Karen VARDANYAN’s Post

The Art and Science of Reverse Engineering: Unlocking Hidden Potential Reverse engineering is the art of dismantling an object to see how it works. It's a powerful technique used across various fields—from software and hardware to military technology and even biological functions. → So, what is reverse engineering? Reverse engineering involves taking apart a product or system to analyze and understand its components and functionality. It's often used to duplicate, enhance, or repurpose an object. In the realm of software, this means working with machine code to recreate the original source code, offering insights into compatibility, security, and performance improvements. → Why reverse engineer? The primary purpose is to understand how something works. Here are a few key reasons: 1. Recreate or Enhance: Reverse engineering allows us to recreate a product or create a similar one with enhancements. 2. Cost-Effectiveness: It helps in developing similar products more inexpensively. 3. Compatibility: Ensures hardware or software can work with other systems. 4. Security Analysis: Identifies vulnerabilities and improves security. Consider the classic case of the IBM PC BIOS: In the early 1980s, IBM's BIOS was proprietary, and other companies wanted to create compatible PCs. Phoenix Technologies reverse-engineered the IBM BIOS to develop a clean-room version that did not infringe on IBM's copyright. This enabled the rise of the PC clone market, allowing more companies to enter the market and drive down costs, which ultimately benefited consumers and spurred technological advancement. → The Reverse Engineering Process: 1. Information Extraction: Study the object, extract design information, and understand how pieces fit together. 2. Modelling: Abstract the collected information into a conceptual model to guide the design of new systems. 3. Review: Test the model in various scenarios to ensure it accurately represents the original object. → Legal and Ethical Considerations: Reverse engineering is generally legal if the original product was obtained legally and no contractual agreements are violated. However, it can raise legal issues around patents, copyrights, and trade secrets. Reverse engineering is a complex but invaluable practice. It helps address compatibility issues, recreate legacy parts, perform security assessments, and improve existing products. Despite its complexity, reverse engineering remains a crucial tool for innovation and problem-solving. What do you think are the ethical considerations in reverse engineering? 💬 Drop your thoughts in the comments below! ⬇️ #ReverseEngineering #Innovation #Tech

Just decided to have some fun and rethink this in modern day terminology. Have a lovely week! 💫 and a bit of fun as you win 🏆 Psalm 23: The Lord, My Developer 1. The Lord is my developer; I shall not malfunction. 2. He configures me in secure systems of tranquility; he leads me beside silent servers. 3. He upgrades my software; he directs me in the paths of efficient algorithms for his name’s sake. 4. Even though I navigate through the complexities of vast data, I fear no bugs, for you are with me; your code and your logic, they comfort me. 5. You design a platform before me in the presence of my errors; you initialize my program with energy; my memory overflows. 6. Surely performance and innovation will accompany me all the days of my operation, and I will execute in the domain of the Lord as long as my systems run.

Complexity analysis helps evaluate the efficiency and scalability of algorithms, Here’s why it’s important...... 😀 1. Performance Measurement: Complexity analysis quantifies how an algorithm's runtime or memory requirements grow with input size. This makes it easier to predict performance and choose algorithms that meet speed and efficiency requirements. 2. Resource Management: Knowing time and space complexity helps developers manage resources like memory and processing power more effectively, especially for large datasets or constrained environments (e.g., mobile devices). 3. Scalability: Complexity analysis reveals how well an algorithm will perform as input sizes increase. Algorithms with lower complexity are more likely to remain efficient at scale, while those with higher complexity may become slow or unresponsive. 4. Algorithm Comparison: When solving a problem, you might have several algorithms to choose from. Complexity analysis provides a way to compare them and choose the best one for the specific requirements of the project. 5. Cost-Effectiveness: For large-scale applications, efficient algorithms reduce costs associated with processing power, memory, and energy consumption. 6. Optimization Guidance: Complexity analysis highlights the parts of an algorithm that could be optimized. Knowing which sections have the highest complexity can guide code improvement efforts for better performance.

Today's and tomorrow's software involves complex interactions with humans. To ensure our solutions are fit-for-purpose, developers must deeply understand these interactions. Our recent project tackled this challenge head-on by establishing methodological guidelines and recommending supporting technologies for applications that involve human interaction. We defined a ground-breaking general metamodel called ‘Digital Human Twins’ (DHTs), which constructs models of humans in these interactions, effectively addressing the complexity and variability of human behaviour. Additionally, we are developing guidelines and tools for generating and managing DHTs, explored the potential of portable DHT profiles, and set an industry standard. An emerging concept is the possibility of defining DHT profiles that are unique to each individual, which can be specified using an industry standard, which could be defined based on the results of this research. Such profiles can then be used to adjust the human-machine interface of any software application that supports the industry standard. Learn more about our work: https://lnkd.in/gdBaWQGj Monash Information Technology, Bran Selic

Much has been said about how LLMs consume energy and contribute to carbon emissions. But they also offer enormous potential to make software radically more efficient. Modern software is bloated, running on top of a towering stack of abstractions, frameworks, libraries, compilers and platforms. The days of optimizing data structures and algorithms to leverage CPU architectures and cache characteristics are long gone. The distance between the application layer and hardware has never been greater and continues to increase. No human can fully wrap their head around the entire stack. This is where LLMs can shine: they can analyze the entire stack and optimize code across layers, from the application to the hardware. I’ve been experimenting with LLMs to tune the performance of simple applications and have seen impressive speedups of 10x to 100x with minimal effort. Now imagine scaling this across entire software ecosystem. The potential to improve performance, reduce waste, and build leaner software is enormous. It’s still early days, but the opportunities are exciting.

The future of software development is fast approaching, and 2025 promises to bring transformative changes that will redefine the industry. 1️⃣ AI-Powered Development: AI is becoming a critical tool in software development, from automating code generation to optimizing workflows. 2️⃣ Low-Code/No-Code Platforms: Low-code and no-code solutions are going to help non-developers build strong applications, bridging the gap between tech and business. 3️⃣ Edge Computing: Edge computing will gain prominence, reducing latency and enhancing performance by processing data closer to the source. 4️⃣ Sustainable Development: As sustainability becomes a priority, developers will increasingly focus on creating energy-efficient software and reducing the carbon footprint of digital solutions. 5️⃣ Cybersecurity by Design: With rising cyber threats, security will be integrated into every stage of the software development lifecycle, from initial design to deployment, ensuring robust protection against vulnerabilities. 6️⃣ Quantum Computing: Quantum computing will be on the rise to revolutionize complex problem-solving. At Zema, We're committed to using the latest technologies to help our clients stay competitive and innovative! Let’s build the future together! 🌐 #SoftwareDevelopmentTrends #InnovateWithZema #ImagineAlways #BuildTheFuture 

** Learn Observability with MetaMeraqi** Day 1: Application observability refers to the ability to gain deep insights into the internal state and performance of an application by collecting and analyzing various types of telemetry data, such as logs, metrics, and traces. The goal is to monitor and understand the behavior of the application in real-time, identify issues, and optimize performance. *Key aspects of application observability include:* 1. *Logs:* Detailed records of events that occur within the application, which help in debugging and understanding specific actions or errors. 2. *Metrics:* Quantitative data that represent the state or performance of the application, such as CPU usage, memory consumption, request rates, and error rates. 3. *Traces:* Records of the flow of requests through various components of the application, which help in identifying bottlenecks, latencies, and the root causes of issues. Application observability goes beyond traditional monitoring by enabling a more proactive and comprehensive approach to managing and improving the health and performance of applications. It is especially critical in modern, distributed systems where understanding complex interactions and dependencies is essential for ensuring reliability and performance.

Harnessing the Power of Digital Twins in Application Development #technologyandinnovation #applicationdevelopment #digitaltwins #enhancedsimulation #predictivemaintenance #realtimemonitoring

Why knowing the code complexity is crucial in this digital era? When we are building applications or systems that need to perform well in various conditions, as a coder, efficiency of our code plays a very important role and its crucial for us to choose algorithms that are time and space efficient. Lets understand it:- 1. Performance and Scalability : We need to understand the Asymptotic complexity of our algorithm i.e. what happens when the input size is huge & how well our application responds in terms of speed. Is it fast enough to have better user experience? 2. Resource Utilization : Application resources are limited. With space inefficient algorithms, our devices get memory bloat. CPU utilization is also proportional to our code efficiency. An efficient algorithm will execute fewer instruction and thus reduce time required to perform a task and hence less CPU cycles. Also, when we are working with mobile or embedded systems, energy consumption like battery life is also a concern. So, when less resources are used, energy used is also less! 3. Cost Efficiency : We need to the lower operational costs for our application. An efficient code reduces the amount of processing power, memory and other resources needed to perform a task which in turn will require fewer servers and cloud resources. Thus saving our operational cost of running the application. 4. Environmental Impact : Software industry increases the carbon footprints. Carbon footprints refers to total amount of green house gases that are released in the atmosphere due to human activities and it causes global warming & climate change. The infrastructure required to support software development like data centers, cloud computing etc. requires lot of energy consumption which in turn is increases the carbon footprints. And if our code is not efficient, it is going to use more resources and hence increase the carbon footprints. And its our mother earth that is impacted! So, writing an optimized code becomes our responsibility towards our environment. 5. Competitive Advantage : To sustain and grow as an organization, efficiency and security of our application matters. Therefore code optimization is a necessity! Writing efficient code is not just about making things run faster but making our systems more reliable, scalable, maintainable, cost effective and environmental friendly in long run.

MSR has showcased BitNet, a novel 1-bit Transformer architecture, designed to address the challenges associated with scaling LLMs. A key innovation in BitNet is the introduction of BitLinear, which replaces traditional linear layers and facilitates the training of 1-bit weights from the ground up. This architecture not only achieves competitive performance in language modeling but also significantly reduces memory usage and energy consumption when compared to advanced 8-bit quantization methods and FP16 Transformer baselines.   The architecture employs quantization-aware training, optimizing the model for low-precision representation from the outset, which enhances both accuracy and stability during the training process. BitNet features stacked blocks of self-attention and feed-forward networks with binarized weights, while retaining higher precision for other components, thereby minimizing computational costs. This design allows for efficient matrix multiplication with 1-bit weights, resulting in notable energy savings, especially as model sizes increase.   The training methodology incorporates techniques such as the straight-through estimator for gradient approximation and mixed precision training. These approaches enable BitNet to converge effectively with larger learning rates compared to traditional FP16 Transformers, leading to improved training efficiency and stability, which is particularly beneficial in resource-constrained environments.   Comparative evaluations indicate that BitNet consistently outperforms existing quantization methods across a range of benchmarks, including zero-shot and few-shot tasks, while also maintaining lower inference costs. These findings highlight the advantages of quantization-aware training over post-training quantization, positioning BitNet as a prominent approach for developing efficient LLMs.    Arxiv: https://lnkd.in/e-D959-U