Beyond Boundaries - How Technology is Reshaping Industries - Technology Org
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Beyond Boundaries - How Technology is Reshaping Industries - Technology Org
With every year passing, industries are undergoing a transformative revolution in the software world.
In this article, we dive into the software’s important role in reshaping businesses and industries.
From collaborative platforms to advanced automation, discover how technology transcends boundaries, revolutionizing operations and propelling industries into a dynamic future.
Let’s get into it!
Enhanced Software Security Measures
Security stands as the cornerstone of every technological advancement. Industries, aware of the evolving threat landscape, have adopted comprehensive security measures to fortify their digital presence, such as active directory MFA on premise.
Cybersecurity Advancements
Multi-Factor Authentication (MFA) – On-premise MFA introduces an additional layer of security, requiring users to authenticate their identity through multiple means. This significantly reduces the risk of unauthorized access.
Advanced Encryption Protocols – Cutting-edge encryption techniques ensure that sensitive data remains unintelligible to unauthorized parties. This formidable barrier thwarts even the most sophisticated cyber threats.
AI-Powered Threat Detection – Machine learning algorithms analyze patterns in real-time, swiftly identifying anomalies indicative of potential breaches. This proactive approach preempts cyberattacks.
Data Protection and Privacy
As data emerges as the lifeblood of modern enterprises, safeguarding its integrity and privacy is paramount.
Industries now prioritize robust data protection protocols, ensuring compliance with stringent privacy regulations.
From end-to-end encryption to secure cloud storage solutions, every facet of data management is fortified, engendering trust and resilience in an age of information vulnerability.
The Role of Software in Industry Transformation
Automation and Efficiency
Process optimization – Software uses logic and algorithms to automate processes, minimizing the need for human interaction.
Process Monitoring and Control – Software systems provide real-time monitoring and control of industrial processes.
Supply Chain Management – Advanced software solutions manage the end-to-end supply chain, optimizing inventory levels, demand forecasting, and order fulfillment.
Predictive Maintenance – Industries use predictive maintenance software to monitor real-time equipment performance.
Data Management and Analysis
Industries are harnessing the power of big data software to process and analyze large volumes of data generated from various sources.
This allows for identifying trends, patterns, and correlations that inform business strategies.
Advanced analytics software utilizes machine learning algorithms to extract insights from data.
Industries can predict customer behavior, optimize operations, and even develop new products based on data-driven insights.
Software platforms are crucial in integrating IoT devices. These devices collect data from sensors and machinery, which is then processed and analyzed by rendering software to provide valuable insights for decision-making.
Operational Efficiency
In the relentless pursuit of operational excellence, modern industries have turned to technological innovations that redefine efficiency standards.
Automation and Robotics
Precision and Consistency – Machines execute tasks with unparalleled accuracy, minimizing errors inherent to human intervention.
Enhanced Speed – Automation accelerates production cycles, enabling industries to meet demands swiftly.
Cost Savings – By reducing labor costs and optimizing resource allocation, automation is a financially astute investment.
Safety and risk reduction – Robots execute risky activities, providing worker safety while reducing operational risks.
IoT and Connectivity
A new era of seamless communication has arrived thanks to the Internet of Things (IoT) revolution. Real-time communication across machines, gadgets, and systems now supports a synchronized ecosystem.
Because of this connectivity, sensors may provide vital information for prompt treatments, enabling predictive maintenance.
Additionally, it facilitates data-driven decision-making, allowing businesses to optimize processes based on accurate, up-to-the-minute information.
The result is a dynamic operational environment where every element collaborates harmoniously, driving efficiency to unprecedented heights.
Software and the Future of Work
Workflow Optimization
Workflow optimization is a critical facet of modern business operations, enabling organizations to enhance efficiency, reduce redundancies, and ultimately, boost productivity.
BPM systems play a pivotal role in workflow optimization. They facilitate the modeling, execution, and monitoring of business processes.
These systems help identify inefficiencies, automate manual tasks, and streamline operations for improved outcomes.
Enterprise Resource Planning (ERP) systems act as a centralized hub for various functions within an organization.
They integrate data and processes across departments, ensuring seamless communication and coordination. This integration leads to more streamlined workflows and faster decision-making.
This approach focuses on handling complex and dynamic cases that may not follow a predefined path.
It provides the flexibility needed to address unique situations, allowing for adaptability in workflows.
Adaptive case management systems are particularly valuable in industries with unpredictable processes.
Remote Work and Flexibility
Collaboration Platforms – Microsoft Teams, Slack, and Zoom facilitate seamless communication and collaboration among remote teams.
Cloud-Based Storage and Document Management – Tools like Google Drive, Dropbox, and OneDrive provide secure cloud storage for files and documents.
Virtual Private Networks (VPNs) – VPN software like Cisco AnyConnect or NordVPN ensures secure connections to company networks.
Remote Desktop Software – Software such as TeamViewer or Chrome Remote Desktop allows employees to access their work computers from anywhere.
Conclusion
In a rapidly evolving technological landscape, industries are experiencing an unprecedented renaissance. Cutting-edge technology acts not just as an enhancer but as a strategic imperative.
This article uncovers the dynamic interplay between industry and technology, where innovation knows no bounds.
Scroll up, and you can read about how industries adapt and flourish in the face of disruption.
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step 1: build a professional-looking, ad-free site that will perform formatting and validation on a wide variety of file types used for configuration and data transfer (.properties, YAML, .config, JSON, etc), and decrypt file contents if people will just paste in their handy dandy private key/shared secret/etc. Yes, there are already sites for this stuff, but they're fairly scattered and ad-infested.
step 2: perform whatever SEO skullduggery is needed to get your site to the top of the Google search rankings.
step 3: once your site has established itself, pass every single thing that gets pasted into it to a backend service. If you wanna be coy about it, continue doing the validation in Javascript and pretend that the backend calls are metrics. Listen, if you make the URL something like https://admin.yourhosthere.com/datadog-agent then 90% of devs are gonna go 'yeah that seems legit, it's just my good friend Datadog :)' and investigate no further.
step 4: parse every message for strings like 'username' and 'password'.
step 5: now that you have production credentials for about 40% of international corporations and governments, hold the planet hostage.
step 6: rule the world from a flying volcano lair staffed with jumpsuit-wearing henchmen.
(traditionally the henchmen would come before the world conquest, but like most things, supervillainy has gone through massive changes thanks the internet)
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20 key points on becoming a Junior Full-Stack Web Developer | Resource ✨
I follow a user on Twitter called Swapna Kumar Panda! He's a tech educator and mentor from India. He tweeted a thread about mentoring someone into getting their first tech job. He laid out what he did to help him in the tweet and I thought I would bullet point them here for anyone interested!
But do go ahead and read the full thread because he does do into details on what he did!
20 key points on becoming a Junior Full-Stack Web Developer:
[ 1 ] Save time by being smart
[ 2 ] Stop comparing Education
[ 3 ] Practice during learning
[ 4 ] Avoid Tutorial Hell & FOMO (Fear of Missing out)
[ 5 ] Learn and start using Git as early as possible
[ 6 ] Start with simple HTML & CSS (which he provides a roadmap)
[ 7 ] Learn basic JavaScript (which he provides a roadmap)
[ 8 ] Build small projects (he provides 150+ projects)
[ 9 ] Learn TypeScript
[ 10 ] Be modular
[ 11 ] Learn React
[ 12 ] Learn Next.js
[ 13 ] Problem Solving Skills (which he provide practice Algorithms for various programming languages)
[ 14 ] Back-End with Node.js & Express
[ 15 ] Database with MySQL & MongoDB
[ 16 ] Build complete projects (which he provides 150+ Full-Stack Web projects)
[ 17 ] Make Personal Portfolio
[ 18 ] Build Resume
[ 19 ] Build Connections
[ 20 ] Be ready for a few failures
Hope this helps people! But make sure to check out the full thread on Twitter! Have a nice day programming! (✿◡‿◡)
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// it took me to make ig a criminal au for him to finally be at peace with technology
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If anyone wants to know why every tech company in the world right now is clamoring for AI like drowned rats scrabbling to board a ship, I decided to make a post to explain what's happening.
(Disclaimer to start: I'm a software engineer who's been employed full time since 2018. I am not a historian nor an overconfident Youtube essayist, so this post is my working knowledge of what I see around me and the logical bridges between pieces.)
Okay anyway. The explanation starts further back than what's going on now. I'm gonna start with the year 2000. The Dot Com Bubble just spectacularly burst. The model of "we get the users first, we learn how to profit off them later" went out in a no-money-having bang (remember this, it will be relevant later). A lot of money was lost. A lot of people ended up out of a job. A lot of startup companies went under. Investors left with a sour taste in their mouth and, in general, investment in the internet stayed pretty cooled for that decade. This was, in my opinion, very good for the internet as it was an era not suffocating under the grip of mega-corporation oligarchs and was, instead, filled with Club Penguin and I Can Haz Cheezburger websites.
Then around the 2010-2012 years, a few things happened. Interest rates got low, and then lower. Facebook got huge. The iPhone took off. And suddenly there was a huge new potential market of internet users and phone-havers, and the cheap money was available to start backing new tech startup companies trying to hop on this opportunity. Companies like Uber, Netflix, and Amazon either started in this time, or hit their ramp-up in these years by shifting focus to the internet and apps.
Now, every start-up tech company dreaming of being the next big thing has one thing in common: they need to start off by getting themselves massively in debt. Because before you can turn a profit you need to first spend money on employees and spend money on equipment and spend money on data centers and spend money on advertising and spend money on scale and and and
But also, everyone wants to be on the ship for The Next Big Thing that takes off to the moon.
So there is a mutual interest between new tech companies, and venture capitalists who are willing to invest $$$ into said new tech companies. Because if the venture capitalists can identify a prize pig and get in early, that money could come back to them 100-fold or 1,000-fold. In fact it hardly matters if they invest in 10 or 20 total bust projects along the way to find that unicorn.
But also, becoming profitable takes time. And that might mean being in debt for a long long time before that rocket ship takes off to make everyone onboard a gazzilionaire.
But luckily, for tech startup bros and venture capitalists, being in debt in the 2010's was cheap, and it only got cheaper between 2010 and 2020. If people could secure loans for ~3% or 4% annual interest, well then a $100,000 loan only really costs $3,000 of interest a year to keep afloat. And if inflation is higher than that or at least similar, you're still beating the system.
So from 2010 through early 2022, times were good for tech companies. Startups could take off with massive growth, showing massive potential for something, and venture capitalists would throw infinite money at them in the hopes of pegging just one winner who will take off. And supporting the struggling investments or the long-haulers remained pretty cheap to keep funding.
You hear constantly about "Such and such app has 10-bazillion users gained over the last 10 years and has never once been profitable", yet the thing keeps chugging along because the investors backing it aren't stressed about the immediate future, and are still banking on that "eventually" when it learns how to really monetize its users and turn that profit.
The pandemic in 2020 took a magnifying-glass-in-the-sun effect to this, as EVERYTHING was forcibly turned online which pumped a ton of money and workers into tech investment. Simultaneously, money got really REALLY cheap, bottoming out with historic lows for interest rates.
Then the tide changed with the massive inflation that struck late 2021. Because this all-gas no-brakes state of things was also contributing to off-the-rails inflation (along with your standard-fare greedflation and price gouging, given the extremely convenient excuses of pandemic hardships and supply chain issues). The federal reserve whipped out interest rate hikes to try to curb this huge inflation, which is like a fire extinguisher dousing and suffocating your really-cool, actively-on-fire party where everyone else is burning but you're in the pool. And then they did this more, and then more. And the financial climate followed suit. And suddenly money was not cheap anymore, and new loans became expensive, because loans that used to compound at 2% a year are now compounding at 7 or 8% which, in the language of compounding, is a HUGE difference. A $100,000 loan at a 2% interest rate, if not repaid a single cent in 10 years, accrues to $121,899. A $100,000 loan at an 8% interest rate, if not repaid a single cent in 10 years, more than doubles to $215,892.
Now it is scary and risky to throw money at "could eventually be profitable" tech companies. Now investors are watching companies burn through their current funding and, when the companies come back asking for more, investors are tightening their coin purses instead. The bill is coming due. The free money is drying up and companies are under compounding pressure to produce a profit for their waiting investors who are now done waiting.
You get enshittification. You get quality going down and price going up. You get "now that you're a captive audience here, we're forcing ads or we're forcing subscriptions on you." Don't get me wrong, the plan was ALWAYS to monetize the users. It's just that it's come earlier than expected, with way more feet-to-the-fire than these companies were expecting. ESPECIALLY with Wall Street as the other factor in funding (public) companies, where Wall Street exhibits roughly the same temperament as a baby screaming crying upset that it's soiled its own diaper (maybe that's too mean a comparison to babies), and now companies are being put through the wringer for anything LESS than infinite growth that Wall Street demands of them.
Internal to the tech industry, you get MASSIVE wide-spread layoffs. You get an industry that used to be easy to land multiple job offers shriveling up and leaving recent graduates in a desperately awful situation where no company is hiring and the market is flooded with laid-off workers trying to get back on their feet.
Because those coin-purse-clutching investors DO love virtue-signaling efforts from companies that say "See! We're not being frivolous with your money! We only spend on the essentials." And this is true even for MASSIVE, PROFITABLE companies, because those companies' value is based on the Rich Person Feeling Graph (their stock) rather than the literal profit money. A company making a genuine gazillion dollars a year still tears through layoffs and freezes hiring and removes the free batteries from the printer room (totally not speaking from experience, surely) because the investors LOVE when you cut costs and take away employee perks. The "beer on tap, ping pong table in the common area" era of tech is drying up. And we're still unionless.
Never mind that last part.
And then in early 2023, AI (more specifically, Chat-GPT which is OpenAI's Large Language Model creation) tears its way into the tech scene with a meteor's amount of momentum. Here's Microsoft's prize pig, which it invested heavily in and is galivanting around the pig-show with, to the desperate jealousy and rapture of every other tech company and investor wishing it had that pig. And for the first time since the interest rate hikes, investors have dollar signs in their eyes, both venture capital and Wall Street alike. They're willing to restart the hose of money (even with the new risk) because this feels big enough for them to take the risk.
Now all these companies, who were in varying stages of sweating as their bill came due, or wringing their hands as their stock prices tanked, see a single glorious gold-plated rocket up out of here, the likes of which haven't been seen since the free money days. It's their ticket to buy time, and buy investors, and say "see THIS is what will wring money forth, finally, we promise, just let us show you."
To be clear, AI is NOT profitable yet. It's a money-sink. Perhaps a money-black-hole. But everyone in the space is so wowed by it that there is a wide-spread and powerful conviction that it will become profitable and earn its keep. (Let's be real, half of that profit "potential" is the promise of automating away jobs of pesky employees who peskily cost money.) It's a tech-space industrial revolution that will automate away skilled jobs, and getting in on the ground floor is the absolute best thing you can do to get your pie slice's worth.
It's the thing that will win investors back. It's the thing that will get the investment money coming in again (or, get it second-hand if the company can be the PROVIDER of something needed for AI, which other companies with venture-back will pay handsomely for). It's the thing companies are terrified of missing out on, lest it leave them utterly irrelevant in a future where not having AI-integration is like not having a mobile phone app for your company or not having a website.
So I guess to reiterate on my earlier point:
Drowned rats. Swimming to the one ship in sight.
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How An Advanced Survey Analysis Software Can Predict The Growth Trajectory Of Your Business
The severe competition that prevails in every industry demands that you accurately predict your growth trajectory so that you can make strategic decisions. While traditional forecasting methods offer some insights, they often overlook a valuable source of information namely, customer and employee feedback. This is where advanced survey analysis software steps in, offering a powerful tool to not only understand your current state but also predict your future course. Let us explore how these sophisticated software solutions can empower you to map a path to sustainable business growth.
Sophisticated survey analysis software extends beyond elementary data gathering and visualisation. It employs advanced statistical methods to unveil concealed patterns and connections within your survey data. Consider the ability to pinpoint particular customer segments at risk of churn or to identify employee engagement factors directly linked to heightened productivity. These insights surpass mere descriptive statistics, uncovering deeper trends in customer behaviour and workforce sentiments crucial for foreseeing future outcomes. By harnessing these predictive capacities, you can pre-emptively tackle potential hurdles and seize emerging opportunities before they become evident.
Top rated survey software goes even further by offering scenario modelling. This allows you to directly test the potential effects of various business decisions on critical metrics like customer satisfaction, brand loyalty, and employee retention. Imagine simulating the launch of a new product to see its impact on specific customer segments, or analysing how a revised employee benefits package might influence overall engagement. This empowers you to make data-driven decisions with greater confidence. By testing different scenarios beforehand, you can mitigate risks and maximise the potential for positive outcomes. In simpler terms, you can predict the success of a marketing campaign before allocating significant resources, or gauge employee receptiveness to a new company policy before implementation.
In addition to its essential features, advanced survey analysis software frequently integrates with other business intelligence tools. This seamless merging enables you to blend survey data with other pertinent datasets, such as sales data, website analytics, and social media engagement metrics. By constructing a comprehensive view of your business landscape, you acquire a deeper comprehension of the elements shaping your growth path. For example, you can uncover connections between customer satisfaction levels and particular marketing initiatives, or evaluate how employee morale influences overall customer service quality. This thorough data analysis empowers you to identify pivotal growth drivers and make informed decisions that enhance all facets of your business operations.
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How do I learn data science in 50 days?
Hi,
Learning data science in 50 days is challenging. But if you plan your day properly, then you can make a significant progress.
In the initial few(5–6) days, refresh your understanding of essential mathematical concepts like linear algebra (matrices, vectors), statistics (mean, median, standard deviation), and probability theory.
In the next step, (10 days) you can learn Python basics. Focus on variables, data types, control flow (if statements, loops), functions, and basic data structures (lists, dictionaries, tuples).
In the next few days(7 days) learn basic SQL(Structured Query Language) queries for data manipulation and retrieval from databases.
Once you are comfortable with maths, python, and SQL, learn techniques for handling missing data, data transformations, and data aggregation.
In the next few days introduce yourself to machine learning. Understand supervised and unsupervised learning concepts and model evaluation metrics (accuracy, precision, recall). Implement basic machine learning algorithms like linear regression, K-Nearest Neighbors (KNN), or decision trees using Python libraries like sci-kit-learn.
Lastly, work on small data science projects, or use open datasets to apply your newly acquired skills. This hands-on experience is crucial for solidifying your learning.
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got this from @trendsetterrrrrrr #funny #memes
got this from @trendsetterrrrrrr #funny #memes
Click to set custom HTML
https://igrowsalons.weebly.com/i-grow-salons/got-this-from-trendsetterrrrrrr-funny-memes
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SQLite Sistem Manajemen Basis Data Relasional Yang Ringan
SQLite adalah sistem manajemen basis data (DBMS) tertanam yang dikenal karena keberatannya yang rendah, efisiensinya, dan kemampuannya untuk beroperasi secara lintas platform. Khususnya cocok untuk aplikasi dengan sumber daya terbatas seperti aplikasi seluler dan proyek kecil. SQLite membanggakan konfigurasinya yang sederhana, integrasi yang mudah, dan dukungannya terhadap standar SQL.
Kelebihan…
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i rlly hate that so much software's "pRivAcY pOLiCy" amounts to "hey, we're gonna take your usage statistics and personal data and keystrokes to iMpRoVe oUr pRoDuCtS and there's nothing you can do about it 🤗 fuck you 🥰"
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