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tudipblog · 2 months ago

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IoT in Action: Transforming Industries with Intelligent Connectivity

The Power of Connectivity

The Internet of Things (IoT) has become a cornerstone of innovation, as it reimagines industries and redefines the way business is conducted. In bridging the physical and digital worlds, IoT enables seamless connectivity, smarter decision-making, and unprecedented efficiency. Today, in the competitive landscape, intelligent connectivity is no longer just a technology advancement; for businesses wanting to be relevant and continue to thrive, it is now a strategic imperative.

IoT is not simply about connecting devices; it’s about creating ecosystems that work collaboratively to drive value. With industries relying heavily on real-time data and actionable insights, IoT-powered connectivity has become the backbone of operational excellence and growth. Let’s explore how this transformative technology is revolutionizing key sectors, with a focus on how businesses can leverage it effectively.

Applications of IoT in Key Industries

1.Smart Manufacturing: Efficiency Through Connectivity

Manufacturing has embraced IoT as a tool to streamline operations and boost productivity. By embedding sensors in machinery and integrating real-time monitoring systems, manufacturers can:

Predict and Prevent Downtime: IoT-enabled predictive maintenance reduces unplanned outages, saving time and money.

Optimize Resource Allocation: Smart systems track inventory, raw materials, and energy consumption, ensuring optimal usage.

Enhance Quality Control: Real-time data from production lines helps identify defects early, maintaining high-quality standards.

Example: A global automotive manufacturer integrated IoT sensors into its assembly lines, reducing equipment downtime by 25% and improving production efficiency by 30%. The ability to monitor machinery health in real time transformed their operations, delivering significant cost savings.

2.Healthcare: Improve Patient Outcomes

In healthcare, IoT has been a game-changer in enabling connected medical devices and systems that enhance patient care and operational efficiency. The main applications include:

Remote Patient Monitoring: Devices track vital signs in real time, allowing healthcare providers to offer timely interventions.

Smart Hospital Systems: IoT-enabled equipment and sensors optimize resource utilization, from patient beds to medical supplies.

Data-Driven Decisions: IoT integrates patient data across systems, providing actionable insights for personalized treatment plans.

Example: A major hospital has put into operation IoT-enabled wearables for chronic disease management. This solution reduced the number of readmissions to hospitals by 20% and empowered patients to take an active role in their health.

3.Retail: Revolutionizing Customer Experiences

IoT is revolutionizing retail through increased customer interaction and streamlined operations. Connected devices and smart analytics allow retailers to:

Personalize Shopping Experiences: IoT systems track customer preferences, offering tailored recommendations in real time.

Improve Inventory Management: Smart shelves and sensors keep stock levels optimal, reducing wastage and improving availability.

Enable Smooth Transactions: IoT-driven payment systems make checkout easier and much faster, increasing customers’ convenience

Example: A retail chain leveraged IoT to integrate smart shelves that automatically update inventory data. This reduced out-of-stock situations by 40%, improving customer satisfaction and driving higher sales.

Role of Intelligent Connectivity in Business Transformation

Intelligent connectivity lies at the heart of IoT’s transformative potential. By connecting devices, systems, and processes, businesses can:

Accelerate Decision-Making: Real-time data sharing enables faster, more informed decisions, giving companies a competitive edge.

It increases collaboration by allowing smooth communication between departments and teams, making the entire system more efficient.

Adapt to Market Dynamics: IoT enables companies to respond quickly to changes in demand, supply chain disruptions, or operational challenges.

Intelligent connectivity is not just about technology; it’s about creating value by aligning IoT solutions with business objectives. This strategic approach guarantees that IoT investments will deliver measurable outcomes, from cost savings to improved customer loyalty.

How Tudip Technologies Powers Intelligent Connectivity

Tudip Technologies specializes in designing and implementing IoT solutions that drive meaningful transformation for businesses. With a focus on innovation and collaboration, Tudip ensures that its clients achieve operational excellence through intelligent connectivity.

Tailored Solution for Every Business Industry

Tudip understands that no two businesses are alike. By customizing IoT strategies to address specific challenges, Tudip helps clients unlock the full potential of connectivity. Examples include:

Smart Supply Chains: Implementing IoT systems that provide real-time visibility into inventory and logistics, reducing delays and improving efficiency.

Energy Management: Developing IoT frameworks to monitor and optimize energy usage, driving sustainability and cost savings.

Healthcare Innovations: Designing networked medical devices that allow remote patient monitoring and data integration without a hitch.

The Future of Connected Systems

The demand for intelligent connectivity will keep increasing as the industries continue to evolve. Emerging trends in IoT include edge computing, 5G networks, and AI-powered analytics, which promise to redefine possibilities for connected ecosystems.

Businesses that embrace these advancements stand to gain:

Greater Resilience: IoT enables adaptive systems that can withstand market fluctuations and operational challenges.

Enhanced Innovation: Connected technologies open doors to new business models, revenue streams, and customer experiences.

Sustainable Growth: IoT optimizes resources and processes, contributing to long-term environmental and economic sustainability.

The future belongs to those who see connectivity not just as a technological tool but as a strategic enabler of transformation. The right partner will help businesses transform IoT from a concept into a competitive advantage.

Conclusion: Embracing Intelligent Connectivity with Tudip

IoT is not just changing the way businesses operate—it’s redefining what’s possible. From manufacturing and healthcare to retail and beyond, intelligent connectivity is driving innovation, efficiency, and growth across industries.

Tudip Technologies is at the forefront of this transformation, offering customized IoT solutions that deliver real results. By prioritizing collaboration, adaptability, and measurable outcomes, Tudip ensures that its clients stay ahead in an increasingly connected world.

Now is the time to embrace the power of IoT and unlock its potential for your business. With Tudip as your partner, the journey to intelligent connectivity is not just achievable—it’s inevitable.

Click the link below to learn more about the blog IoT in Action: Transforming Industries with Intelligent Connectivity https://tudip.com/blog-post/iot-in-action-transforming-industries-with-intelligent-connectivity/

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innovativecomp · 19 days ago

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jcmarchi · 2 months ago

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DeepSeek's AIs: What humans really want

New Post has been published on https://thedigitalinsider.com/deepseeks-ais-what-humans-really-want/

DeepSeek's AIs: What humans really want

Chinese AI startup DeepSeek has solved a problem that has frustrated AI researchers for several years. Its breakthrough in AI reward models could improve dramatically how AI systems reason and respond to questions.

In partnership with Tsinghua University researchers, DeepSeek has created a technique detailed in a research paper, titled “Inference-Time Scaling for Generalist Reward Modeling.” It outlines how a new approach outperforms existing methods and how the team “achieved competitive performance” compared to strong public reward models.

The innovation focuses on enhancing how AI systems learn from human preferences – a important aspect of creating more useful and aligned artificial intelligence.

What are AI reward models, and why do they matter?

AI reward models are important components in reinforcement learning for large language models. They provide feedback signals that help guide an AI’s behaviour toward preferred outcomes. In simpler terms, reward models are like digital teachers that help AI understand what humans want from their responses.

“Reward modeling is a process that guides an LLM towards human preferences,” the DeepSeek paper states. Reward modeling becomes important as AI systems get more sophisticated and are deployed in scenarios beyond simple question-answering tasks.

The innovation from DeepSeek addresses the challenge of obtaining accurate reward signals for LLMs in different domains. While current reward models work well for verifiable questions or artificial rules, they struggle in general domains where criteria are more diverse and complex.

The dual approach: How DeepSeek’s method works

DeepSeek’s approach combines two methods:

Generative reward modeling (GRM): This approach enables flexibility in different input types and allows for scaling during inference time. Unlike previous scalar or semi-scalar approaches, GRM provides a richer representation of rewards through language.

Self-principled critique tuning (SPCT): A learning method that fosters scalable reward-generation behaviours in GRMs through online reinforcement learning, one that generates principles adaptively.

One of the paper’s authors from Tsinghua University and DeepSeek-AI, Zijun Liu, explained that the combination of methods allows “principles to be generated based on the input query and responses, adaptively aligning reward generation process.”

The approach is particularly valuable for its potential for “inference-time scaling” – improving performance by increasing computational resources during inference rather than just during training.

The researchers found that their methods could achieve better results with increased sampling, letting models generate better rewards with more computing.

Implications for the AI Industry

DeepSeek’s innovation comes at an important time in AI development. The paper states “reinforcement learning (RL) has been widely adopted in post-training for large language models […] at scale,” leading to “remarkable improvements in human value alignment, long-term reasoning, and environment adaptation for LLMs.”

The new approach to reward modelling could have several implications:

More accurate AI feedback: By creating better reward models, AI systems can receive more precise feedback about their outputs, leading to improved responses over time.

Increased adaptability: The ability to scale model performance during inference means AI systems can adapt to different computational constraints and requirements.

Broader application: Systems can perform better in a broader range of tasks by improving reward modelling for general domains.

More efficient resource use: The research shows that inference-time scaling with DeepSeek’s method could outperform model size scaling in training time, potentially allowing smaller models to perform comparably to larger ones with appropriate inference-time resources.

DeepSeek’s growing influence

The latest development adds to DeepSeek’s rising profile in global AI. Founded in 2023 by entrepreneur Liang Wenfeng, the Hangzhou-based company has made waves with its V3 foundation and R1 reasoning models.

The company upgraded its V3 model (DeepSeek-V3-0324) recently, which the company said offered “enhanced reasoning capabilities, optimised front-end web development and upgraded Chinese writing proficiency.” DeepSeek has committed to open-source AI, releasing five code repositories in February that allow developers to review and contribute to development.

While speculation continues about the potential release of DeepSeek-R2 (the successor to R1) – Reuters has speculated on possible release dates – DeepSeek has not commented in its official channels.

What’s next for AI reward models?

According to the researchers, DeepSeek intends to make the GRM models open-source, although no specific timeline has been provided. Open-sourcing will accelerate progress in the field by allowing broader experimentation with reward models.

As reinforcement learning continues to play an important role in AI development, advances in reward modelling like those in DeepSeek and Tsinghua University’s work will likely have an impact on the abilities and behaviour of AI systems.

Work on AI reward models demonstrates that innovations in how and when models learn can be as important increasing their size. By focusing on feedback quality and scalability, DeepSeek addresses one of the fundamental challenges to creating AI that understands and aligns with human preferences better.

See also: DeepSeek disruption: Chinese AI innovation narrows global technology divide

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tejkohli25 · 3 months ago

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AI vs. AGI: What’s the Difference?

Artificial Intelligence (AI) is transforming industries, but its evolution is still in progress. Artificial General Intelligence (AGI) is the next frontier—capable of independent reasoning and learning. While AI excels at specific tasks, AGI aims to replicate human-like cognitive abilities. Understanding the key differences between AI and AGI is essential as technology advances toward a more autonomous future.

For a deeper insight into the role of AGI and its potential impact, check out this expert discussion.

What is Artificial Intelligence (AI)?

AI is designed for narrow applications, such as facial recognition, chatbots, and recommendation systems.

AI models like GPT-4 and DALL·E process data and generate outputs based on pre-programmed patterns.

AI lacks self-awareness and the ability to learn beyond its training data.

AI improves over time through machine learning algorithms.

Deep learning enables AI to recognize patterns and automate decision-making.

AI remains dependent on human intervention and structured data for continuous improvement.

Common applications of AI include:

Healthcare: AI-powered diagnostics and drug discovery.

Finance: Fraud detection and algorithmic trading.

Autonomous Vehicles: AI assists in self-driving technology but lacks human intuition.

What is Artificial General Intelligence (AGI)?

AGI aims to develop independent reasoning, decision-making, and adaptability.

Unlike AI, AGI would be able to understand and perform any intellectual task that a human can.

AGI requires self-learning mechanisms and consciousness-like functions.

AGI is designed to acquire knowledge across multiple domains without explicit programming.

It would be able to solve abstract problems and improve its performance independently.

AGI systems could modify and create new learning strategies beyond human input.

Potential applications of AGI include:

Advanced Scientific Research: AGI could revolutionize space exploration, climate science, and quantum computing.

Fully Autonomous Robots: Machines capable of human-like decision-making and reasoning.

Ethical & Philosophical Thinking: AGI could assist in policy-making and ethical dilemmas with real-world implications.

Key Differences Between AI & AGI

Scope:

AI is narrow and task-specific.

AGI has general intelligence across all tasks.

Learning:

AI uses supervised and reinforcement learning.

AGI learns independently without predefined rules.

Adaptability:

AI is limited to pre-defined parameters.

AGI can self-improve and apply learning to new situations.

Human Interaction:

AI supports human decision-making.

AGI can function without human intervention.

Real-World Application:

AI is used in chatbots, automation, and image processing.

AGI would enable autonomous research, problem-solving, and creativity.

Challenges in Achieving AGI

Ethical & Safety Concerns:

Uncontrolled AGI could lead to unpredictable consequences.

AI governance and regulation must ensure safe and responsible AI deployment.

Computational & Technological Barriers:

AGI requires exponentially more computing power than current AI.

Quantum computing advancements may be needed to accelerate AGI development.

The Role of Human Oversight:

Scientists must establish fail-safe measures to prevent AGI from surpassing human control.

Governments and AI research institutions must collaborate on AGI ethics and policies.

Tej Kohli’s Perspective on AGI Development

Tech investor and tech entrepreneur Tej Kohli believes AGI is the next major revolution in AI, but its development must be approached with caution and responsibility. His insights include:

AGI should complement, not replace, human intelligence.

Investments in AGI must prioritize ethical development to prevent risks.

Quantum computing and biotech will play a crucial role in shaping AGI’s capabilities.

Conclusion

AI is already transforming industries, but AGI represents the future of true machine intelligence. While AI remains task-specific, AGI aims to match human-level cognition and problem-solving. Achieving AGI will require breakthroughs in computing, ethics, and self-learning technologies.

#Artificial Intelligence #Tej Kohli #AI vs AGI #Machine Learning #Deep Learning #Future of AI #AGI Development #AI Ethics #Quantum Computing #Autonomous Systems #AI Innovation

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link-layer · 4 months ago

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Advanced Cooling Technologies: The Future of Thermal Management

An exploration of the physics, engineering, and environmental aspects of modern cooling technologies in computing.

Introduction to the Thermal Challenge

The exponential growth in computing power has created an unprecedented challenge in thermal management. At its most fundamental level, cooling in computing systems involves managing energy that manifests as heat. When electricity flows through semiconductors and other computing components, some energy is inevitably lost to heat through a process called Joule heating. This creates a fascinating challenge: we must remove this heat efficiently while maintaining precise temperature control for optimal component performance.

Modern processors can generate heat densities exceeding 100 watts per square centimeter - comparable to the surface of a nuclear reactor core. This intense heat concentration necessitates innovative cooling solutions that go far beyond traditional approaches. The stakes are particularly high because even small temperature variations can significantly impact performance, with many systems automatically throttling their processing speed when temperatures rise too high.

The Physics Behind Liquid Cooling

To understand why liquid cooling has become so crucial, we should first examine the fundamental physics involved. Liquids typically have thermal conductivity values 20-30 times higher than air, and their heat capacity per unit volume can be over 3,000 times greater. This means that liquid cooling systems can remove heat much more efficiently while requiring less volumetric flow, leading to more compact and efficient cooling solutions.

Heat Transfer Mechanisms

Three primary mechanisms govern heat transfer in computing systems:

Conduction: This occurs when heat moves directly through materials. In processors, heat conducts from the silicon die through thermal interface materials (TIMs) to heat spreaders. The efficiency of conduction depends on thermal conductivity - copper, for instance, conducts heat about 1,000 times better than air. This explains why metal heat sinks are so crucial in cooling systems.

Convection: This involves heat transfer through fluid movement (including air). Natural convection occurs when heated fluid rises due to density differences, while forced convection uses pumps or fans to move the fluid. The effectiveness of convection depends on factors like fluid velocity, surface area, and temperature difference. This is why heat sinks have fins - they increase surface area for better convection.

Radiation: This transfers heat through electromagnetic waves, though it plays a minor role in most computing cooling systems. However, it becomes more significant in high-temperature scenarios or specialized applications like satellite computing systems.

Modern Cooling Systems

Immersion Cooling

Immersion cooling represents one of the most fascinating developments in thermal management. This approach can be broken down into two main categories:

Single-Phase Immersion Cooling: In this method, the dielectric fluid remains in a liquid state throughout the cooling cycle. The fluid, typically a specially engineered synthetic coolant, absorbs heat from the components and transfers it to a heat exchanger. The constant liquid state provides extremely stable temperatures across all components, virtually eliminating hot spots that can plague traditional cooling methods.

Two-Phase Immersion Cooling: This more advanced approach utilizes the liquid-to-vapor phase change of the coolant. When the fluid contacts hot components, it boils and vaporizes, carrying away large amounts of heat through the latent heat of vaporization. The vapor then rises, condenses at a heat exchanger, and falls back into the liquid pool, creating a highly efficient natural circulation system.

Direct-to-Chip Cooling

The science behind direct-to-chip cooling involves sophisticated microfluidic engineering. Modern systems employ micro-channels as small as 50 micrometers in diameter, carved directly into the processor's heat spreader or integrated into specialized cold plates. These channels create laminar flow patterns that maximize heat transfer while minimizing pumping power requirements. The coolant flows through these channels in carefully designed patterns that ensure uniform cooling across the entire chip surface.

Advanced Air Cooling

While liquid cooling often takes the spotlight, significant advances in air cooling technology shouldn't be overlooked. Modern air cooling systems incorporate several sophisticated elements:

Heat Pipe Technology: Advanced heat pipes now use specialized wick structures and working fluids that can transfer heat at rates approaching those of simple liquid cooling systems. Some cutting-edge designs incorporate variable conductance features that automatically adjust their heat transfer rate based on temperature.

Advanced Fan Designs: Contemporary cooling fans utilize computational fluid dynamics-optimized blade designs and magnetic levitation bearings to maximize airflow while minimizing noise and energy consumption. Some systems now incorporate active flow control elements that can direct cooling precisely where it's needed most.

Engineering Challenges in Modern Cooling Systems

The implementation of advanced cooling technologies presents several intricate engineering challenges that require innovative solutions.

Hybrid Cooling Systems

The implementation of hybrid cooling systems requires sophisticated control systems that can dynamically balance multiple cooling methods. These systems use machine learning algorithms to predict heating patterns and adjust cooling strategies proactively rather than reactively. For example, a hybrid system might use liquid cooling for high-heat components while maintaining air cooling for lower-power peripherals, with the control system continuously optimizing the balance between the two methods.

Thermal Interface Materials

One critical challenge lies in the thermal interface between components. Even seemingly smooth surfaces have microscopic irregularities that create air gaps, severely impeding heat transfer. Modern solutions include:

Phase Change Materials (PCMs): These substances change from solid to liquid at specific temperatures, filling microscopic gaps and improving thermal conductivity. Advanced PCMs can achieve thermal conductivities above 5 W/m·K while maintaining stability over thousands of thermal cycles.

Liquid Metal TIMs: Gallium-based alloys provide exceptional thermal conductivity (around 70 W/m·K), but present challenges with electrical conductivity and potential corrosion. Engineers must carefully design containment systems and choose compatible materials.

Microfluidic Engineering

The design of microfluidic cooling channels involves complex fluid dynamics considerations:

Flow Distribution: Engineers must ensure uniform coolant distribution across heat-generating surfaces. This often requires sophisticated manifold designs and careful consideration of pressure drops throughout the system.

Phase Change Management: In two-phase cooling systems, engineers must manage bubble formation and flow patterns to prevent dry spots or flow instabilities. This requires precise control of surface properties and channel geometries.

Scaling Challenges

As computing systems grow larger, new engineering challenges emerge:

Fluid Distribution: Large-scale systems must maintain uniform coolant distribution across multiple racks or servers. This requires careful balancing of flow rates and pressure drops throughout the system.

Redundancy and Reliability: Critical systems need backup cooling capabilities, often requiring complex control systems and redundant fluid paths.

Environmental Implications and Sustainability

The environmental impact of cooling systems extends far beyond energy consumption, encompassing several interconnected aspects.

Water Usage and Conservation

Data center cooling can consume enormous quantities of water, both directly and through power generation. Modern solutions address this through:

Closed-Loop Systems: Advanced recycling and filtration systems can reduce water consumption by over 90% compared to traditional cooling towers.

Alternative Coolants: Engineered fluids with better thermal properties can reduce the volume of coolant needed, though their environmental impact over their lifecycle must be carefully considered.

Energy Efficiency and Carbon Footprint

The energy efficiency of cooling systems affects both operating costs and environmental impact:

Heat Recovery Systems: Modern facilities can repurpose waste heat for various applications, from office heating to greenhouse agriculture, significantly improving overall energy efficiency.

Smart Control Systems: AI-driven cooling management can reduce energy consumption by 20-30% by optimizing cooling distribution based on real-time load patterns.

Natural Cooling Integration: Some facilities use geothermal cooling or night air cooling when ambient conditions permit, dramatically reducing energy consumption during favorable weather conditions.

The Future of Thermal Management in Computing

Looking ahead, several emerging and promising technologies are reshaping the future of computing cooling:

Advanced Materials

New materials are enabling better cooling solutions:

Carbon Nanotubes: These structures can achieve thermal conductivities exceeding 3,000 W/m·K, potentially revolutionizing heat spreading in compact devices.

Graphene-Based Solutions: Single-layer graphene has demonstrated theoretical thermal conductivity above 5,000 W/m·K, though practical applications currently achieve lower values.

Advanced materials like graphene, with its extraordinary thermal conductivity, are being developed into ultra-thin heat spreaders that could revolutionize device-level cooling.

Quantum Technologies

Emerging quantum technologies offer new approaches to cooling:

Magnetic Cooling Systems: Using magnetocaloric effects could provide highly efficient, solid-state cooling solutions. These systems rely on the magnetocaloric effect, where certain materials heat up when magnetized and cool down when removed from the magnetic field. By cyclically magnetizing and demagnetizing these materials, magnetic cooling systems can achieve refrigeration without the need for traditional refrigerants.

Quantum Well Cooling Systems: This technology uses quantum effects to create highly efficient solid-state cooling devices that could eventually replace traditional refrigeration cycles. These systems utilize quantum wells, which are thin layers of semiconductor material that confine electrons or holes in a two-dimensional plane. By controlling the energy levels within these wells, they can achieve cooling effects through processes like laser cooling or evaporative cooling.

Thermoelectric Improvements: New materials and quantum well structures are improving the efficiency of thermoelectric cooling devices.

Conclusion

The landscape of computing cooling technologies represents a fascinating convergence of fundamental physics, cutting-edge engineering, and environmental stewardship. As we look toward the future of computing, the importance of efficient thermal management cannot be overstated. The challenges we face in this domain are not merely technical problems to be solved, but opportunities to revolutionize how we build and operate computing systems.

The evolution from simple air cooling to sophisticated liquid immersion systems demonstrates how far we've come in our understanding of thermal management. Yet, this evolution continues at an accelerating pace. The integration of artificial intelligence in cooling system management, combined with advances in material science and quantum cooling technologies, suggests we are on the cusp of another major leap forward in cooling efficiency.

Perhaps most importantly, the future of cooling technology is increasingly intertwined with environmental sustainability. As computing power demands grow exponentially, the solutions we develop must not only be technically sophisticated but also environmentally responsible. The emergence of closed-loop systems, heat recovery technologies, and smart control systems points to a future where computing can expand without proportionally increasing its environmental impact.

The interdisciplinary nature of modern cooling solutions – drawing from fields as diverse as fluid dynamics, materials science, artificial intelligence, and environmental engineering – highlights how complex challenges often drive the most innovative solutions. As we push the boundaries of computing performance, the cooling technologies we develop will play a crucial role in enabling the next generation of technological advancement, from quantum computing to artificial intelligence infrastructure.

In essence, the future of computing cooling is not just about removing heat – it's about doing so in ways that are more efficient, more sustainable, and more intelligent than ever before. As we continue to develop these technologies, we move closer to a future where the thermal limitations that once constrained computing performance become merely another engineering parameter to be optimized, rather than a fundamental barrier to progress.

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classroomlearning · 4 months ago

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BTech CSE: Your Gateway to High-Demand Tech Careers

Apply now for admission and avail the Early Bird Offer

In the digital age, a BTech in Computer Science & Engineering (CSE) is one of the most sought-after degrees, offering unmatched career opportunities across industries. From software development to artificial intelligence, the possibilities are endless for CSE graduates.

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#n the digital age #a BTech in Computer Science & Engineering (CSE) is one of the most sought-after degrees #offering unmatched career opportunities across industries. From software development to artificial intelligence #the possibilities are endless for CSE graduates.#Top Job Opportunities for BTech CSE Graduates #Software Developer: Design and develop innovative applications and systems.#Data Scientist: Analyze big data to drive business decisions.#Cybersecurity Analyst: Safeguard organizations from digital threats.#AI/ML Engineer: Lead the way in artificial intelligence and machine learning.#Cloud Architect: Build and maintain cloud-based infrastructure for global organizations.#Why Choose Brainware University for BTech CSE?#Brainware University provides a cutting-edge curriculum #hands-on training #and access to industry-leading tools. Our dedicated placement cell ensures you’re job-ready #connecting you with top recruiters in tech.#👉 Early Bird Offer: Don’t wait! Enroll now and take the first step toward a high-paying #future-ready career in CSE.#Your journey to becoming a tech leader starts here!#BTechCSE #BrainwareUniversity #TechCareers #SoftwareEngineering #AIJobs #EarlyBirdOffer #DataScience #FutureOfTech #Placements

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thepastisalreadywritten · 5 months ago

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Punched Cards

In 1955, 4.5 megabytes of data was stored on 62,500 punched cards, illustrating just how far technology has come since then.

Punched cards were a primary method of data storage and input in the mid-20th century. Each card could hold a small amount of information, typically one line of code or a set of instructions.

This process required significant physical space and manpower, with each card needing to be carefully organized and processed by a machine.

The image of 62,500 punched cards serves as a striking reminder of the limitations of early computing technology.

The massive volume of cards needed to store a relatively small amount of data highlights the inefficiency of early data storage methods.

As computers evolved, more compact and efficient ways of storing and processing data emerged, eventually leading to the development of modern digital storage devices.

Looking back at the use of punched cards emphasizes the rapid technological advancements in computing that have occurred over the last several decades.

Today, we can store far more data in incredibly small devices, thanks to innovations in memory storage and processing power.

The 1955 punched card system now stands as a historical marker, showcasing the ingenuity and persistence that led to the computing revolution.

#punched cards #megabyte #data #data storage #data input #computer technology #technology #information #code #computing technology #digital storage devices #memory storage #processing power #punched card system #innovation

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foldingfittedsheets · 11 months ago

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Mattresses, unbeknownst to many, are a lot like cars. Every year new ones roll out, they’re always tweaking and innovating and you’ll never find the same one you loved decades ago when buying a new one.

Where I sold mattresses had a three month return or exchange program for this reason. New beds take a while to break in, and they’re a big expense. Your body is used to the old one. So we made sure people were loving it. If a bed got returned we’d take it back, sanitize and clean it, then sell it again on clearance.

To sell these we always had to disclose what clearance meant to customers, and they had to sign that they knew what they were getting. (FYI, not every company is as… forthright about the used bed situation)

In clearance we had beds that were floor models, we had returns, and more rarely we had old models whose line had been discontinued. These clearance beds were always final sale, so a bed could only be sold twice.

Now, the manager at the store I was working at had realized a vital fact. Clearance beds in the warehouse didn’t sell, especially old models that salespeople weren’t familiar with. And even more especially in odd sizes, like twin extra longs. So he set up a split king on the showroom floor to exhibit clearance beds, pulling all those forgotten twin extra longs out onto the showroom.

Almost all of these were brand new discontinued models. Beds I’d never learned in training were exhumed to be displayed. The manufacturers had moved on to new lines and they’d been left behind. Why would he take such in interest in selling old stock, you might wonder? Because we made double commission on the sales margin of clearance beds, and if we’d had a bed long enough they dropped the cost in the system so it was a fucking cash cow to sell these. Even with huge discounts the commissions were wonderful so it was a win win.

When I got started I was jazzed about this program, I was so on board to sell weird old brand new beds and make a ton of money. I had a wonderful older couple come in, looking for a split king adjustable set. This was a white whale sale.

The current clearance models on the floor were a latex mattress that was brand new despite being of an age to start first grade, and a tempurpedic floor model. The couple laid down and it was like magic. They each loved the bed they’d laid down on. They wanted to buy the whole shebang.

I. Was. Thrilled. I told them about the clearance program and what that meant, and they weren’t bothered in the least. I wrote up the sale then dashed into the back, fizzing with excitement to tell my manager what I’d done.

“You sold the death bed?!” He asked in delight.

I pulled up short, my smile freezing in place. “What…?”

“Didn’t you check the notes?”

I hesitated for a long beat then slowly shook my head. You see, dear reader, all beds had a personal history. Every clearance bed had logs written up by the person who took the return, as well as warehouse crew after sanitizing. It helped us know what to expect when selling them. “Wasn’t it just a floor model? You said it was a floor model…”

He slowly shook his head. I checked the notes.

It turned out, it had been sold as a floor model. The first time. But the company had made an exception and taken it back as a return two months later. Why? Because it’s owner had passed away.

I stared at the computer in horror and my manager shrugged. “They signed the clearance form. Technically it was a floor model.”

“We know for a fact that a man died in that bed!”

“What they don’t know can’t haunt them,” he said philosophically.

The man came back a week later for more sheets, utterly delighted to tell me how well they were sleeping. I clamped my teeth down around the secret of the deathbed, choosing to let them love their new bed without the stigma. Only one person would be haunted by that deathbed, and it was me.

#ramblies #ffs foibles #that sale was over ten thousand dollars #and I made a thousand dollars in that one sale #I cried about it later because I couldn’t even conceive of making that much money #story #writing #funny

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