Trending November 2023 # Top 10 Trending Decentralised Applications You Should Know In 2023 # Suggested December 2023 # Top 18 Popular

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The top trending decentralized applications in 2023 that run on blockchain networks without the need for third parties.

The technological world is constantly evolving, bringing with it new trends. Decentralized applications, or dApps, are one of the most intriguing trends that have been gaining traction. There is a high demand for these trending DApps in major industries such as finance, banking, e-commerce, social media, and gaming.

Axie Infinity

Axie Infinity is a non-fungible token-based online video game developed by the Vietnamese studio Sky Mavis, best known for its in-game economy based on Ethereum-based cryptocurrencies. It has been described as a “pyramid scheme that relies on cheap labour from countries such as the Philippines to fuel its growth.”


    Compound is a DeFi protocol that enables functions such as lending and borrowing in a decentralized ecosystem. Compound, which is built on Ethereum, allows its users to earn money by providing liquidity to borrowers on the platform. The protocol automatically matches lenders and borrowers using an Automated Market Maker (AMM) to facilitate a simple and seamless function. Users who own the COMP token can also vote on important issues.

      Maker DAO

      MakerDAO is a decentralised platform best known for the DAI stablecoin. DAI is a stablecoin that is linked to the US dollar. It makes it easier to trade crypto tokens without being concerned about their volatility. It provides financial freedom and the benefits of decentralised payments while also maintaining fiat currency stability. MakerDAO is a Decentralized Autonomous Organization (DAO) that is not managed by a single company or entity. It is instead controlled by its users and MKR token holders. 

        NBA Top Shot

        NBA Top Shot is a marketplace where digital basketball collectibles are sold as NFTs. Similarly, fans can use the NFT marketplace to trade, buy, and sell collectibles. The NBA Players Association, Dapper Labs, and the NBA collaborated to create the blockchain-based NBA trading cards. The Dapper Labs FLOW blockchain is used to power the NFT platform.


          OpenSea is a decentralised exchange protocol based on Ethereum that allows users to trade on their own terms with no middlemen. To ensure fair trading conditions between buyers and sellers, it combines smart contracts and blockchain technology.


            PancakeSwap is a blockchain-based decentralised exchange. It makes use of smart contracts to enable swaps between BEP-20 standard tokens. The platform also provides several ways for users to earn money from it. Yield farming, staking, lotteries, and even NFT collectibles are examples of these. The platform’s native token Is CAKE. It provides users with benefits such as staking and even participation in platform governance.


              Splinterlands is a digital card game in which you can earn money by playing. Furthermore, the game employs NFTs to provide players with ownership of their in-game assets and digital playing cards. Splinterlands is a Web3-powered gaming platform designed to address the shortcomings of traditional card trading games. Splinterlands has its own blockchain, which provides in-game stability. Aside from that, the gaming platform can be easily upgraded.

                Step App

                Step App is a popular Move to Earn app that lets you earn money while working out. It tracks your steps with your phone and rewards you with cryptocurrency for walking or running. The FITFI token powers the Step App. Users can stake this token to earn passively. Staking also enters users into random draws for new avatars, badges, and even sneakers.


                  Uniswap, like PancakeSwap, is a decentralised exchange (DEX). Uniswap, on the other hand, is built on the Ethereum blockchain and allows buyers and sellers to trade without the use of an intermediary. It has a larger token base because it supports all Ethereum ERC-20 tokens. Uniswap assists users in lowering gas fees by reducing the number of transactions that occur on Ethereum.

                    Yearn Finance

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                    Top 10 Applications Of Artificial Neural Networks In 2023

                    The Top 10 Applications of Artificial Neural Networks in 2023

                    Artificial Neural Networks (ANNs) are rapidly emerging as one of the most powerful and versatile technologies of the 21st century. They are a subset of machine learning that is inspired by the structure and function of the human brain and are capable of learning and adapting to complex patterns in data. In recent years, ANNs have found their way into numerous industries and applications, ranging from speech recognition and image processing to financial forecasting and medical diagnosis.

                    In this article, we will explore the top 10 applications of ANNs in 2023 and what makes them so effective in these domains.

                    Image Recognition and Computer Vision

                    Image recognition is one of the most well-known applications of ANNs. In computer vision, ANNs are used to identify objects, people, and scenes in images and videos. ANNs can learn to identify patterns in pictures and make predictions about what is in the image. This technology is already being used in many fields, including surveillance, autonomous vehicles, and medical imaging.

                    Speech Recognition and Natural Language Processing (NLP)

                    Speech recognition and NLP are other popular applications of ANNs. In speech recognition, ANNs are used to transcribe spoken words into text, while in NLP, they are used to analyze and understand the meaning of the text. These technologies are being used in virtual assistants, customer service chatbots, and other applications that require the ability to understand and respond to human speech.

                    Financial Forecasting and Trading

                    Financial forecasting and trading are areas where ANNs are being used to make predictions about market trends and stock prices. ANNs can analyze large amounts of financial data and identify patterns and relationships that can be used to make informed decisions. This technology is being used by hedge funds, banks, and other financial institutions to improve their investment strategies and minimize risk.

                    Medical Diagnosis and Treatment Planning

                    Medical diagnosis and treatment planning are critical applications of ANNs. In medical diagnosis, ANNs are used to analyze medical images and patient data to identify diseases and disorders. In treatment planning, ANNs are used to develop personalized treatment plans based on a patient’s individual characteristics and medical history. These technologies are helping to improve the accuracy and effectiveness of medical diagnoses and treatments, making healthcare more accessible and affordable for everyone.

                    Autonomous Vehicles

                    Autonomous vehicles are one of the most exciting applications of ANNs. In autonomous vehicles, ANNs are used to analyze sensor data and make decisions about how the vehicle should respond to its environment. This technology is being used to develop self-driving cars, drones, and other autonomous vehicles that can operate without human intervention.

                    Recommender Systems

                    Recommender systems are another application of ANNs that are changing the way we interact with technology. In recommender systems, ANNs are used to analyze user behavior and make recommendations about products, services, and content that are likely to be of interest to the user. This technology is being used by e-commerce websites, streaming services, and other online platforms to improve the user experience and increase engagement.

                    Natural Language Generation

                    Natural language generation is a relatively new application of ANNs that is rapidly gaining popularity. In natural language generation, ANNs are used to generate text that mimics human writing. This technology is being used in news articles, reports, and other forms of content that require the ability to write in a natural and engaging style.

                    Fraud Detection

                    Fraud detection is an important application of ANNs that is being used to prevent financial losses and protect businesses and consumers. In fraud detection, ANNs are used to analyze financial transactions and identify patterns that indicate fraudulent activity. This technology is being used by banks, credit card companies and other financial institutions to improve their security measures and reduce the risk of fraud.

                    Supply Chain Optimization

                    Supply chain optimization is another area where ANNs are being used to improve efficiency and reduce costs. In supply chain optimization, ANNs are used to analyze data from various stages of the supply chain, from raw materials to finished products, to identify bottlenecks and inefficiencies. This technology is helping companies to streamline their supply chains, reduce waste, and improve their overall performance.

                    Predictive Maintenance

                    Top 10 Applications Of Deep Learning In Cybersecurity In 2023

                    Deep learning tools have a major role to play in the field of cybersecurity in 2023.

                    Deep learning

                    which is also known as Deep Neural Network includes machine learning techniques that enable the network to learn from unsupervised data and solve complex problems. It can be extensively used for


                    to protect companies from threats like


                    , spear-phishing, drive-by attack, a

                    password attack

                    , denial of service, etc. Learn about the top 10 applications of

                    deep learning

                    in cybersecurity. 


                    Detecting Trace of Intrusion

                    Deep learning

                    , convolutional neural networks, and Recurrent Neural Networks (RNNs) can be applied to create smarter ID/IP systems by analyzing the traffic with better accuracy, reducing the number of false alerts, and helping security teams differentiate bad and good network activities. Notable solutions include Next-Generation Firewall (NGFW), Web Application Firewall (WAF), and User Entity and Behavior Analytics (UEBA).


                    Battle against Malware


                    Spam and Social Engineering Detection

                    Natural Language Processing (NLP), a deep learning technique, can help you to easily detect and deal with spam and other forms of social engineering. NLP learns normal forms of communication and language patterns and uses various statistical models to detect and block spam. You can read this post to learn how Google used TensorFlow to enhance the spam detection capabilities of Gmail.  

                    Network Traffic Analysis

                    Deep learning

                    ANNs are showing promising results in analyzing HTTPS network traffic to look for malicious activities. This is very useful to deal with many cyber threats such as SQL injections and DOS attacks.


                    User Behavior Analytics

                    Tracking and analyzing user activities and behaviors is an important deep learning-based security practice for any organization. It is much more challenging than recognizing traditional malicious activities against the networks since it bypasses security measures and often doesn’t raise any flags and alerts. User and Entity Behavior Analytics (UEBA) is a great tool against such attacks. After a learning period, it can pick up normal employee behavioral patterns and recognize suspicious activities, such as accessing the system in unusual hours, that possibly indicate an insider attack and raise alerts.  

                    Monitoring Emails

                    It is vital to keep an eye on the official Email accounts of the employees to prevent any kind of cyberattacks. For instance, phishing attacks are commonly caused through emails to employees and asking them for sensitive data. Cybersecurity software along with deep learning can be used to avoid these kinds of attacks. Natural language processing can also be used to scan emails for any suspicious behavior.


                    Analyzing Mobile Endpoints

                    Deep learning

                    is already going mainstream on mobile devices and is also driving voice-based experiences through mobile assistants. So using deep learning, one can identify and analyze threats against mobile endpoints when the enterprise wants to prevent the growing number of malware on mobile devices.


                    Enhancing Human Analysis

                    Deep learning

                    in cybersecurity can help humans to detect malicious attacks, endpoint protection, analyze the network, and do vulnerability assessments. Through this, humans can decide on things better by bringing out ways and means to find the solutions to the problems.


                    Task Automation

                    The main benefit of deep learning is to automate repetitive tasks that can enable staff to focus on more important work. There are a few cybersecurity tasks that can be automated with the help of machine learning. By incorporating deep learning into the tasks, organizations can accomplish tasks faster and better.  


                    WebShell is a piece of code that is maliciously loaded into a website to provide access to make modifications on the Webroot of the server. This allows attackers to gain access to the database. Deep learning can help in detecting the normal shopping cart behavior and the model can be trained to differentiate between normal and malicious behavior.  

                    Network Risk Scoring

                    10 Artificial Intelligence (Ai) Startups In India You Should Know


                    Check out 10 of the most awesome Artificial Intelligence (AI) startups in India

                    We focus on a diverse field of AI startups, including startups from Healthcare, Logistics, FinTech, among others


                    “AI is the new electricity.” – Andrew Ng

                    When Andrew Ng speaks, you drop everything and pay attention. That’s what I (and thousands of others) did when Andrew Ng compared our age of AI to the discovery of electricity.

                    We are truly living in the age of artificial intelligence. Companies are spending billions of Dollars just to stay relevant in today’s ever-changing environment. If you’re not AI-compatible, the consensus is that you will soon be an also-ran in the industry.

                    This got me thinking – could I bring out the AI startups in India truly bringing about a global revolution? It was an interesting quest. I put down a framework or criteria to filter the top AI startups in India (which we will see soon).

                    So, this article highlights the top Indian AI startups, the top initiators, that are using AI to build a better future for us. Ready to dive in and look at the framework I used to create this list?

                    If you’re entirely new to artificial intelligence, here are a few articles to get you started:

                    Framework to shortlist the startups

                    This list has been curated based on certain parameters which act as indicators for the success of startups. The parameters on which startups have been evaluated are:

                    The Sector / Market: The industry in which the startup plays and the opportunities that it can leverage in that market

                    Investment: Whether the startup has raised funds, the amount and quality of funding received

                    Of course, the primary criteria was that the startup should be in India!

                    Our Pick of the Best AI Startups in India:

                    Conversational AI Startups



                    Healthcare Startups



                    Logistics Startups



                    Fintech Startups



                    Other Awesome AI Startups



                    Conversational AI Startups

                    Chatbots are ubiquitous these days. From businesses to the research lab, they have become an integral part of an organization’s strategy. Learning how to create a chatbot from scratch is a much-vaunted skill in data science.

           is the world’s largest conversational AI platform. I’m a huge Haptik fan. The company was launched back in 2013 as a one-stop app for all everyday tasks and is now undoubtedly a market leader in its field.

                    Haptik provides 16 different channels of texts and voices for the users to build, deploy and manage a conversational application. Haptik only focuses on use cases to enable end consumer engagement on the back of real conversational data of a billion+ messages.

                    With the help of AI, Haptik helps millions in their day to day tasks including me. It’s quick response and query solving capability is quite remarkable. It’s indeed a personal assistant.

                    Avaamo is another conversational startup that is currently serving six industries: Insurance, Financial Services, Healthcare, Telecommunications, Retail.

                    Founded in 2014, Avaamo has created a name for itself in around 40 countries now. Pretty impressive!

                    Avaamo specializes in conversational interfaces to solve specific, high impact problems in the enterprise. It uses AI to make conversational computing for the enterprise a reality.

                    Unlike the first generation conversational AI where a user could only pass commands like play music or open camera, Avaamo brings in second generation conversational AI experience that executes rich multi-turn conversations capable of handling queries in customer service, generating quotes in insurance, or answering claims inquiries in healthcare.

                    Healthcare Startups

                    This AI startup will resonate with a lot of you. Healthcare is one domain where AI needs to make its mark. Progress has been slow due to various reasons but things have been looking promising in the last couple of years.

                    NIRAMAI stands for “Non-Invasive Risk Assessment with Machine Intelligence”. In Sanskrit, Niramai means being free from illness. It is a novel breast cancer screening solution.

                    Breast cancer is the leading cause of cancer death today in women.

                    According to WHO, one in every 12 women have the risk of a breast abnormality. Indian women have only about 50% chance of survival.

                    Niramai provides a cost-efficient way to detect breast cancer and is a better alternative to the existing method of mammography which requires a high capital cost. The major drawback of mammography is that it can be used only for women above 45 years of age because it cannot identify tumors effectively for younger women.

                    The core of the NIRAMAI solution is Thermalytix, a computer-aided diagnostic engine that is powered by Artificial Intelligence. Go through this link to know more about how Niramai uses AI for the early detection of cancer.


                    Doxper is another Indian AI startup working in the healthcare sector.

                    Have you ever seen hospital records? Keeping the records of patients is a hectic task – it is truly a formidable function. Doxper helps in simplifying the way healthcare data is recorded.

                    Doxper helps doctors, hospitals, and patients as well. It improves the doctor-patient interaction. Doxper aims to provide easy storage & retrieval, automated patient follow-ups, and auto data transcription.

                    When information is written using Doxper, it gets stored in the cloud and is automatically shared with the patient immediately. This helps in keeping the records safe and makes communicating with the patient really simple.

                    Hospitals can use it to immediately digitize Casualty/Emergency, OPD, and ICU. This can also be used in existing HIS systems through intuitive APIs.

                    This is one remarkable product helping the healthcare sector to digitize the records of their patients.

                    Logistics Startup

                    AI has left no stone untouched. It has found its niche in almost every sector, even the previously gigantic and manual logistics field. There are quite a few AI-powered logistics startups springing up and LogiNext is definitely among the leaders right now.

                    LogiNext helps in managing field services. It tracks and optimizes field agent movements in real time on a single map interface.

                    LogiNext helps organizations plan and manage the dispatch schedule, delivery routes and capacity in the most cost-optimized way.

                    It empowers its users by providing them the power to track (real-time) the shipment every single minute. It provides insights and visualizations based on predictive and big data analytics. Logistics analytics helps the user to accurately predict the future with algorithm-enabled location intelligence and optimize the logistics and field service management operations.

                    It provides every single detail right from pickup to delivery. It also provides complete field service management analytics.

                    The logistics and field workforce management solutions are fully automated, effective, secure and can be seamlessly integrated with multiple platforms providing complete logistics automation.

                    Another logistics startup in the list is Locus. Started back in 2023, the company provides facilities like route planning and optimizing, real-time fleet tracking, insights and analytics, and automated shipment sorting and rider allocation.

           offers a number of features, including:

                    Smart Geocoding: It converts the fuzziest addresses into precise geographical coordinates. This increases order delivery in fewer vehicles and reduces fuel costs

                    Dispatch Overview: This feature allows you to visualize your plans on a screen in the post-planning session via three different metrics – geography, time & vehicle. This helps in visualizing the cluster forms and ensures a minimum overlap of routes

                    Predictive alerts: It warns you in any kind of breach. This feature helps in keeping track of on-ground resources. You get instant alerts in case of any deviation from the planned route, undesignated halts, exceeding break times, etc.

                    Live Tracking:’s system allows to share the dispatch details of the order with the customers. Current latitude and longitude, contact details of the driver, etc. are provided which result in improved customer engagement

                    Enroute Analytics: This feature helps in comparing planned versus executed delivery routes. This also helps in analysing driver’s riding behaviour

                    Fintech Startups

                    AI in finance just intuitively makes sense. Finance is all about number crunching (well, almost!) and machines are well equipped at this point to work with numbers. It is a perfect match. So it’s no surprise that the FinTech sector has seen a massive surge in AI applications.

                    Rubique is one such FinTech startup that is contributing to making finance simple by using AI.Rubique wants to revolutionize the finance industry by introducing predictability. This helps its users to find the best match to his/her credit requirements with the help of an AI-based recommendation engine and Rubique’s financial matchmaking platform.

                    Rubique’s multi-sided lending platform provides features like e-KYC, bank statement analysis, credit bureau check, credit memo generation & MCA integration along with real-time application tracking to make it a paperless experience.

                    Rubique is doing a great job of providing top-notch solutions to the entire lending spectrum.

                    LendingKart is another brilliant startup tackling the financial sector by providing loans to small businesses. Spread over 1300+ cities, LendingKart is on its way to becoming one of the leading FinTech companies in the world. Here’s their official statement:

                    LENDINGKART Group aims to make working capital finance available at the fingertips of entrepreneurs, so that they can focus on business instead of worrying about the gaps in their cash-flows.

                    LendingKart has developed technology tools based on big data analysis which facilitates lenders to evaluate borrower’s creditworthiness. They don’t focus on past records of the vendor. The main aim of LendingKart is to make capital funds available to help entrepreneurs focus on their business instead of worrying about cash-flow flaws.

                    Other Awesome AI Startups

                    The agriculture sector is synonymous with Indian values. It is an integral part of this country and what we are all about. So what can AI do to accelerate progress in this field? CropIn, a Bengaluru-based startup, provides a glimpse into the future of agriculture.

                    CropIn is a smart farming app that provides future-ready farming solutions to the entire sector. It is an intuitive, intelligent and self-evolving system. The company provides real-time solutions to predict trends, archive patterns and to make a blueprint for future businesses.CropIn has the capability of live reporting, and it also provides geographical analysis, interpretation, and insight. Data gathering through a smartphone app ensures efficient operations, lower costs and better visibility for your field agents at all times.

                    CropIn’s vision is to maximize per acre value and the mission is to make every farm traceable.

                    Real-time insights help to take planned and responsive business decisions. The predictability of quantity & quality of yield combined with a reduced cost of operations results in higher productivity for businesses.

                    Imagine an AI helping you perform online transactions end-to-end. Sounds too futurustic to be true. But here’s the good news – it’s already here!

           is a virtual agent that does end-to-end online transactions for you in various domains. Everything from postpaid recharges to hotel bookings, Niki covers the length and breadth of it.

                    Niki uses AI to book and make payments after taking a few inputs from the users. This reduces the end-to-end interaction time of the customer and service provider and helps in providing fast and responsive services.

                    Booking movie tickets, household bills, bus booking, event booking, etc. – you name and it and Niki does it.

                    End Notes

                    In this article, we saw some of the most amazing AI startups in India that are changing the way we live. This is in no way an exhaustive list. There are a LOT of AI startups springing up right now that bring their own unique brand to the table.


                    Top 10 Applications Of Object

                    Watch out for these top 10 applications of object-oriented programming using the python language

                    When organizing a program, object-oriented programming (OOP) groups together similar characteristics and behaviors into distinct objects. Objects are conceptually comparable to a system’s parts. Consider a program as a type of factory assembly line. A system component processes some material at each stage of the assembly line, turning raw materials into finished goods in the end. An object has both behavior, such as the action that each component of an assembly line does, and data, such as the raw or pre-processed materials at each stage. In this article, we’ll know about the top 10 applications of object-oriented programming using python language.

                    About OOP in Python: The programming paradigm known as “object-oriented programming” offers a way to organize programs so that individual objects are composed of properties and behaviors. An approach for modeling physical, real-world objects, such as cars, as well as relationships between objects, such as between businesses and employees, students and teachers, etc., is called object-oriented programming. Real-world entities are represented by OP as software objects that have certain data attached to them and are capable of carrying out specific tasks. Another popular paradigm for programming is procedural programming, which organizes a program similar to a recipe by offering a series of steps (in the form of functions and code blocks) that must be followed to finish a task. The main lesson learned from this is that Python’s object-oriented programming revolves around objects.

                    Let us know the

                    top 10 applications of Object-Oriented Programming: 

                    In parallel programming, an issue is split up into smaller subproblems that can all be worked on simultaneously utilizing different computing resources. OOPs, are utilized to streamline the procedure by improving the network’s capacity for approximation and prediction.

                    When creating client-server systems, OOPs principles are quite helpful. To construct Object-oriented server internet, or OCSI, applications, the IT infrastructure is created using Object-oriented client-server systems.

                    OOP can be used to reduce the amount of work required in manufacturing and designing applications. It can be applied, for instance, when creating flowcharts and blueprints. So, it makes it possible to produce these flowcharts and blueprints accurately.

                    OOP is helpful in hypertext and hypermedia. It aids in laying the framework for hypertext and hypermedia

                    Simulation and modeling systems are imitations of the real-world product. The system’s workings can be checked and analysed using object-oriented programming.

                    OOP helps users to minimize the work required and can be applied in both application design and manufacturing. For example, it can be applied when creating flowcharts and blueprints. The ability to precisely construct these flowcharts and blueprints is therefore made possible.

                    OOP is beneficial in hypermedia and hypertext. It assists in establishing the foundation for hypertext and hypermedia.

                    Systems used for simulation and modeling are an emulation of real-world products. Using object-oriented programming, the operation of the system may be examined.

                    The conventional form of storing data, known as the relational model, saves every single piece of data in tables made up of rows and columns. Today, every single piece of data is stored and processed in object-oriented databases.

                    It is beneficial in computer-aided design (CAD), which uses workstations or computers to assist in the creation, modification, analysis, or optimization of a design.

                    Bottlenecking: Everything You Should Know

                    Bottlenecking is a natural result of an unbalanced PC build. When you build your own, or at least pick the parts, you might feel tempted to grab whatever you can afford. Or just the most expensive one out there – that’s usually not the best approach. Finding the right balance between parts, especially CPU and GPU, is the key to having a powerful PC that keeps up as games and software evolve.

                    That might mean waiting a little to be able to afford a slightly pricier part that fits better. Or actually selecting a cheaper alternative that works better in your setup. If you make the wrong choice, you end up with a bottleneck.

                    The quite descriptive term refers to when a certain element of your PC hardware – usually CPU or GPU – is unable to keep up with the performance of other parts. A computer can only perform as well as its weakest part. So pairing a powerful CPU with a weak GPU means it won’t be able to work at capacity as it’ll be limited by the GPU.

                    Why Is It a Problem?

                    When you put money into a PC, one part slowing down the rest of the system essentially means wasting the money you invested in the parts that are being slowed down. In some cases, it can also lead to increased wear and tear on the bottleneck part since it might cause it to overheat if it’s forced to run at capacity all the time. Depending on the part, a bottleneck can outright prevent you from playing certain games or running certain programs – or it might just make them sluggish and slow. Either way, it’s best to avoid them or fix them as soon as possible.

                    What Are Common Bottlenecks?

                    The most common two bottleneck points are CPU and GPU. Both are relatively pricey parts that can be particularly expensive to upgrade – and therefore, they are often replaced one at a time, preventing the improved part from reaching its potential. Technically, any part can be a bottleneck, at least in some tasks – here are some of the most common ones.


                    The CPU is the heart of the computer. It controls basically everything that happens and performs the vast majority of the computer’s processing. There are two factors in CPU performance, core count and processing power. Both can cause bottlenecks but in slightly different scenarios.

                    CPU Core Count

                    The CPU core count is the number of processing cores a CPU has, and each of these cores can run a separate process simultaneously. This has overall performance benefits, but some programs benefit more than others. Some programs have logic that can be neatly divided into multiple processes. Each process can then be run on a separate CPU core simultaneously. This can provide a performance boost of up to two times running on a single CPU core.

                    A lot of software, especially older software, can only run on one process on one core at a time. Even in this case, though, there can be some performance increase, as two or more of these programs can be run at once, depending on the number of cores.

                    CPU Processing Power

                    Processing power is typically measured with the clock rate through other factors like the IPC. A clock rate is simply how many processor cycles the CPU can complete per second. It is typically measured in GHz (pronounced gigahertz), with typical values between 2 and 5GHz, or between two and five billion cycles per second.

                    Raw processing power can sometimes be a bottleneck as single processes may not complete fast enough, leaving other parts waiting. This is especially the case when the CPU doesn’t get enough cooling. If this happens, it automatically slows itself down to reduce the heat it produces, thus preventing any damage to your hardware and slowing down any tasks it is running, increasing the chance of your CPU bottlenecking something else.


                    GPUs are generally limited by power or by heat. Like CPUs, cooling is important, so make sure that you’ve also got good airflow to keep your GPU cool so it can run fast.

                    RAM SSD/HDD

                    If you think storage capacity will be a bottleneck issue, you’ll probably want to use HDDs. However, if you need to read or write data quicker, you’ll want an SSD. A combination of both can work well, so you can store infrequently needed data on a cheap HDD and files you’ll need more often on a fast SSD.

                    At least in gaming, a slow hard drive often causes things like slow loading times. It can also cause your computer to be slow at booting up. This doesn’t really affect your performance in-game as the hard drive isn’t used so much then and isn’t a bottleneck. Still, while reading a lot of data from a slow hard drive, it can be a bottleneck.


                    The display is rarely a bottleneck, but that’s not to say it can’t be. If you want to visualize a lot of data at once, you will be limited by the screen’s resolution. You can display more detailed images or graphs on higher resolution screens. It may even be helpful to get a second screen.

                    In gaming specifically, not just resolution but also the refresh rate of the screen can also be a bottleneck. Standard monitors display 60 frames per second. However, if you’ve got a powerful enough graphics card compared to the graphical requirements of the game you’re playing, you may be able to produce more frames than that, potentially substantially more. All of that data and processing power go to waste if your monitor can’t show that many frames per second. Then again, some people may be happy with 60 frames per second and want to get a higher resolution monitor instead.


                    The motherboard is basically the spine of your computer. Everything attaches to it and communicates through it. Budget motherboards cut features to reduce costs. These are obvious and easy enough to work around in some cases, such as a lack of integrated Wi-Fi. Unfortunately, you also often don’t get the latest feature sets. This can, for example, force your expensive PCIe5 SSD to operate at PCIe3 speeds. In that case, cutting potential SSD performance by three quarters. You need to make sure your motherboard is compatible with all your parts. However, you also don’t want to spend too much on a motherboard that has features you don’t want or need, as you may be able to better spend that money elsewhere.

                    With motherboards, the bottleneck isn’t a direct performance of the motherboard. But more if it can enable optimum performance of the rest of your components.

                    Power supply

                    Computers need power, and all of this comes through the PSU. It’s important to determine how much power your computer will draw when under load. Then ensure that your PSU can provide more than that, ideally by 20-30%. There are online calculators where you can enter your components and estimate the total power draw. This is followed by recommendations for PSU power capacities.

                    Realistically, most standard computers will be fine with a 650W PSU. Gaming computers often have high-performance GPUs under heavy load combined with a mid to high CPU and can need more like 850W. You can need even more if you’re running particularly high-end gear and overclocking it. Generally, however, you shouldn’t need a 1600W power supply. That will just be overkill, and the money can be better spent elsewhere.

                    Realistically, a PSU doesn’t affect performance unless it can’t provide enough power, in which case your computer will likely crash. Again, aim for 20-30% more than you need, and you should be fine.

                    How Can You Fix/Avoid It?

                    By definition, it is worth noting that if any part is running at 100,% you have a bottleneck, as that part is then holding back other parts. This is generally bad but may not be avoidable, especially if you already have the best-performing version of the relevant part. For example, video games require a huge GPU processing power and comparatively little CPU processing power. A flagship GPU will run 100% in most computers with even mid-tier modern components. This is simply a limitation of what is currently possible with graphics hardware and the imbalance of processing requirements in games.


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