The Bybit crypto hack, the largest in history, shines a spotlight again on central crypto exchange security. DePIN, the rising star of Web3, could have prevented the hack.

Is The Bybit Crypto Hack The Future? DePIN Looks To Make Web3 Much Safer

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Is NVIDIA Corporation (NVDA) Among the Stocks ChatGPT Predicts Could Make You Wealthy in 10 Years?

Make

make

Make is a conceptually simple build-system, and it is packaged with major Linux distributions. However, Make should rarely be your build-system of choice when starting a new C++ project. That’s a bold claim, so here’s why… Writing a Large, Correct Makefile is Really Hard The dependency graph of a C++ library is actually quite complex. It must be rebuilt every time a linked object-file changes… But an object-file should be rebuilt every time its translation-unit changes, or the compilation flags change, or if any of its included headers change, or if the compiler changes… You can query this information using the compiler and encode all of this in a Makefile, but it’s going to get really verbose. There’s a high chance that you will miss a dependency. If this happens, then you might have stale artefacts after a change. Cue a clean build! No Sandboxing The lack of sandboxing is the root problem. Make has a dependency graph, but it does not actually enforce it. What this means is that a build-rule can read a file even if it is not declared as a dependency in Make! Caches are Not Portable Make does not allow you to share build artefacts across a network, so everyone on your development team will be performing the same build-steps over and over! Yes, you can use CCache, but that is not general (only compiler calls are cached) and it isn’t provided out-of-the-box. No Language Abstractions are Provided Make’s simplicity is a big strength, but it is also a major weakness. Make doesn’t provide any language-specific abstractions, so you have to write them yourself. This can get very tedious, and the odds of making a mistake are high. This also goes for tests, deployments and so on. You need to build everything yourelf. Timestamps, Not Hashes In determining when to rebuild, Make uses the timestamps of build inputs rather than their hashes. This results in many unnecessary rebuilds when a file has been touched, but has not actually changed. If this happens deep in the dependency tree, then the rebuild will take a long time! So When Should You Use Make? It’s not all bad; Make still has its place as a bootstrapping tool. If you have extreme dependency requirements (for example you are building Linux from source), then you need something simple and self-contained. But for most developers this just isn’t the case. We already fetch Clang, GCC and Visual Studio as prebuilt binaries, so why not download a prebuilt build-system? Since You’re Here… We recently announced , a new tool to optimize your C/C++ build scripts. ! BuildInfer Take a look

Why Not Make?

There are quite a few ways to create new maps and slices in Go. Which one is best? Or perhaps better asked, which one is best in your situation? Let's take a look. Slices varStyle [] literalStyle := [] {}

newStyle := ([] )

makeStyle := ([] , ) var string string new string make string 0 is the idiomatic way to declare an slice. The slice is actually , which means it will be when marshalled to JSON and will succeed nil checks. var varStyle []string empty nil null should probably only be used when the literal is going to , as in . Otherwise prefer literalStyle := []string{} start with values in it literalStyle := []string{"cat", "dog", etc} make() returns a pointer to the slice. Same as . newStyle := new([]string) ptrStyle := &[]string{} Only use if you want a pointer. is the same as the literal style, but is preferred for idiomatic reasons when the . Make() allows the slice to be initialized with a starting length and capacity, which can have good performance implications in some circumstances: makeStyle := make([]string, 0) slice doesn't need non-zero starting values makeStyle := make([]string, len, cap) Maps varStyle [ ] literalStyle = [ ] {}

newStyle := ( [ ] )

makeStyle := ( [ ] ) var map int int map string int new map string int make map string int creates a nil map. Writing (but not reading interestingly enough) will cause a panic. You probably don't want a nil map. var varStyle map[int]int using the literal syntax is just fine, though idiomatically its probably best to use a make function. Developers are more used to seeing a make function and make offers some additional features. literalStyle := map[string]int{} := creates a pointer to a nil map... very often not what you want. newStyle new(map[string]int) ! If you know your space requirements you can optimize for allocation by passing in a size: makeStyle := make(map[string]int) This is probably what you want makeStyle := ( [ ] , ) // Give me a map with room for 10 items before needing to allocate more space make map string int 10 Want to learn more? Check out the Article! How To: Global Constant Maps and Slices Thanks For Reading Follow us on Twitter if you have any questions or comments @q_vault Take game-like coding courses on Qvault Classroom to our Newsletter for more educational articles Subscribe

Go (Golang): Your Cheatsheet On Ways To Create New Maps And Slices

Like many other technical people I learned to code from a young age, I specifically remember learning the likes of HTML, CSS and PHP in order to build out my long imagined dream of running my own successful Club Penguin(rip) blog. I relied solely on the teachings of W3school at the time and while I had a great respect for code and a huge amount of pride every time I printed “Hello World!” on to my screen, it was deeply frustrating. See, I didn’t want to code. I hated the concept of coding. Coding to me was a means to an end, never a passion. I recognised that I wanted something and realised that the only way I could get it was to build it myself and that required code. I hated making anything because I knew I would have to code. The day I became a Maker. Fast forward to 2018 and I’m building a simple crowdsourced based web app and I stumble across a WYSIWYG editor called WebFlow, reeling from past experiences, I was super skeptical but I gave it a shot. Within 2 hours I had a website with a dynamic CMS fully up and running. Then, I came across Airtable and realised I can hook up my apps submission form to the database and low and behold without writing a line of code I built an app that automatically updated records and displayed user inputs on the front end. I was building a thing I quickly started to realise that this was much bigger than I had thought. I didn’t stumble on to one not-so-known app. I had stumbled on to an entire movement. Around that time I also came across another website, Product Hunt (who I ended up working for) and discovered that people were making full apps in a day without code. Products such as: Kith Elements Copy paste reusable elements into your Bubble projects 🚧 Kiths reusable elements help you get started quickly on Bubble. You can easily drag and drop the elements you need into your projects. And customise them however you like. Growth List A crowd-sourced list of growth hacks GrowthList is a crowd-sourced list of actionable, proven Growth hacks sourced by the community MakerPad Free program to learn to build mini-startups without code The concept here is to teach anyone and everyone how to build in minutes and validate with real paying customers via a custom, tailored plan. All products that if I were to build would have required me booting up a code editor and working for hours to build a prototype. All products that, being honest I probably would not have finished. Needless to say, my mind was well and truly blown. That day was important to me as it changed my outlook on making completely.No longer did it feel like an elite club reserved for serious code wranglers but it seemed now that anyone can be a maker, as long as you have an idea, you can make it. I realised anyone can be a Maker The no-code movement is breaking down barriers many would-be Makers have previously faced; the barrier of code. Fuelled by the accessibility of new tools and a growing community of Makers supporting each other, building apps has never been more accessible. Anyone can be a Maker. In less than a day you can build: A crowdsourced, data centric app powered by Airtable A toolkit to run better meetings with Coda A dynamic, CMS driven blog with Webflow An entire Alexa skill using Voiceflow Any assortment of site / app using Sheet2Site A mobile app using Glide These products provide the means for a maker to make. They remove the barrier of code and let Makers focus on what truly matters: the idea. A celebration of Making As I mentioned before I now work for Product Hunt and the Maker community is the bread and butter behind everything we do. My day to day life has changed dramatically just because of products built by Makers launching on Product Hunt and we want to celebrate the genius that is Making. Last year, we launched our first ever Makers Festival where over 2,000 makers from all walks of life, built, tinkered and hacked their way to building a product to showcase to the world. This year we have partnered with for the Makers Festival no-code edition. We want to showcase the true potential of the no-code community and the apps powering it. If you’re passionate about building and you want to discover more about no-code then . Registrations are open until Sunday the 24th of March. We have a prize for every category and if you build with Coda there is a special Grand Prize. Coda sign up here We have a a few themes coupled with collections below to inspire you in your maker journey: Tools for teams Life Hacks Social Impact Health & Wellness Tiny Apps We are in a unique period of time where an idea is the only requirement needed to start making. I truly believe everyone can be a Maker and the community is proving that every day. No code is still in its infancy, but it is a growing movement with some serious potential to shake things up. I’m excited to be part of that. Me now when I go to make something

I hate coding but I love making

This article is part of the series: 'Towards Vaadin Developer Certification,' which aims to explain the fundamentals of Vaadin while I study for this certification. The topics covered here are an integral part of the 'Vaadin Developer' Certification. Styling in Vaadin goes beyond simple theme definitions. Some components, like Grid, require dynamic styling. Additionally, fonts and images must be properly managed within the theme folder. This article explores advanced styling techniques. Grid 1. Dynamic Styling in Grid Components 1. Dynamic Styling in Grid Components Grids often need row- or cell-specific styles based on data. This can be achieved using part names: Then, define styles in styles.css: styles.css 2. Lazy Loading Stylesheets 2. Lazy Loading Stylesheets If a stylesheet is required only in certain views, it can be loaded dynamically using @StyleSheet: @StyleSheet 3. Styling Using Custom Fonts 3. Styling Using Custom Fonts In Vaadin, custom fonts are treated as static resources, just like images or other assets. While the recommended approach is to leverage Vaadin’s built-in styling options, there are cases where overriding the default Lumo font style is necessary to achieve a unique look for your application. Below, we will guide you through the process of integrating a custom font into your Vaadin theme. 3.1 Steps to Define a Custom Font 3.1 Steps to Define a Custom Font a) Obtain the Font Files a) Obtain the Font Files You have two options to acquire the necessary font files: Google Fonts: Many fonts are freely available and can be used directly.
Font Conversion Tools: If you have a .TTF or .OTF font file, use an online tool such as Webfont Generator to convert it into web-friendly formats. Google Fonts: Many fonts are freely available and can be used directly. Google Fonts Font Conversion Tools: If you have a .TTF or .OTF font file, use an online tool such as Webfont Generator to convert it into web-friendly formats. Font Conversion Tools .TTF .OTF Once converted, the ZIP file will contain: .WOFF – Web Open Font Format (recommended for modern browsers)
.WOFF2 – Optimized version of .WOFF, offering better compression
stylesheet.css – Defines @font-face rules for the font .WOFF – Web Open Font Format (recommended for modern browsers) .WOFF .WOFF2 – Optimized version of .WOFF, offering better compression .WOFF2 .WOFF stylesheet.css – Defines @font-face rules for the font stylesheet.css @font-face b) Add the Font to Your Theme b) Add the Font to Your Theme Inside your project’s theme folder (frontend/themes/your-theme), create a new fonts directory and place the following files inside: frontend/themes/your-theme fonts c) Inject the Font into Your Stylesheet c) Inject the Font into Your Stylesheet Inside your theme’s global stylesheet (styles.css), import the font definitions: styles.css d) Override Lumo’s Default Font d) Override Lumo’s Default Font To apply the custom font across your entire application, override the --lumo-font-family property: --lumo-font-family Conclusion Conclusion Advanced styling in Vaadin includes dynamic grid styling, lazy-loaded stylesheets, and custom fonts. Mastering these techniques ensures highly customizable and performant applications. Disclaimer Disclaimer This content is drawn from studies and notes related to the Vaadin 24 certification. For deeper details, please consult the official documentation and learning materials for Vaadin certification. AI tools assisted in the structuring of this content; however, the writing and original content are solely done by the author. Authors Authors Paulo B. A. is an 'Oracle Certified Java Developer' and 'Spring Certified Professional' with a deep passion for Vaadin Flow. He crafts UIs with Vaadin and strives to make it the leading frontend framework for full-stack Java developers worldwide. He loves teaching, sharing knowledge, and creating content. While he enjoys learning theory through certifications, he always advocates for a practical approach. Linkedin.com/in/pbalves
X.com/p_b_alves
Mastodon.social/@pbalves Linkedin.com/in/pbalves X.com/p_b_alves Mastodon.social/@pbalves Staff Writer: Fábio A. P. is a technology enthusiast, self-taught writer, and scholar of society's relationship with technology. Passionate about sharing insights, he provides reliable perspectives on how technology shapes our lives. With clear and precise writing, Fábio simplifies complex topics, empowering readers to navigate the digital age with confidence. Staff Writer: Staff Writer: Medium.com/@fabioape Medium.com/@fabioape

Make Your Vaadin App Smarter, Prettier, and Faster With These Advanced Styling Hacks

This Dev Hacked EF Core Just to Make SQL LIKE Work in .NET 8 — And It Actually Does

Breaking Down Jamba: How Mixing Attention and State Spaces Makes a Smarter LLM

What Makes a Scene Important? This AI Knows

Authors:
(1) Anh Pham, Deloitte Consulting LLP;
(2) Andrew Vlasic, Deloitte Consulting LLP. Authors: Authors: (1) Anh Pham, Deloitte Consulting LLP; (2) Andrew Vlasic, Deloitte Consulting LLP. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of error Mitigation Methods II. Overview of error Mitigation Methods III. Methodology III. Methodology IV. Results and Discussions, and References IV. Results and Discussions, and References The study examines the efficacy of the Fire Opal error suppression and AI circuit optimization system integrated with IBM’s quantum computing platform for a multi-modal distribution loading algorithm. Using Kullback-Leibler (KL) divergence as a quantitative error analysis, the results indicate that Fire Opal can improve on the time-dependent distributions generated by our Conditional Quantum Generative Adversarial algorithm by 30-40% in comparison with the results on the simulator. In addition, Fire Opal’s performance remains consistent for complex circuits despite the needs to run more trials. The research concludes that Fire Opal’s error suppression and circuit optimization significantly enhanced quantum computing processes, highlighting its potential for practical applications. In addition, the study also reviews leading error mitigation strategies, including zero noise extrapolation (ZNE), probabilistic error cancellation (PEC), Pauli twirling, measurement error mitigation, and machine learning methods, assessing their advantages and disadvantages in terms of technical implementation, quantum resources, and scalability. I. INTRODUCTION Data loading is critical for many quantum algorithms and applications. However, it is a challenging problem when executing on NISQ quantum hardware due to the longer circuit depth in this quantum subroutine. In this report, we demonstrate the utility of error suppression and AI-enabled circuit optimization in Fire Opal to load multi-modal distributions on IBM Kyoto as implemented in our Conditional Quantum Generative Adversarial Network (C-QGAN) algorithm. Specifically, the distributions generated with Fire Opal on quantum hardware produced results much closer to the ideal distributions, and showed an approximate 30% − 40% improvement in results relative to running without it based KL divergence analysis. Previously we proposed a new quantum algorithm known as C-QGAN [1] to load multiple non-uniform distributions using condition registers. This data loading technique was then applied in conjunction with quantum amplitude estimation [2] to evaluate complex financial instrument called Asian option. The state preparation based on C-QGAN was shown to be less computationally expensive than other well known technique like GroverRudolph [3] and QGAN [4] which can potentially negate the quadratic speedup of algorithm like QAE [5] when they are approximating stochastic processes. Due to the noisy nature of current NISQ hardware, various error mitigation techniques have been applied to enable improve the outputs when quantum algorithms are run on quantum hardware. Error mitigation techniques are post-processing algorithms, which happen at the software level, that can improve on the distorted values obtained from quantum hardware due to different noise sources. In addition, these techniques have been applied to improve in many variational quantum algorithms [6] which have many important applications in quantum machine learning, chemistry and optimization. However, a potential drawbacks of many error mitigation techniques can be that they require extra classical and quantum computational resources to recover the noisefree values, which limits their applications only for shortdepth circuits [7]. As a result, our report aims to explore error suppression technique, which happens at the hardware level, as implemented in Fire Opal to understand their utility in the state-preparation process. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

How Fire Opal Makes Quantum Data Loading Smarter and More Accurate

Market share calculation is crucial for making effective management decisions. While the market share calculation formula is quite simple, there’s one major mistake in how data is presented and interpreted. In the following, you’ll discover in an entertaining format where the catch lies, so you can avoid this in your business. Before you dive in, make sure you’re subscribed so you don’t miss the next expert insights packed with practical recommendations. Let’s get started! One Late Evening in the Office One Late Evening in the Office The soft glow of the Sydney skyline painted the perfect backdrop as Alex sat back in his chair, staring intently at the numbers on his screen. His SaaS company, specializing in cybersecurity solutions, had been growing steadily for the past few years. But now, the numbers painted a troubling picture—his market share had remained static for the last three months. Was his company stagnating? Alex had built his business from the ground up. From late nights developing the product to aggressive marketing campaigns, everything had been pointing towards success. Yet, despite all his hard work, he couldn’t ignore the numbers staring back at him. The market share calculation for the first quarter was steady at 10%—January, February, March—unchanged. Was this a sign of failure? He couldn’t help but feel disheartened. His dream of dominating the cybersecurity space was slipping away. He had always believed that growth was the key to business success, but now, stagnation seemed to have set in. It was a silent killer that crept in, unnoticed, yet it threatened everything he had worked so hard for. Just then, Daniel, his old friend and a successful entrepreneur, stepped into the office. Daniel’s calm confidence was palpable, a stark contrast to Alex’s frustration. "Mate, you look like you need a break," he said, his voice full of understanding. "Let’s grab a drink and talk about it. Whatever it is, it’s not the end of the world." Alex shook his head. "It’s not that simple, Daniel. My company’s market share is stuck. I don’t know how to fix it. I think we’re stagnating." Daniel paused, sizing up his friend. “Alright, enough work for today. Let’s dig into this together. I’m sure there’s more to this than just the numbers. Let’s figure it out.” Together, they dove into the market share calculations. Daniel leaned over, pointing to the numbers on the screen. “You see,” Daniel said, “you’re calculating market share by looking at the percentage, but that’s only part of the picture. You need to look at the underlying trends, not just the surface level. The raw percentages without decimals can be misleading. It’s important to understand the subtle shifts that may be invisible at first glance.” Daniel then took Alex through three different market share scenarios, each one showing how small changes could significantly impact the interpretation of data when broken down into decimals and hundredths of a percent: Scenario 1: The Market Share is GrowingJanuary: 10.15%, February: 10.29%, March: 10.48%.The market share is gradually increasing. Small increments like this might seem insignificant, but over time, they lead to growth.


Scenario 2: The Market Share is DecliningJanuary: 10.45%, February: 10.20%, March: 10.05%.Despite appearing stable at first, this pattern indicates a decline in market share, which would be missed if only looking at the rounded numbers.


Scenario 3: The Market Share is StableJanuary: 10.25%, February: 10.24%, March: 10.26%.Here, the market share remains essentially the same, a slow but steady position in the market. Scenario 1: The Market Share is GrowingJanuary: 10.15%, February: 10.29%, March: 10.48%.The market share is gradually increasing. Small increments like this might seem insignificant, but over time, they lead to growth. Scenario 1: The Market Share is GrowingJanuary: 10.15%, February: 10.29%, March: 10.48%.The market share is gradually increasing. Small increments like this might seem insignificant, but over time, they lead to growth. Scenario 2: The Market Share is DecliningJanuary: 10.45%, February: 10.20%, March: 10.05%.Despite appearing stable at first, this pattern indicates a decline in market share, which would be missed if only looking at the rounded numbers. Scenario 2: The Market Share is DecliningJanuary: 10.45%, February: 10.20%, March: 10.05%.Despite appearing stable at first, this pattern indicates a decline in market share, which would be missed if only looking at the rounded numbers. Scenario 3: The Market Share is StableJanuary: 10.25%, February: 10.24%, March: 10.26%.Here, the market share remains essentially the same, a slow but steady position in the market. Scenario 3: The Market Share is StableJanuary: 10.25%, February: 10.24%, March: 10.26%.Here, the market share remains essentially the same, a slow but steady position in the market. "So, as you can see," Daniel continued, "the raw numbers can be misleading if you don’t look deeper. What seems like stagnation might actually be gradual growth or decline when you break it down into decimals. You need to account for the smallest changes to get a real picture of where you stand." Alex’s eyes lit up with understanding. “Wait a minute… So, if I look at the numbers like this, I can see that my company’s market share is actually growing, even if it seems like it's stagnant at first glance!” Daniel nodded with a smile. “Exactly. Sometimes the biggest mistake is misinterpreting what seems like stagnation. When you break it down and use the right calculations, you can see the true growth potential.” Alex felt a surge of relief and excitement. The realization that his company was indeed growing—albeit slowly—gave him a renewed sense of purpose. The pressure he had been feeling began to lift. With his newfound clarity, he and Daniel left the office, heading toward the evening’s celebration. Alex couldn’t help but feel a sense of pride in his understanding of the numbers. Armed with the right approach, Alex was ready to take his company to new heights. With a sense of gratitude, he raised his glass to Daniel. "Thanks, mate. I think I just found the key to unlocking our growth." Daniel grinned. "Just remember: numbers are powerful, but understanding them is even more so." Market Share Calculation Formula Market Share Calculation Formula Market share is a critical metric for understanding a company’s position in the market. It’s calculated using the following formula: Market Share = (Company's Sales / Total Market Sales) × 100 While the formula is straightforward, how you handle the resulting numbers can make a significant difference. Without decimal places, a company’s market share can seem much more stable or volatile than it really is. That's why it’s important to consider both the whole numbers and the decimals (tenths and hundredths) to get a more accurate picture of trends. Why Precision Matters in Market Share Calculation Why Precision Matters in Market Share Calculation Rounding a company’s market share to whole numbers can lead to the following drawbacks and potential errors in analysis: Loss of Precision – The difference in tenths and hundredths can be critical, especially in highly competitive markets with companies having similar market shares. For example, a market share of 4.4% and 4.6% when rounded to whole numbers will become 4% and 5%, distorting perception.
Distorted Comparisons – Small changes in market share (e.g., from 4.9% to 5.1%) can lead to a "jump" when rounded (from 5% to 5%), creating the illusion of significant growth when, in reality, there is none.
Summing Issues – If all market shares are rounded to whole numbers, the sum might not add up to 100%, which could create confusion and questions about data reliability.
Forecasting Errors – Analyzing trends based on rounded data can lead to less accurate forecasting models, as part of the dynamics of changes is lost.
Simplifying the Competitive Picture – Smaller players might be excluded from the analysis if their share is too small to be rounded (e.g., 0.4% becomes 0%, though in reality, this is already a significant segment in niche markets). Loss of Precision – The difference in tenths and hundredths can be critical, especially in highly competitive markets with companies having similar market shares. For example, a market share of 4.4% and 4.6% when rounded to whole numbers will become 4% and 5%, distorting perception. Loss of Precision Distorted Comparisons – Small changes in market share (e.g., from 4.9% to 5.1%) can lead to a "jump" when rounded (from 5% to 5%), creating the illusion of significant growth when, in reality, there is none. Distorted Comparisons Summing Issues – If all market shares are rounded to whole numbers, the sum might not add up to 100%, which could create confusion and questions about data reliability. Summing Issues Forecasting Errors – Analyzing trends based on rounded data can lead to less accurate forecasting models, as part of the dynamics of changes is lost. Forecasting Errors Simplifying the Competitive Picture – Smaller players might be excluded from the analysis if their share is too small to be rounded (e.g., 0.4% becomes 0%, though in reality, this is already a significant segment in niche markets). Simplifying the Competitive Picture In general, using more precise values (tenths and hundredths) provides a more realistic view of the company's position in the market and helps avoid analysis errors. About About MTP Business Boost is a series of expert publications filled with real-world strategies, transforming complex ideas into simple, actionable advice you can apply immediately (MTP stands for Marketing, Technology, and Psychology). The author Gene Misiev is a marketing expert with over seven years of experience in digital marketing. He thrives in highly competitive "red ocean" B2B markets. His focus is on growth marketing, market research, and strategic marketing development for a complex SaaS product—a tender platform for government and private procurement. If the knowledge you gained from this article has been valuable and helpful to you, you can show your appreciation to the author by making a donation via this link or giving a like and sharing it. this link this link Stay Connected and Dive Deeper into My Content Thanks for reading! Want to explore more of my thoughts, insights, and updates? Follow me on LinkedIn, Medium so you don’t miss the next publication to help you expand your business empire with the power of marketing, technology and psychology. LinkedIn Medium

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