#document metadata api

[owlservice]

A search across different repositories you should start via (meta-)search engines. We recommend the following discovery services:

Datacite - is the DOI registry for research data and offers both a web interface for searching DOI datasets and an API for retrieving its metadata.

Base -Bielefeld Academic Search Engine - is one of the world's largest search engines for scientific web documents, which can be searched specifically for the publication type "research data".

B2Find - is a discovery service based on metadata harvested from EUDAT data centres and other repositories of the European Union.

In addition, you can use Google Dataset Search to find open data in the web. Google Dataset Search finds datasets if the provider uses the open standard schema.org to describe the content of their web pages.

Guys..let's find a dataset

[innovationalofficesolution]

Integrating Power BI with Existing BI Infrastructure Post-Migration

Successfully completing a Tableau to Power BI migration is a significant milestone—but it’s only the beginning of fully unlocking Power BI’s potential. For many organizations, Power BI must coexist with existing BI infrastructure such as data warehouses, legacy tools, and embedded analytics solutions. A seamless integration strategy ensures business continuity, maximizes return on investment, and accelerates the adoption of Power BI across teams.

Understand Your Existing BI Ecosystem

Before you dive into integrating Power BI, it’s critical to assess the components of your existing BI ecosystem. Many enterprises still rely on traditional SQL-based reporting systems, SAP BusinessObjects, or custom dashboards. Power BI can complement these systems rather than replace them immediately. This hybrid model is particularly important during transition periods, where some departments continue to use Tableau or legacy tools while others move forward with Power BI.

Optimize Data Connectivity

Power BI offers broad data connectivity options—from on-premise databases and Azure services to third-party APIs. Leveraging Power BI Gateway allows secure, real-time access to on-premise data without complex reengineering. Ensure your existing ETL pipelines are compatible or gradually reconfigure them using Power Query for better integration with Power BI’s semantic models.

Using tools like Pulse Convert by OfficeSolution during migration ensures that datasets, relationships, and measures from Tableau are translated accurately to the Power BI environment. This foundation makes it easier to plug Power BI into your existing data flows.

Maintain Governance and Security

Integrating Power BI within an enterprise BI framework requires a robust governance model. Many organizations already have security protocols and access policies tied to legacy tools. With Power BI, use Microsoft Purview, Azure Active Directory, and Row-Level Security (RLS) to enforce similar compliance. Integrate these with your existing governance tools to avoid policy gaps and ensure consistent access control across platforms.

Coexistence Strategy: When Tableau and Power BI Run Together

It’s common for organizations to maintain Tableau for specific use cases while adopting Power BI for broader business intelligence. In such cases, set clear usage guidelines—use Power BI for enterprise-wide dashboards and reporting, while Tableau continues to serve more specialized or departmental analytics.

Encourage interoperability by exporting data models to formats that both platforms can understand. Standardizing data definitions and adopting a centralized metadata repository helps avoid duplication and confusion.

Train Users and Enable Change Management

A successful integration isn’t just technical—it’s cultural. Offer hands-on training sessions, documentation, and support to help users transition and understand how Power BI fits into the broader BI landscape. Power BI’s integration with Microsoft 365 tools such as Excel and Teams can also help increase adoption among non-technical users.

Conclusion

Integrating Power BI with your existing BI infrastructure post-migration is crucial for long-term success. With the right data connectivity, governance, and training strategies, organizations can build a modern, flexible BI ecosystem. OfficeSolution, through tools like Pulse Convert, helps ensure that Power BI becomes a seamless part of your analytics journey. For more insights and resources, visit https://tableautopowerbimigration.com.

[flexiconsultancy]

Accounting Data Disasters: Common Mistakes and How to Avoid Them

Ever feel like you're drowning in spreadsheets? You're not alone. As financial data grows more complex, even seasoned accountants make critical errors that can ripple through an entire business.

The Data Danger Zone: 5 Common Accounting Mistakes

1. Trusting Data Without Verification

We recently worked with a London retailer who based an entire quarter's strategy on sales projections from unreconciled data. The result? A £45,000 inventory mistake that could have been prevented with basic data validation.

Quick Fix: Implement a "trust but verify" protocol—cross-reference data from multiple sources before making significant decisions. At Flexi Consultancy, we always triangulate data from at least three sources before presenting insights to clients.

2. Excel as Your Only Tool

Excel is brilliant, but it's not designed to be your only data analysis tool when working with large datasets. Its limitations become dangerous when handling complex financial information.

Quick Fix: Supplement Excel with specialized data tools like Power BI, Tableau, or industry-specific financial analytics platforms. Even basic SQL knowledge can transform how you handle large datasets.

3. Ignoring Data Visualization

Numbers tell stories, but only if you can see the patterns. Too many accountants remain stuck in endless rows and columns when visual representation could instantly reveal insights.

Quick Fix: Learn basic data visualization principles and create dashboard summaries for all major reports. Your clients will thank you for making complex data digestible.

4. Overlooking Metadata

The context around your data matters just as much as the numbers themselves. When was it collected? Who entered it? What methodology was used?

Quick Fix: Create standardized metadata documentation for all financial datasets. Something as simple as "last modified" timestamps can prevent major reporting errors.

5. Manual Data Entry (Still!)

We're shocked by how many London accounting firms still manually transfer data between systems. Beyond being inefficient, this practice introduces errors at an alarming rate.

Quick Fix: Invest in proper API connections and automated data transfer protocols between your accounting systems. The upfront cost is nothing compared to the errors you'll prevent.

The Real Cost of Data Mistakes

These aren't just technical issues—they're business killers. One of our clients came to us after their previous accountant's data analysis error led to a six-figure tax miscalculation. Another lost investor confidence due to inconsistent financial reporting stemming from poor data management.

Beyond immediate financial implications, data mistakes erode trust, which is the currency of accounting.

Beyond Fixing: Building a Data-Strong Accounting Practice

Creating reliable financial insights from large datasets requires more than avoiding mistakes—it demands a systematic approach:

Document your data journey: Track every transformation from raw data to final report

Create repeatable processes: Standardize data handling procedures across your practice

Build data literacy: Ensure everyone touching financial information understands basic data principles

Implement peer reviews: Fresh eyes catch mistakes others miss

Need Help Navigating Your Data Challenges?

If you're struggling with financial data management or want to elevate your approach, reach out to our team. We specialize in helping London businesses transform financial data from a headache into a strategic asset.

This post was brought to you by the data nerds at Flexi Consultancy who believe financial insights should be both accurate AND actionable. Follow us for more practical accounting and financial management tips for London SMEs and startups.

[nishatjahannupur0102]

Telegram Data: A Deep Dive into Usage, Privacy, and Potential

Introduction

In the age of digital communication, data is king. From simple text messages to large multimedia files, our conversations generate vast amounts of data every day. One of the platforms at the center of this data revolution is Telegram, a cloud-based instant messaging app with over 800 million active users globally. Telegram is known not only for its secure messaging but also for the unique way it handles and stores data. This article explores the concept of Telegram data, its structure, uses, privacy implications, and potential for businesses, developers, and users alike.

What is Telegram Data?

Telegram data refers to all the information transmitted, stored, and processed through the Telegram messaging platform. This includes:

User data: Profile information like phone number, username, profile picture, bio, and last seen.

Message data: Texts, voice notes, videos, documents, and multimedia shared in individual or group chats.

Bot interactions: Data generated from automated bots, including user commands, bot replies, and API calls.

Channel and group metadata: Member counts, post histories, reactions, polls, and engagement statistics.

Cloud storage data: Files saved in Telegram's cloud, including saved messages and media.

This dataset is massive and can be useful for analytics, personalization, moderation, and even machine learning applications.

Why Telegram Data Matters

1. User Communication and Experience

Telegram's data-centric approach enables features like:

Seamless multi-device synchronization.

Unlimited cloud storage for messages and files.

Advanced search across messages and media.

Easy retrieval of old conversations and documents.

2. Privacy and Security

Telegram markets itself as a secure platform. It offers:

End-to-end encryption for Secret Chats.

Self-destructing messages.

Two-factor authentication.

Anonymous group management.

While not all Telegram data is end-to-end encrypted (only Secret Chats are), it uses its proprietary MTProto protocol to encrypt and securely store data on its servers. This dual approach offers both speed and privacy.

3. Data for Developers

Telegram offers APIs for accessing data:

Telegram Bot API allows developers to create bots that interact with users and respond to messages.

Telegram API and TDLib (Telegram Database Library) are more powerful tools used to build custom Telegram clients and extract deeper data insights (with user permission).

These APIs open a world of possibilities in automation, customer service, content delivery, and data analysis.

Types of Telegram Data

1. Chat and Messaging Data

This includes:

Messages (text, images, videos, stickers, etc.)

Voice and video calls metadata

Reactions, replies, and forwarded messages

Message timestamps, edits, and deletions

2. User Metadata

Even if content is encrypted, Telegram collects metadata such as:

IP addresses

Device types

App version

Contact lists (if synced)

Telegram stores this data to improve user experience, detect abuse, and comply with regional regulations when necessary.

3. Bot and API Data

Bots can log:

Commands issued

User interactions

Poll responses

API call logs

External service data linked to bots

This makes bots powerful tools for collecting and analyzing structured user inputs.

4. Group and Channel Data

Number of users

Posting activity

Comments and reactions

Engagement levels over time

Link clicks and post views (in public channels)

Admins can export this data for marketing, community building, and strategic planning.

How Telegram Stores and Manages Data

Telegram uses a cloud-based infrastructure to store data. It splits data into multiple data centers across the world and uses the MTProto protocol for encryption. Key features of their architecture include:

Client-server/server-client encryption for normal chats.

End-to-end encryption for Secret Chats (data is not stored on Telegram’s servers).

Data localization based on local laws.

Self-destructing content with timers for extra privacy.

Telegram's architecture ensures speed and reliability while maintaining a level of privacy suitable for most users.

Telegram Data and Privacy Concerns

Despite being privacy-focused, Telegram is not immune to scrutiny. Some key concerns include:

1. Not Fully Encrypted by Default

Only Secret Chats are fully end-to-end encrypted. Normal cloud chats are encrypted between the client and the server but stored on Telegram servers.

2. Metadata Collection

Telegram collects metadata like IP address, device information, and contact lists (if synced), which can theoretically be used to build user profiles.

3. Third-Party Bots and Data Leakage

Bots are not bound by Telegram’s privacy policy. A poorly designed or malicious bot can collect sensitive user data without consent.

4. Government Requests

Telegram claims it has never handed over user data to any government. However, its privacy policy leaves room for cooperation with legal investigations, especially in cases of terrorism or child abuse.

Applications of Telegram Data

1. Marketing and Analytics

Telegram channels and groups are increasingly used for brand promotion, telegram data content distribution, and user engagement. Admins use data to:

Track engagement rates

Measure reach and retention

Understand user behavior

Tailor content strategies

2. Customer Service Automation

Businesses use Telegram bots to automate:

Order tracking

FAQs

Feedback collection

Appointment bookings All these interactions generate valuable data that can improve customer experience.

3. Research and Sentiment Analysis

Academics and analysts extract Telegram data (via APIs or web scraping) to study:

Political discourse

Misinformation

Public sentiment on global events Especially in countries with internet censorship, Telegram becomes a vital source for open discussions.

4. Machine Learning and AI Training

Developers can train chatbots, recommender systems, and NLP models using anonymized Telegram data, particularly from public groups or channels.

5. Education and E-learning

Telegram’s structure supports sharing courses, lectures, and learning materials. Educators can analyze engagement data to improve course delivery.

Legal and Ethical Considerations

Using Telegram data raises several ethical and legal issues:

Data scraping without consent may violate Telegram’s terms of service.

GDPR compliance: In the EU, users have rights over their data (access, portability, deletion). Telegram has mechanisms for data download and account deletion.

Consent and transparency: Especially in research or analytics, informing users about data usage is critical.

Bias and misinformation: Public groups can become echo chambers or spread fake news. Responsible data use includes efforts to counterbalance these issues.

Telegram Data Export

Telegram offers a Data Export Tool in its desktop app:

Accessible via Settings > Advanced > Export Telegram Data

Exports chat history, media, account info, and even stickers

Data is provided in JSON and HTML formats

This tool enhances transparency and gives users control over their digital footprint.

Future of Telegram Data

As Telegram continues to grow, the scope and significance of its data will increase. Here are some trends to watch:

1. AI Integration

Telegram bots powered by AI will create new data types (e.g., chatbot conversations, smart assistant queries).

2. Decentralized Communication

Telegram may adopt decentralized storage or blockchain technologies to further secure user data.

3. Data Monetization

Though Telegram is ad-free in private chats, it may allow channel monetization or ad-based analytics, leveraging aggregate user data.

4. Stricter Privacy Regulations

With rising global focus on privacy (e.g., India’s Data Protection Bill, U.S. AI regulation talks), Telegram may need to adapt its data practices to avoid legal issues.

[marketingspecialist5980]

Understanding Telegram Data: Uses, Privacy, and the Future of Messaging

In the age of digital communication, messaging platforms have become central to our personal and professional lives. Among these, Telegram has emerged as a prominent player known for its speed, security, and versatile features. However, as with any digital service, the term "Telegram data" raises important questions about what information is collected, how it is stored and shared, and how it can be used by users, developers, marketers, or even state actors. This article provides a comprehensive look into Telegram data, dissecting its components, usage, and implications.

1. What is Telegram Data?

Telegram data refers to the entire range of information generated, transmitted, and stored through the Telegram platform. This can be broadly categorized into several components:

a. User Data

Phone numbers: Telegram accounts are tied to mobile numbers.

Usernames and profile information: Including names, bios, and profile pictures.

Contacts: Synced from the user’s address book if permission is granted.

User settings and preferences.

b. Chat and Media Data

Messages: Both individual and group chats. Telegram offers two types of chats:

Cloud Chats: Stored on Telegram’s servers and accessible from multiple devices.

Secret Chats: End-to-end encrypted and stored only on the users’ devices.

Media Files: Photos, videos, voice messages, and documents shared via chats.

Stickers and GIFs.

c. Usage Data

Log data: Includes metadata such as IP addresses, timestamps, and device information.

Activity patterns: Group participation, usage frequency, and interaction rates.

d. Bot and API Data

Telegram allows developers to build bots and integrate third-party services using its Bot API. Data includes:

Commands and messages sent to bots.

Bot logs and interactions.

Callback queries and inline queries.

2. Where is Telegram Data Stored?

Telegram is a cloud-based messaging platform. This means that most data (excluding secret chats) is stored on Telegram’s distributed network of data centers. According to Telegram, these centers are spread across various jurisdictions to ensure privacy and availability. Notably, Telegram’s encryption keys are split and stored in separate locations, telegram data a measure intended to protect user privacy.

For regular cloud chats, data is encrypted both in transit and at rest using Telegram’s proprietary MTProto protocol. However, Telegram—not the users—retains the encryption keys for these chats, meaning it can technically access them if compelled by law enforcement.

On the other hand, secret chats use end-to-end encryption, ensuring that only the sender and receiver can read the messages. These messages are never uploaded to Telegram’s servers and cannot be retrieved if one device is lost.

3. How is Telegram Data Used?

a. For User Functionality

The main use of Telegram data is to enable seamless messaging experiences across devices. Users can:

Access their chats from multiple devices.

Restore messages and media files after reinstalling the app.

Sync their contacts and communication preferences.

b. For Bots and Automation

Developers use Telegram data via the Telegram Bot API to create bots that:

Provide customer support.

Automate tasks like reminders or notifications.

Conduct polls and surveys.

Offer content feeds (e.g., news, RSS).

Telegram bots do not have access to chat history unless explicitly messaged or added to groups. This limits their data access and enhances security.

c. For Business and Marketing

Telegram’s growing popularity has made it a platform for digital marketing. Data from public channels and groups is often analyzed for:

Tracking trends and discussions.

Collecting feedback and user sentiment.

Delivering targeted content or product updates.

Some third-party services scrape public Telegram data for analytics. These activities operate in a legal grey area, especially if they violate Telegram’s terms of service.

4. Telegram’s Approach to Privacy

Telegram has built a reputation for being a privacy-focused platform. Here’s how it addresses user data privacy:

a. Minimal Data Collection

b. No Ads or Tracking

As of 2025, Telegram does not show personalized ads and has stated that it does not monetize user data. This is a significant departure from other platforms owned by large tech corporations.

c. Two-Layer Encryption

Telegram uses two layers of encryption:

Server-client encryption for cloud chats.

End-to-end encryption for secret chats.

While this model allows for cloud-based features, critics argue that Telegram’s control of the encryption keys for cloud chats is a potential vulnerability.

d. Self-Destruct and Privacy Settings

Users can:

Set messages to auto-delete after a specific period.

Disable forwarding of messages.

Hide last seen status, phone number, and profile picture.

Enable two-factor authentication.

5. Risks and Controversies Around Telegram Data

While Telegram markets itself as a secure platform, it has not been free from criticism:

a. MTProto Protocol Concerns

Security researchers have criticized Telegram’s proprietary MTProto protocol for not being independently verified to the same extent as open protocols like Signal’s. This has raised questions about its true robustness.

b. Use by Malicious Actors

Telegram’s relative anonymity and support for large groups have made it attractive for:

Illegal marketplaces.

Extremist propaganda.

Data leaks and doxxing.

Governments in countries like Iran, Russia, and India have, at various times, tried to ban or restrict Telegram citing national security concerns.

c. Data Requests and Compliance

Telegram claims it has never shared user data with third parties or governments. However, it does reserve the right to disclose IP addresses and phone numbers in terrorism-related cases. To date, Telegram reports zero such disclosures, according to its transparency reports.

6. Telegram Data for Researchers and Analysts

Telegram data scraping from public channels and groups has become a valuable resource for researchers studying:

Social movements.

Disinformation campaigns.

Public opinion on political events.

Online behavior in encrypted spaces.

Tools like Telethon and TDLib (Telegram Database Library) are used for accessing Telegram’s API. They allow developers to build advanced tools to collect and analyze public messages.

However, scraping Telegram data comes with legal and ethical responsibilities. Researchers must ensure:

Data anonymity.

Respect for Telegram’s API rate limits.

Avoidance of private or personally identifiable information.

7. Future Trends in Telegram Data

As Telegram continues to grow—reportedly reaching over 900 million monthly active users in 2025—the data generated on the platform will increase in scale and value. Here are some expected trends:

a. Monetization Through Premium Features

Telegram launched Telegram Premium offering additional features like faster downloads, larger uploads, and exclusive stickers. These premium tiers may lead to more data on user preferences and consumption patterns.

b. AI Integration

With the AI revolution in full swing, Telegram may integrate or allow AI-powered bots for content generation, moderation, and summarization, all of which will involve new types of data processing.

c. Regulatory Scrutiny

As governments worldwide tighten data protection laws (e.g., GDPR in Europe, DPDP Act in India), Telegram will face increased scrutiny over how it handles user data.

Thanks for Reading…..

SEO Expate Bangladesh Ltd.

[seodigital7]

Secure API Development: Protecting Your Data in the Digital Age

Introduction: Why Secure API Development is Essential

In today's interconnected world, APIs are the backbone of modern software. They connect services, platforms, and users. However, with this connectivity comes risk. Cyberattacks, data breaches, and unauthorized access are growing threats. That's why secure API development has become a non-negotiable priority for developers and businesses.

In this comprehensive guide, we will explore what secure API development means, why it's more important than ever in 2025, how to implement security practices, the best tools for securing APIs, and answer common questions.

What is Secure API Development?

Secure API development is the process of designing, building, and managing APIs with a primary focus on data security, access control, and compliance. It involves a range of practices including:

Authentication & Authorization

Encryption of data in transit and at rest

Input validation

Rate limiting and throttling

Monitoring and logging

Why It Matters in 2025

API Attacks Are Increasing: APIs are a top target for attackers due to weak security implementations.

Strict Regulations: Laws like GDPR, HIPAA, and CCPA require secure data handling.

Brand Trust: A single breach can ruin consumer confidence.

Business Continuity: Secure APIs reduce downtime and financial loss.

IoT and Mobile Expansion: With billions of devices connected, secure APIs are vital.

Key Principles of Secure API Development

Least Privilege Access: Only grant access to what’s necessary.

Secure Authentication: Use OAuth 2.0, OpenID Connect, and strong token systems.

Data Encryption: Use HTTPS/TLS and encrypt sensitive data at rest.

Input Sanitization: Prevent injection attacks with proper input validation.

Rate Limiting: Protect APIs from abuse and DDoS attacks.

Monitoring & Logging: Track API usage and detect anomalies early.

Secure API Development Best Practices

PracticeDescriptionUse HTTPSAlways encrypt data in transit.Implement OAuth 2.0Modern standard for API authorization.Validate InputsAvoid SQL injection and XSS attacks.Token ExpirationUse short-lived tokens for sessions.CORS PoliciesRestrict cross-origin requests.API GatewayCentralize security and traffic management.LoggingLog all API calls with metadata for audits.

Tools for Secure API Development

Postman Security Suite: For testing vulnerabilities.

Swagger + OpenAPI: Document and test API access securely.

Kong Gateway: Secure API traffic and enforce policies.

Okta / Auth0: Authentication and authorization.

OWASP ZAP: For automated security testing.

DataDog: For monitoring API traffic and threats.

Common Threats in API Security

ThreatDescriptionBroken AuthenticationImproperly implemented login mechanisms.Excessive Data ExposureAPIs revealing more data than needed.Rate Limiting FailureAPIs can be abused without restrictions.Injection AttacksMalicious data sent to manipulate databases.Lack of LoggingNo trail of usage makes incident response hard.

How to Test for API Security

Penetration Testing: Simulate attacks to identify vulnerabilities.

Static Analysis: Analyze source code for security flaws.

Dynamic Testing: Test APIs during runtime.

Fuzz Testing: Send random data to uncover bugs.

Audit Trails: Review logs for unusual patterns.

Real-World Case Study: API Security in FinTech

A leading FinTech startup experienced a near-breach due to excessive data exposure in its open banking API. After adopting secure API practices:

Implemented OAuth 2.0 and JWT-based token system

Added rate limiting and IP whitelisting

Regularly audited logs and monitored API traffic

Result: No breaches since the update and a 40% increase in client trust and onboarding.

Review: Is Secure API Development Worth It?

Absolutely. In an era where APIs are integral to business, securing them is essential. The upfront investment in security reduces long-term costs and protects brand reputation.

Pros:

Reduced risk of data breaches

Regulatory compliance

Improved user trust

Lower long-term maintenance

Cons:

Increased initial development time

Need for continuous monitoring

Overall Rating: ⭐⭐⭐⭐⭐ (4.9/5)

FAQs: Secure API Development

Q1: Is HTTPS enough to secure an API? No. HTTPS is vital but not sufficient. You also need proper authentication, input validation, and access control.

Q2: What is OAuth 2.0? It’s a secure authorization protocol that allows users to grant apps access to their data without sharing passwords.

Q3: How often should I test my API security? Regularly—ideally during every release and after any major update.

Q4: Are open APIs less secure? Not necessarily. Open APIs can be secure if properly implemented with access control and monitoring.

Q5: Can rate limiting stop all attacks? It’s a useful defense but should be used in combination with other security measures.

Final Thoughts

Secure API development is no longer optional—it’s a fundamental requirement for digital businesses. From authentication to encryption, every step in your API design must consider security. Organizations that prioritize API security not only protect data but also build trust with users, stakeholders, and regulators.

Stay ahead in API security trends with more guides at diglip7.com. Invest in protection today for a safer tomorrow.

[validateaddressapis]

Batch Address Validation Tool and Bulk Address Verification Software

When businesses manage thousands—or millions—of addresses, validating each one manually is impractical. That’s where batch address validation tools and bulk address verification software come into play. These solutions streamline address cleansing by processing large datasets efficiently and accurately.

What Is Batch Address Validation?

Batch address validation refers to the automated process of validating multiple addresses in a single operation. It typically involves uploading a file (CSV, Excel, or database) containing addresses, which the software then checks, corrects, formats, and appends with geolocation or delivery metadata.

Who Needs Bulk Address Verification?

Any organization managing high volumes of contact data can benefit, including:

Ecommerce retailers shipping to customers worldwide.

Financial institutions verifying client data.

Healthcare providers maintaining accurate patient records.

Government agencies validating census or mailing records.

Marketing agencies cleaning up lists for campaigns.

Key Benefits of Bulk Address Verification Software

1. Improved Deliverability

Clean data ensures your packages, documents, and marketing mailers reach the right person at the right location.

2. Cost Efficiency

Avoiding undeliverable mail means reduced waste in printing, postage, and customer service follow-up.

3. Database Accuracy

Maintaining accurate addresses in your CRM, ERP, or mailing list helps improve segmentation and customer engagement.

4. Time Savings

What would take weeks manually can now be done in minutes or hours with bulk processing tools.

5. Regulatory Compliance

Meet legal and industry data standards more easily with clean, validated address data.

Features to Expect from a Batch Address Validation Tool

When evaluating providers, check for the following capabilities:

Large File Upload Support: Ability to handle millions of records.

Address Standardization: Correcting misspellings, filling in missing components, and formatting according to regional norms.

Geocoding Integration: Assigning latitude and longitude to each validated address.

Duplicate Detection & Merging: Identifying and consolidating redundant entries.

Reporting and Audit Trails: For compliance and quality assurance.

Popular Batch Address Verification Tools

Here are leading tools in 2025:

1. Melissa Global Address Verification

Features: Supports batch and real-time validation, international formatting, and geocoding.

Integration: Works with Excel, SQL Server, and Salesforce.

2. Loqate Bulk Cleanse

Strengths: Excel-friendly UI, supports uploads via drag-and-drop, and instant insights.

Ideal For: Businesses looking to clean customer databases or mailing lists quickly.

3. Smarty Bulk Address Validation

Highlights: Fast processing, intuitive dashboard, and competitive pricing.

Free Tier: Great for small businesses or pilot projects.

4. Experian Bulk Address Verification

Capabilities: Cleans large datasets with regional postal expertise.

Notable Use Case: Utility companies and financial services.

5. Data Ladder’s DataMatch Enterprise

Advanced Matching: Beyond address validation, it detects data anomalies and fuzzy matches.

Use Case: Enterprise-grade data cleansing for mergers or CRM migrations.

How to Use Bulk Address Verification Software

Using batch tools is typically simple and follows this flow:

Upload Your File: Use CSV, Excel, or database export.

Map Fields: Match your columns with the tool’s required address fields.

Validate & Clean: The software standardizes, verifies, and corrects addresses.

Download Results: Export a clean file with enriched metadata (ZIP+4, geocode, etc.)

Import Back: Upload your clean list into your CRM or ERP system.

Integration Options for Bulk Address Validation

Many vendors offer APIs or direct plugins for:

Salesforce

Microsoft Dynamics

HubSpot

Oracle and SAP

Google Sheets

MySQL / PostgreSQL / SQL Server

Whether you're cleaning one-time datasets or automating ongoing data ingestion, integration capabilities matter.

SEO Use Cases: Why Batch Address Tools Help Digital Businesses

In the context of SEO and digital marketing, bulk address validation plays a key role:

Improved Local SEO Accuracy: Accurate NAP (Name, Address, Phone) data ensures consistent local listings and better visibility.

Better Audience Segmentation: Clean data supports targeted, geo-focused marketing.

Lower Email Bounce Rates: Often tied to postal address quality in cross-channel databases.

Final Thoughts

Batch address validation tools and bulk verification software are essential for cleaning and maintaining large datasets. These platforms save time, cut costs, and improve delivery accuracy—making them indispensable for logistics, ecommerce, and CRM management.

Key Takeaways

Use international address validation to expand globally without delivery errors.

Choose batch tools to clean large datasets in one go.

Prioritize features like postal certification, coverage, geocoding, and compliance.

Integrate with your business tools for automated, real-time validation.

Whether you're validating a single international address or millions in a database, the right tools empower your operations and increase your brand's reliability across borders.

SITES WE SUPPORT

Validate Address With API – ​​​Wix

[annabelledarcie]

How Does AI Agent Development for Local Plugin-Based System Enhance Performance?

In recent years, artificial intelligence has transformed how we interact with technology, particularly with the rise of autonomous AI agents capable of performing complex tasks with minimal human intervention. A particularly promising and flexible approach in this domain is AI Agent Development for Local Plugin-Based System. This approach not only enhances user control and data privacy but also offers a scalable and customizable framework suitable for diverse applications ranging from personal assistants to industrial automation.

Let’s explore what this development approach entails, its architecture, benefits, use cases, and the steps to build one.

Understanding the Concept: What is a Local Plugin-Based System?

Before diving into AI agent development itself, it’s essential to understand the concept of a plugin-based system.

A plugin-based system allows software functionalities to be extended dynamically through independently developed modules called plugins. These plugins act like modular building blocks that integrate with the main software without altering its core codebase. When this system is run locally—i.e., on a user's machine rather than on a centralized server—it gives users control over their data and application behavior.

Combining this plugin architecture with AI agents allows developers to create intelligent, modular, and extensible systems that can be customized according to specific workflows.

Core Components of a Local Plugin-Based AI Agent System

To effectively build such a system, developers must understand its core components:

1. AI Agent Core

This is the brain of the system. It includes:

Natural Language Processing (NLP): Understands and interprets human input.

Task Planning & Execution: Uses logic and possibly machine learning to make decisions and act on commands.

Memory Module: Stores past interactions and learns user preferences over time.

2. Plugin Interface Layer

This acts as the communication bridge between the AI core and plugins. It should support:

Plugin discovery and registration

API standardization (so the AI can interact with various plugins uniformly)

Permission management for plugin access

3. Local Plugin Modules

These are functional blocks that can be developed to do specific tasks like:

Sending emails

Managing files

Fetching weather data

Automating spreadsheet workflows

Each plugin typically includes:

A manifest file (describing metadata and capabilities)

Executable code

Input/output schemas for interoperability with the AI core

Benefits of Local Plugin-Based AI Agents

Developing AI agents using a local plugin-based model brings significant advantages:

1. Enhanced Privacy

All processes occur on the user’s machine, ensuring sensitive data never leaves local storage unless explicitly permitted.

2. Customization & Scalability

Users can enable, disable, or develop plugins tailored to their unique needs, resulting in a flexible, user-centric solution.

3. Offline Functionality

Since everything is local, the AI agent can function even without an internet connection—a critical advantage in industries with limited connectivity.

4. Security

A local system reduces the attack surface. Since the codebase and data reside locally, vulnerabilities associated with cloud storage and data breaches are mitigated.

Real-World Use Cases

1. Personal Productivity Agents

Imagine an AI assistant that can schedule meetings, manage to-do lists, summarize documents, and automate emails—all through locally installed plugins.

2. Enterprise Workflow Automation

Companies can deploy AI agents on employee machines to automate repetitive tasks (e.g., report generation, CRM updates), integrated with internal tools via custom plugins.

3. IoT Device Management

Smart home systems or factory automation setups can benefit from local AI agents controlling devices without needing cloud connectivity.

4. Healthcare Assistants

In clinics where patient data must remain confidential, a local AI agent can fetch records, summarize diagnostic information, and manage appointments without breaching compliance regulations.

Development Process: Building an AI Agent for Local Plugin-Based Systems

Step 1: Define Agent Capabilities

Start by outlining what tasks the agent should be able to perform. These may include voice command recognition, task execution, and contextual learning.

Step 2: Choose a Programming Language & Framework

Languages like Python, Rust, or Go are popular for building such systems due to their rich ecosystems and plugin support. Frameworks like LangChain, AutoGPT, and PrivateGPT can be adapted for local execution.

Step 3: Design the Plugin Interface

Create a standard communication protocol (such as RESTful APIs or local sockets) that plugins will use to talk with the AI core. Include permission and sandboxing mechanisms to ensure plugins cannot access unauthorized data.

Step 4: Build or Integrate Plugins

Start with a few core plugins—e.g., file system manager, email handler, or calendar integrator. Ensure each plugin has a manifest that describes its function, input parameters, and output format.

Step 5: Implement Natural Language Understanding

Use models like LLama 3, GPT-J, or BERT to process user queries. Fine-tune them locally for better privacy and performance. Link user inputs to plugin actions via a command parsing system or a prompt-based logic chain.

Step 6: Add Feedback & Learning Loops

Allow the agent to learn from user feedback. Incorporate reinforcement learning or basic rule-based improvement systems for better personalization over time.

Step 7: Testing & Debugging

Test the AI agent with different plugin combinations and edge cases. Ensure the system fails gracefully and handles user commands safely.

Challenges and Considerations

While this architecture is powerful, developers should keep a few challenges in mind:

Memory and Resource Usage: Local agents must be optimized to run efficiently on user machines with varying specs.

Plugin Compatibility: Managing dependencies and compatibility between plugins can become complex as the ecosystem grows.

Security Updates: A centralized update system may be lacking, so mechanisms for secure plugin updates and vulnerability scanning are crucial.

Future Outlook

The rise of open-source AI models, growing emphasis on data privacy, and increasing user demand for personalized tools indicate a strong future for local plugin-based systems. Moreover, with initiatives like Apple’s on-device AI push and Meta’s LLMs for offline devices, the groundwork is being laid for widespread adoption.

As AI continues evolving, plugin-based systems may become the norm for anyone wanting both flexibility and control in their digital environments.

Conclusion

As we move into an era where personalized automation is no longer a luxury but a necessity, the need for decentralized, intelligent, and modular systems becomes evident. AI Agent Development for Local Plugin-Based System presents a paradigm shift—one that empowers users with more control, better customization, and tighter security. By leveraging local resources and a flexible plugin framework, developers can create intelligent agents that are not only powerful but also respect user privacy and autonomy. The future of AI Agent Development lies in this balance of intelligence, adaptability, and local empowerment.

[souhaillaghchimdev]

Content Management System (CMS) Development

A Content Management System (CMS) is a software application that allows users to create, manage, and modify content on a website without needing specialized technical knowledge. CMS development is essential for businesses and individuals who want to manage their web presence effectively. In this post, we’ll explore the core aspects of CMS development, key features, and popular platforms.

What is a Content Management System?

A CMS provides a user-friendly interface to manage digital content such as text, images, and videos. It separates content management from content presentation, allowing users to publish and edit content easily.

Types of Content Management Systems

Traditional CMS: Focused on web content management (e.g., WordPress, Joomla).

Headless CMS: Decoupled from the front end, providing content via APIs (e.g., Contentful, Strapi).

Decoupled CMS: Combines both traditional and headless features, allowing for flexibility in how content is served.

Enterprise CMS: Designed for large organizations with complex content needs (e.g., Adobe Experience Manager).

Core Features of a CMS

User Management: Control user roles and permissions for content creation and editing.

Content Creation and Editing: WYSIWYG editors for easy content formatting and media management.

Template Management: Support for customizable templates and themes to manage layout and design.

SEO Optimization: Tools for managing metadata, sitemaps, and URLs to improve search engine visibility.

Version Control: Keep track of changes and revert to previous versions of content.

Analytics and Reporting: Integrate with analytics tools to track content performance.

Popular CMS Platforms

WordPress: The most popular CMS, open-source with extensive plugins and themes.

Joomla: Flexible and powerful, suitable for complex sites.

Drupal: Highly customizable, suitable for developers and enterprise solutions.

Shopify: Specialized for e-commerce, easy to set up and use.

Contentful: A headless CMS offering API-driven content management.

Developing a Custom CMS

If you're interested in building your own CMS, consider the following steps:

Define Requirements: Identify key features and functionalities needed for your CMS.

Choose Technology Stack: Select languages and frameworks (e.g., PHP, Python, Node.js) and database (e.g., MySQL, MongoDB).

Design Database Schema: Structure your database for content, users, and settings.

Develop Frontend: Create a user-friendly interface for content creation and management.

Implement Backend Logic: Handle CRUD operations, user authentication, and content serving.

Testing: Ensure functionality, security, and usability through rigorous testing.

Deployment: Host your CMS on a server and make it accessible to users.

Best Practices for CMS Development

Ensure responsive design for mobile and desktop users.

Focus on user experience and simplicity in the content management interface.

Implement security measures like HTTPS, input validation, and user authentication.

Regularly update your CMS to fix vulnerabilities and improve performance.

Provide documentation and support for users.

Conclusion

Developing a Content Management System can empower users to manage their online presence with ease. Whether using a popular CMS platform or building a custom solution, understanding the essential features and best practices can lead to successful implementation. Embrace the power of CMS to enhance content management and user engagement!

[whatsappbusiness]

WhatsApp Issue: Integration Challenges with Enterprise Systems

For enterprises seeking to leverage WhatsApp's massive user base and familiar interface, system integration represents one of the most significant technical challenges. While WhatsApp Business API theoretically enables connections between the messaging platform and enterprise systems, the reality often falls short of expectations. This WhatsApp issue creates substantial obstacles for businesses attempting to incorporate the platform into their broader technology ecosystem.

The WhatsApp Integration Issue Landscape

The fundamental WhatsApp issue stems from the platform's relatively closed architecture compared to purpose-built enterprise messaging solutions. Despite improvements to the WhatsApp Business API, organizations continue to encounter significant challenges when attempting to create seamless connections with critical systems like:

Customer Relationship Management (CRM) platforms

Enterprise Resource Planning (ERP) systems

Customer Service and Helpdesk solutions

Marketing Automation platforms

Business Intelligence and Analytics tools

Document Management Systems

Internal Communication platforms

These integration challenges represent a substantial WhatsApp issue that can undermine the platform's business value if not properly addressed.

Primary WhatsApp Issues in Enterprise Integration

1. Webhook Reliability and Message Delivery

A critical WhatsApp issue involves the reliability of its webhook system, which enterprises rely on to receive incoming messages and status updates. Organizations frequently report:

Delayed webhook delivery during high-volume periods

Inconsistent callback patterns requiring complex retry logic

Missing notifications for important status changes

Duplicate webhook deliveries requiring deduplication handling

These reliability issues create a significant WhatsApp issue for enterprise systems that expect consistent and timely event notifications.

2. Rate Limiting and Throughput Constraints

Another substantial WhatsApp issue involves the platform's strict rate limits, which can severely impact high-volume business operations:

Message sending throttling that limits burst communications

Conversation initiation restrictions that complicate marketing campaigns

Template message approval bottlenecks for time-sensitive communications

Quality rating impacts that can further reduce allowed throughput

For enterprises accustomed to controlling their communication infrastructure, these constraints represent a challenging WhatsApp issue that requires significant workflow adaptations.

3. Data Synchronization Complexities

Maintaining consistent data between WhatsApp and enterprise systems creates another significant WhatsApp issue:

Limited historical data access through official APIs

Challenges matching WhatsApp users with existing customer records

Complex conversation state tracking across systems

Media file handling and storage synchronization

Contact information management between platforms

These synchronization challenges often force enterprises to develop complex middleware solutions to address this WhatsApp issue.

4. Authentication and Security Framework Misalignment

Enterprise security requirements frequently conflict with WhatsApp's authentication approach, creating a WhatsApp issue where organizations must reconcile different security models:

WhatsApp's token-based authentication versus enterprise SSO systems

Challenges implementing fine-grained access controls

Difficulties auditing user actions within the WhatsApp environment

Limited integration with enterprise security monitoring systems

These security misalignments represent a significant WhatsApp issue that often requires specialized integration development.

5. Metadata and Context Limitations

A persistent WhatsApp issue involves the platform's limited support for rich contextual metadata:

Restricted custom attributes for message categorization

Limited conversation labeling capabilities

Challenges in maintaining conversation threading

Minimal support for business process context

For enterprises requiring rich metadata to drive automated workflows, this WhatsApp issue necessitates the development of external systems to maintain crucial context.

Business Impact of WhatsApp Integration Issues

These integration challenges create substantial business impacts:

Extended Implementation Timelines: WhatsApp projects typically require 30-50% more development time than comparable messaging integrations

Increased Development Costs: Specialized expertise requirements drive higher project expenses

Operational Inefficiencies: Staff often need to manually bridge gaps between systems

Customer Experience Inconsistencies: Information silos can create disjointed customer interactions

Analytical Blind Spots: Incomplete data synchronization limits business intelligence capabilities

For many enterprises, these consequences represent the true cost of the WhatsApp issue beyond simple technical challenges.

Strategic Approaches to WhatsApp Integration Issues

Despite these challenges, enterprises can implement effective strategies to address WhatsApp integration issues:

1. Middleware Development

Rather than attempting direct connections between WhatsApp and enterprise systems, successful organizations address this WhatsApp issue by developing specialized middleware:

Message Queuing Systems: Implementing robust queuing to handle webhook inconsistencies

Transformation Layers: Creating adapters that normalize data between WhatsApp and enterprise formats

State Management Services: Maintaining conversation context separate from WhatsApp

Retry and Resilience Logic: Building systems that handle communication failures gracefully

This abstraction layer approach effectively mitigates many aspects of the WhatsApp issue by isolating enterprise systems from the platform's integration challenges.

2. Certified Integration Partner Relationships

Another effective approach to this WhatsApp issue involves working with officially certified WhatsApp Business Solution Providers (BSPs):

BSPs often have prebuilt connectors for common enterprise systems

Their experience with WhatsApp's specific integration challenges speeds implementation

They typically maintain relationships with Meta that can help address critical WhatsApp issues

Their specialized knowledge can identify potential problems before they impact production systems

While this approach typically requires additional investment, it can significantly reduce the technical complexity of the WhatsApp issue.

3. Hybrid Communication Architecture

Some organizations address the WhatsApp issue by implementing hybrid architectures:

Using WhatsApp primarily for customer-facing communications

Maintaining separate systems for internal processes and data management

Creating limited, well-defined integration points rather than attempting deep integration

Developing clear boundaries for which processes belong in each system

This pragmatic approach acknowledges the WhatsApp issue of integration challenges but limits its impact on core business functions.

4. Comprehensive Testing Frameworks

Successful enterprises address this WhatsApp issue through robust testing:

Developing automated test suites specifically for WhatsApp integration points

Implementing continuous monitoring of WhatsApp API connectivity

Creating synthetic transaction testing to verify end-to-end functionality

Establishing performance testing regimes that simulate high-volume conditions

These testing approaches help identify potential WhatsApp issues before they affect production systems.

5. Phased Implementation Strategy

Rather than attempting comprehensive WhatsApp integration immediately, successful organizations often address this WhatsApp issue through phased approaches:

Beginning with limited use cases that demonstrate clear value

Gradually expanding integration points as expertise develops

Continuously refining the integration architecture based on operational experience

Building institutional knowledge before attempting more complex integrations

This measured approach helps manage the WhatsApp issue by limiting initial complexity while building valuable implementation experience.

Case Study: Overcoming WhatsApp Integration Issues

A large retail organization successfully addressed WhatsApp integration issues through a comprehensive strategy:

They implemented a dedicated integration layer between WhatsApp and their existing systems

They developed robust queuing systems to handle webhook reliability issues

They created a separate database for maintaining conversation context and customer relationships

They implemented comprehensive monitoring and alerting for integration points

Results included:

99.7% message delivery reliability despite webhook inconsistencies

Seamless customer context preservation across channels

42% reduction in customer service resolution times

Successful handling of seasonal volume spikes without degradation

Future Outlook for WhatsApp Integration

While integration remains a significant WhatsApp issue, several trends suggest potential improvements:

Meta continues enhancing the WhatsApp Business API with enterprise needs in mind

The growing ecosystem of integration partners is developing more sophisticated solutions

Third-party middleware platforms increasingly include robust WhatsApp connectors

Enterprise systems are adding native WhatsApp integration capabilities

These developments indicate that while the WhatsApp issue of integration challenges will persist, its severity may gradually decrease as the ecosystem matures.

Conclusion: Addressing the WhatsApp Integration Issue

The WhatsApp issue of enterprise integration challenges represents a significant but surmountable obstacle for organizations seeking to leverage the platform's reach and familiarity. By implementing thoughtful architectural approaches, working with experienced partners, developing appropriate middleware, establishing robust testing, and adopting phased implementation strategies, enterprises can successfully navigate these technical challenges.

Organizations that effectively address this WhatsApp issue position themselves to deliver superior customer experiences while maintaining operational efficiency. As WhatsApp continues its evolution from consumer messaging app to business communication platform, those who master its integration challenges gain significant competitive advantages in customer engagement and operational excellence.

[alcoanalysis]

Intro to Web Scraping

Chances are, if you have access to the internet, you have heard of Data Science. Aside from the buzz generated by the title ‘Data Scientist’, only a few in relevant fields can claim to understand what data science is. The majority of people think, if at all, that a data scientist is a mad scientist type able to manipulate statistics and computers to magically generate crazy visuals and insights seemingly out of thin air.

Looking at the plethora of definitions to be found in numerous books and across the internet of what data science is, the layman’s image of a data scientist may not be that far off.

While the exact definition of ‘data science’ is still a work in progress, most in the know would agree that the data science universe encompasses fields such as:

Big Data

Analytics

Machine Learning

Data Mining

Visualization

Deep Learning

Business Intelligence

Predictive Modeling

Statistics

Data Source: Top keywords

Image Source – Michael Barber

Further exploration of the skillset that goes into what makes a data scientist, consensus begins to emerge around the following:

Statistical Analysis

Programming/Coding Skills: - R Programming; Python Coding

Structured Data (SQL)

Unstructured Data (3-5 top NoSQL DBs)

Machine Learning/Data Mining Skills

Data Visualization

Big Data Processing Platforms: Hadoop, Spark, Flink, etc.

Structured vs unstructured data

Structured data refers to information with a high degree of organization, such that inclusion in a relational database is seamless and readily searchable by simple, straightforward search engine algorithms or other search operation

Examples of structured data include numbers, dates, and groups of words and numbers called strings.

Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents.

Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files, and unstructured text such as the body of an e-mail message, Web pages, or word-processor document. Source: Unstructured data - Wikipedia

Implied within the definition of unstructured data is the fact that it is very difficult to search. In addition, the vast amount of data in the world is unstructured. A key skill when it comes to mining insights out of the seeming trash that is unstructured data is web scraping.

What is web scraping?

Everyone has done this: you go to a web site, see an interesting table and try to copy it over to Excel so you can add some numbers up or store it for later. Yet this often does not really work, or the information you want is spread across a large number of web sites. Copying by hand can quickly become very tedious.

You’ve tried everything else, and you haven’t managed to get your hands on the data you want. You’ve found the data on the web, but, alas — no download options are available and copy-paste has failed you. Fear not, there may still be a way to get the data out. Source: Data Journalism Handbook

As a data scientist, the more data you collect, the better your models, but what if the data you want resides on a website? This is the problem of social media analysis when the data comes from users posting content online and can be extremely unstructured. While there are some websites who support data collection from their web pages and have even exposed packages and APIs (such as Twitter), most of the web pages lack the capability and infrastructure for this. If you are a data scientist who wants to capture data from such web pages then you wouldn’t want to be the one to open all these pages manually and scrape the web pages one by one. Source: Perceptive Analytics

Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis. Source: Wikipedia

Web Scraping is a method to convert the data from websites, whether structured or unstructured, from HTML into a form on which analysis can be performed.

The advantage of scraping is that you can do it with virtually any web site — from weather forecasts to government spending, even if that site does not have an API for raw data access. While this method is very powerful and can be used in many places, it requires a bit of understanding about how the web works.

There are a variety of ways to scrape a website to extract information for reuse. In its simplest form, this can be achieved by copying and pasting snippets from a web page, but this can be unpractical if there is a large amount of data to be extracted, or if it spread over a large number of pages. Instead, specialized tools and techniques can be used to automate this process, by defining what sites to visit, what information to look for, and whether data extraction should stop once the end of a page has been reached, or whether to follow hyperlinks and repeat the process recursively. Automating web scraping also allows to define whether the process should be run at regular intervals and capture changes in the data.

https://librarycarpentry.github.io/lc-webscraping/

 Web Scraping with R

Atop any data scientist’s toolkit lie Python and R. While python is a general purpose coding language used in a variety of situations; R was built from the ground up to mold statistics and data. From data extraction, to clean up, to visualization to publishing; R is in use. Unlike packages such as tableau, Stata or Matlab which are skewed either towards data manipulation or visualization, R is a general purpose statistical language with functionality cutting across all data management operations. R is also free and open source which contributes to making it even more popular.

To extend the boundaries limiting data scientists from accessing data from web pages, there are packages based on ‘Web scraping’ available in R. Let us look into web scraping technique using R.

Harvesting Data with RVEST

R. Hadley Wickham authored the rvest package for web scraping using R which will be demonstrated in this tutorial. Although web scraping with R is a fairly advanced topic it is possible to dive in with a few lines of code within a few steps and appreciate its utility, versatility and power.

We shall use 2 examples inspired by Julia Silge in her series cool things you can do with R in a tweet:

Scraping the list of districts of Uganda

Getting the list of MPs of the Republic of Rwanda

[itsusernotfound]

Plex is a bit hard to explain these days. Even if you don't know its roots as an outgrowth of a Mac port of the Xbox Media Center project, Plex is not your typical "streaming" service, given how most people use it. So as Plex announces its first price increase to its Plex Pass subscription in more than 10 years, it has its work cut out explaining why, what's included, and what is changing.

Starting April 29, the cost of a Plex Pass rises from $5 to $7 monthly, from $40 to $70 annually, and a lifetime pass now costs $250, previously $120. In a blog post, Plex cites rising costs and its commitment to an independent service that supports "personal media."

"We are all in on the continued success of Plex Pass and personal media," the post states. "This price increase will ensure that we can keep investing dedicated resources in developing new features, while supporting and growing your favorites." The post cites a roadmap that contains an integration with Common Sense Media, a new "bespoke server management app" for managing server users, and "an open and documented API for server integrations," including custom metadata agents.

[thoughtportal]

Plex is a bit hard to explain these days. Even if you don't know its roots as an outgrowth of a Mac port of the Xbox Media Center project, Plex is not your typical "streaming" service, given how most people use it. So as Plex announces its first price increase to its Plex Pass subscription in more than 10 years, it has its work cut out explaining why, what's included, and what is changing.

Starting April 29, the cost of a Plex Pass rises from $4.99 to $6.99 monthly, from $39.99 to $69.99 annually, and a lifetime pass now costs $249.99, previously $119.99. In a blog post, Plex cites rising costs and its commitment to an independent service that supports "personal media."

"We are all in on the continued success of Plex Pass and personal media," the post states. "This price increase will ensure that we can keep investing dedicated resources in developing new features, while supporting and growing your favorites." The post cites a roadmap that contains an integration with Common Sense Media, a new "bespoke server management app" for managing server users and "an open and documented API for server integrations," including custom metadata agents.

Someone in a remote video stream must have a Pass

And then, after that note, Plex hits the big change: Streaming "personal media"—i.e. video files, not audio, photos, or offerings from Plex's ad-supported movies and TV—from outside your own network will no longer be a free Plex feature, starting April 29. "Fully free" might be the better way to put it, because if a server owner has a Plex Pass subscription, their users can still access their server for free.

But if you've been hosting your own Plex server to maintain access to your stuff while you're away or relying on the kindness of non-Pass-having friends with servers, either you or your server-owning friends will need a Plex Pass subscription by the end of April.

Alternatively, you, as a non-server-running Plex viewer, can get a cheaper Remote Watch Pass, $1.99 per month or $19.99 a year. That doesn't include Plex Pass features like offline downloads, skipping a show intro or credits, or the like, but it does keep you connected to your "personal media" vendors.

[gloriousfestgentlemen02]

Sure, here is the article formatted according to your specifications:

Yoast SEO API scripts TG@yuantou2048

Yoast SEO is a powerful tool that helps website owners and content creators optimize their websites for search engines. One of its key features is the Yoast SEO API, which allows developers to integrate Yoast SEO functionalities into custom scripts and applications. This can be incredibly useful for automating SEO tasks, enhancing user experience, and improving overall site performance.

The Yoast SEO API provides a range of endpoints that enable developers to interact with various aspects of the plugin's functionality. For instance, you can use the API to retrieve metadata, generate sitemaps, and even programmatically manage content analysis. By leveraging the API, you can create custom solutions that streamline SEO processes and provide more dynamic interactions with the plugin's features.

To get started with the Yoast SEO API, you first need to authenticate your requests using an API key. Once authenticated, you can access a variety of data points and functionalities. Here are some of the things you can do with the API:

1. Retrieve Metadata: You can fetch metadata for posts and pages, including titles, descriptions, and focus keywords. This is particularly useful for large-scale sites where manual configuration might be impractical or inefficient. By integrating these scripts into your workflow, you can automate tasks such as generating structured data, updating meta tags, and managing redirects. This not only saves time but also ensures consistency across your site. For example, you can write scripts that automatically update meta descriptions and titles based on predefined rules or external data sources. This can be especially beneficial for sites with frequently updated content, ensuring that all new and existing content is optimized without manual intervention.

Another significant advantage is the ability to programmatically manage your site’s SEO settings. Imagine a scenario where you have a news website with hundreds of articles published daily. Instead of manually setting up each piece of content, you can write scripts that dynamically adjust SEO settings based on specific criteria. For instance, you could write a script that automatically generates optimized titles and descriptions for new posts, reducing the workload for content managers and editors.

Moreover, the API supports bulk operations, allowing you to make changes to multiple pieces of content at once. This is particularly handy for large-scale websites where manual updates would be cumbersome. Developers can build tools that analyze content in real-time, suggesting improvements or automatically applying best practices. The API offers methods to fetch and modify metadata, making it easier to maintain high-quality SEO configurations across your entire site. Additionally, the API supports fetching and modifying metadata for individual posts or pages, streamlining the process of keeping your site’s SEO settings up-to-date. For e-commerce platforms, this means maintaining consistent SEO optimization across thousands of products or categories. By automating these tasks, you ensure that every piece of content meets your SEO guidelines, thereby saving hours of manual labor.

For developers, the possibilities are endless. You might develop a dashboard that suggests improvements based on predefined rules or even create plugins that enhance the SEO of your site without needing to navigate through the WordPress admin interface. This level of automation can significantly improve efficiency and accuracy, ensuring that every piece of content adheres to your SEO strategy.

However, it's important to understand the structure and capabilities of the API. Documentation is comprehensive and well-documented, making it accessible even for those who are not deeply versed in SEO best practices. With the right setup, you can create workflows that adapt to changing SEO strategies or apply bulk updates efficiently. Whether you're building a custom plugin or working on complex setups involving multiple authors or contributors who may not have the technical knowledge required for optimal SEO settings.

In summary, the Yoast SEO API opens up a world of possibilities for both small and enterprise-level projects. It's worth noting that while the API is robust, it requires a basic understanding of how Yoast SEO works internally. If you're looking to implement advanced SEO strategies, the API documentation provides detailed guides on how to interact with the plugin directly from within your own applications or third-party systems. As you explore its potential, remember to test thoroughly before deploying any scripts in a production environment. Always keep security in mind when implementing these scripts. Make sure to handle authentication securely and follow best practices for API usage. By leveraging the API, you can build custom solutions tailored to your unique needs. Whether you're a developer looking to extend the plugin’s features or a business owner aiming to scale SEO efforts without compromising on quality. Remember, the API is designed to work seamlessly with WordPress hooks and functions, enabling seamless integration with other tools and services. By doing so, you can create custom solutions that fit your specific requirements. Overall, the Yoast SEO API scripts offer a powerful way to leverage the plugin's features beyond what's possible through the standard WordPress backend. Start by exploring the official documentation and testing environments to avoid disrupting live content. Experimentation in a sandbox environment is recommended before rolling out updates to your live site. This flexibility makes it a valuable asset for anyone looking to take their SEO game to the next level. By harnessing its power, you can tailor-make solutions that align with your unique needs. Keep an eye on rate limits and error handling to avoid overloading your server resources. With careful planning and implementation, you can unlock new levels of control over your site’s SEO settings. Ultimately, the API serves as a bridge between your site’s SEO settings and your custom workflows. By familiarizing yourself with the available endpoints and methods, you can craft sophisticated integrations that boost productivity and maintain high standards of SEO management.

加飞机@yuantou2048

谷歌留痕

负面删除

[braininventoryusa]

Why You Should Hire Next.js Developers for Your Web Project

If you have been searching for web development options for your business, you must have definitely heard about Next.js development services. It’s a popular framework and a preferred choice among developers, and for all good reasons. Whether you’re building a profitable e-commerce website, a SaaS product, or a blog, hiring Next.js developers could be the best decision you will ever make. So let’s learn about how Next.js developers can give your cross-platform applications a competitive edge. 

Lightning Fast Performance: Speed matters, especially in today’s time when the attention spans are limited. 53% of mobile users tend to leave a site if it takes longer than 3 seconds to load, crazy right? It is utmost important for your application to load within the expected time, otherwise you might lose many of your users. Next.js is designed with high performance in mind. It offers features like static site generation and server-side rendering to ensure your website/app loads quickly. So hire Next.js developers to keep the speed optimized from the start–making it a reason for visitors to stay, and make a purchase. Imagine running an e-commerce store, with a huge display of product images in HD. With Next.js, your pages will load almost instantly, leaving no user frustrated.

SEO-Friendly Architecture: Search engine optimization can have a great impact on your website’s visibility. Unlike other frameworks, Next.js offers built-in features like metadata management, dynamic routing, faster loading times, etc, to support in building a good reputation in front of search engines. By hiring Next.js experts, you can use these SEO features to help your website rank higher in the search engine result pages (SERPs). Developers can also use Next.js’ crucial feature SSR to create fully rendered pages, ensuring the website’s content is always indexed correctly.

Cross-platform Compatibility: Today’s user is present on every digital platform–smartphones, laptops, tablets, and smart TVs. So your website should be accessible and give a consistent experience across all devices. But this isn’t easy. Next.js developers can create highly responsive and cross-platform compatible applications. They make sure your site adapts to different screen sizes and gives a similar experience to the users wherever they are.

Future-proof Technology: Next.js is backed by a company named Vercel, which is constantly focusing towards updating the framework to stay ahead of the web development trends. So when you hire Next.js developers, you are not just getting access to a robust framework, but also setting yourself up for a future-proof platform. With Next.js, developers can also integrate modern features like WebSockets, ensuring your app stays relevant for future generations.

Cost-effective Development: Next.js brings efficiency in every aspect of web development, which means projects are often completed faster than other frameworks. And faster development times mean low investment for businesses. Additionally, Next.js can combine SSR and SSG, which can reduce the need for backend servers, saving you more money on the expensive infrastructure in the long run.

Built-in Support for APIs: APIs are an essential part of custom software development services. Whether you are building your own API from scratch, or integrating a third-party service into your existing application, Next.js makes the process easier. Experienced developers can create robust APIs and integrate them directly in your application, saving you time and simplifying the process further.

Active Community: One of the best advantages of using Next.js is getting access to its thriving community. From detailed documentation to live support, developers gain access to some useful resources that can address complex queries and speed up the development process. By hiring Next.js developers, you can leverage these open-source tools and resources to build an application for you in no time.

Scalability: Whether you are running a startup, or a mature business, scalability is crucial. Your web application should be capable of handling more traffic in the future and manage customer data without giving a single problem. Next.js developers can create a scalable application. With features like serverless operation, and API routes, your app can scale up efficiently without needing to build it from scratch. Imagine your website/application starts with 500 daily users, but the business grows to handle a traffic of 100,000 users daily, a Next.js app can scale up effortlessly.

How to Find and Hire Next.js Developers?

The first step to find and hire Next.js developers or a reputed web design agency is clearly defining your project’s requirements. List down the goals, and the technical tools you might need to develop your project and make it fully functional. Understanding these basic things will help you identify the best Next.js developer from the lot. 

Next step is to research on those platforms which attract a good number of Next.js experts. Freelancer, Toptal, Upwork, and Indeed are some of the great options. LinkedIn is another professional networking site where you can connect with Next.js candidates and hire them. You can also post a job listing on LinkedIn. But before you post one, understand what you need in the candidate: 

Technical Expertise: Experience with server-side rendering, JavaScript/TypeScript, custom web development, React, and static site generation. Also, some proficiency in smooth API integration is a must. 

Work Portfolio: Check their previously done Next.js projects and look for similar technologies as your requirements. 

Problem Solving: Should be capable of solving problems quickly, and implementing performance improving techniques like lazy loading, image optimization, and more.

Communication Skills: Should be able to communicate clearly and stay in constant touch with the business they are associated with. Providing timely updates and keeping the team in the loop.

Top Sites to Hire Next.js Developers

1) Toptal: Toptal is one of the best websites to hire Next.js developers for your projects. You can find highly skilled professionals, as they only accept the best applicants for their platform. Toptal has a candidate assessment policy which includes comprehensive skill assessments, language tests, personality assessment tests, and technical proficiency tests. 

    Key points: 

The platform has a Trustpilot rating of 4.8 out of 5

Enjoy a risk-free trial for up to 5 days

2) LinkedIn: LinkedIn is a professional networking platform where you can build connections from your industry. You can find potential Next.js developers by using their search filters, or posting a job listing. Also, using a premium profile, you can send as many requests as you want. 

     Key points: 

Millions of qualified candidates are active on the platform

Peer validations help in finding ideal candidates 

Send personalized messages to potential candidates with Next.js experience 

3) Glassdoor: Glassdoor displays featured business reviews and average salary listings which make it a perfect site for candidates seeking transparency. They have also partnered with a popular site called Indeed, which gives double exposure to the job seekers. When you post a job requirement on Glassdoor, it is displayed on Indeed also. 

     Key points: 

Highly targeted ads can increase your chances of finding the best Next.js developer 

Custom company profiles attract not only active job applicants but also passive ones

It provides detailed candidate analytics, which makes the hiring decisions easier for businesses

4) Upwork: Upwork is the largest platform that boasts a global pool of freelancers. The optimized search engine feature can help you find developer profiles that list experience, and reviews.  After you have selected a candidate, you can collaborate with them using the Upwork’s workspace and make payments via their platform. If you find any problem during the hiring process, you can find a resolution through their 24/7 support system. 

     Key points: 

Developers can earn badges if they receive positive client feedback. This also serves as an authenticity mechanism for businesses who want to hire only the best candidates

Businesses can find the top 1% of Next.js developers with the Upwork’s expert vetted program

Find talented freelancers from around the world

5) Freelancer: Freelancer is a great platform if you are looking for qualified candidates but on a budget. When you post a job listing, multiple candidates will bid on your project, thereby making it easier to compare rates and hire the best Next.js developer who is ready to work under your budget. The platform also features experienced professionals in large project management and small tasks.

     Key points: 

Global network of more than 73 million freelancers

Applicants detail their work portfolio and experience in their proposals, which makes the selection process easier

Developers available for both short-term projects and longer contracts. 

Wrapping up

Partnering with a Next.js developer helps your website or app stand out. They offer expertise, speed, scalability, SEO benefits, and user-focused solutions. All of this is essential for creating a successful project. 

Don’t keep watching until your competitors beat your business. Start your research for Next.js developers today and take the first step toward creating a product to wow your audience. 

Contact us to hire Next.js experts now.