3Box Labs is a crypto company focused on building protocols and tools for Web3 application development

3Box Labs

.box is bridging Web3 and Web2 TORONTO, Ontario, January 18th, 2024/Chainwire/-- Breaking new ground in the domain name industry, My.box Inc. announces the upcoming launch of its Public Beta on January 18th, 2024 at 12:00 PM ET. This kicks off public availability of the first ever domain name system supported natively by browsers, email, and wallets. The Future Of Digital Identity .box domains offer secure and seamless identity on Web3 and Web2. The holder of ‘example.box’ is empowered with decentralized ownership and access to DNS record management, ENS integration, payments, Web3 authentication, Web3 messaging, websites, and email. Founder of .box, Josh Brandley states, “There are many great things happening at the intersection of Web3, decentralized identity, and domain names – we are the first to tie it all together with a powerful, easy to use product”. .box solves the major problem with blockchain domains .box sets a new standard in the world of blockchain-based top-level domains. While other new blockchain domain systems generate excitement, they ultimately fall short because they do not work with traditional browsers and email services. In contrast, .box is ICANN-accredited, recognized by the global Domain Name System (DNS), and supported by the Ethereum Name Service (ENS). Both DNS and ENS have established substantial network effects, solidifying their positions as core infrastructure. .box is pioneering an approach that natively integrates the features of both systems, thus delivering a better experience for users. The Team Behind .box The .box TLD is owned and operated by Intercap Registry, an ICANN Accredited Registry Operator. Intercap has dedicated over a decade to the development of .box. Core partners include 3DNS and ENS Labs, whose leaders share the vision of bringing the benefits of Web3 to the domain industry. Nick Johnson, Founder of ENS, states: “We have always had a deep respect for the DNS and are very excited to witness this unique .box implementation that supports seamless interoperability between the two systems”. Paul Gauvreau, CEO of 3DNS adds, “Bringing real world domain assets onchain improves almost every use case there is – we anticipate that this product will be widely adopted by Web3 enthusiasts and newcomers alike.” Launch Pricing During launch, .box domains will run through a premium pricing phase, ensuring fair access for everyone. Prices start at $7,680 USDC and then decay exponentially for 6 days until stopping at a fixed rate of $120 USDC. All domains will renew at $120 USDC per year. Domains are minted and managed using the dApp hosted at my.box. Payment is made with ETH or USDC. Join the .box community Secure your .box domain now at http://www.my.box Follow .box at https://x.com/boxdomains Join the .box to: Discord server Learn how to buy a .box and take control of your decentralized identity. Qualify for your referral link to earn $18 for each successful referral. Receive support, updates, and connect with the team. For More Information: Visit our website at  __ __or contact Omar Hamed, omar@intercap.com http://www.my.box Contact Community Manager Omar Hamed My.box contact@omar.box This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Introducing .box – The World’s First Blockchain Native DNS Routable Domain

In today’s world, for many people, conversing with AI has become as routine as discussing one’s coffee preferences with a barista (or it soon will be!). Yet, here lies an irony: . the more we interact with AI, the more elusive our understanding of these conversations becomes This irony essentially represents a modern twist on the “black box” dilemma, which has perplexed the ML community for years. The “black box” problem refers to the opaque decision-making processes of ML models (large language models (LLMs) including), where the rationale behind any given response is shrouded in complexity. Despite advances in technology, the inner workings of these models, governed by billions or soon trillions of parameters, remain largely inscrutable. Their decision-making is a puzzle, complicated by nonlinear interactions that defy straightforward interpretation. Prompting — the buzzing word of 2023 — doesn’t make it any better: we now have obscured layers of communication activated with every prompt. What we see — the prompt we type — is merely the surface. Beneath lies a hidden dialogue, an augmented system prompt, which is a complex, coded conversation the model conducts with itself, away from our understanding. And who knows what a model whispers to itself? So, if you were confused about prompting amid the avalanche of articles, blogs, and tutorials about it — you should be. As Ethan Mollick’s reveals, contrary to intuition, , such as pretending to navigate a Star Trek episode or a political thriller, demonstrating that traditional logical or direct prompts may not always yield the best responses from AI. research the most effective prompts involve imaginative scenarios But it also reveals that it’s not coherent and might change with a new version of the model. He mentions the futility of seeking a universal “magic phrase” for AI interaction, the effectiveness of specific prompting techniques like adding context, few-shot learning, and Chain of Thought, and the significant impact that prompts can have on AI performance. But for me — and I’ve been using AI a lot — many times, the most straightforward prompts, or “magic words,” can be surprisingly effective. How to explain it? A few years back, Explainable AI (XAI) was heralded as a solution to the “black box” issue, with entities like DARPA leading the charge (they created the , which has not been updated since 2021). However, the buzz around XAI seems to have dimmed, overtaken by a broader focus on Responsible AI. Is Responsible AI the solution? XAI toolkit So, that’s what we end up with: How do machines make decisions? — We don’t know! How to talk (prompt) to them? — We don’t know as well! But, please, keep shipping to us new, larger (though we will also take smaller) models! Why? — We don’t know! But we can’t stop. Great Article Covering the Topic An explanatory article about the concept of a black box: https://hackernoon.com/deep-learning-and-artificial-neural-networks-solving-the-black-box-mystery-rl7h32wa?embedable=true 🎁 Bonus: The Freshest Research Papers, Categorized for Your Convenience Special Category: Definitely Worth Reading : Discusses the development and advantages of 1-bit LLMs, promising significant cost reductions and efficiency improvements. The Era of 1-bit LLMs Read the paper : Introduces bGPT, a model that simulates the digital world beyond traditional modalities, predicting and diagnosing algorithms or hardware behavior. Beyond Language Models Read the paper Language Models in Specialized Domains : Integrates music understanding and generation capabilities into LLMs, demonstrating LLMs’ potential in music composition. ChatMusician Read the paper : Aims to bridge LLMs’ gap in interpreting structured data, enhancing their ability to ground knowledge in tables, graphs, and databases. StructLM Read the paper : Focuses on responsibly creating Code LLMs, contributing to advancements in coding benchmarks, and emphasizing model openness. StarCoder2 and The Stack v2 Read the paper : Discusses video generation’s potential as a unified interface for diverse tasks, outlining challenges and future directions. Video as the New Language for Real-World Decision Making Read the paper Enhancing and Merging Language Model Capabilities : Proposes a method to fuse knowledge from multiple chat models, improving chat model performance through a novel merging technique. FUSECHAT Read the paper : Details a multilingual language model that showcases superior performance in coding tasks and multilingual capabilities. Nemotron-4 15B Technical Report Read the paper : Explores latent multi-hop reasoning in LLMs, revealing their inherent capabilities and limitations in complex reasoning tasks. Do Large Language Models Latently Perform Multi-Hop Reasoning? Read the paper Scaling and Efficiency in Model Training : Discusses a system for training LLMs on over 10,000 GPUs, tackling efficiency and stability challenges in large-scale model training. MegaScale Read the paper : Proposes a theory for optimizing LLM learning, aiming for reduced training steps and improved performance. Towards Optimal Learning of Language Models Read the paper : Introduces a model combining gated linear recurrences with local attention, offering an efficient alternative for language processing tasks. Griffin Read the paper : Investigates the effects of scaling on fine-tuning LLMs, providing insights into data, model, and method impacts on bilingual tasks. When scaling meets LLM finetuning Read the paper Improving Robustness and Diversity in AI : Generates diverse adversarial prompts to enhance LLM robustness, employing an open-ended search method for prompt discovery. Rainbow Teaming Read the paper : Proposes a deterministic sampling technique for code generation, improving sample diversity and model performance in compiler optimization. Priority Sampling of Large Language Models for Compilers Read the paper Generative Models and Interactive Environments : Trains a generative model to create interactive virtual worlds from various inputs, advancing generative AI and simulation capabilities. Genie r Read the pape Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. Subscribe for free using the button below and be the first to read the latest stories. The goal of Turing Post is to equip you with comprehensive knowledge and historical insights, so you can make informed decisions about AI and ML. Join over 50,000 readers from the main AI labs, forward-thinking startups, and major universities 👇🏼

Explainable AI and Prompting a Black Box in the Era of Gen AI

From Chat-Bots to Killer-Bots?

How to Create a Blog using Golang

The field of Artificial Intelligence (AI) is built using neural networks that mimic the neural pathways in our own brains. Yet despite the powerful capabilities of current AI technology, its software can’t yet be classed as being independently intelligent. Further, the issue of hallucinations and black box phenomenons highlights the stark gap between the exciting results AI can deliver and our understanding of why computational models have reached a given outcome. This is also true with the human condition when neurodegenerative conditions strike with unpredictable impacts on the behavior of patients. Understanding these anomalies will be central to advancing our fundamental understanding of intelligence as a concept. This work is of such rising importance that a new field of study is forming, with the ‘Physics of Intelligence’ bridging the gap between computer science, brain science, and physics to better understand the universal mathematical principles that form intelligence. Now, this emerging field of study is set to receive a boost through the creation of an ambitious new fellowship program Fellowships for the Physics of Intelligence The NTT Research Foundation announced this week a gift to establish the Harvard University Center for Brain Science (CBS)-NTT Fellowship Program to support the research into the physics of intelligence. The two-year gift, renewable for up to three more years, will create a fund to support post-doctoral research in the physics of intelligence, an emerging field that uses physics to tackle fundamental questions in intelligence, bridging computer science, neuroscience, and psychology. If renewed, the donation could total over $1.7 million. Harvard University will have full control over the administration of the fund, which is set to be used to support two post-doctoral researchers, including support for technology, facilities, travel, and guest speakers for seminars. Although the Fellowship Program is newly established, Harvard CBS and the NTT Research Physics & Informatics Lab have been collaborating on similar themes since 2021 under a joint research agreement since 2024. The new program will amplify themes that emerged from shared research into natural and artificial intelligence. Physicists and AI researchers from the PHI Lab’s Intelligent Systems Group, led by Hidenori Tanaka, who is also a CBS associate, have established fruitful alliances with neuroscientists and psychologists at Harvard. “We are thrilled to support the Harvard Center for Brain Science at the dawn of the Physics of Intelligence,” NTT Research President and CEO Kazuhiro Gomi said. “As history teaches us, inventions – consider the steam engine, electricity, liquid crystals – can lead to new fields in physics.” “Today, AI is playing that role and giving us an opportunity to explore fundamental questions involving the science of intelligence, as well as address some urgent practical problems, like the need for computational systems that are unbiased, trustworthy and green. We believe this gift will advance those shared goals,” added the executive. A fundamental understanding of intelligence The Harvard CBS is pursuing an ambitious mission. Scientists affiliated with the Harvard CBS study the structure and function of neural circuits, the computations that govern thought and behavior, and how these circuits and patterns develop and vary. Harvard CBS Director Murthy said, “We’re grateful to the NTT Research Foundation for this generous gift, which comes on top of several years of unique intellectual contributions and collaboration. The Harvard CBS looks forward to developing ideas around the Physics of Intelligence through fellowships, seminars, talks, and other activities that both enhance and align with our diverse approach to neuroscience.” Shaping this ongoing academic cross-pollination is the dramatic rise of powerful AI, which provides a new subject of study for the science of intelligence. “The interdisciplinary environment at Harvard’s Center for Brain Science has been crucial in nurturing this emerging field, which harnesses physics-based approaches to investigate foundational questions about intelligence,” PHI Lab Scientist and Harvard CBS Associate Tanaka said. “By conducting quantitative experiments and theoretical modeling involving AI systems, which are more accessible to testing and measurement than the human brain, we aim to uncover the universal mathematical principles that underlie intelligence in all its forms, both natural and artificial.” In addition to its work on the CIM and initiatives in neuroscience, the PHI Lab is engaged in groundbreaking research into quasi-particles and non-linear optical materials. The PHI Lab also has a JRA with the NASA Ames Research Center. NTT Research opened its offices in July 2019 as a new Silicon Valley startup to conduct basic research and advance technologies that promote positive change for humankind. This article was published by Katie Konyn on The Sociable. The field of Artificial Intelligence (AI) is built using neural networks that mimic the neural pathways in our own brains. Yet despite the powerful capabilities of current AI technology, its software can’t yet be classed as being independently intelligent. Artificial Intelligence Further, the issue of hallucinations and black box phenomenons highlights the stark gap between the exciting results AI can deliver and our understanding of why computational models have reached a given outcome. This is also true with the human condition when neurodegenerative conditions strike with unpredictable impacts on the behavior of patients. computational models Understanding these anomalies will be central to advancing our fundamental understanding of intelligence as a concept. This work is of such rising importance that a new field of study is forming, with the ‘Physics of Intelligence’ bridging the gap between computer science, brain science, and physics to better understand the universal mathematical principles that form intelligence. Now, this emerging field of study is set to receive a boost through the creation of an ambitious new fellowship program Fellowships for the Physics of Intelligence Fellowships for the Physics of Intelligence The NTT Research Foundation announced this week a gift to establish the Harvard University Center for Brain Science (CBS)-NTT Fellowship Program to support the research into the physics of intelligence. The two-year gift, renewable for up to three more years, will create a fund to support post-doctoral research in the physics of intelligence, an emerging field that uses physics to tackle fundamental questions in intelligence, bridging computer science, neuroscience, and psychology. If renewed, the donation could total over $1.7 million. If renewed, the donation could total over $1.7 million. Harvard University will have full control over the administration of the fund, which is set to be used to support two post-doctoral researchers, including support for technology, facilities, travel, and guest speakers for seminars. Although the Fellowship Program is newly established, Harvard CBS and the NTT Research Physics & Informatics Lab have been collaborating on similar themes since 2021 under a joint research agreement since 2024. The new program will amplify themes that emerged from shared research into natural and artificial intelligence. Physicists and AI researchers from the PHI Lab’s Intelligent Systems Group, led by Hidenori Tanaka, who is also a CBS associate, have established fruitful alliances with neuroscientists and psychologists at Harvard. “We are thrilled to support the Harvard Center for Brain Science at the dawn of the Physics of Intelligence,” NTT Research President and CEO Kazuhiro Gomi said. “As history teaches us, inventions – consider the steam engine, electricity, liquid crystals – can lead to new fields in physics.” “Today, AI is playing that role and giving us an opportunity to explore fundamental questions involving the science of intelligence, as well as address some urgent practical problems, like the need for computational systems that are unbiased, trustworthy and green. We believe this gift will advance those shared goals,” added the executive. A fundamental understanding of intelligence A fundamental understanding of intelligence The Harvard CBS is pursuing an ambitious mission. Scientists affiliated with the Harvard CBS study the structure and function of neural circuits, the computations that govern thought and behavior, and how these circuits and patterns develop and vary. Harvard CBS Director Murthy said, “We’re grateful to the NTT Research Foundation for this generous gift, which comes on top of several years of unique intellectual contributions and collaboration. The Harvard CBS looks forward to developing ideas around the Physics of Intelligence through fellowships, seminars, talks, and other activities that both enhance and align with our diverse approach to neuroscience.” Shaping this ongoing academic cross-pollination is the dramatic rise of powerful AI, which provides a new subject of study for the science of intelligence. “The interdisciplinary environment at Harvard’s Center for Brain Science has been crucial in nurturing this emerging field, which harnesses physics-based approaches to investigate foundational questions about intelligence,” PHI Lab Scientist and Harvard CBS Associate Tanaka said. “By conducting quantitative experiments and theoretical modeling involving AI systems, which are more accessible to testing and measurement than the human brain, we aim to uncover the universal mathematical principles that underlie intelligence in all its forms, both natural and artificial.” In addition to its work on the CIM and initiatives in neuroscience, the PHI Lab is engaged in groundbreaking research into quasi-particles and non-linear optical materials. The PHI Lab also has a JRA with the NASA Ames Research Center. NTT Research opened its offices in July 2019 as a new Silicon Valley startup to conduct basic research and advance technologies that promote positive change for humankind. This article was published by Katie Konyn on The Sociable. This article was published by Katie Konyn on The Sociable. Katie Konyn The Sociable.

NTT Research Foundation, Harvard Announce Fellowship for Physics of Intelligence Research

এনটিটি রিসার্চ ফাউন্ডেশন, হার্ভার্ড বুদ্ধিমত্তা গবেষণার পদার্থবিজ্ঞানের জন্য ফেলোশিপ ঘোষণা করেছে

NTT Research Foundation und Harvard kündigen Stipendium für Forschung im Bereich der Intelligenzphysik an

NTT Research Foundation y Harvard anuncian una beca para la investigación en física de la inteligencia

La NTT Research Foundation et Harvard annoncent une bourse pour la recherche en physique du renseignement

एनटीटी रिसर्च फाउंडेशन, हार्वर्ड ने इंटेलिजेंस रिसर्च के भौतिकी के लिए फेलोशिप की घोषणा की

NTTリサーチ財団とハーバード大学が知能物理学研究のためのフェローシップを発表

NTT 연구 재단, 하버드, 지능 물리학 연구 펠로우십 발표

NTT Research Foundation e Harvard anunciam bolsa para pesquisa em física da inteligência

Исследовательский фонд NTT и Гарвард объявляют о стипендии для исследований в области физики интеллекта

NTT Araştırma Vakfı, Harvard Zeka Araştırmaları Fiziği Bursunu Duyurdu

Quỹ nghiên cứu NTT, Harvard công bố học bổng nghiên cứu vật lý về trí thông minh

NTT 研究基金会与哈佛大学宣布为智能物理学研究提供奖学金

After getting fed up with React, SPAs, and Javascript around 2021 I decided to re-write my webpage in Rust, after which on how to build a simple blog entirely with Rust. It ended up becoming one of my most popular articles and for good reason; Rust is exciting, fun to write, and blazingly fast. After a while though, I started to feel frustrated with the development process for adding new features to my site: the feedback loop was simply too long. I wrote an article I've always been interested in solo entrepreneurship and technology. But, as I'm getting older, I realize that I might have been more interested in trying out new technologies. Great for learning and growing as an engineer, bad for shipping projects, and starting to see that sweet MRR grow. I decided to re-re-write my site once again, this time in Go for multiple reasons: I write Go for a living Simple language, with a decent type system and fast performance Blazingly fast compile times In this post, I will show how you can create your blog using . I'll assume you're familiar with Go, and know how to configure a router/database/server, and so on. Should you not, feel free to grab a clone of my Go starter template that has lots of things configured for you out of the box. Go Grafto Foundations I write all my stuff in markdown; if you're a developer who also wants to start blogging, chances are you also are quite familiar with it. After Go 1.16 where we got the package included in the standard library most of our work is already done. We basically only need to have a method for storing some filenames, associating them with an ID or a slug, grabbing the file, and serving it to the user. Pretty simple. embed Whether you've created your own setup or grabbed a copy of Grafto, create a new directory in the root of your project called and in there, create a file called . posts posts.go Open it and add the following: package posts

// imports omitted

//go:embed *.md
var Assets embed.FS Now, any file with the extension will get included in the binary that ultimately gets built once we run . We can simply grab the files from our global variable using to handle any error that might occur or return the file as a string, e.g: .md go run build Assets Assets.ReadFile(name-of-file) file, err := Assets.ReadFile("my-post.md")
if err != nil {
	return err
}

return string(file) It won't be pretty (we'll fix that later), but it gets the basic idea out, which we can build on. We have a way to include our blog posts in the binary; let's add a way to associate them with a slug. You could use an ID here, but it just looks better to have URLs like: " " compared to " ". https://acme.com/my-first-blog-post http://acme.com/44a2530b-567d-472f-9495-e2ee64e7ae6d So, assuming you have a database up and running, add this table: create table posts (
    id uuid primary key,
    created_at timestamp not null,
    updated_at timestamp not null,
    title varchar(255) not null,
    filename varchar(255) not null,
    slug varchar(255) not null
); Not much exciting going on here. Basically, for the unaware readers, the slug column above will be the title but URL-friendly. So if you have a post with the title "My First Blog Post" the slug equivalent would be "my-first-blog-post" easy. Lastly, we need to be able to serve this to readers. That flow would typically involve them hitting a landing page showing a list of articles they can choose from, which links to the article. The implementation of this will depend a bit on your setup, but let us implement the handler to deal with grabbing a specific article using Echo as our router. Assuming you have a route like , create the following: /posts/:slug type ArticleStorage interface {
	GetPostBySlug(slug string) (Post, error)
}

func ArticleHandler(ctx echo.Context, storage ArticleStorage) error {
	postSlug := ctx.Param("postSlug")
	
	postModel, err := storage.GetPostBySlug(postSlug)
	if err != nil {
		return err
	}
	
	postContent, err := posts.Assets.ReadFile(postModel.Filename)
	if err != nil {
		return err
	}

	return ctx.String(http.StatusOK, string(postContent))
} I'm putting some decisions on your here in terms of implementing the ArticleStorage. We just need that grabs the data on the post from the DB, based on the slug. something This is the foundation of what we need...but it's not pretty let's fix that by letting the server do what it was always supposed to do: return HTML. Enter templ If you've spent time in the Go ecosystem, chances are you've probably heard about . It lets you write templates as Go packages and it's just such a pleasant way of building out a UI. Add some HTMX and alpine.js and you've at least 95% of what you get with SPAs, with the added complexity. templ HTML It's good practice to have a base template that wraps around your other templates, so we have a single point for adding things like stylesheets, javascript, metadata, etc. Create a directory in root called views and add the following to a file called . base.templ package views

templ base() {
	<!DOCTYPE html>
	<html lang="en">
		<nav>
			<a href="/">MBV Labs</a>
		</nav>
		<body>
			{ children... }
			<footer>
				<aside>
					<p>Copyright ©2024 </p>
					<p>All right reserved by MBV Labs</p>
				</aside>
			</footer>
		</body>
	</html>
} For this to work, you'll need to install templ and run which will produce a file called that we can then import into other templates to wrap around them. For the sake of brevity, we'll only create the template to show the actual article. templ generate base_templ.go Create a file called , and add the following: article.templ type ArticlePageData struct {
	Title             string
	Content           string
}

templ ArticlePage(data ArticlePageData) {
	@layouts.Base() {
		<div>
			<div>
				<h1>{ data.Title }</h1>
			</div>
			<article>
				@unsafe(data.Content)
			</article>
		</div>
	}
} and run once again. templ generate We can now go back and update our ArticleHandler handler: func ArticleHandler(ctx echo.Context, storage ArticleStorage) error {
	postSlug := ctx.Param("postSlug")
	
	postModel, err := storage.GetPostBySlug(postSlug)
	if err != nil {
		return err
	}
	
	postContent, err := posts.Assets.ReadFile(postModel.Filename)
	if err != nil {
		return err
	}

	return views.ArticlePage(
		views.ArticlePagedata{
			Title: postModel.Title,
			Content: postContent,
		},
	).Render(
		ctx.Request().Context(), 
		ctx.Response().Writer,
	)
} If you run your application now and visit a valid URL, you should see a (rather ugly) page showing the markdown of your article but this time, with some sweet hypertext markup. Making things (slightly) less ugly In terms of styling things, throwing some tailwind or vanilla CSS at what we have now will get you a long way. But, we still show raw markdown to the user when they visit our articles. Additionally, we might want to show some nicely formatted code snippets in our articles. Let's fix this now. For this, we need something that can transform the markdown into HTML components e.g ## Some sub header into <h2>Some sub header</h2> Luckily, a create library already exists for this: Goldmark. So let's refactor the file to parse the content we store using embed. posts/posts.go //go:embed *.md
var assets embed.FS // unexport assets

type Manager struct {
	posts embed.FS
	markdownParser goldmark.Markdown
}

func NewManager() Manager {
	md := goldmark.New(
		goldmark.WithParserOptions(
			parser.WithAutoHeadingID(),
			parser.WithAttribute(),
		),
		goldmark.WithRendererOptions(
			html.WithHardWraps(),
			html.WithXHTML(),
			html.WithUnsafe(),
		),
	)

	return Manager{
		posts:           assets,
		markdownHandler: md,
	}
}

func (m *Manager) Parse(name string) (string, error) {
	source, err := m.posts.ReadFile(name)
	if err != nil {
		return "", err
	}

	// Parse Markdown content
	var htmlOutput bytes.Buffer
	if err := m.markdownHandler.Convert(source, &htmlOutput); err != nil {
		return "", err
	}

	return htmlOutput.String(), nil
} Lastly, update the ArticleHandler to use the Manager: func ArticleHandler(
	ctx echo.Context, 
	storage ArticleStorage,
	postManager posts.Manager
) error {
	postSlug := ctx.Param("postSlug")
	
	postModel, err := storage.GetPostBySlug(postSlug)
	if err != nil {
		return err
	}
	
	postContent, err := postManager.Parse(postModel.Filename)
	if err != nil {
		return err
	}
	

	return views.ArticlePage(
		views.ArticlePagedata{
			Title: postModel.Title,
			Content: postContent,
		},
	).Render(
		ctx.Request().Context(), 
		ctx.Response().Writer,
	)
} Try and edit your article by adding some code blocks and they will now get nicely formatted. You can add custom themes to the parser so your code snippets will be shown with your favorite theme.

How to Build a Blog with Go

RootStock L2 : Pioneering Dapps on Bitcoin

Liquid Re-staking Tokens (LRTs) and the emergence of Layer2 solution Blast are powering a new wave in DeFi staking. In this article, we’ll look at the backdrop behind this development, what it means for DeFi staking and what are the major trends in the space. Liquid Re-staking Tokens: The Newest Innovation in LSDs Liquid staking derivatives (LSD) have been an area in the Ethereum ecosystem that has demonstrated exemplary innovation and adaptability, especially after the Shanghai upgrade. Lido Finance, a platform that played a critical role in refining the Ethereum staking mechanism, was where it all started. With its innovative solution, Lido unlocked the liquidity of staked assets in Ethereum, solving a major issue in the Ethereum staking mechanism. Lido allows users to earn staking rewards on Ethereum without locking up assets, marking a major shift from the traditional rigid paradigm of staking. Pendle Finance was the next to chart new territories in the LSD space. It pioneered yield tokenization on Ethereum, enabling users to trade yield and earn a fixed yield on their assets. This new model opened up new approaches for yield optimization and risk management. The quest for higher yields and fuller utilization of staked assets, however, has no end. Restaking protocol Eigenlayer emerged with a more complex model. Eigenlayer allows stakers to reallocate their staked assets to secure additional decentralized services on the Ethereum network. This not only increases stakers’ overall rewards but also enhances cryptoeconomic security and resilience of the entire Ethereum network. However, a major challenge emerged. Restaked assets became fragmented and illiquid, hampering the activity and composability in the DeFi space. This was where Liquid Restaking Tokens (LRT) came into play. This innovation unlocks the liquidity of restaked assets and further increases stakers’ rewards by enabling them to participate in DeFi. Users can deposit LRTs to liquid restaking protocols for additional earnings. With these developments, the LSD space has evolved from a starting point of single-layer staking into a more complex and multifaceted ecosystem. Some more recent happenings such as the emergence of Blast reflect this trend. Blast is an EVM-compatible Ethereum Layer 2 network that offers native yield staking of ETH and stablecoins on its chain. This has further democratized staking rewards and elevated the overall user experience by streamlining the process. The evolvement of the LSD space demonstrated the dynamic environment and adaptability of the Ethereum ecosystem where innovations are constantly being created to meet users’ needs for higher liquidity and yield while navigating the intricacy and risks of DeFi. DeFi’s Next Big Thing: LRTs In the realm of DeFi, the emergence of Liquidity Re-Staking Tokens represents a profound revolution in the traditional staking paradigm. LRTs represent a breakthrough not only by thinking out of the box of single-asset staking but also by introducing and facilitating the development of multi-protocol staking. Through EigenLayer, users can transcend the boundaries of a single protocol and diversify their rewards by staking ETH assets across multiple Actively Validated Services (AVS) on Eigenlayer, i.e. applications and networks that are secured via restaking. This significantly enhances network security and, in doing so, injects a more intricate layer of security into the whole DeFi ecosystem. The advantages embodied by LRTs are manifold: a significant improvement in capital efficiency, enhanced network security, and substantial savings for developers in resource allocation. However, we must remain vigilant about the accompanying risks, including potential penalty risks, protocol centralization concerns, and revenue dilution caused by fierce market competition. Regarding the market prospects of LRTs, we believe LRT protocols will have a tremendous allure for users with their high earnings potential. LRT protocols offer additional revenue streams for ETH stakers, encompassing not only the basic staking rewards but also rewards from EigenLayer and the potential benefits of token issuance. Moreover, through incentive measures such as airdrops, LRTs have the potential to attract a wider range of participants, thereby driving overall DeFi growth and creating a trend that could replicate or even surpass the heat of the DeFi summer of 2020. The trend is worth paying attention to. With LRT providing fresh impetus, the DeFi space is expected to see increased competition similar to the previous “Curve War”, and token economics may evolve toward more complex models. In the meantime, as more AVSs are launched, the additional rewards that they offer to LRT holders could become a key driver for user acquisition in the LRT space. veTokenomics In conclusion, LRTs have demonstrated their unique value in unlocking liquidity, enhancing yields, and optimizing governance and risk management. As such, LRTs are not only a staking tool but also a crucial catalyst for DeFi innovation and growth. Their development and application could bring profound changes to the DeFi ecosystem, opening new possibilities for users, developers, and the entire blockchain world. Associated Risks and Challenges Stader Labs has recently emerged as a dark horse with its launch of Ethereum mainnet’s first liquid restaking token rsETH garnering widespread attention. With rsETH, users will be able to “restake” liquid staked tokens such as Coinbase’s wrapped staked ETH (cbETH), Lido’s staked ETH (stETH), and Rocket Pool’s rETH on multiple different networks, minting a liquid token to represent their share in a restaking contract. This allows greater liquidity and flexibility of staked assets. With the support of EigenLayer, rsETH has successfully achieved large-scale restaking of ETH by streamlining the process for users entering the restaking ecosystem, further reinforcing the decentralization of the Ethereum network. However, such innovations also bring risks. Ethereum’s founder, Vitalik Buterin, and the co-founders of EigenLayer have both pointed out that restaking may lead to complex scenarios, posing a threat to the security of the main network. Additionally, with the proliferation of AVSs and LRTs, excessive dispersion of industry funds and attention could result in market instability and weakened governance structures. As an emerging force, Layer 2 network Blast added more vitality to the space with its unique automatic compounding feature. Its innovation lies in the design that it not only provides users with basic staking yields by working with liquid staking protocols such as Lido, but also enables users to receive additional yields from its auto-rebasing stablecoin USDB by working with protocols like MakerDAO. Although such a model is user-friendly and offers users more returns, owing to its impact on the TVL composition, it also brings in centralization concerns and increases its sensitivity to market risk. How Blast’s strategy influences the actual utilization of assets and the dynamics of capital flows within its ecosystem will be the key. Blast should aim for its TVL composition to shift more from capital preservation to asset growth so that it can maintain good network activity and sufficient liquidity in dApps. Only in this way can it truly achieve its goal of driving the growth of its blockchain network, rather than merely serving as a deposit platform. Overall, while both Stader Labs and Blast signify innovation and progress in the staking models of the DeFi sector, they are not without risks that we should remain vigilant about, to ensure the safety and well-being of the Ethereum network in the long run. A robust and sustainable balance needs to be reached between these innovations and risk management in the future. Other New Paradigms for Ethereum Staking We believe dynamic staking strategies like LRTs are having a profound impact on the Ethereum staking market. Firstly, the automation and efficiency of these strategies have lowered the investment threshold, attracting more individual investors. Secondly, as more funds flow in, market liquidity significantly improves, contributing to the stability of the entire DeFi ecosystem. Moreover, dynamic staking strategies, by offering diversified and customized investment options, are driving market innovation and development. However, implementing these strategies faces technical challenges, particularly in constructing efficient and accurate algorithmic models and ensuring system security and resilience against network attacks. Meanwhile, there are other new paradigms of staking under exploration. Let’s take a look. A new model known as the Layered Staking System emerged in response to a maturing market with diverse investor demands. This system distributes staked assets across different risk layers, each with varying profit potential and risk configurations. Low-risk layers offer relatively stable returns, suitable for risk-averse investors, while high-risk layers cater to those with a higher risk tolerance. The flexibility of this system allows investors to allocate assets based on their risk preferences and market forecasts. Implementing a Layered Staking System requires complex smart contracts and sophisticated risk management strategies to ensure liquidity and security between different layers. Its introduction provides investors with more choices and flexibility, propelling the Ethereum staking market towards maturity and diversification. Contract-based automated staking strategies are an interesting development too. They enhance asset management efficiency and effectiveness by automatically executing staking, unstaking, and asset reallocation. The risk management capability of automated staking strategies is enhanced through specific risk parameters set within smart contracts. Once market conditions trigger these parameters, smart contracts automatically adjust staking positions. Despite challenges such as ensuring the security and reliability of smart contracts, as the security of smart contracts strengthens with the advancement of technology, automated staking strategies are expected to see broader applications. Additionally, collective staking and profit-sharing models enable multiple investors to collectively stake their assets and share the generated profits proportionally. This model utilizes smart contracts to automatically distribute earnings, offering collaborative wealth growth opportunities for small-scale investors. Smart contracts play a pivotal role in collective staking by executing profit distribution and ensuring fair returns for each participant. Collective staking provides small-scale investors with opportunities to reduce risk and costs, enhancing inclusivity within the DeFi ecosystem. Lastly, linking staked assets with Real World Assets (RWAs) is another innovative approach in the DeFi space. Under this model, real-world assets such as real estate and artwork are tokenized on the blockchain and serve as the underlying assets for staked assets. This approach provides liquidity for typically illiquid assets and offers new sources of funding for owners of real-world assets. Although this model faces challenges such as accurate assessment and tokenization of real-world assets, as well as dealing with regulatory and compliance issues related to these assets, the future holds the promise of broader applications for linking staked assets with RWAs. This can provide traditional asset holders with new funding channels and offer cryptocurrency investors new investment opportunities, deepening the integration of cryptocurrency markets with traditional finance markets. In conclusion, the future of DeFi staking promises further advancements. It’s an area worth paying attention to for both investors and DeFi players.

The New Trend in DeFi Staking: Liquid Re-Staking Tokens and Blast

ডিফাই স্টেকিংয়ের নতুন প্রবণতা: লিকুইড রি-স্টেকিং টোকেন এবং ব্লাস্ট

Der neue Trend beim DeFi-Staking: Liquid Re-Staking Tokens und Blast

La nueva tendencia en las apuestas DeFi: tokens de reapuesta líquida y explosión

La nouvelle tendance du jalonnement DeFi : jetons de re-jalonnement liquide et explosion

डेफी स्टेकिंग में नया चलन: लिक्विड री-स्टेकिंग टोकन और ब्लास्ट

DeFi ステーキングの新しいトレンド: リキッド再ステーキング トークンとブラスト

DeFi 스테이킹의 새로운 트렌드: 유동적 재스테이킹 토큰 및 폭발

A nova tendência em staking DeFi: Tokens de re-staking líquidos e Blast

Новый тренд в стейкинге DeFi: ликвидные токены повторного стейкинга и Blast

DeFi Staking'de Yeni Trend: Liquid Re-Stake Tokenları ve Blast

Xu hướng mới trong đặt cược DeFi: Token đặt lại chất lỏng và vụ nổ

DeFi 质押新趋势：流动性代币再质押和爆炸

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Since n.d.

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