DeFi Lending: How to Earn Passive Income with Crypto

Are you bored of watching your digital assets gather dust, doing nothing to help you pad your wallet? Fear not, for the beautiful world of DeFi lending is here to assist you in putting your crypto to work and earning some delicious, sweet passive income!

DeFi lending is a decentralized and trustless way to lend your cryptocurrency to other users as you relax and enjoy your beverage. And, as of September 2021, there was over $90 billion in crypto asset locked up in DeFi lending mechanisms, indicating that this is not a fleeting trend.

But why has DeFi grown so popular? It’s all owing to blockchain technology, which enables transparent, secure, and automated transactions via smart contracts. You may lend, borrow, trade, and stake your cryptocurrency with DeFi dApps without dealing with any annoying intermediaries or centralized organizations. Not to mention the sweet, sweet prizes that may be earned by engaging in the DeFi ecosystem.

So, if you’re looking to spice things up and earn some serious crypto cash, buckle up and join the DeFi revolution!

Unraveling DeFi, Blockchain, and the Evolution of Decentralized Money

DeFi or Decentralized Finance proposes a paradigm shift in the financial sector by establishing an open, transparent, and accessible financial environment.

This novel concept uses blockchain technology to bypass traditional intermediaries like banks and financial organizations, giving individuals complete control over their assets and transactions.

DeFi comprises numerous financial services like lending, borrowing, trading, and investing, all developed on decentralized platforms. The usage of smart contracts – self-executing agreements with terms incorporated in the code – is the foundation of DeFi, allowing for frictionless and safe transactions between parties.

DeFi offers a more inclusive and democratized approach to finance by eliminating the need for intermediaries and embracing decentralization, benefiting users worldwide.

Another distinguishing feature of DeFi is liquidity pools that enable users to contribute their assets to a common pool and receive passive income through interest or fees. This mechanism encourages participation and assures that decentralized platforms have enough liquidity to conduct seamless and efficient transactions.

As DeFi evolves, it is becoming a more appealing alternative to established financial institutions, promising a brighter and more egalitarian financial future.

The Role of Blockchain Technology in DeFi

Let’s take a moment to admire DeFi’s backbone: blockchain technology, primarily Ethereum. You may be wondering what makes blockchain unique and why it is so important to DeFi’s success.

Blockchain, on the other hand, is a distributed, decentralized, and secure digital ledger that records transactions in blocks. It enables the development of trustless, transparent, and tamper-proof systems – exactly what DeFi needs!

The true strength of Ethereum blockchain technology is its ability to enable the development of decentralized applications (dApps) that run on its network. DeFi services such as lending, borrowing, trading, and investing are now feasible without the requirement for a central authority.

It also allows for the production of decentralized digital assets such as cryptocurrencies and tokens, which feed the expansion of DeFi.

The Origin of DeFi

DeFi can be traced back to the birth of Bitcoin in 2008. Although Bitcoin is not a DeFi application in and of itself, it does lay the groundwork for decentralized financial services by showcasing the possibilities of a decentralized, peer-to-peer payment system.

However, the ultimate birth of DeFi occurred with the launch of Ethereum in 2015. DeFi’s development and expansion were aided by Ethereum’s release of smart contracts and the ability to construct decentralized apps (dApps) on its platform.

Expand Your Knowledge: Ethereum Starter Kit: Smart Contract Development For Beginners

Charting DeFi’s Development: Key Milestones

DeFi has seen important achievements that have brought it to the forefront of the financial world in its relatively short history. Among the major landmarks are:

MakerDAO is the first decentralized autonomous organization (DAO) to create the stablecoin DAI, which is pegged to the US dollar. This was the start of DeFi’s venture into lending and borrowing.
Uniswap’s launch (2018): Uniswap, a decentralized exchange (DEX), transformed the way users trade cryptocurrencies by utilizing liquidity pools rather than traditional order books, resulting in a more efficient and decentralized trading experience.
DeFi saw significant growth in 2020, with total value locked (TVL) on DeFi platforms jumping from over $1 billion at the start of the year to more than $14 billion by the end of 2020.
Mainstream adoption: The success of DeFi has piqued the interest of major financial institutions, with many investigating the feasibility of incorporating DeFi solutions into their services.

Is DeFi crypto’s version of a stock exchange?

While there are similarities between DeFi and stock exchanges, they are not the same.

DeFi is a broader idea that includes a variety of financial services, such as decentralized trading platforms similar to stock exchanges.

However, it goes beyond trading to include lending, borrowing, and other financial services enabled by decentralized technology. Let’s look at how they differ in the DeFi ecosystem.

Decentralization

The core notion of decentralization is the most significant distinction between DeFi and regular stock exchanges.

Traditional stock exchanges are centralized institutions that govern and regulate the trading activity, whereas DeFi platforms, including DEXs, run on decentralized networks such as Ethereum, allowing users to trade with one another directly.

Intermediaries

Traditional stock exchanges rely on middlemen to enable transactions, such as brokers, clearinghouses, and custodians.

Smart contracts, on the other hand, are used by DeFi platforms to automate and secure transactions, removing the need for intermediaries and lowering associated expenses.

Inclusion and Access

Traditional stock exchanges impose access barriers like minimum investment amounts, accreditation requirements, and geographical restrictions.

However, DeFi systems are open and available to anybody with an internet connection, fostering financial inclusion and democratizing access to financial services.

Trading Hours

Traditional stock exchanges have set trading hours, whereas DeFi platforms and DEXs offer 24/7 trading, allowing users to trade whenever they want.

Transparency

Because all transactions are recorded on a public blockchain, they are easily verifiable and auditable, DeFi platforms and DEXs provide greater transparency than traditional stock exchanges.

Centralized vs. Decentralized Finance (CeFi vs. DeFi)

Centralized Finance (CeFi) and Decentralized Finance (DeFi) are two separate approaches to financial services, each with its own set of benefits and drawbacks. Let’s look at their main distinctions.

Governance and Control

CeFi operates under centralized control, which means that banks, traditional finance, and financial institutions are in charge of the transactions and services they offer. These bodies are in charge of making decisions, enforcing rules, and regulating the flow of money in the system.

This centralized governance model ensures compliance with existing laws and regulations by providing a structured and regulated environment for financial services.

DeFi, on the other hand, uses blockchain technology to build decentralized platforms that give consumers more control over their assets and transactions. Smart contracts and decentralized governance models, which are often based on consensus algorithms, are used to enforce decisions and norms.

This decentralization provides greater autonomy and flexibility, but it also implies that DeFi platforms may be less regulated and prone to greater dangers.

Agents of distribution

Centralized exchange relies on intermediaries to enable transactions, such as banks, brokers, and payment processors. Account administration, payment processing, and transaction execution are just a few of the services offered by these intermediaries.

While this paradigm provides convenience and dependability, it also adds inefficiencies, greater costs, and significant delays in transaction processing.

DeFi eliminates the need for intermediaries by automating and securing transactions with smart contracts. Smart contracts are self-executing contracts with terms and conditions encoded straight into code. These contracts can automatically facilitate, verify, and enforce contract fulfillment, eliminating the need for middlemen and associated fees.

DeFi platforms can provide faster, more efficient, and less expensive financial services by eliminating intermediaries.
Accountability

When compared to CeFi institutions, DeFi platforms provide more transparency. DeFi platform transactions are recorded on public blockchains, making them easily traceable and auditable. This transparency ensures that users may rely on the platform’s operations and check transaction accuracy without the need for third-party audits.

CeFi institutions, on the other hand, have private, centralized ledgers, making it harder for users to obtain insight into their activities. Furthermore, CeFi institutions are not obligated to disclose all of their operations, which can lead to inconsistency and potential conflicts of interest.

Availability

DeFi systems are open and available to interested investors with an internet connection, lowering entry barriers and boosting financial inclusion. Users can use DeFi services regardless of their location, income, or financial status, allowing underserved groups to engage in the global financial system.

CeFi services, on the other hand, have eligibility requirements and regional restrictions that can preclude potential users. To obtain CeFi services, investors may need to fulfill certain income requirements, have a certain credit score, or live in a given nation.

These entrance obstacles can impede financial inclusion while also perpetuating existing inequities in the financial system.

Safety and custody

CeFi institutions are often custodians of their users’ assets, storing and managing funds on their behalf. Because consumers do not have to handle their assets directly, this custody model can be convenient. It does, however, introduce a single point of failure, leaving CeFi institutions vulnerable to hacking, fraud, and mismanagement.

DeFi systems, on the other hand, allow users to keep control of their assets by using non-custodial wallets and smart contracts. Users are in charge of managing their private keys, which allow them to access their funds.

This non-custodial architecture decreases the possibility of centralized failures while also requiring users to assume greater responsibility for the protection of their assets.

Advantages of DeFi

DeFi is crucial for various reasons, including its potential to change the global financial system and yield enormous advantages. Among the many benefits of DeFi are:

Financial Democratization

DeFi has the potential to democratize access to financial services by reducing entry barriers and creating a more inclusive financial environment.

Traditional financial systems frequently contain constraints such as minimum investment amounts, credit scores, and regional restrictions, which can prevent a huge section of the world’s population from gaining access to financial services.

DeFi systems are open and available to anybody with an internet connection, fostering financial inclusion and empowering people to participate in the global financial system.

Enhanced Accessibility

DeFi platforms are accessible 24/7, allowing users to access financial services at their convenience. Traditional financial systems, such as banks and stock exchanges, frequently have limited operating hours, which might limit service availability.

The availability of DeFi platforms around the clock provides customers with greater freedom and can promote more smooth worldwide financial transactions.

Cost Cutting

DeFi platforms can help cut financial service costs by eliminating intermediaries and automating procedures with smart contracts. Traditional financial systems frequently involve several middlemen, such as banks, brokers, and payment processors, which can result in higher fees and longer transaction times.

DeFi can provide more efficient and cost-effective financial services by eliminating these intermediaries.

Better Security and Control

Through non-custodial wallets and decentralized protocols, DeFi systems allow users to keep control over their assets. This additional control has the potential to lower the danger of centralized failures, hacks, and mismanagement which are common in traditional financial organizations.

Furthermore, DeFi platforms frequently include blockchain technology, which, due to its decentralized and tamper-resistant nature, provides additional security and transparency.

Financial Ingenuity

DeFi has fostered a new wave of financial innovation by providing a platform for new financial products and services. DeFi projects’ open-source nature encourages cooperation and experimentation, which leads to the invention of novel financial instruments and application cases.

Decentralized lending and borrowing platforms, tokenized assets, yield farming, and decentralized insurance products are among the examples.

Enhanced Transparency

Because transactions are recorded on public blockchains, DeFi platforms provide greater transparency than traditional financial institutions. Users may trust the platform’s operations and check transaction accuracy without relying on third-party audits or locked ledgers because of this transparency.

DeFi Building Blocks: The Key Protocols Powering Decentralized Finance

DeFi protocols serve as the foundation for a wide range of DeFi applications and services. In this section, we will go over some essential DeFi protocols and how they enable various applications in the decentralized finance ecosystem:

AMMs (Automated Market Makers)

Decentralized trading is facilitated by AMMs such as Uniswap and Balancer, which use liquidity pools and algorithms to determine asset values. Users can earn trading fees by providing liquidity to these pools.

AMMs allow for the construction of DEXs and are crucial in guaranteeing effective price discovery and low-slippage trading.

Lending Protocols

Smart contracts are used by lending protocols like Aave and Compound to support decentralized lending and borrowing. They let users contribute assets to liquidity pools in exchange for interest or borrow assets in exchange for collateral.

These protocols eliminate the need for traditional middlemen, resulting in more transparency and access to financial services.

Stablecoins Protocols

Stablecoin protocols, such as MakerDAO and Terra, generate digital assets with a fixed value, often a fiat currency or a basket of assets.

They serve as a medium of exchange for numerous applications like lending, borrowing, and trading, as well as providing stability to the DeFi ecosystem.

Yield Farming and Liquidity Mining Protocols

Yearn.Finance and Harvest Finance, for example, use yield farming and liquidity mining techniques to help customers maximize their returns by allocating funds to the best-performing strategies across several DeFi platforms.

They automate the process of identifying high-yielding possibilities, allowing customers to better optimize their investments.

Derivatives Protocols

Derivatives protocols such as Synthetix and dYdX allow for the development and trading of decentralized derivatives, allowing users to hedge against a variety of assets such as cryptocurrencies, equities, and commodities.

In the DeFi arena, these protocols open up new investment opportunities and risk management measures.

Governance Protocols

Governance protocols like Aragon and DAOstack enable DeFi platforms to make decentralized decisions.

They give token holders the ability to vote on proposals and modifications, promoting community-driven development and management.

Oracle Protocols

Oracle protocols, such as Chainlink and Band Protocol, offer DeFi applications with precise and dependable off-chain data, such as price feeds and external events.

They are critical to the security and correct operation of many DeFi technologies that rely on real-world data.

Tokenization Protocols

Real-world assets can be converted into digital tokens via tokenization protocols such as RealT and Centrifuge, allowing for fractional ownership, easier trading, and higher liquidity.

They broaden the spectrum of assets available in the DeFi ecosystem.

Exploring the DeFi Universe: A Dive into Diverse Decentralized Apps

DeFi encompasses numerous apps aimed to support various financial services in a decentralized manner. Here’s a rundown of some of the most prevalent types of DeFi applications and how they fit into the DeFi ecosystem:

DEXs (Decentralized Exchanges)

Users can trade cryptocurrencies and tokens on decentralized exchanges without relying on a centralized intermediary.

DEXs support trading by utilizing liquidity pools, automated market makers (AMMs), and smart contracts, giving a more decentralized and secure alternative to traditional centralized exchanges.

Uniswap, SushiSwap, and Curve Finance are among popular DEXs.

Platforms for lending and borrowing

Users can earn interest on their deposits or borrow assets against their collateral using DeFi lending and borrowing systems.

Smart contracts are used on these platforms to automate and secure the loan and borrowing process, making them a more transparent and efficient alternative to traditional lending institutions.

Aave, Compound, and MakerDAO are a few famous lending and borrowing platforms.

Stablecoins

Stablecoins are cryptocurrencies with a fixed value, generally tied to a fiat currency such as the US dollar or a basket of assets.

They are utilized in numerous DeFi applications, such as lending, borrowing, and trading, and give stability in the turbulent crypto market.

DAI (pegged to the US dollar and collateralized by other cryptocurrencies), USDT (Tether), and USDC (USD Coin) are examples of popular stablecoins.

Liquidity mining and yield farming

DeFi members use yield farming and liquidity mining tactics to optimize returns on their holdings. Users can earn interest, trading fees, or governance tokens by depositing their assets into DeFi platforms or liquidity pools.

These tactics have gained popularity due to the potential for high profits, but they can also be risky. Yearn Finance, Harvest Finance, and Balancer are several platforms that provide yield farming and liquidity mining opportunities.

Derivatives and synthetic assets

Synthetic assets and derivatives that track the value of underlying assets such as stocks, commodities, or other cryptocurrencies can be created using DeFi systems.

Users can acquire exposure to numerous markets by using synthetic assets rather than purchasing the underlying assets. Synthetix, dYdX, and UMA are examples of DeFi platforms that provide synthetic assets and derivatives.

Decentralized Insurance

Decentralized insurance platforms offer a decentralized method to risk management and protection against unfavorable events like hacks or smart contract failures.

Insurance plans can be purchased by users, and claims are resolved through decentralized governance or automated procedures. Decentralized insurance platforms include Nexus Mutual and Cover Protocol.

Aggregators and Asset Management

DeFi asset management systems and aggregators assist consumers in optimizing their investment plans and properly managing their assets.

Portfolio management features, automated investing methods, and access to numerous DeFi applications are frequently provided by these platforms through a single interface.

Zapper, Zerion, and TokenSets are examples of asset management and aggregator platforms.

Diverse Use-Cases of DeFi’s Potential in the Decentralized World

Asset Management

DeFi platforms assist users in managing and optimizing their digital assets by offering portfolio management tools, automated investing techniques, and access to a variety of DeFi applications.

Zapper, Zerion, and TokenSets are among the examples.

Compliance and KYT (Know Your Transaction)

DeFi initiatives can use KYT and compliance solutions to detect and prevent unlawful actions such as money laundering and terrorist funding. Chainalysis, Elliptic, and Merkle Science are a few examples.

DAOs (Decentralized Autonomous Organizations)

DAOs are organizations that are administered by code and have decentralized decision-making processes, allowing the community to manage resources and make collective decisions.

MakerDAO, Kyber Network, and Aragon are a few examples.

Data and Analytics: Data and analytics solutions for DeFi projects provide insights into project performance, risk, and other pertinent parameters. DeFi Pulse, Dune Analytics, and Nansen are a few examples.

Derivatives

DeFi platforms provide decentralized derivatives, which allow users to trade and hedge against a variety of assets such as cryptocurrencies, equities, and commodities. Synthetix, dYdX, and UMA are a few examples.

Developer and Infrastructure Tooling

Developer tools and infrastructure allow DeFi applications to be created, tested, and deployed. Truffle Suite, Infura, and Alchemy are a few examples

DEXs (Decentralized Exchanges)

DEXs allow for the trustless exchange of cryptocurrencies and tokens without the use of a centralized intermediary. Uniswap, SushiSwap, and Curve Finance are a few examples.

Gaming

DeFi gaming platforms incorporate decentralized financial concepts into gaming experiences, such as tokenization and NFTs. Axie Infinity, The Sandbox, and Decentraland are a few examples.

Identification

Decentralized identification solutions give people greater control over their digital identities while also improving privacy and security. Civic, uPort, and SelfKey are a few examples.

Insurance

Decentralized insurance platforms protect against unforeseen catastrophes like hacking or smart contract failures. Nexus Mutual and Cover Protocol are two examples.

Lending and Borrowing

DeFi lending and borrowing platforms enable users to earn interest on deposits and borrow assets in exchange for collateral. Aave, Compound, and MakerDAO are a few examples.

Decentralized Margin Trading Platforms

Decentralized margin trading platforms allow users to trade assets with leverage, increasing possible gains and losses. dYdX, Fulcrum, and DeversiFi are a few examples.

Marketplaces

DeFi marketplaces make it easier to buy, sell, and trade various digital assets, such as NFTs and tokenized assets. OpenSea, Rarible, and SuperRare are other examples.

Payments

Decentralized payment solutions offer cost-effective, borderless payment choices. Flexa, Request Network, and Lightning Network are a few examples.

Prediction Markets

Users can speculate on the outcomes of certain events via decentralized prediction markets. Augur, Gnosis, and Polymarket are a few examples.

Savings

Through DeFi savings systems, individuals can earn money on their assets by placing them in lending pools or staking them in other protocols. Yearn.Finance, Anchor Protocol, and PoolTogether are a few examples.

Stablecoins

Stablecoins keep their value stable by being linked to a fiat currency or a basket of assets. DAI, USDT, and USDC are a few examples.

Staking

Staking is storing digital assets in a wallet or smart contract to support blockchain network activities such as transaction validation and network security. This process of staking assets allows users to gain rewards.

Synthetic Assets

DeFi platforms generate synthetic assets that track the value of underlying assets like stocks, commodities, or other cryptocurrencies. Users can receive market exposure without owning the underlying assets.

Synthetix, Mirror Protocol, and Tokenlon are a few examples.

Tokenization

Tokenization is the process of transforming real-world assets, like real estate, art, or commodities, into digital tokens on a blockchain, allowing for fractional ownership, easier trade, and higher liquidity.

RealT, Centrifuge, and NFTfi are a few examples.

Trading

DeFi trading systems enable the purchase, sale, and exchange of various digital assets such as cryptocurrencies, tokens, and NFTs. Binance, Wazirx, and bybit are a few examples.

Navigating the Decentralized Finance Landscape: Comparing Popular DeFi Platforms

Here we will go over some of the most popular DeFi platforms.

Compound

Compound is yet another prominent DeFi lending and borrowing platform that allows users to supply and borrow multiple cryptocurrencies. For decentralized decision-making, it employs its native governance token (COMP).

SushiSwap

SushiSwap is a DEX and yield farming platform that provides services similar to Uniswap but with added features like Onsen (custom liquidity pools) and BentoBox (optimal yield techniques). It governs using the SUSHI token.

MakerDAO

MakerDAO is a decentralized credit platform that enables users to create the DAI stablecoin by encrypting collateral in smart contracts. It also provides a decentralized governance mechanism through the use of its MKR coin.

Synthetix

Synthetix is a DeFi platform that allows users to create and trade synthetic assets, allowing them to obtain exposure to other markets without owning the underlying assets. The SNX token is used for collateralization and governance.

Understanding Risks and Ensuring Security of the DeFi Minefield

Before you headfirst into Decentralized Finance lending, remember that it, like any investment, comes with its set of hazards. The value of cryptocurrency can fluctuate, and smart contract faults or hacks are always a risk.

However, with proper research and caution, DeFi lending can be an excellent way to put your crypto asset holdings to work and make some real passive income.

Security Flaws in Smart Contracts

Smart contracts are used by DeFi platforms to automate transactions and enforce agreements. Smart contracts, however, are still vulnerable to coding faults and weaknesses that hackers can exploit.

Users should look for platforms with certified smart contracts and a history of secure operations to mitigate this risk.

Impermanent Loss

Liquidity providers in AMM-based DEXs face the danger of impermanent loss. It occurs when the relative price of assets in a liquidity pool changes, resulting in a decline in the value of a user’s deposit.

Users might consider investing in stablecoin pairs or using advanced AMM platforms with greater risk management capabilities to limit impermanent loss.

Risk of Liquidation

Users must offer collateral while borrowing assets through DeFi lending networks. If the collateral’s value falls below a specific threshold, the user’s position may be liquidated, resulting in a loss.

Users should maintain a healthy collateral ratio and closely monitor their positions to prevent liquidation risk.

Regulatory Risk

As the Decentralized Finance industry expands, governments may enact new regulations that have an impact on the ecosystem.

Users should be aware of any regulatory changes that may have an impact on their DeFi investments and alter their plans accordingly.

Risks of Centralization and Governance

Some DeFi implementations retain centralization aspects, such as admin keys or centralized oracles, which could introduce possible points of failure or manipulation.

Platforms with strong decentralized governance and security measures should be sought after by users.

Platform and Token Risks

DeFi coins’ value can be variable, and some platforms may underperform or fail. Users should diversify their assets across several platforms and tokens and undertake rigorous research before investing to reduce platform and token risks.

Users should choose trustworthy platforms with a proven track record and audited smart contracts to assure security and mitigate dangers in Decentralized Finance.
For further security, they should secure their private keys and employ hardware wallets.
Use risk management measures like diversification and keeping a healthy collateral ratio.
Keep up to current on legislative changes, platform updates, and market circumstances.
When investing in fresh or unproven systems and tokens, proceed with caution.

How to earn passive income with DeFi?

Decentralized Finance offers several chances for passive income. Here’s a step-by-step guide to investing in DeFi initiatives, as well as advice for analyzing and selecting viable investments, to get you started:

Step 1: Research and Select a DeFi Platform

Begin by learning about several platforms, such as loan protocols, DEXs, and yield farming platforms. When making your decision, consider considerations such as security, user experience, and historical performance.

Platform evaluation tips:

Look for platforms with audited smart contracts and a proven track record of security.
Platforms with a large user base and a significant total value locked (TVL) should be prioritized.
Examine the platform’s reputation and possibilities by reviewing community discussions, development updates, and news articles.

Step 2: Get a Cryptocurrency and a Supported Wallet

You’ll need a compatible DeFi wallet, such as MetaMask, Ledger, or Trezor, to engage with the chosen platforms.

Fund your wallet with a cryptocurrency that is compatible with the DeFi platform of choice, often Ethereum (ETH) or a stablecoin such as DAI or USDC.

Step 3: Connect Your Wallet to the DeFi Platform

Connect your wallet to the DeFi platform by following the on-screen instructions. To avoid phishing scams, make sure you’re on the platform’s official page.

Step 4: Invest in DeFi Opportunities

You can now invest in a variety of DeFi opportunities, depending on the platform you’ve chosen:

Lending platforms: Provide assets in exchange for interest on your deposit.
DEXs with liquidity pools: Earn trading fees and/or governance tokens by providing liquidity.
Stake assets to receive yield farming rewards in the form of governance tokens on yield farming platforms.
To deposit your assets and begin receiving passive income, follow the platform’s instructions.

Step 5: Manage and Monitor Your Investments

Check in on your DeFi investments regularly, assess their success, and alter your plan as appropriate. Keep an eye out for market changes and platform improvements that may influence your assets.

Suggestions for Choosing Promising DeFi Investments:

Diversification: To lessen the risk, spread your investments across numerous platforms and assets.
Investigate each platform thoroughly, including its tokenomics, governance mechanism, and team.
Risk management entails evaluating each investment’s potential risks and benefits and adjusting your portfolio accordingly.
Follow community discussions and developer updates to remain up to know on potential possibilities and advancements.

DeFi Technology Trailblazers: Case Studies of Successful Apps Shaping the Industry

Numerous successful applications have emerged in the DeFi ecosystem, each contributing to the industry’s growth and evolution. Let’s take a look at three prominent DeFi apps, their rise to prominence, and the impact they’ve had on the DeFi sector.

Uniswap

Background: Uniswap, launched in 2018 by Hayden Adams, is a decentralized exchange (DEX) that leverages automated market makers (AMMs) to conduct smooth token swaps.

Growth: Due to its novel AMM approach, user-friendly design, and strong community support, Uniswap has garnered huge popularity. With the release of Uniswap V2 in 2020, new features such as liquidity provider fees, enhanced pricing oracles, and direct token swaps were implemented.

Impact: The success of Uniswap has spawned a slew of additional DEXs and AMM-based platforms, considerably increasing the liquidity and accessibility of the DeFi ecosystem. It has grown to be one of the major DEXs in terms of trading volume and total value locked (TVL).

Aave

Background: Stani Kulechov developed Aave in 2017 as ETHLend, a decentralized peer-to-peer lending network. It then changed its name to Aave and included new features like flash loans, rate switching, and a native governance token (AAVE).

Growth: Aave’s novel features and attention to user experience led to quick growth, attracting both users and developers. Aave moved to a more decentralized governance style and increased its supported assets in 2020.

Impact: Aave has emerged as one of the major DeFi lending platforms, pushing the boundaries of decentralized finance with unique features such as flash loans. It has been critical in increasing the use of DeFi loans and borrowing services.

Yearn.Finance

Background: Andre Cronje founded Yearn.Finance in 2020 as a DeFi platform that focuses on yield farming and aggregation. It optimizes customers’ investments by allocating funds automatically to the best-performing strategies across multiple DeFi platforms.

Growth: Yearn.Finance had tremendous growth thanks to its ground-breaking yield optimization techniques and the spectacular development of its native governance token, abbreviated YFI. The platform’s services were enhanced by integrating with other DeFi platforms and offering new products such as yVaults and yInsure.

Impact: Yearn.Finance has democratized access to yield farming prospects and prompted the establishment of other yield optimization platforms. Its success has aided DeFi’s expansion and spurred the development of fresh strategies and cross-platform collaborations.

The Future of DeFi

The future of the DeFi space is bright as the ecosystem evolves and matures. While predicting the exact direction of DeFi is hard, some important trends and developments are likely to affect its future:

Mainstream Adoption

DeFi platforms are anticipated to draw a larger user base as they become more user-friendly, secure, and accessible.

Integration with traditional financial services and the advent of new use cases may help drive DeFi systems’ mainstream adoption.

Cross-Chain Interoperability

The expansion of DeFi has resulted in a greater emphasis on cross-chain interoperability, allowing for frictionless communication and asset transfers between multiple blockchain networks.

This tendency is likely to continue, resulting in a more connected and versatile DeFi ecosystem.

Enhancement of Security and Regulation

As DeFi grows more popular, the need for greater security and regulatory compliance becomes more pressing.

We can anticipate continued work to strengthen smart contract security, eliminate risks, and ensure compliance with developing legislation, resulting in a more secure environment for DeFi users.

Expansion of DeFi Application Case

DeFi’s current use cases are likely to be expanded to include a broader range of financial services and products.

Real-world asset tokenization, decentralized insurance, and prediction markets are examples of innovations that will continue to push the frontiers of what DeFi can offer.

Layer 2 Scaling Solutions

To meet the increasing demand for DeFi, developers will continue to deploy Layer 2 scaling solutions such as rollups and sidechains, which enable faster, cheaper, and more efficient blockchain transactions.

Focus on Decentralized Governance

DeFi platforms will increasingly embrace decentralized governance models, giving consumers a vote in platform creation and management.

This tendency will encourage community-driven innovation while also increasing the reliability of DeFi systems.

AI and Machine Learning Integration

Artificial intelligence and machine learning, for example, might be integrated into DeFi systems to optimize investment strategies, increase risk management, and boost overall platform efficiency.

Explore more: Neural Networks: The Driving Force Behind Modern AI Revolution

Partner with OnGraph to Build Cutting-Edge DeFi Apps

OnGraph Technologies is a blockchain development company with an in-house team of qualified and experienced blockchain developers on staff who specialize in developing DeFi apps and other blockchain-based solutions.

OnGraph is well-equipped to help you manage the complexity of DeFi development, thanks to a comprehensive grasp of the DeFi ecosystem and a track record of successful projects.

By working with OnGraph, you receive access to a plethora of knowledge in smart contract development, tokenomics, decentralized governance, and more. Our staff remains on top of DeFi trends and technology to ensure your project is based on a solid basis and is optimized for success.

May 8, 2023March 13, 2024

AI and ML Weekly Digest: Top Stories and Innovations

Today we’ll discuss two interesting advancements in the AI and ML space. First, we’ll explore the influence of OpenAI’s GPT technology on employment markets, shining light on the potential implications for different occupations. Then, we’ll turn our attention to the exciting ways that AI/ML is improving the e-commerce landscape, providing unprecedented opportunities for personalization, efficiency, and customer satisfaction.

Let’s dive right in and have a look at the fascinating effects of these developments.

The Growing Influence of GPT Models on the U.S. Workforce

As artificial intelligence and machine learning improve, OpenAI’s GPT models will have a substantial impact on the U.S. workforce across numerous industries, resulting in both opportunities and challenges in the job market.

According to OpenAI research, GPT technology will have a significant impact on the jobs of US workers, with 80% of jobs being affected in some way. Higher-paying jobs are more vulnerable, and approximately 19% of workers might see at least 50% of their duties disrupted across practically all industries.
Because of their diverse applicability, we compare GPT models to general-purpose technologies such as steam engines or printing presses. The researchers assessed the possible influence of GPT models on various occupational tasks using the O*NET database, which contains 1,016 jobs.
Mathematicians, tax preparers, authors, web designers, accountants, journalists, and legal secretaries are among the occupations most exposed to GPT technology. The research anticipated that data processing services, information services, publishing businesses, and insurance companies will get most affected.
Food production, wood product manufacture, and agricultural and forestry support activities are projected to have the least impact.
The study has some limitations, such as human annotators’ familiarity with GPT models and lack of vocations measured, and GPT-4’s sensitivity to prompt wording and composition.
Google and Microsoft are already embedding AI into their office products and search engines, demonstrating the growing acceptance of AI technologies. Startups are using GPT-4’s coding ability to cut costs on human developers, highlighting the possibilities of AI in a variety of industries.

Researchers believe that the economic impact of GPT models will continue to expand even if new capabilities are not developed today.

How AI and ML are Transforming E-commerce

The incorporation of artificial intelligence and machine learning in e-commerce is defining the future of online shopping experiences, allowing for greater personalization, customer service, and efficiency. Here’s a closer look at how AI and machine learning can alter e-commerce.

E-commerce has a significant impact on customer experiences since it represents how people perceive their interactions with brands.
Creating seamless experiences is critical in the digital environment to avoid cancellations, abandoned carts, refunds, and negative feedback.
According to Oberlo, 79% of shoppers make online purchases at least once a month, therefore seamless e-commerce experiences are in high demand.

With a few key integration tactics, AI and ML have the ability to greatly improve e-commerce user experiences:

Personalize Product Recommendations

AI algorithms can examine user data, browsing history, and purchase behavior to deliver personalized product suggestions, streamlining the shopping experience and boosting the possibility of sales. Amazon, Netflix, and numerous online supermarkets are great places for personalized recommendations.

Use of chatbots and virtual assistants

AI-powered chatbots and virtual assistants provide real-time customer care and support around the clock, managing everything from answering queries to processing orders and resolving issues without the need for human participation.

Use Visual Search

Visual search technology and QR codes use AI algorithms to evaluate images and match them with relevant products, allowing customers to easily locate what they’re looking for even if they don’t have a specific description.

E-commerce enterprises can improve their consumer experiences and remain ahead of the competition by implementing these AI and ML integration tactics.

Conclusion

Lastly, the incorporation of artificial intelligence and machine learning in e-commerce is transforming the way businesses connect with their customers. Companies may create tailored experiences, improved customer service, and efficient shopping procedures by implementing AI and ML methods, ultimately increasing consumer happiness and loyalty.

By developing personalized AI/ML solutions, OnGraph Technologies can assist organizations in staying ahead of the competition. OnGraph blends cutting-edge technologies with a team of trained experts to design creative, customer-centric e-commerce solutions that promote growth and success.

Businesses can use the revolutionary power of AI and ML by teaming with OnGraph to optimize their e-commerce platforms and create amazing consumer experiences.

May 4, 2023January 18, 2024

Ethereum Starter Kit: Smart Contract Development for Beginners

Are you ready to use the latest innovations to transform your business operations? Smart contracts come to change the way you conduct contracts and transactions by bringing unparalleled efficiency and transparency to your fingertips.

Indeed, the global smart contracts market is expected to increase at an 18.1% CAGR from 2021 to 2028, reaching an incredible $345.4 million by 2028.

This beginner’s tutorial is designed for company owners like you who want to learn how to use Ethereum, the leading platform for smart contract development. You’ll learn Ethereum’s Solidity programming language, set up your ideal working environment, and write your first smart contract during this instructive and exciting session.

So gather your zeal, grab your favorite energy drink, and let’s explore the vibrant world of Ethereum development together!

So, What are Smart Contracts?

Smart contracts are self-executing digital agreements that automate transactions and simplify company operations. Smart contracts eliminate the need for middlemen by running on decentralized networks like Ethereum, resulting in a trustless, transparent, and tamper-proof system.

The benefits of adopting smart contracts are difficult to deny. They streamline processes, save expenses, and improve security by automating procedures that were previously dependent on human intervention. Let’s look at some real-world instances of businesses that have effectively adopted smart contracts:

Supply Chain Management: Smart contracts can be used to trace products from the manufacturer to the end consumer, providing transparency, eliminating fraud, and accelerating payment settlements.
Real Estate Transactions: Smart contracts can automate property transfers, escrow services, and leasing agreements, reducing paperwork, lowering expenses, and streamlining the entire process.
Insurance: Insurance businesses can use smart contracts to automate the claims process, ensuring prompt reimbursements and lowering the risk of fraud.

Now that you’re familiar with the concept of smart contracts and their real-world applications, it’s time to look deeper into why Ethereum is the best choice for smart contract creation.

Ethereum: The Ideal Smart Contract Development Platform

Ethereum has established itself as a prominent platform for smart contract creation; nevertheless, what makes it the best choice for businesses? Here are several major features that distinguish Ethereum:

Developer Community

Ethereum has a large and active developer community that is always contributing to the platform’s growth and enhancement. This dynamic ecosystem encourages creativity and offers a wealth of tools, making it easier for newbies to learn and build on the platform.

Robust Ecosystem

Ethereum’s ecosystem contains a variety of tools, frameworks, and libraries that make building and implementing smart contracts easier. This comprehensive bundle of materials assists developers in overcoming obstacles and expediting the development process.

Native Cryptocurrency

Ether (ETH) is the Ethereum platform’s native money, which supports smart contract execution and incentivizes developers to create and maintain decentralized apps (dApps).

Now that we’ve established Ethereum’s dominance in the smart contract ecosystem, let’s move on to Solidity, the programming language that enables Ethereum development.

Solidity: Ethereum’s Powerful Programming Language

Solidity is the primary programming language used on the Ethereum platform to create smart contracts. It is designed expressly for blockchain and smart contract development and has distinct features and capabilities that set it apart from other languages.

Here are some key features of Solidity:

Object-Oriented

Solidity is an object-oriented language that allows developers to write modular and reusable programs. This functionality facilitates the organization and maintenance of complex smart contract projects.

Static Typing

Solidity is a statically typed language, which means that variable data types are explicitly defined during compile time. This feature aids in the detection of potential mistakes early in the development process, hence improving the overall security and reliability of smart contracts.

Similarities to JavaScript

Solidity contains syntax similarities with JavaScript, making it easier for developers who are already familiar with JavaScript to learn and adapt to the language.

Contract Structure

Smart contracts in Solidity are arranged into a contract structure, which comprises features like state variables, functions, events, and modifiers. Understanding these aspects and their interactions is critical for the effective development of smart contracts.

Inheritance

Contract inheritance is supported by Solidity, allowing developers to build new contracts that inherit properties and methods from existing ones. This feature encourages code reuse and modularization, resulting in smart contract projects that are more controllable and organized.

Error Handling

To manage exceptional conditions and ensure that contracts execute only when particular requirements are met, Solidity provides error handling features such as ‘require’, ‘assert’, and ‘revert’. Implementing adequate error handling is crucial for smart contract security and stability.

Gas Optimization

Smart contracts on Ethereum consume ‘gas,’ which translates to transaction fees paid in Ether. Developers of Solidity could optimize their code to reduce gas usage, making smart contract interactions more cost-effective for users.

Best Security Practices

Writing secure smart contracts is critical in the realm of blockchain. Familiarizing yourself with common security patterns, such as the ‘Checks-Effects-Interactions’ pattern, as well as maintaining up to speed with Solidity’s latest security guidelines, can assist you in building robust and safe smart contracts.

Setting Up Your Ethereum Development Environment

Before getting into smart contract development, it’s critical to set up an appropriate development environment. Having the correct tools at your disposal can help you to streamline your process and increase your productivity.

Here is a list of crucial Ethereum development tools and resources:

Integrated Development Environment (IDE)

An IDE, such as Remix or Visual Studio Code, provides a user-friendly interface for creating, testing and deploying smart contracts. These IDEs provide syntax highlighting, auto-completion, and debugging features, making development easier and more efficient.

Solidity Compiler

To compile your Solidity code into bytecode that can be executed on the Ethereum Virtual Machine (EVM), you’ll need a Solidity compiler, such as ‘solc’. Most IDEs come with a built-in compiler or offer plugins to incorporate one.

Truffle Framework

Truffle is a popular Ethereum development framework that makes smart contract compilation, deployment, and testing easier. It also includes capabilities such as migrations, network management, and connection with well-known front-end technologies such as React and Angular.

Ganache

Ganache is an Ethereum development personal blockchain that allows you to run tests, issue commands, and view state while controlling the execution environment. It’s a fantastic resource for mimicking blockchain interactions during development and testing.

MetaMask

MetaMask is a browser extension that acts as an Ethereum wallet and a bridge between your browser and the Ethereum network. It allows you to communicate with smart contracts and dApps without having to run an entire Ethereum node.

Learning tools

Solidity documentation, Ethereum Stack Exchange, and online courses like ConsenSys Academy or CryptoZombies are all excellent tools for learning and troubleshooting while working on Ethereum.

Continue Reading: The Ultimate Guide to Blockchain Development [Plus Use-Cases]

A Step-by-Step Guide to Creating and Deploying Your First Smart Contract

You’re ready to construct and deploy your first smart contract now that you’ve set up your development environment and have a basic understanding of Solidity and Ethereum.

Here’s a step-by-step tutorial to help you get started. However, if you feel you’re phasing out, you can partner with industry professionals.

Create the Smart Contract

Write the Solidity code for your smart contract in your preferred IDE. Begin with a small example, such as a token or a simple voting system, to become acquainted with the language and the development process

Convert the Smart Contract

When your code is finished, use the Solidity compiler (solc) integrated into your IDE or Truffle framework to convert it into bytecode that can be executed on the Ethereum Virtual Machine (EVM).

Testing

Before releasing your smart contract to the Ethereum network, extensively test it with tools such as Ganache and Truffle. This stage guarantees that your smart contract works as intended and aids in the identification of any defects or vulnerabilities.

Deploy the Smart Contract

Once your smart contract has been tested, it is time to deploy it to the Ethereum network. To deploy a smart contract, you can use the Remix IDE, Truffle migrations, or even a web3-enabled script, depending on your tools.

Interact with the Smart Contract

After you’ve launched your smart contract, you may interact with it via MetaMask or a custom front-end interface. This step lets you put your smart contract to the test in a real-world setting and obtain a better knowledge of how people will interact with it.

Monitor and maintain

It is critical to monitor your smart contract after deployment for any unusual behavior, security issues, or performance bottlenecks. Regular maintenance will keep your smart contract secure and efficient, allowing it to respond to changes in the Ethereum ecosystem.

Congratulations! You have successfully created and deployed your first Ethereum smart contract. As you explore Ethereum development and design more complex smart contracts, know that the key to success is continuous learning, practice, and staying current with the latest blockchain breakthroughs.

Smart Contract Scaling: Integrating with Decentralized Applications (dApps) and Beyond

As your knowledge and comfort in smart contract development expand, you may want to consider integrating your smart contracts with decentralized applications (dApps) and scaling them to fit your business’s needs.
Here’s what you should know:

Creating Decentralized Applications (dApps)

Decentralized applications (dApps) are programs that operate on decentralized networks such as Ethereum, integrating the power of smart contracts with user interfaces to offer interesting and secure digital experiences.

Integrating your smart contracts with dApps can help you open up new business prospects while also improving user experience.

Interoperability with Other Protocols

As the blockchain ecosystem evolves, new protocols and platforms emerge, allowing for cross-chain interactions and greater functionality. Learning how to combine your smart contracts with various blockchain protocols will assist you in staying ahead of the curve and entering new industries.

Layer 2 Scaling Solutions

As your smart contracts grow in complexity and popularity, you may encounter issues with transaction throughput and gas expenses. Layer 2 scaling solutions such as Optimistic Rollups and zk-Rollups can assist you in overcoming these difficulties by offloading computation and storage from the main Ethereum network, resulting in faster and cheaper transactions.

Upgradability and Governance

As the complexity and user base of your smart contracts expand, adding upgradability and decentralized governance methods becomes crucial. These characteristics enable your smart contracts to adapt and evolve over time, ensuring long-term viability and harmony with your company objectives.

Security Audits and Best Practices

As your smart contracts manage more valuable and vital business functions, their security becomes increasingly important. Regular security audits, adherence to best practices, and being current on the latest security research can assist you in maintaining resilient and secure smart contracts that can survive future threats.

Using web3.js and Creating a Simple Web Interface to Interact with Your Smart Contract

You can interact with a deployed smart contract using libraries such as web3.js, which provide a straightforward way to communicate with the Ethereum blockchain. We’ll show you how to make a simple web interface for users to interact with a smart contract in this guide.

We’ll utilize the SimpleMessage contract we built before in this example.

Create your project

Make a new folder for your project and run npm init to get it started. Run the following command to install the essential dependencies:

Connect to the Ethereum network

Make a file called index.js in your project folder. Set up a connection to the Ethereum network using web3.js in this file:

Make a basic web interface

In your project folder, create an HTML file called index.html with the following structure:

Use web3.js to interact with the smart contract

To communicate with the smart contract, add the following functions to your index.js file:

Serve your dApp

To test your dApp, use a simple HTTP server such as http-server. Using npm, install it globally:

Then, in your project folder, execute the server: http-server

To interact with your dApp, open your browser and go to http://localhost:8080.

Using web3.js, you’ve now constructed a simple web interface for people to interact with your smart contract. As needed, this basic configuration can be enhanced and adapted to accommodate more complicated smart contracts and user interactions.

Get the Full Story: Clutch Recognizes OnGraph Among India’s Top Blockchain Developers For 2022

Partner with OnGraph for Smart Contract Development

For organizations wishing to harness the power of blockchain, OnGraph provides skilled smart contract development services. Our expert smart contract developer team has vast experience in Ethereum-based smart contract creation and adheres to the best security principles.

Being a leading Blockchain development company, we provide complete smart contract platform development solutions from smart contract logic, smart contract design, and smart contract code, as well as bespoke integration with current systems and continuous support and maintenance with our leading Blockchain developer team.

Besides our wide range of smart contract development services, we also specialize in NFT marketplace development, (dApps) decentralized application development, and quality assurance of these apps suited according to your business needs.

Choose OnGraph as your Ethereum smart contract development company to explore the possibilities of blockchain technology for your business.

May 1, 2023March 18, 2024

Why React Native is Ideal for Building Cross-Platform Business Apps

React Native is the second most popular cross-platform mobile app development framework and holds the sixth rank among all development frameworks with a market share of 38%.

Since its creation, React Native has been on an upward trend, with more and more developers and businesses opting to use it as their primary framework for creating mobile applications due to its reliability and scalability.

Around 53% of developers have experience with React Native, making it the second most popular mobile development framework according to the 2021 State of JavaScript report.

In this blog, we will uncover various aspects of React Native app development, from how to use and benefits to comparison with other cross-platform app development frameworks.

What is React Native App Development?

React Native is an open-source cross-platform mobile app development framework developed by Facebook that enables developers to build native-like mobile apps by leveraging the React.js library.

With React Native app development, you can design high-performing, feature-rich, mobile apps with a single codebase in JavaScript that runs on various platforms like Android and iOS. You can also use the same techniques, tools, and workflows that are utilized for web development, making easier transitions from web development to mobile app development.

Instead of web-based components, React Native app development employs native components, hence providing a better app performance. React Native also boasts a large developer community, that builds third-party plugins, integrations, and libraries to extend its functionalities and assist other developers.

What’s New in the React Native Ecosystem

The React Native ecosystem is always changing, with new updates, tools, and features coming out all the time. In case you’re interested in what’s new in the React Native ecosystem, here you go:

React Native 0.64: React Native released version 0.64 with various new features, improvements, and bug fixes. Hermes runtime configuration, accessibility improvements, and iOS and Android components are important features.
Expo SDK 43: Expo SDK is a popular React Native app development and deployment toolkit. SDK 43 supports iOS 15, Android 12, and TypeScript 4.4.
React Native Navigation v6: A popular React Native navigation library. v6 adds stack presentation, a tab bar component, and performance improvements.
React Native Firebase: React Native Firebase integrates Firebase, Google’s mobile developer platform, easily. The latest upgrades support Firebase Authentication v9, Performance Monitoring, and ML Kit.
React Native CLI: This command-line interface builds and manages React Native projects. The latest updates enhance TypeScript support, project initiation, and third-party integration.

The Architecture of React Native

React Native adheres to a unidirectional data flow design, meaning that data travels in a single path via the application’s components. The architecture is based on the Facebook-developed Flux architectural pattern.

The primary components of React Native are:

View: This component is in charge of displaying the user interface. It also can have additional elements like text and pictures.
Props: They are the data supplied to a child component by its parent. They cannot be altered within the component as they are immutable.
State: It is a component’s internal data. It is modifiable by the component itself, and when it is, the component is re-rendered.
Redux: It is a library for state management that is compatible with React Native. It keeps the application state in a single store, from which the components can access the state.
Actions: They are events that initiate state changes within an application. These are sent to the store, which then updates the state and re-renders the components.
Reducers: They are functions that manage activities and change the state of the application.

React Native employs a Virtual DOM (VDOM) to efficiently update the user interface. It is a lightweight clone of the actual DOM that determines the least number of updates required to update the user interface. This makes React Native apps more efficient and quicker.

How is React Native Better Than Flutter For App Development?

Both React Native and Flutter are well-known cross-platform mobile app development frameworks for developing. Although they have some similarities, they also have substantial differences. Here are some important distinctions between React Native app development and Flutter app development

Languages

React Native uses JavaScript, and Flutter uses Dart. JavaScript is a more popular language that developers find easier to learn and utilize. Dart, on the other hand, is a more recent programming language that was created expressly for app development.

React Native uses native components, whereas Flutter employs its own collection of widgets. Native components offer superior performance and a more consistent user experience, whereas Flutter widgets offer greater customization and flexibility.

Development environment

For each platform, React Native requires a distinct development environment, such as Xcode for iOS and Android Studio for Android. Flutter offers a unified development environment across both platforms.

Hot reloading

React Native supports hot reloading, which allows developers to observe changes in the app without having to rebuild or restart the app. Flutter has a similar functionality called “hot reload,” although it isn’t as fast as React Native

Community support

Because React Native has a larger and more established developer community, it has access to a broader choice of tools and third-party libraries. The Flutter community is fast increasing, yet it is still young in comparison to React Native.

Performance

Compared to React Native, Flutter has better performance because it compiles code to native ARM machine code, while React Native uses a bridge to talk to native components.

Difference between React Native App Development and Xamarin App Development?

React Native and Xamarin are both popular cross-platform mobile app frameworks. Here are the key differences between React Native app development and Xamarin app development:

Languages

React Native uses JavaScript, whereas Xamarin uses C#. Developers may find JavaScript easier to learn. Modern app development language C# is ideal for enterprise apps.

User interface

React Native uses native components, whereas Xamarin employs its own set of controls. Xamarin controls are more customizable, but native components run better and offer a more consistent user experience.

Development environment

Xcode for iOS and Android Studio for Android are required for React Native development. Xamarin’s cross-platform programming environment simplifies development.

Community support

React Native has a larger developer community with more tools and third-party libraries. Xamarin’s community is powerful but smaller than React Native’s.

Performance

As Xamarin converts code to native machine code, it outperforms React Native, which uses a bridge to interface with native components.

Cost

Xamarin requires a license for full functionality, which might be expensive for small enterprises and individual developers. Smaller teams can use React Native because it’s open source.

How to Start React Native App Development?

Install Node.js

React Native can’t run without Node.js and the Node Package Manager (npm) is installed. Node.js can be downloaded from its official website and set up in a matter of minutes if you read and follow the instructions carefully.

Install the React Native CLI

React Native includes a command-line interface (CLI) tool for creating and managing projects. You can install it in your terminal by typing the following command:

npm install -g react-native-cli

Start a new project

The react-native init command can be used to create a new React Native project. For instance, you can enter this command in the terminal to initiate the creation of a new project with the name “MyApp”:

react-native init MyApp

This will create a new project with the basic file structure and dependencies.

Run the project

You can run the project using the following command:

cd MyApp

react-native run-ios

This will start the iOS simulator and launch the app.

Write the code

You can now begin developing your app’s code by making use of React Native’s APIs and components. React Native comes with a set of fundamental components that you can use to design your user interface, including text, image, view, ScrollView, and others.

Debugging and testing

You can test and debug your app using the React Native development tools. For example, you can analyze the component hierarchy and state using the React Native Debugger tool, and log warnings and errors using the console.

Create and deploy

When you’ve designed and tested your app, use tools like Xcode and Android Studio to build and push it to app stores.

What is the Difference between React Native App Development For Android and iOS?

While React Native allows for the creation of cross-platform mobile applications, development for the Android and iOS platforms differs. Here are some of the notable differences:

UI Components

React Native includes a set of UI components that may be used to create applications. However, the UI components for Android and iOS differ slightly. Developers must verify that the application’s UI components are compatible with both platforms.

Platform-Specific APIs

Android and iOS have platform-specific APIs that the other platform does not have. While React Native gives access to the majority of the core APIs, developers may need to leverage platform-specific APIs to provide platform-specific functionality in some cases.

Platform-Specific Styling

Because the styling for the Android and iOS platforms changes greatly, developers must ensure that the application’s design and layout adhere to the platform-specific requirements. React Native allows developers to easily generate platform-specific styles by providing a means to declare styles for both platforms.

Navigation

React Native includes a navigation library that allows developers to design screen navigation. The navigation styles, however, change across Android and iOS. Developers must ensure that the navigation styles follow platform-specific rules.

Debugging

Debugging React Native apps for Android and iOS differ differently. While both platforms include debugging tools, the debugging process differs. To effectively debug apps, developers must be familiar with platform-specific debugging tools.

How can Businesses Benefit From React Native App Development?

React Native offers numerous benefits to businesses wishing to develop mobile applications. Among the primary benefits are:

Cross-platform development

Using React Native, developers can create apps for both the iOS and Android platforms using a single codebase. Developers can save time and money by not having to design different codebases for each platform.

Reduced development cycles

The hot reloading feature of React Native allows developers to see changes in the app instantaneously, without having to rebuild or restart the app. This can hasten the development process and shorten the time to market.

Code reusability

Developers can save time and energy by reusing code between projects, thanks to React Native’s modular design and component-based architecture.

High performance

React Native renders the user interface using native components, which gives greater performance than hybrid application development frameworks that rely on web-based components.

Scalability

The modular architecture of React Native enables businesses to easily extend their apps, adding new features and capabilities without having to completely rewrite the app.

Large developer community

React Native offers a huge and active developer community that provides access to a variety of resources, tools, and third-party libraries that can expand the functionality of an app.

Business Challenges Faced During React Native App Development

While there are many benefits for organizations to use React Native, there are also potential drawbacks. Some of the major obstacles include:

Absence of native functionality

While React Native gives you access to numerous native device capabilities and APIs, some of them may be unavailable or have restricted usefulness. This can be difficult for organizations that need unique app functionalities.

Issues with compatibility

React Native may not be compatible with all third-party libraries and frameworks, which can be difficult for organizations that rely on these tools in their development workflow.

Support for legacy systems is limited

Because React Native is a new technology, it may not be well-suited for enterprises that need to integrate with older legacy systems or technologies.

Developer recruitment and training

It can be difficult to find skilled React Native developers, and firms may need to invest time and resources in training their developers on this technology.

Debugging and testing

Debugging and testing React Native apps can be difficult because the technology is still emerging and developers may have limited resources and tools.

Popular Apps Built By Leveraging React Native App Development

Several well-known apps have been built with React Native, making it a popular approach to developing cross-platform mobile applications. These are some examples of popular React Native apps:

Facebook: In 2015, Facebook rebuilt parts of its app using React Native. React Native powers numerous Facebook features.
Instagram: Instagram, which is owned by Facebook, also employs React Native in some portions of its app. This contains the Explore tab, main feed, and profile page elements.
Skype: Skype, a popular video-conferencing app, uses React Native for its mobile app. The program lets users phone, text, and transfer files smoothly.
Tesla: Tesla’s mobile app uses React Native to control automobiles, and monitor battery conditions, and other capabilities.
Bloomberg: Bloomberg’s real-time market data, news, and analysis mobile app leverages React Native.
Uber Eats: Uber Eats’ mobile app leverages React Native to order meals from local restaurants and track deliveries in real-time.

Best React Native Practices You Should Integrate with Your Development Strategy

If you’re developing an app with React Native, consider the following guidelines:

Use a modular approach: Avoid constructing monolithic components and instead utilize modular code that can be readily reused and maintained.
Keep the code as simple as possible: Avoid over-engineering or over-optimization by keeping the code basic and clear.
Use Redux to handle state: Redux provides a centralized store for state management, making it easier to maintain and debug the application’s state.
Improve performance: To boost app performance, employ strategies such as lazy loading, caching, and image optimization.
Thoroughly test the app: Employ automated testing tools such as Jest or Enzyme to thoroughly test the app and ensure it functions as anticipated.
Utilize version control: To manage the codebase and track changes, use a version control system such as Git.
Follow the platform’s guidelines: To guarantee that the app appears and feels like a native app, adhere to the platform-specific design guidelines.
Keep up with new releases: Stay up to speed with new React Native and its components, and make sure you update the codebase regularly to avoid compatibility concerns.
Debug the app efficiently: Utilize debugging tools such as React Native Debugger or Flipper to effectively debug the app and swiftly address bugs.
Document the code: Describe the code and keep it up to date so that new developers can join the project and understand the source.

Partner With OnGraph To Build Native-like Apps With React Native App Development

OnGraph is a leading React Native app development company providing cutting-edge and industry-leading React Native app development services with over 15+ years of industry experience and an in-house team of dedicated and proficient React Native developers.

Hire React Native developers from OnGraph to build growth-driven, cross-platform, scalable, and flexible React Native apps that will scale your business growth.

Contact our experts to know more about our services and for free 1:1 React Native consultation.

April 17, 2023January 18, 2024

Neural Networks: The Driving Force Behind Modern AI Revolution

Welcome to the world of neural networks, the fascinating new frontier of artificial intelligence. They are the brilliant engine that has moved the field forward. Now more than ever, neural networks are the backbone of modern AI and data science, powering development and creativity in a world where game-changing technologies like ChatGPT and other generative AI marvels rule the day.

Here we’ll explore the rudiments of neural networks, dissecting their many topologies to discover their secret sauce and the reason for their meteoric rise to prominence. From healthcare to banking and beyond, we’ll look at real-world applications influencing the future of these and other industries.

Our motto as we explore the field of neural networks will be “Unlocking AI’s True Potential: One Neuron at a Time.” We can’t wait to help you learn about and implement AI’s powerful neural networks. Let’s start this exciting journey together and honor the profound effect that neural networks have had on our lives and the globe.

What are Neural Networks?

A neural network is a collection of algorithms that attempts to identify underlying links in a data set by simulating how the human brain works. In this context, neural networks are systems of neurons that can be either organic or synthetic in origin.

According to Allied Market Research, the neural network market worldwide was valued at 14.35 billion US dollars in 2020, and it is predicted that it will reach 152.61 billion US dollars by 2030 with a CAGR of 26.7 percent from 2021-2030.

Since neural networks can adapt to changing input, the network can produce the best outcome without changing the output criterion. The artificial intelligence-based idea of neural networks is quickly gaining prominence in the design of trading systems.

Types of Neural Networks

Before going for the deep dive, let’s understand the different types of neural networks. While it is hard to cover all of them, we have managed to organize them into a few intriguing categories.

We’re starting with the perceptron, the neural network’s ancestor. The perceptron, invented in 1958 by the brilliant Frank Rosenblatt, is the most basic type, with only one neuron.

Remember, this is merely the tip of the iceberg in the huge and ever-changing world of neural networks.

Feedforward Neural Network (FNN)

Feedforward neural networks or multi-layer perceptrons (MLPs) have an input layer, one or more hidden layers, and an output layer. Because the data goes in a single direction with no loops, they are ideal for applications like as image recognition and language processing.

RNNs (Recurrent Neural Networks)

RNNs feature loops that allow information to persist, making them suited for dealing with sequential data such as time series or text. They can recall past inputs and use what they’ve learned to forecast future outcomes.

Convolutional Neural Network (CNN)

CNNs excel in processing grid-like data such as photographs because they are designed to mimic the human visual system. To recognize patterns and features in images, they use convolutional layers, pooling layers, and fully connected layers.

Long Short-Term Memory (LSTM) Networks

LSTMs are a form of RNN that can remember long-term dependencies while avoiding the vanishing gradient problem. They are commonly employed in tasks such as speech recognition, machine translation, and text production.

Radial Basis Function Network (RBFN)

These networks approximate any continuous function by using radial basis functions as activation functions. Interpolation, approximation, and pattern recognition activities frequently employ RBFNs.

Autoencoders

Autoencoders are a form of unsupervised learning model that consists of an encoder and a decoder. In the encoder, they compress data and rebuild it in the decoder, learning a compact representation of the input data.

GANs (Generative Adversarial Networks)

GANs are made up of two competing neural networks, a generator, and a discriminator. The generator generates bogus data, whereas the discriminator attempts to discern between real and bogus data. They are used for image synthesis, style transfer, and data augmentation, among other things.

What is a Biological Neural Network?

Biological neural networks are complex networks of interconnected biological neurons present in live animals’ brains and nervous systems. These networks serve an important role in information processing, transmission, and storage, enabling a wide range of tasks such as sensation, perception, cognition, and motor control.

Electrical and chemical signals are used to process and transfer information in biological brain networks. When a biological neuron receives enough information from other neurons, it produces an action potential, which is an electrical signal that travels through the axon and eventually releases neurotransmitters at the synapse.

These neurotransmitters interact with postsynaptic neurons, which may trigger their action potentials if the input is powerful enough, thus continuing the information transmission process.

The structure and function of biological neural networks inspire artificial neural networks, which are utilized in machine learning and artificial intelligence. They seek to imitate biological systems’ learning and processing skills to execute tasks such as pattern recognition, decision-making, and prediction.

Neural Network Architecture

Here are the primary components of the architecture of neural networks:

Input Layer

The first layer of a neural network, the input layer is in charge of taking in raw data. The dimensionality of the input data (e.g., the number of pixels in a picture or the number of characteristics in a dataset) determines the number of neurons in this layer.

Hidden levels

The intermediate levels between the input and output layers are the hidden layers. They handle the vast majority of network computation. The number of hidden layers and neurons in each hidden layer gets determined by the difficulty of the task and the architecture utilized.

Output Layer

The output layer is the final layer of the neural network and is responsible for generating predictions or classifications. The number of neurons in this layer is determined by the problem’s number of classes or target values.

Activation Function

These are mathematical functions that determine a neuron’s output based on its input. They give the network non-linearity, allowing it to learn complicated relationships. ReLU (Rectified Linear Unit), sigmoid, tanh, and softmax are examples of common activation functions.

Weights and Biases

Weights and biases are neural network parameters learned during the training process. While weights describe the strength of connections between neurons, biases determine the activation threshold of the neuron.

Loss Function

The loss function calculates the discrepancy between the predicted values of the network and the actual target values. It is used to update the network’s weights and biases during training. Mean squared error, cross-entropy, and hinge loss are all common loss functions.

Optimizer

An optimizer is an algorithm that modifies the weights and biases of the network to minimize the loss function. Gradient descent, stochastic gradient descent (SGD), and adaptive algorithms such as Adam and RMSprop are popular optimizers.

How are Neural Networks and Deep Learning Related?

Neural networks are computer models that mimic the structure and function of the human brain, with linked layers of neurons processing and transferring information. On the other hand, deep learning is a subset of machine learning that uses neural networks with multiple layers to automatically learn complex, hierarchical data representations.

Deep learning has transformed industries such as computer vision, audio recognition, and natural language processing due to its capacity to learn characteristics automatically and generalize effectively to new, previously unknown data. Deep learning models can be difficult to interpret and require large amounts of training data and computer capacity.

In essence, neural networks serve as the foundation, while deep learning applies them to address more complicated problems, making it a strong tool for artificial intelligence breakthroughs.

What are the Top Eight Neural Network Libraries?

Here are the top eight neural network libraries.

Keras

Keras is a user-friendly, high-level, and robust API that works with Theano, CNTK, and backend flow for neural network tests. It also runs on GPUs and CPUs and comes with ten different training API modules and neural network modeling.

Pytorch

PyTorch is an optimized, open-source, deep-learning Python library built by Facebook’s AI Lab. It utilizes a Tensor, specifically a torch. Tensor to operate and store rectangular arrays of numbers.

Tensors are like the NumPy array that can operate in the GPU and the torch.nn library has several classes that serve as building blocks to design a neural network.

TensorFlow

TensorFlow is a user-friendly, open-source, machine-learning platform designed by Google Brain. Although TensorFlow wasn’t especially created for neural networks, it is mainly used for it.

A few fundamental areas where TensorFlow is used are speech, text, and image recognition, Natural Language Processing (NLP), handling deep neural networks, abstraction capabilities, partial differential equations, and so on.

Microsoft Cognitive Toolkit (CNTK)

Developed by Microsoft, CNTK is a robust library for developing and training deep learning models. It supports numerous neural network topologies and provides efficient, scalable processing on both CPU and GPU.

Apache MXnet

Apache MXnet is a flexible, open-source framework for deep learning that enables you to train, design, and deploy neural networks for various devices, from mobile phones to cloud infrastructure. It supports several programming languages, including Python, Scala, and R.

Caffe

Caffe is a deep learning framework developed at the Berkeley Vision and Learning Center that focuses on speed, modularity, and expressiveness. It is highly popular for computer vision tasks like picture categorization and object detection.

Chainer

Chainer is a deep learning framework that emphasizes adaptability and user-friendliness. It enables developers to design complex neural network topologies through the “define-by-run” technique, making it suitable for study and experimentation.

Theano

Theano is an open-source library for numerical computing that enables programmers to swiftly design, improve, and test mathematical expressions. Although it is terminated, it is still an essential library in the deep learning community.

Must Read: 10 Best Python App Development Frameworks in 2023

Neural Network Usage

Consider every node having its linear regression model, which would have input data, weights, a bias (or threshold), and an output. The equation would resemble something like this:

The commercial applications of most technologies center on complicated signal processing or pattern recognition issues. Since 2000, there have been many notable commercial uses for technologies, including handwriting recognition for check processing, speech-to-text transcription, data analysis for oil exploration, weather forecasting, and facial recognition.

Many processors, typically stacked in tiers and running simultaneously, are usually used in an ANN.

Like the optic nerves in the human vision system, the first tier receives the raw input data. As neurons farther away from the optic nerve get signals from them, each succeeding tier receives the output from the layer before the raw input. The system’s output is created in the bottom tier.

Each processing node has its little area of expertise, including the things it has seen and whatever rules it created or was initially programmed with. Since the tiers are closely related, each node in tier n will be connected to numerous nodes in tier n-1, which serves as its input, and tier n+1, which supplies input data for those nodes. The output layer may have one or more nodes, and the produced answer can be read from these nodes.

Artificial neural networks are renowned for their ability to adapt, which means that they change as they gain knowledge from initial training and additional data from later runs. The most fundamental learning model is based on input stream weighting, where each node assigns a value to the significance of the input data from each of its predecessors. The weight of inputs that help provide accurate replies is higher.

Neural Network Implementation Using Python

A paradigm for information processing that draws inspiration from the brain is called an artificial neural network (ANN). ANNs learn via imitation, just like people do. Through a learning process, an ANN is tailored for a particular purpose, such as pattern recognition or data classification.

The synaptic connections that exist between the neurons change as a result of learning. Computer scientists simulate this process by utilizing matrices to build “networks” on a computer.

These networks can be viewed as an abstraction of neurons without considering all the biological complexity. There are two training processes: Forward Propagation and Back Propagation.

Here are some prerequisites for implementing neural networks with Python:

At least Python 3.6
Tensorflow 2. X
Numpy
Jupyter Notebook or Google Colab
Scikit-Learn
Pandas

Optimization of Neural Networks

We address non-convex optimization while considering optimization in the context of neural networks.

Convex optimization involves a function with just one optimal value corresponding to the optimal global value (maximum or minimum). Convex optimization issues are not subject to local optima, making them very simple to resolve.

A function with several optima, but only one is the global optima, used in non-convex optimization. Finding the global optima might take much work, depending on the loss surface.

The curve or surface we refer to is the neural network’s loss surface. The goal of neural network training is to find the global minimum on this loss surface since we are attempting to reduce the network’s prediction error.

So, to tackle these problems, there are specific ways in which you can train your network for better optimization.

Local Optima

Local minima were considered a significant issue in neural network training. According to recent research, the genuine global minimum is no longer very crucial; instead, a local minimum with a respectably low error is acceptable. This is because many local minima suffer a minimal cost when utilizing reasonably large neural networks.

Saddle Points

When we consider the information from recent studies in high dimensions, Local minima are less prevalent than saddle points. Local minima are less complex than saddle points since the gradient might be much smaller. Gradient descent will therefore produce minor network updates, ending network training.

Poor Conditioning

The specific way the error function depicts the learning problem is significant. The fact that the error function’s derivatives are typically not well-conditioned has long been known. Error landscapes with many saddle points and flat areas demonstrate this unconditioning.

Top Optimization Algorithms Used in Neural Networks

These days, several algorithms are used in Neural Networks, which we have noted down below, so let’s take a look at them.

Gradient Descent

The first-order optimization process known as gradient descent requires a loss function’s first-order derivative. It determines how the weights should be changed for the function to reach a minima. The loss is propagated from one layer to the next using backpropagation, and the model’s weights are adjusted by the losses to reduce the loss.

Algorithm: θ=θ−α⋅∇J(θ)

Stochastic Gradient Descent

It is a Gradient Descent version. It aims to perform more frequent parameter updates for the model. The model parameters changes after the computation loss on every training instance. Therefore, if the dataset has 1000 rows, SGD will do it 1000 times instead of updating the model parameters once, like in Gradient Descent.

Algorithm: θ=θ−α⋅∇J(θ;x(i);y(i))

Mini-Batch Gradient Descent

Of all gradient descent methods, it is the best. Both standard gradient descent and SGD are improved by it. After each batch, the model’s parameters are updated. The dataset is split into different batches, and the parameters are changed.

Algorithm: θ=θ−α⋅∇J(θ; B(i))

Nesterov Accelerated Gradient

It is a method that prepares. Since we already know we’ll be altering the weights, we can infer the future location from V(t1). Now, rather than using the current parameter to determine the cost, we will use this future one.

Momentum

Momentum was devised to lower the high variance in SGD and smooth out the convergence. It decreases the fluctuation to the irrelevant direction and speeds up convergence in the order that matters. This approach uses a further hyperparameter called momentum, denoted by the symbol “γ”

Algorithm: V(t)=γV(t−1)+α.∇J(θ)

Adagrad

This optimizer alters the learning rate. At each time step, ‘t’ and for each parameter, it modifies the learning rate “η.” It is an algorithm for second-order optimization. It operates using an error function’s derivative.

AdaDelta

AdaDelta is an addition to AdaGrad that helps to address the issue of decaying learning rates. It restricts accumulated prior gradients to some specified size w rather than accumulating all previously squared gradients.

Instead of using the average of gradients, an exponential moving average is employed in this case.

Algorithm: E[g²](t)=γ.E[g²](t−1)+(1−γ).g²(t)

Adam

Adaptive Moment Estimation (Adam) deals with first-order and second-order momentums. The idea of Adam is that instead of rolling quickly only to clear the minimum, slow down to allow for a more thorough search.
Adam additionally preserves a decaying average of past gradients M and maintains a decaying average of past squared gradients like AdaDelta (t).

Comparing the Neural Network Models

The best optimizer is Adam.

Adam is considered an excellent optimizer due to its ability to integrate the finest characteristics of two prominent optimization approaches, momentum, and RMSprop. It adjusts learning rates for each parameter, resulting in faster convergence and better performance.

However, depending on the exact problem and model architecture, the “best” optimizer may differ. As a result, experimenting with different optimizers to discover the best fit for your task is always a smart idea.

Min-batch gradient descent is the best choice if you wish to apply the gradient descent algorithm.

What are the Advantages and Disadvantages of Neural Networks?

Neural Networks have proved more efficient than simple analytic models and humans by working tirelessly. You can also program them to understand previous inputs given and, based on them, predict future outcomes.

Neural networks also mitigate risks when integrated with cloud solutions and perform numerous tasks simultaneously. It has found applications in various sectors like agriculture, medicine, science, and security.

Although neural networks operate online, it still requires a hardware component to create neural networks, creating a network risk relying on set-up prerequisites, the complexity of systems, and physical maintenance.

Since the algorithm of neural networks is complex, developing an algorithm for one task can take months. It also proves difficult to detect bugs, especially when the results have theoretical ranges or estimates.

Also, neural networks need more transparency and are easier to audit. Their processes take time to analyze and track how they learn from prior inputs.

Here’s the list of advantages and disadvantages of Neural Networks.

Advantages

Powerful learning capacities: Neural networks are well suited for a variety of applications due to their ability to generalize to new inputs, learn from enormous volumes of data, and identify intricate patterns.
Non-linear modeling: Unlike linear models, neural networks can describe non-linear interactions between inputs and outputs.
Noise resistance: Because neural networks can manage noisy inputs and partial data, they are suited for noisy and incomplete datasets.
Parallel processing: Because neural networks can conduct several computations at the same time, they are ideal for large-scale and real-time processing.
Adaptability: Neural networks may modify their internal settings in response to fresh input data, making them suited for dynamic and changing contexts.

Disadvantages

Complexity: Neural networks, particularly deep learning models with numerous layers, can be complex and difficult to design, train, and analyze.
Black-box nature: Neural networks, particularly deep learning models, can be difficult to read, making it challenging to understand how they make judgments.
Overfitting: If not addressed effectively, neural networks can overfit the training data, resulting in poor generalization of fresh data.
Computationally Demanding: Neural networks need a substantial amount of processing resources, making them unsuitable for resource-constrained environments.
Data requirements: Because neural networks require a substantial quantity of training data, they are not appropriate for small datasets or areas with limited data.

What are the Application of Neural Networks?

Neural networks have become a key part of various industries like healthcare, defense, finance, and automotive. Their ability to adapt makes them the foundational basis of Artificial Intelligence.

Neural networks find applications in daily life, from online shopping and social media platforms to personalized recommendations, voice-to-text, and search recommendations.

Here are some significant applications of Neural Networks.

Stock Market Prediction

Neural networks examine past stock market data and uncover patterns that aid in forecasting future trends, helping investors to make more educated decisions. They can, for example, forecast price fluctuations, volatility, and trade volume.

Facial Recognition

In facial recognition systems, neural networks evaluate and detect distinct facial traits, enabling applications like unlocking cellphones, identifying friends in social networking photographs, and improving security systems.

Social Media

In social media platforms, neural networks play an important role in analyzing user behavior, interests, and preferences. They improve user experience and engagement by enabling personalized content curation, targeted advertising, and sentiment analysis.

Aerospace

Neural networks are used in the aerospace industry for activities like as autopilot systems, malfunction detection, and aircraft performance monitoring. They aid in the prediction of possible problems and the provision of effective solutions to ensure safety and efficiency.

Defense

Neural networks in defense systems detect possible threats and improve surveillance capabilities. They can process massive volumes of data to discover patterns, enabling the development of advanced military technologies like drones and self-driving cars.

Weather Forecasting

Because neural networks can handle massive volumes of meteorological data, they make more accurate weather forecasts. They can simulate complicated atmospheric dynamics, allowing for better short- and long-term forecasting of phenomena such as hurricanes and tornadoes.

Healthcare

Neural networks in healthcare evaluate medical pictures, diagnose diseases, and predict patient outcomes. They can detect early symptoms of diseases such as cancer, enabling doctors in developing the most effective treatment strategies for patients.

Signature and Handwriting Verification

To detect forgeries and verify documents, neural networks are used in the study and verification of signatures and handwriting. They can detect minor changes and patterns, improving security and fraud detection.

Explore this read: Leverage the Power of Conversational AI to Augment Business

What are the Ethical Concerns Regarding Neural Networks?

Bias and Discrimination

This refers to neural networks that perpetuate or magnify existing biases in data, resulting in unfair outcomes. For example, due to past prejudices, a hiring algorithm may accidentally favor male candidates over female prospects.

To overcome this issue, strategies such as re-sampling, re-weighting, or adversarial training can be used to reduce data biases and assure fair decision-making.

Privacy

This worry centers around neural networks’ gathering, storage, and use of sensitive information, which may violate individuals’ privacy. A facial recognition system, for example, might study photos of people without their knowledge.

To address this issue, developers might employ privacy-preserving approaches such as federated learning, differential privacy, or data anonymization to safeguard users’ data.

Lack of Transparency

This ethical dilemma stems from the “black-box” aspect of many neural network models, which makes understanding how they make judgments challenging. A medical diagnosis model, for example, may deliver an output without explaining its reasoning, causing clinicians and patients to lose trust.

To improve transparency, researchers can create explainable AI strategies that aid in elucidating the decision-making process of neural networks.

Misuse and Malicious Applications

This refers to the usage of neural networks for malicious reasons, such as the creation of deepfakes or the generation of fake news.

For example, an AI-generated deepfake movie could spread falsehoods and destroy people’s reputations. To address these problems, robust detection mechanisms for malicious AI-generated content can be created, coupled with legal and regulatory measures to hold perpetrators accountable.

Job Displacement

Advances in neural networks and automation may result in the replacement of human labor in a variety of industries, resulting in job loss and significant societal unrest. Self-driving vehicles, for example, may reduce the need for human drivers.

To lessen the impact, governments and organizations can invest in reskilling programs and implement policies that support the creation of new job prospects in the age of AI and automation.

Collaborate With OnGraph To Build Neural Network Apps

As we wrap up our thrilling exploration of the fantastic universe of neural networks, it becomes abundantly clear that these astounding innovations have fundamentally altered the AI environment.

Neural networks continue to open up previously unimaginable opportunities for businesses and individuals by improving our knowledge of complex systems and giving machines the ability to learn and adapt. The advent of neural networks as the backbone of current AI has ushered in a new era of intelligent systems that expand our horizons.

Remember that OnGraph is here to help realize your ideas in the exciting realm of neural networks. Our team of experts specializes in developing neural network and deep learning-powered software applications that can elevate your business and reshape your industry.

Partner with OnGraph and let’s go on this exciting journey together, letting AI live up to its full promise and making the future smarter and more innovative for everyone.

April 10, 2023March 18, 2024

SVELTE 101 For Busy Business Owners

The Stack Overflow developer survey of 2021 rated SVELTE, a relatively new online UI framework for designing a web-based interface with components as the most favored web framework. Several major organizations like The New York Times, Razorpay, Avast, Square, IBM, and GoDaddy leverage the SVELTE framework as a part of their development strategy.

Some Twitter tweets also show Spotify and Apple also are using SVELTE for building web applications to some extent. But what is SVELTE? Why is it so popular among the developer community? How can business owners benefit by leveraging this framework for their business?

Let’s dive into this blog post to find out more.

What is SVELTE?

SVELTE is an open-source JavaScript-based front-end web development framework written in TypeScript for creating dynamic web pages that offer various features and functionalities. It is a revolutionary UI development methodology that you can utilize to create either discrete components of an interface or a complete web app most easily.

With SVELTE, you may create a whole app or incrementally incorporate it into an existing application. Without relying on a typical framework, components can also be delivered as independent packages that function elsewhere. It shares goals with other JS frameworks like Vue.js and React, which make it simpler to build dynamic and engaging UI.

However, there is a remarkable difference: SVELTE transforms your app into perfect JavaScript at build time rather than reading your application code at run time. As a result, neither a performance cost for the component framework’s abstractions nor a cost when your app first loads applies to you.

What are the Features of SVELTE?

Here are the three basic features of SVELTE.

Truly Reactive

SVELTE performs precise DOM modifications at build time. Because of this, users may create applications that meet their needs without worrying about extra overhead.

This web framework is also utilized for its responsiveness in its language to further simplify things for the user. The user must use hooks in Vue or React to update the state. Hooks are an essential component in updating the state, but they burden the Garbage Collector with extra work.

Absent Virtual DOM

The Virtual DOM, in its most basic form, is a method of updating the state by contrasting the current tree of customized objects with the snapshot of a prior one. React makes use of this.

Since SVELTE is a compiler, running its code does not need loading a UI library into the browser. Instead, the app is rendered on the page by loading a straightforward.js file. At compilation time, all object modifications are made.

This aids SVELTE in lowering the overhead of virtual DOM operations. Additionally, by not loading the entire library, the file size is greatly reduced. Mobile devices notably benefit from this.

Lesser Coding Required

Being written in TypeScript, SVELTE has a simpler format and allows writing fewer code lines enhances code readability, saves resources and time, and reduces bugs. A simple “Hello World” program written in the SVELTE framework would look something like this:

In the code above, there are:

HTML heading tag, <h1>, that templates the name variable and writes the word “Hello”, and
A <script> tag that encloses the program written in JS

The same code would be about 30 percent to 40 percent bigger in Vue.js or React.js. React utilizes the useState hook, making the source code heavier.

SVELTE doesn’t restrict developers to a single additional top-level element and lets allows updating the variable’s local state with ease by using the “=” assignment operator.

Here’s a good read: Jamstack For Business Growth: Translating the Buzzword

How is SVELTE Development Different From React?

Here’s the thing: developers can build successful and powerful web apps using both SVELTE and React. However, although having a similar overall function, they operate very differently.

Performance

Performance is one of the primary advantages of using a frontend framework like SVELTE. It employs a compile-time method for code generation, resulting in faster load times and improved performance.

Svelte, unlike React, compiles the components into plain JS that directly manipulates the DOM. When working with big or complicated applications, this strategy can result in significant performance advantages.

Ecosystem

React has a large ecosystem of libraries and tools built around it, which makes it easier for developers to find solutions to common problems.

However, Svelte is a newer framework with a smaller ecosystem. It does, however, have a thriving development community and toolkits and libraries are being developed all the time.

Learning Curve

Svelte’s learning curve is usually believed to be smaller than React’s. It has a syntax comparable to HTML, making it more approachable to developers who are acquainted with web development. Furthermore, it has an approach to state management that is easier than React’s, which can assist newcomers to minimize the learning curve.

Size

Svelte’s small size is one of its advantages. The compile-time method used by the component framework produces optimized code, resulting in reduced bundle sizes and faster load times. React’s virtual DOM, on the other hand, might add overhead, resulting in larger bundle sizes and slower load times.

Testing

Both Svelte and React provide testing tools for developers. Jest, Enzyme, and the React Testing Library are just a few of the testing libraries and tools available for React. Svelte testing tools are the official Svelte testing library and third-party tools such as Cypress and Jest.

However, due to its compile-time methodology, some developers have observed that testing Svelte components prove tougher.

Prerequisites of SVELTE

As experts, we advise that you have a working familiarity with the terminal and command line (CLI), as well as the fundamentals of HTML, CSS, and the JavaScript framework. The SVELTE compiler creates lean and highly efficient JavaScript code from our sources; to compile and build your app, you’ll need a terminal with node + npm installed.

You can also keep reading this SVELTE tutorial to learn more.

How does SVELTE work?

SVELTE files can expand HTML, CSS, and JavaScript because it is a compiler, producing the best JavaScript code with no runtime overhead. To do this, the SVELTE compiler adds the following features to standard web technologies:

To achieve real reactivity and simplify component state management, it extends JavaScript by reinterpreting several of the language’s directives.
The scope method it adds to CSS improves its capabilities by letting each component specify its template styles without worrying about them clashing with those of other components.
By enabling JavaScript expressions in markup and offering directives that leverage conditions and loops in a way akin to handlebars, it expands HTML.
Only when SVELTE components are involved and only in very narrow circumstances does the compiler step in. To avoid breaking JavaScript syntax or alienating developers, extensions to the JavaScript language are kept to a minimum and are carefully chosen. Actually, you will be using standard JavaScript most of the time.
SVELTE also leverages context API to share data between the different components without the need for a prop.

If you feel this is going over your head, you can consider outsourcing and successfully complete building your web applications.

What are the Different SVELTE Frameworks?

Here are the two most used SVELTE frameworks:

Svelte Native

Another illustration is Svelte Native, which makes it simple for Svelte developers to create native apps for iOS and Android. The best features of Native Script and Svelte are combined in Svelte Native, which was published in February 2019.

SvelteKit

Several different frameworks have been constructed on top of Svelte, and its ecosystem is expanding quickly. For starters, SvelteKit, a more recent framework that took the role of Sapper, was made available in March 2021. It has complex capabilities like server-side rendering, file-based routing, code splitting, and offline support and is the quickest way to create a Sveltekit app. Svelte’s version of Next.js is called SvelteKit.

Advantages of SVELTE

The benefits of SVELTE should be clear by now. There’s more, though. Your developers will gain a few advantages over competing tools by using this new framework. These benefits include:

Reduced Boilerplate Code

SVELTE eliminates the need for boilerplate code, making application development easier and faster. This allows developers to devote more time to tackling challenging problems and adding new features and details to the application.

Reactive Variables

With this novel framework, developers can quickly generate reactive variables by prefixing the declaration with $. This makes it simple to handle program state changes and construct dynamic user interfaces.

Faster and More Reliable Apps

SVELTE does away with the requirement for a virtual DOM, making apps faster and more dependable. The built components are highly optimized and run directly in the browser, resulting in a more efficient and responsive application.

Scoped Styling with JavaScript Framework

Using scoped styling with JavaScript, Svelte allows developers to insert template styles mid-document that explicitly target a given element and its children. This simplifies CSS management and lowers the possibility of style clashes in larger apps.

Built-in State Management

Svelte includes its own simple and easy-to-use built-in state management system. This eliminates the requirement for external state management libraries, lowering the application’s complexity.

No Framework Traces

Because Svelte converts the components into normal JavaScript, the resulting apps have no traces of the framework. This eliminates the need for transitions, and reduces the bundle sizes and load times, making the application more efficient.

High Performance

A SVELTE application runs faster than those built with competing frameworks. Because the compiled source code in the src folder is highly optimized, it loads faster and performs better. As a result, SVELTE is an excellent solution for developing high-performance web apps.

Disadvantages of Leveraging SVELTE Development

Nevertheless, there are a few drawbacks to adopting Svelte, which include:

Lack of Significant Backing

Svelte lacks the support of a large firm, such as React (Facebook) or Angular (Google), which may make it less appealing to some organizations.

Insufficient IDE Support

Svelte has some IDE support, but it isn’t as robust as some other frameworks. This can be difficult for developers who rely largely on their IDE.

Limited Dev Toolkits

In comparison to other frameworks, the number of Svelte dev toolkits available is currently rather limited. This may make it more difficult for developers to discover the correct tools for their specific requirements.

Limited Open-Source Ecosystem

Svelte’s open-source ecosystem is still relatively tiny, which means there are fewer third-party libraries and tools available.

Steep Learning Curve

Although Svelte has a lower learning curve than some other frameworks, reactive programming, newbies find it difficult to get started with it.

Limited Svelte Community Support

Svelte is a relatively new framework, hence the amount of available community help may be restricted. This may make it more difficult for developers to find assistance with more sophisticated difficulties.

About SVELTE 1.0: The Latest Release

SVELTE 1.0, the latest version released in the SVELTE ecosystem, was launched a few weeks back on December 14, 2022, with vitePreprocess as its default preprocessor. Here are the new developments in the SVELTE ecosystem that came with releasing the latest SVELTE version.

SvelteKit now utilizes Vite 4 and needs a Svelte peer Dependency of 3.54.0. sveltekit() now returns a commitment for an array of Vite plugins. __data.json is no longer included in URLs.
A new embedded feature, which is disabled by default, improves link clicks when SvelteKit is embedded.
Builder has replaced the automated fallback generation (generateFallback(fallback))
SvelteKit now will throw an error if a load response is invalid.
+server.js files and +(layout|page)(.server)?.js no longer permit unknown exports (other than those beginning with an underscore).
Ensure prerender remains false while ssr also is false
The choices.
Custom transition functions can now accept a direction parameter.
You can modify variables now within a @const function.
svelte/elements have now been introduced for Svelte/HTML type specifications
Invalid() is renamed as fail() and ValidationError is renamed to ActionFailure.

New Additions to the SVELTE Ecosystem

SVELTE now comes with new libraries and components.

Free Svelte Accelerators: A set of Sveltekit and Svelte open-source code to kickstart development
Konsta UI: A library of mobile user-interface components created with Tailwind CSS for Svelte, React, and Vue
JetBrains WebStorm 2022.3 now has SVLETE built-in support
probablykasper/modal-svelte: A modal SVELTE component
NextAuth.js is now available for SvelteKit
scrolltron/deepcrayon: Crawler overlay for OBS Studio
SvelteKit Tailwind Blog Starter: An easily customizable and configurable blog starter for SvelteKit and Tailwind CSS
tRPC-SvelteKit: Provides end-to-end typesafe APIs for your SvelteKit applications
SvelteKit CAS authentication: A list of functionalities for using CAS/SSO in SvelteKit
@svelte-plugins/tooltips: A fundamental SVELTE tooltip component
@macfja/sveltekit-session: A simpler session management for SvelteKit

SVELTE 1.0 needs these minimum version specifications for languages and frameworks.

SVELTE version 3.55
Node version 16
Typescript version 3.49

Also read: The Era of Generative AI: ChatGPT vs Bard

What are the Use Cases of SVELTE?

You can use Svelte to create both individual user interface elements and complete programs. You can either build your user interfaces from scratch using a Svelte file or you can gradually include it into an already-existing application.

However, Svelte is especially suitable to deal with the following circumstances:

Web programs designed for low-power gadgets: You can use smaller bundle sizes of the Svelte app for devices with sluggish network connections and poor processing power. Less code necessitates downloading, parsing, executing, and keeping in memory fewer KBs.
Highly interactive sites or intricate visualizations: Performance advantages from a framework with minimal runtime overhead will guarantee that user interactions are quick and responsive if you are developing data visualizations that must display a lot of DOM elements.
Svelte features a short learning curve, making it easy to onboard users with no prior web programming experience. Web developers with a foundational understanding of HTML, CSS, and JavaScript may quickly pick up and begin developing web apps.
You can also create apps with sophisticated capabilities like server-side rendering, file-based routing, code splitting, and offline support with the aid of Sapper (a framework based on Svelte). Svelte Native is another tool that enables you to create native mobile applications.

Build Reactive SVELTE Application with OnGraph

SVELTE is a novel and innovative approach to the JavaScript framework that enables the development of very responsive and quick applications. You can also leverage Github actions for your SVELTE project. It should be on your radar if you want to assist your developers in bringing your web application to the next level of efficiency and simplicity.

If you think SVELTE is the right fit for you but don’t know where to start, you can consider partnering with a SVELTE web development agency. OnGraph is a SVELTE development company that assists you in leveraging the benefits of SVELTE web development for your business.

To learn more about SVELTE development and start developing web apps, schedule a 1:1 consultation with our experts.

April 2, 2023January 18, 2024

Automated Testing Vs Manual Testing: Which is Right for Your Business

Since we started our schooling, we’ve been given tests to evaluate our progress academically, professionally, or personally. Tests have proven as a measurable tool to evaluate knowledge, skills, and expertise in any particular area.

Similarly, the software also goes through tests to evaluate its performance and quality and look for compliance issues before rolling it out to the public. It is the process of analyzing a software application or product to find errors, bugs, defects, and other issues and it is a crucial part of the software development cycle that cannot be overlooked.

Software testing is divided into two broad categories: automated testing and manual testing. In this blog, we will uncover the software testing facts by comparing automated testing vs manual testing and help you understand which would be a better option for your business.

Let’s get started!

What is Automated Testing?

Automated testing is a software testing technique that involves executing test cases and verifying the results of software applications using automation tools and test scripts. It is the practice of carrying out tests automatically with the aid of scripts and specialized software to determine whether or not they passed or failed in comparison to predetermined standards.

The global automation testing market size is about 20 billion USD and Global Market Trends has predicted that it will have a CAGR of 15% between 2023-32.

Automated testing is a more effective and dependable method of testing software applications since it decreases the amount of time and effort necessary to test the program while increasing the correctness and consistency of the test findings. As it helps the automation testing team to conduct tests rapidly and repeatedly without the chance of human error, it is very helpful for testing big, complicated, and frequently tested systems.

Automated testing is useful for a wide range of tests, including

Functional testing

Functional testing determines whether the software satisfies functional specifications or requirements is known as functional testing.

Repetitive testing

If the testing involves a repetitive task or several repetitive tasks that need to be completed regularly, automated testing can be a viable solution since it reduces testing time and improves accuracy.

Performance Testing

Performance Testing looks at how well the software works in different situations, such as when there is a lot of traffic or a lot of work to do.

Large-scale testing

When the testing involves a large number of test cases or a complex app, automated testing can ensure that all test cases are completed fully and efficiently since it can cover different situations, data sets, and settings.

Security Testing

Security testing assesses the security features of the software to ensure that they fulfill the appropriate standards.

Regression testing

Regression testing checks the application’s functioning after updates. Automated testing saves effort and time since this testing is time-consuming and requires testing of various scenarios.

Compatibility Testing

Compatibility testing assesses the software’s compatibility with various operating systems, browsers, and hardware.

It is crucial to note, however, that not all testing can or should be automated, and human testing may still be necessary for certain types of testing.

What is Manual Testing?

Manual testing is a type of software testing that is carried out by hand, typically by human testers. The goal of manual testing is to find bugs and other problems in the software by running through test cases and scenarios.

Manual testing involves the tester going through the software application step by step to check that it fulfills the quality and performance standards. To guarantee that the software meets the functional and non-functional requirements, the tester uses numerous testing methodologies such as exploratory testing, regression testing, and user acceptability testing (UAT).

Manual testing requires knowledge of the software’s functionality. The tester must identify and report difficulties, errors, and defects to the development team. Manual testers utilize the SRS document to create detailed test cases for each module or feature. They then go over the test cases with the project owner before running them one by one to check that the feature or module fits the specifications.

This procedure assures that the software system is bug-free and fulfills the quality requirements specified in the SRS document. Small projects with limited testing requirements and a low budget often use manual testing. It can also be used in complex projects that require human judgment to spot flaws.

While manual testing is an effective testing method, it is time-consuming, repetitive, and prone to human error. As a result, many firms are shifting to automated testing, which can assist enhance testing productivity and accuracy.

Here are a few types of manual testing:

Exploratory Testing

Exploratory testing is a method of testing in which the tester explores the software program to uncover faults and other issues.

Regression Testing

Regression testing is a type of testing that is done after making modifications to the software to ensure that the changes do not affect the software’s existing functioning.

Ad hoc testing

Testing the application without a plan or script is ad hoc testing. Manual testing is better for ad hoc testing since it lets the tester explore the program and find defects or issues, not in the test plan.

User Acceptance Testing (UAT)

UAT is a sort of testing that assesses the usability of software and whether it meets the needs of its intended users.

Automated Testing vs Manual Testing: What is the difference?

Here’s how automated testing and manual testing are distinct from each other.

Execution

Manual testing involves a human intervention to conduct test cases, whereas automated testing executes test cases using software tools and scripts. Also, in manual testing involves testers performing test cases step by step and manually documenting the results, whereas automated testing involves test scripts being conducted automatically and results being created.

Examples of manual testing include exploratory testing and usability testing, whereas automated testing examples include regression testing, load testing, and API testing.

Speed

Because it can perform test cases at a considerably faster rate, automated testing is faster than manual testing. Automated testing can run a huge number of test cases in a short period, but manual testing can take longer.

For example, if there are 100 test cases, automated testing can finish them in a matter of hours, whereas manual testing can take several days.

Accuracy

Because it eliminates human errors and inconsistencies, automated testing gives greater accuracy and precision. Because human testers can make mistakes, neglect specific areas, or interpret results differently, manual testing is more prone to errors and inconsistencies.

For example, results in automated testing are derived by comparing actual and expected outcomes, whereas results in manual testing are susceptible to human judgment and interpretation.

Scope

Because it can manage high test volumes, automated testing is ideally suited for repetitive and routine tests such as regression testing or load testing.

Manual testing is ideal for complicated and exploratory tests that involve human intuition, inventiveness, and critical thinking. Automated testing, for example, is used to test mobile applications, whereas manual testing is utilized to test gaming applications.

Cost

Automated testing can be costly to set up and maintain at first, but it can be more cost-effective in the long run because it saves time and effort by eliminating the need for manual testing. Manual testing may be less expensive at first, but it requires more resources, time, and effort to complete, making it more expensive in the long term.

For example, automated testing necessitates the purchase of testing instruments, whereas manual testing necessitates the purchase of human resources.

Parameters	Automated Testing	Manual Testing
Execution	Performed using automated tools and scripts	Performed manually by human testers
Speed	Automated testing is faster than manual testing since they are executed quicker	Slower than automated testing
Accuracy	Automated testing is more accurate than manual testing	Less accuracy in comparison to automated testing
Scope	Automated testing can cover a wider range of tests and scenarios	Manual testing is limited by the available time and resources.
Cost	Automated testing can be more expensive than manual testing	Fewer resources are needed compared to automated testing
Repeatability	Can be easily repeated	Manual tests require significant effort to be repeated

What are the Advantages of Automated Testing?

Here are the advantages of automated testing:

Faster Execution

Because automated testing does not require human intervention, it can be completed significantly faster than manual tests. This can help to shorten the overall testing time and accelerate the release cycle.

You can also conduct the test execution of automated tests on numerous platforms and multiple devices at the same time, speeding up the testing process even more.

Enhanced Accuracy

Because they are free of human errors and inconsistencies, automated tests are more trustworthy and accurate than manual testing. Automated tests can be conducted often and consistently, ensuring that any flaws are identified and corrected before they affect end users.

Because automated tests can be run whenever a new build is deployed, they help reduce the possibility of regression issues.

Expanded Coverage

Manual tests cannot cover as many tests and scenarios as automated tests. This increases the overall quality of the product by ensuring that all important and edge cases are tested. Automated tests can also be done on a wide range of configurations and situations, ensuring that the program is adequately tested across all potential circumstances.

Reusability

Once you’ve established automated test scripts, you can reuse them across different software builds and versions. If you have a software program that is frequently updated and released, for example, you may utilize automated testing to verify that the software is tested consistently across different versions.

Scalability

Depending on the testing requirements, automated testing can be simply scaled up or down. You can, for example, employ automated testing to mimic thousands of virtual users accessing a website to assess its scalability and performance under high-traffic conditions.

What are the Disadvantages of Automated Testing?

Here are some disadvantages of leveraging automation testing.

High Initial Investment

An automated testing infrastructure might be expensive to set up at first because it necessitates investments in tools, technology, and experience. This can be a substantial impediment for small and medium-sized businesses.
The expense of maintaining and upgrading automated tests can also add up over time, especially if the software is updated frequently.

Limited Flexibility

Automated tests are less versatile than manual tests since they are meant to examine certain scenarios. This can make exploratory testing or testing new features or functions that were not covered in the initial test plan challenging.

Automated tests also necessitate a high level of technical competence, making participation in the testing process challenging for non-technical team members.

Negatives and False Positives

Automated testing can generate false positives and negatives, which can be deceptive and necessitate extra effort to detect and remedy the problems. False positives arise when a test fails despite the absence of a software problem.
False negatives arise when a test passes despite a flaw in the software. Both can be time-consuming and annoying for the automation testing team, and they can undermine trust in the automated testing process.

What are the Advantages of Manual Testing?

Here are some advantages of leveraging manual testing.

Early fault detection

Consider a development team creating an online shopping web app. The manual testers discover a bug in the application’s shopping cart that allows users to add more products than inventory.

Early detection of this flaw can help the development team correct it before the application goes into production, avoiding costly rework and delays.

Flexibility and adaptability

Imagine a team developing a complex app. Manual testers discover that some test cases are irrelevant or unnecessary. The manual tester can prioritize more important test cases to optimize the testing process for the project.

Enhanced collaboration

Consider a financial application development team working on sophisticated computations and operations. Manual testers can collaborate with the development team to fix bugs and provide application behavior comments. This collaboration can assist guarantee the application satisfies needs and specifications, improving product quality and development efficiency.

Improved usability

Consider an e-commerce website development team. The product description font size is too small, making it hard to read, according to manual testing. Early detection can assist the development team fix the issue and improve user experience, increasing customer happiness.

Cost-Effective

Manual testing involves little tools and infrastructure, making it cost-effective for small projects. To save money and launch faster, a business may select manual testing over automated testing.

What are the Disadvantages of Manual Testing?

Here are a few drawbacks of leveraging manual software testing.

Labor-intensive and time-consuming

Manual testing involves running and analyzing test cases. Testing complicated or large-scale applications can be laborious and time-consuming. Manual testing takes more time and resources per release, making it hard to scale.

Risk-based or exploratory testing can prioritize key test cases and enhance testing efficiency, mitigating this disadvantage. Test case management systems automate test case execution and analysis, saving time and effort.

Error-prone

Human judgment, intuition, and experience make manual testing error-prone. Testing errors missed faults, and inconsistent feedback can lower application quality.

A thorough testing procedure, comprising test case design, execution, and analysis, helps reduce error risk. Testers can also work with developers and stakeholders to ensure the app meets requirements.

Low scalability

Large-scale or complicated applications might make manual testing difficult. Manual testing makes it hard to thoroughly test each release. This causes application delays, bottlenecks, and quality concerns.

Automated testing can offset this drawback. Automated testing lets testers test the software more often, thoroughly, and efficiently. Testers can manage and prioritize test cases using test case management solutions to optimize the testing process for the project.

Automated Testing vs Manual Testing: What to Choose?

Here are the factors you should consider before deciding whether automated or manual testing is better suited for your business.

Test Type

Take into account the kind of testing needed. Some tests are better suited for manual testing than others for automated testing. Exploratory and usability testing, for example, may be more effective when performed manually, although regression and load testing can be automated.

Test Coverage

Evaluate the required level of test coverage. automated testing is likely to be more effective if the application involves testing across different platforms, configurations, and data types. Manual testing, on the other hand, may be sufficient if the testing requirements are modest.

Time Restriction

Evaluate the project timeframe and the testing time available. Automated testing may run tests more quickly and efficiently, making it an excellent choice when time is of the essence. Manual testing, on the other hand, takes longer to complete but provides more detailed input.

Budget Restriction

Assess the amount of money available for testing. Automated testing necessitates a larger initial investment in tools, infrastructure, and staff. Yet, once the initial setup is complete, it may be less expensive in the long run. Manual testing, on the other hand, could be more costly in terms of both personnel and time.

Test Maintenance

Take into account the continuing upkeep needed for the tests. As the application changes and the tests need to be updated, automated tests may demand extra upkeep. Manual tests, on the other hand, may necessitate less maintenance but must be performed more regularly.

Skills and Expertise

Take into account the testing team’s qualifications and experience. Automated testing necessitates expertise in scripting, programming, and automated technologies. Manual testing, on the other hand, may necessitate more subject knowledge and analytical abilities.

Risks and Complexity

Take into account the application’s hazards and the testing needs’ complexity. Automated testing is excellent for testing complicated and high-risk systems, whereas manual testing is better for discovering edge situations and nuanced flaws.

Here’s a good read: Start Your First Application with Agile Project Management

Various Automation Testing Tools for Web and Mobile Applications

Here are some popular automated testing tools that you can use for testing your web and mobile apps.

Selenium

Selenium is a popular automated testing tool for web applications. It provides a robust collection of APIs for automating browser tasks like clicking, typing, choosing, and more. It’s a flexible testing tool because it works with many different languages, including Java, Python, C#, and more.

Selenium automation testing enables running tests on a variety of browsers and platforms, including Windows, Mac, Linux, and mobile devices. You can reach us for our leading Selenium Automation Testing services.

Cypress

Cypress is a free and open-source end-to-end testing tool for online applications. It stimulates the production set to ensure a quick, dependable, and user-friendly testing experience. Cypress also includes a robust set of APIs for simulating user behaviors, network traffic, and more.

Appium

Appium is a popular mobile application automated testing tool. It’s cross-platform, meaning you can use it on your Android or iOS device, and it lets you write tests in languages like Java, Ruby, Python, and more.
Appium provides APIs for replicating mobile device user activities like touch, swipe, and more.

Katalon Studio

Katalon Studio is a free web and mobile application testing automated solution. The intuitive user interface aids in the rapid development of test scenarios. Katalon Studio supports multiple scripting languages, such as Groovy, Java, and more. This makes it easy for testers to write automated scripts.

Espresso

Espresso is an automated testing tool for Android application testing. It offers APIs for emulating user activities on Android devices, such as touch and swipe. By providing support for Java and Kotlin, Espresso makes it simple for Android programmers to create automated scripts.

TestComplete

TestComplete is a commercial automated testing tool for web and desktop applications. It has a record and playback function that allows testers to quickly develop automated tests without having to write any code.
Scripting languages are also supported by TestComplete, so testers can use tools like Python, JavaScript, and VBScript to build more sophisticated test scenarios.

Robot Framework

Robot Framework is an open-source automated testing solution for online and mobile apps. It provides a keyword-driven approach for developing test cases and works with a wide range of pro languages. Robot Framework features a robust ecosystem of modules and plugins for extending its functionality.

XCUITest

XCUITest is an automated testing tool for iOS applications. It includes APIs for imitating iOS device user behaviors such as touch, swipe, and more. Because of XCUITest’s compatibility with languages like Swift and Objective-C, creating automated scripts for iOS is a breeze.

Real-World Examples of How Automated Testing has Benefited Businesses

Here are a few real-world examples of businesses that have successfully utilized automated testing:

Amazon

Amazon is one of the world’s top e-commerce enterprises, and it has substantially invested in automated testing to increase the quality of its software. To test its web and mobile applications, the organization employs a vast array of technologies and methodologies for automated testing.

Amazon has its testing platform called AWS Device Farm, where developers can test their mobile apps on numerous real devices.

Google

Google is another industry giant that has poured resources into automating test cases. To test its web and mobile applications, the company employs a diverse set of automated testing tools and approaches.

Google has also created its testing framework, Espresso for testing Android apps. The company also employs Selenium automated testing for testing its online apps.

Netflix

Netflix, the world’s largest streaming service, uses automated testing to ensure the quality of its web and mobile apps. The company tests its web applications with a wide range of automated testing tools and methods, such as Selenium automated testing.

To ensure the security and reliability of its online services, Netflix built an internal testing system it calls Simian Army.

Facebook

When it comes to the quality assurance of its web and mobile applications, Facebook, the largest global social networking platform relies heavily on automated testing.

With automation testing tools like WebDriverAgent for iOS and UIAutomator for Android, the Facebook testing team utilizes both automated and manual testing to guarantee the reliability of the platform’s software.

Spotify

Spotify, a popular music streaming platform leverages automation testing and manual testing to maintain the quality of its apps. The company has also created its automation testing tool, Detox, which is used to test its iOS and Android applications.

Best Practices for Automated Testing

Here are some best practices for automated testing:

Establish clear goals

Define the goals and objectives explicitly before beginning the automated testing process. This will assist in identifying areas that require automated testing as well as ensuring that the testing methodology is aligned with the business goals.

Choose the most appropriate automation tool

You have options to choose from numerous automated testing tools on the market. Select the best automation tool for your project’s demands, technical requirements, and finances.

Develop an effective testing framework

To guarantee that the automated testing process is dependable and consistent, it is critical to developing a robust testing framework that contains a set of principles and standards.

Design and prioritize test cases

Design and prioritize test cases to ensure the proper testing of your application’s most critical sections. This procedure will minimize the probability of bugs and guarantee that the application works as planned. You can also hire a human tester to fulfill your requirements.

Keep code quality high

The quality of the code used for automated testing is crucial to the testing process’s success. Make sure the code is well-documented, comprehensible, and easy to maintain.

Perform code reviews regularly

Frequent code reviews can help uncover potential issues and enhance the quality of automated testing code.

Leverage version control

To manage changes to the code used for automated testing, utilize a version control system. This will make it easier to track changes, work with team members, and keep the code up to date.

Carry out continuous testing

Ongoing testing is required to guarantee that the application functions properly throughout the development lifecycle. This includes performing automated tests regularly, reviewing results, and swiftly resolving errors.

Combine with other tools

Tools such as bug tracking, project management, and continuous integration, should be connected with automated testing technologies. This will aid in streamlining the testing process and increasing efficiency.

Update and maintain tests regularly

Update and maintain the automated tests regularly to ensure that they are relevant and up to date with the latest changes in the application. This will help to increase the testing process’s accuracy and dependability.

Common Mistakes to Avoid when Using Automated testing

Here are some common mistakes to avoid when using automated testing:

Inadequate planning

One of the most typical errors is to begin automated testing without adequate planning. It is critical to specify specific goals, identify areas that require automated testing, and design the testing process accordingly.

Using the incorrect automation tools

Using the incorrect automated testing tool can result in a variety of issues, including inadequate test coverage, expensive maintenance costs, and inaccurate results. Make sure to investigate and select the best tool for your project’s demands and technical requirements.

Failure to maintain the test script

Test scripts must be updated frequently to reflect the most recent changes in the application. Failure to keep the scripts up to date can lead to false-positive or false-negative findings.

Overuse of Test Automation

Automated testing should not completely replace the manual testing process. To ensure proper testing of your application, utilize a combination of manual and automated testing.

Communication breakdown

A lack of communication among team members can lead to problems and errors during the testing process. Verify that the developer, QA tester, manual tester, and stakeholder are communicating clearly.

Ignoring problems with the test automation environment

Issues with the test environment can have an impact on the accuracy and dependability of the automated testing process. Ensure that you set up the test environment correctly and consistently throughout the testing process.

Not taking accessibility and localization into account

In automated testing, accessibility, and localization testing are frequently disregarded. Use these considerations in the testing process to guarantee that the program is accessible and usable by all users.

Concentrating on incorrect test automation cases

While selecting test cases for automated testing, it is vital to focus on the most critical aspects of the application. Choosing the wrong test cases might lead to insufficient coverage and inaccurate results.

Incompatibility with other tools

You should integrate other tools, such as bug tracking, project management, and continuous integration automated testing technologies. This will aid in streamlining the testing process and increasing efficiency.

Failure to analyze test results

Evaluating the test result is critical for identifying problems and improving the testing process. Establish a habit of periodically analyzing the test result and taking action to address any issues that appear.

Start Automated Testing With OnGraph

OnGraph is a web and mobile app development company that provides comprehensive unit testing and automated testing solutions. Our developers hold expertise in various automation testing tools like Selenium, Cypress, Katalon, and so on.

We also have an in-house team of QA testers and manual testers to ensure that your software is bug-free and provides a seamless user experience.

Hire a QA tester from OnGraph to get complete testing solutions from automated software testing and stress testing to regression test and manual test with an innovative testing technique and industry-leading expertise.

To know more about our manual testing process and automated testing services, contact us for a free consultation.

March 24, 2023January 18, 2024

Jamstack For Business Growth: Translating the Buzzword

Jamstack is one of the latest technology lingos gaining widespread popularity among major organizations because of its promise of offering a revolutionary approach to web app development that is simpler, cost-effective, productive, secure, and has enhanced performance.

The State of Jamstack Report 2022 showed that 44% of developers choose 44% to build apps with stability and reliability. 43% of the developers chose Jamstack for productivity, and 40% leveraged Jamstack for its security benefits.

Jamstack’s wide range of offerings like better speed, high scalability, better developer experience, and saving resources has completely changed the way we think about web application development. You can learn more about its purpose and how to get started with the aid of this comprehensive guide.

What is Jamstack?

Jamstack, which stands for Javascript, APIs, and Markup, is first and foremost a software architecture and philosophy.

Nowadays, the term “Jamstack” describes an architectural method for creating websites. Although there are differing views on what exactly the Jamstack approach signifies today, the majority of websites that make this claim include the following characteristics:

Decoupled

The frontend and backend use separate tooling. Developers typically use a static site generator to create the front end, while APIs are commonly utilized throughout the building process to interface the back end with the front end. Additionally, serverless functions can be used to perform server-side procedures.

Static-First

Although there are many techniques for adding dynamic functionality to Jamstack sites, the majority of them are pre-rendered, meaning that the front end was written and compiled into HTML, CSS, and JavaScript files.

Progressively Enhanced

Pre-rendered websites can incorporate the JavaScript framework when necessary, enhancing browser performance.

What makes up the Jamstack Ecosystem?

The Jamstack architecture consists of Javascript, APIs, and Markup. Its origins can be traced to the development of the word “static site” into something more significant (and marketable). Although the end product is ultimately a static site, it has been expanded to incorporate top-notch tooling at every stage.

JavaScript

The JavaScript framework has arguably contributed the most to Jamstack’s popularity. All of the dynamic and interactive elements that we might not have if we were serving plain HTML markups without our preferred browser language are now possible thanks to it.

This is where UI frameworks like React, Vue, and more recent additions like Svelte frequently find use. By offering component APIs and technologies that output a straightforward HTML file, they make creating Jamstack applications easier and more organized.

These HTML markup files contain a collection of assets, such as photos, CSS, and the actual JS, which you can eventually serve to a browser via your preferred CDN (content delivery network).

Here’s a good read: A detailed comparison between JavaScript Libraries and Frameworks

APIs

The key to creating dynamic websites is to make use of APIs’ strengths. Your application will use Javascript to send an HTTP request to another provider, whether it’s for authentication or search, which will ultimately improve the user experience in some way.

You can contact as many hosts as you need for an API.

For instance, if you host your blog entries using a headless WordPress API, store your specialized media in a Cloudinary account, and run your site’s search engine using an Elasticsearch instance, all of these components work together to give visitors to your site a seamless experience.

HTML Markup

This is the essential component. It’s the initial piece you serve to the client, whether it’s your handwritten HTML or the code that generates the HTML. This is essentially a de facto component of every website, but the most crucial aspect is how you provide it.

The HTML must be delivered statically for it to qualify as a Jamstack app, which entails that it cannot be dynamically rendered from a server.

Benefits of Leveraging Jamstack Development

Here are some proven benefits of leveraging Jamstack web development for your business:

Speed

Because Jamstack apps are typically served directly via a CDN as static files, your app will probably load extremely quickly. It’s no longer necessary for the server to create the page first before replying; instead, you can deliver the page “as is” as plain HTML or with client-side hydration, like with React.

Great for SEO

Websites built with Jamstack load swiftly and effectively. You want a website that loads quickly and easily when it comes to Google. A website of this kind will rank highly on Google. a high ranking, which will increase visitors to your website.

Jamstack also allows you complete freedom over where and how you position your content. You can add page titles, meta descriptions, alt texts, and so much more with Jamstack. All of these details will increase your website’s search engine exposure.

Cost

Jamstack sites cost less to maintain than their server-side equivalents. Static asset hosting is inexpensive, and your page is now being served at the same speed.

Scalability

Since you’re serving your files from static hosting, most likely a CDN, you essentially have infinite scalability by default. The majority of companies will make this guarantee, so you won’t have any issue letting any influx of visitors enter through the front door.

Great Developer Experience

As a Jamstack developer, you have unlimited flexibility because you can select your own tech stack. Developers are free to work without being constrained by a particular platform or technology. Additionally, it’s getting much easier to reuse functionality across different websites or applications with the emergence of microservice APIs and reusable APIs.

Maintenance

You don’t need to maintain a web server because it isn’t the basis of your static website. No matter which provider you choose, your static HTML markup, CSS, and JS are kept up to date without causing you any headaches.

Security

Considering that you don’t have to directly manage a web server, you don’t have to worry as much about securing entry points for outsiders.

To secure private material and reassure your consumers that their personal information isn’t made public, you should instead concentrate mostly on permissions.

Drawbacks of Leveraging Jamstack

Here are some drawbacks of using Jamstack for development. Although the drawbacks are not major, it is necessary to understand them and mitigate any risks that can arise during the development process.

Dependence on Third-party Systems

You depend on third-party systems and APIs to function consistently because your website depends so heavily on them. Your website is affected if a third-party system or API is unavailable (or parts of it).

It’s the same as when a regular website’s server goes down, but with a Jamstack site, there are very few things you can do to solve the issue if it’s a problem with a third-party supplier.

Tougher Implementation of Updates

You’ll need to use code to change your templates if you want to do so. Since the content is separated from your front end, editors cannot easily alter the templates. Since they can’t be quickly updated in other ways, this will frequently imply that developers will have to spend extra time creating the updates.

Not Suited with Content Editors

Though content marketers and editors may not necessarily enjoy it, developers probably do. Your content editors must be fairly technically proficient to develop and update material because you must serve your content as Markup.

As your editors lose editing features they’re familiar with from a content management system, it can slow down content development and often require you to teach your editors new skills for Jamstack CMS. They will also be in charge of effective media management, which can be time-consuming.

Problems with Dynamic Features

As long as we’re creating pages with content and photos, Jamstack sites are fantastic. You can encounter problems when your site needs dynamic content and features. You will have to conduct more of the labor-intensive work yourself using API calls or your code if you don’t have a database to handle your requests.

This doesn’t mean that you can’t do it, but since your architecture lacks these dynamic elements, it will require more resources to complete.

Tools You Can Use For Jamstack Development

You can utilize several tools available to get started with Jamstack development and provide amazing web experiences. They might be a bit rough on the edges since this is a brand-new tooling area.

To help you get started, here is a compiled a list of the best tools for each development phase and with our recommendations.

Application Construction with Jamstack

You can choose your preferred user-interface framework and start working on the development process of your app. Here are some popular frameworks that you can choose from. We recommend Gatsby for app development since it is the most popular choice.

Gatsby
Scully
Hugo
11ty
Nift

Application Deployment with Jamstack

If you wish to have more control, AWS is one of the best options. However, tools like Zeit and Netlify CMS make configurations easier by hooking into your GitHub repository and building whenever you push a new commit.

AWS
Zeit
GCP
Azure
Netlify
GitHub Pages
Surge

Adding API and other Dynamic Functionality with Jamstack

Here are some ideas and tools for API and other dynamic features.

Google Analytics – Website traffic analytics
Auth0 – Authentication processes
headlesscms.org – Unlimited lists of headless content management systems
Cloudinary – Media management
Snipcart – E-commerce
Sanity – CMS
Stripe – Payments
Serverless Framework – DIY, easy-to-deploy serverless resources

Some Use Cases of Jamstack

The use cases and tips discussed below will enable you to leverage the best out of Jamstack development.

Content delivery network (CDN)

When you have already created all the assets and markup, content delivery network (CDN) delivery is a great option for results in improved performance and increased scalability.

Cache invalidation

After uploading your build is complete, the content delivery network clears its cache, indicating that your fresh construction is operational now.

Version control

You can store your codebase in a version management tool like Git. The main advantages include traceability, collaboration, and history changes.

Atomic deploys

In atomic deploys, each deployment builds an exact site’s duplicate to ensure it is accessible everywhere in a uniform version.

Automated builds

When a new build becomes essential, your server gets alerted, mostly through webhooks. After the server creates the project and updates the content delivery networks, the site goes live.

Is Jamstack the Future?

Yes! Jamstack is a good option for the proper project, and the decoupled architecture instead of the traditional monolithic architecture has many benefits for many websites.

However, Jamstack requires certain technical expertise from both developers and editors to thrive. Thus, you might not get the outcomes you want if your team lacks the ability to use Jamstack effectively.

If all a visitor sees on your website is the same old content that your editors are fighting to update, it won’t matter how quickly it loads.

Consider adopting a headless CMS if you want to eliminate the editor experience, which is the main drawback of a typical Jamstack site. By doing this, you can maintain the editor experience while still relying on a decoupled architecture with RESTful APIs supplying and rendering the content.

Start Jamstack Development with OnGraph

The best way to kickstart your Jamstack development journey with guaranteed success is to partner with a reliable Jamstack development company with expertise in various industry verticals.

Being a leader in providing the most innovative Jamstack website development services, OnGraph is an agile organization with a team of proficient developers and 15+ years of industry experience in giving scalable, trustworthy, and customized Jamstack development services.

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March 20, 2023January 18, 2024

How Machine Learning is Reimagining User Experience

New is always better! One of Barney’s many laws that actually apply to technology, especially with advancements in AI/ML.

In recent years, the popularity of machine learning has risen as organizations recognize the benefits it offers to a broad spectrum of uses. Grand View Research predicts that the worldwide machine learning industry will be worth $117.19 billion by 2027, growing at a CAGR of 39.2 percent between 2020 and 2027.
This growth is being driven by the growing amount of data and the need to make sense of it, as well as the growing demand for more personalized and effective software applications.

Machine learning (ML) is increasingly being adopted by enterprises across a wide range of sectors, from healthcare and banking to retail and entertainment. It is emerging as a crucial competitive differentiator in the modern online marketplace.

Businesses are constantly looking for new methods to harness the potential of ML because of its capacity to automatically learn and grow from the experience.

In this blog, we will uncover the benefits of integrating ML into web and mobile applications with popular examples.

What is Machine Learning (ML)?

Machine learning (ML) is a branch of AI that enables software to learn and improve from data over time without explicit programming. ML algorithms use statistical approaches to examine data, detect patterns, and generate predictions based on the identified patterns.

This makes it an effective resource for several fields like computer vision, NLP, predictive analytics, and more. Because of its numerous benefits like personalization and increased productivity ML is increasingly being included in mobile and web apps.

According to a recent poll by Gartner, 37% of businesses have already incorporated AI, with machine learning being the most widely adopted technique. With the help of ML algorithms, organizations can analyze massive volumes of consumer data to generate precise predictions about user behavior and preferences. This can help businesses enhance the user experience and boost revenue.

Types of Machine Learning Algorithms For Web And Mobile Apps

Here’s a list of the types of ML algorithms that are incorporated into web and mobile applications.

Supervised Learning

While training a supervised learning algorithm, the input data is labeled to indicate the expected output or target variable. The algorithm then adapts to fresh data to better forecast the dependent variable.

It is a common practice to employ supervised learning for NLP, speech recognition, and image classification. Supervised learning allows online and mobile apps to provide in-depth, data-driven predictions and suggestions for each user.

Personalized product suggestions are an application of supervised learning in a web or mobile app. An individual’s browsing and purchasing habits can be utilized to inform the algorithm’s predictions about what kinds of things will pique a user’s interest.

By tailoring recommendations to each user’s tastes, this approach boosts engagement and ultimately revenue.

Unsupervised Learning

In contrast to supervised learning algorithms, unsupervised learning algorithms are taught with data without a target variable. The algorithm then learns to identify such structures in the data, whether through clustering or dimensionality reduction.

Typical applications of unsupervised learning include spotting outliers in data, visualizing patterns, and segmenting audiences. Unsupervised learning can be used to evaluate user behavior in online and mobile apps, yielding insights for optimization and customization.

Customer segmentation is an example of unsupervised learning in a web or mobile app through which you can categorize people into subsets with shared interests, preferences, and other characteristics. This information proves useful for the app’s owner to better target specific demographics with targeted ads and improved features.

For example, e-commerce software utilizes unsupervised learning to identify a set of high-spending clients who are most likely to respond to tailored promotions.

Reinforcement Learning

Reinforcement learning algorithms learn by interacting with their surroundings and getting feedback in the form of rewards or punishments. The algorithm then learns to maximize the predicted reward by taking actions. Games, robots, and recommendation systems are just a few examples of common applications for reinforcement learning.

By dynamically altering app features and content, reinforcement learning may be used to improve user engagement and conversion for both online and mobile apps.

An example of reinforcement learning in a web or mobile app is enhancing user experience. Based on user behavior and comments, the system can learn to dynamically alter app features and content.

For instance, a fitness app can employ reinforcement learning to alter workout intensity based on user performance, or a social media app might prioritize content that is most likely to engage each user.

Deep Learning

Deep learning algorithms are neural networks capable of learning complicated patterns and relationships from massive data volumes. It is frequently used for image and speech recognition, natural language processing, and predictive modeling. Content filtering, fraud detection, and user profiling are some areas where the app’s accuracy and performance can improve with deep learning.

A popular application of deep learning in web and mobile apps is image recognition. After being trained on a large collection of photos, the technique can be used to recognize objects or patterns in new images. This can be used to identify product logos or recognize people in pictures, among other things.

For example, a shopping app employs deep learning to recognize brand logos in user-generated material, or a social media app utilizes deep learning to automatically tag friends in images.

Transfer Learning

Transfer learning is a method that permits a previously trained model to be utilized for a new task with little extra training. When the new task has similar qualities or properties to the original task, transfer learning is frequently applied. Transfer learning can be used in online and mobile apps to swiftly adjust pre-trained models for tasks such as sentiment analysis, object identification, and language translation.

Sentiment analysis is a good example of transfer learning in a web or mobile app. Pre-training the algorithm on a large dataset of text data for a comparable task, such as language translation or sentiment analysis for a different language or topic, is possible. The pre-trained model can get fine-tuned using a smaller batch of data according to the needs of the app.

For example, you can use transfer learning for a customer service app to quickly change a pre-trained sentiment analysis model to classify user feedback as positive, negative, or neutral.

How Machine Learning can Enhance App Performance?

Here are the benefits of integrating ML for enhancing app performance.

Personalization

Personalization customizes an app’s content or features for each user. ML algorithms can construct user profiles from behavior, demographics, location, and device data. The app can customize recommendations, content, and features based on these profiles.

For example, you can integrate ML algorithms into a music app to assess a user’s listening history, behavior, and preferences to produce tailored playlists or propose songs and artists they’ll like.

Customization boosts user engagement and happiness, improving app performance. When users view relevant material, they spend more time in the app, increasing user retention and income for the app owner. Personalization also lets app owners target users with individualized marketing messaging, increasing conversion rates and ROI.

Real-time Decision Making

ML algorithms employ real-time data or user inputs to make app decisions in real-time. Examples are identifying user intent, optimizing network traffic, or automating activities in response to triggers.

For example, meal delivery software employs real-time decision-making to assign orders to nearby drivers depending on their availability and proximity to the restaurant and customer. This improves order fulfillment speed and accuracy, increasing user pleasure and loyalty.

Online shopping software can employ real-time decision-making to recommend products based on browsing behavior and purchase history, enhancing conversion and revenue.

Real-time decision-making helps apps adapt to changing conditions, user preferences, and company goals. This improves user experience, efficiency, and app owner outcomes.

Predictive Analytics

ML algorithms are used in predictive analytics to assess past data and anticipate future events. Predictive analytics may predict user behavior and app performance in an app.

A fitness app employs ML algorithms to anticipate exercises for a user based on their workout history, activity levels, and other data. Based on the user’s fitness objectives and preferences, this data can also recommend new training schedules.

Similarly, a ride-hailing service can optimize the allocation of drivers and decrease wait times for consumers by using predictive analytics to forecast demand for rides in different sections of the city.

Predictive analytics can improve app performance by anticipating user needs and responding proactively. This can reduce user irritation and boost user happiness, resulting in higher user retention and app owner revenue.

Automation with Machine Learning Algorithms

Developer chores can be automated using ML algorithms. Bug discovery and testing can be automated in an app.
For example, a mobile game app can employ ML algorithms to automatically discover defects and crashes during gameplay. Bug fixes and app performance can be prioritized using this data.

Another example is where a banking app can employ automation with ML algorithms to test new features and upgrades, saving time and money and letting developers focus on more complex tasks.

Automation can boost app performance by lowering the time and resources needed for common operations, freeing up developers’ time to focus on more complicated and high-priority tasks. This can lead to shorter development cycles, higher app quality, and higher user satisfaction.

Resource Optimization

Using ML algorithms to assess app usage patterns and improve resource utilization, such as CPU and memory usage, is what resource optimization entails.

For example, a photo editing app can employ ML algorithms to assess a user’s photo editing behavior and optimize the usage of CPU and memory resources, leading to faster processing times and a better user experience.

Similarly, a music streaming app saves power consumption by altering audio quality dependent on the user’s network connection and device capability.

Resource optimization can boost app performance by lowering the app’s resource consumption, resulting in faster processing times, lower battery usage, and overall performance improvements.

Anomaly Detection

Anomaly detection is utilizing ML techniques to detect odd/unexpected behavior within an app, such as excessive CPU or memory utilization.

For example, e-commerce software can utilize ML algorithms to detect anomalies in website traffic, such as unexpected spikes or dips in user activity. This data can be utilized to identify and address possible issues before they become serious difficulties.

Similarly, by examining a user’s health data, such as blood pressure and heart rate, a healthcare app can employ anomaly detection to discover potential health hazards.

Anomaly detection can boost app performance by helping developers to identify and address possible issues before they become serious difficulties. This can aid in reducing downtime, preventing problems, and ultimately improving user happiness.

Challenges of Integrating Machine Learning Into Web And Mobile Apps

Although ML is a very promising approach for enhancing mobile and web apps, it is not without some drawbacks.

Data privacy and security

ML models learn and predict using massive volumes of data. This data, however, may contain sensitive information that must be safeguarded. A healthcare app, for example, uses patient data to provide recommendations, but this information must be kept secure to comply with HIPAA laws.

To prevent unauthorized access or data breaches, developers must ensure that data is collected, stored, and processed safely. To safeguard the data, encryption, access controls, and other security measures may be implemented.

Integration with current ML systems

Online and mobile applications frequently rely on pre-existing systems and databases. Incorporating ML into these systems can be difficult since developers must assure compatibility with various technologies and data formats.

An e-commerce app, for example, interacts with a legacy inventory management system that uses a different data format than the ML model. To tackle this difficulty, developers may need to employ data transformation tools or create bespoke connections to connect the systems.

Training and Maintenance of Machine Learning Models

To guarantee that ML models remain accurate and up to date, they require constant training and maintenance. Developers must have the knowledge and resources to manage these activities, which include retraining models when new data becomes available.

This entails building automated data collecting, model retraining methods, and monitoring model performance to detect and remedy faults.

Expertise

Data science and machine learning professionals with specialized knowledge integrate ML into online and mobile apps. Unfortunately, many developers lack this knowledge, posing difficulties in building, implementing, and maintaining ML models.

To address this difficulty, developers need to train in development or collaborate with outside specialists to supply the required skills. They could also employ pre-trained models or off-the-shelf ML technologies that require less specific knowledge.

Examples of Businesses With Successful Machine Learning Integration Into Their Apps

Here are some examples of popular apps that have successfully used ML algorithms.

Netflix

Netflix successfully uses ML algorithms to propose content to subscribers based on history, ratings, and other data. Netflix’s recommendation algorithm uses collaborative and content-based filtering.

Collaborative filtering analyzes the viewing habits and preferences of many individuals to find commonalities and provide recommendations. Content-based filtering analyzes movies and TV shows to provide user-specific suggestions.

Amazon

Amazon has effectively integrated ML algorithms to customize product recommendations and searches. Through machine learning techniques, Amazon also advertises based on customers’ browsing and purchase histories. To create accurate predictions about what customers want, Amazon’s Machine Learning algorithms sift through mountains of data.

This enables Amazon to make individualized recommendations to its users, increasing customer engagement and sales.

Spotify

Spotify leverages ML algorithms to personalize recommendations and playlists for its customers based on their listening behavior and tastes. The algorithm behind Spotify’s suggestions utilizes data like past listening habits, playlists, and content created by other Spotify users.

To deliver even more personalized recommendations, the system considers aspects such as the user’s location, time of day, and mood.

Pinterest

Pinterest integrates machine learning algorithms to enhance its image search and recommendation algorithms. This allows its users to discover new content based on their interests. Pinterest’s algorithms look at things like an image’s colors and forms to find commonalities and provide suggestions. To deliver more relevant recommendations, the system considers the user’s previous searches and interests.

Uber

Uber has successfully integrated machine learning algorithms into its app to optimize travel pricing and match drivers with passengers based on location and availability. Their algorithm considers factors like location, time, and ride history to forecast demand and set pricing accordingly. The technology also matches drivers with passengers based on proximity and availability, reducing wait times and increasing customer satisfaction.

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February 21, 2023January 18, 2024

The Era of Generative AI: ChatGPT vs. Bard

Generative AI has become increasingly popular because of its potential to generate original content and boost human creativity in areas ranging from images and text to music and even films. Deep learning and natural language processing advances have allowed generative AI models to create content that is frequently impossible to differentiate from human-created content.

The two most trending generative AI tools are ChatGPT and Bard. Launched a couple of weeks ago, ChatGPT bought forth a new revolution in the field of artificial intelligence and gained over 1 million users within 5 days.

To embrace the revolution, Google introduced Bard, which will be available for public use within a few weeks.
Let’s dive in to learn more about both leading generative AI tools and how they are different.

What is ChatGPT?

OpenAI created the ChatGPT AI language model. It is a subset of the Generative Pre-trained Transformer, or GPT line of language models, that are built with deep neural networks and trained on massive volumes of text data.

ChatGPT’s primary objective is to produce human-like prose in response to a specific prompt or query. It understands and responds to natural language input, which makes it suitable for a variety of applications including chatbots, customer support, and personal assistants.

ChatGPT has been trained on a huge corpus of text data ranging from books and news articles to forums and social media posts. As a result, it can produce text that is cohesive, relevant, and contextually suitable.

GPT-2 and GPT-3 are two versions of GPT made accessible to the public by OpenAI. GPT-3 is the model’s most recent and powerful version, and it has been widely employed in a number of applications such as chatbots, content production, and language translation.

The following are examples of popular AI-generated material for ChatGPT:

Social media posts
easy explanations of difficult topics
Summaries of podcasts, meetings, and transcripts
Written code
Translation
Drafts for emails
Blogs
product descriptions
Law briefs
Even Memes and Jokes

What is Bard?

Bard is Google’s AI generative tool that uses LaMDA, Google’s Language Model for Dialogue Applications to get responses from the internet. Through this linguistic approach, Bard provides more extensive information to queries than a standard Google search. LaMDA’s lighter and second version utilizes less computational resources, allowing it to expand for more individuals to use and offer feedback.

Bard is was rolled out on February 7th and currently in beta testing. It will be available to all users in a few weeks.

Like digital assistants Siri and Alexa, Bard’s main purpose is to obtain information in a concise answer rather than a search engine results page, but with hyperlinks for users to gather further information. Bard will also serve as a personal assistant, assisting with chores such as trip planning, discovering existing reservations, and meal preparation.

What are the Differences Between ChatGPT and Bard?

Although both Generative AI Bard and ChatGPT are quite similar tools, there are some notable differences between them.

Features

At their foundation, the qualities of these two bots are very similar. Both need you to enter a question or request, and if you do, an answer will be returned. You can then ask follow-up questions or make additional requests, and the bot will keep conversing with you.

Bard is an extended version of the search engine into which they have been integrated. They add more context to answers.

ChatGPT, on the other hand, may have a broader range of applications. When utilized via its interface on OpenAI’s site, the “Generative ai Chatbot vs Bard” AI chatbot may generate content for news articles, fiction poems, blogs, product descriptions, etc.

ChatGPT can also support specific programming languages, enabling it to give the code needed to develop a simple website. It is not unlikely that Bard will be unable to manage such requests, but such features have yet to be shown.

Pricing

Both Bard and ChatGPT provide free versions. ChatGPT is now available for a free experimental preview on OpenAI’s website, with a paid membership model dubbed ChatGPT Plus that costs $20 per month and grants customers priority access and quicker speeds. ChatGPT Plus is only available to individuals who have been approved by OpenAI, therefore you’ll need to join the waitlist.

The Generative AI Bard now only offers a free model, but you must be an authorized tester to use the AI chatbot. Google revealed that some AI-based capabilities have been incorporated into products such as Lens and Maps, but Bard remains unavailable to the public. According to Google, public accessibility to Bard will be disclosed in the following weeks.

Accuracy

Google and OpenAI both acknowledge that Bard and ChatGPT can deliver false or improper information.

This is largely due to the way these chatbots operate. They use language models — LaMDA for Bard and GPT-3.5 for ChatGPT that require massive quantities of information to work. In the case of GPT and LaMDA, much of this information is obtained through the internet, and in the case of GPT-3.5, only until 2021, after Open AI discontinued training its language model.

Bing’s version of GPT is more up-to-date since, like Bard AI, it draws relevant information from the internet.

There are certain drawbacks to this training, such as the fact that the data it pulls may be erroneous or prejudiced, and the bot is not necessarily trained to realize this. Chatbots are simply trained to deliver outputs connected with inputs; they cannot assess whether or not the information provided is correct or if the answer provided is free of inherent biases.

Integrations

Open AI, Google, and Microsoft all want their chatbots to be integrated into their own ecosystems as soon as possible. ChatGPT is already available in three Microsoft products: Bing, Edge, and Teams.

Microsoft Teams Premium has been available for a while and includes features like AI-generated chapters and automated meeting notes to help you browse through meeting recordings more easily, and other features, all driven through the same GPT-3.5 language model as ChatGPT. It costs $10 per month per user, however, businesses may presently obtain it for $7 per month.

Microsoft has recently introduced the new Bing, an enhanced version of the Bing search engine driven by GPT-3.5. ChatGPT will be incorporated into the Opera browser in the near future.

Google’s Bard will be included in the search, although Google Search. Users will be able to search using the AI-powered chatbot, similar to Bing, rather than the usual search box. Google has also integrated AI-based technologies into Maps and Lens, albeit they are not exclusively Google Bard integrations.

However, Google has announced that third-party developers will be able to use Bard, thus it will be fascinating to observe what companies come up with. Similarly, OpenAI grants access to GPT-powered capabilities to certain firms, while only Microsoft has the rights to the source code from outside OpenAI.

Citing Sources

When it comes to the employment of generative AI tools like Bard or ChatGPT, plagiarism is a major source of concern. To function, the AI language models that run the chatbots must be developed on existing knowledge sets, which involves feeding them massive amounts of content provided by third parties.

ChatGPT, on the other hand, does not cite the generated data. When properly prompted, it can offer sources, although this is not the default setting. As a result, you must exercise extreme caution when utilizing the chatbot or risk accidentally stealing intellectual property.

Bard also does not automatically offer references for its responses.

However, Bing’s latest GPT-powered bot cites its sources. It uses annotations to cite the site from which the material was gathered, but you’ll have to press the links for further information.

A Third in the Running: ERNIE Bot

Baidu, also known as China’s Google, has revealed that it is experimenting with its own ChatGPT-style bot named Ernie Bot internally. The bot, which will be available next month, is based on its Large language models (LLMs), ERNIE, or ‘Enhanced Representation via Knowledge Integration,’ which was published in 2019.

ERNIE, a bilingual model that is anticipated to understand both Chinese and English, is capable of performing a variety of tasks like language generation and comprehension and text-to-image generation. Ernie Bot will be based on ERNIE 3.0 Titan, a language model with over 260 billion parameters, which is 50 percent more than ChatGPT.

ERNIE has a succession of advanced LLMs that can do a range of activities, and while language generation is provided by ERNIE 3.0 Titan, text-to-image generation is provided by ERNIE-ViLG.

Other Generative AI Alternatives

Following ChatGPT’s introduction into the market, prominent tech companies entered the fray by launching their generative AI tools. Jasper AI, ChatSonic, Wordtune, and OpenAssistant, are some startups embarking on their own initiatives.

The future of artificial intelligence in marketing is changing and adapting as swiftly as it began, with multiple uses besides content generation including email optimization, customer service, social media posts, and product suggestions.

What Does the Future of Generative AI Looks Like?

The potential of generative AI appears bright, with numerous interesting possibilities regarding how this technology may expand and advance in the next years. Listed below are some possible sites for growth:

Ethical and societal repercussions

As generative AI advances in capability and sophistication, it will pose new social and ethical concerns concerning the use and exploitation of simulated data and media. Researchers and policymakers must collaborate to guarantee that all these technologies are used responsibly and fairly.

More Control and Modification over Generated Outputs

One limitation of existing generative models is that they frequently lacked fine-grained command over the output they produce. Researchers are investigating new methods for altering and fine-tuning such models in order to create more particular and tailored outcomes. This could lead to new applications in industries such as product design, architecture, and fashion, where the capacity to swiftly and correctly develop original designs could be incredibly beneficial.

Multimodal generative models

Another prominent field of research is the creation of generative models capable of producing outputs in numerous modalities like audio, text, images, etc. These models have the potential to enable new types of human-computer interaction as well as new methods of expressing and producing complex, multi-modal data.

Advanced and more realistic generative models

As processing power grows and researchers advance in constructing more complicated and effective neural network designs, generative models are anticipated to become more complex and capable of generating more compelling and realistic outputs. This could open up new possibilities in domains such as literature, music, and art, music, and more practical applications such as generating information for teaching other AI systems.

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