sher, this translates to lesser involvement from the user during a bridging operation, as seen in Kontos’ design, which allows users to bridge across multiple chains via a single interface. Sher, Kontos is designed around the user’s intent. If someone intends to get tokens on a particular chain, they can do so using any assets they have on any chain(s). This is possible because the protocol lets users combine assets from various chains and move them to the target chain. It uses smart contracts and a network of participants to handle the complexities of gas estimation and payment behind the scenes. “Kontos' approach to gas abstraction represents a significant innovation in improving blockchain usability,” says Sher. ”By tackling this complex problem, Kontos is addressing one of the major pain points in blockchain interaction, potentially setting a new standard for user experience in the multi-chain ecosystem.” says Sher. A New Standard for On-chain User Experience Bridge abstraction is a significant leap forward in simplifying interactions across blockchain networks. Users typically had to navigate complex processes when bridging assets, ensuring they had enough gas tokens on the source chain, selecting both the source and target chains, and managing specific assets across networks. With bridge abstraction, these barriers are minimized. Bridging no longer requires holding gas tokens on the source chain or keeping track of which chain assets are located. Instead, utilizing assets from multiple chains in a single task is possible. The system will calculate and optimize costs behind the scenes. This evolution promises a smoother, more intuitive on-chain experience that allows users to focus on their transactions rather than the intricacies of blockchain mechanics. It sets a new standard for blockchain accessibility and usability, paving the way for a more inclusive and user-friendly web3 ecosystem.

Bridge Abstraction - The Key to Unlocking True Blockchain Interoperability

My Top Ten Upwork Conspiracy Theories: True or False

Les meves deu millors teories de la conspiració: vertader o fals

Mis diez principales teorías de conspiración sobre Upwork: ¿verdaderas o falsas?

Kymmenen parasta salaliittoteoriaani: totta vai tarua

עשרת תיאוריות הקונספירציה המובילות שלי: נכון או לא נכון

私のトップ10のUpwork陰謀説：真実か嘘か

Менин мыкты он Upwork Conspiracy Theories: Чын же жалган

Миний шилдэг арван хуйвалдааны онол: үнэн эсвэл худал

Chunka Aswan allin Upwork Conspiración Teoríasniy: Chiqap utaq llulla

Cele mai bune zece teorii ale conspirației mele: adevărat sau fals

My Top Ten Upwork Conspiracy Theories: ሓቂ ድዩ ሓሶት።

En İyi On Upwork Komplo Teorim: Doğru mu Yanlış mı

Iithiyori zam eziPhezulu zokuSebenza kweNyelelo: Yinyani okanye bubuxoki

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 4 Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Beginning in 1950, almost exactly at the same time that Turing’s Mind paper appeared, there was an explosion in von Neumann/Turing style computer hardware. At that time, all of the existing computers, such as Colossus and EDVAC, were one-off machines. Although UNIVAC, the company spun off from Penn’s ENIAC project by Eckert and Mauchley, had promised a massproduced machine, none had yet been delivered when RAND decided to build the JOHNNIAC.[30] This was the machine on which Shaw, Newell, and Simon pioneered the IPL series of programming languages, which they used to implement what were are almost certainly the first real AIs: “IPL was first utilized to demonstrate that the theorems in Principia Mathematica which were proven laboriously by hand, by Bertrand Russell and Alfred North Whitehead, could in fact be proven by computation. According to Simon’s autobiography Models of My Life, this application was originally developed first by hand simulation, using his children as the computing elements, while writing on and holding up note cards as the registers which contained the state variables of the program. IPL was used to implement several early artificial intelligence programs, also by the same authors: the Logic Theorist (1956), the General Problem Solver (1957), and their computer chess program NSS (1958). Several versions of IPL were created: IPL-I (never implemented), IPL-II (1957 for JOHNNIAC), IPL-III (existed briefly), IPL-IV, IPL-V (1958, for IBM 650, IBM 704, IBM 7090, Philco model 212, many others.”[5] IPL introduced what became the most important concepts in classical Artificial Intelligence (and, in fact, in computing more generally), including list processing, symbolic computing (as foreshadowed by Ada Lovelace!), and recursion.[5] Notably, although Turing’s bombe, the code-breaking machine, was not a “modern” computer, in the sense that is was hardwired, as opposed to being of the von Neumann/Turing stored program type, the program that it ran was explicitly engaged in mechanical heuristic search, that is, in trying a huge number of operators (e.g., combinations of code keys) in a cleverly arranged order (e.g., beginning with the most probable), in hopes of reaching a goal (e.g., the key that would break the day’s code). Much later, Newell and Simon described heuristic search as a fundamental process of intelligence,[35], and remains one of the most important tools of AI.[2] However, IPL was an ugly programming language – not very far from assembly language – and, as an interpreted language it was slower than assembly or the soon-to-follow “high level” compiled languages such as COBOL and Fortran. These promised programmers both high performance and simplicity of expression. This brings us to SLIP and Lisp, which took different paths to bring simplicity and additional power to the important AI concepts pioneered in IPL. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. [2] Allen Newell once told me, in passing, that AI’s only had one tool, the ability to search huge multi-dimensional spaces efficiently, but that one tool is all you need. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 4 Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Beginning in 1950, almost exactly at the same time that Turing’s Mind paper appeared, there was an explosion in von Neumann/Turing style computer hardware. At that time, all of the existing computers, such as Colossus and EDVAC, were one-off machines. Although UNIVAC, the company spun off from Penn’s ENIAC project by Eckert and Mauchley, had promised a massproduced machine, none had yet been delivered when RAND decided to build the JOHNNIAC.[30] This was the machine on which Shaw, Newell, and Simon pioneered the IPL series of programming languages, which they used to implement what were are almost certainly the first real AIs: “IPL was first utilized to demonstrate that the theorems in Principia Mathematica which were proven laboriously by hand, by Bertrand Russell and Alfred North Whitehead, could in fact be proven by computation. According to Simon’s autobiography Models of My Life, this application was originally developed first by hand simulation, using his children as the computing elements, while writing on and holding up note cards as the registers which contained the state variables of the program. IPL was used to implement several early artificial intelligence programs, also by the same authors: the Logic Theorist (1956), the General Problem Solver (1957), and their computer chess program NSS (1958). Several versions of IPL were created: IPL-I (never implemented), IPL-II (1957 for JOHNNIAC), IPL-III (existed briefly), IPL-IV, IPL-V (1958, for IBM 650, IBM 704, IBM 7090, Philco model 212, many others.”[5] IPL introduced what became the most important concepts in classical Artificial Intelligence (and, in fact, in computing more generally), including list processing, symbolic computing (as foreshadowed by Ada Lovelace!), and recursion.[5] Notably, although Turing’s bombe, the code-breaking machine, was not a “modern” computer, in the sense that is was hardwired, as opposed to being of the von Neumann/Turing stored program type, the program that it ran was explicitly engaged in mechanical heuristic search, that is, in trying a huge number of operators (e.g., combinations of code keys) in a cleverly arranged order (e.g., beginning with the most probable), in hopes of reaching a goal (e.g., the key that would break the day’s code). Much later, Newell and Simon described heuristic search as a fundamental process of intelligence,[35], and remains one of the most important tools of AI.[2] However, IPL was an ugly programming language – not very far from assembly language – and, as an interpreted language it was slower than assembly or the soon-to-follow “high level” compiled languages such as COBOL and Fortran. These promised programmers both high performance and simplicity of expression. This brings us to SLIP and Lisp, which took different paths to bring simplicity and additional power to the important AI concepts pioneered in IPL. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [2] Allen Newell once told me, in passing, that AI’s only had one tool, the ability to search huge multi-dimensional spaces efficiently, but that one tool is all you need.

Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs

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