The Future of Quantum Computing: 6 Quantum Hardware Companies to Watch in 2025

Quantum computing is the next frontier in information technology, promising unprecedented computational power. While still in its infancy, quantum technology is rapidly evolving, and several companies are at the forefront of this exciting new field. In this article, we will explore six quantum hardware companies that are set to make significant strides in 2025.

A Closer Look at Quantum Computing

Quantum computing is a type of computation that utilizes quantum bits, or “qubits”, instead of the binary bits used in classical computing. These qubits can exist in multiple states at once, a concept known as superposition, thereby enabling quantum computers to process information at a much faster rate than traditional computers.

As the technology matures and becomes more accessible, many industries stand to benefit. From the pharmaceutical sector to finance and beyond, quantum computing could potentially revolutionize our world. The following are some of the companies leading the charge in quantum hardware development.

1. IBM Quantum

IBM is a pioneer in the field of quantum computing and continues to be a major player. With their open-source quantum computing platform, IBM Q Experience, and their cloud-based service, IBM Quantum, the company is dedicated to making quantum computing more accessible to researchers, developers, and businesses.

2. Google Quantum AI

Google’s Quantum AI team is another key player in the quantum computing space. In 2019, they achieved “quantum supremacy” by demonstrating that a quantum computer could solve a problem faster than a classical computer. Google Quantum AI continues to refine and expand their quantum hardware capabilities, aiming to harness the potential of this technology to solve complex problems.

3. Rigetti Computing

Rigetti Computing is a start-up that has quickly become a leading voice in the quantum computing industry. They build quantum integrated circuits, develop high-speed quantum processors, and offer their quantum cloud services. Rigetti’s focus is on building practical quantum computers that can solve real-world problems.

4. D-Wave Systems

D-Wave Systems is a Canadian quantum computing company known for its quantum annealing processors. They have been commercializing their quantum computers since 2011, making them available to businesses, researchers, and developers through their cloud service, Leap.

5. IonQ

IonQ is a spin-off from the University of Maryland and Duke University, and it’s one of the most promising quantum computing start-ups. They boast the world’s most powerful quantum computer, based on a unique trapped-ion technology. IonQ aims to build universal quantum computers that can outperform classical computers in a wide range of applications.

6. Quantum Circuits, Inc. (QCI)

Quantum Circuits, Inc., founded by three Yale scientists, is working on developing, manufacturing, and selling quantum computers. Their approach involves modular and error-corrected quantum computers, aiming to mitigate the fragility and instability of qubits, which is one of the major challenges in scaling up quantum computers.

Conclusion

The race to harness the power of quantum computing is heating up, with numerous companies investing heavily in research and development. The six companies listed here are just a few of the many players in this sector, but they are undoubtedly ones to watch. As we move towards 2025, their contributions will likely shape the future of quantum computing and, potentially, our digital world as we know it.

The post 6 Quantum Hardware Companies to Watch in 2025 first appeared on FROMDEV.

Building Your Own GPT Model: A Comprehensive Guide

Transforming your data into a powerful, intelligent, and responsive language model is no longer a distant dream. Thanks to advancements in artificial intelligence and machine learning, you can now create a customized GPT (Generative Pretrained Transformer) model that understands and responds accurately to your specific data. This article will provide a step-by-step guide on how to train your custom GPT model using your data.

Understanding GPT Models

Before diving into the training process, it’s crucial to understand what a GPT model is and what it does. A GPT model is a type of transformer-based machine learning model, known for its efficiency in understanding and generating human-like text. Trained on a large corpus of internet text, it has been used to write articles, answer questions, translate languages, and even create poetry.

Training a custom GPT model means that you’re training it on your data, which enables it to understand and respond based on that specific data. This can be incredibly beneficial for businesses that need to handle customer queries, automate content generation, or any other task that requires understanding and generating text.

Steps to Train Your Own GPT Model

Training your custom GPT model involves several steps, including data collection, data preprocessing, model training, and finally, testing your model. Each of these stages is critical in creating a well-functioning GPT model that generates accurate responses.

Data Collection

The first step in training your GPT model is data collection. The quality and quantity of your data will greatly impact your model’s performance.

• Ensure that your data is relevant to your model’s purpose. For example, if your model is meant to answer customer queries, your data should consist of past customer interactions.
• The more data, the better. A larger dataset will allow your model to learn and understand a greater variety of patterns and responses.
• Ensure that your data is clean and free from errors. Inaccurate data can lead to inaccurate responses from your model.

Data Preprocessing

Once you’ve collected your data, the next step is to preprocess it. This involves cleaning and formatting your data to make it suitable for training. Common preprocessing steps include:

• Removing unnecessary characters and spaces.
• Converting all text to lowercase to maintain consistency.
• Tokenizing your text, which means splitting it into individual words or phrases.

Training Your Model

With your preprocessed data ready, you can now begin training your GPT model. This process involves feeding your data into the model, allowing it to learn and understand the patterns within. Depending on the size of your data and the capabilities of your hardware, this process can take anywhere from a few hours to several days.

Testing Your Model

Once your model is trained, it’s important to test it to ensure that it’s generating accurate and relevant responses. This process involves feeding your model a series of prompts and assessing the quality of its responses. Make sure to test a variety of prompts to fully assess your model’s capabilities.

Choosing the Right Tools

Training a GPT model requires the right tools. OpenAI provides a GPT-3 model that can be fine-tuned using your data. There are also other tools like Hugging Face’s Transformers library that provide pre-trained models that can be fine-tuned on your data.

Conclusion

Training a custom GPT model with your own data can revolutionize the way your business operates, automating processes and providing intelligent, accurate responses. While the process may seem complex, with the right tools and approach, it’s entirely achievable. Remember, the success of your model lies in the quality of your data and the efficiency of your training process. Happy training!

The post How to Train a Custom GPT Model with Your Own Data first appeared on FROMDEV.

Transforming Voice Memos into Musical Masterpieces using Android Apps

Have you ever had a melody or a song idea that you quickly recorded as a voice memo on your phone? We all have those moments of inspiration that come at unexpected times. But what if you could take those raw voice memos and transform them into full-fledged songs? With the help of certain Android apps, you can now convert your voice memos into songs. This article will guide you through the process and recommend some of the best apps to use.

Understanding the Process

Before we delve into the how, it’s important to understand the why. Converting voice memos into songs allows you to capture your creativity whenever it strikes. These initial recordings can then be polished and enhanced using various audio processing tools to create a complete musical piece. It’s an innovative and practical approach to songwriting and musical composition that leverages the power of technology.

With Android apps, you can record, edit, mix, and produce music right from your mobile device. From simple enhancements like adding reverb or adjusting volume to more complex tasks like adding instruments or harmonies, these apps offer a range of features to help you create high-quality music.

Selecting the Right App

There are numerous apps available on the Google Play Store that can convert voice memos into songs. When selecting an app, consider factors such as ease of use, available features, and compatibility with your device. Here are a few recommended apps:

1. BandLab

BandLab is a music creation platform that lets you record, edit, and mix your music. It offers a variety of features such as multi-track recording, a collection of virtual instruments, and a large library of loops and samples. With BandLab, you can transform your voice memos into complete songs with ease.

2. FL Studio Mobile

FL Studio Mobile is a comprehensive music production app. It allows you to record and edit your voice memos, add effects, and mix your tracks. The app also includes a variety of virtual instruments and synthesizers, making it ideal for creating music of all genres.

3. GarageBand

Although initially exclusive for iOS, GarageBand is now available for Android users as well. Known for its intuitive interface and powerful features, it allows you to record, edit, and mix music. It also offers a range of virtual instruments to enhance your songs.

Converting Voice Memos into Songs

Once you’ve selected an app, follow these general steps to convert your voice memos into songs:

1. Open the app and import your voice memo.
2. Use the editing tools to clean up the recording, remove background noise, and adjust the volume.
3. Add musical elements using the virtual instruments provided by the app.
4. Arrange the tracks to create a complete song structure.
5. Use the mixing tools to balance the volume levels of the different tracks, add effects, and polish the overall sound.
6. Finally, export your song to your desired format.

Conclusion

Converting voice memos into songs with Android apps is a great way to harness your creativity and produce music on-the-go. With the right app and some basic knowledge of music production, you can transform your fleeting ideas into beautiful songs. So the next time inspiration strikes, remember that your Android device is a powerful tool that can help you bring your musical ideas to life.

The post How to Convert Voice Memos into Songs with Android Apps first appeared on FROMDEV.

The Power of OpenSL ES for Low-Latency Audio on Android

Audio latency can be a major hurdle for developers working on real-time audio applications on Android. Fortunately, Android provides OpenSL ES, a powerful tool that can be effectively used to address this challenge. This article will guide you on how to leverage OpenSL ES to achieve low-latency audio in your Android applications.

Understanding OpenSL ES

OpenSL ES (Open Sound Library for Embedded Systems) is a cross-platform audio API designed for embedded systems. It provides a rich set of features for playing and recording audio, offering lower latency than other audio APIs on Android, such as AudioTrack and MediaPlayer. OpenSL ES is especially useful for real-time audio applications, including music and game apps.

Before you start using OpenSL ES in your Android app, it’s crucial to understand that it operates at the native level. Hence, you’ll need to use the Java Native Interface (JNI) to interact with it from your Java or Kotlin application code.

Setting up OpenSL ES

Before diving into code, you’ll need to set up your Android project to use OpenSL ES. Follow these steps:

• Ensure you’ve installed the NDK (Native Development Kit) in your Android Studio.
• Add the required permissions in your AndroidManifest.xml file. For recording audio, you’ll need the RECORD_AUDIO permission. For playing audio, INTERNET permission might be necessary if you’re streaming the audio from the internet.
• Create a native library and include it in your project. You’ll write your OpenSL ES code in this library, typically in C or C++.
• Use JNI to call functions from your native library in your Java or Kotlin code.

Creating an OpenSL ES Engine

The first step in using OpenSL ES is to create an engine. This is done using the function slCreateEngine(). After creating the engine, you’ll need to realize it using the Realize function. Realizing an object in OpenSL ES means it’s ready for use.

Creating an Audio Player

Once you have an engine, you can create an audio player. This involves the following steps:

• Define the audio source. This could be a URI, a file descriptor, or a buffer queue.
• Create an audio player object and set the audio source as one of its parameters.
• Realize the audio player.
• Get the play interface and use it to start, stop, or pause playback.

Creating an Audio Recorder

Creating an audio recorder in OpenSL ES is similar to creating an audio player. The main difference is that instead of defining an audio source, you define an audio sink, which is where the recorded audio will be stored. Once you have the recorder object, realize it, and get the record interface to start or stop recording.

Conclusion

OpenSL ES is a powerful tool for achieving low-latency audio in Android applications. It offers a rich set of features and operates at the native level, making it a top choice for real-time audio applications. While it involves a learning curve, especially due to its native nature, the pay-off in terms of audio performance is immense. So, start experimenting with OpenSL ES and give your Android audio apps the performance boost they deserve.

The post How to Use OpenSL ES for Low-Latency Audio on Android first appeared on FROMDEV.

Unraveling CORS Issues with Chrome DevTools

Web development has its fair share of challenges, and one of these is dealing with Cross-Origin Resource Sharing (CORS) issues. These problems often manifest as error messages on your browser console, blocking you from accessing resources from different origins. Thankfully, Chrome DevTools, a set of web developer tools built into Google Chrome, can be a lifesaver. This article explores how you can effectively identify and troubleshoot CORS issues using Chrome DevTools.

Understanding CORS

Before delving into the identification and troubleshooting process, it’s essential to understand what CORS is. Cross-Origin Resource Sharing (CORS) is a security feature that prevents web pages from making requests to a different domain than the one from which they originated. This is a crucial safety measure to prevent malicious scripts from making unauthorized requests to a different website on the user’s behalf.

However, the CORS policy can sometimes block legitimate requests, causing your web application to malfunction. When this happens, identifying and resolving the issue is crucial, and Chrome DevTools is a handy tool for this task.

Identifying CORS Issues with Chrome DevTools

With Chrome DevTools, you can inspect network activity in your application and identify where CORS errors are occurring. Here’s how you can do it:

Step 1: Open Chrome DevTools

To get started, you need to open Chrome DevTools. You can do this by pressing F12 or right-clicking on your web page and selecting ‘Inspect’. This will open the DevTools panel.

Step 2: Navigate to the Network Tab

Once you have the DevTools panel open, navigate to the ‘Network’ tab. This tab provides a detailed log of all network activity happening in your application.

Step 3: Refresh Your Web Page

After navigating to the Network tab, refresh your web page to start monitoring network activity. As the page reloads, DevTools will log all network requests made by your application.

Step 4: Inspect the Logs for CORS Errors

With the logs displaying, you can now inspect them for CORS errors. These will typically be flagged with the text ‘CORS’ in the ‘Type’ column, and the status will be ‘failed’. Clicking on the failed request will provide more details about the error.

Resolving CORS Issues

Once you’ve identified a CORS issue, the next step is to resolve it. The specific solution will depend on the nature of the error, but here are some common fixes:

• Adjust your server to accept requests from your web page’s domain. This is the most straightforward solution and involves configuring your server to include the appropriate CORS headers in its responses.
• Use a proxy server. If you don’t have control over the server with which your web page is communicating, you can use a proxy server to relay requests and responses between your page and the target server.
• For development purposes, you can disable the same-origin policy in Chrome. However, this approach isn’t recommended for production as it compromises browser security.

Conclusion

Dealing with CORS issues can be frustrating, but with Chrome DevTools, the process becomes less daunting. By understanding what CORS is and how to use Chrome DevTools to identify and resolve CORS issues, you can save yourself time and keep your web application running smoothly. Remember, while CORS can sometimes get in the way of development, it’s a critical web security feature, and understanding it can make you a better web developer.

The post Identifying CORS Issues with Chrome DevTools first appeared on FROMDEV.

Unfolding the Power of Quantum Gates in Real Code

Quantum computing is a revolutionary technology that promises to change the way we process data and solve complex problems. Central to this technology are quantum gates, which are the building blocks of quantum algorithms. In this article, we will dive into the intricacies of using quantum gates in real code, offering you hands-on knowledge on how to harness the power of quantum computing.

Understanding Quantum Gates

Before we delve into how to use quantum gates, it’s essential to understand what they are. Quantum gates are basic operations that can be performed on quantum bits or qubits, the fundamental units of quantum information. Unlike classical bits that can be either 0 or 1, qubits can be both at the same time due to the principle of superposition.

Quantum gates manipulate these qubits, changing their state in a way that’s not possible with classical bits. This is where the power of quantum computing comes from. Common quantum gates include the Pauli-X, Pauli-Y, Pauli-Z, Hadamard, and the CNOT gate.

Using Quantum Gates in Real Code

Now, let’s look at how we can use quantum gates in real code. We’ll use Qiskit, an open-source quantum computing framework supported by IBM, in our examples.

Setting Up Qiskit

First, install Qiskit with pip:

pip install qiskit

Creating and Manipulating Qubits

Next, let’s create a quantum circuit with two qubits:

from qiskit import QuantumCircuit
qc = QuantumCircuit(2)

To apply quantum gates, you can use methods like .x(), .y(), .z(), .h(), and .cx() on the QuantumCircuit object:

• Apply a Pauli-X gate (bit-flip) to the first qubit: qc.x(0)
• Apply a Pauli-Y gate to the second qubit: qc.y(1)
• Apply a Pauli-Z gate to the first qubit: qc.z(0)
• Apply a Hadamard gate to the second qubit: qc.h(1)
• Apply a CNOT gate: qc.cx(0, 1)

Visualizing Quantum States

After applying quantum gates, you might want to visualize the resulting quantum state. Qiskit provides the plot_bloch_multivector() function for this purpose:

from qiskit.visualization import plot_bloch_multivector
plot_bloch_multivector(state)

This function plots Bloch vectors for the qubits in your quantum circuit, giving you a visual representation of their state.

Running Quantum Circuits

To run your quantum circuit, you can use the execute() function from qiskit, which runs the circuit on a simulator or real quantum computer:

from qiskit import execute, Aer
result = execute(qc, Aer.get_backend('statevector_simulator')).result()

The state of the qubits after executing the circuit can be obtained with the get_statevector() method:

state = result.get_statevector(qc)

Conclusion

Quantum gates are the core of quantum computing, providing the means to manipulate qubits and perform complex computations. By understanding how to use these gates in real code, you can start to unlock the potential of quantum computing. While the journey may be challenging, the rewards are immense as you dive into this exciting new frontier of technology.

The post How to Use Quantum Gates in Real Code first appeared on FROMDEV.

Understanding Memcpy and Memmove: A Comparative Analysis

In the realm of programming and data manipulation, two functions that often surface are memcpy and memmove. These are standard library functions in the C programming language used for memory manipulation. This article offers a comprehensive analysis of memcpy vs. memmove, including their key differences and practical use cases.

What is Memcpy?

The memcpy function is a common method used in C for copying data from one memory location to another. It’s a part of the string.h library, and its primary purpose is to copy a given number of bytes from the source address to the destination address.

However, memcpy doesn’t provide any protection against overlap. This means if the destination and source areas overlap, memcpy can lead to unexpected results. This is one of the crucial differences when comparing memcpy vs. memmove.

What is Memmove?

Memmove, like memcpy, is a function that copies bytes from one memory location to another. It’s also part of the string.h library in C. However, unlike memcpy, memmove provides safeguarding against overlap.

Handling Overlapping Regions

Memmove ensures that it correctly handles overlapping memory regions. When the source and destination addresses overlap, memmove prevents data corruption by taking a more cautious approach to data transfer. This is accomplished by first copying the data to a temporary location before moving it to the destination.

• Memmove is safer to use when the source and destination overlap.
• Memmove’s ability to handle overlapping memory regions makes it slightly slower than memcpy.
• Memmove guarantees that the copied data is correctly transferred, even with overlap.

Key Differences: Memcpy vs. Memmove

When comparing memcpy vs. memmove, the primary difference lies in their approach to handling overlapping memory regions. While memcpy is faster, it doesn’t provide safeguards for overlap, which could result in data corruption. On the other hand, memmove is slightly slower due to its cautionary approach but ensures safe data transfer even when memory overlaps.

Here are the key differences between memcpy and memmove:

• Memcpy is faster but doesn’t handle overlapping memory locations well.
• Memmove is slower but provides protection against overlap, preventing data corruption.
• Memcpy should be used when you’re certain that memory regions do not overlap.
• Memmove should be used when there’s a chance of overlapping memory regions.

Use Cases for Memcpy and Memmove

Understanding the use cases for memcpy and memmove can help programmers decide which function to use in various scenarios.

Memcpy is ideal when you need to copy data quickly, and you’re confident that the source and destination memory locations do not overlap. It’s commonly used in scenarios where speed is a priority, and the risk of overlap is minimal.

Conversely, memmove is the function of choice when there’s a risk of overlap between the source and destination. Despite its slightly slower speed, it guarantees the safe transfer of data. It’s often used in cases where data integrity is more important than speed.

Conclusion

In conclusion, both memcpy and memmove play crucial roles in memory manipulation in C programming. When comparing memcpy vs. memmove, the decision to use one over the other depends largely on the specific requirements of your program and the nature of the data you’re working with. If speed is your priority and there’s no risk of overlap, memcpy is your best bet. However, if there’s a chance of overlapping memory regions, memmove’s safeguarding against data corruption makes it the safer choice.

The post Memcpy vs. memmove: Key Differences and Use Cases first appeared on FROMDEV.

Strengthening Cryptocurrency Security: An Essential Guide to Wallet Protection

As the popularity of cryptocurrencies continues to surge, the need for robust security measures and wallet protection is more critical than ever. With the increasing number of crypto investors and traders, the risk of cyber-attacks and fraudulent schemes aiming to steal valuable crypto assets is also growing. This article provides practical tips and measures you can adopt to bolster the security of your cryptocurrency wallet and protect your digital investments.

Understanding Cryptocurrency Wallets

A cryptocurrency wallet is a digital wallet where investors can store, send, and receive cryptocurrencies like Bitcoin, Ethereum, and others. These wallets can be software-based (on your computer or mobile device) or hardware-based (physical devices designed for secure crypto storage).

While hardware wallets provide a high level of security, they aren’t immune to risks. A lost or stolen hardware wallet can result in a permanent loss of assets. On the other hand, software wallets, while convenient, are susceptible to cyber-attacks and malware.

Key Security Measures for Cryptocurrency Wallets

Securing your cryptocurrency wallet involves a combination of technical measures and safe practices. Here are some crucial steps that you should consider:

Use Strong, Unique Passwords

One of the basic yet most effective security measures is using strong, unique passwords for your cryptocurrency wallets. Avoid using easily guessable passwords or those that you’ve used for other accounts.

• Ensure your password is at least 16 characters long and includes a mix of upper and lower case letters, numbers, and special characters.
• Consider using a reputable password manager to generate and store your passwords securely.
• Regularly update your passwords for added security.

Enable Two-Factor Authentication (2FA)

Two-Factor Authentication (2FA) adds an extra layer of security to your wallet. It requires not only a password and username but also something that only the user has on them, like a physical token or a specific mobile application.

• Use 2FA for any crypto-related accounts, including email accounts used for crypto transactions.
• Avoid SMS-based 2FA, as it’s susceptible to SIM swapping attacks. Instead, opt for app-based 2FA like Google Authenticator.

Keep Your Private Keys Private

Your private key is like your digital signature; it proves your ownership of the crypto assets in your wallet. Therefore, it’s imperative to keep it private and secure.

• Never share your private keys with anyone.
• Store your private keys offline, in a secure location.
• Consider using hardware wallets or cold storage methods for storing your private keys.

Additional Precautions

Besides these measures, you should also be wary of phishing scams, always use secure and updated software, regularly backup your wallets, and consider using multi-signature wallets for added security.

Remember, the security of your cryptocurrency wallet is as strong as the weakest link. Therefore, stay vigilant, stay updated, and always prioritize your wallet’s security.

Conclusion

The conveniences and potential profits offered by cryptocurrencies come with substantial security risks. However, by understanding these risks and implementing robust security measures, you can significantly reduce the chances of losing your valuable digital assets. In the world of cryptocurrencies, being security-savvy is not an option—it’s a necessity.

The post Cryptocurrency Security and Wallet Protection first appeared on FROMDEV.

AI and ML teams do not usually struggle to gather some public web data. The harder challenge is collecting enough of it, from enough regions, with enough stability to make the dataset useful for training, evaluation, or refresh cycles. At that point, proxies stop being a backend detail and become part of the data pipeline itself. They influence regional coverage, request success, crawl continuity, and the cost of keeping a dataset current.

That is also why infrastructure choices matter early. When a team evaluates a dedicated proxy provider like dataimpulse.com for reliable access, flexible scaling, and multiple IP types, the real concern is not only whether requests go through today, but whether the collection setup will remain usable as volume rises and target sites become less forgiving. DataImpulse’s current offer aligns with the B2B need through residential, mobile, datacenter, pay-per-GB billing, and non-expiring traffic.

Why Scale Changes the Proxy Conversation

At small volume, teams can often crawl from a narrow IP footprint and still gather enough material for early experiments. AI and ML data collection becomes much more demanding once the goal shifts from a proof of concept to a repeatable dataset. Common Crawl’s December 2025 archive contained 2.16 billion web pages and 364 TiB of uncompressed content. Those numbers show how quickly “basic scraping” turns into infrastructure planning.

Large datasets also expose a second problem: uneven visibility. If the collection comes from one region or one network type, the resulting data may miss localized pages, region-specific search results, or market-dependent product information. Google’s documentation notes that search results can be customized by country or region, meaning a crawler’s network context affects what it sees. For AI dataset collection, the goal is not just more requests — it’s broader, more realistic coverage.

Cost Planning Starts With Workload Shape

The most useful way to think about proxy spend is not by chasing the cheapest rate, but by mapping spending to the shape of the crawl. AI and ML collections rarely stay flat. A team may run a short validation crawl, expand into multiple countries, slow down while tuning extraction logic, then launch a larger recrawl when the dataset needs refreshing.

Proxy costs usually rise first at these points:

• Parser testing and validation, when engineers repeatedly fetch samples to refine the extraction logic
• Regional expansion, when the dataset has to reflect multiple countries or local search environments
• Recrawling unstable sources, where page structure changes often or access becomes inconsistent
• Dataset refresh cycles, when newer public data matters for model quality or drift control.

This is where usage-based billing becomes attractive. A pay-per-GB model with non-expiring traffic aligns naturally with pilot-to-scale collection because it doesn’t force teams into a fixed monthly burn when workloads pause or shift.

Coverage and Crawl Stability Must Be Designed Together

Coverage is often treated as a geography issue, while crawl stability is framed as an engineering problem. In practice, they are closely linked. If a crawler gets blocked too quickly, it loses access to long-tail sources and region-specific pages. If it runs from only one IP profile, it may stay fast but narrow.

A practical split by proxy type:

• Residential proxies suit region-sensitive collection — localized search pages, retailer catalogs, and marketplace monitoring
• Mobile proxies make more sense when app flows, mobile-first experiences, or carrier-network context affect what the system returns
• Datacenter proxies are often the most efficient option for high-volume, lower-friction crawling where throughput matters more than household or carrier identity.

The best proxies for data collection are not always the most authentic, nor the cheapest. They are the ones that deliver enough access realism where needed without making the whole crawl unnecessarily expensive.

Stable Crawling Still Depends on Policy Discipline

Even a strong proxy setup won’t rescue a poor crawl policy. The Robots Exclusion Protocol, standardized in RFC 9309, defines how crawlers are expected to interpret robots.txt rules. Crawl stability depends not only on avoiding blocks, but on building a collection process that is repeatable and governable. Proxies support access management — they don’t replace sensible pacing, source prioritization, or robot-aware scheduling.

Strong crawl planning typically includes source segmentation, request pacing, regional sampling rules, proxy-type matching, and defined refresh thresholds.

The Real Benchmark Is Usable Data per Dollar

AI teams should judge web scraping proxies by one practical metric: how much usable, relevant, and stable data they can collect for the money spent. For some jobs, datacenter IPs deliver the best economics. 

For others, residential or mobile traffic justifies the premium by improving access to regional or harder-to-reach content. The strongest setup is rarely the one that only maximizes crawl speed — it’s the one that keeps coverage broad, failure rates manageable, and proxy spend aligned with the real shape of the collection workflow.

The post Using Proxies for AI and ML Data Collection: Cost, Coverage, and Crawl Stability first appeared on FROMDEV.

Leveraging AI for Daily Productivity Enhancements

In today’s fast-paced world, productivity is key to managing time and achieving our goals. Artificial Intelligence (AI) offers an exciting avenue for enhancing daily productivity. The advent of AI has brought about a revolution, making our lives easier and more organized. This article will explore various strategies to utilize AI tools for daily productivity boosts.

Understanding the Role of AI in Productivity

AI, with its ability to mimic human intelligence, is increasingly becoming part of our daily lives. From virtual assistants to AI-powered apps, these intelligent systems help automate tasks, reduce errors, and save time. By integrating AI into our daily routines, we can focus on more critical tasks and significantly boost productivity.

AI can help manage emails, schedule tasks, automate home systems, provide reminders, and even help with health and fitness goals. Whether it’s for personal use or business, AI can be a game-changer for productivity.

AI Tools for Daily Productivity

There are numerous AI tools available today that can assist in enhancing daily productivity. Here are some of the most popular ones:

Virtual Assistants

Virtual assistants, such as Siri, Alexa, and Google Assistant, are AI-powered tools that can help manage your day-to-day tasks. You can use these technologies to:

• Set reminders and alarms
• Make phone calls and send messages
• Manage calendars and to-do lists
• Control home automation devices
• Provide real-time weather and traffic updates

AI-Powered Email Management

Email management can be a time-consuming task. AI tools like SaneBox, Astro, and Spark use intelligent algorithms to sort and prioritize your emails, helping you focus on the important ones and saving you valuable time.

AI Task Automation

AI can also automate routine tasks. Tools like Zapier, IFTTT, and Microsoft Flow enable you to set up automated workflows between different apps and services, reducing manual work and increasing efficiency.

Integrating AI into Your Daily Routine

Integrating AI into your daily routine can seem daunting at first, but with a little planning, it can be straightforward. Here are some steps to get you started:

Firstly, identify tasks that consume a significant amount of your time or could be automated. These might include checking emails, scheduling appointments, or managing social media. Once you’ve identified these tasks, you can start looking for AI tools that offer solutions for these specific needs.

Start with one AI tool and learn how it works. Gradually introduce more tools as you become comfortable with their usage. Remember, the goal is to enhance productivity, not to become overwhelmed with new technology.

Conclusion

In conclusion, AI offers a myriad of opportunities to enhance daily productivity. From managing emails to automating tasks, AI tools can save time and allow us to focus on more critical tasks. While it may take some time to get used to these technologies, the benefits they offer in terms of productivity are well worth the effort. So why not take the plunge and start exploring how AI can boost your productivity today?

The post How to Use AI for Daily Productivity Boosts first appeared on FROMDEV.