Solana and its Key Properties

Before diving into ZK Compression it is import to understand some the key fundamental properties of Solana blockchain that makes it a suitable platform for such innovation.

1. Synchronous Atomic Composability:

It means that multiple independent Solana programs can interact with each other in a single transaction while guaranteeing that either all the interactions will succeed and returns the result or all will fail. This process insures that all phases of transactions are processed together so you don't up worrying about a partial transaction state.

How it is relevant to ZK compression:

Well here ZK Compression leverages this property by enabling interactions between compressed and non-compressed accounts within the same transaction. For instance, multiple accounts that have been compressed can still maintain atomic interactions with other on-chain accounts without breaking composability. This ensures that compressed data can interact seamlessly with other on-chain programs, facilitating full composability within Solana’s L1 ecosystem.

2. Concurrency

Solana's architecture is designed in such a way that allows multiple transactions to be processed in a parallel run. This is usually done by Solana's use of "accounts" an an independent state, that allows transactions that can access different accounts to execute simultaneously.

Relevance to this ZK Compression benefits from Solana's concurrent processing by enabling multiple account states to be compressed and managed independently. Compressed accounts can be processed simultaneously in parallel with non-compressed accounts, optimizing overall performance and ensuring that the compression does not become a bottleneck. Concurrency enhances the scalability that ZK Compression seeks to achieve by reducing transaction latency even when interacting with large datasets.

3. Safety

In Solana safety here means to guarantee that system will always perform in a valid state and will never process any invalid transaction. This ensure zero invalid transaction and hence does not corrupt the blockchain state.

In relevance to this ZK Compression must ensure that compressed data remains secure and valid. The zero-knowledge proofs used to compress account states provide cryptographic assurances that the compressed data corresponds to the correct on-chain state. This maintains Solana’s safety guarantees, ensuring that even when working with compressed data, all transactions remain verifiable and correct.

4. Liveness

This property ensures that the system continues to process transactions and progress forward, regardless of network conditions or temporary failures.

ZK Compression adds to Solana's liveness by improving the efficiency of state storage and management. By reducing the storage footprint and transaction size, ZK Compression helps ensure that the blockchain continues to function smoothly, even under high load conditions. This means that applications using compressed states can handle large user bases or frequent small transactions without negatively impacting the network's ability to progress.

5. Censorship Resistance

Censorship resistance ensures that no single entity (or set of entities) can prevent specific transactions from being included in a block or delay them indefinitely. This is very essential feature of decentralized systems, that ensures fair access to the network for all participants.

Now the question arises Why we need ZK Compression? What it was introduced into Solana ecosystem? How it helps and improve Solana blockchain state? Lets understand all of these questions step wise.

Why ZK Compression?

As it is well know that storing data on chain is very expensive, to put this into a wider context lets understand it with a short example.

Drip Haus which was founded by Vibhu Norby, which is a creator platform built upon Solana, that enables artists and creators to distribute digital works, such as art and music, directly to their followers while facilitating payments and tips. On the platform it usually cost users roughly around "70 SOL" to store "10MB" of account data on Solana, which is huge. To resolve this challenge, ZK Compression offers a way to significantly reduce these storage costs by compressing account data off-chain while maintaining a verifiable state on-chain.

By using Zero-Knowledge Proofs, ZK Compression allows developers to prove that a transaction was executed correctly without storing the entire state on-chain. This keeps the state lightweight and accessible while still following Solana’s composability and concurrency principles.

How ZK Compression works?

ZK (Zero-Knowledge) Compression was introduced by Light Protocol in 2021 , which is a mechanism to reduce the on-chain data storage and bandwidth requirements in Solana transactions. To understand ZK Compression properly it's essential to aware about Solana's traditional transaction lifecycle. a Solana program loads the state of all accounts from storage to memory, processes the transaction, and then writes the modified state back to disk. However, with ZK Compression, the accounts' data is passed as part of the transaction payload rather than loaded from disk.

Typical lifecycle of Solana Transaction

1. 1.Transaction Validation: This step includes verifying the transaction's age, ensuring that it hasn’t been processed before, performing structural verification, gas fee calculations, and signature checks.

2. 2.Program Execution: The Solana Virtual Machine (SVM) loads the program bytecode and executes the transaction.

3. 3.Account Loading: Accounts referenced in the transaction are loaded from storage (disk) into memory.

4. 4.Account Updates: Any modifications to accounts (such as balance updates) are made and saved back to storage.

Following process of ZK Compression

1. 1.State Compression: Multiple account states are recursively hashed into a compact 32-byte state root. This root is stored on-chain while the detailed account data is stored off-chain in the Solana ledger.

2. 2.Merkle Proofs and Zero-Knowledge Proofs: To interact with the compressed data, a Merkle proof is generated, which shows that the off-chain data corresponds to the on-chain state root. However, instead of using large proofs that would exceed Solana’s transaction size limit, Groth16 zk-SNARKs (Succinct Non-Interactive Arguments of Knowledge) are used to compress the Merkle proofs into a smaller, constant-sized proof of 128 bytes. This combination of Merkle proofs and ZK-SNARKs is key to making ZK Compression efficient. It allows Solana to maintain full composability with its L1 structure without compromising the speed and cost benefits that Solana provides.

3. 3.Proof Verification: When a transaction attempts to modify or access a compressed account, it provides a proof that the data aligns with the state root. This proof is verified on-chain, allowing the transaction to proceed if valid.

One of the key benefits of open source is that it allows developers to build upon the work of others, rather than starting from scratch. This not only saves time and resources but also enables the creation of more complex and innovative products. For example, the Linux operating system, which powers a large portion of the world's servers and devices, is built on top of several open-source projects. Similarly, the popular web browser Firefox is also built on top of open-source projects such as the Gecko rendering engine.

In addition to saving time and resources, open source also promotes collaboration and community involvement. Developers from all over the world can contribute to open-source projects, which leads to a diverse range of perspectives and ideas. This diversity can lead to more creative and innovative solutions, as well as a stronger sense of community and belonging among contributors.

Collaboration is also essential for the growth and success of open-source projects. By working together, developers can share their knowledge and expertise, as well as their resources and tools. This helps to ensure that open-source projects are of high quality and can meet the needs of their users.

One of the best examples of the power of open source and collaboration is the growth of the internet. The internet is built on open standards and protocols, which have enabled anyone with an internet connection to access and share information with the rest of the world. This has led to the creation of a global network of information and knowledge, which has had a profound impact on society and the way we live our lives.

In conclusion, open source and collaboration have played a crucial role in the growth and development of the technology industry. By allowing developers to build upon the work of others and collaborate towards common goals, open source has enabled the creation of more complex and innovative products, as well as foster a sense of community and belonging among contributors. The internet is a prime example of the power of open source and collaboration, and we can expect to see many more exciting developments in the future as a result of these practices.

Where and how you can also collaborate

Here are many ways to collaborate on open-source projects and engage with communities, including the following:

1. Join an existing open-source project on a platform like GitHub, where you can fork the repository, make changes, and submit a pull request for review. You can also create your open-source project on GitHub and invite others to contribute.

2. Attend hackathons, which are events where people come together to work on projects over a short period. Hackathons can be a great way to collaborate on open-source projects and meet other like-minded individuals.

3. Participate in online communities, such as forums, chat rooms, or social media groups, where you can connect with others who are interested in open source and collaboration. Some examples of online communities for open source and collaboration include Reddit, Stack Overflow, and LinkedIn Groups.

4. Contribute to open source projects through organizations or initiatives that support and coordinate open source contributions, such as the Open Source Initiative, the Linux Foundation, or Google's Open Source Program Office.

GitHub

Joining an existing open-source project on a platform like GitHub is a common way to collaborate on open-source projects. Here's a step-by-step guide to getting started:

1. Find a project that interests you and that you would like to contribute to. You can search for open-source projects on GitHub, or you can look for projects that are related to your area of expertise or interests.

2. Create a GitHub account if you don't already have one. GitHub is a platform that hosts and manages open-source projects, and it provides a range of tools and features to support collaboration.

3. Fork the repository of the project you want to contribute to. A repository is a collection of files and resources that make up a project. By forking the repository, you create a copy of the project that you can work on without affecting the original.

4. Clone the forked repository to your local machine. This will allow you to make changes and test your code locally before you submit it for review.

5. Make changes to the code or resources in the repository as needed. You can add new features, fix bugs, or improve the documentation, depending on the needs of the project and your skills and expertise.

6. Test your changes to make sure they work as intended. This can involve running the code, reviewing the results, and fixing any issues that arise.

7. Commit your changes and push them to your fork of the repository. A commit is a snapshot of your changes, and pushing the changes to your fork updates the repository with your changes.

8. Submit a pull request to the original repository. A pull request is a request for the maintainers of the original repository to review and consider your changes. They may ask you to make additional changes or provide more information before they accept the pull request.

If your pull request is accepted, your changes will be incorporated into the original repository, and you will have contributed to the open-source project. If your pull request is rejected or you decide not to submit it, you can continue to work on your fork of the repository and submit a new pull request later.

Hackathons

Hackathons are events where people come together to work on projects over a short period, often a few days. Hackathons can be a great way to collaborate on open-source projects, as they provide a focused and energizing environment for working on projects. Here's how you can participate in a hackathon:

1. Find a hackathon that interests you and meets your availability. Hackathons can be in-person or virtual, and they can have a specific theme or focus, such as a particular technology or social issue.

2. Register for the hackathon if necessary. Some hackathons have a registration process, while others are open to anyone who wants to participate.

3. Prepare for the hackathon by gathering the necessary resources and tools, such as a laptop, software, and any project materials. You may also want to review the hackathon's guidelines and expectations.

4. Attend the hackathon and participate in the activities. Hackathons usually involve a mix of workshops, presentations, and project work, and they may have a variety of prizes or awards for the most creative or impactful projects.

5. Work on an open-source project during the hackathon. You can either join an existing open-source project or create your own, depending on your interests and goals.

6. Collaborate with others on your project. Hackathons are a great opportunity to meet and work with new people, so be open to working with others and sharing ideas and expertise.

7. Present your project at the end of the hackathon. Many hackathons have a final presentation or showcase where participants can share their work with others. This can be a great opportunity to get feedback on your project and connect with potential collaborators.

Overall, hackathons can be a fun and rewarding way to collaborate on open-source projects and learn from others. They can also be a great way to network and build your professional skills and connections.

Online communities

Online communities like "WeMakeDev", "Eddiehub" and "4C" are platforms or groups where people with shared interests can connect and communicate with each other. There are many online communities for open source and collaboration, including forums, chat rooms, and social media groups. Here's how you can participate in online communities related to open source and collaboration:

1. Find an online community that interests you and aligns with your goals and interests. You can search for open-source and collaboration communities on social media platforms, forums, or specialized websites.

2. Join the community if necessary. Some communities may require you to register or apply to join, while others may be open to anyone interested.

3. Familiarize yourself with the community's guidelines and expectations. Each community may have its own rules and norms for behavior and communication, so it's important to follow these guidelines to ensure a positive and productive experience.

4. Introduce yourself and let others know what you are interested in and what you hope to contribute to the community. This can help you connect with others who have similar interests and goals.

5. Participate in discussions and contribute to the community. You can ask questions, share your own experiences and insights, and provide feedback and support to others.

6. Collaborate with others on open-source projects. Many online communities provide opportunities to connect with others who are interested in open source and collaboration. You can work on projects together, share resources and expertise, and get feedback on your work.

Online communities can be a great way to connect with others who are interested in open source and collaboration, learn from others, and contribute to the community.

Open Source Projects

There are many organizations and initiatives that support and coordinate open source contributions, including the Open Source Initiative (OSI), the Linux Foundation, and Google's Open Source Program Office. Here's how you can contribute to open-source projects through these organizations:

1. Find an organization or initiative that aligns with your interests and goals. The OSI, for example, promotes and protects open-source software, while the Linux Foundation supports the development of the Linux operating system and other open-source projects.

2. Learn about the organization's mission, goals, and programs. This will help you understand the scope and focus of the organization and how you can contribute.

3. Find open-source projects that the organization supports or coordinates. Many organizations have a list of projects or initiatives that they support, and they may have specific guidelines and expectations for contributions.

4. Review the project's contribution guidelines and follow the process for submitting changes or contributions. This may involve creating a fork of the repository, making changes, and submitting a pull request for review.

5. Collaborate with others on the project. Many open-source projects involve contributions from multiple people, and you can work with others to share ideas, coordinate efforts, and review each other's work.

6. Stay up to date with the project's progress and any new opportunities to contribute. Many open-source projects have a regular release cycle, and they may have ongoing needs for contributions and updates.

Contributing to open-source projects through organizations and initiatives can be a great way to get involved in high-impact projects and make a meaningful contribution to the open-source community.

Communities value in my journey

Participating in online communities has been a valuable experience for me in terms of connecting with others who share my interests and goals, learning from others, and getting feedback on my work.

One of the main benefits of online communities is that they allow you to connect with others who have similar interests and goals, even if you are geographically distant from each other. This can be especially useful if you are working on a niche topic or if you are in an area where there is not a strong local community around your interests. Online communities can provide a sense of belonging and connection that can be hard to find elsewhere.

In addition to connecting with others, online communities can also be a great way to learn from others and get feedback on your work. By participating in discussions, asking questions, and sharing your own experiences and insights, you can learn from others and get valuable feedback on your work. This can help you grow and improve as a professional or enthusiast, and it can also help you build your reputation and credibility within the community.

Overall, participating in online communities has been a rewarding experience for me, and I highly recommend it to others who are looking to connect with others, learn from others, and get feedback on their work.

How Open-Source can play a major role in your career

Open source can play a significant role in advancing your career and professional development, as it can provide opportunities for leadership, exposure, and recognition.

One of the main ways that open source can advance your career is by providing leadership opportunities. By contributing to open-source projects, you can take on leadership roles, such as project maintainer or community leader, which can help you develop your leadership skills and build your reputation. Open source can also provide opportunities for mentorship, where you can share your knowledge and expertise with others and help them grow and develop as professionals.

In addition to leadership opportunities, open source can also provide exposure and recognition for your work. By contributing to open-source projects, you can showcase your skills and expertise to a wider audience, which can help you build your professional network and reputation. Open source can also provide opportunities for recognition, such as awards or accolades, which can help you stand out in the job market and advance your career.

Overall, open source can be a valuable resource for advancing your career and professional development, as it can provide opportunities for leadership, exposure, and recognition. By contributing to open-source projects, you can build your skills, network, and reputation, and make a meaningful contribution to the greater good.

Challenges and how to overcome them

Collaborating on open-source projects and participating in communities can present a range of challenges, including managing conflicts, communicating effectively with team members, and coordinating contributions from different time zones and cultural backgrounds. Here are some best practices for addressing these challenges:

1. Manage conflicts effectively. Conflicts are a normal part of the collaboration, and they can occur for a variety of reasons, such as differing opinions, misunderstandings, or miscommunication. To manage conflicts effectively, it's important to stay calm, listen to others, and communicate openly and honestly. You can also seek out mediation or other forms of support if needed.

2. Communicate effectively with team members. Communication is key to successful collaboration, and it's important to keep lines of communication open and clear. This can involve using a variety of communication tools and channels, such as email, chat, or video conferencing, and it can also involve being responsive, respectful, and proactive in your communication.

3. Coordinate contributions from different time zones and cultural backgrounds. Collaborating with others from different time zones and cultural backgrounds can present challenges, such as coordinating schedules and overcoming language and cultural barriers. To address these challenges, it's important to be flexible, patient, and understanding, and to use tools and strategies that can help you coordinate effectively, such as time zone conversion tools and language translation tools.

Artificial intelligence (AI) and machine learning (ML) are two fields that are changing how we live and work. AI is the comprehensive concept of machines performing tasks in a "smart" manner, whereas ML is a subset of Artificial intelligence that includes the use of algorithms to enable machines to learn from data and enhance their performance on a specific task over a period.

One of the greatest significant benefits of AI and Machine Learning is their ability to analyze large amounts of data quickly and precisely. This enables businesses to make better decisions based on real-time data, and it enables the development of previously unimaginable new products and services. ML algorithms, for example, are being used to develop personalized recommendations for online shoppers, detect fraud in financial transactions, and more accurately diagnose medical conditions.

An even major benefit of AI and ML is their ability to automate repetitive tasks, which can help businesses enhance their efficiency and productivity. This is especially true in industries like manufacturing and logistics, where machines are progressively being used to perform tasks previously carried out by humans.

Despite the numerous potential benefits of AI and machine learning, there are some issues with their impact on society. The use of AI and ML in decision-making processes, for example, can be biased if the algorithms are not trained on diverse and representative data sets. There is also the possibility that AI and ML will displace human workers, which could have serious economic and social consequences.

Are Artificial Intelligence And Machine Learning The Same Things 🤔?

No, they are not, here are seven key points that differentiate AI from machine learning:

1. Definition: AI refers to the overall field of building intelligent systems, while ML is a specific technique for training models to learn from data.

2. Scope: AI covers a wide range of technologies and approaches, including machine learning, natural language processing, robotics, and more. ML is a specific technique within the field of AI.

3. Training: ML algorithms and models are trained using data, while traditional AI systems may be programmed with rules and instructions.

4. Adaptability: ML models can adapt and improve based on new data, while traditional AI systems may require manual updating.

5. Decision-making: ML models can make predictions and decisions based on data, while traditional AI systems may use rules and logic to make decisions.

6. Types of tasks: ML is often used for tasks that involve making predictions or decisions based on data, such as image classification or fraud detection. AI can be used for a wide range of tasks, including decision-making, natural language processing, and more.

7. Level of intelligence: ML models can perform specific tasks based on the data they were trained on, but they do not have general intelligence like humans. AI systems can exhibit a wide range of intelligence, from simple rule-based systems to more advanced systems that can exhibit human-like intelligence.

What Is Deep Learning And How It Is Different From AI-ML?

Deep learning falls under machine learning, which itself falls under artificial intelligence. But what are their genuine contrasts and similarities?

The relationship between them is sometimes represented as a set of concentric circles, with DL at the center and AI on the outside.

Deep learning (DL) is a type of machine learning that uses artificial neural networks to learn and make decisions. It is called "deep" learning because the neural networks have multiple layers, or "depths," that allow them to learn and make decisions based on multiple levels of abstraction.

Deep learning has been successful in a wide range of applications, including image and speech recognition, natural language processing, and more. It has been particularly successful at tasks that require the model to learn complex relationships in data and make decisions based on those relationships.

Deep learning is often implemented using neural network architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). It requires large amounts of labeled data and significant computational resources to train.

Deep learning, in contrast, uses several layers, and it's these additional, "hidden," processing levels that give deep learning its name. Deep learning algorithms are essentially self-training in that they can evaluate and improve their accuracy over time by analysing their own predictions and outcomes. Deep learning algorithms have the ability to learn on their own.

Why AI-ML Is Important In Real Life?

Artificial intelligence (AI) and machine learning are significant because they enable computers to learn and carry out tasks without explicit programming. This enables them to learn and adapt over time, as well as carry out a variety of activities more effectively and precisely than humans can.

To free up human attention for more difficult and innovative work, AI can also be utilized to automate regular or repetitive jobs. AI can assist to increase efficiency and accuracy in sectors like healthcare and finance, which would benefit customers and patients.

AI/ML Examples And Use Cases 🤯:

1. Healthcare⚕️:

   Machine learning can be used to analyze medical images, such as X-rays and CT scans, to detect abnormalities and assist with diagnosis. It can also be used to predict the likelihood of certain diseases based on patient data, such as age, gender, and medical history.

2. Finance💹:

   Machine learning can be used to detect fraudulent transactions and to make predictions about market trends and the likelihood of certain outcomes. It can also be used to personalize financial products and services, such as recommending investment portfolios or credit card offers.

3. Manufacturing👷🏻:

   Machine learning can be used to optimize production processes, predict equipment failures, and improve supply chain efficiency. It can also be used to analyze customer data to predict demand for certain products and to optimize inventory management.

4. Retail🛍️:

   Machine learning can be used to personalize recommendations for online shoppers and to optimize pricing and marketing strategies. It can also be used to improve the accuracy of demand forecasting and to optimize the placement of products in stores.

5. Transportation🚄:

   Machine learning can be used to optimize routing and scheduling for delivery companies and to predict maintenance needs for vehicles. It can also be used to improve traffic flow in cities by analyzing traffic data and making recommendations for changes to traffic signals and road layouts.

Pros 👍🏻 And Cons 👎🏻 Of This Technology.

The advantages and disadvantages of AI (artificial intelligence) depend on how it is used and the context in which it is applied.

Some potential advantages of AI include:

• Improved accuracy 🎯and speed: AI algorithms are capable of processing large amounts of data quickly and accurately, which can enable businesses to make more informed decisions and develop new products and services.

• Increased efficiency and productivity: AI can automate repetitive tasks, which can help businesses to increase their efficiency and productivity. This is particularly relevant in industries such as manufacturing and logistics.

• Enhanced customer experience: AI-powered chatbots and other applications can provide personalized, real-time support to customers, which can improve the overall customer experience.

However, there are also some potential disadvantages of AI, including:

• Bias: AI algorithms can incorporate bias if they are trained on biased data sets, which can result in unfair and discriminatory outcomes.

• Job displacement: The automation of tasks previously performed by humans has the potential to displace workers, which could have significant economic and social implications.

• Security and privacy risks: AI algorithms can be vulnerable to hacking and other security threats, which could compromise sensitive data and personal information.

Some Artificial Intelligence Job Roles That Are Most In Demand 🧑🏻‍💻.

There are many different job roles within the fields of machine learning and artificial intelligence. Some of these roles include:

• Machine learning engineer: This role involves developing and implementing machine learning models and algorithms.

• Data scientist: This role involves using machine learning and statistical techniques to analyze and interpret data.

• AI engineer: This role involves developing and implementing AI systems and applications.

• NLP engineer: This role involves developing and implementing natural language processing systems and algorithms.

• Computer vision engineer: This role involves developing and implementing computer vision algorithms and systems.

• Robotics engineer: This role involves developing and implementing machine learning and AI algorithms for use in robotics applications.

• Deep learning engineer: This role involves developing and implementing deep learning algorithms and models.

• Reinforcement learning engineer: This role involves developing and implementing reinforcement learning algorithms and systems.

Skills Required To Get A Job In This Field.

There are many different roles and specialties within the field of artificial intelligence (AI) and machine learning (ML), and the specific skills required for a job in this field will depend on the role and the industry.

In general, some common skills that may be useful for a job in AI or ML include:

1. Strong math skills, including linear algebra, calculus, and statistics

2. Proficiency in programming languages such as Python, R, and C++

3. Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, and sci-kit-learn

4. Knowledge of data preprocessing and manipulation techniques

5. Experience with data visualization tools such as Matplotlib and Seaborn

6. Strong problem-solving and critical-thinking skills

7. Ability to work with large, complex datasets

8. Experience with software development practices, including version control and testing

NEEDS OF BRIDGES

Bridges exist to connect blockchain networks. They enable connectivity and interoperability between blockchains.

Bridges allow isolated blockchain environments to communicate with one another. They create a pathway for tokens, messages, arbitrary data, and even SmartContract calls to be transferred from one blockchain to another.

Blockchains exist in siloed environments, which means they cannot naturally trade and communicate with one another. As a result, while an ecosystem may have significant activity and innovation, it is constrained by a lack of connectivity and interoperability with other ecosystems.

BENEFITS OF BRIDGES:

• the transfer of any data, information, and assets across chains.

• unlocking new features and use cases for protocols as bridges expand the design space for what protocols can offer.

• the opportunity to leverage the strengths of different blockchains. For example, developers can benefit from the lower fees offered by the different L2 solutions by deploying their dapps across rollups, and sidechains and users can bridge across them.

• collaboration among developers from various blockchain ecosystems to build new products.

• attracting users and communities from various ecosystems to their Dapps.

HOW DO BRIDGES WORK?

While there are many types of bridge designs, three ways to facilitate the cross-chain transfer of assets stand out:

1. Lock and mint – Lock assets on the source chain and mint assets on the destination chain.
2. Burn and mint – Burn assets on the source chain and mint assets on the destination chain.
3. Atomic swaps –Swap assets on the source chain for assets on the destination chain with another party.

TYPES OF BRIDGES

Bridges can usually be classified into one of the following buckets:

1. Native bridges – These bridges are typically built to bootstrap liquidity on a particular blockchain, making it easier for users to move funds to the ecosystem. For example, the Arbitrum Bridge is built to make it convenient for users to bridge from Ethereum Mainnet to Arbitrum. Other such bridges include Polygon PoS Bridge, Optimism Gateway, etc.

2. Validator or oracle based bridges – These bridges rely on an external validator set or oracles to validate cross-chain transfers. Examples: Multichain and Across.

3. Generalized message passing bridges – These bridges can transfer assets, along with messages and arbitrary data across chains. Examples: Nomad and LayerZero.

4. Liquidity networks – These bridges primarily focus on transferring assets from one chain to another via atomic swaps. Generally, they don’t support cross-chain message passing. Examples: Connext and Hop.

Alchemy is under everyone's spotlight after it got valued above $10 billion, but what is Alchemy , and how a startup which was founded in 2017 hit a $10 billion+ evaluation in just matter of time.

📷 Image credit: Co-founders Joe Lau and Nikil Viswanathan Nikil / Alchemy

Alchemy is not any ordinary startup. It was founded by NIKIL VISWANATHAN (CEO) and JOE LAU (co-founder) in 2017. It is a blockchain developer platform which provides software developing tools for developers. It's main aim is to provide ease for developers and remove complexity and costs of building products while improving applications through "necessary" developers tools.

Alchemy wants to do same for blockchain what AWS (Amazon Web Services) is doing for internet. It provides the leading blockchain development platform powering millions of users in 197 countries worldwide. It's mission is to provide developers with the fundamental building blocks they need to create the future of technology.

Developers building DApps which interact with Ethereum can use Alchemy's powerful APIs to supercharge their apps. They also provide services like analytics, monitoring, altering, logging and debugging.

Alchemy majorly solves these 3 challenges:

Alchemy is the only scalable, reliable, and accurate blockchain infrastructure provider.

Scalability

• Alchemy's scaling solutions ensure that you'll be able to rapidly scale your business with no rate limiting or crashing.

📷 Image credit: Alchemy

Reliability

• Alchemy's 99.9% up-time ensures that you'll be able to interact with the blockchain whenever you need to.

📷 Image credit: Alchemy

Accuracy

• Alchemy has 100% data-correctness, ensuring that your users won't experience faulty transactions or incorrect blockchain info.

📷 Image credit: Alchemy