2025-11-03 06:44:38
Tokyo, Japan, October 31st, 2025/Chainwire/--In a landmark evolution, the ARCS (ARX) project has transitioned from visionary foundations to tangible, real-world impact. Launched in 2019 with a bold mission to empower data sovereignty, enabling individuals to control and monetize their personal data, gaining fair economic value from it, ARCS has now entered its ARCS 2.0 phase.
This strategic pivot integrates blockchain with physical assets, forging a decentralized economic ecosystem that bridges digital innovation and everyday utility. Recent milestones affirm the team's unwavering trajectory toward a sustainable, user-centric future.
ARCS 1.0 introduced a groundbreaking "data bank" model, rewarding users with ARX tokens for anonymous data contributions. While pioneering, it faced hurdles: the "cold start" challenge of simultaneous user and enterprise adoption, limited real-world use cases, and regulatory uncertainties.
These insights crystallized a core truth that true value demands practical application. ARCS 2.0 responds decisively, anchoring the token in high-frequency, verifiable economic activity.
At the forefront of ARCS 2.0 is a transformative July 2025 partnership with SSG Holdings Co., Ltd. and its subsidiary Sun Sun House Co., Ltd., both based in Tokyo, to integrate traditional Japanese kominka homes with blockchain technology.
This partnership marks the beginning of a new phase for ARCS, where it bridges the gap between traditional real estate and blockchain utility. The partnership aims to create an ecosystem where ARCS tokens are used for property transactions, rentals, and loyalty rewards, thereby providing a real-world utility to the token.
This alliance reimagines kominka, Japan's historic pre-WWII wooden homes, as global investment and hospitality assets, with ARX as the exclusive settlement currency.
Kominka's appeal, rustic authenticity, natural harmony, and immersive experiences tap into booming tourism trends. Japan's inbound travel has surged post-pandemic, with the vacation rental market expanding 145% year-over-year in 2023 (Japan Tourism Agency).
Valued at $1.91 billion USD, this segment favors privacy and locality, positioning ARCS as a functional bridge to a $2.14 trillion property market.
ARCS 2.0 transforms ARX into a token built for active use, circulation, and real-world integration. Every function is tied to verifiable transactions within the ecosystem, ensuring value is created, spent, and rewarded in a self-sustaining loop.
Minting authority remains with the ARCS team, executed transparently and with disciplined supply control. All new ARX enters circulation solely through ecosystem activity like lodging, memberships, and data interactions, and never through speculative or unanchored distribution.
This structure powers a dual flywheel:
Web3 enhances real estate with immutable transparency, democratizing global access and streamlining transactions.
Key initiatives include:
Market response underscores confidence: ARX has trended upward since June 2025, reflecting trust in this utility-driven model.
ARCS2.0 is steadily advancing toward its goal of creating a decentralized economic ecosystem, with a focus on real-world assets, data banks, and sustainable growth.
By fusing Japanese heritage with blockchain technology, through partnership with SSG Holdings, the team delivers a usable token that circulates value across physical and digital realms.
Supported by national policy, market tailwinds, and stakeholder incentives, ARCS is on track to achieve its vision of a decentralized and sustainable economic future.
The ARCS project is dedicated to empowering individuals by establishing data sovereignty, where users manage their own data as a sacred asset and rightfully enjoy its value.
At the heart of ARCS2.0 lies the fusion of a Real World Asset (RWA) ecosystem and a secure data bank, creating a robust platform that prioritizes user control and benefits.
At ARCS, they believe in data sovereignty, where individuals have the right to control and benefit from their own data. ARCS' platform is designed to empower users, provide practical value, and drive growth through our native token, ARX.
The native token, ARX, plays a pivotal role in this ecosystem by serving as a means for discounted payments at accommodation facilities and as a reward for service usage.
This not only provides clear practical value to users but also drives engagement and growth within the platform. By turning real-world economic activity into a self-reinforcing growth cycle, or "flywheel", ARCS creates a dynamic environment where users can thrive.
Through its innovative approach, ARCS is poised to revolutionize the way data is managed and valued. By putting users at the center and offering tangible benefits, ARCS is building a platform that empowers individuals and fosters a community driven by data sovereignty and economic opportunity.
Users can follow updates on ARCS' socials:
Website: https://www.arcs-chain.com
Medium: https://medium.com/arcs-arx-official
X (formerly Twitter): https://x.com/ARCS_HQ
Telegram: https://t.me/ARCS_ARX_EN
Whitepaper: https://www.arcs-chain.com/whitepaper_en.pdf
Arcs Team
IFA Co., Ltd.
:::tip This story was published as a press release by Btcwire under HackerNoon’s Business Blogging Program. Do Your Own Research before making any financial decision.
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2025-11-03 05:05:22
Momentum is heating up fast for Mutuum Finance (MUTM), a rising DeFi crypto that’s redefining decentralized credit systems. The project’s Presale Phase 6 is already 80% sold out, showing overwhelming investor demand as it gears up for the much-anticipated Testnet launch of its lending and borrowing protocol.
Priced at just $0.035, MUTM is attracting attention from analysts, calling it one of the best crypto to buy right now before its next presale phase pushes the price higher.
With only 20% of tokens remaining in Phase 6 and the mainnet roadmap already in motion for 2025, investors are racing to secure allocations ahead of what could be one of the most anticipated DeFi crypto launches of the next bull run.
As the crypto market shifts its focus toward utility-driven projects, Mutuum Finance stands out as a next-generation DeFi crypto combining innovation, real use cases, and early-stage growth potential. With its presale nearing sellout and product testing about to begin, MUTM may be the best crypto to buy for early entry opportunity with tangible on-chain value, a combination that historically defines future market leaders.
Mutuum Finance (MUTM) continues to capture massive attention across the DeFi landscape, reinforcing its standing as one of the top crypto projects of 2025 and a strong contender to become the next token to reach $1. Now advancing through Phase 6 of its presale, MUTM is priced at $0.035, a 20% increase from the previous phase, marking the final opportunity for investors to join before the next price rise.
With Phase 7 set to push the price up to $0.04, investor anticipation has reached fever pitch as participants rush to secure positions ahead of the next milestone. To date, Mutuum Finance has attracted over 17,600 investors and raised more than $18.25 million in total contributions.
With Phase 6 already more than 80% complete, the surging demand highlights growing market confidence in Mutuum Finance’s long-term value and its strong potential as both a short-term profit opportunity and a lasting DeFi crypto investment. Positioned at the intersection of real-world utility and high-growth potential, MUTM continues to solidify its reputation as the best crypto to buy heading into 2025.
Mutuum Finance is preparing to enter a major development milestone with the upcoming launch of its decentralized lending and borrowing protocol on the Sepolia testnet in Q4 2025. Designed to merge efficiency with innovation, the platform integrates on-chain functionality with token-based operations to provide a user-friendly experience for both lenders and borrowers.
Investors will be able to deposit assets and earn passive yield through mtTokens, tokenized receipts that automatically accrue value over time. Borrowers can leverage ETH or USDT as collateral to access liquidity without selling their holdings, while also earning additional MUTM incentives by staking mtTokens.
The Sepolia testnet rollout marks a defining phase in Mutuum Finance’s roadmap, allowing the team to stress-test its risk management framework, optimize lending algorithms, and refine its interest rate models before mainnet deployment.
This approach underscores Mutuum Finance’s commitment to transparency, scalability, and long-term ecosystem integrity. Beyond a record-breaking presale, Mutuum Finance is proving itself as a fully realized DeFi crypto, one built to redefine decentralized finance and potentially emerge as the next $1 crypto of 2025, making it the best crypto to buy for investors seeking early exposure.
For more information about Mutuum Finance (MUTM) visit the links below:
Website: https://mutuum.com/
Linktree: https://linktr.ee/mutuumfinance
:::tip This story was published as a press release by Btcwire under HackerNoon’s Business Blogging Program. Do Your Own Research before making any financial decision.
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2025-11-03 03:02:12
My current product has hundreds of APIs. Every time I need to refer to the API specification, I have to navigate to the Swagger link, scroll endlessly, or use browser search to find what I need—and then manually filter things out. It’s frustrating and painfully slow. Even worse, every developer who needs to integrate with the API has to go through the same experience.
This frustration led me to explore how AI could improve the process. This post is a deep dive into that journey and how it evolved into something simple, robust, and effective.
As a first step, I reviewed entire api documentation to make sure it has clear summary and description details in the swagger docs. This is a critical step for better discoverability in later stages. I also made sure the summary and description are unique for each api.
paths": {
"/users/accounts/{account-id}": {
"put": {
"tags": [
"Account API"
],
"summary": "Update Test suite by test-suite-id",
"externalDocs": {
"description": "Robust get account details description",
"url": "https://mydocs.com/description"
},
\
With hundreds of APIs available, it can be challenging to identify which ones are related. Categorizing the APIs makes management more efficient and later simplifies selecting the right API based on natural language input. This categorization was implemented using the tagging concept defined in the OpenAPI specification.
\
"paths": {
"/users/accounts/{account-id}": {
"put": {
"tags": [
"Account API"
],
"summary": "Update Test suite by test-suite-id",
"externalDocs": {
"description": "Robust get account details description",
"url": "https://mydocs.com/description"
},
\
Users would enter the natural language question related to the API.
Example: How to retrieve account details?
At this stage, the question and all the available categories are sent to the LLM. The LLM is tasked to return the high-level category the question falls into. The output of this step is one of the categories.
Based on the LLM’s identified category, the system sends another request to the model with the same question — but this time, it includes all API details within that specific category detected in the previous step.
This is where the earlier preparation pays off: the more descriptive and well-structured the API documentation, the better the results. Clear descriptions help the LLM accurately determine which API the user is requesting information about.
The output of this step is a single, specific API.
The OpenAPI specification of the API is then provided to the LLM to generate a detailed, context-rich description of the API alongside the original question.
For example, if the user asks, “How can I retrieve account details using an account ID?”, the response will include the relevant specification details of the Account API.
With the system’s enhanced ability to accurately detect the appropriate API, users can now go a step further — generating code snippets to interact with various APIs directly.
For example:
“Share Python code to call the Get Account Details API for a given ID.”
“Provide a cURL command to fetch account details by ID.”
“Generate a Go client to retrieve account details for a specific ID.”
\
As the number of available APIs continues to grow, exploring and managing them requires a new approach. With the rise of AI agents powered by large language models (LLMs), developers now have a more intuitive and efficient way to discover and interact with APIs—saving countless hours previously spent searching for the right endpoints.
The potential doesn’t stop there. This concept can evolve into a standalone product capable of seamlessly ingesting OpenAPI specifications at runtime and exposing them through a natural language interface—offering users an out-of-the-box solution for API exploration.
Hopefully, this article has illustrated how to leverage LLMs effectively and how well-structured API documentation can create a smoother, more intelligent discovery experience.
\n \n
\n \n
2025-11-03 02:30:04
I taught myself HTML a long time ago, on a software called HotDog (Pro?). There wasn't such a thing as What You See Is What You Get capabilities at the time. However, HotDog had an amazing feature: the toolbar had all HTML tags (there weren't that many at the time) as buttons, and you could learn them by clicking on them and watching the results. The only downside was that you had to click on another button to close the tag.
\ Then came Dreamweaver. It was the first WYSIWYG editor, and it immediately became very popular. People who had no clue about HTML started to use it: the number of websites skyrocketed. I used it once and looked at the generated HTML. Having learned to write HTML "by hand", I found it generated by Dreamweaver overtly verbose. I continued to write my HTML by hand or with the help of IDEs.
\ Fast forward fifteen years. HTML went beyond websites to be an ubiquitous format that Sir Berners-Lee wouldn't have dreamed it would become. Coupled with web browsers, it went on to become the medium for web applications. Yet, it didn't stop there. Even at the time, I was amazed that JavaDocs generated HTML. Python docstring? Generates HTML. Ruby rdoc? Generates HTML. Rust rustdoc? Generates HTML.
\
While you can write HTML snippets, e.g., in JavaDocs, writing HTML by the line becomes a bore quite fast. It's easy to miss a slash in the closing tag, or to get a <table> right on the first attempt, especially if it involves spans. Yet, most documentation doesn't need the full power of HTML, especially the kind brought by its more modern versions.
\ To address this issue, John Gruber and Aaron Swartz invented Markdown in 2004.
Markdown is a lightweight markup language for creating formatted text using a plain-text editor. John Gruber created Markdown in 2004 as an easy-to-read markup language.
-- Markdown
\ Markdown took the world by storm. I think that it's even more popular than HTML, at least in the tech world. It's been available on GitHub for ages. Java integrated it into its JavaDocs in version 25. And nowadays, LLMs use Markdown for input and output!
Markdown is amazing, but it has strong limitations. I wrote my latest book with Markdown, in the Doc-as-Code tradition. The experience was so painful that I vowed never to do it again.
\ Here's a simple excerpt from my book-writing experience. I wanted to feature code snippets to illustrate my points. However, I wanted them to be valid: compiled and tested. I wrote a project, complete with a build configuration.
\ Yet, Markdown doesn't allow the inclusion of external files. I had to copy and paste the required snippets. Worse, because writing a book takes some time, I kept the version of the dependencies up-to-date. Each time I did, I had to copy-paste the updated code at every occurrence. Interestingly enough, documentation aimed at developers has the same use case.
\ Another widespread limitation is wanting to draw attention to an item. Here's a styling option:
Markdown doesn't offer anything similar. For this reason, Python Markdown does. What about other languages? I don't know; I wish it were part of Markdown.
\
Though there are many more, my final point will be about tables. Markdown handles tables, up to a point, which is column headers. In my blog posts, I regularly need rowspan and colspan, which aren't supported. Fortunately, HTML is valid Markdown. However, the formatting inside the table, code, etc., must be transformed into HTML along with it.
Asciidoc is the solution to the above limitations.
AsciiDoc is a plain text markup language for writing technical content. It’s packed with semantic elements and equipped with features to modularize and reuse content. AsciiDoc content can be composed using a text editor, managed in a version control system, and published to multiple output formats.
-- Asciidoc
\ Let's see how Asciidoc addresses limitations one by one:
An include directive imports content from a separate file or URL into the content of the current document. When the current document is processed, the include directive syntax is replaced by the contents of the include file. Think of the include directive like a file expander.
\
There are certain statements you may want to draw attention to by taking them out of the content’s flow and labeling them with a priority. These are called admonitions. This page introduces you to admonition types AsciiDoc provides, how to add admonitions to your document, and how to enhance them using icons or emoji.
\
Asciidoc has a specification as well as a TCK, both managed by the Eclipse Foundation. Note, though, that Asciidoctor is the sole implementation of Asciidoc. The Asciidoctor site is actually hosting the Asciidoc documentation. For most intents and purposes, you can treat them as one, as I do.
\
I listed above how Asciidoc solves three limitations I regularly encounter in Markdown, but it provides many more benefits. Tons of features are available. Here are a couple of them, which I use regularly:
A sample is more descriptive than a complete description:
[source]
----
video::RX9zwgHuNmA[youtube,width=840,height=473]
----
[quote,'https://asciidoc.org/[Asciidoc^]']
____
AsciiDoc is a plain text markup language for writing technical content.
____
[%collapsible]
====
This content is only revealed when the user clicks the block title.
====
Materializing menus and buttons is another useful feature for workshops and technical documentation in general:
Click on the OK button.
Go to the Text > Text Filters > JSON item.
\
With the right toolchain, you can generate HTML from Asciidoc and publish the result on GitHub/GitLab Pages. Here's a non-trivial example that showcases several features:
A tool is only as useful as its surrounding ecosystem. Here are a couple of such tools and benefits:
README.adoc and watch the magic happen. Here's a non-exhaustive list of Asciidoc integration items with GitHub.Asciidoctor Diagram is a set of Asciidoctor extensions that enable rendering of plain text diagrams that are embedded in your AsciiDoc document as part of the Asciidoctor conversion process.
I use it a lot with PlantUML. You might have noticed it on this blog already.
Asciidoc allows you to generate regular HTML documents, but Reveal.js enables the creation of slide-based presentations. When I taught at university, I used both regular HTML for seminar works and slides for courses. My old Java EE course is available as a GitHub Pages site (in French).
\ Icing on the cake, you can leverage the diagram integration.
Markdown is everywhere, and I'm more than happy if it meets your needs. I had several experiences where it didn't meet my expectations: technical documentation, workshops, courses, and book writing. Asciidoc is the perfect tool to fill Markdown's gaps.
\ I hope this post gave you enough arguments to try it.
To go further:
Originally published at A Java Geek on October 26th, 2025
2025-11-03 01:45:03
There’s more need for open data infrastructure for AI than ever. In this article, we would love to share our learnings from the past, what has changed, what is broken, and why we decided to work on CocoIndex (https://github.com/cocoindex-io/cocoindex) - a next-generation data pipeline built for AI-native workloads — designed from the ground up to handle unstructured, multimodal, and dynamic data and an open system, at scale.
Traditionally, people build data frameworks heavily in this space to prepare data for humans. Over the years, we’ve seen massive progress in analytics-focused data infrastructure. Platforms like Spark and Flink fundamentally changed how the world processes and transforms data, at scale.
\ But with the rise of AI, entirely new needs — and new capabilities — have emerged. A new generation of data transformations is now required to support AI-native workloads.
The new patterns require capabilities to
\ On top of all this, we need to think about:
It’s not something that can be fixed with small patches to existing data pipelines. Many traditional frameworks fall short in several key ways:
\ With so many limitations, developers start to handle AI-native data “natively”, with hand-written Python, and wrap it in orchestrations. Begin from a demo, then start to worry about scale, tolerating backend failures, picking up from where it left off when pipelines break, rate limiting and backpressure, building tons of manual integrations when data freshness is needed, making sure stale input data is purged from output, and all of the issues are hard to handle when it runs at scale, and things start to break.
\ There are so many things to “fix”, and patching existing systems doesn’t work anymore. A new way of thinking about it, from the ground up, is needed.
Users declare "what", we take care of "how". Once the data flow is declared, you have a production-ready pipeline - infrastructure setup (like creating target tables with the right schema, and schema upgrades), data processing and refresh, fault tolerance, rate limiting, and backpressure, batching requests to backends for efficiency, etc.
Most traditional data pipelines treat data processing as transient things – they terminate as long as all data has been processed. If anything changes (data or code), you process it again from scratch. We treat pipelines as live things with memory of existing states and only perform necessary reprocessing on data or code changes, making the output data continuously reflect the latest input data and code.
\ This programming model is essential for AI-native data pipelines. It unlocks out-of-the-box incremental processing - the output data is continuously updated as the input data and code change with minimal computation, and provides the ability to trace the lineage of the data for explainable AI.
The output of pipelines is data derived from source data via certain transformation logic, so each row of data created by the pipeline can be traced back to the specific rows or files from the data source, plus certain pieces of logic. This is essential for refreshing the output on data or logic updates, and also makes the output data explainable.
The schema of data created by each processing step is determined at pipeline declaration time with validation – before running on specific items of data. This catches issues earlier and enables automatic inference of the output data schema for automatic target infrastructure setup.
An open system that allows developers to dock their choice of ecosystem as building blocks. AI agents should be tailored to specific domains, and there will be different technology choices, from source to storage to domain-specific processing. It has to be an open data stack that is easily customizable and brings in the user's own building blocks.
\ With the rapid growth of the ecosystem - new sources, targets, data formats, transformation building blocks, etc., the system shouldn’t be bound to any specific one. Instead of waiting for specific connectors to be built, anyone should be able to use it to create their own data pipelines—assembling flexible building blocks that can work directly with internal APIs or external systems.
We believe this AI-native data stack must be open.
\ The space is moving too fast — closed systems can’t keep up. Open infrastructure enables:
\ It should be something everyone can contribute to, learn from, and build upon.
CocoIndex fits seamlessly into the broader data ecosystem, working well with orchestration frameworks, agentic systems, and analytical pipelines. As an open, AI-native data infrastructure, it aims to drive the rise of the next generation of applied AI.
We are just getting started. AI is notoriously bad at writing data infrastructure code. The abstractions and data feedback loop, and programming model from CocoIndex are designed deliberately, thought from the ground up, and are tailored for the AI co-pilot.
\ This unlocks the full potential of CocoIndex on the path of a self-driving data pipeline, with data auditable and controllable along the way.
\ :rocket: To the future of building!
Thank you, everyone, for your support and contributions to CocoIndex. Thank you so much for your suggestions, feedback, stars, and for sharing the love for CocoIndex.
\ We are especially grateful for our beloved community and users. Your passion, continuous feedback, and collaboration as we got started have been invaluable, helping us iterate and improve the project every step of the way.
\ Looking forward to building the future of data for AI together!
\ ==:star: Star CocoIndex on GitHub here to help us grow!==
2025-11-03 01:30:45
The United Kingdom’s gig economy is going from strength to strength, and at a time when more businesses are wary of tightening operational budgets, the appeal of freelancers has never been stronger. But how can your startup make itself an attractive prospect for the world’s brightest digital nomad talent?
In the wake of the pandemic, the appeal of freelancers is growing as remote work becomes one of the most significant impacts of the new normal workplace.
Solo self-employed workers contributed £366 billion to the UK economy last year, marking a significant rise from the £331 billion recorded in 2023. As more startups and small businesses navigate an uncertain economic climate, gig economy workers offer a critical layer of protection against onboarding new hires when cash flow is far from assured.
Last year’s Autumn Budget also created fresh challenges that many startups are still coming to terms with. Increases in national insurance contributions (NICs) for employers have been linked to a slower jobs market and a fall in employer vacancies nationwide.
Because the cost of a bad hire could threaten the stability of many startups in these conditions, digital nomads offer a greater degree of flexibility that can help to drive more sustainable growth for smaller businesses. But how can employers attract the most talented freelancers in their sector?
Let’s take a deeper look at the importance of getting the rules of attraction right in the gig economy and explore five effective ways to grow your appeal to skilled remote workers:
There’s no getting away from it. Digital nomads want fair pay for the work they do and can tap into a global network of employers to get the pay that they feel they deserve.
This means that you have little chance of wooing freelancers with low-ball offers and weak benefits packages, and instead you should allocate more time towards creating a pay structure that boosts your appeal to skilled prospects.
Getting compensation right can be challenging when it comes to hiring remote workers. For instance, if one freelancer is based in Berlin and the other in Mumbai, the different local living costs will significantly impact salary expectations and the amount of money needed to cover daily expenses. Striking a balance between remote salaries for different locations and worker needs is crucial.
Depending on your industry, the best digital nomads could be more likely to be found in certain global regions. If you’re finding competitive compensation packages too challenging to come up with, consider benefits packages that include perks like mobile plans or even stock options, where applicable.
Always be as transparent as possible when it comes to listing remote jobs. Remote work is far more flexible than in-house positions, and this means that there can be a lot of grey areas that need filling when advertising for a role online.
As well as listing clear job requirements such as education, certifications, responsibilities, and core competencies to clearly establish expectations, you should also clearly seek to answer key questions, like the type of vacancy you’re advertising for, expected hours, any core business hours that could be impacted by time zones, travel required, and whether freelancers would need to spend time in-house.
By clearly listing what your job requires, you can ensure that no time is wasted for either party in the hiring process.
Nobody wants to join a startup with a drab company culture, but how can you showcase why you’re such an enjoyable business to work for?
In your job posting, be sure to highlight your startup’s values, whether it’s a commitment to innovation, transparency, collaboration, or inclusivity. To support your values, share stories and behind-the-scenes looks at your workplace, and add employee interviews to your company website. This means that when candidates research your startup, they’ll be able to envision themselves as part of your team.
Employee testimonials always go a long way in building social proof for prospective hires, and they can help digital nomads learn what a typical day would be like working for your startup. For remote workers based overseas, this approach can be a particularly impactful measure.
Because freelancers may never walk through the doors of your office for as long as they work for you, you must show them you care about their efforts in the same way as you care for your in-house staff.
This means creating a benefits package that motivates them to value you as an employer and remain loyal to your startup.
Because digital nomads crave job security, creating a paid time off (PTO) scheme for remote hires can be especially appealing and shows that you’re keen to care for your employees, whether they’re hired to support one specific project or are kept on the payroll on an ongoing basis.
Implementing a freelancer PTO scheme shows talented digital nomads that you care about their efforts and are ready to support them while they work for you.
When attracting the world’s best remote talent, you must build a strong presence across a variety of channels.
Making use of social media can be especially beneficial. You may already have utilised niche job search boards to broaden your access to the global talent pool, but keeping your Facebook, Instagram, LinkedIn, and X accounts up to scratch can make all the world of difference for freelancers looking for new opportunities online.
Yes, be sure to post new vacancies on social media, but also focus on creating a positive and vibrant presence online that supports your company culture. When digital nomads search for your startup on social networks, you’ll need them to be bowled over by how enjoyable working for your business appears to be.
If your startup has already created a fun and vibrant workplace culture, your task is to simply replicate this appeal online for all to see.
Creating a positive online presence through your website and on social media shouldn’t be too time-consuming, but ensuring that all your workers can chip in with testimonials and multimedia content can be a major help.
Fundamentally, digital nomads crave fair compensation packages and on-the-job benefits that can support their more flexible working lifestyles. Deploying a fair pay package that caters to local expectations while showcasing your appeal on the world stage can really help to put your startup in good standing when showing why it’s the best business to work for when it comes to talented digital nomads.
