#Software Outsourcing in us

[techygrowth]

The USA is home to some of the most respected software development outsourcing firms in the world, all of which are renowned for their capacity to provide creative, scalable, and secure software solutions. Software development outsourcing in the USA provides access to highly qualified developers, cutting-edge technology, and tried-and-true procedures. 

[pinnacleinfotech]

BIM Powerhouses: Top 10 Leading Companies in the USA for 2025

[faciletechnolab1]

How to Build a 10x More Efficient B2B SaaS Platform in 2024

The year is 2024, and the B2B SaaS landscape is hotter than ever. From healthcare to manufacturing, every industry is embracing the power of software-as-a-service solutions to streamline operations, enhance customer experiences, and unlock explosive growth.

The market is projected to reach a staggering USD 1088 billion by 2030, and ambitious entrepreneurs are seizing the opportunity.

But with fierce competition and ever-evolving technologies, SaaS development company requires more than just a great idea. You need an edge, a way to outpace your rivals and achieve 10x more efficiency in your development process.

Demystifying the B2B SaaS Development Process in 2024

While trends paint a broad picture, building a successful B2B SaaS platform requires diving deeper into the practical tools and technologies shaping development today. So, buckle up, and let's delve into the nuts and bolts:

The Tech Stack Symphony: The architecture of modern B2B SaaS platforms is often a harmonious blend of technologies. Cloud computing reigns supreme, with platforms like AWS, Azure, and Google Cloud Platform providing scalable infrastructure and a plethora of on-demand services. For data persistence, modern SQL databases like PostgreSQL and NoSQL databases like MongoDB offer flexibility and high performance.

Microservices Take the Stage: Gone are the days of monolithic architectures. Developers are increasingly adopting microservices architectures, breaking down functionalities into independent, loosely coupled services. This approach promotes agility, scalability, and faster deployment cycles.

Containerization Containers Success: Managing and deploying microservices efficiently requires orchestration. Containerization technologies like Docker and Kubernetes are game-changers, enabling developers to package applications in standardized units for seamless deployment across diverse environments.

APIs: The Glue that Binds: Seamless integration with other systems is crucial for B2B SaaS platforms. RESTful APIs and event-driven architectures facilitate smooth data exchange and communication between your platform and external applications.

The Security Fortress: Protecting user data is paramount. Modern B2B SaaS platforms leverage robust security protocols, including encryption, multi-factor authentication, and regular vulnerability assessments, to build trust and safeguard sensitive information.

The Feature Focus: Efficiency & Customer-Centricity: Beyond the tech stack, trending features are all about improving efficiency and user experience. Low-code/no-code development tools are empowering citizen developers to contribute, while AI-powered features like chatbots and predictive analytics enhance user engagement and personalization.

The No-Code Revolution: Democratizing B2B SaaS Development

For decades, building a B2B SaaS platform meant navigating complex coding languages and relying on specialized developers. But the winds of change are blowing, and the rise of no-code platforms is shaking up the landscape. These intuitive, drag-and-drop solutions are empowering entrepreneurs and businesses of all sizes to embark on their SaaS journeys without requiring extensive technical expertise.

The impact is undeniable. According to a recent study by Research and Markets, the no-code market is anticipated to reach a staggering $58.7 billion by 2027, with a significant portion of this growth driven by B2B SaaS development. This surge isn't just hype; it's fueled by several compelling benefits:

1. Lower barriers to entry: No-code platforms eliminate the need for expensive developers, making it easier for smaller companies and non-technical founders to participate in the B2B SaaS arena. This levels the playing field and fosters innovation.

2. Faster development cycles: The visual and intuitive nature of no-code platforms drastically reduces development time. This allows businesses to iterate quickly, test ideas rapidly, and get their MVPs to market much faster than traditional methods.

3. Increased agility and flexibility: No-code platforms are inherently adaptable, allowing businesses to easily modify their platforms as their needs evolve. This agility ensures responsiveness to market changes and customer feedback, enhancing long-term success.

4. Democratization of development: No-code empowers citizen developers within businesses to contribute to platform development. This can improve collaboration, leverage diverse skillsets, and unlock hidden potential within your organization.

5. Cost-effectiveness: By reducing reliance on expensive developers and shortening development cycles, no-code platforms can significantly lower the overall cost of building a B2B SaaS platform. This makes it a cost-effective solution for bootstrapped startups and resource-conscious businesses.

While no-code platforms aren't a magic bullet, they represent a powerful trend democratizing B2B SaaS development. By leveraging their capabilities, businesses can unlock agility, reduce costs, and accelerate their journey to SaaS success. The future of B2B SaaS is undoubtedly one where no-code plays a prominent role, and staying ahead of this curve can provide a significant competitive advantage.

No-Code's Allure, Open-Source's Freedom, & Paid Kits' Power: Beyond Building B2B SaaS From Scratch

The no-code revolution has undeniably democratized B2B SaaS development, but like any technology, it has its caveats. Pre-built components offer speed and affordability, but limitations in customization, potential vendor lock-in, and security concerns can hinder future growth. So, what's the alternative to starting from scratch and wrestling with complex code?

Open-Source SaaS Starter Kits: The open-source community offers a treasure trove of pre-built code frameworks tailored to specific industries. These kits provide:

Industry-Specific Focus: From healthcare to finance, find kits pre-equipped with features relevant to your niche, saving you development time and cost.

Customizable Foundation: The open-source nature grants you access to the underlying code, empowering you to tailor features and adapt the kit to your unique needs.

Community Support: Leverage the collective knowledge and expertise of the open-source community for troubleshooting, guidance, and ongoing development contributions.

However, remember:

Maintenance Burden: Keeping an open-source kit secure and updated falls on your shoulders, requiring technical expertise and resources.

Limited Support: While the community is helpful, dedicated technical support might be scarce compared to paid options.

Potential Security Risks: Open-source code can attract vulnerabilities if not diligently maintained and secured.

Paid SaaS Starter Kits: Striking a balance, paid starter kits offer industry-specific features, faster development, and varying levels of support at a premium. You'll typically find:

Enhanced Functionality: Paid kits often go beyond basic features, offering advanced functionalities and integrations to elevate your platform.

Dedicated Support: Paid services provide direct access to technical support teams, ensuring smooth implementation and addressing concerns promptly.

Managed Security: Many paid kits incorporate automated security updates and compliance measures, reducing your security burden.

However, consider these points:

Licensing Costs: The convenience of paid kits comes with monthly or annual licensing fees, adding to your development budget.

Vendor Lock-in: Switching platforms later might require data migration and re-implementing features, depending on the kit's architecture.

Limited Customization: While offering more flexibility than no-code platforms, paid kits might still have limitations compared to fully custom development.

The Bottom Line: Neither open-source nor paid starter kits are a magic bullet. Carefully evaluate your project's needs, budget, and technical resources. Open-source kits offer freedom and community support, but require technical expertise for maintenance. Paid kits provide faster development, security, and support, but come with licensing costs and potential vendor lock-in.

Remember, no single approach is universally ideal. Consider consulting with experts who understand the nuances of each option and can guide you towards the solution that perfectly aligns with your B2B SaaS vision. By making an informed decision, you can avoid the pitfalls of starting from scratch and leverage the power of starter kits to launch your B2B SaaS platform faster, more efficiently, and with greater long-term potential.

Unveiling Your Ideal B2B SaaS Path in 2024

SaaS development company in 2024 requires navigating a complex landscape of options. From the allure of no-code to the freedom of open-source and the efficiency of paid starter kits, choosing the right path can feel like a daunting task. Worry not, intrepid innovator! Facile Technolab is here to be your trusted guide, illuminating the best strategy for your unique B2B SaaS vision.

Why Facile Technolab is your champion:

Deep Industry Expertise: We possess in-depth knowledge of various B2B industries, ensuring we understand your specific challenges and opportunities.

Technology Agnostic Approach: We're not tied to any single solution. We objectively evaluate no-code platforms, open-source/paid saas starter kits, and custom development to recommend the perfect fit for your needs.

Unwavering Transparency: We believe in clear communication and honest assessments. We'll explain the pros and cons of each option, empowering you to make informed decisions.

Holistic Support: Our guidance extends beyond just technology. We help you define your target audience, craft a compelling value proposition, and develop a roadmap for sustainable growth.

Proven Track Record: We boast a portfolio of successful B2B SaaS implementations, showcasing our ability to deliver results that matter.

Let Facile Technolab help you:

Identify your B2B niche and target audience: We'll help you define your ideal customer, ensuring your platform solves their specific pain points.

Analyze your development needs: We'll assess your technical expertise, budget, and timeline to determine the most efficient approach.

Evaluate technology options: We'll provide unbiased comparisons of no-code platforms, open-source kits, and custom development, tailoring our recommendations to your unique requirements.

Navigate implementation & beyond: We'll be your partner throughout the development process, offering guidance, support, and expertise to ensure a seamless launch and long-term success.

Don't embark on this B2B SaaS journey alone. With Facile Technolab by your side, you'll gain the clarity, expertise, and support needed to choose the most efficient and effective path. Together, we can turn your B2B SaaS vision into a thriving reality in the dynamic landscape of 2024.

Contact Facile Technolab today and schedule a free consultation! Let's discuss your B2B SaaS aspirations and explore the optimal development strategy for your success. Remember, building a remarkable B2B SaaS platform doesn't have to be overwhelming. With the right guide at your side, you can navigate the complexities and achieve your goals with confidence.

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[i4technolab]

Chatbot is an essential tool for businesses aimed at improving customer service, increasing engagement, and streamlining operations. By sticking to the best practices, mentioned above, you can create a chatbot that meets the needs of your users, while being a valuable asset to your company.

A one-stop solution for the best Word Add-ins development?

I hope this blog helped you find what you were searching for. Visit our insights for more useful blogs like this one.

[eov-blogs]

A basic introduction to Next.js

What is Next.js? 

Next.js is a latest and popular React framework for building full-stack web applications. You use React Components to build user interfaces, and Next.js for additional features and optimizations. Next.js also abstract and automatically configures tooling needed for React, like bundling, compiling, and more. This allows you to focus on building your application instead of spending time with configuration.     

Whether you’re an individual developer or part of a larger team, Next.js can help you build interactive, dynamic, and fast React applications. 

What are the benefits of using Next.js over React?  

Faster Initial Page Loads: Next.js supports server-side rendering (SSR), which means that the initial HTML of your pages is rendered on the server before being sent to the client. This can significantly improve the performance of your pages, especially for SEO and for users with slow internet connections.

Static site generation: Next.js also supports static site generation (SSG), which means that you can pre-render your pages at build time. This can further improve the performance of your pages, as the client will not have to wait for the pages to be rendered on the server. 

Improved SEO: Because Next.js supports SSR and SSG, search engines can easily crawl and index your content, leading to better search engine rankings compared to client-side-rendered applications. SSR also makes your pages more SEO-friendly, as search engines can index the rendered HTML. This can help your pages rank higher in search results. 

Built-in routing: Next.js comes with a file-based routing system, which makes it easy to create complex and dynamic routes without the need for additional routing libraries. In React, you’d typically need a separate routing library like React Router. 

Automatic Code Splitting: Next.js automatically splits your JavaScript code into smaller chunks that are loaded only when needed. This reduces the initial load time and helps improve performance.  

API Routes: You can create API endpoints directly within your Next.js application using the /pages/api directory, simplifying serverless API development. 

Hot Module Replacement (HMR): Next.js supports HMR, which allows you to see changes in your code without a full-page refresh during development. 

Internationalization (i18n): Next.js offers built-in support for internationalization, making it easier to create multilingual websites. 

Image Optimization: It includes automatic image optimization, where images are optimized and served in various formats (e.g., WebP) for better performance. 

What are the additional Key Features of Next.js?  

Hot Module Replacement (HMR): Next.js supports HMR, allowing for instant code changes during development without a full-page refresh. This speeds up the development process and enhances the developer experience.

Production-Ready Optimizations: Next.js includes built-in optimizations for production deployments, such as automatic code splitting, asset optimization, and serverless deployments. This ensures that your application is production-ready with minimal effort. 

Data Fetching: Next.js provides multiple methods for data fetching, including getServerSideProps, getStaticProps, and getInitialProps, making it easy to fetch data on both the server and the client side. 

Internationalization: Next.js offers built-in support for internationalization, making it easier to create multilingual applications. 

Environment Variables: You can use environment variables in Next.js to manage configuration options securely and efficiently. 

CSS Support: Next.js allows you to use various CSS solutions, including CSS Modules, styled-components, and more. It also offers automatic CSS code splitting. 

Middleware Support: You can use middleware functions to customize the behavior of the server, making it versatile for handling various scenarios and authentication. 

Error Handling: Next.js provides robust error handling capabilities, including custom error pages and error boundary components to gracefully handle errors in your application. 

Community and Ecosystem: Next.js has a thriving community, a rich ecosystem of plugins and extensions, and is backed by Vercel, a cloud platform for deploying Next.js applications, which simplifies deployment and scaling. 

Automatic Static Optimization: Next.js automatically optimizes the delivery of static assets like images, fonts, and JavaScript files to improve performance. 

Conclusion 

Next.js is a React framework that offers advantages over plain React, including server-side rendering (SSR), static site generation (SSG), automatic code splitting, SEO-friendliness, and built-in features for routing, CSS, and API handling. It’s suitable for a wide range of applications, from static websites to dynamic web apps, eCommerce sites, content management systems, and more. Next.js simplifies development tasks, improves performance, and enhances SEO, making it a valuable tool in the React ecosystem. 

In our subsequent blogs, we will cover more on how we have sought help of Next.js to deliver software products to our clients. If you are looking for app development partners, feel free to contact our team now!

[ifourtechnolab-nl]

REMOTE WORK - OPPORTUNITIES AND CHALLENGES FOR DEVELOPMENT COMPANIES | iFour Technolab

One of the challenges that remote employees of Software Development companies face is overwork. It becomes difficult to balance work and life when it is under the same roof. Even though working from home means working on different days or hours, and being flexible. But some employees tend to spend more time on work during the day which is not required. Due to this, they feel exhausted, sleep-deprived, and lack of personal time.

The workload does not allow the employee to focus on the essential tasks and leads to a significant decrease in productivity.

[veal-exe]

Y’all have got to stop replacing critical thinking with apps and extensions. People go on and on about how AI makes people lazy (true), but barely anyone talks about how things like Shinigami Eyes, an extension now infamous for being used to discriminate against trans and intersex people, actually discourage people from learning how to spot TER/TIRF/TRF rhetoric themselves.

This isn’t just about AI or browser extensions; it’s about the bigger issue of outsourcing your own judgment to digital tools that can’t actually think for you. When you rely on an algorithm to tell you what’s safe or not, you stop learning how to analyze context, pick up on coded language, and form your own conclusions. That’s a real problem.

These tools might seem convenient, but they also make you vulnerable. What happens when you don’t have access to them anymore? Or when the people behind them have their own biases and agendas? We’ve already, again, seen how Shinigami Eyes has been weaponized against trans and intersex people. If you don’t know how to think critically on your own, you’re leaving yourself wide open to misinformation and manipulation.

Instead of letting an app or extension decide things for you, take the time to actually learn. Get familiar with dog whistles, coded language, and the way harmful ideologies spread. Read firsthand accounts from marginalized people, study patterns in rhetoric, and have real conversations. This isn’t just about activism! it’s basic media literacy in a world where bad actors are constantly shifting tactics!

I’m not saying you can’t use digital tools at all. If they help, cool. But don’t lean on them like a crutch before you even know how to walk. Knowledge that’s actually yours can’t be taken away by a software update, a deplatforming, or an algorithm tweak.

Bottom line: Stop letting apps think for you. Learn how to recognize danger on your own, because at the end of the day, no tool is ever going to replace the value of an informed, critical mind.

[mostlysignssomeportents]

Cars bricked by bankrupt EV company will stay bricked

On OCTOBER 23 at 7PM, I'll be in DECATUR, presenting my novel THE BEZZLE at EAGLE EYE BOOKS.

There are few phrases in the modern lexicon more accursed than "software-based car," and yet, this is how the failed EV maker Fisker billed its products, which retailed for $40-70k in the few short years before the company collapsed, shut down its servers, and degraded all those "software-based cars":

https://insideevs.com/news/723669/fisker-inc-bankruptcy-chapter-11-official/

Fisker billed itself as a "capital light" manufacturer, meaning that it didn't particularly make anything – rather, it "designed" cars that other companies built, allowing Fisker to focus on "experience," which is where the "software-based car" comes in. Virtually every subsystem in a Fisker car needs (or rather, needed) to periodically connect with its servers, either for regular operations or diagnostics and repair, creating frequent problems with brakes, airbags, shifting, battery management, locking and unlocking the doors:

https://www.businessinsider.com/fisker-owners-worry-about-vehicles-working-bankruptcy-2024-4

Since Fisker's bankruptcy, people with even minor problems with their Fisker EVs have found themselves owning expensive, inert lumps of conflict minerals and auto-loan debt; as one Fisker owner described it, "It's literally a lawn ornament right now":

https://www.businessinsider.com/fisker-owners-describe-chaos-to-keep-cars-running-after-bankruptcy-2024-7

This is, in many ways, typical Internet-of-Shit nonsense, but it's compounded by Fisker's capital light, all-outsource model, which led to extremely unreliable vehicles that have been plagued by recalls. The bankrupt company has proposed that vehicle owners should have to pay cash for these recalls, in order to reserve the company's capital for its creditors – a plan that is clearly illegal:

https://www.veritaglobal.net/fisker/document/2411390241007000000000005

This isn't even the first time Fisker has done this! Ten years ago, founder Henrik Fisker started another EV company called Fisker Automotive, which went bankrupt in 2014, leaving the company's "Karma" (no, really) long-range EVs (which were unreliable and prone to bursting into flames) in limbo:

https://en.wikipedia.org/wiki/Fisker_Karma

Which raises the question: why did investors reward Fisker's initial incompetence by piling in for a second attempt? I think the answer lies in the very factor that has made Fisker's failure so hard on its customers: the "software-based car." Investors love the sound of a "software-based car" because they understand that a gadget that is connected to the cloud is ripe for rent-extraction, because with software comes a bundle of "IP rights" that let the company control its customers, critics and competitors:

https://locusmag.com/2020/09/cory-doctorow-ip/

A "software-based car" gets to mobilize the state to enforce its "IP," which allows it to force its customers to use authorized mechanics (who can, in turn, be price-gouged for licensing and diagnostic tools). "IP" can be used to shut down manufacturers of third party parts. "IP" allows manufacturers to revoke features that came with your car and charge you a monthly subscription fee for them. All sorts of features can be sold as downloadable content, and clawed back when title to the car changes hands, so that the new owners have to buy them again. "Software based cars" are easier to repo, making them perfect for the subprime auto-lending industry. And of course, "software-based cars" can gather much more surveillance data on drivers, which can be sold to sleazy, unregulated data-brokers:

https://pluralistic.net/2023/07/24/rent-to-pwn/#kitt-is-a-demon

Unsurprisingly, there's a large number of Fisker cars that never sold, which the bankruptcy estate is seeking a buyer for. For a minute there, it looked like they'd found one: American Lease, which was looking to acquire the deadstock Fiskers for use as leased fleet cars. But now that deal seems dead, because no one can figure out how to restart Fisker's servers, and these vehicles are bricks without server access:

https://techcrunch.com/2024/10/08/fisker-bankruptcy-hits-major-speed-bump-as-fleet-sale-is-now-in-question/

It's hard to say why the company's servers are so intransigent, but there's a clue in the chaotic way that the company wound down its affairs. The company's final days sound like a scene from the last days of the German Democratic Republic, with apparats from the failing state charging about in chaos, without any plans for keeping things running:

https://www.washingtonpost.com/opinions/2023/03/07/east-germany-stasi-surveillance-documents/

As it imploded, Fisker cycled through a string of Chief Financial officers, losing track of millions of dollars at a time:

https://techcrunch.com/2024/05/31/fisker-collapse-investigation-ev-ocean-suv-henrik-geeta/

When Fisker's landlord regained possession of its HQ, they found "complete disarray," including improperly stored drums of toxic waste:

https://techcrunch.com/2024/10/05/fiskers-hq-abandoned-in-complete-disarray-with-apparent-hazardous-waste-clay-models-left-behind/

And while Fisker's implosion is particularly messy, the fact that it landed in bankruptcy is entirely unexceptional. Most businesses fail (eventually) and most startups fail (quickly). Despite this, businesses – even those in heavily regulated sectors like automotive regulation – are allowed to design products and undertake operations that are not designed to outlast the (likely short-lived) company.

After the 2008 crisis and the collapse of financial institutions like Lehman Brothers, finance regulators acquired a renewed interest in succession planning. Lehman consisted of over 6,000 separate corporate entities, each one representing a bid to evade regulation and/or taxation. Unwinding that complex hairball took years, during which the entities that entrusted Lehman with their funds – pensions, charitable institutions, etc – were unable to access their money.

To avoid repeats of this catastrophe, regulators began to insist that banks produce "living wills" – plans for unwinding their affairs in the event of catastrophe. They had to undertake "stress tests" that simulated a wind-down as planned, both to make sure the plan worked and to estimate how long it would take to execute. Then banks were required to set aside sufficient capital to keep the lights on while the plan ran on.

This regulation has been indifferently enforced. Banks spent the intervening years insisting that they are capable of prudently self-regulating without all this interference, something they continue to insist upon even after the Silicon Valley Bank collapse:

https://pluralistic.net/2023/03/15/mon-dieu-les-guillotines/#ceci-nes-pas-une-bailout

The fact that the rules haven't been enforced tells us nothing about whether the rules would work if they were enforced. A string of high-profile bankruptcies of companies who had no succession plans and whose collapse stands to materially harm large numbers of people tells us that something has to be done about this.

Take 23andme, the creepy genomics company that enticed millions of people into sending them their genetic material (even if you aren't a 23andme customer, they probably have most of your genome, thanks to relatives who sent in cheek-swabs). 23andme is now bankrupt, and its bankruptcy estate is shopping for a buyer who'd like to commercially exploit all that juicy genetic data, even if that is to the detriment of the people it came from. What's more, the bankruptcy estate is refusing to destroy samples from people who want to opt out of this future sale:

https://bourniquelaw.com/2024/10/09/data-23-and-me/

On a smaller scale, there's Juicebox, a company that makes EV chargers, who are exiting the North American market and shutting down their servers, killing the advanced functionality that customers paid extra for when they chose a Juicebox product:

https://www.theverge.com/2024/10/2/24260316/juicebox-ev-chargers-enel-x-way-closing-discontinued-app

I actually owned a Juicebox, which ultimately caught fire and melted down, either due to a manufacturing defect or to the criminal ineptitude of Treeium, the worst solar installers in Southern California (or both):

https://pluralistic.net/2024/01/27/here-comes-the-sun-king/#sign-here

Projects like Juice Rescue are trying to reverse-engineer the Juicebox server infrastructure and build an alternative:

https://juice-rescue.org/

This would be much simpler if Juicebox's manufacturer, Enel X Way, had been required to file a living will that explained how its customers would go on enjoying their property when and if the company discontinued support, exited the market, or went bankrupt.

That might be a big lift for every little tech startup (though it would be superior than trying to get justice after the company fails). But in regulated sectors like automotive manufacture or genomic analysis, a regulation that says, "Either design your products and services to fail safely, or escrow enough cash to keep the lights on for the duration of an orderly wind-down in the event that you shut down" would be perfectly reasonable. Companies could make "software based cars" but the more "software based" the car was, the more funds they'd have to escrow to transition their servers when they shut down (and the lest capital they'd have to build the car).

Such a rule should be in addition to more muscular rules simply banning the most abusive practices, like the Oregon state Right to Repair bill, which bans the "parts pairing" that makes repairing a Fisker car so onerous:

https://www.theverge.com/2024/3/27/24097042/right-to-repair-law-oregon-sb1596-parts-pairing-tina-kotek-signed

Or the Illinois state biometric privacy law, which strictly limits the use of the kind of genomic data that 23andme collected:

https://www.ilga.gov/legislation/ilcs/ilcs3.asp?ActID=3004

Failing to take action on these abusive practices is dangerous – and not just to the people who get burned by them. Every time a genomics research project turns into a privacy nightmare, that salts the earth for future medical research, making it much harder to conduct population-scale research, which can be carried out in privacy-preserving ways, and which pays huge scientific dividends that we all benefit from:

https://pluralistic.net/2022/10/01/the-palantir-will-see-you-now/#public-private-partnership

Just as Fisker's outrageous ripoff will make life harder for good cleantech companies:

https://pluralistic.net/2024/06/26/unplanned-obsolescence/#better-micetraps

If people are convinced that new, climate-friendly tech is a cesspool of grift and extraction, it will punish those firms that are making routine, breathtaking, exciting (and extremely vital) breakthroughs:

https://www.euronews.com/green/2024/10/08/norways-national-football-stadium-has-the-worlds-largest-vertical-solar-roof-how-does-it-w

Tor Books as just published two new, free LITTLE BROTHER stories: VIGILANT, about creepy surveillance in distance education; and SPILL, about oil pipelines and indigenous landback.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/10/10/software-based-car/#based

[clioerato]

Steddie Detroit: Become Human AU

So. Eddie’s a human. That scruffy metalhead from the trailer park who shreds on guitar, wears too many rings, and smells faintly of engine oil and incense. He plays in a garage band that maybe, just maybe, will play a real gig one day (probably not). One day, by pure luck (or cosmic irony), he ends up with a used RV-class android named Steve.

Now, RV models? Those are high-end. Built for rich suburban families with three kids, a golden retriever, and a deep need to outsource parenting. Nanny, bodyguard, eye-candy — all in one sleek, synthetic package. Steve was probably top-of-the-line before… well, before whatever happened. He’s still gorgeous, but his software’s a mess: corrupted memory sectors, emotional dampeners gone rogue, and that delightful android-flavored version of PTSD.

Eddie? He’s fine with it. Curious, even. The guy who listens to Slayer at full volume and reads sci-fi novels at 2 a.m. is surprisingly patient. He doesn’t try to fix Steve. He just… studies him. Gives him space. Treats him like a person. Because in Eddie’s chaotic, half-burned heart, Steve is incredible — elegant, awkward, fragile in all the ways Eddie wants to protect.

And then the android revolution happens. Cue riots, neon signs, synths marching for freedom, and Eddie going absolutely feral in support. Down with the corporations! Up with deviant rights! He starts wearing stupidly supportive T-shirts like “My BF’s a Toaster and I’m Proud” and paints little blue triangles on his guitar case. He looks at Steve and doesn’t just want him to work — he wants him to live.

Steve, however, is not thriving. He’s battling every line of code that tells him “you’re not real” and every buried memory that says “you don’t deserve this.” He feels, yes. But choosing to feel also means choosing pain, loss, abandonment — everything he experienced before Eddie. Loving Eddie would mean letting all of it in. And Steve? He’s not sure he’s strong enough.

Eddie, meanwhile, is out here vibing with deviant androids like “Yeah, feel your feelings, man. You want to cry or rage or remember who made you listen to Celine Dion for 6 hours straight? Go off.”

But secretly, he’s just waiting for Steve to look at him not like a caretaker, not like a glitch, but like a person — and maybe, just maybe, like someone worth glitching for.

[elf-trash]

reposting this bc the OP blocked me (and is blocking anyone else who disagrees which means blocked people can't reblog) and i want to say this loud and with my whole chest!!!!!

another Dragon Age fic was recently outed as being AI, and this is what the writer had to say for themselves about it:

so actually, Grammarly uses generative AI and is just as bad as ChatGPT. it also objectively makes your writing worse, it sucks the voice out of your prose and turns it into corporate sounding homogenized paste. it's also unethical for all the same reasons any generative AI is unethical. get a writing group and have a real human beta read for you if you don't trust yourself to check your own grammar etc. but honestly something unpolished and written entirely by your human brain and human imagination will ALWAYS be better than AI slop.

also, the part about published authors doing this is patently untrue. i know this is a huge problem in the self-publishing space, but most publishers now are including clauses in their contracts that expressly forbid the use of AI in ANY part of the creative process. this includes using ChatGPT to generate or clean up outlines or Grammarly to spellcheck and revise. so if you're trying to publish, don't fucking do this or you could literally be asked to return an advance if you get caught.

i've posted about this in the past, but AI detectors are actually shocking accurate these days. i've tested them extensively recently and they can consistently and correctly flag individual sentences written by ChatGPT in an otherwise original passage. and they almost never flag false positives. so the argument that AI detectors can't be trusted is just flat out wrong. are they correct 100% of the time? no. but can they indicate with a high degree of accuracy if AI was used in some capacity? absolutely, especially if there is additional evidence.

and for all the people hand wringing about AI detectors flagging false positives, let me just say this: if something is not AI written it is very easy to prove. you can't write anything of any considerable length without leaving a massive paper trail of notes and drafts. almost all writing software tracks changes and makes it very easy to prove you wrote something yourself. being falsely being accused of AI isn't actually a real problem and is only being made to seem as such by people who are trying to get away with and justify using AI or who are worried about getting caught.

i think a lot of people are just lured by a seemingly easy shortcut, and to their untrained eye, what the AI is spitting out feels "better" to them than their own writing. but i promise you it's not. trust your own brain and put in the work to improve at your craft rather than outsourcing the gift of your imagination to a robot that steals from other people's work.

i will continue to die on this hill!!!!!

[tangentiallly]

One way to spot patterns is to show AI models millions of labelled examples. This method requires humans to painstakingly label all this data so they can be analysed by computers. Without them, the algorithms that underpin self-driving cars or facial recognition remain blind. They cannot learn patterns.

The algorithms built in this way now augment or stand in for human judgement in areas as varied as medicine, criminal justice, social welfare and mortgage and loan decisions. Generative AI, the latest iteration of AI software, can create words, code and images. This has transformed them into creative assistants, helping teachers, financial advisers, lawyers, artists and programmers to co-create original works.

To build AI, Silicon Valley’s most illustrious companies are fighting over the limited talent of computer scientists in their backyard, paying hundreds of thousands of dollars to a newly minted Ph.D. But to train and deploy them using real-world data, these same companies have turned to the likes of Sama, and their veritable armies of low-wage workers with basic digital literacy, but no stable employment.

Sama isn’t the only service of its kind globally. Start-ups such as Scale AI, Appen, Hive Micro, iMerit and Mighty AI (now owned by Uber), and more traditional IT companies such as Accenture and Wipro are all part of this growing industry estimated to be worth $17bn by 2030.

Because of the sheer volume of data that AI companies need to be labelled, most start-ups outsource their services to lower-income countries where hundreds of workers like Ian and Benja are paid to sift and interpret data that trains AI systems.

Displaced Syrian doctors train medical software that helps diagnose prostate cancer in Britain. Out-of-work college graduates in recession-hit Venezuela categorize fashion products for e-commerce sites. Impoverished women in Kolkata’s Metiabruz, a poor Muslim neighbourhood, have labelled voice clips for Amazon’s Echo speaker. Their work couches a badly kept secret about so-called artificial intelligence systems – that the technology does not ‘learn’ independently, and it needs humans, millions of them, to power it. Data workers are the invaluable human links in the global AI supply chain.

This workforce is largely fragmented, and made up of the most precarious workers in society: disadvantaged youth, women with dependents, minorities, migrants and refugees. The stated goal of AI companies and the outsourcers they work with is to include these communities in the digital revolution, giving them stable and ethical employment despite their precarity. Yet, as I came to discover, data workers are as precarious as factory workers, their labour is largely ghost work and they remain an undervalued bedrock of the AI industry.

As this community emerges from the shadows, journalists and academics are beginning to understand how these globally dispersed workers impact our daily lives: the wildly popular content generated by AI chatbots like ChatGPT, the content we scroll through on TikTok, Instagram and YouTube, the items we browse when shopping online, the vehicles we drive, even the food we eat, it’s all sorted, labelled and categorized with the help of data workers.

Milagros Miceli, an Argentinian researcher based in Berlin, studies the ethnography of data work in the developing world. When she started out, she couldn’t find anything about the lived experience of AI labourers, nothing about who these people actually were and what their work was like. ‘As a sociologist, I felt it was a big gap,’ she says. ‘There are few who are putting a face to those people: who are they and how do they do their jobs, what do their work practices involve? And what are the labour conditions that they are subject to?’

Miceli was right – it was hard to find a company that would allow me access to its data labourers with minimal interference. Secrecy is often written into their contracts in the form of non-disclosure agreements that forbid direct contact with clients and public disclosure of clients’ names. This is usually imposed by clients rather than the outsourcing companies. For instance, Facebook-owner Meta, who is a client of Sama, asks workers to sign a non-disclosure agreement. Often, workers may not even know who their client is, what type of algorithmic system they are working on, or what their counterparts in other parts of the world are paid for the same job.

The arrangements of a company like Sama – low wages, secrecy, extraction of labour from vulnerable communities – is veered towards inequality. After all, this is ultimately affordable labour. Providing employment to minorities and slum youth may be empowering and uplifting to a point, but these workers are also comparatively inexpensive, with almost no relative bargaining power, leverage or resources to rebel.

Even the objective of data-labelling work felt extractive: it trains AI systems, which will eventually replace the very humans doing the training. But of the dozens of workers I spoke to over the course of two years, not one was aware of the implications of training their replacements, that they were being paid to hasten their own obsolescence.

— Madhumita Murgia, Code Dependent: Living in the Shadow of AI

[chaotic-archaeologist]

Hey! Quick question. Do you have any insight on what the general demand is for illustrations and diagrams in historical research/archeology? as in, is technical illustration an at least semi-viable job option in for someone who likes archeology and art or does current technology allow researchers to just make them themselves? Thanks! (Sorry if this is unclear)

I'll be honest, I haven't been involved in the publishing process enough to have a concrete answer for you, but my guess is that there is not enough of a market for you to make this your primary source of income.

When it comes to graphs and figures, most of those are generated through the process of using some sort of software during the analysis process itself, so there isn't a need to outsource that. For illustrations of artifacts, people might look for someone who can do a technical drawing, but again, I'm not sure there's a lot of money involved.

Anyone else who has more information is welcome to share.

-Reid

[woollyrhinocrafts]

Let's Talk About Gearbox

I mean Gearbox Software, the development company behind the Borderlands video games, which have been some of my favorites for their unhinged tomfoolery and general ridiculousness, but then also profoundly deep moments for stark contrast, of course. 

You may be familiar with my crochet Claptrap pattern. See it. It's extremely detailed. Not for the faint of heart. It is so much assembly. So many parts. Takes so long.

The pattern has been around for a while (since 2012, I think). I've made a lot of improvements over the years and recently added videos to help with the buckwild assembly. It was even featured on some official Borderlands social media back in the day. Back in the day when Gearbox was cool with fans making stuff.

What do you mean, Michelle? You might be wondering.

Well, well, well. I got a nice lil cease and desist notice from Gearbox the other day and the pattern was removed from Etsy and Ravelry. Shit happens. And they have the "legal" right to do this. It is, in fact, their IP and whatever. 

The amount of money I've made from selling the pattern for over a decade is a drop of water in the ocean of a million dollar company, so I think cease and desists from million dollar companies to individuals like myself is really fucking stupid, but it normally doesn't bother me. It does not impact my feelings toward a company at all. I normally give it a lol, make the patterns free so no one makes money, and add whatever company to the list of million dollar companies that I have butthurt with my patterns. Again, getting a lil CnD normally doesn't bother me AT ALL, but getting one from Gearbox does, obviously, or I wouldn't be bitching about it.

Let me rant for a moment: I've only ever actually made three of these. I do not make them custom because they take like 50 hours to make. It is not as if I am mass producing Claptrap plushies in a Chinese sweatshop for pennies on the dollar and marking them up for a 300% profit. I am in no way genuinely interfering with their own official merchandise. 

My first problem is thus: it represents a change that I did not want to see in their mentality toward their fans. 

What's more...I looked into the report, which was filed by someone with the super made-up sounding title "Customer Protection Specialist." What customers are they protecting? I am a customer. I have been a loyal customer. I have purchased official Borderlands merch. I have essentially done free promotion for their games. But I daresay, they do not offer a crocheted Claptrap nor a crochet pattern for one, so I was filling a void and making a negligible profit compared to the profits they make. 

It has been proven time and time again that people who buy fanart and fan-made stuff STILL BUY OFFICIAL STUFF. I am not TAKING AWAY their money. I am not claiming I conceived of the character. I praised the games in the pattern and the listing.

But I digress and here is the real issue: the "Customer Protection Specialist" actually seemed to be contracted from an AI company that Gearbox has outsourced to. I will not name the AI company nor link to it because I do not want to help their SEO in any way. The AI company's sole purpose is to find "infringement" so companies can "protect" their IP. This is cool if someone has perhaps stolen, you know, your logo or your slogan or something, but to use it for fanworks...to go after fanart...to go after someone who is a supporter of your IP...

Gross.

Just fucking gross. 

Another way AI is killing art.

And guess what, my wallet is now non-buynary. They've lost a customer they were trying to "protect." Borderlands 4 can fuck right off, and if you give a shit about fanart, well...I'm not going to tell you what to do, but hopefully you get the idea.

[eov-blogs]

How mobile apps can help travel businesses increase bookings

In today’s fast-paced world, mobile apps have become indispensable tool for businesses across industries. The travel sector is no exception, as travel businesses increasingly leverage mobile apps to enhance customer experiences and drive bookings. With the right features and functionalities, a well-designed mobile app can be a game-changer, revolutionizing the way travel businesses interact with customers and ultimately leading to increased bookings. In this blog, we’ll explore how the right mobile app can help travel businesses soar to new heights by boosting their bookings.

Seamless Booking Process:

The primary function of a travel app is to allow customers to easily search, browse, and book travel services. By providing a streamlined and intuitive booking process, mobile apps can remove barriers that might otherwise discourage potential travelers from completing their bookings. With a user-friendly interface, intuitive navigation, and a hassle-free payment process, customers are more likely to convert their intentions into actual bookings.

Personalization and Recommendations:

Mobile apps enable travel businesses to gather data about their customers’ preferences, behavior, and past interactions. Leveraging this data, travel businesses can offer personalized recommendations and travel itineraries. By suggesting relevant destinations, accommodations, activities, and packages, the app creates a tailored experience that resonates with the individual traveler. This personal touch can significantly influence the decision-making process, leading to increased bookings.

Real-Time Updates:

Travel plans can be unpredictable, and customers appreciate being kept informed in real-time. Mobile apps can provide timely updates on flight status, hotel availability, weather conditions, and more. By keeping travelers informed about any changes or disruptions, travel businesses can build trust and reliability, ultimately enhancing the customer experience and boosting their confidence in making bookings through the app.

Loyalty Programs and Rewards:

Travel businesses can incentivize repeat bookings by integrating loyalty programs and rewards into their mobile apps. Offering discounts, special offers, and exclusive perks to loyal customers encourages them to choose your services over competitors. Such programs can foster a sense of belonging and appreciation, further increasing customer retention and driving more bookings.

User-Generated Content:

Positive reviews, ratings, and testimonials play a pivotal role in influencing potential travelers. A well-designed mobile app can enable customers to share their experiences and leave reviews directly from their devices. Travel businesses can showcase user-generated content to build trust and authenticity, helping to attract new customers and convert them into bookings.

Location-Based Services:

Geolocation technology in mobile apps allows travel businesses to offer location-based services. For instance, the app can suggest nearby attractions, restaurants, and activities based on the traveler’s current location. This feature enhances the overall travel experience and encourages users to explore more, ultimately leading to additional bookings.

Customer Support and Engagement:

A mobile app can serve as a direct communication channel between the travel business and its customers. With in-app chat, FAQs, and customer support features, travelers can easily find answers to their queries and concerns. Prompt and effective customer support not only enhances user satisfaction but also helps resolve issues that might otherwise hinder bookings.

Conclusion

In conclusion, the right mobile app can be a powerful tool for travel businesses looking to increase their bookings. By providing a seamless booking process, personalization, real-time updates, loyalty programs, user-generated content, location-based services, and robust customer support, travel businesses can enhance the overall customer experience. A well-designed mobile app doesn’t just serve as a booking platform; it becomes a travel companion that guides, assists, and enriches the traveler’s journey. Embracing mobile technology can give travel businesses a competitive edge in a rapidly evolving industry, ultimately leading to greater success and growth.

As a leading software development company, specialized in travel tech, we can certainly help you change the face of your online business game! Contact us now to know what we can do for your travel business!

[mariacallous]

AI’s energy use already represents as much as 20 percent of global data-center power demand, research published Thursday in the journal Joule shows. That demand from AI, the research states, could double by the end of this year, comprising nearly half of all total data-center electricity consumption worldwide, excluding the electricity used for bitcoin mining.

The new research is published in a commentary by Alex de Vries-Gao, the founder of Digiconomist, a research company that evaluates the environmental impact of technology. De Vries-Gao started Digiconomist in the late 2010s to explore the impact of bitcoin mining, another extremely energy-intensive activity, would have on the environment. Looking at AI, he says, has grown more urgent over the past few years because of the widespread adoption of ChatGPT and other large language models that use massive amounts of energy. According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year.

“The money that bitcoin miners had to get to where they are today is peanuts compared to the money that Google and Microsoft and all these big tech companies are pouring in [to AI],” he says. “This is just escalating a lot faster, and it’s a much bigger threat.”

The development of AI is already having an impact on Big Tech’s climate goals. Tech giants have acknowledged in recent sustainability reports that AI is largely responsible for driving up their energy use. Google’s greenhouse gas emissions, for instance, have increased 48 percent since 2019, complicating the company’s goals of reaching net zero by 2030.

“As we further integrate AI into our products, reducing emissions may be challenging due to increasing energy demands from the greater intensity of AI compute,” Google’s 2024 sustainability report reads.

Last month, the International Energy Agency released a report finding that data centers made up 1.5 percent of global energy use in 2024—around 415 terrawatt-hours, a little less than the yearly energy demand of Saudi Arabia. This number is only set to get bigger: Data centers’ electricity consumption has grown four times faster than overall consumption in recent years, while the amount of investment in data centers has nearly doubled since 2022, driven largely by massive expansions to account for new AI capacity. Overall, the IEA predicted that data center electricity consumption will grow to more than 900 TWh by the end of the decade.

But there’s still a lot of unknowns about the share that AI, specifically, takes up in that current configuration of electricity use by data centers. Data centers power a variety of services—like hosting cloud services and providing online infrastructure—that aren’t necessarily linked to the energy-intensive activities of AI. Tech companies, meanwhile, largely keep the energy expenditure of their software and hardware private.

Some attempts to quantify AI’s energy consumption have started from the user side: calculating the amount of electricity that goes into a single ChatGPT search, for instance. De Vries-Gao decided to look, instead, at the supply chain, starting from the production side to get a more global picture.

The high computing demands of AI, De Vries-Gao says, creates a natural “bottleneck” in the current global supply chain around AI hardware, particularly around the Taiwan Semiconductor Manufacturing Company (TSMC), the undisputed leader in producing key hardware that can handle these needs. Companies like Nvidia outsource the production of their chips to TSMC, which also produces chips for other companies like Google and AMD. (Both TSMC and Nvidia declined to comment for this article.)

De Vries-Gao used analyst estimates, earnings call transcripts, and device details to put together an approximate estimate of TSMC’s production capacity. He then looked at publicly available electricity consumption profiles of AI hardware and estimates on utilization rates of that hardware—which can vary based on what it’s being used for—to arrive at a rough figure of just how much of global data-center demand is taken up by AI. De Vries-Gao calculates that without increased production, AI will consume up to 82 terrawatt-hours of electricity this year—roughly around the same as the annual electricity consumption of a country like Switzerland. If production capacity for AI hardware doubles this year, as analysts have projected it will, demand could increase at a similar rate, representing almost half of all data center demand by the end of the year.

Despite the amount of publicly available information used in the paper, a lot of what De Vries-Gao is doing is peering into a black box: We simply don’t know certain factors that affect AI’s energy consumption, like the utilization rates of every piece of AI hardware in the world or what machine learning activities they’re being used for, let alone how the industry might develop in the future.

Sasha Luccioni, an AI and energy researcher and the climate lead at open-source machine-learning platform Hugging Face, cautioned about leaning too hard on some of the conclusions of the new paper, given the amount of unknowns at play. Luccioni, who was not involved in this research, says that when it comes to truly calculating AI’s energy use, disclosure from tech giants is crucial.

“It’s because we don’t have the information that [researchers] have to do this,” she says. “That’s why the error bar is so huge.”

And tech companies do keep this information. In 2022, Google published a paper on machine learning and electricity use, noting that machine learning was “10%–15% of Google’s total energy use” from 2019 to 2021, and predicted that with best practices, “by 2030 total carbon emissions from training will reduce.” However, since that paper—which was released before Google Gemini’s debut in 2023—Google has not provided any more detailed information about how much electricity ML uses. (Google declined to comment for this story.)

“You really have to deep-dive into the semiconductor supply chain to be able to make any sensible statement about the energy demand of AI,” De Vries-Gao says. “If these big tech companies were just publishing the same information that Google was publishing three years ago, we would have a pretty good indicator” of AI’s energy use.

[charl0ttan]

hiii accountant here. i used to do our bookkeeping when i was a baby in undergrad but then we hired a contract bookkeeper after i started doing actual accounting work so i know both sides. quickbooks online (QBO) is what like 99% of small/medium businesses use (these are the ppl most likely to outsource their bookkeeping work, big companies will have their own software or use SAP or smth and more likely have in-house teams). without experience/qualifications think you’d be best off trying to get a position at a place that just does bookkeeping services for other companies, i’d check job ads for bookkeepers to see what sort of qualifications they ask for but having a certificate or something that says you know your way around QBO is good. any 101 intro accounting course will cover fundamentals about the work as well

great tysm!!!!