Solana: The Next in Blockchain

Blockchain, a term synonymous with Bitcoin or Dogecoin, disrupted the global equity market when first launched. A highly hyped terminology, blockchain is nothing more than a digital system for recording transactions and related data in multiple places at the same time.

It is a type of distributed ledger technology, where every transaction in the ledger is authorized by the digital signature of the owner. This makes ledgers tamper-proof. Hence the information in the digital ledger is highly secure.

Now, its application has expanded to many areas. From supply chain and logistics to BFSI, from manufacturing to entertainment, blockchain has helped streamline processes and increase efficiency.

It is a common belief that blockchain and cryptocurrencies like Bitcoin, and Solana are the same. But in reality, cryptocurrencies rely on blockchain technology to be secure.

What makes Blockchain so popular?

Highly Secure

As blockchain technology uses digital signatures it is almost impossible to corrupt or change one user’s data by the other user without a specific digital signature.

Decentralized System

There is no need for regulatory bodies like the government or banks to approve transactions. In blockchain, transactions are done with the mutual consensus of users resulting in safer and faster transactions.

Automation Capability

It is programmable and can generate systematic actions, events, and payments automatically when the criteria of the trigger are met. So validating transactions is completely automated.

How Does Blockchain Technology Work?

Blockchain is a combination of three leading technologies:

Cryptographic keys

A peer-to-peer network containing a shared ledger

A means of computing, to store the transactions and records of the network

Each individual has two cryptographic keys – A private key and a Public key. The data is digitally signed using the Private key and can be verified using the public key.

Also if user-1 wants to send some transaction data to user-2 then he/she will hash the data with user-2’s public key, so only user-2 can confirm the transaction using his/her private key.

The digital signature is merged with the peer-to-peer network; a large number of individuals who act as authorities use the digital signature to reach a consensus on transactions.

Blockchain users employ two cryptography keys to perform different types of digital interactions over the peer-to-peer network.

Secure hashing in blockchain

Blockchain technology uses hashing and encryption to secure the data, relying mainly on the SHA256 algorithm.

Secure Hash Algorithm-256(SHA-256) is a cryptographic hash function designed by the United States National Security Agency (NSA). SHA 256 produces fixed size 256 bits output for variable-size input.

The sender’s private key and public key, the receiver’s public key, and the transaction are hashed using SHA256 and transmitted all over the network, and added to the blockchain after verification. The SHA256 algorithm makes it almost impossible to hack the hash encryption, which in turn simplifies the sender and receiver’s authentication.

What is Solana?

Solana is a blockchain platform designed to host decentralized, scalable applications.

Founded in 2017 by Anatoly Yakovenko, and co-founded by Raj Gokul (COO at Solana), Solana (Solana’s cryptocurrency is SOL) is currently backed by experiences from top organizations like Google, Microsoft Intel, etc.

It is a web-scale blockchain that provides fast, secure, scalable, and decentralized apps. The system currently supports 50,000 TPS (Transactions per second) and 400ms Block Times. The overarching goal of the Solana software is to demonstrate that there is a possible set of software algorithms using the combination to create a blockchain. So, this would allow transactions to scale proportionally with network bandwidth satisfying all properties of a blockchain: scalability, security, and decentralization. Furthermore, the system can support an upper bound of 710,000 TPS on a standard gigabit network and 28.4 million TPS on a 40-gigabit network.

The core innovation that underlays the Solana Network is Proof of History, — a proof of historical events. Utilizing Proof of History creates a historical record that proves that an event has occurred at a specific moment in time. Whereas other blockchains require validators to talk to one another to agree that time has passed, each Solana validator maintains its clock by encoding the passage of time in a simple SHA-256, sequential-hashing verifiable delay function (VDF).

One of the most difficult problems in distributed systems is agreement on time. I believe Proof of History provides this solution and Solana is built using blockchain-based on it.

Nodes in the blockchain network which is a distributed system can’t trust an external source of time or any timestamp that appears in a message. There are solutions like Hashgraph which verify if the timestamp in a message is accurate but these methods are very slow.

What if instead of trusting the timestamp you could prove that the message occurred sometime before and after an event? When you take a photograph with the cover of the Times of India, you are creating a proof that your photograph was taken after that newspaper was published, or you have some way to influence what Times of India publishes. With Proof of History, you can create a historical record that proves that an event has occurred at a specific moment in time.

Proof of History

The Proof of History (POH) is a high-frequency Verifiable Delay Function. A Verifiable Delay Function requires a specific number of sequential steps to evaluate, yet produces a unique output that can be efficiently and publicly verified.

For a SHA256 hash function, this process is impossible to parallelize without a brute force attack.

We can then be certain that real-time has passed between each counter as it was generated, and that the recorded order of each counter is the same as it was in real-time.

Verification in POH

While the recorded sequence can only be generated on a single CPU core, the output can be verified in parallel.

Each recorded slice can be verified from start to finish on separate cores in 1/(number of cores) time it took to generate.

Architecture on how to interact with Solana

Client programs are exposed to users through web applications or CLI. Client code is language agnostic. It can be written in programming languages such as Python, Rust, JavaScript, C++, etc. The client program makes requests to JSON RPC. JSON RPC routes data to the Solana program that is on the chain. Solana currently supports writing programs in Rust and C/C++. The program modifies the state of the blockchain which is called Account. JSON RPC is the middle layer that routes objects sent by clients to the Solana program. These objects are called transactions(tx). This program further processes the transactions to modify the state of the account.

Clients can also request the data. Data that was written into the account can be requested by the user using JSON RPC.

Goal of Solana programming

As discussed before, the goal of the Solana program is to take in user input to modify the chain state.

https://github.com/solana-labs/example-helloworld.git is a GitHub link to a simple Solana project.

This project comprises:

A simple on-chain hello world Solana program written in Rust.

A client program is written JS using Solana web3.js SDK. The client program can simply send “hello” to an account and get back the number of times “hello” has been sent.

Now let’s look into one of the use cases of the Solana blockchain in the health insurance sector

Blockchain in health insurance to simplify Claim settlement process

Claim process can be divided into three main phases

Phase 1: Insurance Providers register on Public Blockchain

In the first phase, the process will be more or less as defined below:

The main stakeholders involved in the first phase are Insurance Providers, Insurance Brokers, and policy portal admins. Every stakeholder involved in the process would have their private keys to add records to the blockchain. Insurance Providers who provide different types of insurance can add the policy details on the public blockchain. For example, if a health insurance provider has to add the plans, they would save details like claim bonus, types of treatment covered, network hospital details, etc. on the public blockchain.

Insurance Brokers will be accessing the details saved by insurance providers on the public blockchain and can rate the insurance policies in the blockchain. The rating provided will help insurance companies and consumers to make informed decisions. Policy portal admins will fetch the insurance plans from the blockchain and add them to their portal. Using blockchain, policy portals like “Policybazaar” spend less time and manual effort contacting insurance providers like “care health insurance”.

Phase 2: Consumers Search and Buy Policies

The stakeholders involved in the second phase are Consumers and insurance companies. Consumers search for the specific insurance policy using their mobile app or website. A list of relevant policy details saved on the public blockchain will be fetched and displayed.

After a customer selects the insurance plan from a specific insurance provider, the next step is to buy the policy. So, the consumer would have to upload necessary documents such as address proof, income proof, etc. to the distributed database. These documents will have their addresses hashed and stored on the private blockchain.

Insurance Companies get notified as soon as the consumer buys the insurance. Insurance companies start verifying the consumer’s details and add the consumer to their private blockchain after validation. Acknowledgment is sent by insurance providers to the consumers about plan activation because the records of transactions stored on blockchain are immutable and traceable, there will be no insurance fraud chances.

Phase 3: Claim Request

The stakeholders involved in the third phase of the blockchain insurance process are:

Consumers, who require a claim in case of any damage, loss, medical treatment, or accident.

Loss Adjuster/Auditor, who verifies if the consumer is liable to get the claim amount or not.

Insurance Company, which provides the claim to the consumers.

In the case of medical treatment, a consumer requests the claim amount from the insurance provider. For example, suppose some consumer is diagnosed with some illness and wants to undergo treatment with a cashless claim. Consumers would have to share the documents supporting evidence on the private blockchain such as scan reports, doctor’s advice, etc.

The documents will be saved in a private blockchain which will be visible to the insurance company. The insurance company verifies the documents and sends the claim account’s breakdown to the consumer. The claims amount is automatically transferred to the consumer or hospital (cashless claim) with the help of smart contracts.

Current challenges faced in Health Insurance

The healthcare insurance industry is one of the most inefficient, fraud-prone sectors today. It faces multiple challenges with which blockchain technology can help significantly.

With blockchain technology, healthcare insurers can:

Maintain patient privacy
Give data sharing controls to patients
Store time-stamped medical records with cryptographic signatures on a shared ledger.
Enable fine permission settings to ensure regulation compliances

MedRec

Introduced by MIT, MedRec is a decentralized medical records management system that indexes healthcare records on the blockchain and allows access to authorized individuals. It helps to ensure the privacy of patients, along with easing the information verification process.

The first implementation of MedRec was done by using the Ethereum blockchain platform. The code is open-source, and the developers of MedRec are working with new healthcare IT center to develop a deployed network.

In a nutshell

Blockchain is a highly secure decentralized system that eliminates regulatory authorities. This makes transactions made using blockchain secure and fast compared to traditional approaches. Apart from cryptocurrency Blockchain technology can be used in multiple domains like Insurance, real estate, money transfer, manufacturing, etc.

Solana has solved the problem of timestamp verification using Proof of History. It can support up to 50k transactions per second because of POH which is faster than “proof of work” used in bitcoin or “proof of stake”.

As we saw in the blockchain-based insurance use case, blockchain and Solana can revolutionize the insurance industry by streamlining time-consuming insurance processes. Blockchain solves a lot of practical problems that exist in the current health insurance sector, this includes maintaining patient privacy and storing time-stamped medical records with cryptographic signatures which are tamperproof.

About the author:

Imran is a Sr. Software Engineer at Mantra Labs working on AI/ML-related projects. A passionate technologist, he has worked in the field of NLP and Computer Vision. Apart from tinkering with new technologies like blockchain, his interests are playing Badminton and chess.

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
Built-in security: Secure and private by design, with robust data security measures
Extensive connectivity: Over 50 built-in, managed, and secure data connectors
Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

Real-time code suggestions and auto-completion across team members
Automated conflict detection and resolution in merge requests
Personalized coding assistance based on individual developer styles
AI-driven code reviews and quality checks

Benefits for development teams:

Increased productivity: Teams report up to 30-50% faster code completion
Improved code consistency: AI ensures adherence to team coding standards
Reduced onboarding time: New team members can quickly adapt to project codebases
Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

Integration of natural language processing for code explanations and documentation
Advanced code refactoring suggestions based on team-wide code patterns
AI-assisted pair programming and mob programming sessions
Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.

Industry	Potential Annual Value
Banking	$200 billion – $340 billion
Retail and CPG	$400 billion – $660 billion

Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.

Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

Cognitive and Skill-Related Concerns:
- Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
- There’s a risk of developers losing the ability to write or deeply understand code independently.
Technical and Ethical Limitations:
- Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
- Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
- Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
Copyright and Licensing Issues:
- AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
Limited Contextual Understanding:
- AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
Bias in Training Data:
- AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.

Future Trends in Code Automation:

AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.