Laura Bratton is a reporter for Yahoo Finance. Follow her on Bluesky @laurabratton.bsky.social.

Reddit stock falls more than 20% since last week's earnings report as 6-month rally eases

Laura is a science news writer, covering a wide variety of subjects, but she is particularly fascinated by all things aquatic, paleontology, nanotechnology, and exploring how science influences daily life.

Extremely rare ‘Einstein ring’ discovered close to Earth | Popular Science

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Laura

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Many people have discovered ChatGPT’s competencies in code generation. But, how well does it actually perform, and are there alternatives? In this post, I take a look at some scenarios where AI can and the degree to which it can be useful. Finally, I do my best to evaluate its performance. help with coding Intro ChatGPT has been everywhere in the last few months. Reading and hearing about how people use it and ‘force’ it to make mistakes has been fun for a week or two, but it's getting tiring. For some reason, my LinkedIn feed still has at least one "Look how ChatGPT failed"-post every day, and I don't know what people are trying to prove with that. At the same time, I am perplexed by software developers claiming that they are using ChatGPT to do their work. Statements like "This week, I didn't write a single line of code and only used ChatGPT" even made it to podcasts where people discussed which jobs AI might replace in the future. For everyone who has seriously worked as a software developer, it is evident that AI cannot replace software developers soon. Yes, ChatGPT can generate code for a given problem description, but it is not good at solving problems, which is the main task of a software engineer. AI can support specific tasks, though, and I am looking at some examples in this post. AI Pair Programming Several services implement smart code completion, also called AI pair programming. The term is quite misleading and was probably invented by someone without much experience with pair programming. While pair programming is specifically beneficial when two people work together to discuss how something should be implemented, AI [pair programming](https://Pair programming - Wikipedia  Wikipedia https://en.wikipedia.org › wiki › Pair_programming) tools instead help with the next step. As soon as you decide what to implement and where to put the code, it suggests a solution. The two most popular ones are and GitHub Copilot is based on , a model for translating natural language to code based on GPT-3. According to a paper by OpenAI researchers, the model generated working code for given Python docstrings for more than 70% of the attempts. That's quite impressive but still also emphasizes that it requires basic coding skills (i.e., writing a docstring) to retrieve good results. Like ChatGPT, CodeX can also be used for free. GitHub Copilot Tabnine. OpenAI Codex on the OpenAI website I have used Copilot for private projects for quite a while and liked it a lot. In the beginning (when it was still in technical preview), it mainly copied lines from GitHub code. Since then, it has improved and evolved a lot. Especially for code that implements generic tasks (like calculations or conversions), there is a high chance that Copilot suggests a complete function based on a doc string. For example, when you write it would suggest the complete function, including a return value. def reverse(self, x:int) -> int... Copilot also works well for writing tests. Just add the name "test" to a given function name and it does the job. In contrast, Tabnine was a massive disappointment for me, though, and felt like a dumb auto-completion suggesting mostly useless or wrong code. While Copilot uses one large model, Tabnine is based on several smaller models that solve individual problems. This is probably also why it is more convenient to use Tabnine in local mode and train it with your own code (which is not possible with Copilot). Even with a lot of context and well-named variables, the suggestions I received from Tabnine were often far from what you would expect. Using local mode, it worked well in large repositories with a lot of similar code. But this is far too easy compared to the problems I would expect such a model to solve. It was not smart but felt more like a tool for automatic copy and pasting. There might be other use cases and circumstances where Tabnine works well (for example, in huge company code bases where everything is written in a similar style and language) but as someone who is working on many smaller projects, it just didn't work for me. Asking AI to write Code As mentioned in the intro, one of the primary use cases of ChatGPT seems to be code generation. Of course, that was already possible with CodeX, but the boom of ChatGPT made many more people aware of what is possible. Compared to the plugins mentioned before, this approach aims to translate textual problem descriptions into code instead of being integrated into an existing code base. Doing this task is challenging for a model as it involves understanding a problem and generating suitable, functional code that solves this problem. In the past, many models failed to do the former reliably, and even with more specialized models, it still is a considerable challenge that is not solved by ChatGPT. However, let's have a look at the code ChatGPT generates. Asking ChatGPT to generate a weather app I asked ChatGPT to write a Streamlit application that fetches weather data for Dortmund from a free API and displays it in a chart. This is what I got: import streamlit as st
import requests
import pandas as pd
import plotly.express as px

# Set up OpenWeatherMap API key
api_key = "YOUR_API_KEY_HERE"

# Define function to get weather data from OpenWeatherMap API
def get_weather_data():
    url = f"https://api.openweathermap.org/data/2.5/onecall?lat=51.5134&lon=7.4814&exclude=hourly,minutely&appid={api_key}&units=metric"
    response = requests.get(url)
    data = response.json()
    return data

# Get weather data
data = get_weather_data()

# Convert weather data to a Pandas DataFrame
df = pd.DataFrame(data["daily"])

# Format the date column
df["dt"] = pd.to_datetime(df["dt"], unit="s")
df["date"] = df["dt"].dt.date
df = df.set_index("date")

# Create a line chart using Plotly Express
chart = px.line(df, x=df.index, y="temp.day", title="Dortmund Weather Forecast")
chart.update_xaxes(title="Date")
chart.update_yaxes(title="Temperature (°C)")

# Display the chart in the Streamlit app
st.plotly_chart(chart) In general, that's not too bad. ChatGPT also gave some instructions on how to get the API key. Unfortunately, it didn't tell me I also have to install , but as I asked for a Streamlit app, it's OK to assume I know how to install libraries. plotly Nevertheless, there are two issues: The temperature value is not parsed correctly. It is saved as a dictionary and accessing it with will throw an error. A quick and easy fix would be to replace this with . I assume that's a good example of how the model has learned a lot of different ways how to handle the reply of the API but isn't successful in drawing a connection between parsing the data and accessing it for the chart again. y="temp.day" y=df['temp'].str['day'] The app only works if there is a valid API key and throws an error if there is none. The missing error handling is unfortunate as it displays error messages in the front end. However, it could quickly be fixed by either catching the error and displaying a more user-friendly error message (preferred) or implementing the API call so that the error is not displayed in the UI. Apart from these issues, the code looks fine and runs after fixing the first issue. Also, while the number of comments might be too ambitious in general, it's helpful to understand the generated code. Writing tests with ChatGPT Next, I asked ChatGPT to write a unit test for the application. import unittest
from app import get_weather_data

class TestWeatherApp(unittest.TestCase):

    def test_weather_data(self):
        # Call the function to get weather data
        data = get_weather_data()

        # Check that the data contains the expected keys
        self.assertIn("current", data)
        self.assertIn("hourly", data)
        self.assertIn("daily", data)

        # Check that the daily data contains the expected keys
        daily_data = data["daily"][0]
        self.assertIn("dt", daily_data)
        self.assertIn("temp", daily_data)
        self.assertIn("feels_like", daily_data)
        self.assertIn("pressure", daily_data)
        self.assertIn("humidity", daily_data)
        self.assertIn("dew_point", daily_data)
        self.assertIn("wind_speed", daily_data)
        self.assertIn("wind_deg", daily_data)
        self.assertIn("weather", daily_data)
        self.assertIn("clouds", daily_data)
        self.assertIn("pop", daily_data)
        self.assertIn("uvi", daily_data) This one is interesting. First of all, the test calls get_weather_data(), but the API key is defined outside the function. Therefore, it will run without an API key and will always fail. So the test is useless. Apart from that, this is not a unit test but an integration test. It tests if the (external) API returns the correct data. To implement a unit test, expected data should be mocked, so I asked ChatGPT to fix this. This is not a unit test but an integration test. Can you fix this? import unittest
from unittest.mock import patch
from app import get_weather_data

class TestWeatherApp(unittest.TestCase):

    @patch('requests.get')
    def test_get_weather_data(self, mock_get):
        # Set up test data
        expected_data = {'daily': [
            {'dt': 1645808400,
            'temp': {'day': 5.5, 'min': 2.15, 'max': 7.9, 'night': 3.4, 'eve': 7.9, 'morn': 2.15},
            'feels_like': {'day': 0.3, 'night': -3.04, 'eve': 4.06, 'morn': -4.01},
            'pressure': 1028, 'humidity': 51, 'dew_point': -2.57, 'wind_speed': 2.81, 'wind_deg': 194,
            'weather': [{'id': 801, 'main': 'Clouds', 'description': 'few clouds', 'icon': '02d'}],
            'clouds': 16, 'pop': 0, 'uvi': 2.21}],
            'timezone_offset': 3600}

        # Set up mock response from requests.get
        mock_response = type(
                'MockResponse',
                (object,),
                {'json': lambda self: expected_data}
            )
        mock_get.return_value = mock_response

        # Call the function to get weather data
        data = get_weather_data()

        # Check that the function returns the expected data
        self.assertEqual(data, expected_data) This looks much better, but still, I am missing testing for potential error cases, so I asked again: Shouldn't the unittest also check if the app crashes in case something unusual happens. For example, if the API is down? ChatGPT added this part to the code generated earlier: @patch('requests.get')
    def test_get_weather_data_error(self, mock_get):
        # Set up mock response from requests.get to raise an exception
        mock_get.side_effect = Exception("Connection error")

        # Call the function to get weather data
        with self.assertRaises(Exception):
            data = get_weather_data() This addition looks reasonable. However, as the original app does not handle any error cases, this test doesn't make much sense. Testing if the app doesn't crash when we know it will crash leads nowhere, so I stopped asking. Overall, the results presented by ChatGPT were OK. They certainly help with prototyping or giving a template to start from. However, the code still needs to be made production-ready. ChatGPT would certainly help with that, but improving the code step by step by asking the right questions is not an efficient way of working. Outlook Last year, Deepmind's AlphaCode took part in competitive programming contests and The code and some comments by a human competitor can be found . This has been quite a breakthrough as it was the first time an AI coding model proved problem-solving skills. It will be interesting to see how these models improve and find their way into software development. performed like a median human competitor. here Also published . here

The Current Reality and Future Possibilities of AI-Powered Coding

Discovering Web3 Gaming with Long Do and Laura Cullell

The tech workforce in the US is not growing at an optimal pace. The number of schools offering computer science is not enough which is impacting the access of computer science to young women and students from marginalized communities. The truth is that there are over in the US but only graduated into the tech workforce last year. 410,020 open computing jobs 71,226 computer science students This is because only in the US offer computer science which deeply impacts its access to young women and students from marginalized communities. 47% of public high schools To address this pressing issue, we have launched #MyStartInTech, a campaign dedicated to drawing attention to this cause and in turn, help widen access to computer science in schools. As part of this campaign, we present the #MyStartInTech interview series where some of the esteemed tech entrepreneurs and professionals in the industry share details about their journey in tech. With this, we hope to draw attention to the infinite opportunities that will lay open if young women and students are given the opportunity to study computer science. In this interview, Laura Fernández, CEO and Co-Founder, , the first campus for Data Science, Product Management, and UX/UI Design for women, by women - talks about how she got her start in the tech world. allWomen Laura is an expert in tech and innovative learning, as well as community building. She also dedicates time to mentoring young entrepreneurs and enjoys curating tech content for brands. Let’s find out what she has to say! What is the importance of technology to you now and how does it impact your life? I think we all see the importance of technology to our daily lives from picking up our phones in the morning to check the news and social media, contacting our friends, doing a guided workout or meditation through an app, or playing some new music while we get ready for work. Then, of course, there's all of the technology that helps us facilitate our work-life like team organization and collaboration tools. Moreover, there's all the technology that's built specifically for women like period tracking apps, female-based online communities, and more that support the female experience in meaningful ways. This technology is everywhere and it's only becoming more inclusive as we make the tech industry more diverse. How important is it to increase access to computer science and technology to underrepresented communities and young women today? We know that our world is becoming increasingly more digital; we feel the effects of it every day. But only the people who know how to use these technologies will be able to benefit from the prosperity of them. That means having a leg-up in terms of job opportunities across all sectors as technology increasingly penetrates every industry. It's also really important for underrepresented communities and young women to have access to technology and computer science now so that they can help create it in the future. If not, our algorithms will continue to reflect our biased society by being biased themselves. Why do you think access to computer literacy in school is important? Computer literacy from a young age is really important because it sets people up for success. If we allow more people to have access to computers and technology earlier on in life, we'll see this reflected back in our future technology. It will be more meaningful and intuitive, built by people who really understand how it works and its potential. Moreover, the more we give diverse groups of people this opportunity to become computer literate at a younger age, the brighter our technology will be in terms of helping more people in valuable ways. What do you think about .Tech Domains taking up this cause? We think this is a super meaningful cause, and we really applaud your mission and your work. What we say here at allWomen is that it all starts with culture. Our culture tells us that women, particularly women of color, don't build technology - men do. But if we start changing who has access to technology at a younger age, we believe this culture will change along with it! Learn more about #MyStartInTech campaign and lend your support at . https://startin.tech/

#MyStartInTech: An Interview With Laura Fernández, allWomen.Tech

Zug, Switzerland, April 23rd, 2025/Chainwire/--Keycard, a hardware wallet company backed by the Status team, is proud to announce the pre-sale launch of Shell, a revolutionary, fully open-source hardware wallet. Shell Shell Designed with modularity, transparency, and uncompromising security at its core, Shell enables users to seamlessly manage multiple wallets through interchangeable smart cards—called Keycards—for enhanced convenience and safety. Early supporters can now pre-order Shell bundled with two Keycards at a discounted rate and gain access to exclusive pre-launch rewards ahead of its global release in October 2025. Redefining the Hardware Wallet Architecture Current hardware wallets often market themselves as transparent and open source, yet many lack true transparency in their designs—relying on closed-source components, exportable keys, or upgradeable secure elements that weaken user trust. Shell sets a new benchmark in wallet architecture. “Hardware wallets need a complete rethink,” says Guy-Louis Grau, Project Lead at Keycard. “Users rarely realize that most hardware wallets allow private keys to be exported outside secure elements or rely on upgradable secure elements—both of which compromise the integrity and transparency of the device.” “Hardware wallets need a complete rethink,” says Guy-Louis Grau, Project Lead at Keycard. “Users rarely realize that most hardware wallets allow private keys to be exported outside secure elements or rely on upgradable secure elements—both of which compromise the integrity and transparency of the device.” Shell is powered by Keycard, an EAL6+ certified, open-source smart card that signs transactions and stores keys internally—ensuring they never leave secure hardware. Moreover, Keycard firmware is not upgradable by design, providing long-term assurance that the device behavior will never change post-manufacture. Shell Shell Keycard Keycard Shell: Built for Transparency, Security, and Control Shell is about more than security, it’s designed for those who demand verifiable control over their hardware and digital assets: 100% Open Source: Every layer—from chip software to hardware layout and 3D-printable casing—is verifiable and customizable, exceeding the transparency of leading competitors.
Multiple Stealth Smart Cards: Users can store multiple seed phrases, or use backup cards safely—even if one card is lost, others remain secure.
Air-Gapped Operation: Users can sign transactions safely using Shell’s integrated camera and QR-code interface—no USB, Bluetooth, or Wi-Fi connection to devices required.
Duress PIN Support: Defends against physical coercion with a second PIN.
Cross-Wallet Compatibility: Works seamlessly with major EVM and Bitcoin wallets like MetaMask, Rabby, BackPack,imToken, UniSat, BlueWallet and more using QR-based signing.
Future-Proof Design: Easily replaceable battery extends the device life, and upgradable Keycards mean Shell adapts as cryptographic standards evolve—unlike most competitors. 100% Open Source: Every layer—from chip software to hardware layout and 3D-printable casing—is verifiable and customizable, exceeding the transparency of leading competitors. hardware layout hardware layout 3D-printable casing 3D-printable casing leading competitors leading competitors Multiple Stealth Smart Cards: Users can store multiple seed phrases, or use backup cards safely—even if one card is lost, others remain secure. Air-Gapped Operation: Users can sign transactions safely using Shell’s integrated camera and QR-code interface—no USB, Bluetooth, or Wi-Fi connection to devices required. Duress PIN Support: Defends against physical coercion with a second PIN. Cross-Wallet Compatibility: Works seamlessly with major EVM and Bitcoin wallets like MetaMask, Rabby, BackPack,imToken, UniSat, BlueWallet and more using QR-based signing. Future-Proof Design: Easily replaceable battery extends the device life, and upgradable Keycards mean Shell adapts as cryptographic standards evolve—unlike most competitors. Pre-Sale now Live: Limited Perks for Early Adopters Customers who join the pre-sale will receive: A discounted Shell bundle with two Keycards
Status Network KARMA rewards for participating in the growing ecosystem A discounted Shell bundle with two Keycards Status Network KARMA rewards for participating in the growing ecosystem ️To reserve a Shell wallet and secure pre-launch rewards, users can join the pre-launch at: https://keycard.tech/keycard-shell https://keycard.tech/keycard-shell https://keycard.tech/keycard-shell About Keycard Keycard is a proud subsidiary of Status, a pioneer in the crypto industry known for its trusted products like the Status mobile wallet. Keycard Keycard With a focus on security, privacy, and user empowerment, Keycard continues to build hardware solutions that meet the evolving needs of the global crypto community. Users can visit https://keycard.tech/keycard-shell https://keycard.tech/keycard-shell https://keycard.tech/keycard-shell Contact PR Laura Guzik Status laura@status.im 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

Keycard Launches Pre-Sale For Shell: The Most Open, Modular Hardware Wallet To Date

Keycard ለ Shell: በአሁኑ ጊዜ በጣም ውስጣዊ, Modular Hardware Wallet ይጀምራል

Keycard تطلق تحديثاً لشركة Shell: أحدث محفظة أمنية مفتوحة ومتنوعة إلى الآن

Keycard pokreće pretprodaju za Shell: Najotvoreniji, modularni hardverski novčanik do sada

Keycard lanza pre-venta para Shell: la cartera de hardware más abierta y modular hasta la fecha

Keycard lanse Pre-Sale pou Shell: Pi louvri, Modilè Hardware Wallet nan dat

Keycard lancia la pre-vendita per Shell: il portafoglio hardware più aperto e modulare di oggi

"Keycard" pristato "Shell" išankstinį pardavimą: labiausiai atvirą, modulinę aparatūros piniginę iki šiol

Keycard uzsāk pre-pārdošanu Shell: visvairāk atvērto, modulāro aparatūras maku līdz šim

Keycard Melancarkan Pre-Sale Untuk Shell: Dompet Perkakasan Paling Terbuka, Modular Setakat Ini

Keycard запускает предпродаж для Shell: самый открытый, модульный аппаратный кошелек на сегодняшний день

I-Keycard ivelisa i-Pre-Sale ye-Shell: I-Openest, i-Modular Hardware Wallet

What Does the Future of AI Model Training Hold?

Poor cloud performance causes many problems. Is it from poorly performing apps to under-optimized workloads and insecure databases? They’re numerous. And if you run a platform where customers actively engage, that's a potential doom at your doorstep. No person other than Laura Nolan and the other engineers at Slack would’ve explained this doom better after having experienced what I call the "one-day Slack doom." Slack's 2021 major outage was the worst time Slack has ever slacked. Customers could not log in, messages took forever to send, and integrations would not load. Despite the team's effort to fix their infrastructure problem, the outage lasted several hours. Laura Nolan Laura Nolan one-day Slack doom Slack's outage proves that an underperforming cloud infrastructure can cost your business thousands of dollars if you're lucky and a significant percentage of your customer base if you're not. That's why, in this article, I've broken down how you can measure cloud performance, ways to improve it, and specific tools that help the process. Key Metrics For Monitoring Cloud Performance Over 90% of organizations, including small, medium, and enterprise businesses, use the cloud. Yet, a whopping 70% cannot account for most of their cloud spend, which inevitably affects cloud performance. So, how do you monitor cloud performance to see where exactly you're missing it? Here are a few useful metrics: CPU Utilization
CPU utilization measures how often the CPU processes operations within a specific timeframe. If CPU utilization is low, it may mean that the compute resources are higher than what's needed for the workload, and vice versa. Monitoring this metric will help you decide how much to spend on cloud computing for your applications.


Input/Output Operations Per Second (IOPS)
IOPS deals with storage and allows you to quantify the speed of your storage device. It measures the number of input and output operations your storage can take per second. A high IOPS means your storage is fast and can handle large operations in a short time. A low IOPS, on the other hand, will reduce your overall system performance.


Memory
Measuring memory will show you if your system is over-provisioned or not. It measures the total memory the system currently uses to run applications and store data. That way, you know where to cut down on cloud RAM expenses while maximizing performance.


Latency
Latency is simply the time it takes for data to move from its source to its destination. Unlike other metrics, the lower the latency, the faster the data transfer, which signifies swift network communication. This is a very important metric you should keep tabs on, as having a high latency value can negatively affect customer interaction with your business.


Bandwidth
Bandwidth is the amount of data that can be transferred across an internet connection in a specific timeframe. The time frame is usually measured in seconds and can be kilobytes per second (Kbps) or megabytes per second (Mbps). CPU Utilization
CPU utilization measures how often the CPU processes operations within a specific timeframe. If CPU utilization is low, it may mean that the compute resources are higher than what's needed for the workload, and vice versa. Monitoring this metric will help you decide how much to spend on cloud computing for your applications. CPU Utilization CPU Utilization CPU utilization measures how often the CPU processes operations within a specific timeframe. If CPU utilization is low, it may mean that the compute resources are higher than what's needed for the workload, and vice versa. Monitoring this metric will help you decide how much to spend on cloud computing for your applications. Input/Output Operations Per Second (IOPS)
IOPS deals with storage and allows you to quantify the speed of your storage device. It measures the number of input and output operations your storage can take per second. A high IOPS means your storage is fast and can handle large operations in a short time. A low IOPS, on the other hand, will reduce your overall system performance. Input/Output Operations Per Second (IOPS) Input/Output Operations Per Second (IOPS) IOPS deals with storage and allows you to quantify the speed of your storage device. It measures the number of input and output operations your storage can take per second. A high IOPS means your storage is fast and can handle large operations in a short time. A low IOPS, on the other hand, will reduce your overall system performance. Memory
Measuring memory will show you if your system is over-provisioned or not. It measures the total memory the system currently uses to run applications and store data. That way, you know where to cut down on cloud RAM expenses while maximizing performance. Memory Memory Measuring memory will show you if your system is over-provisioned or not. It measures the total memory the system currently uses to run applications and store data. That way, you know where to cut down on cloud RAM expenses while maximizing performance. Latency
Latency is simply the time it takes for data to move from its source to its destination. Unlike other metrics, the lower the latency, the faster the data transfer, which signifies swift network communication. This is a very important metric you should keep tabs on, as having a high latency value can negatively affect customer interaction with your business. Latency Latency Latency is simply the time it takes for data to move from its source to its destination. Unlike other metrics, the lower the latency, the faster the data transfer, which signifies swift network communication. This is a very important metric you should keep tabs on, as having a high latency value can negatively affect customer interaction with your business. Bandwidth
Bandwidth is the amount of data that can be transferred across an internet connection in a specific timeframe. The time frame is usually measured in seconds and can be kilobytes per second (Kbps) or megabytes per second (Mbps). Bandwidth Bandwidth Bandwidth is the amount of data that can be transferred across an internet connection in a specific timeframe. The time frame is usually measured in seconds and can be kilobytes per second (Kbps) or megabytes per second (Mbps). 5 Ways to Improve Cloud Performance 5 Ways to Improve Cloud Performance There are several ways to optimize your cloud performance without breaking the bank. Some of them include: Provisioning Services Accurately
Most people think increasing cloud spend is the solution to many of their cloud issues. However, an effective way to optimize your cloud performance is to make sure your services are right-sized.
In other words, you're using metrics like CPU utilization, IOPS, memory, etc. to ensure that you're not spending more than you should on those services. Instead, you're allocating the optimal resources to each service.

Have A Multi-Cloud Support Strategy
Having a Multi-Cloud Support Strategy allows you to tap into the best features of different cloud providers and easy integration. This means you can customize your cloud infrastructure with services tailored to your applications' needs.
Another benefit of a multi-cloud strategy is that even if one of your providers experiences an outage, your applications will not go down. However, this strategy is only efficient for enterprises that have dedicated cloud teams, as it can be quite demanding.

Use Cloud-Native Architectures
Cloud-native architecture is great for speeding up application development. The principle is that since your application will live on the cloud, it's best to build it using microservices.
Microservices are small services handling a particular operation and can be coupled together to form an extensive application. Using cloud-native architecture is effective and will make your applications more reliable.

Infrastructure as Code
This is configuring your cloud infrastructure to act automatedly. You program a set of codes to automate the processes in your cloud environment.
Infrastructure as code reduces the need for human intervention and, consequently, errors. It also helps you to scale your infrastructure effectively.

Auto-Scale Compute Resources
Many cloud providers offer auto-scaling as a pricing feature. This is more like a pay-as-you-go system, where you only pay for the services you use.
When the demand for a particular resource increases, the provider scales up its capacity. And when it reduces, they scale it down. Provisioning Services Accurately
Most people think increasing cloud spend is the solution to many of their cloud issues. However, an effective way to optimize your cloud performance is to make sure your services are right-sized.
In other words, you're using metrics like CPU utilization, IOPS, memory, etc. to ensure that you're not spending more than you should on those services. Instead, you're allocating the optimal resources to each service. Provisioning Services Accurately Provisioning Services Accurately Most people think increasing cloud spend is the solution to many of their cloud issues. However, an effective way to optimize your cloud performance is to make sure your services are right-sized. In other words, you're using metrics like CPU utilization, IOPS, memory, etc. to ensure that you're not spending more than you should on those services. Instead, you're allocating the optimal resources to each service. Have A Multi-Cloud Support Strategy
Having a Multi-Cloud Support Strategy allows you to tap into the best features of different cloud providers and easy integration. This means you can customize your cloud infrastructure with services tailored to your applications' needs.
Another benefit of a multi-cloud strategy is that even if one of your providers experiences an outage, your applications will not go down. However, this strategy is only efficient for enterprises that have dedicated cloud teams, as it can be quite demanding. Have A Multi-Cloud Support Strategy Have A Multi-Cloud Support Strategy Having a Multi-Cloud Support Strategy allows you to tap into the best features of different cloud providers and easy integration. This means you can customize your cloud infrastructure with services tailored to your applications' needs. Another benefit of a multi-cloud strategy is that even if one of your providers experiences an outage, your applications will not go down. However, this strategy is only efficient for enterprises that have dedicated cloud teams, as it can be quite demanding. Use Cloud-Native Architectures
Cloud-native architecture is great for speeding up application development. The principle is that since your application will live on the cloud, it's best to build it using microservices.
Microservices are small services handling a particular operation and can be coupled together to form an extensive application. Using cloud-native architecture is effective and will make your applications more reliable. Use Cloud-Native Architectures Use Cloud-Native Architectures Cloud-native architecture is great for speeding up application development. The principle is that since your application will live on the cloud, it's best to build it using microservices. Microservices are small services handling a particular operation and can be coupled together to form an extensive application. Using cloud-native architecture is effective and will make your applications more reliable. Infrastructure as Code
This is configuring your cloud infrastructure to act automatedly. You program a set of codes to automate the processes in your cloud environment.
Infrastructure as code reduces the need for human intervention and, consequently, errors. It also helps you to scale your infrastructure effectively. Infrastructure as Code Infrastructure as Code This is configuring your cloud infrastructure to act automatedly. You program a set of codes to automate the processes in your cloud environment. Infrastructure as code reduces the need for human intervention and, consequently, errors. It also helps you to scale your infrastructure effectively. Auto-Scale Compute Resources
Many cloud providers offer auto-scaling as a pricing feature. This is more like a pay-as-you-go system, where you only pay for the services you use.
When the demand for a particular resource increases, the provider scales up its capacity. And when it reduces, they scale it down. Auto-Scale Compute Resources Auto-Scale Compute Resources Many cloud providers offer auto-scaling as a pricing feature. This is more like a pay-as-you-go system, where you only pay for the services you use. When the demand for a particular resource increases, the provider scales up its capacity. And when it reduces, they scale it down. 7 Top Cloud Monitoring Tools 7 Top Cloud Monitoring Tools As you take measures to improve your cloud performance, you want to have a vault of tools that help you monitor their effectiveness. Below are some cloud monitoring tools that I've used: Dynatrace
Dynatrace is a cloud infrastructure monitoring tool that provides end-to-end infrastructure monitoring. It is an all-arounder that can show you, through metrics and logs, how your cloud infrastructure affects your applications.


DataDog
DataDog monitors your server's health through metrics, events, traces, and logs. One of the features I love most is its service maps, which give you an overview of server issues, their effects, and how to fix them.


Redgate
Redgate lets you monitor your database performance and security. It works both on-premise and in the cloud, which makes it perfect for organizations just migrating to the cloud.


Nagios
Nagios helps you monitor and analyze network traffic across your infrastructure. It gives you an overview of traffic sources, threats, and even bandwidth usage.


Grafana
Grafana is an open-source monitoring tool. It monitors your cloud environment, has data visualization features, and displays metrics such as memory usage, disk usage, CPU load, etc.


ELK
ELK, which stands for  Elasticsearch, Logstash, Kibana, and now Beats, is a four-in-one open-source monitoring tool. You'll use Elasticsearch for searching and analysis, Logstash for data ingestion, Kibana for data visualization and analysis, and Beats for collection.


NewRelic
NewRelic is an application performance monitoring tool. In addition to cloud monitoring, it can be used to troubleshoot mobile, web, and on-premises environments. It also integrates with Grafana dashboards so you can visualize the insights it provides. Dynatrace
Dynatrace is a cloud infrastructure monitoring tool that provides end-to-end infrastructure monitoring. It is an all-arounder that can show you, through metrics and logs, how your cloud infrastructure affects your applications. Dynatrace Dynatrace Dynatrace is a cloud infrastructure monitoring tool that provides end-to-end infrastructure monitoring. It is an all-arounder that can show you, through metrics and logs, how your cloud infrastructure affects your applications. DataDog
DataDog monitors your server's health through metrics, events, traces, and logs. One of the features I love most is its service maps, which give you an overview of server issues, their effects, and how to fix them. DataDog DataDog DataDog monitors your server's health through metrics, events, traces, and logs. One of the features I love most is its service maps, which give you an overview of server issues, their effects, and how to fix them. Redgate
Redgate lets you monitor your database performance and security. It works both on-premise and in the cloud, which makes it perfect for organizations just migrating to the cloud. Redgate Redgate Redgate lets you monitor your database performance and security. It works both on-premise and in the cloud, which makes it perfect for organizations just migrating to the cloud. Nagios
Nagios helps you monitor and analyze network traffic across your infrastructure. It gives you an overview of traffic sources, threats, and even bandwidth usage. Nagios Nagios Nagios helps you monitor and analyze network traffic across your infrastructure. It gives you an overview of traffic sources, threats, and even bandwidth usage. Grafana
Grafana is an open-source monitoring tool. It monitors your cloud environment, has data visualization features, and displays metrics such as memory usage, disk usage, CPU load, etc. Grafana Grafana Grafana is an open-source monitoring tool. It monitors your cloud environment, has data visualization features, and displays metrics such as memory usage, disk usage, CPU load, etc. ELK
ELK, which stands for  Elasticsearch, Logstash, Kibana, and now Beats, is a four-in-one open-source monitoring tool. You'll use Elasticsearch for searching and analysis, Logstash for data ingestion, Kibana for data visualization and analysis, and Beats for collection. ELK ELK ELK, which stands for  Elasticsearch, Logstash, Kibana, and now Beats, is a four-in-one open-source monitoring tool. You'll use Elasticsearch for searching and analysis, Logstash for data ingestion, Kibana for data visualization and analysis, and Beats for collection. NewRelic
NewRelic is an application performance monitoring tool. In addition to cloud monitoring, it can be used to troubleshoot mobile, web, and on-premises environments. It also integrates with Grafana dashboards so you can visualize the insights it provides. NewRelic NewRelic NewRelic is an application performance monitoring tool. In addition to cloud monitoring, it can be used to troubleshoot mobile, web, and on-premises environments. It also integrates with Grafana dashboards so you can visualize the insights it provides. Final Thoughts Final Thoughts According to Flexera, the most pressing cloud challenges organizations face are lack of resources, governance, security, managing software licenses, and cloud spending. All these affect cloud effectiveness and reduce the overall performance of their applications. the most pressing cloud challenges organizations face the most pressing cloud challenges organizations face To improve your cloud performance, you must be willing to find and create a strategy that works for you. You should also continually assess your strategy using the metrics I mentioned earlier. Lastly, make sure the right tools are within reach, and your cloud infrastructure will thank you.

If Your Cloud Is Performing Poorly, Here Is What You Should Be Looking Out For

How to Improve the Accuracy of Online Ratings for AI Chatbots and Virtual Assistants

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Data 2.1 Data Sources 2.1 Data Sources 2.2 SS and SI Categories 2.2 SS and SI Categories 3 Methods 3.1 Lexicon Creation and Expansion 3.1 Lexicon Creation and Expansion 3.2 Annotations 3.2 Annotations 3.3 System Description 3.3 System Description 4 Results 4.1 Demographics and 4.2 System Performance 4.1 Demographics and 4.2 System Performance 5 Discussion 5 Discussion 5.1 Limitations 5.1 Limitations 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References SUPPLEMENTARY SUPPLEMENTARY Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Other Supervised Models Other Supervised Models 6 CONCLUSION We offer two open-source NLP systems with different approaches, as well as a manual annotation guideline for identifying SS and SI. The rule-based approach and the LLM approach each have strengths and limitations in performing this challenging task of creating a portable system to identify fine and coarse-grained categories of SS and SI in psychiatric patients. REPRODUCIBILITY All code for rule- and LLM-based systems are available on GitHub. Link: https://github.com/CornellMHILab/Social Support Social Isolation Extraction. Link The annotation guidelines and lexicons are available in the supplementary files. FUNDING This study was funded in part by grants from the National Institutes of Health (R01 MH119177, R01 MH121907, and R01 MH121922). ACKNOWLEDGEMENTS This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai Health System and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. AUTHOR CONTRIBUTIONS BGP and LAL worked on planning, data collection, data annotation, data extraction, software development, writing, and editing. PR worked on LLM implementation and editing. VV helped in data annotation, writing, and editing. MMS, BF, and GH helped in data annotation. JP and PA helped in planning, advising, and editing. MW, JJM, and MO helped in initial category identification, lexicon development, advising, and editing. JASR, ER, JMB, IL, GNN, AT, and AWC helped in advising and editing. JP and AWC provided resources and funding. REFERENCES [1] World Health Organization. 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[36] Christopher M Masi, Hsi-Yuan Chen, Louise C Hawkley, and John T Cacioppo. A metaanalysis of interventions to reduce loneliness. Personality and social psychology review, 15(3):219–266, 2011. [37] Catherine A Heaney and Barbara A Israel. Social networks and social support. Health behavior and health education: Theory, research, and practice, 4:189–210, 2008. [38] James S House. Social support and social structure. Sociological forum, 2(1):135–146, 1987. [39] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26, 2013. [40] NLPLAB. Brat rapid annotation tool. https://brat.nlplab.org. [41] Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. spacy: Industrialstrength natural language processing in python. Zenodo, 2020. [42] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485–5551, 2020. [43] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416, 2022. [44] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744, 2022. [45] Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. Context-faithful prompting for large language models. arXiv preprint arXiv:2303.11315, 2023. [46] Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. Few-shot parameter-efficient fine-tuning is better and cheaper than incontext learning. Advances in Neural Information Processing Systems, 35:1950–1965, 2022. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors;
(2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors;
(3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA;
(4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA;
(5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA;
(6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA;
(7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA;
(11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA;
(12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA;
(13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA;
(17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA;
(18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA. Authors: Authors: (1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors; (2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors; (3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA; (4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA; (5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA; (6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA; (7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA; (11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA; (12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA; (13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA; (17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA; (18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA.

Open-Source NLP Systems for Identifying Social Support and Isolation in Psychiatric Notes

Big data has evolved into a cornerstone of business success, especially for startups navigating competitive landscapes. Harnessing its power can enable deeper customer insights, more efficient operations, and data-driven decision-making that fuels sustainable growth. This guide explores practical strategies for startups to collect, analyze, and apply big data insights effectively in 2025. Why Big Data is Essential for Startups Startups often operate with limited resources, making precise decision-making crucial. Big data empowers these businesses to identify opportunities, mitigate risks, and allocate resources efficiently. From understanding customer behavior to enhancing financial stability, leveraging big data transforms challenges into growth opportunities. Strategies for Using Big Data to Drive Growth 1. Start with Clear Objectives Before diving into data collection, define what you want to achieve. Whether it’s improving customer retention, optimizing marketing campaigns, or managing financial health, having clear objectives ensures your data efforts remain focused. Actionable Steps: Identify key performance indicators (KPIs) that align with your business goals.
Break objectives into measurable milestones, such as increasing customer lifetime value by 20% in six months. 2. Collect Data from Diverse Sources The value of big data lies in its diversity. Startups should collect data from multiple channels to gain a holistic view of their operations and market dynamics. Key Sources: Customer Data: Gather insights from website analytics, social media interactions, and purchase histories.
Market Trends: Monitor industry reports, competitor activities, and consumer sentiment analysis.
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Tableau: Visualize complex data sets for easier interpretation.
Snowflake: Manage and query large-scale data with scalability.
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By setting clear objectives, leveraging the right tools, and applying insights strategically, you can turn data into a competitive advantage. Integrate tools like SoFi's free credit score monitoring service to strengthen financial health and enhance your analytics strategy. Remember, the key to growth lies in not just collecting data but actively using it to refine every aspect of your business. Start unlocking the potential of big data today to fuel your startup’s growth in 2025 and beyond. Big data has evolved into a cornerstone of business success, especially for startups navigating competitive landscapes. Harnessing its power can enable deeper customer insights, more efficient operations, and data-driven decision-making that fuels sustainable growth. This guide explores practical strategies for startups to collect, analyze, and apply big data insights effectively in 2025. Why Big Data is Essential for Startups Startups often operate with limited resources, making precise decision-making crucial. Big data empowers these businesses to identify opportunities, mitigate risks, and allocate resources efficiently. From understanding customer behavior to enhancing financial stability, leveraging big data transforms challenges into growth opportunities. Strategies for Using Big Data to Drive Growth 1. Start with Clear Objectives Before diving into data collection, define what you want to achieve. Whether it’s improving customer retention, optimizing marketing campaigns, or managing financial health, having clear objectives ensures your data efforts remain focused. Actionable Steps: Actionable Steps: Identify key performance indicators (KPIs) that align with your business goals. Break objectives into measurable milestones, such as increasing customer lifetime value by 20% in six months. Identify key performance indicators (KPIs) that align with your business goals. Break objectives into measurable milestones, such as increasing customer lifetime value by 20% in six months. 2. Collect Data from Diverse Sources The value of big data lies in its diversity. Startups should collect data from multiple channels to gain a holistic view of their operations and market dynamics. Key Sources: Customer Data: Gather insights from website analytics, social media interactions, and purchase histories. Market Trends: Monitor industry reports, competitor activities, and consumer sentiment analysis. Financial Data: Use tools like credit score tracking to monitor financial metrics. This can help you manage cash flow, assess loan eligibility, and build credibility with investors. Customer Data : Gather insights from website analytics, social media interactions, and purchase histories. Customer Data Market Trends : Monitor industry reports, competitor activities, and consumer sentiment analysis. Market Trends Financial Data : Use tools like credit score tracking to monitor financial metrics. This can help you manage cash flow, assess loan eligibility, and build credibility with investors. Financial Data credit score tracking credit score tracking “Diverse data sources ensure your insights are well-rounded and actionable,” says Laura Bennett , Data Strategist at Insight Edge. “Diverse data sources ensure your insights are well-rounded and actionable,” Laura Bennett 3. Leverage Cutting-Edge Analytics Tools Big data is only as useful as the tools you use to analyze it. Advanced analytics platforms enable startups to uncover patterns, predict trends, and make informed decisions. Tools to Explore: Google Analytics: Monitor website traffic and customer behavior. Tableau: Visualize complex data sets for easier interpretation. Snowflake: Manage and query large-scale data with scalability. LeadsNavi: Identify and track potential leads based on website visitor behavior, giving startups real-time insights into which prospects are most engaged. By understanding how visitors interact with your website, you can fine-tune marketing strategies, prioritize high-value leads, and personalize outreach for maximum conversion. Google Analytics : Monitor website traffic and customer behavior. Google Analytics Tableau : Visualize complex data sets for easier interpretation. Tableau Snowflake : Manage and query large-scale data with scalability. Snowflake LeadsNavi : Identify and track potential leads based on website visitor behavior, giving startups real-time insights into which prospects are most engaged. By understanding how visitors interact with your website, you can fine-tune marketing strategies, prioritize high-value leads, and personalize outreach for maximum conversion. LeadsNavi “The right tools amplify the impact of your data efforts,” explains James Carter, Analytics Lead at DataScale Systems. “The right tools amplify the impact of your data efforts,” James Carter, 4. Enhance Customer Understanding with Predictive Analytics Predictive analytics uses historical data and machine learning to forecast future outcomes. Startups can use these insights to anticipate customer needs, personalize experiences, and improve satisfaction. Examples: Examples: Behavior-Based Personalization: Tailor email campaigns based on past purchases or browsing habits. Customer Churn Prediction: Identify at-risk customers and implement retention strategies. Behavior-Based Personalization : Tailor email campaigns based on past purchases or browsing habits. Behavior-Based Personalization Customer Churn Prediction : Identify at-risk customers and implement retention strategies. Customer Churn Prediction “Predictive analytics transforms raw data into actionable foresight,” notes Sarah Lopez , CX Specialist at GrowthPath Solutions. “Predictive analytics transforms raw data into actionable foresight,” Sarah Lopez 5. Optimize Operations Through Data-Driven Insights Operational inefficiencies can drain resources, especially for startups. Big data helps identify bottlenecks, streamline processes, and reduce costs. Key Applications: Inventory Management: Use historical sales data to predict demand and avoid overstocking or stockouts. Employee Productivity: Analyze workflows to identify areas for improvement and automation. Inventory Management : Use historical sales data to predict demand and avoid overstocking or stockouts. Inventory Management Employee Productivity : Analyze workflows to identify areas for improvement and automation. Employee Productivity 6. Strengthen Financial Planning with Data Insights Financial stability is critical for startup growth. Big data provides clarity on cash flow, expense tracking, and profitability trends, enabling better financial planning. Practical Use Case: Leverage SoFi's free credit score monitoring service to keep track of your credit health. Strong credit scores can improve access to loans and investments, which are vital for scaling your business. Leverage SoFi's free credit score monitoring service to keep track of your credit health. Strong credit scores can improve access to loans and investments, which are vital for scaling your business. 7. Monitor and Adapt in Real-Time In today’s fast-paced business environment, static strategies no longer suffice. Real-time data monitoring enables startups to pivot quickly and stay ahead of market changes. Tools for Real-Time Monitoring: Power BI: Offers dashboards to track KPIs in real-time. Zapier: Automates notifications when critical data thresholds are met. Power BI : Offers dashboards to track KPIs in real-time. Power BI Zapier : Automates notifications when critical data thresholds are met. Zapier “Agility is the new currency of business success,” emphasizes Emily Rogers, Co-Founder of Agile Growth Labs. “Agility is the new currency of business success,” Best Practices for Implementing a Big Data Strategy 1. Ensure Data Accuracy and Integrity Clean, accurate data is the foundation of effective decision-making. Regularly audit your data for errors, redundancies, and outdated information. 2. Focus on Data Security and Compliance Data breaches can undermine customer trust and derail growth. Use encryption, access controls, and compliance tools to protect sensitive information. 3. Foster a Data-Driven Culture Encourage all team members to embrace data in their roles. Provide training and resources to help employees interpret and apply data insights effectively. Conclusion: Unlock Growth with Big Data Unlock Growth with Big Data Big data isn’t just for large enterprises—it’s a game-changer for startups looking to scale effectively. By setting clear objectives, leveraging the right tools, and applying insights strategically, you can turn data into a competitive advantage. Integrate tools like SoFi's free credit score monitoring service to strengthen financial health and enhance your analytics strategy. Remember, the key to growth lies in not just collecting data but actively using it to refine every aspect of your business. Start unlocking the potential of big data today to fuel your startup’s growth in 2025 and beyond.

 Leveraging Big Data for Startup Growth in 2025

 Jach’a yatiyawinak aprovechañax Qalltañ Jiltawi 2025 maran

Aprovechar el Big Data para el crecimiento de las empresas emergentes en 2025

Tirer parti du Big Data pour la croissance des startups en 2025

 Iskorištavanje velikih podataka za rast startupa 2025

2025 年のスタートアップの成長に向けたビッグデータの活用

 2025-жылы стартаптын өсүшү үчүн чоң маалыматтарды колдонуу

Aproveitando Big Data para o crescimento de startups em 2025

 Folosirea Big Data pentru creșterea startup-urilor în 2025

Использование больших данных для роста стартапов в 2025 году

2025 ஆம் ஆண்டில் தொடக்க நிறுவனங்களின் வளர்ச்சிக்கு பெரிய தரவைப் பயன்படுத்துதல்

 Истифодаи маълумоти калон барои рушди стартап дар соли 2025

 ኣብ 2025 ንዕብየት ስታርትኣፕ ዝኸውን ቢግ ዳታ ምጥቃም

Table of Links Abstract and 1. Introduction 2 Research Settings 3 Methods 3.1 Instruments and 3.2 Quantitative Analysis 3.3 Thematic Analysis 4 Results 4.1 Quantitative Results 4.2 Qualitative Results 5 Discussion and 5.1 Answering the research questions 5.2 Threats to validity and 5.3 Limitations and generalizability 6 Conclusion, Acknowledgments, and References 6 CONCLUSION This preliminary study mapped the psychological aspects of pair programming, analyzed ways of increasing students' intrinsic motivation and improving the pair programming practice in educational settings, and tapped into the psychology of pairing. Pilot-navigator roles are more motivating than both members coding simultaneously. The areas for improvement of pairing include task complexity, pairing constellations, feedback, soft and social skills practice, psychological aspects, role-specific insights, and following the “perfect programmer’s” traits. With further advancement of theoretical foundations around the psychology of pairing in software engineering and the development of artificially intelligent pairing programs, the further research should orient toward the experimental study of the cooperation between humans and artificial intelligence. ACKNOWLEDGMENTS This work was supported by an internal grant funding scheme (F4/61/2023) administered by the Prague University of Economics and Business. REFERENCES [1] Valový, Marcel. “Experimental Pair Programming: A Study Design and Preliminary Results.” In Proceedings of the 33rd Psychology of Programming Interest Group (PPIG’22), 2022: 107-112 [2] Valový, Marcel. “Effects of Pilot, Navigator, and Solo Programming Roles on Motivation.” New Perspectives in Software Engineering: Proceedings of the 11th International Conference on Software Process Improvement (CIMPS 2022). Cham: Springer International Publishing, 2022: 84-98. [3] Arisholm, Erik, Hans Gallis, Tore Dybå, and Dag IK Sjøberg. "Evaluating pair programming with respect to system complexity and programmer expertise." IEEE Transactions on Software Engineering 33.2 (2007): 65-86. [4] Bird, Cindy M. "How I stopped dreading and learned to love transcription." Qualitative inquiry 11, no. 2 (2005): 226-248. [5] Braun, Virginia, and Victoria Clarke. "Using thematic analysis in psychology." Qualitative research in psychology 3, no. 2 (2006): 77-101. [6] Feldt, Robert, Lefteris Angelis, Richard Torkar, and Maria Samuelsson. "Links between the personalities, views and attitudes of software engineers." Information and Software Technology 52, no. 6 (2010): 611-624. [7] Graziotin, Daniel, Per Lenberg, Robert Feldt, and Stefan Wagner. "Psychometrics in behavioral software engineering: A methodological introduction with guidelines." ACM Transactions on Software Engineering and Methodology (TOSEM) 31, no. 1 (2021): 1-36. [8] Guest, Greg, Arwen Bunce, and Laura Johnson. "How many interviews are enough? An experiment with data saturation and variability." Field methods 18, no. 1 (2006): 59-82. [9] Hannay, Jo E., Erik Arisholm, Harald Engvik, and Dag IK Sjøberg. "Effects of personality on pair programming." IEEE Transactions on Software Engineering 36, no. 1 (2009): 61-80. [10] Hannay, Jo E., Tore Dybå, Erik Arisholm, and Dag IK Sjøberg. "The effectiveness of pair programming: A meta-analysis." Information and software technology 51.7 (2009): 1110-1122. [11] Katira, Neha, Laurie Williams, Eric Wiebe, Carol Miller, Suzanne Balik, Ed Gehringer. "On understanding compatibility of student pair programmers." Proceedings of the 35th SIGCSE technical symposium on Computer science education. 2004. [12] Latham, Gary P. Work motivation: History, theory, research, and practice. Sage, 2012. [13] Lenberg, Per, Robert Feldt, and Lars Göran Wallgren. "Behavioral software engineering: A definition and systematic literature review." Journal of Systems and software 107 (2015): 15-37. [14] Sach, Rien. The Impact of Feedback on the Motivation of Software Engineers. Open University (United Kingdom), 2013. [15] Ryan, Richard M. "Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory." Journal of personality and social psychology 43.3 (1982): 450. [16] Ryan, Richard M., and Edward L. Deci. "Self-determination theory." Basic psychological needs in motivation, development, and wellness (2017). [17] Self-Determination Theory. 2022. Intrinsic Motivation Inventory. (January 2023). Retrieved January 12, 2023 from http://selfdeterminationtheory.org/intrinsic-motivation-inventory/ [18] Vanhanen, Jari, and Casper Lassenius. "Effects of pair programming at the development team level: an experiment." In International Symposium on Empirical Software Engineering, (2005)., pp. 10-pp. IEEE, 2005. [19] Williams, Laurie, and Robert R. Kessler. Pair programming illuminated. Addison-Wesley Professional, 2003. [20] Williams, Laurie, Lucas Layman, Jason Osborne, Neha Katira. "Examining the compatibility of student pair programmers." AGILE 2006 (AGILE'06). IEEE, 2006 Author:
(1) Marcel Valový, Department of Information Technologies, Prague, Czech Republic (marcel.valovy@vse.cz). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Research Settings 2 Research Settings 3 Methods 3 Methods 3.1 Instruments and 3.2 Quantitative Analysis 3.1 Instruments and 3.2 Quantitative Analysis 3.3 Thematic Analysis 3.3 Thematic Analysis 4 Results 4 Results 4.1 Quantitative Results 4.1 Quantitative Results 4.2 Qualitative Results 4.2 Qualitative Results 5 Discussion and 5.1 Answering the research questions 5 Discussion and 5.1 Answering the research questions 5.2 Threats to validity and 5.3 Limitations and generalizability 5.2 Threats to validity and 5.3 Limitations and generalizability 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 6 CONCLUSION This preliminary study mapped the psychological aspects of pair programming, analyzed ways of increasing students' intrinsic motivation and improving the pair programming practice in educational settings, and tapped into the psychology of pairing. Pilot-navigator roles are more motivating than both members coding simultaneously. The areas for improvement of pairing include task complexity, pairing constellations, feedback, soft and social skills practice, psychological aspects, role-specific insights, and following the “perfect programmer’s” traits. With further advancement of theoretical foundations around the psychology of pairing in software engineering and the development of artificially intelligent pairing programs, the further research should orient toward the experimental study of the cooperation between humans and artificial intelligence. ACKNOWLEDGMENTS This work was supported by an internal grant funding scheme (F4/61/2023) administered by the Prague University of Economics and Business. REFERENCES [1] Valový, Marcel. “Experimental Pair Programming: A Study Design and Preliminary Results.” In Proceedings of the 33rd Psychology of Programming Interest Group (PPIG’22), 2022: 107-112 [2] Valový, Marcel. “Effects of Pilot, Navigator, and Solo Programming Roles on Motivation.” New Perspectives in Software Engineering: Proceedings of the 11th International Conference on Software Process Improvement (CIMPS 2022). Cham: Springer International Publishing, 2022: 84-98. [3] Arisholm, Erik, Hans Gallis, Tore Dybå, and Dag IK Sjøberg. "Evaluating pair programming with respect to system complexity and programmer expertise." IEEE Transactions on Software Engineering 33.2 (2007): 65-86. [4] Bird, Cindy M. "How I stopped dreading and learned to love transcription." Qualitative inquiry 11, no. 2 (2005): 226-248. [5] Braun, Virginia, and Victoria Clarke. "Using thematic analysis in psychology." Qualitative research in psychology 3, no. 2 (2006): 77-101. [6] Feldt, Robert, Lefteris Angelis, Richard Torkar, and Maria Samuelsson. "Links between the personalities, views and attitudes of software engineers." Information and Software Technology 52, no. 6 (2010): 611-624. [7] Graziotin, Daniel, Per Lenberg, Robert Feldt, and Stefan Wagner. "Psychometrics in behavioral software engineering: A methodological introduction with guidelines." ACM Transactions on Software Engineering and Methodology (TOSEM) 31, no. 1 (2021): 1-36. [8] Guest, Greg, Arwen Bunce, and Laura Johnson. "How many interviews are enough? An experiment with data saturation and variability." Field methods 18, no. 1 (2006): 59-82. [9] Hannay, Jo E., Erik Arisholm, Harald Engvik, and Dag IK Sjøberg. "Effects of personality on pair programming." IEEE Transactions on Software Engineering 36, no. 1 (2009): 61-80. [10] Hannay, Jo E., Tore Dybå, Erik Arisholm, and Dag IK Sjøberg. "The effectiveness of pair programming: A meta-analysis." Information and software technology 51.7 (2009): 1110-1122. [11] Katira, Neha, Laurie Williams, Eric Wiebe, Carol Miller, Suzanne Balik, Ed Gehringer. "On understanding compatibility of student pair programmers." Proceedings of the 35th SIGCSE technical symposium on Computer science education. 2004. [12] Latham, Gary P. Work motivation: History, theory, research, and practice. Sage, 2012. [13] Lenberg, Per, Robert Feldt, and Lars Göran Wallgren. "Behavioral software engineering: A definition and systematic literature review." Journal of Systems and software 107 (2015): 15-37. [14] Sach, Rien. The Impact of Feedback on the Motivation of Software Engineers. Open University (United Kingdom), 2013. [15] Ryan, Richard M. "Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory." Journal of personality and social psychology 43.3 (1982): 450. [16] Ryan, Richard M., and Edward L. Deci. "Self-determination theory." Basic psychological needs in motivation, development, and wellness (2017). [17] Self-Determination Theory. 2022. Intrinsic Motivation Inventory. (January 2023). Retrieved January 12, 2023 from http://selfdeterminationtheory.org/intrinsic-motivation-inventory/ [18] Vanhanen, Jari, and Casper Lassenius. "Effects of pair programming at the development team level: an experiment." In International Symposium on Empirical Software Engineering, (2005)., pp. 10-pp. IEEE, 2005. [19] Williams, Laurie, and Robert R. Kessler. Pair programming illuminated. Addison-Wesley Professional, 2003. [20] Williams, Laurie, Lucas Layman, Jason Osborne, Neha Katira. "Examining the compatibility of student pair programmers." AGILE 2006 (AGILE'06). IEEE, 2006 Author: (1) Marcel Valový, Department of Information Technologies, Prague, Czech Republic (marcel.valovy@vse.cz). Author: Author: (1) Marcel Valový, Department of Information Technologies, Prague, Czech Republic (marcel.valovy@vse.cz). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv available on arxiv

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