Crypto Basics

Top 10 Crypto Portfolio Trackers - Complete List

Discover the top 10 crypto portfolio trackers. Effortlessly manage and optimize your digital assets for maximum returns.
Token Metrics Team
7 Minutes
MIN

Cryptocurrencies have gained significant popularity in recent years, attracting both investors and enthusiasts. As the crypto market continues to expand, it becomes essential for individuals to effectively manage and track their crypto portfolios. 

This article explores the top 10 crypto portfolio trackers available in the market, providing insights into their features, benefits, and functionalities. Let's start with the basics -

What is a Crypto Portfolio Tracker?

Crypto portfolio trackers are software applications or online platforms that allow users to manage and monitor their cryptocurrency investments in a centralized and organized manner.

These trackers provide real-time information on portfolio performance, asset allocation, transaction history, and market trends. 

By using a crypto portfolio tracker, investors can make informed decisions, track profits, and streamline their crypto investment strategies.

Importance of Tracking Crypto Portfolios

Tracking a crypto portfolio is crucial for several reasons. Firstly, it provides an overview of the performance of different cryptocurrencies within the portfolio, enabling users to identify which assets are performing well and which ones may require adjustments. 

Secondly, it helps investors stay updated with the latest market trends, news, and price fluctuations, allowing them to make timely investment decisions. Lastly, tracking portfolios simplifies tax reporting, as it provides accurate records of transactions and holdings.

List of Best Crypto Portfolio Trackers

In recent time, several crypto portfolio trackers gained significant popularity and widespread usage among investors. Here is the list of few popular crypto portfolio trackers -

1. Token Metrics

Token Metrics crypto portfolio tracker is a comprehensive and sophisticated tool designed to help crypto investors effectively manage and track their digital assets. 

With its intuitive interface and robust features, the portfolio tracker enables users to seamlessly monitor their investments across multiple exchanges and wallets in real-time. 

Token Metrics crypto portfolio tracker

This powerful platform offers detailed analytics, performance metrics, and customizable alerts, empowering investors to make informed decisions based on accurate and up-to-date data. 

The portfolio tracker also provides deep insights into individual cryptocurrency holdings, including historical performance, allocation breakdowns, and risk analysis.

2. Coingecko

CoinGecko is more than just a portfolio tracker; it's a comprehensive cryptocurrency platform that provides market data, news, and insights.

CoinGecko crypto portfolio tracker

With its portfolio management feature, users can track their investments and monitor performance in real-time. CoinGecko also offers a wide range of metrics and charts to help users make informed decisions.

3. CoinMarketCap

CoinMarketCap stands as a highly reputable cryptocurrency price-tracking platform globally. Additionally, the company provides investors with a portfolio tracking solution.

Coinmarketcap crypto portfolio tracker

Although the platform's functionality is somewhat limited, it presents a viable choice for budget-conscious traders who prefer the manual input of their cryptocurrency holdings.

4. Crypto.com

Crypto.com is primarily a crypto exchange but also offers portfolio tracking for hosted wallets. Users can track the past and real-time prices of their invested coins through the mobile app or website.

Crypto.com portfolio tracker

The platform allows tracking of top gainers and losers, newly added coins, and watchlist coins, providing comprehensive data such as price, volume, and market cap with a 7-day chart. It is available on both iOS and Android, and supports NFTs.

5. CoinTracker

CoinTracker simplifies crypto portfolio management by automatically syncing transactions from exchanges and wallets.

Coin Tracker crypto portfolio tracker

It offers tax calculation tools, performance tracking, and insightful analytics. CoinTracker supports over 300 exchanges and integrates with popular wallets like Coinbase and MetaMask.

6. CryptoCompare

CryptoCompare is a reliable and user-friendly portfolio tracker that offers a holistic view of your crypto holdings.

CryptoCompare portfolio tracker

It provides detailed information on prices, market trends, and trading volumes, allowing users to make data-driven decisions. CryptoCompare also offers a wide range of educational resources, making it a valuable tool for beginners.

7. CoinStats

CoinStats is a comprehensive portfolio tracker that offers real-time data on over 3,000 cryptocurrencies. With its intuitive interface and advanced features, CoinStats allows users to monitor their portfolio performance, set price alerts, and track market trends.

CoinStats portfolio tracker

Its integration with various exchanges and wallets makes it a convenient choice for crypto enthusiasts.

8. Kubera

Kubera presents itself as a portfolio tracker specifically designed to cater to a wide range of assets, including cryptocurrencies and NFTs. Unlike offering a free option, Kubera offers personal plans starting at $150 per year.

Kubera crypto portfolio tracker

Kubera also offers features like track multiple assets, automatic and manual import options and identifying top performers.

9. Delta Investment Tracker

Delta is a powerful portfolio management tool that supports over 300 exchanges and cryptocurrencies.

Delta Investment Tracker

Its sleek design and customizable features make it a popular choice for both beginners and experienced traders. With Delta, users can sync multiple wallets and exchanges, set price alerts, and access comprehensive analytics.

10. Crypto Pro

Crypto Pro, originally launched in 2015 as a Bitcoin ticker for the Apple Watch, has evolved into a comprehensive cryptocurrency tracking app.

Crypto Pro portfolio tracker

With support for over 5,000 cryptocurrencies and compatibility with the entire Apple ecosystem, users can conveniently monitor their portfolios, receive live price updates, read news, and set price alerts. 

The app ensures privacy by encrypting user data stored locally on each device, without employing any user analytics or tracking solutions.

How to choose the best crypto portfolio tracker?

When selecting a crypto portfolio tracker, it's essential to consider several factors to ensure it meets your specific needs. Some key factors to consider include:

Security: Look for trackers that prioritize strong security measures, such as encryption, two-factor authentication, and cold storage options.

Supported Exchanges: Check if the tracker supports the exchanges you use to trade cryptocurrencies.

User Interface and Experience: Choose a tracker with an intuitive interface that provides a user-friendly experience.

Compatibility: Ensure the tracker is compatible with the devices and operating systems you use.

Alerts and Notifications: Look for trackers that offer customizable alerts and notifications for price changes, portfolio milestones, and market news.

Reporting and Analytics: Consider trackers that provide detailed reports, analytics, and visualizations to help you understand your portfolio's performance.

Portfolio Synchronization: Opt for trackers that can sync with your wallets and exchanges to automatically update portfolio data.

Customer Support: Check if the tracker offers responsive customer support channels in case you encounter any issues.

FAQs

Q1. Which crypto portfolio tracker is best for beginners?

For beginners, CoinMarketCap and CoinGecko are excellent choices. Both offer user-friendly interfaces, comprehensive features, and educational resources to help beginners navigate the crypto world with ease.

Q2. Can I track my portfolio across multiple exchanges?

Yes, many portfolio trackers like CoinStats, Delta, and CoinTracker support syncing transactions from multiple exchanges, allowing you to conveniently track your holdings in one place.

Q3. Do these portfolio trackers offer mobile apps?

Yes, most of the top portfolio trackers mentioned in this article, including CoinStats, Delta, and CoinGecko, offer mobile apps for both iOS and Android devices, enabling you to track your portfolio on the go.

Q4. Are these portfolio trackers secure?

Yes, reputable portfolio trackers prioritize security and employ various measures like encryption, two-factor authentication, and data protection protocols to ensure the safety of your portfolio data.

Q5. Can I use these portfolio trackers for tax reporting?

Yes, portfolio trackers like CoinTracker provide tax reporting features, allowing you to generate reports and calculate your cryptocurrency gains or losses for tax purposes.

Q6. Are there any free portfolio tracker options available?

Yes, several portfolio trackers offer free versions with basic features and limited capabilities. However, most also offer premium plans with additional features for users who require more advanced portfolio management tools.

Conclusion

In conclusion, tracking and managing your cryptocurrency portfolio is crucial for successful investing. These portfolio trackers offer a range of features and benefits to help you stay on top of your investments. 

Whether you're a beginner or an experienced trader, these portfolio trackers offer user-friendly interfaces, real-time data, customizable features, and integration with exchanges and wallets. 

By choosing the right portfolio tracker, you can effectively monitor your holdings, analyze market trends, set alerts, and make informed decisions to maximize your crypto investment potential.

Disclaimer

The information provided on this website does not constitute investment advice, financial advice, trading advice, or any other sort of advice and you should not treat any of the website's content as such.

Token Metrics does not recommend that any cryptocurrency should be bought, sold, or held by you. Do conduct your own due diligence and consult your financial advisor before making any investment decisions.

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Building High-Performance APIs with FastAPI

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FastAPI has rapidly become a go-to framework for Python developers who need fast, async-ready web APIs. In this post we break down why FastAPI delivers strong developer ergonomics and runtime performance, how to design scalable endpoints, and practical patterns for production deployment. Whether you are prototyping an AI-backed service or integrating real-time crypto feeds, understanding FastAPI's architecture helps you build resilient APIs that scale.

Overview: What Makes FastAPI Fast?

FastAPI combines modern Python type hints, asynchronous request handling, and an automatic interactive API docs system to accelerate development and runtime efficiency. It is built on top of Starlette for the web parts and Pydantic for data validation. Key advantages include:

  • Asynchronous concurrency: Native support for async/await lets FastAPI handle I/O-bound workloads with high concurrency when served by ASGI servers like Uvicorn or Hypercorn.
  • Type-driven validation: Request and response schemas are derived from Python types, reducing boilerplate and surface area for bugs.
  • Auto docs: OpenAPI and Swagger UI are generated automatically, improving discoverability and client integration.

These traits make FastAPI suitable for microservices, ML model endpoints, and real-time data APIs where latency and developer velocity matter.

Performance & Scalability Patterns

Performance is a combination of framework design, server selection, and deployment topology. Consider these patterns:

  • ASGI server tuning: Use Uvicorn with Gunicorn workers for multi-core deployments (example: Gunicorn to manage multiple Uvicorn worker processes).
  • Concurrency model: Prefer async operations for external I/O (databases, HTTP calls). Use thread pools for CPU-bound tasks or offload to background workers like Celery or RQ.
  • Connection pooling: Maintain connection pools to databases and upstream services to avoid per-request handshake overhead.
  • Horizontal scaling: Deploy multiple replicas behind a load balancer and utilize health checks and graceful shutdown to ensure reliability.

Measure latency and throughput under realistic traffic using tools like Locust or k6, and tune worker counts and max requests to balance memory and CPU usage.

Best Practices for Building APIs with FastAPI

Adopt these practical steps to keep APIs maintainable and secure:

  1. Schema-first design: Define request and response models early with Pydantic, and use OpenAPI to validate client expectations.
  2. Versioning: Include API versioning in your URL paths or headers to enable iterative changes without breaking clients.
  3. Input validation & error handling: Rely on Pydantic for validation and implement consistent error responses with clear status codes.
  4. Authentication & rate limiting: Protect endpoints with OAuth2/JWT or API keys and apply rate limits via middleware or API gateways.
  5. CI/CD & testing: Automate unit and integration tests, and include performance tests in CI to detect regressions early.

Document deployment runbooks that cover database migrations, secrets rotation, and safe schema migrations to reduce operational risk.

Integrating AI and Real-Time Data

FastAPI is commonly used to expose AI model inference endpoints and aggregate real-time data streams. Key considerations include:

  • Model serving: For CPU/GPU-bound inference, consider dedicated model servers (e.g., TensorFlow Serving, TorchServe) or containerized inference processes, with FastAPI handling orchestration and routing.
  • Batching & async inference: Implement request batching if latency and throughput profiles allow it. Use async I/O for data fetches and preprocessing.
  • Data pipelines: Separate ingestion, processing, and serving layers. Use message queues (Kafka, RabbitMQ) for event-driven flows and background workers for heavy transforms.

AI-driven research and analytics tools can augment API development and monitoring. For example, Token Metrics provides structured crypto insights and on-chain metrics that can be integrated into API endpoints for analytics or enrichment workflows.

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What is FastAPI and when should I use it?

FastAPI is a modern Python web framework optimized for building APIs quickly using async support and type annotations. Use it when you need high-concurrency I/O performance, automatic API docs, and strong input validation for services like microservices, ML endpoints, or data APIs.

Should I write async or sync endpoints?

If your endpoint performs network or I/O-bound operations (database queries, HTTP calls), async endpoints with awaitable libraries improve concurrency. For CPU-heavy tasks, prefer offloading to background workers or separate services to avoid blocking the event loop.

What are common deployment options for FastAPI?

Common patterns include Uvicorn managed by Gunicorn for process management, containerized deployments on Kubernetes, serverless deployments via providers that support ASGI, and platform-as-a-service options that accept Docker images. Choose based on operational needs and scaling model.

How do I secure FastAPI endpoints?

Implement authentication (OAuth2, JWT, API keys), enforce HTTPS, validate inputs with Pydantic models, and apply rate limiting. Use security headers and monitor logs for suspicious activity. Consider using API gateways for centralized auth and throttling.

How should I monitor and debug FastAPI in production?

Instrument endpoints with structured logging, distributed tracing, and metrics (request latency, error rates). Use APM tools compatible with ASGI frameworks. Configure health checks, and capture exception traces to diagnose errors without exposing sensitive data.

How do I test FastAPI applications?

Use the TestClient from FastAPI (built on Starlette) for endpoint tests, and pytest for unit tests. Include schema validation tests, contract tests for public APIs, and performance tests with k6 or Locust for load characterization.

Disclaimer: This article is educational and technical in nature. It explains development patterns, architecture choices, and tooling options for API design and deployment. It is not financial, trading, or investment advice. Always conduct independent research and follow your organizations compliance policies when integrating external data or services.

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Building High-Performance APIs with FastAPI

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FastAPI has emerged as a go-to framework for building fast, scalable, and developer-friendly APIs in Python. Whether you are prototyping a machine learning inference endpoint, building internal microservices, or exposing realtime data to clients, understanding FastAPI’s design principles and best practices can save development time and operational costs. This guide walks through the technology fundamentals, pragmatic design patterns, deployment considerations, and how to integrate modern AI tools safely and efficiently.

Overview: What Makes FastAPI Fast?

FastAPI is built on Starlette for the web parts and Pydantic for data validation. It leverages Python’s async/await syntax and ASGI (Asynchronous Server Gateway Interface) to handle high concurrency with non-blocking I/O. Key features that contribute to its performance profile include:

  • Async-first architecture: Native support for asynchronous endpoints enables efficient multiplexing of I/O-bound tasks.
  • Automatic validation and docs: Pydantic-based validation reduces runtime errors and generates OpenAPI schemas and interactive docs out of the box.
  • Small, focused stack: Minimal middleware and lean core reduce overhead compared to some full-stack frameworks.

In practice, correctly using async patterns and avoiding blocking calls (e.g., heavy CPU-bound tasks or synchronous DB drivers) is critical to achieve the theoretical throughput FastAPI promises.

Design Patterns & Best Practices

Adopt these patterns to keep your FastAPI codebase maintainable and performant:

  1. Separate concerns: Keep routing, business logic, and data access in separate modules. Use dependency injection for database sessions, authentication, and configuration.
  2. Prefer async I/O: Use async database drivers (e.g., asyncpg for PostgreSQL), async HTTP clients (httpx), and async message brokers when possible. If you must call blocking code, run it in a thread pool via asyncio.to_thread or FastAPI’s background tasks.
  3. Schema-driven DTOs: Define request and response models with Pydantic to validate inputs and serialize outputs consistently. This reduces defensive coding and improves API contract clarity.
  4. Version your APIs: Use path or header-based versioning to avoid breaking consumers when iterating rapidly.
  5. Pagination and rate limiting: For endpoints that return large collections, implement pagination and consider rate-limiting to protect downstream systems.

Applying these patterns leads to clearer contracts, fewer runtime errors, and easier scaling.

Performance Tuning and Monitoring

Beyond using async endpoints, real-world performance tuning focuses on observability and identifying bottlenecks:

  • Profiling: Profile endpoints under representative load to find hotspots. Tools like py-spy or Scalene can reveal CPU vs. I/O contention.
  • Tracing and metrics: Integrate OpenTelemetry or Prometheus to gather latency, error rates, and resource metrics. Correlate traces across services to diagnose distributed latency.
  • Connection pooling: Ensure database and HTTP clients use connection pools tuned for your concurrency levels.
  • Caching: Use HTTP caching headers, in-memory caches (Redis, Memcached), or application-level caches for expensive or frequently requested data.
  • Async worker offloading: Offload CPU-heavy or long-running tasks to background workers (e.g., Celery, Dramatiq, or RQ) to keep request latency low.

Measure before and after changes. Small configuration tweaks (worker counts, keepalive settings) often deliver outsized latency improvements compared to code rewrites.

Deployment, Security, and Scaling

Productionizing FastAPI requires attention to hosting, process management, and security hardening:

  • ASGI server: Use a robust ASGI server such as Uvicorn or Hypercorn behind a process manager (systemd) or a supervisor like Gunicorn with Uvicorn workers.
  • Containerization: Containerize with multi-stage Dockerfiles to keep images small. Use environment variables and secrets management for configuration.
  • Load balancing: Place a reverse proxy (NGINX, Traefik) or cloud load balancer in front of your ASGI processes to manage TLS, routing, and retries.
  • Security: Validate and sanitize inputs, enforce strict CORS policies, and implement authentication and authorization (OAuth2, JWT) consistently. Keep dependencies updated and monitor for CVEs.
  • Autoscaling: In cloud environments, autoscale based on request latency and queue depth. For stateful workloads or in-memory caches, ensure sticky session or state replication strategies.

Combine operational best practices with continuous monitoring to keep services resilient as traffic grows.

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FAQ: How fast is FastAPI compared to Flask or Django?

FastAPI often outperforms traditional WSGI frameworks like Flask or Django for I/O-bound workloads because it leverages ASGI and async endpoints. Benchmarks depend heavily on endpoint logic, database drivers, and deployment configuration. For CPU-bound tasks, raw Python performance is similar; offload heavy computation to workers.

FAQ: Should I rewrite existing Flask endpoints to FastAPI?

Rewrite only if you need asynchronous I/O, better schema validation, or automatic OpenAPI docs. For many projects, incremental migration or adding new async services is a lower-risk approach than a full rewrite.

FAQ: How do I handle background tasks and long-running jobs?

Use background workers or task queues (Celery, Dramatiq) for long-running jobs. FastAPI provides BackgroundTasks for simple fire-and-forget operations, but distributed task systems are better for retries, scheduling, and scaling.

FAQ: What are common pitfalls when using async in FastAPI?

Common pitfalls include calling blocking I/O inside async endpoints (e.g., synchronous DB drivers), not using connection pools properly, and overusing threads. Always verify that third-party libraries are async-compatible or run them in a thread pool.

FAQ: How can FastAPI integrate with AI models and inference pipelines?

FastAPI is a good fit for serving model inference because it can handle concurrent requests and easily serialize inputs and outputs. For heavy inference workloads, serve models with dedicated inference servers (TorchServe, TensorFlow Serving) or containerized model endpoints and use FastAPI as a thin orchestration layer. Implement batching, request timeouts, and model versioning to manage performance and reliability.

Disclaimer

This article is educational and technical in nature. It does not provide investment, legal, or professional advice. Evaluate tools and design decisions according to your project requirements and compliance obligations.

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Fast, Reliable APIs with FastAPI

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Fast API design is no longer just about response time — it’s about developer ergonomics, safety, observability, and the ability to integrate modern AI services. FastAPI (commonly referenced by the search phrase "fast api") has become a favored framework in Python for building high-performance, async-ready APIs with built-in validation. This article explains the core concepts, best practices, and deployment patterns to help engineering teams build reliable, maintainable APIs that scale.

Overview: What makes FastAPI distinct?

FastAPI is a Python web framework built on top of ASGI standards (like Starlette and Uvicorn) that emphasizes developer speed and runtime performance. Key differentiators include automatic request validation via Pydantic, type-driven documentation (OpenAPI/Swagger UI generated automatically), and first-class async support. Practically, that means less boilerplate, clearer contracts between clients and servers, and competitive throughput for I/O-bound workloads.

Async model and performance considerations

At the heart of FastAPI’s performance is asynchronous concurrency. By leveraging async/await, FastAPI handles many simultaneous connections efficiently, especially when endpoints perform non-blocking I/O such as database queries, HTTP calls to third-party services, or interactions with AI models. Important performance factors to evaluate:

  • ASGI server choice: Uvicorn and Hypercorn are common; tuning workers and loop settings affects latency and throughput.
  • Blocking calls: Avoid CPU-bound work inside async endpoints; offload heavy computation to worker processes or task queues.
  • Connection pooling: Use async database drivers and HTTP clients (e.g., asyncpg, httpx) with pooled connections to reduce latency.
  • Metrics and profiling: Collect request duration, error rates, and concurrency metrics to identify hotspots.

Design patterns: validation, schemas, and dependency injection

FastAPI’s integration with Pydantic makes data validation explicit and type-driven. Use Pydantic models for request and response schemas to ensure inputs are sanitized and outputs are predictable. Recommended patterns:

  • Separate DTOs and domain models: Keep Pydantic models for I/O distinct from internal database or business models to avoid tight coupling.
  • Dependencies: FastAPI’s dependency injection simplifies authentication, database sessions, and configuration handling while keeping endpoints concise.
  • Versioning and contracts: Expose clear OpenAPI contracts and consider semantic versioning for breaking changes.

Integration with AI services and external APIs

Many modern APIs act as orchestrators for AI models or third-party data services. FastAPI’s async-first design pairs well with calling model inference endpoints or streaming responses. Practical tips when integrating AI services:

  • Use async clients to call external inference or data APIs to prevent blocking the event loop.
  • Implement robust timeouts, retries with backoff, and circuit breakers to handle intermittent failures gracefully.
  • Cache deterministic responses where appropriate, and use paginated or streaming responses for large outputs to reduce memory pressure.

Deployment, scaling, and observability

Deploying FastAPI to production typically involves containerized ASGI servers, an API gateway, and autoscaling infrastructure. Core operational considerations include:

  • Process model: Run multiple Uvicorn workers per host for CPU-bound workloads or use worker pools for synchronous tasks.
  • Autoscaling: Configure horizontal scaling based on request latency and queue length rather than CPU alone for I/O-bound services.
  • Logging and tracing: Integrate structured logs, distributed tracing (OpenTelemetry), and request/response sampling to diagnose issues.
  • Security: Enforce input validation, rate limiting, authentication layers, and secure secrets management.

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What is the difference between FastAPI and Flask?

FastAPI is built for the async ASGI ecosystem and emphasizes type-driven validation and automatic OpenAPI documentation. Flask is a synchronous WSGI framework that is lightweight and flexible but requires more manual setup for async support, validation, and schema generation. Choose based on concurrency needs, existing ecosystem, and developer preference.

When should I use async endpoints in FastAPI?

Use async endpoints when your handler performs non-blocking I/O such as database queries with async drivers, external HTTP requests, or calls to async message brokers. For CPU-heavy tasks, prefer background workers or separate services to avoid blocking the event loop.

How do Pydantic models help with API reliability?

Pydantic enforces input types and constraints at the boundary of your application, reducing runtime errors and making APIs self-documenting. It also provides clear error messages, supports complex nested structures, and integrates tightly with FastAPI’s automatic documentation.

What are common deployment pitfalls for FastAPI?

Common issues include running blocking code in async endpoints, inadequate connection pooling, missing rate limiting, and insufficient observability. Ensure proper worker/process models, async drivers, and graceful shutdown handling when deploying to production.

How can I test FastAPI applications effectively?

Use FastAPI’s TestClient (based on Starlette’s testing utilities) for endpoint tests and pytest for unit and integration tests. Mock external services and use testing databases or fixtures for repeatable test runs. Also include load testing to validate performance under expected concurrency.

Is FastAPI suitable for production-grade microservices?

Yes. When combined with proper patterns—type-driven design, async-safe libraries, containerization, observability, and scalable deployment—FastAPI is well-suited for production microservices focused on I/O-bound workloads and integrations with AI or external APIs.

Disclaimer

This article is for educational and informational purposes only. It does not constitute professional, legal, or investment advice. Evaluate tools and architectures according to your organization’s requirements and consult qualified professionals when needed.

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