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We developed a method that combines financial knowledge with advanced technology, which saved over 20,000 employees a cumulative amount of nearly a billion shekels.

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While you’re burning hours searching for the cheapest flight or trying to refinance a mortgage, the big money is actually in places that many people don’t know how to take advantage of. “It’s called financial procrastination,” explains Tal Ekroni. “The method we developed at Better starts with a [free] 45-minute Zoom meeting. From there, our advanced technology comes into play: it can save everyone huge amounts of money. Our goal is for your meeting with us to be the most profitable decision you’ve ever made,” explains the Better team.
Ekroni (middle): “We realized that people simply don’t have the strength to deal with big money. It weighs them down. Technology makes life easier for them – financially and mentally.”

“People who love data love control over numbers. They can talk for an hour at lunch about how much they pay for Netflix or how much a beer costs them at the pub, but as soon as the conversation turns to their pension or mortgage or life insurance – suddenly there’s an itchy head and silence,” says Tal Akoni, founder and CEO of Better.

“We know we pay commissions, we know there’s a chance our management fees are high, but in the end we tell ourselves it’s ‘small money’ or find another reason to postpone dealing with it. The reality is that it’s not small money, certainly not for someone who earns salaries of 15,000 shekels or more. We’re talking about hundreds of thousands of people with whom you can save a minute, 0-3 shekels. For us, managing to save over 700,000 shekels is crazy!”

“People imagine an insurance agent with 60 binders – and give up”

According to Ekroni, people always knew he was the person to turn to with questions about money. “I’m used to family and friends approaching me with questions – is it time to refinance a mortgage? Where should I put my pension? Buy dollars? Where to invest savings?”

“But what really surprised me is that even people who are ‘data people’ by definition – people who work in high-tech, engineers, coders and software people – when it comes to big money, they shy away from dealing with it. They imagine a classic meeting in an office with an insurance agent – and give up. It stresses them out and they simply prefer not to deal with it,” he says.

When the symbolic principle realized that there was a real need here, he teamed up with Eran Orenstein and Uri Weiss – leading investment experts and technology professionals. The three’s goal was to establish a company that would appeal to exactly their type of people – people with a technological orientation, high salaries, little time, those who are used to high standards of work and quick results. Thus, Better Technology was born.

“The process is built on the understanding that there is such a thing as ‘financial procrastination.’ It is something that exists and we have no business coming to judge and criticize it or discover the deep reasons for it. We simply bypass it and do everything for the client. We meet for a 30-minute conversation on Zoom and from there everything happens on its own. The system checks all the relevant information at any given time and at any given time. To optimize the financial portfolio across the entire range of products in it: from pensions to mortgages, from life insurance to taxes,” explains Ekroni.

“That is, from the moment you give consent to the process, our system aims to save you money, and if it finds such an opportunity, you will receive a notification on WhatsApp and will only have to confirm the action.”

Better’s process is simple and allows you to schedule an efficient half-hour consultation where you can get started right away. During the session, you’ll get a general idea of “what to do and how much you can save.” You can schedule your appointment online by signing up here and the Better team will walk you through what you need to bring to the appointment (which isn’t too complicated).

Michael Shapir, senior executive at a cyber company: “I saved 348 thousand shekels”

Michael Shapir, a senior executive at Cyber, was skeptical. As someone who works at a large technology company, he’s heard dozens of stories about algorithms performing magic tricks and entrepreneurs promising mountains and hills. “I approach these projects with great caution. What’s more, my hobbies include Excel spreadsheets, investing in stocks, and I’m usually on the pulse of everything related to life’s ‘big’ financial expenses,” he says.

But when Shafir received Order 8, his and his family’s expenses increased – and he felt the need to clean up the mess. In a long post Shafir wrote on LinkedIn, he said that it was in the reserves that he first realized he needed help. “The decision to devote half an hour to the story was excellent and worthwhile,” he says. “I saved 348,000 shekels in 30 minutes and with zero investment.”

TikTok Customer Manager: “I saved over 272,000 shekels”

D., an account manager at TikTok, was also surprised to discover how much money she could repay in such a short period of time. “I earned about 20,000 shekels a month, and it was important to me to make sure I was saving for the future. I had an agent from work, a nice 60-year-old man, I trusted him, and in retrospect I realized that the terms were not good,” she says.

“I saved 272,992 NIS with Better, they did all the work for me, and I didn’t have to spend a single shekel out of pocket to find the savings.”

How much does it cost us to save hundreds of thousands of shekels?

Finally, Ekroni explains the payment model: “Our business model is built on the fact that customers do not pay for locating savings. In other words, what our technology does, it does for you for free. If we find that you can save, for example, 80,000 shekels on a mortgage or 150,000 shekels in retirement, if we send you an app, we will not accept the action. You will be rewarded for it, and if not, nothing will happen.

“This is a fair model for both parties and is based solely on finding savings and customers’ agreement to implement it.”

To schedule a [free] Zoom savings meeting with a Better financial expert, choose a time that is convenient for you for an online meeting:

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A team of Harvard researchers introduces TxAgent: an artificial intelligence agent for personalized care

 

TxAgent: A Breakthrough in Personalized Medical Care

The Zitnik Lab at Harvard University recently introduced TxAgent, an innovative artificial intelligence agent designed to revolutionize the therapeutic decision-making process. The development comes at a critical time as healthcare systems around the world seek ways to improve the efficiency of medical care and reduce medical errors.

Led by Prof. Marinka Zitnik, the team of researchers developed a system that combines advanced multi-step analysis and real-time dynamic access to huge medical databases. The system is able to assess complex drug interactions, identify potential contraindications, and suggest treatment strategies tailored to the specific patient based on their entire medical data.

“TxAgent represents a completely new approach to precision medicine,” explains Prof. Zitnik. “Instead of relying on standard protocols that are not always appropriate for every patient, our system analyzes each patient’s unique situation and offers a personalized treatment plan.”

TxAgent’s complex architecture integrates 211 different data analysis tools, ranging from traditional statistical models to advanced deep learning algorithms, all working in harmony to provide accurate and reliable treatment recommendations.

 

Advanced capabilities and technological innovation

One of TxAgent’s significant advantages is its ability to perform complex analyses of medical data in real time. The system can analyze a patient’s medical history, laboratory test results, genetic data, and information about previous treatments, and combine all of this with the latest scientific knowledge from global medical databases.

Unlike traditional clinical support systems, TxAgent goes beyond providing basic alerts, but offers in-depth, layered analysis:

  • Drug interaction analysis : The system identifies not only direct interactions between drugs, but also combined effects of several drugs together.
  • Genetic matching : TxAgent takes into account the patient’s genetic profile to predict drug efficacy and side effects.
  • Continuous learning : The system is constantly improving with each new case, updated in real time from new studies and clinical protocols.
  • Transparency in decision-making : Unlike “black box” systems, TxAgent provides clear explanations for each recommendation, which increases the medical team’s trust in the system.

Dr. James Chen, one of the senior researchers on the project, emphasizes: “One of the biggest challenges in medicine today is the enormous amount of information that doctors have to process. TxAgent helps filter the most relevant information and presents it clearly, allowing doctors to make more informed decisions in less time.”

 

The potential to improve treatment outcomes and patient safety

The clinical application of TxAgent holds significant potential for improving treatment outcomes and patient safety in several aspects:

  1. Reduce medical errors : According to studies, medication errors cause many unnecessary hospitalizations each year. TxAgent can reduce these errors by accurately identifying drug interactions and contraindications.
  2. Dose personalization : The system is able to recommend dosage adjustments based on many factors, including patient weight, kidney and liver function, medical history, and genetic profile.
  3. Improving treatment compliance : Through personalized recommendations, TxAgent can help reduce side effects and improve patients’ compliance with drug treatment.
  4. Streamlining decision-making processes : Making relevant information available in real time allows the medical team to make faster and more informed decisions.

In initial trials in a controlled clinical setting, TxAgent showed a 28% improvement in identifying potentially dangerous drug interactions, and a 34% improvement in tailoring treatment protocols to unique patient profiles.

 

Ethical challenges and questions

Despite the great promise, the development and implementation of systems like TxAgent also raises significant challenges:

  • Privacy and data security : The system requires access to sensitive medical information, which raises questions about data security and privacy.
  • Medical liability : Questions regarding liability in cases of error or system failure have not yet been fully resolved.
  • Integration into existing systems : Integrating the system into existing medical information systems poses a significant technical challenge.
  • Training of medical teams : The success of the system depends largely on the ability of medical teams to use it effectively.

Prof. Zitnik is aware of these challenges: “We take the ethical and practical questions seriously. TxAgent is intended to be a decision-support tool, not a substitute for medical judgment. The physician always remains the ultimate authority in the decision-making process.”

Summary: The Future of Personalized Medicine

TxAgent represents a significant step forward in the era of personalized medicine. The system demonstrates how artificial intelligence can be used as a powerful tool in the hands of medical staff, enabling more informed decisions tailored to the unique needs of each patient.

“Personalized medicine is not just a trend, but the future of medicine,” concludes Prof. Zitnik. “With tools like TxAgent, we are moving toward a world where medical care will be more precise, more efficient, and perfectly tailored to each patient.”

The research team is now planning larger clinical trials in collaboration with several hospitals across the United States, with the aim of testing the system’s effectiveness in a wide range of clinical scenarios and patient populations. If the trials are successful, TxAgent could be available to medical institutions within two to three years.

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