The S&P 500 fell 1.2% for its ninth drop in 11 days and touched its lowest level in five months. Another steep fall for Big Tech dragged on the market, sending the Nasdaq composite to a market-leading loss of 1.8%, while the Dow Jones Industrial Average sank 251 points, or 0.8%.

Stock market today: More drops for Big Tech pull Wall Street nearly 10% below its summertime high

Big Tech has given workers an ultimatum: Come back to the office, or find a new job. Elon Musk, Mark Zuckerberg and a host of other leaders of America’s biggest companies have made it clear that their success hinges upon everyone working in person.

Big Tech said return to the office. She had another solution

Latoya Staten, director of Impact at Fearless, a company that helped design Baltimore’s bid, told CBS News that the tech hub designation was “exciting news” and “is going to be able to bring lots of economic impact and jobs.” Announcing the ...

White House unveils 31 U.S. tech hubs to boost industry

In the tumultuous battlefield of digital technology, Chinese titan Huawei continues its relentless march, unfurling a tapestry of innovation and resilience. Amid the debris of controversies and challenges,

Huawei’s Tech Innovations Continue to Shine Amidst Challenges

In this article, I will delve into whether artificial intelligence (AI), particularly the emergence of tools like generative AI (GenAI), will replace classical software outsourcing as we know it.

AI vs software outsourcing: An opportunity or a threat?

A national settlement with the software company Blackbaud for “deficient data-security practices and deficient response to a 2020 ransomware event” shows the company would pay a total of $49.5 ...

Settlement reached with software company Blackbaud after multiple data breaches impact schools, hospitals and nonprofits

CMU’s expertise in computer science and robotics is helping to draw high-tech jobs to the Pittsburgh metro area, as shown in a new study by the Burning Glass Institute ranking cities in terms of the “future readiness” of their labor forces.

How Pittsburgh Has Become a Hot Tech Center. Sort of.

A San Antonio technology entrepreneur hopes to enhance the experience of art through a new mobile application.

San Antonio technology entrepreneur combines augmented reality, art with new application

A new study has revealed that choosing “inferior but quick” solutions over the “right” solution is costing businesses more than just cash as they risk “technical debt.”The report from DXC Technology explains that businesses can find themselves in tech debt after a series of trade-offs and poor decisions,

Is tech debt holding back your business?

Chetu

chetu

In our rapidly evolving work-from-anywhere world, employee downtime and productivity are rising concerns. Just ask Chetu— (sidenote: wrongful termination laws were not on their side). the Florida-based company that came under fire for terminating an employee who turned off their webcam While I’m not on board with you watching your employees via webcam all day, the productivity concerns are real and valid. The important thing to note is that most employees to be productive. They aren’t shirking responsibilities behind a turned-off webcam; they are trying to get their work done as efficiently as possible—and they’re being slowed down by poor DEX. want What Is DEX? DEX, or the digital employee experience, is the way employees interact with technology during their workday. And it’s a key factor in evaluating how an organization stacks up to the rest in the race for effective tech. DEX measures the quality of your workforce’s interaction with the tools they use daily. These tools help them get their jobs done and your company running. So, why are some companies still behind in adapting to new technologies that can help their employees stay productive? Many companies are stuck using tech tools that don’t serve the modern workforce. These inefficient tools slow down operations and ultimately make your employees feel unproductive. In a survey facilitated by Lakeside Software, respondents indicated that they lose about one hour of weekly work time to lousy tech. Naturally, this leaves them frustrated and feeling less than satisfied with the tools their company provides them, which could ultimately lead to dissatisfaction with the company overall. Here are a few tips on how companies can better support their employees. Evaluate Your DEX Maturity. The first step is to look honestly at your company’s DEX efforts and see how you can improve. helps you evaluate where you’re lacking and be honest about what changes need to be made to benefit your employees and, ultimately, your bottom line. Exploring your DEX maturity After all, the two are intrinsically linked—companies with poor turnover rates burn through a lot of cash trying to find new employees, only to have them leave again a year later. The proof is in the pudding: 14% of the employees surveyed said they’d previously left an organization due to poor DEX, and over 60% said they would consider it. When looking into DEX, start at the beginning: clearly evaluate where your company is at to see how you can best improve. A true DEX leader is fully committed to improving the digital employee experience and seeks to make it part of the business's core strategy. They’re willing to invest in IT resources as part of their proactive approach and closely track the impact of their efforts across functions—whether that’s human resources, finance, operations, or another area. They know that optimized digital experiences drive better customer experiences and invest in them accordingly. Quell employee frustrations through comprehensive DEX efforts. Investing in your employees should seem like an obvious step. Still, many employees aren’t just frustrated with the loss of productivity—they’re also frustrated with the overall IT infrastructure that supports their workplace. When investing in DEX, enterprises need to consider how they can and begin to take proactive steps toward improving their tech stack. remove silos between departments Comprehensive DEX efforts take on the following qualities: They’re proactive instead of reactive. They don’t just respond to emergencies as they come up. Instead, they create systems to prevent these issues from ever happening in the first place. They break down silos and become more integrated. They do away with disparate teams and workflows and see the company from a holistic point of view. They go from manual to automated. They adapt to automated systems that can remediate, update, patch, and more. They go from being IT-dependent to self-reliant. This doesn’t mean you don’t need IT—it means you have an “always on” IT infrastructure that supports users with self-help remediations instead of limiting their support to when IT is available. They use complete data instead of incomplete information. They can gather objective system details to make better decisions with accurate metrics. They go from static to predictive analysis. They leverage the power of AI and machine learning to study patterns and trends that could negatively impact their digital environments. They go from one-size-fits-all to personalized IT. This one is really important to pay attention to and is perhaps the biggest mindset shift for companies. Personalized IT provides end users with exactly what they need, like specific hardware and software that suit their unique job responsibilities and work styles. For some companies, the above list will seem like a major change. But for companies looking to become DEX leaders (rather than webcam spies), this list will be essential moving forward. DEX can reduce employee turnover and frustration, increase your output, and heighten your productivity—this is one investment that’s really a no-brainer. Photo by on Annie Spratt Unsplash

How Effective Tech Can Reduce Employee Downtime and Attrition

Collaboration between healthcare and technology industries is moving faster than light. The Healthcare Information and Management Systems Society (HIMSS) in their 2021 Future of Healthcare research that most healthcare systems are planning to increase their investments in digital healthcare tools. reported There is a myriad of reasons for that. The increase in the demand for personalized medicine and the pressure to cut down on healthcare costs has further impacted the need to improve healthcare and technology collaborations further. And that’s not all of it. Following the pandemic, there has been an increase in the need to digitize healthcare further. This has prompted many healthcare providers to lean on the technology that provides digitized delivery of healthcare services in the future. Now, we will look at the top technology trends that your healthcare facility can latch onto in 2022. So, put on your seat belts! 7 Emerging Technology Trends in Healthcare in 2022 Here is a list of top healthcare technology trends expected to transform the healthcare industry in 2022: Internet of Medical Things (IoMT) Cognitive automation (CA) Telemedicine Virtual Reality (VR) AI-empowered Big Data and data analytics Digital Therapeutics Robotic Process Automation (RPA) 1. Internet of Medical Things (IoMT) - think IoT but medically There is the Internet of Things (IoT). So, we shouldn’t be surprised if something else pops up. The Internet of Medical Things is a connected infrastructure of healthcare systems and services that provides adequate care to rural and isolated communities. This technology also aims to provide adequate care to patients who cannot physically meet up with their healthcare provider. IoMT is propelled by innovation and data integration that enables patients to receive timely and high-quality treatment in the comfort of their homes. Here’s how it works: it involves connected digital devices that help to collect, store and convey data through a digital network without human assistance. These connected digital devices include wearables and trackers, which are a massive part of the development of IoMT. For instance, trackers allow people to access real-time data on their behavior daily and proffer recommendations. IoMTs software encourages patients to increase their motivation to lead a healthy lifestyle with features that allow them to track their progress daily or weekly. Through this technology, your business can easily see your patients in a more active capacity than ever. Plus, it’s cost-effective! The data set from this emerging technology includes the patient’s sleep patterns, nutrition habits, average activity level, and other parameters. Healthcare providers can build a more realistic tracking program with these essential parameters to accurately track patients’ progress. With IoMT facilitating telehealth, here are a few ways different healthcare businesses or facilities can take advantage: assisted living technologies; disability and rehabilitation assistive tech; speech assistive tech; chronic disease monitoring technologies. 2. Cognitive Automation - who says your healthcare facility can have its own Alexa? Cognitive automation is a new and fast-emerging technology in digital transformation. It is listed as one of the emerging IT trends in healthcare, and its popularity is expected to increase by 2022. The Cognitive Automation (CA) technology seeks to mimic the behavior of humans beyond repetitive tasks. It serves as the digital brain of the healthcare organization. CA takes the automation capabilities of current software providers and applies Machine Learning algorithms to introduce decision velocity in the healthcare industry. This technology makes it possible to process zettabytes of data within seconds, thereby providing decision-makers with recommendations readily available and backed with real-time data. The end goal of this emerging healthcare technology is to establish a self-driving enterprise whereby operational processes are automated. What say you try out cognitive automation programs to automate your operational processes, cut costs, and maximize your business efforts? 3. Telemedicine - reach more without expanding The pandemic has provided the healthcare industry with the option of turning telemedicine into a current trend for the industry. COVID-19 contributed to the increase in the provision of virtual healthcare, which includes scheduling online appointments, provision of remote care, and video conferencing. Telemedicine is a healthcare technology practice that unlocks the hands-free communication channel. Healthcare providers and patients can get across in real-time virtually. The pandemic opened the potential in this emerging technology as monitoring cases of patients in isolation can be done remotely. In general, it helps to contribute to health equity. This means that healthcare is provided for people of all groups, no matter their geographical, economic, political, and social standing conditions. The emergence of this new technology is a giant leap in how healthcare is provided and is expected to be popularized in the coming years. No physical appointments; no schedules, except a patient’s condition calls for it. For your healthcare facility, this means wider reach without having to allocate half of your budget to that expansion. 4. Virtual Reality (VR) - give your doctors more tools to work with There has been a recent use in augmented reality and virtual reality, but these areas will need applications in the coming years. This technology serves as a supplement for many activities undertaken by healthcare providers, whether done on-site or on a remote basis. Virtual reality technology has been used in healthcare to: provide surgical training; visualize real-case scenarios to clinical students; make clinic tours; advance the quality of analysis provided to lung scans and treat critical health conditions. Virtual Reality technology helps people with developmental disorders such as ASD to train their social and coping skills in virtual environments. Thanks to virtual reality in healthcare, doctors can continuously train and improve their skills by using virtual environments. It significantly decreases the probability of medical errors and improves value-based care. This is a plus for your healthcare facility as it helps to ensure all your medical providers have all the resources to meet the needs of a patient. 5. AI-empowered Big Data and data analytics - process data faster The introduction of AI technology in the healthcare industry and data analytics will save the industry a significant ton of money in the coming years. Let’s put it this way: it will save YOUR healthcare facility a ton of money. The long-distance analysis and measurement of patient data are healthcare technology trends in the coming years. A large volume of information is created every second, culminating in extensive data needed to be processed. Processing this data manually could lead to the loss of a massive amount of data, which is why AI-powered data processing exists to help prevent this. Concerning healthcare, Big Data can derive from the following: electronic health data (e.g., EMR, ENR, images demographics); biometric data from portable wellness trackers; biobanks to aid with research-design efforts. The need for the automated process of Big Data instead of humans is inevitable as it provides real-time analytics for decision making. AI-empowered Data analytics also helps make predictions in which accurate predictive analysis is completed. This helps to gain insights on previous patient interactions, thereby making predictive analysis needed for changes to occur. This technology will help patients get accurate treatment plans and timely services. It also, in turn, helps healthcare providers enjoy the optimization of their activity and a greater level of control over the supply chain. 6. Digital Therapeutics - take evidence-based care to level 100! Digital Therapeutics is a patient-centered trend in healthcare that delivers evidence-based care via software. It represents a digital health sector that comprises technologies, products, and services spanning the healthcare industry. Digital Therapeutics is an evidence-based behavioral treatment delivered digitally and facilitated by software that helps enhance the accessibility and effectiveness of healthcare. It also helps to prevent, manage and treat diseases. Digital Therapeutics is also used with drugs and medical devices that help improve the care provided to patients. The digital therapeutics technology helps monitor blood sugar readings, offer medication regimens, diet, and offer clinicians a complete picture of the status of the patient’s health. These services provided by this technology helps to provide a compelling survey of patients’ lifestyles and daily activities. This helps to manage some critical health conditions in the patient offering personalized therapies for anxiety and depression. An increasing number of healthcare companies are taking a thoughtful approach to Digital Therapeutics, and it is expected to be an emerging technology in the healthcare sector by 2022. 7. Robotics Process Automation (RPA) Robotic process automation empowered technology has become a significant milestone in healthcare information technology development. This emerging technology makes it possible for health providers to: experience accurate automation; cost reduction; staff optimization; and the introduction of transformational changes. The inclusion of AI algorithms in this technology is helping to provide a guide to patients in need of healthcare providers through the scanning of their symptoms. Thus offering cutting-edge diagnosis techniques for them more accurately than the conventional search engines. The Robotics Process Automation has introduced some robots that can mimic the behavior of humans to the healthcare sector. This robot in development helps to offer numerous capabilities, including the detection of screenwriting, the input of data, and the implementation of pre-defined actions. The RPA technology enables quick scheduling and accurate appointment setting in the hospital. It helps to detect the relevant personal information about the patient quickly, pulls rapid diagnostics of the patient’s symptoms, and pulls the patient’s insurance details. Besides, RPA helps to schedule appointments with a doctor as it checks for free spots in the schedules of the doctors working in all hospitals close to the patient’s location. In recent times, healthcare organizations have widely applied the Robotics Process Automation to: collect and handle medical information more efficiently, enhance appointment scheduling, deliver optimal care to their patients, and improve their hospital organization management processes. Moreover, this is predicted to further develop in the coming years, making it an emerging healthcare technology area in 2022. Now, keep in mind that executing any or all of these trends is as crucial as gaining knowledge. You want to work with companies in the technology sector that can expertly develop these types of software and effectively integrate them into your system for optimal functionality. We have identified the top 3 custom software development companies for healthcare providers. We selected these companies based on testimonials, healthcare-related case studies, regulatory-bound procedures, customer satisfaction, and awards. Here they are: Top 3 Healthcare Software Development Providers 1. Altoros Labs Altoros Labs is one of the largest healthcare software development companies out there. Their wide range of healthcare solutions is designed to improve your business functions and minimize costs. They cater to clients’ unique needs from the healthcare background and render unsurpassable and result-oriented healthcare software development services. Altoros Labs is situated in 10 different countries globally to provide scalable healthcare solutions readily for their clients. With Altoros Labs, you get GDPR- and HIPAA-compliant healthcare technology solutions. Their services include: creation of patient portals; designing solutions for clinical management; development of hospital management systems; development of remote patient monitoring (RPM) systems; custom pharmacy management solutions; revenue cycle management software building. to this healthcare software development provider to get started with discovering the scalable software that is right for your business and take advantage of all the opportunities that 2022 brings. Reach out 2. Chetu Chetu is a healthcare software development company that has earned a reputation of being one of the best and preferred healthcare software development companies globally. They help to provide the right strategy and the right technology needed to develop software for healthcare organizations, enabling them to move in the forward direction. The company has gotten diverse experience in assisting healthcare organizations upscale their business delivering back-to-back successful projects for their clients. Their software products are enriched with sophisticated modern-day technology, which helps to provide their user with an unparalleled user experience. Reach out to them to develop healthcare technology apt to boost your business operations. here 3. Intellectsoft Intellectsoft has carved a unique position among the most reliable healthcare software development agencies. Its cutting-edge transformative solutions and customer-centric approach make it one of the most sought-after software development firms for healthcare providers. The company provides its clients with: healthcare web/mobile applications; digital healthcare solutions; IoT-powered medical solutions; EHR systems; clinical data management. Reach out to them to get started with your healthcare facility to keep up with digital transformation in 2022. here The Bottom Line And there you have it, the top healthcare technology trends in 2022 that your business can latch onto quickly towards efficacy. The technology industry is infiltrating every business sector, and the healthcare industry is no different. This means that to move ahead, stay ahead and be relevant; your health center needs to make do with this trend. And what's more, the technology infiltration is a welcomed one for your business as it can single-handedly transform your operations for the better. So, what do you say?

7 Healthcare Technology Trends to Watch Out for in 2022

Technology influences all aspects of human life, and healthcare is no exception. The rapid technological evolution makes hospitals and other healthcare providers look for solutions that simplify and accelerate their routine. The market for healthcare software development , accounting for USD 13,100 million in 2017 and being forecasted to reach USD 19,300 million by the end of 2025. More and more companies join the competition every year, so it has become difficult to find a reliable provider with proper skills, competencies, certifications, and domain knowledge. is already huge Below, you will find a list of medical software development companies that have proved to be experienced partners when creating robust healthcare software for providers in the US and worldwide. This compilation was based on a thorough assessment of the market leaders based on the following criteria: major healthcare clients, market presence, experience in the healthcare industry, and clients’ reviews. 1. Itransition Colorado-based Itransition is a custom software development company that delivers software solutions for medical organizations and care providers. It creates patient portals, solutions for clinical management, mobile health applications, and more. Employing virtual & augmented reality, IoT, and artificial intelligence, Itransition provides solutions for advanced diagnostics. Itransition’s healthcare software is compliant with HIPAA and other healthcare-related policies. The company’s team of more than 1500 IT specialists delivers healthcare software development to more than 30 countries. <a href="https://medium.com/media/f45777b0568fe3f2a0a9eef2d446b79d/href">https://medium.com/media/f45777b0568fe3f2a0a9eef2d446b79d/href</a> Denver, CO Location: 1998 Founded: $10,000+ Min. Project Budget: Summer Infant, Maximus Major Healthcare Customers: 1000–9999 Average Number of Employees: Website: https://www.itransition.com/ 2. Chetu Since 2000, Chetu has been a medical software development company. The company’s service list includes the development of HIPAA-compliant electronic health records (EHR) and health information exchange (HIE) solutions, electronic prescription services, patient management systems, and many more. The company also delivers solutions for individuals with disabilities. <a href="https://medium.com/media/9c19c7a2e8e80d5e1d5e4d336d1cf281/href">https://medium.com/media/9c19c7a2e8e80d5e1d5e4d336d1cf281/href</a> Plantation, FL Location: 2000 Founded: $10,000+ Min. Project Budget: Memorial Healthcare System, Conifer Health Solutions, MedAdept Major Healthcare Customers: 1000–9999 Average Number of Employees: Website: https://www.chetu.com/ 3. Macadamian Founded in 1997, Macadamian is a full-service software development firm with a focus on healthcare solutions delivering. The company develops of a wide range of healthcare solutions, including cloud-based care management products, digital healthcare solutions for patients, and many more. All of the products are made in compliance with HIPAA and FDA regulations. <a href="https://medium.com/media/47a5910134419ca2c8b5801044afb196/href">https://medium.com/media/47a5910134419ca2c8b5801044afb196/href</a> Gatineau, Canada Location: 1997 Founded: $50,000+ Min. Project Budget: Siemens Healthineers, Bio-Optronics, Fresenius Medical Care Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://www.macadamian.com/ 4. Cabot Technology Solutions Ohio-based Cabot Technology is a software development company that provides solutions for Healthcare. The company offers software for appointment making, health centre search, real-time monitoring, and more. Cabot also creates HIPAA-compliant Electronic Medical Record and Electronic Health Record solutions and builds medical devices. Highland Heights, OH Location: 2006 Founded: $10,000+ Min. Project Budget: Envision, ATMEL Wearable App, Erudyte Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://www.cabotsolutions.com/ 5. Velvetech Illinois-based Velvetech is an award-winning medical software company. It delivers the development of health monitoring solutions, medical education platforms, web/mobile applications for post-acute care coordination, practice management systems for medical practices, and more. Velvetech also empowers its healthcare software products with such emerging technologies as blockchain, artificial intelligence, and the internet of things. <a href="https://medium.com/media/64a5d44eb6bfcf5e7b17414514b50345/href">https://medium.com/media/64a5d44eb6bfcf5e7b17414514b50345/href</a> Northbrook, IL Location: 2004 Founded: $25,000+ Min. Project Budget: American Academy of Family Physicians, MedAdept, Healix Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://www.velvetech.com/ 6. IT Craft With more than 17 years of experience and 280 professionals aboard, IT Craft is a software development company that offers healthcare solutions. The company delivers the development of healthcare-related mobile/web applications, custom healthcare portals, IoT-powered medical solutions, electronic health record systems, data management systems, and more. Berlin, Germany Location: 2001 Founded: $10,000+ Min. Project Budget: Arctrieval, DRG Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://itechcraft.com/ 7. Seamgen Established in 2008, Seamgen is a California-based software development enterprise that offers healthcare applications development. It delivers solutions for e-prescribing, remote diagnosis, electronic medical records management, patient management portals, etc. Seamgen serves healthcare organizations of all sizes and specialities, from insurance teams to large hospitals. All of the company’s technical solutions are HIPAA-compliant. San Diego, CA Location: 2008 Founded: $75,000+ Min. Project Budget: DizzyDoctor, WellMed, WellRx Major Healthcare Customers: 50–249 Average Number of Employees: Website: https://www.seamgen.com/ 8. SunNet Solutions Established in 2011, SunNet Solutions is an award-winning medical software company. SunNet delivers the development of a wide variety of healthcare-related software solutions, from tools for health progress monitoring to medical cases management systems. SunNet has delivered a large number of digital healthcare projects, including the end-to-end creation of FACN website and Act Early Texas website. Houston, TX Location: 2001 Founded: $25,000+ Min. Project Budget: FACN, Act Early Texas Major Healthcare Customers: 10–49 Average Number of Employees: Website: https://sunnet.us/ 9. Nisos Technologies Nisos Technologies is a healthcare software development company that delivers custom software development to healthcare providers, healthcare software organizations, and healthcare insurance firms. The company delivers the creation of the following healthcare software: clinic management software, online appointments systems, health and wellness software for employers, physician referral management systems, and more. Brooklyn, NY Location: 2012 Founded: $25,000+ Min. Project Budget: Magnacare, Brighton Health Plan, Mount Sinai Major Healthcare Customers: 10–49 Average Number of Employees: Website: https://nisostech.com/ 10. BVG Software Group Founded in 2011, California-based BVG Software Group is a company that delivers web and mobile solutions for various industries, including healthcare. The company provides healthcare web/mobile applications, digital healthcare solutions for patients, digital instruments for psychological parameters research and diagnosis, and more. South San Francisco, CA Location: 2011 Founded: $10,000+ Min. Project Budget: SkinData, Almed, Kisquali Games Major Healthcare Customers: 10–49 Average Number of Employees: Website: http://www.bvgsoftwaregroup.com/ <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

Top Healthcare Software Development Companies

As the metaverse develops, it presents opportunities for restaurants to improve critical areas of their business such as online ordering, loyalty programs, and menu management. Software companies can profit by helping restaurants integrate restaurant management software with metaverse-compatible technologies such as augmented reality and virtual reality (VR), the Internet of Things (IoT), blockchains, and cryptocurrencies. Those technologies can be used as part of online worlds and spaces that comprise the metaverse. A good example of the metaverse is , a selection of virtual reality worlds owned by Meta, the parent company of Facebook. These worlds can be explored with the Oculus Quest 2, a VR headset. Horizon Worlds Metaverse Software If you want to take your software company to the next level, it’s time for you to develop metaverse-powered software that will appeal to restaurants. There’s no time to waste, Restaurants are already exploring the use of that software. So, it’s best to find a software development company that offers metaverse development services. With access to those services, software proprietors can use metaverse technologies to enhance restaurant management software features. Those features can include online food ordering, customer loyalty and rewards modules, and staff management. Food Ordering in the Metaverse Restaurant management software platforms often include an online ordering module that is integrated with a point-of-sale (POS) system. This combination makes it possible to create a seamless, convenient digital food ordering experience for customers. Just imagine if you could extend the functionality of a restaurant software platform by bringing online into a metaverse world. It’s possible. food ordering services Metaverse food ordering capabilities can be created through the development of custom e-commerce shopping cart software and designs, payment gateway integration, and digital wallets. The wallets can store payment information from credit cards, debit cards, and bank accounts. A variation of the digital wallet is the crypto wallet, which can be used to store, send, and receive cryptocurrency for online payments. Allowing food to be ordered in the metaverse and delivered in the real world creates an engaging customer experience. That’s why integrating virtual reality and augmented reality (AR) software with restaurant management software would be so powerful. Experienced AR developers could build a metaverse-enabled mobile application incorporating unique 3D modeling capabilities for restaurant menu items. Customers could use the application in conjunction with AR glasses or smartphones to view three-dimensional displays of entrees, appetizers, and drinks. Few things will stimulate a person’s appetite faster than seeing a delicious plate of food. So, software companies have a unique opportunity to acquire restaurants as clients by developing mobile AR software applications that make food look enticing. Customer Loyalty and Rewards Customer loyalty programs help restaurants retain customers. These programs can be launched in online worlds that are part of the metaverse. Of course, customer loyalty and rewards software modules would have to be developed, and those modules would have to contain several important features: integration with POS systems, customer activity tracking, and social sharing. Social sharing features would enable restaurants to reward customers who share offers on social networks. NFTs in the Metaverse It’s also possible to use digital assets known as non-fungible tokens (NFTs) for restaurant loyalty programs. An NFT is a blockchain-based digital unit representing a unique asset such as collectible artwork, media, and real estate property rights. NFTs are unique cryptographic tokens that cannot be replicated. Offering NFT-based memberships can elevate the customer loyalty experience. And, a software development company can make those these memberships possible by building and integrating an NFT marketplace with customer loyalty software. It is on that marketplace that NFTs can be created, bought, and sold. A cryptocurrency wallet would have to be developed specifically for use with the marketplace. Of course, the wallet should store a cryptocurrency such as Ethereum. Customers will then be able to buy the restaurant’s NFTs with Ethereum. A good option is to make the wallet compatible with multiple cryptocurrencies. Doing so will ensure that customers have tremendous flexibility. Staff Management Staff management software helps with managing employee onboarding/offboarding, hours/timesheets, and payroll. Employee training, in particular, could be enhanced through the use of VR technology. VR would make a cost-effective and scalable addition to a restaurant’s training program. The VR software applications could be created with gaming engines such as Unreal Engine and Unity and would have to be compatible with popular VR headsets such as the Oculus Quest 2 and Samsung Gear VR. VR apps can then be integrated with human capital management (HCM)software modules that are used to manage employee training and information while maintaining labor law compliance. Restaurant Management Platform Integrated with Metaverse Software Software proprietors can help restaurants enhance their marketing, customer experience, and operations by implementing custom software solutions that integrate metaverse-compatible technology with a restaurant management platform. Online food ordering, customer loyalty programs, and staff management are all restaurant management software modules that could be taken to the next level. To provide these capabilities, software companies can use the services of an experienced software development company. That company can provide metaverse development services and would be proficient in blockchain technology, augmented and virtual reality, and 3D virtual design. The developers would also ensure that the metaverse network and all components within the network remain secure and in reliable working order through maintenance and upgrades.

Metaverse: The Next Game Changer For Restaurant Management 

Technology is relentless in forcing industries to evolve. Regarding big data, machine learning, and artificial intelligence, processing and analyzing massive volumes of real-time data has become a critical competitive advantage for all companies. However, building and maintaining reliable data platforms capable of handling such scale presents formidable challenges. Now more than ever, software engineers must be creative thinkers and innovators. Rahul Chaturvedi, a Staff Software Engineer at Uber Technologies Inc., is an excellent example of the problem-solver these challenges require. His innovative approaches to data infrastructure are meeting every future-forward standard in the tech industry today. With nearly two decades of experience in software engineering, Chaturvedi has established himself as an authority in big data, streaming technologies, and cloud infrastructure. His expertise, honed through roles at industry giants and bolstered by his academic achievements at IIT Kharagpur and the University of Washington, has been instrumental in tackling some of the most complex data challenges modern tech companies face. At Uber, Chaturvedi has been at the forefront of optimizing one of the world's largest Kafka deployments, a system that processes trillions of messages and petabytes of data daily. His work addresses the immediate needs of real-time data processing and lays the groundwork for robust AI ecosystems that can scale efficiently in cloud environments. The Scale of the Challenge: Uber's Data Ecosystem To truly appreciate the significance of Rahul Chaturvedi's contributions, one must first grasp the sheer scale of Uber's data ecosystem. As one of the world's leading transportation platforms, Uber's operations generate an astronomical volume of data: trillions of messages daily, amounting to petabytes of information that must be processed, analyzed, and acted upon in real-time. This data deluge stems from various sources: ride requests, driver locations, traffic conditions, payment transactions, and countless other data points from Uber's global network of services. Each data stream is critical for the company's operations, feeding into systems that power everything from dynamic pricing algorithms to route optimization and fraud detection. The complexity of Uber's data landscape is further compounded by several factors, including real-time processing requirements, the global distribution of data, seamless scalability without compromising performance or reliability, and cost efficiency. Chaturvedi's challenge was maintaining this system and enhancing its performance while preparing it for significant cloud migration. This required innovative approaches to long-standing problems in distributed systems and data engineering - solutions that would need to work at an unprecedented scale. Innovating on Solutions: Kafka Optimization Strategies Much of Rahul Chaturvedi's work at Uber was defined by his innovative approach to optimizing Kafka, a distributed streaming platform that forms the backbone of Uber's real-time data processing infrastructure. One of Chaturvedi's most significant contributions was leading the effort to co-locate Kafka with other technologies on the same host. Due to the critical nature of the technology, this move had never been attempted before at Uber. This strategy was crucial for enabling Uber's cloud migration while optimizing costs. Co-location introduced new challenges, particularly the "noisy neighbor" problem, where one service could potentially impact the performance of others on the same host. Other challenges included resource constraints and kernel version challenges, but Chaturvedi worked with various teams to solve these problems. The results of these optimization efforts were significant. Not only did they pave the way for a smooth cloud migration, but they also led to substantial improvements in system efficiency and reliability. The co-location strategy alone is estimated to save Uber millions of dollars annually in infrastructure costs. Future-Forward Outcomes: Building Robust AI Ecosystems Rahul Chaturvedi's work at Uber extends beyond optimizing Kafka and managing cloud migration. His efforts have been instrumental in building a robust ecosystem that supports Uber's extensive AI and machine learning initiatives. The optimized Kafka infrastructure allows for the ingestion and distribution of massive data streams necessary for training and feeding live data to AI models, and it can handle the varying and often unpredictable data volume requirements of different ML models and training processes. Besides Kafka, Chaturvedi's work involved integrating Redis into Uber's data infrastructure. This allows ML/AI teams to cache frequently accessed data or pre-processed features, significantly improving AI models' efficiency during training and inference. By strategically using Redis alongside Kafka, Chaturvedi's team created a system that provides ultra-low latency access to critical data essential for real-time AI applications. The smooth combination of Redis with an efficiently co-located Kafka infrastructure is just the kind of future-focused innovation that engineers should aspire to. Chaturvedi's work lays the foundation for future advancements in Uber's AI capabilities thanks to a data platform designed with the flexibility to incorporate new AI technologies and methodologies as they emerge. Chaturvedi has played a pivotal role in creating an ecosystem where AI and ML can flourish at Uber's massive scale through his work on Kafka, Redis, and the overall data infrastructure. Industry Impact: The Engineering of the Future Rahul Chaturvedi's contributions to Uber's data infrastructure have had far-reaching implications for the company and the broader tech industry. The co-location of Kafka with other technologies is projected to save Uber millions of dollars annually in infrastructure costs. Latency reduction in critical Kafka clusters has improved overall system responsiveness, directly impacting user experience. The optimized infrastructure can now handle Uber's massive daily data volumes - trillions of messages - more efficiently. The significance of Chaturvedi's work has not gone unnoticed. He has received three coveted "Search Storage Data (SSD) Impact Awards" in 2023 at Uber Technologies Inc., and his innovative approaches have garnered attention in the tech community, positioning him as a thought leader in big data and infrastructure. Rahul Chaturvedi's work at Uber is an excellent example of the critical role that innovative data engineering plays in modern technology companies. His strategies for building reliable, scalable, and efficient data platforms have solved immediate challenges and positioned Uber at the forefront of big data and AI technologies. Solutions like these, whether conducted on a massive scale like Uber or smaller scales by ambitious startups and small businesses, will inspire new technology and new strategies in future decades. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Technology is relentless in forcing industries to evolve. Regarding big data, machine learning, and artificial intelligence, processing and analyzing massive volumes of real-time data has become a critical competitive advantage for all companies. However, building and maintaining reliable data platforms capable of handling such scale presents formidable challenges. Now more than ever, software engineers must be creative thinkers and innovators. Rahul Chaturvedi , a Staff Software Engineer at Uber Technologies Inc., is an excellent example of the problem-solver these challenges require. His innovative approaches to data infrastructure are meeting every future-forward standard in the tech industry today. Rahul Chaturvedi Rahul Chaturvedi With nearly two decades of experience in software engineering, Chaturvedi has established himself as an authority in big data, streaming technologies, and cloud infrastructure. His expertise, honed through roles at industry giants and bolstered by his academic achievements at IIT Kharagpur and the University of Washington, has been instrumental in tackling some of the most complex data challenges modern tech companies face. At Uber, Chaturvedi has been at the forefront of optimizing one of the world's largest Kafka deployments, a system that processes trillions of messages and petabytes of data daily. His work addresses the immediate needs of real-time data processing and lays the groundwork for robust AI ecosystems that can scale efficiently in cloud environments. The Scale of the Challenge: Uber's Data Ecosystem To truly appreciate the significance of Rahul Chaturvedi's contributions, one must first grasp the sheer scale of Uber's data ecosystem. As one of the world's leading transportation platforms, Uber's operations generate an astronomical volume of data: trillions of messages daily, amounting to petabytes of information that must be processed, analyzed, and acted upon in real-time. This data deluge stems from various sources: ride requests, driver locations, traffic conditions, payment transactions, and countless other data points from Uber's global network of services. Each data stream is critical for the company's operations, feeding into systems that power everything from dynamic pricing algorithms to route optimization and fraud detection. The complexity of Uber's data landscape is further compounded by several factors, including real-time processing requirements, the global distribution of data, seamless scalability without compromising performance or reliability, and cost efficiency. Chaturvedi's challenge was maintaining this system and enhancing its performance while preparing it for significant cloud migration. This required innovative approaches to long-standing problems in distributed systems and data engineering - solutions that would need to work at an unprecedented scale. Innovating on Solutions: Kafka Optimization Strategies Much of Rahul Chaturvedi's work at Uber was defined by his innovative approach to optimizing Kafka, a distributed streaming platform that forms the backbone of Uber's real-time data processing infrastructure. One of Chaturvedi's most significant contributions was leading the effort to co-locate Kafka with other technologies on the same host. Due to the critical nature of the technology, this move had never been attempted before at Uber. This strategy was crucial for enabling Uber's cloud migration while optimizing costs. Co-location introduced new challenges, particularly the "noisy neighbor" problem, where one service could potentially impact the performance of others on the same host. Other challenges included resource constraints and kernel version challenges, but Chaturvedi worked with various teams to solve these problems. The results of these optimization efforts were significant. Not only did they pave the way for a smooth cloud migration, but they also led to substantial improvements in system efficiency and reliability. The co-location strategy alone is estimated to save Uber millions of dollars annually in infrastructure costs. Future-Forward Outcomes: Building Robust AI Ecosystems Rahul Chaturvedi's work at Uber extends beyond optimizing Kafka and managing cloud migration. His efforts have been instrumental in building a robust ecosystem that supports Uber's extensive AI and machine learning initiatives. The optimized Kafka infrastructure allows for the ingestion and distribution of massive data streams necessary for training and feeding live data to AI models, and it can handle the varying and often unpredictable data volume requirements of different ML models and training processes. Besides Kafka, Chaturvedi's work involved integrating Redis into Uber's data infrastructure. This allows ML/AI teams to cache frequently accessed data or pre-processed features, significantly improving AI models' efficiency during training and inference. By strategically using Redis alongside Kafka, Chaturvedi's team created a system that provides ultra-low latency access to critical data essential for real-time AI applications. The smooth combination of Redis with an efficiently co-located Kafka infrastructure is just the kind of future-focused innovation that engineers should aspire to. Chaturvedi's work lays the foundation for future advancements in Uber's AI capabilities thanks to a data platform designed with the flexibility to incorporate new AI technologies and methodologies as they emerge. Chaturvedi has played a pivotal role in creating an ecosystem where AI and ML can flourish at Uber's massive scale through his work on Kafka, Redis, and the overall data infrastructure. Industry Impact: The Engineering of the Future Rahul Chaturvedi's contributions to Uber's data infrastructure have had far-reaching implications for the company and the broader tech industry. The co-location of Kafka with other technologies is projected to save Uber millions of dollars annually in infrastructure costs. Latency reduction in critical Kafka clusters has improved overall system responsiveness, directly impacting user experience. The optimized infrastructure can now handle Uber's massive daily data volumes - trillions of messages - more efficiently. Rahul Chaturvedi's contributions Rahul Chaturvedi's contributions The significance of Chaturvedi's work has not gone unnoticed. He has received three coveted "Search Storage Data (SSD) Impact Awards" in 2023 at Uber Technologies Inc., and his innovative approaches have garnered attention in the tech community, positioning him as a thought leader in big data and infrastructure. Chaturvedi's work Chaturvedi's work Rahul Chaturvedi's work at Uber is an excellent example of the critical role that innovative data engineering plays in modern technology companies. His strategies for building reliable, scalable, and efficient data platforms have solved immediate challenges and positioned Uber at the forefront of big data and AI technologies. Solutions like these, whether conducted on a massive scale like Uber or smaller scales by ambitious startups and small businesses, will inspire new technology and new strategies in future decades. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure : This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure

Mastering Real-Time Data: Rahul Chaturvedi's Strategies for Building Reliable Data Platforms

রিয়েল-টাইম ডেটা আয়ত্ত করা: নির্ভরযোগ্য ডেটা প্ল্যাটফর্ম তৈরির জন্য রাহুল চতুর্বেদীর কৌশলগুলি

Echtzeitdaten beherrschen: Rahul Chaturvedis Strategien zum Aufbau zuverlässiger Datenplattformen

Dominar los datos en tiempo real: estrategias de Rahul Chaturvedi para crear plataformas de datos confiables

Maîtriser les données en temps réel : les stratégies de Rahul Chaturvedi pour créer des plates-formes de données fiables

रियल-टाइम डेटा में महारत हासिल करना: विश्वसनीय डेटा प्लेटफ़ॉर्म बनाने के लिए राहुल चतुर्वेदी की रणनीतियाँ

リアルタイム データの習得: 信頼性の高いデータ プラットフォームを構築するための Rahul Chaturvedi の戦略

실시간 데이터 마스터하기: 안정적인 데이터 플랫폼 구축을 위한 Rahul Chaturvedi의 전략

Dominando dados em tempo real: estratégias de Rahul Chaturvedi para construir plataformas de dados confiáveis

Освоение данных в реальном времени: стратегии Рахула Чатурведи по созданию надежных платформ данных

Gerçek Zamanlı Verilerde Uzmanlaşmak: Rahul Chaturvedi'nin Güvenilir Veri Platformları Oluşturma Stratejileri

Làm chủ dữ liệu thời gian thực: Chiến lược xây dựng nền tảng dữ liệu đáng tin cậy của Rahul Chaturvedi

掌握实时数据：Rahul Chaturvedi 构建可靠数据平台的策略

Panama, Panama, April 1st, 2025/Chainwire/--Backed by strong investment, record-breaking NFT sales, and a 60-million user base, SEED is setting the new trend for the GameFi narrative. SEED SEED GameFi GameFi As traditional Play-to-Earn fades, SEED brings back the NFT Gaming, combining opportunities for sustainable rewards with a real playing experience. With a bold vision and long-term strategy, SEED aims to pioneer the first sustainable Web3 gaming ecosystem on Sui and set a blueprint for the future of Web3 gaming. Strong Metrics Ahead of Listing SEED has gained the community’s attention with notable milestones, starting with investment secured from the Sui Foundation. Building on this traction, their NFT collection recently claimed the top 1 on Sui, proving the strong demand and real value of SEED’s in-game assets. Meanwhile, $SLOVE reached Top 2 trending on DexScreener, amassing 25K+ holders in just 3 days. With support from Mysten Labs, SEED is proving that Web3 gaming is more than a trend. SEED officially lists on April 1, 2025, at 10:00 AM (UTC) on leading exchanges like KuCoin, BingX, MEXC, and Cetus. With staking rewards and strategic token burns, including the burn of 37.5 million $SEED from the Mon Box launch, SEED is set up for long-term growth and a potential price increase of 200% or more. The Comeback of NFT Gaming SEED Go is the team's next big step in Play-to-Earn - a location-based game that brings Web3 gaming into the real world. By combining Play-to-Earn with real-world exploration, SEED Go is set to make Web3 gaming more interactive, immersive, and rewarding than before. Instead of playing on-screen only, players will walk to the nearest tree on the SEED map and send their SEED Mon on a hunt for $SLOVE, $SEED, and hidden treasures. $SLOVE $SLOVE $SEED $SEED Launched on Mar 26, SEED Go has already drawn 50,000+ players, increasing every single day, into the hunt with earning potential from $5 - $50 per day. SEED Mon NFTs have already generated $2M+ in sales, fueling the demand for its in-game economy. Looking Ahead: Expanding Gameplay and Real-World Integration After TGE, the team's priority is to onboard more users, keep them engaged, and make sure they stay for the long run. They're also rolling out Adventure Mode and Mon Battle. Adventure Mode is where players hunt SEED Mons in the real world using their Mons. Comparable to Pokémon Go, but for blockchain gaming. Meanwhile, Mon Battle offers strategic combat against Mons for glory and rewards. And beyond all that, SEED is building a Real World Network - RWA Layer 1, integrating real-world assets, to take Web3 gaming beyond the screen. Instead of chasing short-term hype, SEED is committed to building lasting value, real engagement, and an immersive gaming experience through its dual-token model and well-designed economy. SEED holders will continuously benefit from all DApps within the SEED ecosystem. With a clear vision for the future, SEED aims to not only lead the charge in GameFi innovation but also lay the foundation for a new era of sustainable, real-world-integrated gaming that may shape the industry for years to come. About SEED From a Telegram Miniapp with over 60M users, SEED is evolving into the top RPG in Web3 gaming, inspired by the success of games like Pokémon Go and Axie Infinity. With the Sui Foundation’s backing, SEED is transforming from a Telegram Miniapp into the first 100M-user Web3 gaming ecosystem on the Sui blockchain. SEED SEED Contact COO Alex Zhang SEED alex@seeddao.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

SEED Opens A New Chapter For GameFi Narrative After Hitting Top 1 NFT Collection On Sui

The Myth of Connectivity: Uncovering the Global Blueprint of Digital Deception

Integrated Speech Language Models Face Critical Safety Vulnerabilities

Hello people! Welcome to the Programming and Doodles blog. Today, we’ll be talking about DeepSeek in-depth— including its architecture, and most importantly, how it’s any different from OpenAI’s ChatGPT. You don’t have to subscribe to read this but I highly recommend subscribing. In a Glance, DeepSeek is an open-source large language model (or as we call them, LLM), developed by a Chinese AI research company. It’s designed to compete with models like OpenAI’s GPT series, especially through its latest R1 model. Behind the scenes, it’s built with an MoE (mixture of experts) architecture, incorporating transformed layers for natural language processing. It predicts multiple words at once (instead of one-by-one like ChatGPT and some other LLMs do), uses smarter memory tricks (summarizing key points instead of writing everything down as ChatGPT does), and is trained on both English and Chinese data (which might have been stolen, according to Open AI’s CEO), making it strong in coding, math, and in general, reasoning. If any of the above jargon feels fancy, don’t worry, everything’s explained down below; keep reading. The Architecture of DeepSeek One of the reasons for the popularity DeepSeek received is that it’s not just another ChatGPT clone. It’s unique, and because of its uniqueness, it’s faster, cheaper, and more efficient. One such unique feature is that DeepSeek-V3 has 671 billion total parameters, but only 37 billion parameters are activated per token (word). Why does this matter? You see, this means that it doesn't use all of its resources at once; only the necessary parts of the model are used, and this makes it faster + efficient than other LLMs. What are these parameters? Think of parameters like this: you’re baking a cake, and the recipe calls for ingredients like flour, sugar, and eggs but the exact amounts vary depending on the cake you want to make. Parameters are just like these specific measurements of these ingredients. If you tweak the sugar amount, the cake might be sweeter; if you adjust the amount of flour, it might be denser or fluffier. Similarly, in AI models, parameters control how the model processes information. They are the internal settings that the model adjusts during training to make better predictions or generate accurate responses. 1. Mixture of Experts (MoE) Architecture Most of the AI models, like the earlier versions of ChatGPT (GPT-3), use a monolithic transformer architecture for their core. This means that every single part of the model is active all the time, even when it’s not needed. But here’s the catch: Some other new models, like GPT-4, are also rumored to use Mixture of Experts architectures. The difference? How they use it. DeepSeek’s MoE design is hyper-specialized. It means that instead of treating every task like a five-alarm fire, it activates only the most relevant “experts“ in its network for each input. Simply put in bullet points: Instead of using all of its parameters at once, DeepSeek only activates a subset of experts for each task. This (obviously) reduces computation waste and makes DeepSeek run faster and cheaper. You can think of this as a team of specialists; instead of asking every professor in a university about a math problem, you ask the math department, not the biology or psychology departments. 2. Multi-head Latent Attention (MLA) Deepseek uses Multi-head attention—MLA, in short— instead of standard self-attention like some ChatGPT models. Simply put, this means that instead of keeping track of everything in memory, MLA compresses and stores only the most important details from past interactions. Think of it as reading a book. Deepseek doesn’t memorize every word and write them down; instead, it summarizes and stores the key ideas. ChatGPT, on the other hand, tries to memorize and write down every word, which makes it slower and inefficient in comparison. Well, that makes sense. This also makes DeepSeek a better model for long conversations, as it doesn’t drift away from reality and produces chaotic outputs when handling complex discussions. 3. Multi-Token Prediction (MTP) If you have read this article where I explain ChatGPT’s behind the scenes, you might remember that ChatGPT predicts one word at a time. When we ask ChatGPT a question, it usually generates one word at a time. Sure, we can see its beautiful front end making it look like it’s basically messaging with you like a real-time person, but that’s not what’s really happening.
It’s also similar to the game of “20 Questions“, where you gradually build the answer based on each previous guess.— How ChatGPT works, Programming and Doodles blog DeepSeek, on the other hand, uses Multi-Token Prediction (MTP), which predicts multiple words at once and also allows pre-planning sentences, making text generation smoother and faster compared to other models. Think of it like typing on your phone: instead of predicting just the next word, it suggests entire phrases. Chaotic when it happens to us, yes, but in AI generation, this is much faster. 4. FP8 Mixed Precision One of the biggest challenges with training AI models is GPU memory and cost. DeepSeek solves this by using FP8 mixed precision training. That means, it stores data in smaller memory units (FP8 instead of FP16 or FP32). This saves GPU memory, allowing training on less expensive hardware. In my view, the best benefit of using this method is that it allows DeepSeek to be trained with fewer resources than GPT-4, yet achieve similar performances. 5. Load Balancing A common problem with AI models is their uneven workload distribution. Some parts of the model work too hard, while others do nothing. To fix this bottleneck, DeepSeek uses auxiliary-loss-free load balancing which evenly distributes the workload, thereby preventing “traffic jams“ in AI processing. It improves stability as well, avoiding sudden performance drops. And that’s about it, the behind-the-scenes. If you have more time to research DeepSeek, reading the DeepSeek-V3 paper on GitHub is totally worth it. Why do People Choose Deepseek Over ChatGPT? The main reason, as for any other tool, is its cost. Nobody’s comfortable with paying $200/month when there’s a free, open-source alternative. Also, its ability to run locally has grabbed the popularity of a lot of programmers and developers as well. But for me, there’s another reason: DeepSeek feels unbiased and direct; This isn’t something proper for a Python developer to say as it has no technical base and is about a “feeling“, but I gotta put it here. For example, I created new accounts on both DeepSeek and ChatGPT (so there’s nothing about me in memory at all), and told them I’m the founder of an AI/ML startup, looking for someone to work with me as head of content. Then I copy-pasted my resume, personal website, and LinkedIn. As I guessed, ChatGPT’s response was a bit of a mess. It has probably missed the part of my message “Give me a direct reply, recruit them or not.“ Based on Chenuli Jayasinghe's profile and website, she seems like a strong candidate for the Head of Content position, especially if you're looking for someone with a mix of technical expertise in AI/ML and a proven track record in content creation.
— ChatGPT’s response DeepSeek, on the other hand, started with my request: Recruitment Recommendation: Recruit Her
— DeepSeek’s response Note: both of them did pay attention to she/her pronouns on LinkedIn profile. Both of the responses were also followed by a summary of the web content, resume, and LinkedIn, but overall, I’d prefer DeepSeek’s straight-to-the-point reply. (Not because it told me to recruit myself, of course,) Summing Up While we can’t say that this one is the best LLM, DeepSeek does have some bonus points for its cost-effectiveness and efficiency. Refer to the following image on performance benchmark testing results on DeepSeek and ChatGPT, for more information. But also be aware, that DeepSeek’s policy states that it stores the information for “further training“ of the chatbot in Chinese servers. While it’s not something to get panicked about (most of the applications follow the same principle, despite not being overly open about it), it’s best to use precautions and run it locally through a service like Ollama. If you loved this article, make sure to subscribe using your email, so you can read all my content inside your inbox without missing any! It’s totally free of charge and I don’t even have time to send spam emails: https://codedoodles.substack.com/ Hello people! Welcome to the Programming and Doodles blog. Today, we’ll be talking about DeepSeek in-depth— including its architecture, and most importantly, how it’s any different from OpenAI’s ChatGPT. You don’t have to subscribe to read this but I highly recommend subscribing. In a Glance, DeepSeek is an open-source large language model (or as we call them, LLM), developed by a Chinese AI research company. It’s designed to compete with models like OpenAI’s GPT series, especially through its latest R1 model. designed Behind the scenes, it’s built with an MoE (mixture of experts) architecture, incorporating transformed layers for natural language processing. It predicts multiple words at once (instead of one-by-one like ChatGPT and some other LLMs do), uses smarter memory tricks (summarizing key points instead of writing everything down as ChatGPT does), and is trained on both English and Chinese data (which might have been stolen, according to Open AI’s CEO), making it strong in coding, math, and in general, reasoning. predicts multiple words at once smarter memory tricks both English and Chinese data If any of the above jargon feels fancy, don’t worry, everything’s explained down below; keep reading. The Architecture of DeepSeek One of the reasons for the popularity DeepSeek received is that it’s not just another ChatGPT clone. It’s unique, and because of its uniqueness, it’s faster, cheaper, and more efficient. One such unique feature is that DeepSeek-V3 has 671 billion total parameters, but only 37 billion parameters are activated per token (word). Why does this matter? You see, this means that it doesn't use all of its resources at once; only the necessary parts of the model are used, and this makes it faster + efficient than other LLMs. What are these parameters? Think of parameters like this : you’re baking a cake, and the recipe calls for ingredients like flour, sugar, and eggs but the exact amounts vary depending on the cake you want to make. Parameters are just like these specific measurements of these ingredients . If you tweak the sugar amount, the cake might be sweeter; if you adjust the amount of flour, it might be denser or fluffier. Think of parameters like this Parameters are just like these specific measurements of these ingredients Similarly, in AI models, parameters control how the model processes information. They are the internal settings that the model adjusts during training to make better predictions or generate accurate responses. 1. Mixture of Experts (MoE) Architecture Most of the AI models, like the earlier versions of ChatGPT (GPT-3), use a monolithic transformer architecture for their core. This means that every single part of the model is active all the time, even when it’s not needed. But here’s the catch: Some other new models, like GPT-4, are also rumored to use Mixture of Experts architectures. The difference? How they use it. DeepSeek’s MoE design is hyper-specialized. It means that instead of treating every task like a five-alarm fire, it activates only the most relevant “experts“ in its network for each input. Simply put in bullet points: Instead of using all of its parameters at once, DeepSeek only activates a subset of experts for each task. Instead of using all of its parameters at once, DeepSeek only activates a subset of experts for each task. This (obviously) reduces computation waste and makes DeepSeek run faster and cheaper. You can think of this as a team of specialists; instead of asking every professor in a university about a math problem, you ask the math department, not the biology or psychology departments. You can think of this as a team of specialists; 2. Multi-head Latent Attention (MLA) Deepseek uses Multi-head attention—MLA, in short— instead of standard self-attention like some ChatGPT models. Simply put, this means that instead of keeping track of everything in memory, MLA compresses and stores only the most important details from past interactions. Think of it as reading a book. Deepseek doesn’t memorize every word and write them down; instead, it summarizes and stores the key ideas. ChatGPT, on the other hand, tries to memorize and write down every word, which makes it slower and inefficient in comparison. Well, that makes sense. This also makes DeepSeek a better model for long conversations, as it doesn’t drift away from reality and produces chaotic outputs when handling complex discussions. doesn’t drift away from reality and produces chaotic outputs when handling complex discussions. 3. Multi-Token Prediction (MTP) If you have read this article where I explain ChatGPT’s behind the scenes, you might remember that ChatGPT predicts one word at a time. this article When we ask ChatGPT a question, it usually generates one word at a time. Sure, we can see its beautiful front end making it look like it’s basically messaging with you like a real-time person, but that’s not what’s really happening. It’s also similar to the game of “20 Questions“, where you gradually build the answer based on each previous guess. — How ChatGPT works, Programming and Doodles blog When we ask ChatGPT a question, it usually generates one word at a time. Sure, we can see its beautiful front end making it look like it’s basically messaging with you like a real-time person, but that’s not what’s really happening. one word at a time. It’s also similar to the game of “20 Questions“, where you gradually build the answer based on each previous guess. — How ChatGPT works, Programming and Doodles blog — How ChatGPT works, Programming and Doodles blog DeepSeek, on the other hand, uses Multi-Token Prediction (MTP), which predicts multiple words at once and also allows pre-planning sentences, making text generation smoother and faster compared to other models. Think of it like typing on your phone : instead of predicting just the next word, it suggests entire phrases. Chaotic when it happens to us, yes, but in AI generation, this is much faster. Think of it like typing on your phone 4. FP8 Mixed Precision One of the biggest challenges with training AI models is GPU memory and cost. DeepSeek solves this by using FP8 mixed precision training. That means, it stores data in smaller memory units (FP8 instead of FP16 or FP32). This saves GPU memory, allowing training on less expensive hardware. In my view, the best benefit of using this method is that it allows DeepSeek to be trained with fewer resources than GPT-4, yet achieve similar performances. DeepSeek to be trained with fewer resources than GPT-4, 5. Load Balancing A common problem with AI models is their uneven workload distribution. Some parts of the model work too hard, while others do nothing. To fix this bottleneck, DeepSeek uses auxiliary-loss-free load balancing which evenly distributes the workload, thereby preventing “traffic jams“ in AI processing. It improves stability as well, avoiding sudden performance drops. And that’s about it, the behind-the-scenes. If you have more time to research DeepSeek, reading the DeepSeek-V3 paper on GitHub is totally worth it. DeepSeek-V3 paper on GitHub Why do People Choose Deepseek Over ChatGPT? The main reason, as for any other tool, is its cost . Nobody’s comfortable with paying $200/month when there’s a free, open-source alternative. Also, its ability to run locally has grabbed the popularity of a lot of programmers and developers as well. its cost But for me, there’s another reason: DeepSeek feels unbiased and direct; This isn’t something proper for a Python developer to say as it has no technical base and is about a “feeling“, but I gotta put it here. For example, I created new accounts on both DeepSeek and ChatGPT (so there’s nothing about me in memory at all), and told them I’m the founder of an AI/ML startup, looking for someone to work with me as head of content. Then I copy-pasted my resume, personal website , and LinkedIn. personal website As I guessed, ChatGPT’s response was a bit of a mess. It has probably missed the part of my message “Give me a direct reply, recruit them or not.“ Based on Chenuli Jayasinghe's profile and website, she seems like a strong candidate for the Head of Content position, especially if you're looking for someone with a mix of technical expertise in AI/ML and a proven track record in content creation. — ChatGPT’s response Based on Chenuli Jayasinghe's profile and website, she seems like a strong candidate for the Head of Content position, especially if you're looking for someone with a mix of technical expertise in AI/ML and a proven track record in content creation. — ChatGPT’s response DeepSeek, on the other hand, started with my request: Recruitment Recommendation: Recruit Her — DeepSeek’s response Recruitment Recommendation: Recruit Her — DeepSeek’s response Note: both of them did pay attention to she/her pronouns on LinkedIn profile. Note: both of them did pay attention to she/her pronouns on LinkedIn profile. Both of the responses were also followed by a summary of the web content, resume, and LinkedIn, but overall, I’d prefer DeepSeek’s straight-to-the-point reply. (Not because it told me to recruit myself, of course,) Summing Up While we can’t say that this one is the best LLM, DeepSeek does have some bonus points for its cost-effectiveness and efficiency. Refer to the following image on performance benchmark testing results on DeepSeek and ChatGPT, for more information. But also be aware, that DeepSeek’s policy states that it stores the information for “further training“ of the chatbot in Chinese servers. While it’s not something to get panicked about (most of the applications follow the same principle, despite not being overly open about it), it’s best to use precautions and run it locally through a service like Ollama . service like Ollama If you loved this article, make sure to subscribe using your email, so you can read all my content inside your inbox without missing any! 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