#cost to develop an AI app

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12+Best Grocery Delivery Apps in USA to Shop Online

When COVID hits, people are not allowed to go outside for grocery shopping. Instead, they prefer grocery delivery apps to get delivery at the doorstep. These are essential parts of human life for day-to-day survival, and that creates one of the biggest needs in the market. 

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Generative AI: Use cases, Benefits and Models in 2025

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Generative AI: Use Cases, Benefits and Models in 2025

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Jason Knight is Co-founder and VP of ML at OctoAI – Interview Series

New Post has been published on https://thedigitalinsider.com/jason-knight-is-co-founder-and-vp-of-ml-at-octoai-interview-series/

Jason Knight is Co-founder and VP of ML at OctoAI – Interview Series

Jason Knight is Co-founder and Vice President of Machine Learning at OctoAI, the platform delivers a complete stack for app builders to run, tune, and scale their AI applications in the cloud or on-premises.

OctoAI was spun out of the University of Washington by the original creators of Apache TVM, an open source stack for ML portability and performance. TVM enables ML models to run efficiently on any hardware backend, and has quickly become a key part of the architecture of popular consumer devices like Amazon Alexa.

Can you share the inspiration behind founding OctoAI and the core problem you aimed to solve?

AI has traditionally been a complex field accessible only to those comfortable with the mathematics and high-performance computing required to make something with it. But AI unlocks the ultimate computing interfaces, that of text, voice, and imagery programmed by examples and feedback, and brings the full power of computing to everyone on Earth. Before AI, only programmers were able to get computers to do what they wanted by writing arcane programming language texts.

OctoAI was created to accelerate our path to that reality so that more people can use and benefit from AI. And people, in turn, can use AI to create yet more benefits by accelerating the sciences, medicine, art, and more.

Reflecting on your experience at Intel, how did your previous roles prepare you for co-founding and leading the development at OctoAI?

Intel and the AI hardware and biotech startups before it gave me the perspective to see how hard AI is for even the most sophisticated of technology companies, and yet how valuable it can be to those who have figured out how to use it. And seeing that the gap between those benefiting from AI compared to those who aren’t yet is primarily one of infrastructure, compute, and best practices—not magic.

What differentiates OctoStack from other AI deployment solutions available in the market today?

OctoStack is the industry’s first complete technology stack designed specifically for serving generative AI models anywhere. It offers a turnkey production platform that provides highly optimized inference, model customization, and asset management at an enterprise scale.

OctoStack allows organizations to achieve AI autonomy by running any model in their preferred environment with full control over data, models, and hardware. It also delivers unmatched performance and cost efficiency, with savings of up to 12X compared to other solutions like GPT-4.

Can you explain the advantages of deploying AI models in a private environment using OctoStack?

Models these days are ubiquitous, but assembling the right infrastructure to run those models and apply them with your own data is where the business-value flywheel truly starts to spin. Using these models on your most sensitive data, and then turning that into insights, better prompt engineering, RAG pipelines, and fine-tuning is where you can get the most value out of generative AI. But it’s still difficult for all but the most sophisticated companies to do this alone, which is where a turnkey solution like OctoStack can accelerate you and bring the best practices together in one place for your practitioners.

Deploying AI models in a private environment using OctoStack offers several advantages, including enhanced security and control over data and models. Customers can run generative AI applications within their own VPCs or on-premises, ensuring that their data remains secure and within their chosen environments. This approach also provides businesses with the flexibility to run any model, be it open-source, custom, or proprietary, while benefiting from cost reductions and performance improvements.

What challenges did you face in optimizing OctoStack to support a wide range of hardware, and how were these challenges overcome?

Optimizing OctoStack to support a wide range of hardware involved ensuring compatibility and performance across various devices, such as NVIDIA and AMD GPUs and AWS Inferentia. OctoAI overcame these challenges by leveraging its deep AI systems expertise, developed through years of research and development, to create a platform that continuously updates and supports additional hardware types, GenAI use cases, and best practices. This allows OctoAI to deliver market-leading performance and cost efficiency.

Additionally, getting the latest capabilities in generative AI, such as multi-modality, function calling, strict JSON schema following, efficient fine-tune hosting, and more into the hands of your internal developers will accelerate your AI takeoff point.

OctoAI has a rich history of leveraging Apache TVM. How has this framework influenced your platform’s capabilities?

We created Apache TVM to make it easy for sophisticated developers to write efficient AI libraries for GPUs and accelerators more easily. We did this because getting the most performance from GPU and accelerator hardware was critical for AI inference then as it is now.

We’ve since leveraged that same mindset and expertise for the entire Gen AI serving stack to deliver automation for a broader set of developers.

Can you discuss any significant performance improvements that OctoStack offers, such as the 10x performance boost in large-scale deployments?

OctoStack offers significant performance improvements, including up to 12X savings compared to other models like GPT-4 without sacrificing speed or quality. It also provides 4X better GPU utilization and a 50 percent reduction in operational costs, enabling organizations to run large-scale deployments efficiently and cost-effectively.

Can you share some notable use cases where OctoStack has significantly improved AI deployment for your clients?

A notable use case is Apate.ai, a global service combating telephone scams using generative conversational AI. Apate.ai leveraged OctoStack to efficiently run their suite of language models across multiple geographies, benefiting from OctoStack’s flexibility, scale, and security. This deployment allowed Apate.ai to deliver custom models supporting multiple languages and regional dialects, meeting their performance and security-sensitive requirements.

In addition, we serve hundreds of fine-tunes for our customer OpenPipe. Were they to spin up dedicated instances for each of these, their customers’ use cases would be infeasible as they grow and evolve their use cases and continuously re-train their parameter-efficient fine-tunes for maximum output quality at cost-effective prices.

Thank you for the great interview, readers who wish to learn more should visit OctoAI.

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What Are the Advantages Of Using AI-Powered Tutoring Systems In Education?

The rise of artificial intelligence has restructured the education system. It has been among the most debated topics of the modern times. The inclusion of AI has made its way into the discussion. Surveys conducted in the United States pinpoint the majority opinion of the public. One similar survey emphasizing the adoption and use of artificial intelligence was conducted by the Forbes Advisor,…

[learning--tech]

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[technovation]

Steps To Develop An App Like DoorDash - Protonshub Technologies

Find out the steps to create your own food delivery app like DoorDash with Protonshub Technologies. Their step-by-step process for developing an app like DoorDash ensures seamless navigation through concept, design, development and testing. They tailor solutions to your unique vision.

[blockverse-infotech]

Android App Development: Building Your First Mobile App - A Step-by-Step Guide for Beginners

In the ever-evolving landscape of web development, crafting designs that seamlessly adapt to various screen sizes has become imperative. Among the key elements of web design, typography plays a pivotal role in conveying the intended message effectively. As users access websites across a myriad of devices, ensuring that typography adjusts gracefully to different screen sizes is crucial for delivering a consistent and user-friendly experience.

At Blockverse Infotech Solutions, we understand the significance of responsive typography in modern web design. In this article, we delve into the fundamentals of responsive typography and share essential tips and techniques to help you create text that adapts flawlessly to the diverse array of devices used by your audience.

Use Relative Units: Embrace relative units such as percentages, ems, or rems instead of fixed pixel sizes for font properties like size, line height, and spacing. Relative units ensure that typography scales proportionally based on the user’s viewport size, facilitating a consistent reading experience across devices.

Viewport-based Typography: Leverage viewport units (vw, vh) to define font sizes relative to the dimensions of the viewport. This approach enables typography to adjust dynamically, maintaining optimal legibility regardless of the screen dimensions.

Media Queries for Typography: Utilize media queries to tailor typography styles based on specific breakpoints corresponding to different device sizes. Adjust font sizes, line heights, and other typographic parameters within media query blocks to optimize readability across various screen resolutions

Hierarchical Scaling: Establish a hierarchical typographic scale consisting of relative font size increments for different heading levels and text elements. By maintaining consistent proportions between typography elements, you ensure visual harmony and readability across all screen sizes.

Optimize Line Length: Pay attention to line length — the number of characters per line — to prevent excessively long or short lines of text. Aim for an optimal line length that promotes comfortable reading without excessive eye movement, adjusting it dynamically for different viewport widths.

Contrast and Readability: Prioritize contrast and readability by selecting font colors and backgrounds that ensure sufficient contrast ratios, especially on smaller screens or in high ambient light conditions. Test typography legibility across diverse devices and environments to guarantee optimal readability.

Fallback Fonts and Typography Stacks: Define robust font stacks incorporating web-safe fonts and appropriate fallback options to maintain typographic consistency across platforms. Specify font fallbacks in CSS declarations to ensure graceful degradation in case primary fonts are unavailable.

Responsive Type Scale Tools: Explore responsive type scale tools and resources that facilitate the generation of typographic scales optimized for responsiveness. Leverage online calculators and frameworks to streamline the creation of scalable typography systems tailored to your design requirements.

User Testing and Iteration: Conduct thorough user testing across various devices and screen sizes to evaluate the effectiveness of your responsive typography implementation. Gather feedback from users regarding readability, legibility, and overall typographic experience, iteratively refining typography styles based on insights gleaned from testing.

In the realm of web design, responsive typography serves as a cornerstone for crafting engaging and accessible user experiences across diverse devices. By employing the tips and techniques outlined in this article, you can elevate your typographic design prowess and ensure that text content adapts seamlessly to the ever-changing digital landscape. At Blockverse Infotech Solutions, we empower businesses with innovative web design solutions that prioritize responsiveness and user-centricity, including meticulous attention to typography for enhanced readability and visual appeal.

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Artificial intelligence, or AI, has brought an unsaid revolution in the technological world. Thus, it can be said that top AI tools have been impressive. It is indicated that AI can add around $15.7 trillion to the global economy by 2030. The concept of AI began in the mid-20th century. However, at the start of the 21st century, AI gained attention. According to another report by Grand View Research, the international AI market is expected to grow at a CAGR of 37.3% between 2023 and 2030. 

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How Generative AI in Commercial Real Estate is Transforming the Future? - Detailed Guide

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How to Build an AI App in 2024: A Step-by-step Guide

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AI in Healthcare: Automating Tasks to Personalizing Treatments

The healthcare industry is currently on the cusp of a revolution- this time, driven by AI. From automating healthcare-related administrative tasks to assisting in groundbreaking medical procedures and discoveries, AI is changing the game, and very efficiently. It is rapidly changing the way we understand, diagnose, and treat health.

Since the entire world is performing half of their daily tasks like connecting with people across the continent, paying their bills, and purchasing groceries through apps, how can the healthcare industry stay behind? Healthcare mobile app development is all the rage now- at least among people who understand the futuristic implications of healthcare combined with AI.

In 2024, the AI in Healthcare market is expected to touch a whopping $67.6 billion (the potential!) worldwide. But do you understand what exactly AI brings to the table in the healthcare arena? Read ahead to understand how AI is driving the healthcare industry, and how you can tap into this potential too.

AI In Healthcare: The Implications

AI in healthcare refers to using certain Artificial Intelligence technologies like software or apps that help enhance various arenas of the healthcare industry. Various aspects of AI help to understand and analyze the medical data already available to facilitate clinical decision-making and even assist in diagnosis.

While AI is not widely implemented in healthcare today, it does have promising and positive implications, some of which are:

Automated Tasks:

A lot of tasks in the healthcare domain can be automated with the help of AI. This involves a lesser burden on already overworked staff at hospitals and clinics where there is usually a huge number of patients, and it is difficult to keep up.

Reduced Healthcare Costs:

One of the biggest implications of leveraging AI in healthcare is reduced healthcare costs. Though the costs wouldn’t come down drastically, AI can help by reducing the time required for consulting specialized healthcare professionals, thus cutting down costs around it.

Personalized Mental Healthcare:

One example of how AI can help in healthcare can be seen in the case of mental health. Over the past couple of years, many tools have come up that are helping people take care of their mental health. These include chatbots that are available to converse with, anytime; along with guided meditation and maintaining all the data that is fed into the app.

The Best Examples of AI In Healthcare

While you wonder about how you can tap into the potential of AI in the Healthcare market, here are some of the best examples of AI in use in the healthcare niche:

Medical Imaging Analysis:

AI models are currently being trained to analyze medical images like X-rays and different types of scans, like CT and MRIs, to assist radiologists in detecting any abnormalities, like tumors or fractures.

For example, Paige.AI can use mammograms to detect any signs of breast cancer.

Drug Discovery And Development:

Traditional drug discovery processes are both money and time-consuming. AI can help accelerate this process by analyzing vast amounts of data to identify potential drug targets and predict the effectiveness of new drugs by analyzing the action of the present components.

For Example, Benevolent AI uses patient data to analyze and predict alternate treatment options for different diseases.

Robot-Assisted Surgery:

Surgical robots guided by AI algorithms have an elevated precision, helping to reduce complications in complex surgical procedures. With enhanced dexterity, surgeons can now perform minimally invasive procedures with better precision.

Use Cases: AI In Healthcare

It might be worth it to ponder upon the cases and situations where AI can be leveraged. This is also the starting point where you begin to think of ideas that might help you break into the market with another app catering to healthcare. Some of these use cases are:

Administrative Efficiency:

Administrative processes take up a lot of time in the healthcare arena.

The use of AI to automate processes like patient registration, appointment scheduling, cancellation, rescheduling, patient data retrieval, insurance claims, generating patient reports etc., can easily be handed over to AI, thus freeing up more time for healthcare professionals to tend to more patients.

Disease Prediction And Prevention:

Often, the correct use of previous data to train any AI model can help to correctly diagnose, and even predict the progression of a disease by analyzing patterns.

Remote Patient Monitoring:

AI-powered wearables and sensors can continuously monitor a patient’s health data and even offer real-time updates and data on how the patient is progressing with the disease.

Drug Dosage Optimization:

For certain patients, after analyzing their health and lab tests, it can be gauged what dosage of a drug would be effective for them. This also partly falls under the arena of personalized healthcare but also deals with already existing drug compositions.

Benefits Of AI In Healthcare

Now when you understand the use cases of AI in Healthcare, it only seems fair to see how beneficial it currently is, and the potential it shows for the future. These benefits apply to doctors, patients, and the entire healthcare system as a whole:

Enhanced Efficiency:

Leveraging AI in the healthcare arena has only proven beneficial, from drug dosage optimization to automating tasks streamlining workflows, and helping patients to understand their health symptoms, AI has been able to bring out efficiency, not just in hospitals, but the overall healthcare arena.

Increased Access To Care:

Patients no longer need to stand waiting in a call for someone to check and inform them of an empty slot to book an appointment, AI can do it for them. Imagine seeing real-time scheduling slots for a healthcare professional, whom anyone can access.

Also, children who do not have an adult nearby can also use AI to identify the symptoms they are having, and AI can automatically inform the nearest hospital of the same as well.

Personalized Healthcare:

This is one of the most revolutionary things that AI is involved within healthcare. Each has different needs regarding healthcare and treatments.

AI is currently being used to study the medical data and patterns of individuals that can benefit them by making up a treatment plan for them that is tailored to their needs. 

Conclusion

While AI is not a replacement for human healthcare professionals, it can certainly be used to assist healthcare professionals in diagnosis, data analysis and treatment for patients.

Beyond the hospitals too, AI is being used in pharmaceutical industries in drug designing and more, as well as in medical research, to better understand disease progression and how AI can help by understanding the progression patterns and more.

As for someone looking to tap into this market, you can always identify niches in healthcare where the use of AI can be leveraged well, and since everything has gone mobile today, invest in a mobile apps development agency tailored for that particular niche. With time, you can break into the market and benefit many patients in the process.