JayDevs - Software Development Company

Happy New Year from the JayDevs team!

This year was full of wins and achievements, surprises and challenges. Thank you all for trusting and supporting us in moving forward: our outstanding team, and our dearest clients, working together in synergy.

Now it's time to recharge your batteries and meet 2023 with ease, joy, and readiness to conquer new heights! Let it be filled with happiness and prosperity 🎄

How outsourcing has helped Apple, Google, GitHub and other IT companies become who they are 👇

Follow the link to find out:

- What are the common reasons for companies to outsource;
- Why did the above companies decide to do that;
- And what lessons we can learn from their experiences.

Why Do Companies Outsource? Lessons Learned and Software Outsourcing Examples - JayDevs Outsourcing is a common practice in IT industry. So let's find out why do companies choose to outsource work with 10 fascinating outsourcing examples.

Learn how PayPal, Visa, IBM, Google, Amazon and others implement ML to improve cybersecurity 👇

In our new article we explored:

- Why you should be aware of cybersecurity issues;
- Impact of recent famous cyberattacks;
- What can machine learning do for cybersecurity;
- And cases of using ML for cybersecurity by big technology companies.

9 Companies Using Machine Learning: Tesla, Facebook, PayPal, and Others As technology and computing becomes more complex, so do cyber attacks. We take a look at the top big companies using machine learning toto improve cybersecurity.

5 tips to improve data quality before training a neural network from Jaydevs Co-Founder and Managing Partner, Alexander Sivov 👇

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First, the data should cover as much of what we want to train the neural network as possible. For example, if we are solving the problem of recognizing chairs, then the data should include all types of chairs: office, bar, stools, etc.

In addition to this, the chairs should be presented in different turns/ projections, with different lighting.

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It is also crucial that there is a relatively even distribution of data in the dataset to provide no gaps.

These gaps are extremely bad: when the network starts working on the real data and the data that was in these gaps comes across, you can get completely inadequate results.

To fix this, synthetic data is sometimes created. This is not the best case, and also leads to other issues, but better than having gaps anyway.

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When we're preparing the data, we need to form 3 datasets. One dataset we'll use to train the neural network. The second one is for testing it. And the third is the validation one. All three datasets must not overlap with each other.

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When a neural network is trained it recognizes the data as a set of features. So it’s essential to identify which set of features the network will learn from.

Thus the data must be prepared (labeled) to show a machine learning model the target features. This is done by labelers.

Once the data is labeled, it is important to make sure it is consistent, and everything is labeled according to the same rules.

A Data Scientist is usually in charge of determining what data is needed for input, preparing rules, preparing sample datasets, and quality control.

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The last thing that matters is the feedback of the ML engineer who works with the data while training the network. For example, the dataset was prepared according to some rules, but during training it something goes wrong; perhaps some critical factors were not considered.

The engineer can suggest what can be changed and how. That is, the learning process must be iterative.

4 steps to minimize the risks and negative impact when switching software development vendors in our new article 👇

Tap the link to find out:

- How to evaluate factors before making a decision to switch vendors;
- How to prepare for switching teams;
- How to quickly find a new potential vendor;
- And how to smoothly complete the transition from one team to another one.

4 Steps IT Project Transition Plan from One Vendor to Another - JayDevs Does software development project transition scare you? Here you will find step-by-step project transition plan and a IT project transition checklist.

Hire pre-vetted senior dedicated developers in just 7 days. Here is how to do it 👇

01. Go to our website - https://jaydevs.com/

02. Click “Hire Developers” and tell us about your requirements. It will be useful for us to know how many resources you need, their expected level, technology stack, approximate start date and the period of involvement.

03. We’ll analyze your requirements, send you CV’s of the available developers, and ask clarifying questions, if there are any. Usually, we also conduct a short meet-and-greet call to answer any questions you may have.

04. 1-2 days after, we will arrange an interview with the developers, where you can assess their level on your own. If necessary, they'll also complete a test task.

05. If everything is ok, we’ll sign a GA, NDA and SoW, so developers can start to work with a trial period of up to 2 weeks.

Still have questions on how to hire our developers? Leave a comment and we’ll help you.

Dedicated team can be a great tool to optimize your IT expenses and provide flexibility. In our new article, we have described all the details you need to know about this software development model 👇

Tap the link to find out:

- What is a dedicated development team;
- What benefits can it bring to your business;
- Who is this model best suited for;
- And how much does it cost to hire DDT.

Enjoy reading: https://jaydevs.com/dedicated-software-development-teams/

Dedicated Software Development Teams: The Four W's - JayDevs All you should know about dedicated software development teams. What? Why? When? Who? Read our article and find out answer on this four W's ✔

Liver cancer is one of the leading causes of cancer deaths worldwide, with more than 800,000 people diagnosed each year.

Researchers from Johns Hopkins Kimmel Cancer Center have developed an AI-based test that can help doctors detect this type of cancer.

The blood testing technology, known as DNA evaluation of fragments for early interception (DELFI), earlier allowed researchers to successfully classify lung cancer in 2021. Now, scientists have used this technology to detect liver cancer.

DELFI works by measuring the amount and size of cell-free DNA present in the blood from different parts across the genome. Cancer cells release DNA fragments into the blood when they die, giving an opportunity to detect tumors at early stages and without invasive tests.

The recent study evaluated samples from 724 people from the U.S., the European Union and Hong Kong to detect hepatocellular cancer, the most common type of primary liver cancer.

The study involved people who either had liver cancer or were at average or high risk of developing the disease. DELFI achieved 88% sensitivity and 98% specificity in the average-risk group, and 85% sensitivity and 80% specificity in the high-risk cohort.

Assistant professor of medicine at the Johns Hopkins University School of Medicine, Amy Kim, M.D., outlined the potential implications of the study.

“Currently, less than 20% of the high-risk population get screened for liver cancer due to accessibility and suboptimal test performance. This new blood test can double the number of liver cancer cases detected, compared to the standard blood test available, and increase early cancer detection.”

Every year, AI is being implemented more and more into the healthcare industry. Jaydevs is honored to be one of the companies taking a part in this movement.

Recently, we participated in a similar medtech project. We have developed a web application and integrated an AI algorithm into it to detect various diseases (cancer, skin diseases, etc.).

So, if you have ideas on how to implement AI in healthcare, contact us. We will be glad to help you with the implementation of your idea -https://jaydevs.com/

3 easy steps to provide specific and actionable feedback to software developers with the SBI framework 👇

Sometimes, it’s hard to stay objective when giving feedback. We all have our sense of right and wrong, and often jump to conclusions based on our own assumptions about people's behavior. While this may be true, this type of feedback is generally unhelpful.

The Center for Creative Leadership created an SBI framework that helps you remove judgment out of your feedback and make it more clear, so the recipient becomes less defensive about it. The framework consists of 3 simple steps.

01. Situation.

When you’re giving feedback, first define where and when the situation occurred. This makes the feedback more specific, so developers can easily relate to it.

02. Behavior.

Next, describe the behavior without judgments and without assuming you already know what they have in mind. This is the most challenging part of the process as you must communicate only the behaviors that you observed directly.

For example, if you observed that a developer made a mistake, you should not assume that he/she prepared poorly. You should simply comment that the mistake was made, ideally note what the mistake was.

By being objective and stating the facts, you lower high emotions that can occur when feedback is given. This also helps you avoid making assumptions that could upset the employee and damage your cooperation.

03. Impact.

Describe your thinking or feeling about the behavior. Keep in mind how developers’ actions have affected the process. Focus on information that can be measurable.

Once you’ve delivered your feedback, encourage developers to think about the situation and understand the impact of their behavior. Allow them time to absorb what you have said, and then go over specific actions that will help them to improve.

When you structure feedback this way, your developers will understand precisely what you are commenting on, and why. This could be a great step for their further growth.

Like the post if it was helpful and share your ideas on providing effective feedback!

Imagine, you need to ship something to the opposite part of the country and you don’t want to look for a box, wait in a queue or go to the post office.

You want someone to pick up the package from your house and deliver it to the desired destination.

This is what the Shyp startup was doing, and people were really appreciating it, but something went wrong.

In this post, we explore their story. How they reached success, where did it go wrong, and what things founders should keep in mind while growing a startup.

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💡 The idea.

After Uber's rise, different companies tried to replicate its success in other niches.

Shyp was one of them. It was founded in 2013 in San Francisco as an on-demand service provider for shipping.

Their app allowed users to take a photo of the item for shipping. Then, for a $5 fee, Shyp would pick it up in 20 minutes to pack and deliver to a shipping company.

Users no longer had to worry about finding a box and packing material, or drive to a shipping store or post office to send their package.

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🥳 Early success and big plans.

Shyp had a great start. Customers loved it and investors wanted to invest in it.

Their app was well-designed. The couriers were personable and often arrived even more swiftly than the promised 20 minutes. People who received shipments admired the quality of Shyp’s custom packaging, which it produced using its own box-cutting machines.

In 2013, Shyp gained a lot of appreciation for the services it provided from people in San Francisco. Due to this initial explosive growth, it even got compared to Uber and called the “Uber of shipping”.

The business began to grow significantly primarily as it focused on supply chain issues that many companies overlooked. Soon Shyp raised $62 million with $250 million in valuations.

The concept was strong enough that the company expanded its services into New York, Los Angeles and Chicago, and introduced new initiatives like its partnership with eBay and a new feature, address-less shipping.

The company had enough resources to become one of the first on-demand startups to turn its contractors into employees. A transition increased its costs but also gave a higher level of quality control.

Shyp started calling itself “the new global standard in shipping” and launched an ambitious ad campaign to ramp up its customer base.

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🙁 Why and how Shyp failed.

While everyone was excited with Shyp's success, there were some fundamental problems with their operating model.

01. Introducing a price for packaging.

A fixed price for pickup and packaging proved a challenge given the wide variance in the size of the packages that people were sending.

Shyp charged a $5 pickup fee, including coming to home, packaging, and delivering. Moreover, all items were not created equal. Shipping shoes versus shipping TV didn’t have a lot of financial sense.

To be more profitable, the company introduced packaging and shipping fees that could vary based upon the size of the item. It started from $3 for a small item and $75 for an extra-large item.

While probably prudent for the company, it likely diminished the value of the service in the eyes of customers.

02. Overestimation of potential customers.

At the beginning of their journey, Shyp had vastly overestimated the number of individuals that would regularly ship items anywhere.

They found out that shipping things is an unusual and frequently intermittent practice, unlike basic utilities like taxi rides and food delivery, which most people now use at least once a week.

To compensate for this, Shyp began to recruit small businesses that ship regularly to join their shipping movement, even providing volume discounts.

Still, they decided to keep the popular but unprofitable parts of their business running, with small teams of their own behind them. The company’s CEO Kevin Gibbon admitted that letting the unprofitable parts run was one of his biggest mistakes.

03. Focus on expansion rather than sustainability.

One of the main reasons for Shyp’s fail was the focus on expansion rather than sustainability. They keep on expanding geographically without thinking it through, which eats up lots of their funds.

In 2018, Gibbon admitted that instead of planning the right demographic, innovative features, and growth tactics, he focused only on the engineering and development part. He stated that the Shyp app fell into the trap of growth.

He also said that "growth, at all costs, is a dangerous trap that many startups fall into, mine included.”

In order to survive, Shyp downsized itself and ended operations in all markets except its largest and most profitable, San Francisco. They fired many of their employees, focused entirely on business customers, and shut off all the parts of the business which weren’t generating revenue.

These moves worked temporarily, as the company started operating at a profit. But due to their earlier mistakes, the company had been left with too little cash flow and insufficient resources to continue pursuing in the new direction. Finally, on March 27, 2018, Shyp shut down its operations.

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🤓 Lessons to learn.

Shyp’s story illustrates the difficulty of establishing a business with a solid foundation.

It shows that software development should go alongside with the proper planning and prudent growth, even when entering a promising niche.

Trying too much too fast, without thought as to sustainability or cost, is a high-risk proposition that few can afford to make.

Too much focus on ideas and technological solutions can lead to a lack of clarity about what the market is and what can be reasonably expected and achieved.

Follow us to get more stories on technology startups and businesspeople!

3 steps to reduce risks while estimating software project requirements, according to Jaydevs CEO Alex Valadzko 👇

“ 01. First if you envision issues with requirements you should ideally involve a business analyst who will ask the right questions and elaborate all the requirements. At the initial project stage, it also makes sense to ask QA engineers for assistance and create detailed test cases. Oftentimes it helps to reveal bottlenecks and unclear moments.

02. Secondly, you should consider frequent product delivery. Agile methodology being really popular nowadays can help you do this. This approach reduces risks due to the ability of seeing the results at an early stage and costs of changes are less.

In general, the customer should be involved in the process of development, not necessarily full time but good enough to evaluate intermediate results and provide feedback. Client's supervision and understanding if everything meets the expectations is the ultimate goal.

The manager from the production side should in a timely manner observe that the client is informed about any requirements misunderstanding resulting in project estimate and costs change. This may not necessarily affect the final project budget, but will surely help you address changes in a more elegant way.

03. The third important aspect to consider is availability of a professional with expertise in the specific domain, having real experience developing and estimating similar applications. People without relevant experience as a rule, estimate based on guesses and assumptions being the main reason for future problems. “

Find more tips on product development, team management and hiring on our blog - https://jaydevs.com/blog/

How do you reduce risks while estimating a software project? Share your thoughts in the comments.

Copilot's potential for business, initial developer reactions, and who it is not suitable for 👇

An AI "pair programmer," GitHub Copilot made its debut in June 2022 following a year of technical previews.

It is based on the OpenAI Codex and assists developers by suggesting the next line of code, complete functions or more complex algorithms when they might be suitable. For individuals, the service costs $10 per user per month or $100 per user per year.

GitHub’s Copilot has proven to be popular with coders. Among core advantages they highlight:

- Less time spent browsing for solutions on the internet;
- Smarter code completion than typical IDEs offer;
- And tiny boosts with contextualized solutions;

All of the above results in a greater focus on the big picture, architecture, and integration.

On the more qualitative front, a majority of Copilot users reported they were less frustrated with coding and more satisfied with their job at 59% and 60%, respectively (according to internal GitHub research).

Although, the majority of developers said Copilot can speed up daily work, they also said it won’t do the job for you. At the end of the day, developers are still responsible for delivering quality code, even if they use AI assistance.

❗️ The GitHub FAQ states that “Inexperienced developers may struggle to use Copilot to effectively generate code, as their lack of experience might inhibit their capability to effectively review and edit suggestions made by Copilot.”

A version for business will be launching soon. Once launched, companies will be able to purchase and manage seat licenses of the tool for their teams of employees, helping them significantly expand their use of AI.

Alongside with expansion, GitHub is experimenting with a new voice-based interaction system for its Copilot. “Hey, GitHub!” will allow programmers to code with just their voice and no keyboard, just like how you’d speak to Siri, Alexa, or Google Assistant.

The feature is currently being tested by the GitHub Next research team. It eventually could speed up the programming process even more, as using a voice is faster than using a mouse and keyboard. It would also make coding more accessible to those who can’t use the standard manual tools.

Have you had the opportunity to test Copilot? How do you assess the prospects of using voice-assistants in software development? Share your thoughts in the comments.