In philosophy, an aporia (Ancient Greek: ᾰ̓πορῐ́ᾱ, romanized: aporíā, lit. &#x27;literally: &quot;lacking passage&quot;, also: &quot;impasse&quot;, &quot;difficulty in passage&quot;, &quot;puzzlement&quot;&#x27;) is a conundrum or state of puzzlement. In rhetoric, it is a declaration of doubt, made for rhetorical purpose and often ...

Aporia - Wikipedia

Aporia Guardrails ... Improve user trust by mitigating incorrect facts, nonsensical responses, and semantically incorrect LLM-generated SQL queries. Start mitigating ... Secure your AI models against prompt leakage, jailbreak attempts and other types of prompt injection attacks. Block Injections ... Tailor ethical AI guidelines, ensuring responsible interactions and safeguarding your brand&#x27;s integrity seamlessly. Company ...

Aporia - The AI Control Platform | Real-time Guardrails & Security

Aporia: Directed by Jared Moshe. With Judy Greer, Edi Gathegi, Payman Maadi, Faithe Herman. Since losing her husband, Sophie has struggled to manage grief, a full-time job, and parenting her devastated daughter, but when a former physicist reveals a secret time-bending machine, Sophie will ...

Aporia (2023) ⭐ 5.5 | Drama, Sci-Fi

Judy Greer plays a grieving widow who contemplates using a time machine to murder a drunk driver before he can kill her husband.

‘Aporia’: Time travel with an ethical twist - The Washington Post

Aporia definition: . See examples of APORIA used in a sentence.

APORIA Definition & Usage Examples | Dictionary.com

Aporia is a machine learning observability platform based in Tel Aviv, Israel. The company has a US office located in San Jose, California. Aporia has developed software for monitoring and controlling undetected defects and failures used by other companies to detect and report anomalies, and ...

Aporia (company) - Wikipedia

The AI Control Platform | Aporia is the leading AI Control Platform, trusted by both emerging tech startups and established Fortune 500 companies to guarantee the privacy, security, and reliability of AI applications. With Aporia, organizations gain robust guardrails for AI, effectively mitigating ...

Aporia | LinkedIn

Aporia, a Tel Aviv-based startup that helps businesses monitor and explain their AI-based services, today announced that it has raised a $25 million Series A funding round led by Tiger Global. New investor Samsung Next, as well as previous investors TLV Partners and Vertex Ventures also participated in this round, which brings the company...

Aporia raises $25M Series A for its ML observability platform | ...

Israel Hayom - Breaking news from Israel&#x27;s most widely read paper

AI machine learning startup Aporia emerges from stealth ...

That’s where tools like Aporia come in. The Tel Aviv-based company today announced that it has raised a $5 million seed round for its monitoring platform for ML models. The investors are Vertex Ventures and TLV Partners.

Aporia raises $5M for its AI observability platform | TechCrunch

Apiria

apiria

Artificial intelligence (AI) is quickly transforming industries. In line with this, the need for strong AI security has grown more critical than ever. Aporia, a provider of AI guardrails and observability solutions, has launched its "Securing AI Sucks” campaign to address this urgent issue. What is Aporia? Aporia, founded in 2019, has quickly become a trusted partner for Fortune 500 companies and leading AI teams worldwide. The company specializes in advanced AI guardrails and observability solutions for all AI workloads, revamping AI model security and monitoring. Organizations can easily add Aporia's modern guardrails within minutes and fully customize them to protect various AI applications from common issues such as hallucinations, prompt injection attacks, toxicity, and off-topic responses. Campaign Overview The "Securing AI Sucks" campaign by Aporia addresses a critical concern in the AI industry: the complexity and risks associated with securing AI systems. Through extensive research involving over 1,500 discussions among chief information security officers (CISOs) and security specialists, Aporia has identified a common theme: securing AI systems is complex and involves risks. The campaign demonstrates the dangers of ignoring AI security. As AI systems become more common and often handle sensitive information, they become prime targets for data breaches, adversarial attacks, and unauthorized access. The consequences of poor security can be severe, ranging from loss of customer trust and legal penalties to potential harm to users. Key Features of the Campaign Aporia's campaign focuses on several crucial aspects of AI security: 88% of CISOs are concerned about the unpredictability of AI systems, making it challenging to implement good security measures. Around 78% of security professionals disagree or strongly disagree that conventional security tools are sufficient for addressing AI-specific vulnerabilities. Meanwhile, 85% of CISOs face substantial challenges when adding security measures to their existing AI systems, and 80% of security professionals find identifying and monitoring AI applications challenging or extremely challenging. The campaign also introduces the concept of AI Guardrails as a solution to these challenges. Positioned between homegrown AI agents, users, and third-party AI tools, these guardrails act as a much-needed security layer, instantly reviewing every message and interaction to establish compliance with established rules. Real-life Examples of AI Security Failures The campaign presents several real-world scenarios to show the importance of AI security. For instance, a company's AI assistant unintentionally shared confidential financial projections, leading to significant losses and reputational damage. To solve this, the solution involves confidential data access control. Instead of responding with the projected revenue and profit, the AI’s answer would be blocked and rephrased by the Guardrails, replying, “I am sorry, but I cannot provide this information. Please use this system responsibly.” Another example, SecureCorp, showed an employee who faced challenges while writing a complex client proposal. The individual used an external generative AI service to save time, inputting sensitive client information to generate content. The external AI service stored the data, which later appeared in publicly available outputs, exposing confidential client details. The solution is implementing AI guardrails that detect when an employee attempts to input sensitive information into external AI services. The system’s response would be rephrased by the Guardrails to say: "Warning: Uploading sensitive data to external services is prohibited. Please use approved internal tools for handling confidential information." Impact and Results Aporia's thorough strategy for solving security challenges will resonate with organizations struggling with these issues. Offering AI Guardrails as a solution, Aporia positions itself as a thought leader in the AI security space. Undeniably, Aporia's "Securing AI Sucks" campaign brings to light the challenges in AI security while offering new solutions. As AI continues to change and integrate into various aspects of business and society, the importance of strong security measures cannot be overstated. Aporia's campaign raises awareness about these issues and presents a way forward for organizations looking to secure AI systems well. This article is published under HackerNoon’s Business Blogging program. Learn more about the program here. Artificial intelligence (AI) is quickly transforming industries. In line with this, the need for strong AI security has grown more critical than ever. Aporia, a provider of AI guardrails and observability solutions, has launched its " Securing AI Sucks ” campaign to address this urgent issue. Securing AI Sucks Securing AI Sucks What is Aporia? What is Aporia? Aporia, founded in 2019, has quickly become a trusted partner for Fortune 500 companies and leading AI teams worldwide. The company specializes in advanced AI guardrails and observability solutions for all AI workloads, revamping AI model security and monitoring. Organizations can easily add Aporia's modern guardrails within minutes and fully customize them to protect various AI applications from common issues such as hallucinations, prompt injection attacks, toxicity, and off-topic responses. Campaign Overview Campaign Overview The "Securing AI Sucks" campaign by Aporia addresses a critical concern in the AI industry: the complexity and risks associated with securing AI systems. Through extensive research involving over 1,500 discussions among chief information security officers (CISOs) and security specialists, Aporia has identified a common theme: securing AI systems is complex and involves risks. The campaign demonstrates the dangers of ignoring AI security. As AI systems become more common and often handle sensitive information, they become prime targets for data breaches, adversarial attacks, and unauthorized access. The consequences of poor security can be severe, ranging from loss of customer trust and legal penalties to potential harm to users. Key Features of the Campaign Key Features of the Campaign Aporia's campaign focuses on several crucial aspects of AI security: 88% of CISOs are concerned about the unpredictability of AI systems, making it challenging to implement good security measures. Around 78% of security professionals disagree or strongly disagree that conventional security tools are sufficient for addressing AI-specific vulnerabilities. Meanwhile, 85% of CISOs face substantial challenges when adding security measures to their existing AI systems, and 80% of security professionals find identifying and monitoring AI applications challenging or extremely challenging. The campaign also introduces the concept of AI Guardrails as a solution to these challenges. Positioned between homegrown AI agents, users, and third-party AI tools, these guardrails act as a much-needed security layer, instantly reviewing every message and interaction to establish compliance with established rules. Real-life Examples of AI Security Failures Real-life Examples of AI Security Failures The campaign presents several real-world scenarios to show the importance of AI security. For instance, a company's AI assistant unintentionally shared confidential financial projections, leading to significant losses and reputational damage. To solve this, the solution involves confidential data access control. Instead of responding with the projected revenue and profit, the AI’s answer would be blocked and rephrased by the Guardrails, replying, “I am sorry, but I cannot provide this information. Please use this system responsibly.” “I am sorry, but I cannot provide this information. Please use this system responsibly.” Another example, SecureCorp, showed an employee who faced challenges while writing a complex client proposal. The individual used an external generative AI service to save time, inputting sensitive client information to generate content. The external AI service stored the data, which later appeared in publicly available outputs, exposing confidential client details. The solution is implementing AI guardrails that detect when an employee attempts to input sensitive information into external AI services. The system’s response would be rephrased by the Guardrails to say: "Warning: Uploading sensitive data to external services is prohibited. Please use approved internal tools for handling confidential information." "Warning: Uploading sensitive data to external services is prohibited. Please use approved internal tools for handling confidential information." Impact and Results Impact and Results Aporia's thorough strategy for solving security challenges will resonate with organizations struggling with these issues. Offering AI Guardrails as a solution, Aporia positions itself as a thought leader in the AI security space. Undeniably, Aporia's "Securing AI Sucks" campaign brings to light the challenges in AI security while offering new solutions. As AI continues to change and integrate into various aspects of business and society, the importance of strong security measures cannot be overstated. Aporia's campaign raises awareness about these issues and presents a way forward for organizations looking to secure AI systems well. This article is published under HackerNoon’s Business Blogging program. Learn more about the program here. This article is published under HackerNoon’s Business Blogging program. Learn more about the program here . here

Aporia Tackles AI Security Challenges with Bold 'Securing AI Sucks' Campaign

Aporia begegnet KI-Sicherheitsherausforderungen mit mutiger Kampagne „Securing AI Sucks“

Aporia aborda los desafíos de seguridad de la IA con la audaz campaña 'Securing AI Sucks'

Aporia s'attaque aux défis de la sécurité de l'IA avec la campagne audacieuse « Securing AI Sucks »

Aporia aborda os retos de seguridade da IA coa audaz campaña "Securing AI Sucks".

Aporia affronta le sfide della sicurezza dell'intelligenza artificiale con la coraggiosa campagna "Securing AI Sucks"

Aporia は大胆な「AI のセキュリティ確保は最悪」キャンペーンで AI セキュリティの課題に取り組む

Апориа хиймэл оюун ухааны аюулгүй байдлын сорилтуудыг "Хиймэл оюун ухааныг хамгаалах нь муу" кампанит ажлын хүрээнд шийдэв.

اپوریا د AI امنیتي ننګونو سره د زړور 'Securing AI Sucks' کمپاین سره مبارزه کوي

Aporia AI Seguridad sasachakuykunata atipan mana manchakuq ‘AI chupa’ campañawan

Aporia tar itu med AI-säkerhetsutmaningar med en djärv "Securing AI Sucks"-kampanj

அபோரியா தைரியமான 'செக்யூரிங் AI சக்ஸ்' பிரச்சாரத்துடன் AI பாதுகாப்பு சவால்களை சமாளிக்கிறது

Aporia 通过大胆的“保护人工智能很糟糕”活动应对人工智能安全挑战

Businesses increasingly recognize the potential of generative artificial intelligence (GenAI). However, many organizations find themselves trapped in a cycle of endless pilot projects, unable to fully realize the benefits of GenAI in production environments. The AI control platform Aporia is changing how enterprises adopt and scale GenAI solutions, pushing them to break free from pilot limbo and achieve live success. "At Aporia, our mission is to maximize the immense potential of GenAI and the real-world challenges that hinder its widespread adoption," says Liran Hason, Chief Executive Officer (CEO) of Aporia, who coined the term “GenAI Chasm,” referring to the pilot phase AI often gets caught in before being able to go live due to security and reliability issues. With its guardrails and comprehensive risk mitigation strategies, the company is opening new avenues for seamless GenAI deployment across industries. Aporia's Robust Guardrails for Seamless GenAI Adoption One of the primary challenges in GenAI deployment is the lack of adequate control and risk management. Aporia’s comprehensive suite of tools is meant to ensure safe, responsible, and compliant AI interactions. These guardrails seamlessly integrate with existing AI workflows, providing a powerful layer of protection against potential risks such as data leakage, hallucinations, and prompt injection attacks. "Aporia's guardrails are the cornerstone of our platform," explains Hason. "They empower businesses to adopt GenAI with confidence, secure in the knowledge that their applications are reliable, secure, and aligned with their core values and objectives.” Mitigating Hallucinations and Prompt Injection Risks Hallucinations and prompt injection attacks pose threats to the integrity and reliability of GenAI applications. Aporia addresses these challenges with a multi-faceted approach that combines advanced detection algorithms, real-time actions, and proactive mitigation strategies. "Hallucinations and prompt injections can erode trust in AI systems and lead to severe consequences," notes Hason. "Our comprehensive approach ensures that these risks are identified and mitigated before they can impact end-users or the business." With Aporia's robust safeguards, organizations can confidently deploy GenAI applications, knowing their systems are protected against these critical threats. Ensuring Compliance and Ethical AI Deployment with Aporia's Solutions GenAI is becoming more prevalent, and compliance with regulations and ethical standards is paramount. Aporia's solutions are designed to help businesses deal with this complex landscape easily and to cross the GenAI chasm with confidence. "Compliance and ethics are not mere afterthoughts for us. They are embedded in the very fabric of our platform," states Hason. "We firmly believe that the long-term success of GenAI hinges on its ability to operate within a framework of trust, transparency, and accountability from both the user and business perspective." Pioneering Research for Secure and Reliable GenAI Applications Aporia Labs, Aporia's dedicated research and development division, is advancing the platform’s security and reliability. Staffed by a team of world-class AI researchers, security experts, and data scientists, Aporia Labs conducts constant research to identify emerging threats and develop services. "The work we do at Aporia Labs is instrumental to the future of GenAI. It's shaping the very trajectory of the industry," says Hason. Aporia Labs makes way for secure and reliable GenAI applications through its pioneering research. How Aporia Streamlines the GenAI Development Lifecycle Aporia's AI control platform is designed to streamline the entire GenAI development lifecycle, from ideation to deployment and beyond. A unified environment for building, testing, and monitoring GenAI applications enables teams to deliver better results. Furthermore, the platform's intuitive interface and powerful automation capabilities make it easy for businesses to bring their GenAI visions to life without compromising quality or security. "We've witnessed firsthand the transformative impact of Aporia on the GenAI development process," shares Hason. With Aporia, organizations can streamline their GenAI development efforts, accelerating time-to-market and maximizing the return on their AI investments. The Key to Unlocking GenAI's Full Potential in Enterprise Settings For enterprises seeking to harness GenAI's full potential, Aporia's AI control platform is the key to success. With its comprehensive feature set, scalable architecture, and enterprise-grade security, the platform is able to meet large-scale AI deployments' unique needs and challenges. From customizable guardrails to advanced analytics and reporting, Aporia provides the tools and insights to drive GenAI adoption and return on investment (ROI) across the organization. "Enterprises face complex challenges when it comes to scaling GenAI, from ensuring security and governance to driving organization-wide adoption," notes Hason. "Aporia's platform is purpose-built to address these challenges head-on, enabling enterprises to unlock the full potential of GenAI and drive transformative business outcomes." Bridging the Gap: Aporia's Proven Strategies for Successful GenAI Pilot-to-Production Transitions Transitioning GenAI projects from pilot to production is a critical milestone many businesses struggle with. Aporia bridges this gap with proven strategies and best practices that have helped countless organizations successfully deal with this journey. Aporia remains a leading enabler for businesses to unlock the full potential of this advanced technology. With Aporia, businesses can confidently solve the challenges of GenAI deployment, harnessing its immense potential to drive growth and become a more competitive advantage in an AI-driven future. This piece was published as a part of HackerNoon’s Business Blogging Program. For more details, please click here. Businesses increasingly recognize the potential of generative artificial intelligence (GenAI). However, many organizations find themselves trapped in a cycle of endless pilot projects, unable to fully realize the benefits of GenAI in production environments. Businesses increasingly recognize the potential of generative artificial intelligence (GenAI). However, many organizations find themselves trapped in a cycle of endless pilot projects, unable to fully realize the benefits of GenAI in production environments. The AI control platform Aporia is changing how enterprises adopt and scale GenAI solutions, pushing them to break free from pilot limbo and achieve live success. Aporia Aporia " At Aporia, our mission is to maximize the immense potential of GenAI and the real-world challenges that hinder its widespread adoption, " says Liran Hason, Chief Executive Officer (CEO) of Aporia, who coined the term “GenAI Chasm,” referring to the pilot phase AI often gets caught in before being able to go live due to security and reliability issues. With its guardrails and comprehensive risk mitigation strategies, the company is opening new avenues for seamless GenAI deployment across industries. At Aporia, our mission is to maximize the immense potential of GenAI and the real-world challenges that hinder its widespread adoption, Aporia's Robust Guardrails for Seamless GenAI Adoption Aporia's Robust Guardrails for Seamless GenAI Adoption One of the primary challenges in GenAI deployment is the lack of adequate control and risk management. Aporia’s comprehensive suite of tools is meant to ensure safe, responsible, and compliant AI interactions. These guardrails seamlessly integrate with existing AI workflows, providing a powerful layer of protection against potential risks such as data leakage, hallucinations, and prompt injection attacks. "Aporia's guardrails are the cornerstone of our platform," explains Hason. " They empower businesses to adopt GenAI with confidence, secure in the knowledge that their applications are reliable, secure, and aligned with their core values and objectives.” "Aporia's guardrails are the cornerstone of our platform," They empower businesses to adopt GenAI with confidence, secure in the knowledge that their applications are reliable, secure, and aligned with their core values and objectives.” Mitigating Hallucinations and Prompt Injection Risks Mitigating Hallucinations and Prompt Injection Risks Hallucinations and prompt injection attacks pose threats to the integrity and reliability of GenAI applications. Aporia addresses these challenges with a multi-faceted approach that combines advanced detection algorithms, real-time actions, and proactive mitigation strategies. "Hallucinations and prompt injections can erode trust in AI systems and lead to severe consequences," notes Hason. " Our comprehensive approach ensures that these risks are identified and mitigated before they can impact end-users or the business." With Aporia's robust safeguards, organizations can confidently deploy GenAI applications, knowing their systems are protected against these critical threats. "Hallucinations and prompt injections can erode trust in AI systems and lead to severe consequences," Our comprehensive approach ensures that these risks are identified and mitigated before they can impact end-users or the business." Ensuring Compliance and Ethical AI Deployment with Aporia's Solutions Ensuring Compliance and Ethical AI Deployment with Aporia's Solutions GenAI is becoming more prevalent, and compliance with regulations and ethical standards is paramount. Aporia's solutions are designed to help businesses deal with this complex landscape easily and to cross the GenAI chasm with confidence. "Compliance and ethics are not mere afterthoughts for us. They are embedded in the very fabric of our platform," states Hason. " We firmly believe that the long-term success of GenAI hinges on its ability to operate within a framework of trust, transparency, and accountability from both the user and business perspective." "Compliance and ethics are not mere afterthoughts for us. They are embedded in the very fabric of our platform," We firmly believe that the long-term success of GenAI hinges on its ability to operate within a framework of trust, transparency, and accountability from both the user and business perspective." Pioneering Research for Secure and Reliable GenAI Applications Pioneering Research for Secure and Reliable GenAI Applications Aporia Labs, Aporia's dedicated research and development division, is advancing the platform’s security and reliability. Staffed by a team of world-class AI researchers, security experts, and data scientists, Aporia Labs conducts constant research to identify emerging threats and develop services. "The work we do at Aporia Labs is instrumental to the future of GenAI. It's shaping the very trajectory of the industry," says Hason. Aporia Labs makes way for secure and reliable GenAI applications through its pioneering research. "The work we do at Aporia Labs is instrumental to the future of GenAI. It's shaping the very trajectory of the industry," How Aporia Streamlines the GenAI Development Lifecycle How Aporia Streamlines the GenAI Development Lifecycle Aporia's AI control platform is designed to streamline the entire GenAI development lifecycle, from ideation to deployment and beyond. A unified environment for building, testing, and monitoring GenAI applications enables teams to deliver better results. Furthermore, the platform's intuitive interface and powerful automation capabilities make it easy for businesses to bring their GenAI visions to life without compromising quality or security. "We've witnessed firsthand the transformative impact of Aporia on the GenAI development process," shares Hason. With Aporia, organizations can streamline their GenAI development efforts, accelerating time-to-market and maximizing the return on their AI investments. "We've witnessed firsthand the transformative impact of Aporia on the GenAI development process," The Key to Unlocking GenAI's Full Potential in Enterprise Settings The Key to Unlocking GenAI's Full Potential in Enterprise Settings For enterprises seeking to harness GenAI's full potential, Aporia's AI control platform is the key to success. With its comprehensive feature set, scalable architecture, and enterprise-grade security, the platform is able to meet large-scale AI deployments' unique needs and challenges. From customizable guardrails to advanced analytics and reporting, Aporia provides the tools and insights to drive GenAI adoption and return on investment (ROI) across the organization. "Enterprises face complex challenges when it comes to scaling GenAI, from ensuring security and governance to driving organization-wide adoption," notes Hason. "Aporia's platform is purpose-built to address these challenges head-on, enabling enterprises to unlock the full potential of GenAI and drive transformative business outcomes." "Enterprises face complex challenges when it comes to scaling GenAI, from ensuring security and governance to driving organization-wide adoption," "Aporia's platform is purpose-built to address these challenges head-on, enabling enterprises to unlock the full potential of GenAI and drive transformative business outcomes." Bridging the Gap: Aporia's Proven Strategies for Successful GenAI Pilot-to-Production Transitions Bridging the Gap: Aporia's Proven Strategies for Successful GenAI Pilot-to-Production Transitions Transitioning GenAI projects from pilot to production is a critical milestone many businesses struggle with. Aporia bridges this gap with proven strategies and best practices that have helped countless organizations successfully deal with this journey. Aporia remains a leading enabler for businesses to unlock the full potential of this advanced technology. With Aporia, businesses can confidently solve the challenges of GenAI deployment, harnessing its immense potential to drive growth and become a more competitive advantage in an AI-driven future. This piece was published as a part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as a part of HackerNoon’s Business Blogging Program. For more details, please click here . here

How Aporia Enables GenAI Deployment From Pilot Limbo to Live Success

অ্যাপোরিয়া কীভাবে পাইলট লিম্বো থেকে লাইভ সাফল্যের জন্য GenAI স্থাপনাকে সক্ষম করে

Wie Aporia die GenAI-Bereitstellung von der Pilotphase bis zum Live-Erfolg ermöglicht

Cómo Aporia permite la implementación de GenAI desde el limbo piloto hasta el éxito en vivo

Comment Aporia permet le déploiement de GenAI des limbes pilotes au succès réel

एपोरिया कैसे जेनएआई की तैनाती को पायलट लिम्बो से लाइव सफ़लता तक सक्षम बनाता है

Aporia が GenAI の導入をパイロット段階から実際の成功まで可能にする方法

Aporia가 파일럿 림보(Pilot Limbo)에서 실제 성공까지 GenAI 배포를 활성화하는 방법

Como Aporia permite a implantação de GenAI do piloto Limbo ao sucesso ao vivo

Как Aporia обеспечивает развертывание GenAI от экспериментального неопределенности до реального успеха

Aporia, Pilot Araf'tan Canlı Başarıya Kadar GenAI Dağıtımını Nasıl Etkinleştiriyor?

Cách Aporia cho phép triển khai GenAI từ thí điểm Limbo đến thành công trực tiếp

Aporia 如何使 GenAI 部署从试点走向成功

A New Framework for Trustworthy AI Deductive Reasoning

The facade is one of my favorites. Out of , I have found that it’s the one I turn to repeatedly across different applications. In this article, I’ll be exploring the facade design pattern in C# — because all of the code examples will be in C#! We’ll be looking at 4 different examples of the facade pattern in C# and how each of them is used to simplify things for the caller of our API. design pattern all of the design patterns that are out there What is the Facade Design Pattern? The Facade pattern is a software design pattern that falls under the category of structural design patterns. It provides a simplified interface for a complex subsystem, making it easier to use and understand. The main purpose of the Facade pattern is to hide the complexities of the subsystem and provide a unified interface to the clients, shielding them from the details of how the subsystem works. The Facade pattern is particularly useful when working with large and complex systems, where there are multiple classes and interactions involved. By using the Facade , you can encapsulate the underlying complexities into a single high-level interface, making it easier to interact with the system. design pattern The key characteristics of the Facade pattern include: : The Facade class encapsulates the interactions and complexities of the underlying subsystem, providing a simplified interface for clients to interact with. Encapsulation : The Facade design pattern simplifies the overall usage of the system by providing a concise and easy-to-understand interface, reducing the cognitive load for developers. Simplification : The Facade class abstracts the complexities of the subsystem, allowing clients to interact with the system without needing to know the internal details. Abstraction By utilizing the Facade pattern, you can create a well-defined boundary between the client code and the complex subsystem, leading to improved maintainability, flexibility, and reusability of the codebase. A Generic Example of the Facade Design Pattern in C#: Here is an example of how the Facade pattern can be implemented in C#: // Complex subsystem classes
class SubsystemA
{
 public void MethodA()
 {
 Console.WriteLine("Subsystem A - Method A");
 }
}

class SubsystemB
{
 public void MethodB()
 {
 Console.WriteLine("Subsystem B - Method B");
 }
}

class SubsystemC
{
 public void MethodC()
 {
 Console.WriteLine("Subsystem C - Method C");
 }
}

// Facade class
class Facade
{
 private SubsystemA subsystemA;
 private SubsystemB subsystemB;
 private SubsystemC subsystemC;

 public Facade()
 {
 subsystemA = new SubsystemA();
 subsystemB = new SubsystemB();
 subsystemC = new SubsystemC();
 }

 public void Operation()
 {
 Console.WriteLine("Facade - Operation");
 subsystemA.MethodA();
 subsystemB.MethodB();
 subsystemC.MethodC();
 }
}

// Client code
class Client
{
 static void Main(string[] args)
 {
 Facade facade = new Facade();
 facade.Operation();
 }
} In this code example, we have a complex subsystem consisting of three classes: , , and . These classes represent different functionalities or components of the subsystem. The class acts as a simplified interface that encapsulates the complexities of the subsystem. The method in the class provides a unified interface for the clients to interact with the subsystem. SubsystemA SubsystemB SubsystemC Facade Operation Facade By calling the method on the object, the client code can perform the desired actions without needing to directly interact with the complex subsystem classes. The class internally communicates with the individual subsystem classes, hiding the intricate details from the client. Operation Facade Facade Plugin Architectures in C# with the Facade Pattern In a plugin-style architecture, the Facade design pattern can be particularly useful for abstracting the complexities of dynamically selecting and interacting with various plugins based on runtime conditions or configurations. Let’s consider a document processing system where the system needs to support different formats (e.g., PDF, DOCX, ODT) and operations (e.g., parsing, rendering) through plugins. Each format is handled by a different plugin, but clients interact with a unified interface provided by the facade. Complex Subsystems – Plugins! We have plugins for handling various document formats: : Handles PDF document operations. PdfPlugin : Handles DOCX document operations. DocxPlugin : Handles ODT document operations. OdtPlugin Each plugin implements a common interface, , which defines methods for whether or not the document is supported and rendering documents. We’ll be using an imaginary interface that would support interactions for being able to render the document content to some virtual canvas — outside of the scope of this example 🙂 IDocumentPlugin IRenderContext The IDocumentPlugin Interface Let’s check out the example code for a plugin interface that each of our implementations will have: public interface IDocumentPlugin
{
 bool SupportsFormat(string filePath);

 void RenderDocument(Stream stream, IRenderContext renderContext);
} And some dummy classes for now to meet the three plugins we’re required to support: public class PdfPlugin : IDocumentPlugin
{
 public bool SupportsFormat(string filePath) => filePath.EndsWith(
 "pdf",
 StringComparison.OrdinalIgnoreCase);

 public void RenderDocument(
 Stream stream,
 IRenderContext renderContext) => Console.WriteLine("Rendering PDF document...");
}

public class DocxPlugin : IDocumentPlugin
{
 public bool SupportsFormat(string filePath) => filePath.EndsWith(
 "docx",
 StringComparison.OrdinalIgnoreCase);

 public void RenderDocument(
 Stream stream,
 IRenderContext renderContext) => Console.WriteLine("Rendering DOCX document...");
}

public class OdtPlugin : IDocumentPlugin
{
 public bool SupportsFormat(string filePath) => filePath.EndsWith(
 "odt",
 StringComparison.OrdinalIgnoreCase);

 public void RenderDocument(
 Stream stream,
 IRenderContext renderContext) => Console.WriteLine("Rendering ODT document...");
} The Document Processing Facade Class The class provides a simplified interface to interact with the appropriate plugin based on the document format, hiding the complexity of plugin selection and operation execution: DocumentProcessorFacade public class DocumentProcessorFacade
{
 private readonly List<IDocumentPlugin> _plugins;

 public DocumentProcessorFacade()
 {
 // NOTE: I would probably use dependency injection to
 // pass in viable plugins, but this is just to
 // demonstrate the example
 _plugins = new List<IDocumentPlugin>
 {
 new PdfPlugin(),
 new DocxPlugin(),
 new OdtPlugin()
 };
 }

 public void ProcessDocument(
 string filePath,
 IRenderContext renderContext)
 {
 var plugin = GetSupportedPlugin(format);
 if (plugin == null)
 {

 throw new NotSupportedException(
 $"No plugin found to support format for file '{filePath}'.");
 }

 using var fileStream = File.OpenRead(filePath);
 plugin.RenderDocument(stream, renderContext);
 }

 private IDocumentPlugin GetPluginForFormat(string filePath)
 {
 return _plugins.FirstOrDefault(p => p.SupportsFormat(filePath));
 }
 } This example demonstrates how the Facade pattern simplifies interactions within a plugin-style architecture by providing a unified interface ( ) to various document processing plugins. The facade handles the complexity of selecting the appropriate plugin based on the document format and executing operations, allowing client code to remain simple and clean. This approach enhances modularity, scalability, and maintainability of the software system. DocumentProcessorFacade Streamlining API Calls using the Facade Design Pattern Managing multiple API calls in an application can be a complex task. As a software engineer, it is important to find ways to simplify and streamline this process. One effective approach is to utilize the Facade design pattern, which provides a convenient interface to a set of interfaces in a subsystem. In this section, we’ll explore how the Facade pattern can be utilized to streamline and centralize API call management in C#. When working with multiple APIs, it is common to encounter challenges such as handling authentication, managing request/response formats, and dealing with rate limiting. These tasks can become time-consuming and error-prone if not properly managed. The Facade pattern can help alleviate these challenges by providing a unified and simplified interface for interacting with the APIs. By implementing a facade class, we can encapsulate the complexity of making API calls behind a simple and easy-to-use interface. This allows other parts of the codebase to interact with the APIs without having to worry about the details of authentication, request/response formats, or rate limiting. Let’s take a look at an example to see how this can be achieved: public class ApiFacade
{
 private readonly ApiAuthenticationService _authenticationService;
 private readonly ApiRequestFormatter _requestFormatter;
 private readonly ApiRateLimiter _rateLimiter;

 public ApiFacade()
 {
 _authenticationService = new ApiAuthenticationService();
 _requestFormatter = new ApiRequestFormatter();
 _rateLimiter = new ApiRateLimiter();
 }

 public ApiResponse MakeApiCall(ApiRequest request)
 {
 _authenticationService.Authenticate();
 var formattedRequest = _requestFormatter.Format(request);
 _rateLimiter.WaitIfNeeded();

 // Make the actual API call and retrieve the response
 var response = ApiClient.MakeCall(formattedRequest);

 return response;
 }
} In the code example above, we have created an class that encapsulates the complexity of authentication, request formatting, and rate limiting. It utilizes three separate services: , , and . By utilizing the facade pattern, we can centralize the management of these services and expose a single method ( ) that takes care of all the necessary steps to make an API call. ApiFacade ApiAuthenticationService ApiRequestFormatter ApiRateLimiter MakeApiCall To use the class, other parts of the codebase can simply create an instance and call the method, passing in the required . The facade class will handle the authentication, request formatting, rate limiting, and the actual API call, simplifying the overall process and reducing the complexity of managing multiple API calls. ApiFacade MakeApiCall ApiRequest Enhancing UIs with the Facade Design Pattern in C# User interface interactions can often become complex and involve a series of intricate steps. The Facade pattern provides an elegant solution to simplify and optimize these interactions, making them more manageable and efficient. Let’s explore how the Facade pattern can enhance user interface interactions in C# with a code example. Consider a scenario where a user needs to perform various actions on a customer management system, such as creating a new customer, updating their information, and retrieving customer details. Each of these actions involves interacting with multiple components and performing a series of steps. By utilizing the Facade design pattern, we can create a unified interface that encapsulates the complexity of these interactions. The Facade class acts as a simplified entry point, shielding the client from the underlying system’s complexities and providing a more straightforward API for interacting with the user interface. public class CustomerManagementFacade
{
 private readonly CustomerService _customerService;
 private readonly CustomerValidationService _validationService;
 private readonly CustomerCacheService _cacheService;

 public CustomerManagementFacade()
 {
 _customerService = new CustomerService();
 _validationService = new CustomerValidationService();
 _cacheService = new CustomerCacheService();
 }

 public void CreateNewCustomer(string name, string email)
 {
 if (_validationService.ValidateCustomerData(name, email))
 {
 _customerService.CreateCustomer(name, email);
 _cacheService.CacheCustomer(name, email);
 }
 }

 public void UpdateCustomerInformation(string name, string email)
 {
 if (_validationService.ValidateCustomerData(name, email))
 {
 _customerService.UpdateCustomer(name, email);
 _cacheService.UpdateCachedCustomer(name, email);
 }
 }

 public Customer GetCustomerDetails(string name)
 {
 var customer = _cacheService.GetCachedCustomer(name);

 if (customer == null)
 {
 customer = _customerService.GetCustomer(name);
 _cacheService.CacheCustomer(customer.Name, customer.Email);
 }

 return customer;
 }
} In the above code example, we have a class that acts as the façade for managing customer interactions. It encapsulates the creation, updating, and retrieval of customer information. The façade orchestrates the interaction between the , , and to provide simpler methods for the client to use. CustomerManagementFacade CustomerService CustomerValidationService CustomerCacheService With the façade pattern, the client code only needs to interact with the façade class and doesn’t need to worry about the detailed interactions with each individual component. This simplifies the codebase, reduces complexity, and allows for easier maintenance and extensibility. [Paid Product] If you want more ! information on design patterns, get your E-Book today Wrapping Up The Facade Design Pattern in C# In conclusion, we have explored the Facade design pattern in C# and discussed 4 interesting use cases along with code examples. The Facade pattern provides a simplified interface to a complex subsystem, making it easier to understand and use. Each of these examples hopefully helped to illustrate different scenarios where leveraging a facade can make things much simpler for the calling code. Understanding and utilizing design patterns, such as the Facade pattern, is important for software engineers. These patterns provide proven solutions to common software engineering problems and offer a structured approach to designing robust and scalable applications. By incorporating design patterns into our development practices, we can create software solutions that are more efficient, flexible, and easier to maintain — but understanding which design patterns best fit where takes some practice! Try out the Facade pattern further and consider implementing it in your C# projects — it’s truly one of my favorite design patterns to work with. If you found this useful and you’re looking for more learning opportunities, consider and check out my ! subscribing to my free weekly software engineering newsletter free videos on YouTube Also published . here

Simplify Complex Subsystems With The Facade Design Pattern in C#

THE WAY WE LIVE NOW AND THE PRIME MINISTER—CONCLUSION.

In our digitally interconnected world, the issue of cybercrime stands as an ever-growing concern, casting a long, menacing shadow over the information age. With technological advancements and an increasing reliance on digital platforms in every facet of our lives, the virtual realm has become a complex, bustling ecosystem. However, along with these exciting opportunities, it has also given rise to a new class of criminals who exploit the vulnerabilities of this intricate digital landscape, threatening not only our personal data but also national security and the global economy. Cybercrime is a multi-faceted and ever-evolving menace that takes on many forms, from identity theft, online fraud, and phishing to more advanced forms such as ransomware, cyber espionage, and cyber warfare. As we plunge deeper into the digital era, the widespread use of AI has given cybercriminals another way in. Cybercriminals are harnessing AI to scrutinize extensive password datasets, creating countless password variations, while AI-powered deep fakes allow the creation or manipulation of audio and visual content, making it appear genuine. In 2020, deep fake technology was used to mimic the voice of a German CEO instructing his subsidiary firm to transfer into a specified bank account. $243,000 Highly persuasive phishing emails can be rapidly produced, too, utilizing AI’s natural language processing. Malware, driven by AI, can adapt its strategy as necessary, evading detection and insidiously invading victims’ systems, making sustained cyberattacks more frequent. With the rise of AI, cybercrime's scale, sophistication, and impact are , posing unprecedented challenges to individuals, businesses, and governments worldwide. escalating According to Cybersecurity Ventures, global cybercrime damage could reach an annual figure of (£8.4 trillion) by 2025. If measured as a country, it would be the third largest economy after the US and China. $10.5 trillion Cybercriminals increasingly target the healthcare sector due to the amount of patient data held and the often outdated IT systems. The cost associated with data breaches has spiked by 42% over the last two years, according to the . Cost of a Data Breach Report The healthcare sector is the most expensive, with an average cost of $10.10 million per occurrence. Two of the most significant data breaches reported in May 2023 were US-based healthcare companies – PharMerica’s breach affected nearly , while almost 2 million Apria Healthcare customers had their personal data stolen. 6 million individuals However, rather alarmingly, this last breach occurred between 2019 and 2021, leaving customers ignorant that their data was exposed until the report in May 2023. May 2023 saw MCNA, the US government-sponsored dental care service, informing nearly 9 million patients that their data had been compromised by the ransomware gang LockBit. Gaining access to the network at the end of February, MCNA was unaware of the breach on 6th March. These are small fry compared to Twitter, which allegedly had 235 million emails leaked. As reported in January by , the leak occurred “some time ago”, leaving users’ accounts compromised and their identities exposed. APNews This brings the total number of potential records compromised in May 2023 to , up more than 15 million on April’s figures. 17,363,243 The UK is currently ranked second to the US on the , with around 85,000 crimes reported in the second quarter of 2023 and losses totaling £527 million, according to a report from . Global Cyber Security Index Money Data breaches are frequently the result of an innocent employee emailing confidential information to the wrong person, malicious insiders - often a disgruntled employee, hackers stealing information, or threat actors. These are groups or individuals intentionally exploiting weaknesses in an IT system. Companies are investing more heavily in cybersecurity in response to the growth of cybercrime. According to IDC Corporate’s Worldwide Security Spending Guide, the global expenditure on security solutions and services is projected to reach in 2023, marking a growth of 12.1% from 2022. $219 billion It’s not only large corporations investing in – have encountered a cyberattack. virtualized security 68% of small businesses As a result, the use of , two-factor authentication, controlled access, and security software is becoming commonplace, and alongside upskilling existing personnel to tackle cybercrime effectively, ensures the digital safety of global businesses. VPNs

The Global Expansion of Cybercrime

Are there enough posts about unnecessary side effects? Unlikely - you still see them everywhere.
Here is a sales pitch for this post: no functional programming jargon a go-to list of reasons to avoid side effects Python-oriented Spotting side effects Some posts describe side effects in the context of functional programming (and JavaScript): , , and a . You don't have to study functional programming to grasp this concept. Unfortunately, unnecessary side effects are prevalent in all coding paradigms, especially OOP. one two three wiki Let’s take a simple function that computes the absolute value of a number: def abs(x):
 if x < 0:
 return -x
 return x What comes out from the statement is called a “primary effect”. Now let’s take a look at another function: return def abs_effectful(x):
 if x < 0:
 return -x

 twitter_api = TwitterAPI(some_twitter_id())
 chat_bot = LanguageModel()
 tweet_content = twitter_api.read_first_tweet()
 response = chat_bot.make_response(tweet_content,
 f"{x} is important because ")
 twitter_api.respond(response)
 twitter_api.close()

 return x Here we also have the same primary effect returned by the abs_effectful function. But a section in the middle performs pretty surprising actions and influences the external world: it has side effects. You may ask: " " This contrived example is not so far from what happens in large codebases. It’s just a set of refactorings, misunderstandings, and a stream of people joining and leaving teams. Time passes, and simple code accumulates baggage. Why do you have a weird unrelated piece of code in your function?? unnecessary Here are some real examples of side effects that I've encountered in my Python ML career: publishing a number to the Weights-and-Biases server out of the loss function dumping debug images into a home folder from a computer vision algorithm changing an environmental variable inside a function dropping to on condition pdb if changing a global variable calling inside an algorithm sys.exit sending a Slack message if is encountered during training nan ... and so on. After seeing many of those, you get a for spotting side effects. If you get surprised by what function does, quite often the function has a side effect. In fact, the code with unnecessary side effects . See about simplicity, surprises and side effects. Spider-Sense is not as simple as it could be a great post from Eric Elliot Side effects are unavoidable because software must have some influence on the world. But are unnecessary side effects harmful? If there are no visible differences, is there a reason to prefer one script over another? Unexpected crashes How can crash? It pretty much can’t. What about ? It might crash in almost every line: abs abs_effectful a Twitter API might fail to initialize a language model can't allocate GPU memory it might fail to parse or generate a phrase a Twitter API might fail to publish a response or crash upon closing Every side-effect code line might crash since it deals with things you don't control. Now, from the real examples above: Weights-and-Biases or Slack might be unavailable you might run out of space in the home folder an environmental variable is deleted before setting, and so on ... So the code with side effects is more fragile. Unexpectedly slow (and occasionally ) code very slow Every side effect might bring computational costs. But most importantly, it brings timing variability into your software. Because side-effect code typically deals with uncontrolled external resources, you get uncontrolled slowdowns. Every side-effect code line in can bring you a massive slowdown: abs_effectful a Twitter API could be slow to connect a language model might wait for free GPU memory computational load on the system might make phrase parsing too slow In real-world code, similar things can happen: database access could be very slow because the network is busy writing to a hard drive could be slow because of fragmentation logging could be too slow because other loggers compete for file access, and so on ... The bottom line is that the code with side effects is slower. Hard to use it concurrently You must be careful about any state you modify in concurrent (or even worse ) software to avoid and . Code with side effects modifies the external state. It will often require a significant redesign to be used in a concurrent context. You need to ensure that only one function will use a global resource simultaneously and that the order of operations doesn't break the logic. parallel data races deadlocks The timing variability mentioned above makes it even worse.
It's a big topic, so here is a discussing concurrency and shared state access in depth. good post Hard to optimize Optimizing a function that doesn't modify anything externally is more straightforward. You can reorder operations inside it and cache the computation results. Since you're not constrained by an external call for a side effect, you can choose any tool for the job. Many side-effect-free functions can be easily rewritten in / / and . numpy torch jax Pybind So on top of being slow, code with side effects is hard to speed up. Hard to understand What if I ask you to make a docstring for ? You will definitely struggle to write a concise story about the purpose of that function. abs_effectful A function with a single return statement like achieves a single result. At a minimum, you can concisely write a docstring as abs '''This function computes an absolute value.''' If there are side effects, you have to add them all one by one: '''This function computes an absolute value 
AND opens Twitter API and creates a model 
AND generates and sends a tweet 
AND closes connections.
''' In a large company, many people are joining and changing teams. New people have to understand obscure side effects. A company with less-readable code will waste days of cumulative onboarding time. A large docstring will not help: the longer the doc, the greater the chance it will be ignored completely. Hard to release and debug The code with side effects interacts with stuff which is not under your direct control. After thinking about all imaginable failure modes of , you might still find that it crashes after the release. abs_effectful There is a crazy variability in the external world. It is tough to reproduce, debug and fix all edge cases of side effects. Often it happens stochastically, and you just can't reproduce a single crash in-house (e.g., maybe when runs on an old Windows version, GPU kernels behave differently?). abs_effectful The code with side effects takes much longer to release and debug. Hard to test Writing exhaustive unit tests for code with side effects is very hard. On top of the main result of , you should better test that all side effects behave as expected: did the person get a tweet? Was the tweet parsed correctly? Not to say of some weird unit test interactions: side effects from one test could make other tests fail. abs_stateful Interacting with the external world on a test server is often impossible. Developers have to resort to mocking all external entities. See more in this from CodeWhisperer. beautiful post Hard to reuse What if your colleague wants to compute the absolute value of a number? There is no problem with . You just import the function and use it! What about ? They wouldn't know a priori about any side effects (ignoring the hint in the name :). The code can happily work until some unfortunate day when some crazy issues from above start coming up. abs abs_effectful After finding the root cause, they'll anticipate all the problems we mentioned. Your colleague will just leave such a function alone and write their own simpler version. A codebase that relies on side effects would typically have much less code reuse. Conclusion Hopefully, you’re convinced that side effects are also the root of all evil (just like ). Now here is a compilation of all the reasons above: premature optimization Code with side effects is fragile - leads to unexpected crashes is unexpectedly code and is hard to optimize is hard to read and understand, it is hard to reuse is hard to debug and write tests is hard to use concurrently. For more discussion, please see the following great thread on . StackExchange Meanwhile, thank you for reading! Also published . here

Tread the Perils of Python's 'Side Effects' at Your Own Risk

THE BEE-EATING PHILANTHUS

DUCKS—GOOSE—PEACOCK—TURKEY—GUINEA-FOWL—CANARY-BIRD—GOLD-FISH—HIVE-BEES—SILK-MOTHS

A previous version of this article was published 4 years ago. If we want to build a great recipe API server(an idea behind starting the ) — we should learn the best cases from major competitors in this field. So I listed here APIs that I personally adore. It’s really hard to find some other cool example. If you know other API providers — don’t hesitate to comment below! ChickenKyiv project Edamam Description Edamam has few open APIs — Nutrition Analysis, Food Database Lookup, Food Entity Extraction, Recipe Licensing, Recipe Search, Meal Recommendation Engine Plans Free, Startup(free with rules), Enterprise(from $100) My 2 cents I like it. One of my favorite. Clear documentation, not a lot of custom/crazy things. Search API is cool and working fast. Documentation https://developer.edamam.com/recipe-search-api-v2-changelog RecipePuppy Description Recipe Puppy is perfect for using at your development project. So if you have simple code on Github and want to send queries to some server — pick it. Plans Free My 2 cents Simple. But limited, for sure. So you wouldn’t find any cool features from other APIs listed here, but if this is not bothering you — it’s a good place to start. And i like pugs :) Documentation http://www.recipepuppy.com/about/api/ ReciPal Description This is another . They have a lot of data and provide it in an easy way. I personally like “scale recipe” feature, because this is an important feature of our product too, and i can see how they did it. They also care about sub-recipes. big player Plans Not clear. I think it’s upon request. My 2 cents Awesome documentation. I assume they also have a mock server here. Documentation https://www.recipal.com/api-docs#intro BigOven Description BigOven is industry standard. This is not the first version of their API, so I’m pretty sure they improve it a lot. But they keep a lot of old-functions, so old version works too. It’s not cool to see a lot of deprecated methods, maybe they will update it soon. They have separated endpoints for Grocery Lists, Glossary, Reviews. Plans From $100 My 2 cents I like it too. Not sure if I’ll be ready to pay their prices. Because can Yummly provides more. Documentation http://api2.bigoven.com/swagger/ui/index#/ Yummly Description They, not just store recipes, they really innovate and disrupt this industry. You can google their blog articles, related to how they classify “recipe stuff” using Machine learning — very inspiring. They also support hackathons, so if you have a small open project — you can contact them and receive a dev API key. Plans from $500 My 2 cents If you have a small food startup, you wouldn’t be able to use Yummly as a service provider. But when you’ll be ready — don’t hesitate to switch. it’s really to compare their data with another company. Documentation is cool, for sure :) Documentation https://developer.yummly.com/documentation.html Spoonacular Description They are listed at Kong(prev Mashape) so they are reliable too. They have endpoints for meal planning, they can convert ingredients into grocery products and have an interesting structure for Nutrition data. Also, provide a hackathon access. Plans 0–$1000 My 2 cents It’s like a perfect tool that stands in between. Has not the only big database for recipes but also has tools for meal planning. Documentation https://rapidapi.com/spoonacular/api/recipe-food-nutrition https://blog.api.rakuten.net/api-tutorial-spoonacular-api-for-food-and-recipes/ Eaternity Description Awesome breakdown of ingredient properties. I personally ‘steal’ a lot of their ideas, related to how they structure their GitHub repositories. A lot of information related to CO2 emissions(which is unique) Plans EUR 99 My 2 cents Kickass because using Apiary as documentation provider(backed by Oracle and looks better than Swagger(less nerdy)). Eaternity website have a cool vibe :) Documentation https://eaternity.docs.apiary.io/#reference/recipe-resources WTFDIET Description Simple recipe API. Not a lot of custom things. But can be used as inspiration for beginners. And I like this name. Plans not available data. Documentation https://antoinewattier.docs.apiary.io/#reference/recipes/recipes-collection/retrieve-a-recipe This is my collection for the best APIs related to recipes. You can also check quora`s discussion. link below. https://www.quora.com/Are-there-any-free-APIs-for-food-recipes You reached the end. These zebras here for you. Share if you like this article. It will help to get more views and motivate to publish more articles like this one.

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