Global Work Visa Alternatives in 2025: H-1B Options, Canada, UK, Australia & More

2025 Guide: Alternatives to the H-1B and the Best Global Work Options

If you are looking beyond the H-1B? Here’s a clear, current, and complete guide to strong alternatives in the United States and the top work destinations worldwide, updated through 24 September 2025.


A) United States: Alternatives to H-1B (2025)

L-1 (Intracompany Transfer)

Who qualifies: Employees of multinational companies.
Core requirements: At least 1 continuous year outside the United States within the last 3 years, working for a parent, branch, subsidiary, or affiliate of the U.S. company; L-1A = manager/executive; L-1B = specialized knowledge.
Pros: No lottery; employer familiarity; L-1A can transition to EB-1C green card.
Cons / caveats: Initial validity up to 3 years (1 year for a new office); L-1A max 7 years, L-1B max 5 years; high scrutiny for “specialized knowledge.”

O-1 (Extraordinary Ability)

Who qualifies: Top performers in science, business, education, arts, athletics.
Core requirements: Sustained acclaim (e.g., awards, publications, memberships, press, judging, etc.).
Pros: No cap or lottery; strong prestige; initial 3-year validity.
Cons / caveats: Extensions are 1 year at a time; costly and evidence-heavy.

Cap-Exempt H-1B

Who qualifies: Roles at universities, nonprofit research organizations, or their qualifying affiliates.
Core requirements: Job offer from a cap-exempt employer.
Pros: Avoids the lottery; can file year-round; dual intent allowed.
Cons / caveats: Typically limited to nonprofit/academic environments; compensation may be lower.

E-1 / E-2 (Treaty Trader / Investor)

Who qualifies: Nationals of treaty countries.
Core requirements: E-1: substantial trade; E-2: substantial investment in a U.S. enterprise.
Pros: Renewable; entrepreneur-friendly.
Cons / caveats: India is not a treaty country (only usable with eligible dual citizenship).

E-3 (Specialty Occupations — Australians)

Who qualifies: Australian nationals.
Core requirements: Similar to H-1B plus LCA.
Pros: Functionally uncrowded; renewable; simpler processing.
Cons / caveats: Australians only.

F-1 (Student Visa)

Who qualifies: Students admitted to U.S. institutions; must maintain status.
Core requirements: OPT of 12 months; STEM OPT up to an extra 24 months (total up to 36 months).
Pros: U.S. education pathway; significant work authorization via OPT for STEM.
Cons / caveats: Tuition costs; not a direct work visa; must later transition.

J-1 (Exchange / Research / Training)

Who qualifies: Exchange visitors, interns, researchers, trainees via an approved sponsor.
Core requirements: Program sponsorship and funding.
Pros: Training/cultural exposure; some waiver options for the 2-year home residency rule.
Cons / caveats: Many categories trigger the 2-year home residency requirement unless waived.

EB-5 (Investor Green Card)

Who qualifies: Investors creating U.S. jobs.
Core requirements: Minimum investment $1,050,000 (or $800,000 in TEAs/infrastructure) creating 10 full-time jobs.
Pros: Direct route to a green card.
Cons / caveats: High capital; complex compliance; visa number availability can fluctuate mid-year.

EB-2 NIW (National Interest Waiver)

Who qualifies: Advanced degree or exceptional ability.
Core requirements: Show an endeavor of national importance; can self-petition.
Pros: No employer or PERM required; realistic for strong profiles.
Cons / caveats: It’s an immigrant petition; Indian nationals face long priority-date backlogs.

H-1B fee update (effective 21 September 2025):

All new H-1B petitions filed on or after 21 September 2025 must include an additional $100,000 fee. This applies to both cap-subject and cap-exempt filings.


B) Other Top Work Destinations (2025)

🇨🇦 Canada

  • Employer-specific permits (TFWP, LMIA-based).

  • Global Talent Stream (GTS): expedited LMIA (service standard ~10 business days) plus 2-week work permit processing for eligible roles under the Global Skills Strategy.

  • Open work permits: Post-Graduation Work Permit (PGWP), spousal open work permits, and more. (Master’s grads can qualify for a 3-year PGWP even if the program was under 2 years, provided it was at least 8 months.)

  • Path to PR: Express Entry (CEC/FSW) or Provincial Nominee Programs, typically after Canadian work experience.

🇬🇧 United Kingdom

  • Skilled Worker visa: Licensed sponsor, English, and salary meeting the higher of the occupation’s going rate or the general threshold of £41,700 (from 22 July 2025).

  • Discounted routes:

    • Immigration Salary List (ISL): certain roles can qualify at £33,400 (and specific going rate rules).

    • PhD tradeable points: lower salary floors apply in limited scenarios.

  • Health & Care visa: lower thresholds with NHS/care pay scales.

  • Graduate Route: 2 years (3 for PhD).

  • Global Talent visa: for leaders/potential leaders in academia, arts, and tech.

  • PR: Indefinite Leave to Remain usually after 5 years (route-specific rules apply).

🇦🇺 Australia

  • Skills in Demand (SID) visa (subclass 482, launched Dec 2024): employer-sponsored up to 4 years, with three streams — Specialist, Core, and Labour Agreement/Essential.

  • PR pathway: Employer Nomination Scheme (subclass 186), including Temporary Residence Transition enhancements.

  • Points-tested visas remain: 189 (independent) and 190 (state-nominated).

🇩🇪 Germany

  • EU Blue Card: job offer of ≥ 6 months. 2025 salary thresholds:

    • General: €48,300

    • Bottleneck/new entrants: €43,759.80

  • Opportunity Card (Chancenkarte): 1-year job-search residence via a 6-point system (education, experience, age, language, financial proof).

  • PR: Blue Card holders can apply for a settlement permit after 27 months (or 21 months with B1 German).

🇸🇬 Singapore

  • Employment Pass (EP): minimum qualifying salary from S$5,600 for new applications from 1 Jan 2025 (higher for financial services and with age-progression); assessed under COMPASS.

  • S Pass: mid-skilled; qualifying salary S$3,300 for new applications from 1 Sept 2025 (higher for financial services); levy harmonised at S$650 from 1 Sept 2025; subject to quotas.

  • ONE Pass: 5-year pass for top professionals (S$30,000/month or equivalent achievements).

  • PR: Work passes do not automatically lead to PR; separate application required.

🇦🇪 United Arab Emirates

  • Standard Work Visa: employer-sponsored, typically valid 1–3 years, renewable.

  • Green Visa: 5-year self-sponsored visa for skilled employees/freelancers; requires bachelor’s, skill level 1–3, and salary ≥ AED 15,000.

  • Golden Visa: 5–10 years for investors, entrepreneurs, students, and skilled professionals (common skilled-track salary benchmark ~AED 30,000/month).

  • PR: Long-term residence via Golden; Green and standard visas are renewable.

🇳🇱 Netherlands

  • Highly Skilled Migrant (HSM) via recognised sponsors; GVVA (combined permit) for stays > 90 days; TWV for short stays; Orientation Year (zoekjaar) for recent grads.

  • 2025 salary thresholds (valid 1 Jan–31 Dec 2025, gross/month excl. 8% holiday pay):

    • ≥30 yrs: €5,688

    • <30 yrs: €4,171

    • Reduced (graduates/zoekjaar): €2,989

    • EU Blue Card: €5,688 (€4,551 for recent graduates)

  • PR: Eligible after 5 years of continuous residence.

🇮🇪 Ireland

  • Critical Skills Employment Permit: shortage occupations at €38,000; other eligible roles at €64,000+. Two-year job offer required.

  • General Employment Permit: broader roles, Labour Market Needs Test, and the 50:50 EEA staff rule (with limited waivers).

  • Intra-company transfers also available.

  • PR: Stamp 4 residence generally after 2 years on Critical Skills; long-term residence usually after 5 years in the State.

🇳🇿 New Zealand

  • Accredited Employer Work Visa (AEWV): up to 5 years; accredited employer & job check. From 10 March 2025, the median-wage requirement was removed for AEWV settings (replaced with tailored thresholds and checks).

  • Green List residence pathways:

    • Tier 1: Straight-to-Residence (immediate eligibility in Tier-1 roles).

    • Tier 2: Work-to-Residence (apply after 24 months in Tier-2 roles).

  • Wage thresholds (residence routes): updated 18 August 2025 using the June 2024 median wage NZD $33.56/hour (role-specific rules apply).


Final Notes & Tips

  • Always check sponsor eligibility (where required) and whether the role meets the current pay and skill thresholds.

  • Dual intent (temporary → permanent) rules vary by country and visa type.

  • Processing times, fees, and quotas can change quickly, build in some buffer time for administrative updates and new policy announcements.

  • While creating this post we have tried to include as much latest and accurate information as possible but there are chances that few points may not be accurate so please check the official websites for latest and accurate information.


Official Sources for Latest Visa & Work Permit Updates

For the most accurate and up-to-date information, always refer to official government websites:


Vibe Coding Explained: The Future of Software Development with AI (2025 Guide)

Vibe Coding Explained: The Future of Software Development with AI (2025 Guide)

When I first heard Andrej Karpathy use the term vibe coding, I was intrigued.
Karpathy isn’t just anyone, he’s one of the most respected names in AI, known for shaping Tesla’s AI team and evangelizing deep learning. So when he tweeted about this new way of writing code where “you give in to the vibes and forget the code even exists,” I had to dig deeper.

And what I found blew my mind.

Vibe coding isn’t just a catchy phrase. It’s an emerging software development paradigm that shifts the developer’s role from writing every line of code to orchestrating AI, expressing intent, validating results, and iterating fast.

Let’s break it down.



What Is Vibe Coding? (Clear Definition)

Vibe coding is natural-language driven programming, where a human describes what they want in plain English (or any language), and an AI model generates working code that fulfills that intent.

Researchers call it a reconfiguration of intent mediation, we no longer “command” the computer line-by-line but instead “collaborate” with AI to achieve the outcome. The cognitive load shifts from syntax and boilerplate to design thinking and oversight.



How Vibe Coding Works (Step-by-Step)

Here’s how a typical vibe coding workflow looks:

  1. Describe the Goal:
    I might type:
    “Build a simple to-do list app with title, description, and due date. Add an option to mark tasks as done.”

  2. AI Generates the Code:
    The tool (ChatGPT, Claude, Cursor IDE, or Replit Ghostwriter) writes the HTML, CSS, and JS for me.

  3. Run & Observe:
    I check how the app looks and behaves.

  4. Refine Through Prompts:
    “Make the layout mobile-friendly, add a dark mode toggle, and ensure tasks are saved in local storage.”

  5. Repeat Until Done:
    In 2–3 iterations, I often have a polished MVP.

This process feels more like guiding a very fast, tireless junior developer but one who occasionally makes mistakes and needs correction.



Why Everyone’s Talking About It

The hype around vibe coding is justified, here’s why:

  • ⚡ Speed: You get to a working prototype 5–10× faster.

  • 🎨 Creativity: Frees up mental bandwidth to focus on design and UX instead of syntax.

  • 🌍 Democratization: Product managers, designers, and even founders without coding experience can build apps.

  • 🚀 Momentum: Seeing results so quickly is motivating — I recently built a newsletter analytics dashboard in two hours that I’d been procrastinating for weeks.

Y Combinator reports that 25% of startups in its Winter 2025 batch had codebases that were 95% AI-generated. Clearly, this is more than a toy.



The Dark Side: Challenges and Risks

But it’s not all sunshine and vibes:

  • Messy, Hard-to-Maintain Code: AI sometimes produces unstructured or redundant code. Technical debt can creep in early.

  • Security Vulnerabilities: You may unknowingly ship code that’s insecure (e.g., SQL injection risks, unsafe API calls).

  • Compliance Headaches: Auditing AI-generated code for regulated industries (finance, healthcare) is still a challenge.

  • Debugging Friction: When something breaks, figuring out why can take longer if you never read the code.

  • Skill Atrophy: Relying 100% on AI can make your fundamentals rusty over time.

Enterprises need governance models, code reviews, CI/CD pipelines, automated security scans before adopting vibe coding at scale.



Vibe Coding vs. Agentic AI

People often confuse vibe coding with agentic AI (AI agents that autonomously plan, execute, and deploy tasks). Here’s the difference:

Vibe CodingAgentic AI
Human-in-the-loopMostly autonomous
Conversational, prompt-drivenGoal-driven, self-directed
Best for prototyping, creative buildsBest for automation, repetitive workflows
Requires active guidanceRequires careful guardrails

Think of vibe coding as you pair-programming with AI, while agentic AI is closer to delegating the entire job to a virtual employee.



Best Practices for Responsible Vibe Coding

Over the past few months, I’ve experimented a lot. Here’s what works:

  • Write clear prompts: Specify frameworks, libraries, constraints (e.g., “use React + Tailwind”).

  • Test after every iteration: Don’t trust blindly, run the code frequently.

  • Review at least high-level logic: Even a quick skim can reveal glaring issues.

  • Version control is your friend: Commit each AI-generated change so you can roll back.

  • Document your prompts: Helps recreate or explain decisions later.

  • Automate checks: Use linters, code scanners, and security tools before pushing to production.



Real-World Examples & Lessons

  • Kevin Roose (NYT Tech Journalist): Built a lunch-scheduling app via vibe coding, functional but buggy, showing that human review is crucial.

  • Enterprise Teams: Internal hackathons use vibe coding to build proof-of-concepts in days. However, compliance and security teams flag the need for post-generation review before deployment.

  • Students: Research shows beginners rely heavily on testing and rarely read generated code which raises questions about long-term skill development.



Future of Vibe Coding

I see three big shifts coming:

  1. Integrated Workflows: IDEs will natively support vibe coding, with testing and debugging auto-wired.

  2. AI Co-Testers: AI won’t just write code, it will write unit tests, run them, and suggest fixes.

  3. New Roles: We may see “AI Software Orchestrators” or “Prompt Architects” who specialize in guiding AI to build reliable software.



My Take: How You Should Approach It

I don’t see vibe coding as a threat, I see it as an amplifier.

It won’t replace developers, but it will replace developers who don’t adopt it.

If you’re a developer or IT professional, here’s my advice:

  • Start with small projects (internal tools, hobby projects).

  • Learn to prompt like a pro, your clarity becomes your superpower.

  • Build your own prompt library and style guide.

  • Keep learning core fundamentals so you can catch bad AI output when you see it.



FAQs on Vibe Coding 

Q: Is vibe coding production-ready?

A: For prototypes: yes. For enterprise production: only with reviews, tests, and security audits.

Q: Can non-developers use vibe coding?

A: Yes, but having basic programming knowledge will help you guide the AI better.

Q: Will vibe coding kill programming jobs?

A: No. It will create demand for developers who can guide AI, review outputs, and integrate them into robust systems.



Final Thoughts

Vibe coding is one of the most exciting developments in software since cloud computing.

It makes coding faster, more creative, and more accessible but it also raises questions about security, maintainability, and developer skill.

In my view: Don’t ignore this trend. Play with it. Learn its strengths and weaknesses. Become the person in your team who knows how to guide AI effectively.

Because in the near future, being AI-literate will be just as important as being code-literate.


If you found this useful, then most miss our most viewed article on Hidden Job Market Opportunities, access it here for FREE => Hidden Job Market Opportunities

Future of IT Outsourcing Jobs: India’s IT Jobs Shift to AI, Cloud, SaaS and Beyond

Is IT Outsourcing Dead? The Real Shift in India’s IT Jobs

I’ve spent over 20 years in the IT industry. A few of those years were in the US, but most of my career was in India. Over this time, I’ve been part of almost every stage of the software development life cycle — from requirements gathering and design to development, testing, and support.

And here’s the truth: all of this was possible because of outsourcing. Outsourcing gave me (and millions of others) the opportunity to work with global clients, learn modern tools, and build careers that our parents’ generation could only dream of.

So when I talk about the future of outsourcing, I’m not speaking as an outsider. I’ve seen it closely, lived it day in and day out, and felt both the opportunities and the challenges.


How Outsourcing Changed India

In the 2000s and 2010s, outsourcing was India’s golden era. The model was simple:

  • Global companies wanted to cut costs.

  • India had a young, English-speaking, educated workforce.

  • Work that was repetitive but essential — call centers, application support, manual testing, routine coding — was moved here.

These jobs didn’t just build companies, they built cities. They gave millions of families upward mobility, exposure to global work culture, and a sense of pride in being part of India’s IT rise.

I still remember how exciting it was to work with teams in the US while sitting in Bangalore or Pune. That was the power of outsourcing.


Why Those Roles Are Fading

But the same model that created those opportunities is now under pressure.

  • Automation is taking over repetitive processes.

  • Cloud platforms have reduced the need for traditional maintenance.

  • AI tools and copilots can generate code, fix bugs, and even answer customer queries.

Outsourcing won’t vanish overnight — companies like TCS, Infosys, and Wipro will continue to manage huge projects and support contracts for years. But the growth — the new jobs being created — is no longer in those traditional areas.


The New Roles Taking Shape

The opportunities of tomorrow look very different from the ones that built my career. We’re now seeing demand for:

  • AI engineers, data scientists, and machine learning specialists

  • Cloud architects and DevOps experts

  • Cybersecurity professionals

  • Product engineers and full-stack developers

  • Domain-specific specialists in fintech, health-tech, climate-tech, and defense

  • Freelance and global remote workers — AI trainers, SaaS consultants, app developers

In simple terms: the jobs are fewer, but they are far more valuable. They pay better, offer global exposure, and require deeper expertise.


Follow the Money Trail

If you want to know where your career should head, look at where the investment is flowing:

  • SaaS: Zoho, Freshworks, Postman

  • Fintech: Paytm, PhonePe, Razorpay

  • Health-tech: Practo, PharmEasy

  • Cybersecurity: exploding global demand

  • Semiconductors: India’s chip design push

  • Climate-tech: green IT and renewable energy

This is where the jobs of the next decade will be created.


India’s Global Role Is Changing

When I started my career, India was mostly executing instructions from abroad. Today, the story is very different.

We’re building digital public infrastructure like UPI, Aadhaar, and CoWIN — platforms that other countries now want to replicate. We’re creating SaaS companies that sell worldwide. And we’re taking a seat at the table in AI governance, cybersecurity, and digital policy on a global stage.

That’s not “back-office work.” That’s leadership.


What This Means for Working Professionals

Here’s the honest reality:

  • The mass hiring of the outsourcing era won’t come back.

  • The future lies in fewer but higher-quality jobs.

  • Change won’t happen overnight — outsourcing will stay for years — but the shift is real.

So, what can you do?

  1. Reskill in AI, cloud, data, or cybersecurity.

  2. Move closer to growth sectors like SaaS, fintech, and semiconductors.

  3. Adopt a global mindset — compliance, governance, collaboration across cultures.

  4. Build visibility — share your knowledge, showcase your expertise, don’t stay invisible.


Looking Ahead

Yes, there are also early experiments with quantum computing, Web3, and the metaverse. But for most professionals, the real opportunities today are in AI, cloud, data, cybersecurity, and sector-focused tech.

The outsourcing wave gave us quantity.
The next wave will reward quality and depth.

I’ve seen outsourcing change lives, including mine. Now I believe the next decade will belong to those who prepare early, learn continuously, and align with the industries driving growth.

The question is: are you preparing now, or waiting until the shift is complete?

For more, refer this YouTube video: https://youtu.be/Qm14U6SYrTw



LLMOps Explained: How Large Language Models Are Managed and Why It Matters for Your Career

 LLMOps Explained: The New Must-Have Skill in the AI Job Market

Large Language Models (LLMs) like ChatGPT, Claude, and Gemini are changing how we work. But who’s managing these powerful tools behind the scenes?

Enter: LLMOps, a rapidly growing field that every tech-savvy professional should understand.

In this post, we’ll break down what LLMOps is, why it matters, what tools are involved, and how you can start exploring this space even if you’re not from a Machine Learning background.



What Are LLMs?

LLMs or Large Language Models are AI systems trained on massive amounts of text data to understand and generate human-like language. Think:

  • ChatGPT answering your queries

  • Claude summarizing long PDFs

  • Google Gemini assisting with search or content generation

These models don’t “know” like humans do, but they’ve learned statistical patterns in language, which makes them remarkably effective at generating relevant and coherent responses.



But Training an LLM Is Not the End Goal

While much of the buzz is around training LLMs, in real-world companies, the bigger challenge is:

➡️ How do you deploy these models safely, efficiently, and at scale?

This is where LLMOps comes in.



What Is LLMOps?

LLMOps is short for Large Language Model Operations. It’s like DevOps for LLMs, the entire set of tools, practices, and workflows that support the deployment, monitoring, and maintenance of LLMs in production environments.

Key responsibilities of LLMOps include:

  • Version Control: Managing different versions of models and prompts

  • Prompt Engineering: Designing, testing, and tuning prompts for optimal results

  • Performance Monitoring: Tracking accuracy, speed, token usage, and hallucination rates

  • Governance: Ensuring compliance, safety, bias checks, and ethical use

  • Scalability: Deploying LLMs across cloud infrastructure, with cost and speed in mind

  • Feedback Loops: Continuously improving model behavior based on real usage data



Why Should You Care?

You may not be training your own LLM today, but many companies are integrating LLMs into:

  • Customer support chatbots

  • Internal knowledge bases

  • Product recommendation engines

  • Code generation tools

  • Workflow automation platforms

If you're in Software Development, QA, Data Engineering, DevOps, Product Management, or even Business Analysis, you’re likely to interact with LLM-powered systems soon (if you haven’t already).

Understanding LLMOps gives you a competitive edge.



Real-World Example: LLMOps in Action

Let’s say a fintech company builds a chatbot that answers customer queries using an LLM.

Here’s how LLMOps fits in:

TaskWho Handles ItWhat’s Involved
Prompt TuningPrompt EngineerWriting effective instructions for accurate answers
MonitoringLLMOps/MLOpsTracking wrong responses, latency, token cost
GovernanceAI Ethics/ComplianceFiltering sensitive content, managing GDPR requests
OptimizationDevOps + LLMOpsCaching frequent queries, controlling compute costs
Fine-tuningML EngineersAdjusting the base model for specific domain accuracy

This is not theoretical, these roles and responsibilities are showing up in job postings today.


What Tools Power LLMOps?

Here are some popular tools and frameworks used in LLMOps today:

CategoryTool/PlatformUse Case
Prompt ManagementPromptLayer, LangChainLogging, testing, and managing prompts
Experiment TrackingWeights & Biases, MLflowMonitoring LLM performance, tuning
DeploymentFastAPI, KServe, Docker, KubernetesPackaging and deploying LLM apps
Feedback/MonitoringHelicone, OpenAI Usage AnalyticsUsage tracking and alerting
Fine-TuningHugging Face, LoRA, QLoRADomain-specific training and adaptation

You don’t need to master all of these, but knowing what they are and how they fit into the LLM lifecycle is key.



Careers in LLMOps: What’s Emerging?

Roles to watch:

  • LLMOps Engineer: A DevOps-like role focusing on running LLMs in production

  • Prompt Engineer: Designs prompts for accurate, ethical, and context-aware outputs

  • AI Product Owner: Manages LLM-based product workflows, business alignment

  • AI QA Specialist: Validates and monitors output quality and relevance

  • Data Pipeline Engineers for LLMs: Prepares clean and optimized input/output flows

These are not futuristic roles, you’ll find many of these in job listings already from companies like Microsoft, OpenAI, Anthropic, Cohere, and enterprise tech teams across sectors.



How to Get Started (Even Without an ML Background)

If you’re not a data scientist, here’s how to dip your toes into LLMOps:

  1. Understand the Ecosystem
    Read case studies of how companies use LLMs in real-world applications.

  2. Try Prompt Engineering
    Use ChatGPT or Claude to create multi-step workflows. Try prompt tuning.

  3. Learn the Tools
    Visit LangChain’s docs or try setting up a simple logging app with PromptLayer.

  4. Watch Job Descriptions
    Search LinkedIn or Google Jobs for “LLMOps,” “Prompt Engineering,” and “AI Product.”

  5. Follow AI Product Builders
    People building GPT apps are often early adopters of LLMOps tools. Follow them on Twitter/X, Substack, or GitHub.



Final Thoughts

LLMOps is not just another tech buzzword.

It’s a fast-growing, high-impact field that bridges the gap between AI research and real-world application and the entry points are open for smart, curious professionals from all domains.

If you want your career to stay relevant and future-proof in the AI era, this is a space worth exploring.


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Have questions or want a deep dive into a specific LLMOps topic?
Drop them in the comments or write to me,  I’d love to hear from you.

Before we sign off, here is a video I published last week on LLM Ops...    

YouTube Video - LLM Ops Explained


Global Work Visa Alternatives in 2025: H-1B Options, Canada, UK, Australia & More

2025 Guide: Alternatives to the H-1B and the Best Global Work Options If you are looking beyond the H-1B? Here’s a clear, current, and comp...