Sergey Laptick

While general AI excels at administrative tasks and prototyping, clinical applications demand purpose-built systems for accuracy, regulatory compliance, and patient safety. A hybrid approach (using general AI for non-critical functions and specialized AI for clinical workflows) allows for innovation while mitigating risk:

While companies accumulate vast data stores, traditional BI tools often fail to turn this information into insight. See how custom data-driven platforms serve as tailored interfaces that connect raw data and AI outputs to human decision-making workflows. Discover how bespoke visualization and interaction layers transform complex data into confident business decisions.

Spec-Driven Development (SDD) offers a structured approach where clear, machine-readable specifications become the source of truth, guiding AI to generate code that is consistent, testable, and aligned with business goals. In this article, we explore how this approach works step by step, review tools like GitHub Spec Kit and BMAD, and share our team’s real-world experience using specs to build better software, showing you how to move from chaotic velocity to controlled, high-quality delivery.

The US healthcare software market is at a pivotal moment. According to the Vantage report, the Digital Health Market is projected to reach an astonishing $2,688 billion by 2035, which shows that digital transformation is now a fundamental operational requirement. This surge is fueled by the rapid maturation of AI-powered diagnostics, cloud-based EHR systems, and patient-centric virtual care platforms.

For healthcare providers, payers, and innovators, selecting the right software development partner is a critical strategic decision that impacts care quality, operational efficiency, and long-term growth. This guide provides a refreshed analysis of 11 leading healthcare software development companies in the USA, enriched with the latest market analytics and a clear-eyed view of the pressing challenges shaping the industry.

In the energy industry, software is no longer a support tool — it’s the engine driving digital transformation. From optimizing field operations and predictive maintenance to managing carbon reporting and renewable integration, the companies that get their energy software right and choose the right energy software development partner in the USA are the ones that stay ahead.

Here’s a COO’s perspective — a mix of industry insight, hands-on experience, and professional respect for those who are helping to shape the digital backbone of modern energy.

Construction industry professionals all around the globe deal with projects that significantly vary in scope, scale, and complexity. From small houses to colossal industrial facilities, each project type presents its unique challenges. To overcome them and deliver projects on time and within budget, it's crucial to have effective management software at hand. Today, we'll discuss different types of construction projects and the specific challenges associated with them. 

AI assistant app development is a unique challenge that blends several disciplines. It includes Natural Language Processing (NLP), UI/UX design, system integration, cloud architecture, and more. Embarking on such a journey requires a solid plan and an understanding of how AI assistant apps operate. 

For companies that provide medical services, optimizing patient care is a top priority. However, patients are not only individuals in need of treatment but also consumers in a vast and diverse healthcare market with specific requirements and expectations. To meet ever-increasing demands and remain competitive, companies must pay attention to numerous small details in addition to solving health problems. Patient management software offers a solution by streamlining many administrative tasks related to patient interactions. 

In every industry, businesses need to remain flexible and provide innovative services to stay afloat. It doesn’t only mean generating groundbreaking ideas but also working with reliable and cutting-edge tools. Since a company can do business in countless ways, software developers must implement their apps efficiently and provide a distribution model suitable for their numerous potential customers. Independent Software Vendors offer a model of cooperation between those who deliver software and those who use it that many businesses may find attractive. 

Ensuring product sustainability has both global and local impacts. As the US Environmental Protection Agency points out, reducing waste and adopting reusing practices prevent pollution by minimizing the need to harvest new raw materials, saving energy, and reducing greenhouse gas emissions contributing to the global climate. Additionally, as a joint study from McKinsey and NielsenIQ shows, people are more likely to consume products that claim to be environmentally responsible.

Considering this, evaluating your business's environmental impacts and keeping them low not only helps save Mother Earth but also makes your product more attractive to potential consumers. So, it's a win-win situation. The question is, how exactly can you assess these factors? Life Cycle Assessment, or LCA, is the exact answer:

Discover how modernizing legacy systems can rejuvenate outdated software, ensuring business growth and innovation. Learn the challenges and solutions for integrating modern functionalities into existing applications, bridging the gap between old and new.

Building solid relationships with donors is vital for those who depend on donations. However, managing a network of supporters, tracking contributions, and nurturing connections can be overwhelming for those who work with spreadsheets and rely on manual data processing. That's where donor management software, or DMS, can become helpful.

If you adopt one of those, you'll get powerful tools that help streamline operations, strengthen relationships, and elevate your fundraising efforts. Today, we'll consider the significant benefits of donor management software, talk about its core features, and try to understand how to implement them using modern development tools.

Artificial intelligence has brought many significant innovations lately. Generative #AI (GenAI) chatbots are at the forefront of this revolution as they don't require special skills, are easy to access, and offer sophisticated human-like interactions. Examples include IBM Watson Assistant, Copilot, Gemini, and ChatGPT, which everyone talks so much about.

Self-supervision combined with massive data and computing power enables generalizable and adaptable representations called foundation models. These large-scale neural networks can be trained and adapted to various tasks. They, for example, power such AI apps as ChatGPT. Any business can take a large pre-trained AI model fed with trustworthy data specific to a given industry and powered with the company's knowledge to tune it to a particular range of use cases. It opens almost endless business possibilities since you can not only use what popular GenAI chatbot developers offer but also adopt an AI solution tailored to your business needs.

The app's source code is where business ideas find their embodiment. However, for the end users to try the features, the code must go a long way from the idea to landing on the server. One of the possible solutions that allow development companies to fill the gap between writing code and putting it into production is #DevOps. Today, we'll speak about what DevOps is, what critical practices it relies on, and what trends will rise in the future.

Tracking systems have become integral to large companies' business processes and average individuals' lives. If you're lost in an unfamiliar city, you can always rely on your smartphone's GPS capabilities to find the quickest path to the railway station. For those who manage a large fleet of vehicles, the same technology can become the basis for a complex management system that tracks their assets and maximizes resource utilization. As one technology can shift forms so quickly and become either a tourist assistant or a key cog in the mechanism of a large enterprise, imagine how many possible tracking systems are out there.

The number of possible combinations of tracking devices and the ways you interpret the data you get from them result in too many types of tracking systems to name them all in one article. Instead, we'll categorize the most widely used of them to help you navigate such a diverse universe.

#Construction projects are often challenging due to multiple reasons. You deal with internal factors, such as workers availability or unpredicted project delays. It's solvable and doesn’t look like a big deal, especially if you have a well-trained team. Unfortunately, there are also external issues that you have no control over. For example, bad weather causing delays in work. Also, the assets you work with are of a different nature. Your management team works with both human and non-human resources, which may be tricky sometimes. You can leave all these factors unattended to prevent the project from going astray.

For quite some time now, React has been the most popular technology. According to Statista, in 2023, React was used by 40.6% of software developers worldwide, which makes it the most popular frontend framework. And we're not talking about some niche React enthusiasts here. It was adopted by such prominent companies as Meta, Netflix, Uber, Airbnb, and many others. According to the StackOverflow annual developer survey, React was one of the two most popular frameworks and technologies for five years in a row, competing for first place with such JavaScript tools as jQuery and Node.js.

React seems to be doing great, but how long can it hold the palm of victory? There are many other efficient JavaScript frameworks and libraries. React should provide new features, and its ecosystem should be replenished with new third-party tools to compete with them. Today, we'll look at what exciting features React offers to JavaScript developers to maintain its leading position in 2024: 

Daily interactions between businesses and customers generate tons of data. It can help companies shift from intuitive to data-driven decision-making when collected and handled right. However, vast raw data datasets are uneasy and barely possible to analyze.

Luckily, many techniques help summarize them into manageable insights. They seem pretty straightforward at first sight, but effective data aggregation demands sophisticated understanding and strategic application:

Generative AI has become a hot topic after the ChatGPT release. The reason is mainly because previous groundbreaking technological leaps, such as the wide distribution of personal computers or the Internet, could only help realize one's potential. In their turn, generative platforms introduced a dramatic change to what a specific individual can do. Such solutions can not only improve the way you perform routine tasks but also allow you to do things beyond your skill set, such as drawing or writing code.

It's no wonder that businesses quickly recognized the importance and potential of generative artificial intelligence. However, since this technology is relatively new and complex, it may be challenging to understand how it functions, which tool better suits specific needs, and its possible drawbacks. Today, we'll try to fix that: What Decision Makers Should Know about Generative AI

Implementing a perfect software solution on the first try is impossible. At some point, developers may start suspecting their codebase is a complete mess and doesn't correspond to what they had in their heads at the beginning of the project. It happens often and sometimes forces developers to make urgent code adjustments even before the product is launched, and users begin to find undetected bugs. As Fred Brooks wrote in The Mythical Man-Month, "The management question, therefore, is not whether to build a pilot system and throw it away. You will do that." It's not an action guide but rather a metaphor that nevertheless gives some clues about the state of affairs in the software development industry.

After the launch, the situation doesn't usually improve in the long run. Technical debt, obsolescence of initially used technologies and techniques, and other factors make code changes unavoidable. Here, developers have multiple options for getting the job done, but the choice is often between rewriting the code from scratch and refactoring it.