Pankaj Kumar

Discover Mockingbird, Vectara’s advanced Retrieval Augmented Generation #RAG and structured output-focused Language Learning Model #LLM. Suleman Kazi, Vivek Sourabh, Rogger Luo & Abhilasha Lodha take you on a technical deep dive into Mockingbird's impressive performance and capabilities. 1️⃣ Why Another LLM? - Learn why Vectara developed its own LLM focused on tasks that matter to our customers. 2️⃣ Training Mockingbird - Explore the rigorous training process for RAG and structured output tasks. 3️⃣ Evaluation Metrics - See how Mockingbird excels in generation quality, citations, and structured output. 4️⃣ Human Ratings - Understand the human evaluation process and how Mockingbird compares to leading models like GPT-4. Mockingbird is integrated into the Vectara platform, offering unparalleled performance with high-quality, secure outputs. 🌐 Dive into the full blog and see how Mockingbird can transform your data handling and generation processes. https://gag.gl/2WqJGM?

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Breaking Barriers: Unveiling the Power of Hindi Text-to-Speech on Windows Explore how enabling text-to-speech functionality on Windows fosters inclusivity, empowers users with diverse needs, and transforms the digital experience. #Accessibility #Windows #Inclusion #DigitalAccessibility" #HindiTts #Google #Flutter #Dart

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A Day at the Apache Kafka 🚀 Today, I had the pleasure of attending an in-person Apache Kafka hosted by CRED in Bangalore. Here’s my feedback on this incredible event: 📍 Venue: Cred Aero, Bengaluru 🔹 Agenda Highlights: - @Vikas Sharma , CRED : Talk: Running Kafka Optimally in Production: Practical Usage Feedback: Vikas shared practical insights on optimizing Kafka in production environments, with valuable strategies for resource management. - Udayaram Kammara , Bazaarvoice : Talk: How Bazaarvoice Utilizes Kafka for Both Stream and Batch Processing Scenarios Feedback: Udayaram's session on Kafka's use at Bazaarvoice was enlightening, covering real-world use cases and challenges. - Diptiman Raichaudhuri , Confluent : Talk:Stream Processing for the Non-JVM World: pyflink and kafka-python Feedback: Diptiman's explanation of Apache Flink's architecture and the demonstration of kafka-python and pyflink were fascinating and beneficial for non-JVM users. What I Learned: - Effective strategies for running Kafka in production. - Real-world use cases and challenges in stream and batch processing. - Leveraging pyflink and kafka-python for stream processing. Overall, this meetup was a treasure trove of knowledge and inspiration. The sessions were well-organized, and the speakers were exceptionally knowledgeable. I’m excited to implement these learnings in my projects and continue exploring the potential of Apache Kafka and related technologies. 🙌 #ApacheKafka #KafkaMeetup #CRED #Bangalore #EventStreaming #StreamProcessing #TechCommunity #Networking #Python #Flink #DataPlatforms

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ok time for new vocabulary. my buddy Vinod Sankar used the term hallucinations in the context of some LLM discussions we were having in a previous post. I had never heard of the word used in the context of LLMs(yes i am not upto date on terminology) so i asked my fav LLM what it meant. Here is the response "In the context of large language models (LLMs) like GPT, hallucinations refer to instances when the model generates text or information that appears plausible but is factually incorrect, misleading, or entirely fabricated. These "hallucinations" occur due to the nature of LLMs, which predict text based on patterns learned from massive datasets rather than understanding facts or accessing a database in real-time." Its brilliant usage and apt terminology. AI models are slowly getting a human face.

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I feel this debate about whether LLMs are intelligent and can actually reason or not is rather moot for most practical applications. Even if LLMs are just pattern matching systems, if the pattern matching is happening over a vast output of human reasoning, does it matter ? LLMs may not solve entirely new problems from first principles. But if they can solve such problems for everyone, that have been solved at least once by someone, that alone is a massive increase of intelligence available on tap to boost productivity.

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Live streaming is all about CDN 🤦‍♂️ If you are one of them, watch this podcast to understand the infra setup, the scaling techniques, and the challenges it takes to livestream a cricket match to 20 million concurrent users without hiccups. I spoke with Prachi Sharma, a Sr. Engineering Director at JioCinema leading the Backend Platform pod to understand the nuances of live streaming at scale and some fun wartime stories. Fun fact, it was Prachi's 6th IPL 🤯 give it a watch - youtu.be/36N1Bz7qW0A I am on a mission to bring out the best engineering stories from around the world and make you all fall in love with engineering. If you resonate with this then follow along, I always keep it no-fluff. #AsliEngineering x #JioCinema

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Top learnings from my discussion with Kishore Gopalakrishna - Language choice for system development should balance team expertise, hiring potential, and ecosystem maturity. A well-implemented system in a familiar language often outperforms a poorly implemented one in a "faster" language. - Distributed systems are complex - they can fail in unexpected ways, making your own computer useless. This challenge is often underestimated. Understand the failure modes. - When building new systems, design for the long-term (10 years) but plan for the near-term (2-3 years). Implement for current needs while keeping the architecture flexible for future growth. Design for long term, plan for short-medium term. - Testing distributed systems requires going beyond known test cases. Develop test frameworks that generate new scenarios automatically to uncover unforeseen issues. - Cost optimization in data systems often involves trade-offs. Kafka, for example, sacrificed transactionality and exact-once delivery for higher throughput and lower cost per log. Its all about the Trade-offs. You get some, you lose some. - When approaching problems, start with first principles. Understand why something is expensive or inefficient before trying to optimize it. Don't jump on a solution. Understand the real problem first. - Open-source success relies heavily on community building. Foster respectful communication, clear processes for resolving conflicts, and a culture of helpfulness to grow a strong contributor base. There are many communities, but only the ones with good culture thrive. - Real-time analytics can significantly boost user engagement. Even small reductions in data latency can lead to measurable improvements in user experience and product value. - When considering real-time analytics, focus on how it can improve your users' lives rather than just technical capabilities. Many support issues can be eliminated by giving users direct access to real-time data. Product isn't useful if it making users' lives better. - Innovation in data products often comes from changing perspective - instead of asking "Do we need this?", ask "How can this improve our users' experience?" This shift can uncover valuable use cases for real-time analytics. Asking the right questions will help you reach the right answers. - When building data products, consider the long-term evolution of technology. Design choices should anticipate future developments, such as improvements in network speed or storage capabilities. Keep your design extensible. - As users become more data-savvy, there's increasing demand for self-serve analytics tools. Empowering users with direct access to data can reduce support overhead and enable more informed decision-making. Empower users to do more with data quickly. ———————————————————————— In case you haven't watched the episode yet, you can watch it here: youtu.be/SmLlJmU9Daw Also subscribe if you like such discussions.

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"𝐁𝐫𝐚𝐜𝐞 𝐟𝐨𝐫 𝐢𝐦𝐩𝐚𝐜𝐭" 𝐰𝐡𝐞𝐧 𝐃𝐡𝐨𝐧𝐢 𝐚𝐫𝐫𝐢𝐯𝐞𝐬: 𝐇𝐨𝐰 𝐉𝐢𝐨𝐂𝐢𝐧𝐞𝐦𝐚 𝐄𝐧𝐬𝐮𝐫𝐞𝐬 𝐅𝐥𝐚𝐰𝐥𝐞𝐬𝐬 𝐈𝐏𝐋 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐭𝐨 𝟐𝟎 𝐌𝐢𝐥𝐥𝐢𝐨𝐧 𝐅𝐚𝐧𝐬 "𝐁𝐫𝐚𝐜𝐞 𝐟𝐨𝐫 𝐢𝐦𝐩𝐚𝐜𝐭," is a term that JioCinema tech team uses when Dhoni enters. - When Dhoni arrives, spikes are gradual - Playback system and playback API's get a spike - When Dhoni gets out, all the APIs on the home page gets a spike as all are exiting the live stream and going to watch something else When millions of IPL fans tune in to watch their favorite teams, the stakes are incredibly high. At JioCinema, the tech team understands the passion and excitement that cricket brings. Here’s a glimpse into the meticulous preparation and innovative solutions that make it possible, shared by 𝐏𝐫𝐚𝐜𝐡𝐢 𝐒𝐡𝐚𝐫𝐦𝐚, 𝐒𝐫 𝐄𝐧𝐠. 𝐃𝐢𝐫. 𝐚𝐭 𝐉𝐢𝐨𝐂𝐢𝐧𝐞𝐦𝐚. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐅𝐚𝐧𝐬' 𝐄𝐦𝐨𝐭𝐢𝐨𝐧𝐬: The IPL is more than just a sporting event, it's an emotion that unites millions. Each match is a high-stakes event. 𝐒𝐭𝐢𝐜𝐤𝐞𝐫 𝐒𝐡𝐨𝐜𝐤: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: A new sticker-sharing feature caused a surge of 1 million requests per second on CDN. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: The team optimized cache TTL and bundled stickers into a single API call, reducing requests from 10 million RPS to 200K RPS. 𝐃𝐍𝐒 𝐃𝐢𝐥𝐞𝐦𝐦𝐚: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Users on a specific ISP couldn't access the app due to DNS resolution failures. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: Google's public DNS and a fallback DNS resolver were implemented within the app. 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Handling unexpected traffic spikes, especially during key moments in the match. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: The databases were pre-scaled based on detailed calculations and increased cache TTLs. They also implemented panic modes with static responses to ensure continuity even under heavy load. 𝐂𝐃𝐍 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Managing multiple CDNs to handle massive traffic and ensure redundancy. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: The multi-CDN optimizer dynamically switches traffic based on real-time performance, ensuring seamless streaming throughout the match. 𝐀𝐝 𝐈𝐧𝐬𝐞𝐫𝐭𝐢𝐨𝐧: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Delivering targeted ads without disrupting the viewing experience. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: Use server-side ad insertion with cohort targeting to ensure personalized ads reach the right viewers without affecting stream quality. 𝐏𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: Preparation for the IPL starts in Nov if the season is from march, with extensive tests and audits to ensure system resilience. From database scaling to CDN optimization, every aspect is fine-tuned to handle the unique challenges each IPL season brings. Watch the full video here: https://lnkd.in/gsivAuzN #IPL2024 #LiveStreaming #TechInnovation #EngineeringExcellence #JioCinema #TechChallenges #CricketPassion #SeamlessStreaming #BackendEngineering #CDNOptimization #DatabaseScaling #AdTech 

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The filtering system, as it exists today at many MAANG companies, has been thoroughly gamed. As leaders, its extremely important to be cognizant of this parallel ecosystem/factory dishing out this pipeline of engineers ready to ace these templatised "interviews". As difficult as it is, We should constantly evolve ourselves and our panelists to stay ahead of the curve to really filter out the fluff from real talent. Also it becomes extremely important to do a thorough background check on prior work and feedback.

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Another one generated through chatgpt by giving a prompt ======================= "Prompt Engineering and Spiritual Wisdom: Guiding the Outcome" Prompt engineering, the art of crafting precise inputs to guide AI's output, parallels the teachings of Bhagavad Gita on focused action and intention. In the Gita, Krishna emphasizes the importance of clear intention and selfless action to achieve desired outcomes. Just as Krishna guides Arjuna to act with clarity and purpose, a well-crafted prompt directs AI systems to generate meaningful and accurate results. In prompt engineering, the quality of the input (the prompt) defines the output. Similarly, Krishna teaches that by focusing on the right intentions (sankalpa), without attachment to the fruits of action, we can align ourselves with higher outcomes. A vague or poorly defined prompt leads to scattered results, just as unfocused action leads to confusion in life. Krishna's wisdom in the Gita is about clarity of thought and precision in action—a principle that underpins prompt engineering. By understanding the context, setting the right parameters, and giving clear instructions, we guide AI to create relevant outputs, much like how Krishna guides Arjuna to take the right course of action. Thus, prompt engineering is like karma yoga—taking intentional actions (prompts) with wisdom and detachment, leading to the best possible outcomes both in AI and life.

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Yajnadevam is a software engineer how has worked in crypto graphy and has used Shannon's information theory to decipher Indus scripts. interesting video to watch. will try to find his research paper to read and get more details.... #crytptogram #linguistics #information #iconography https://lnkd.in/gBBsXaG6

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🏆 𝗭𝗲𝗿𝗼 𝘁𝗼 𝗛𝗲𝗿𝗼 𝗶𝗻 𝗛𝗶𝗴𝗵-𝗟𝗲𝘃𝗲𝗹 𝗗𝗲𝘀𝗶𝗴𝗻 | 🚀 𝗔𝗰𝗲 𝗬𝗼𝘂𝗿 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 | 𝗔𝗽𝗿𝗶𝗹 𝟮𝟬𝟮𝟰 𝗖𝗼𝗵𝗼𝗿𝘁 Starts 20th April The goal of this cohort is to make you learn how to design large-scale distributed systems from scratch so that you'll not only rock interviews but also become a pro in HLD. This means, when you join a company or work on a project, you'll be able to create their HLDs as well. In this cohort, we will not only solve design problems and learn concepts but we will also understand how to make tradeoffs and decide between the choices. This cohort will help you build your intuition on how to approach an HLD problem. For every topic, we will start with a simple system understanding without using any fancy technologies just like the way we used to do in our college and POC projects. From there, we will start understanding issues in terms of scalability, availability, consistency, reliability, etc., and will keep solving them one by one. The full structure of the cohort can be found on registration page below: https://lnkd.in/g-VVEmSU

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Good read

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Today I have gone through an amazing conversation about how JioCinema is able to scale to an amazing level for 20M concurrent devices and the engineering goes behind the scenes. This discussion between Arpit Bhayani and Prachi Sharma is very much helpful to anyone who is passionate about large scale system design. Also, you can relate to it if you are doing similar practices in your organization. These are few key takeaways, how the company does engineering at this mega scale and serve customers asynchronous non-blocking way - 1. How building appropriate alerts and monitoring helps. 2. Importance of feature flags. Different kinds of flags based on severity. 3. Importance of system audits before upcoming peak seasons. Each of the service owners should have a system audit ahead of time and find the breaking point of the system and be ready with appropriate fallback. 4. Perf / load testing to set correct infra. 5. Every system metrics review in terms of load and TPS. 6. Exponential backof retry. 7. Trace a request E2E. 8. Understanding the DB is a difficult scaling factor - immediate auto scaling the kubernetes pod is not always easy as it takes time to trigger the auto scaling. 9. Get the back of the envelope calculation done ahead of time and prescale upfront. 10. How important it is to make the database up. 11. How cache helps and understanding what to cache. 12. How plan B helps and how the fallback for crucial service APIs matters to provide a smooth experience. 13. Panic mode - Invisible to customers and activated personalized fallback. 14. How multi CDN helps to decide which traffic should go to which servers 15. Real engineering - cache offload best practices. 16. As everything comes with cost, so decide which CDN call should go to the server that eventually calls db and which one should utilize cache data. 17. Asynchronous processing during live matches. 18. Kafka - producer rate, consumer rate and decide partitions. 19. How ads get displayed and things happen behind the scenes, etc. Many thanks to Arpit Bhayani and Prachi Sharma for sharing such an insightful conversation! I can relate the same to my organisation practices when peak sale time comes to the eCommerce shopping sites. Check out the conversation here -

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What happens under the hood when you turn on your Android mobile phone When you power on your mobile phone, a meticulous self-test is conducted on all the hardware components. This comprehensive check ensures that every part of your device is functioning flawlessly. Once this rigorous hardware test is successfully completed, the Boot ROM, stored on the CPU, initiates the loading process. The bootloader loads Android OS, validating certain aspects of the initial loading. The kernel loads device drivers and background processes and manages the system resources. The init process loads and mounts directories and files and starts various processes. The Android runtime is initialized and is mainly used by the Android operating system. As the runtime, ART executes the Dalvik executable (DEX) format and DEX bytecode specification. After these system services are started, the activity manager kicks in to bring the user the initial screen to start interacting. Power-On Self-Test (POST): When you press the power button, the phone undergoes a quick self-check process known as POST. This test ensures that essential hardware components like the processor, memory, and battery function correctly. Bootloader: After the POST is successfully completed, the bootloader, a small yet crucial program stored in the phone's read-only memory (ROM), takes charge. It not only initializes the hardware but also orchestrates the booting process, including selecting the operating system to load. Loading the Kernel: The bootloader loads the Android kernel into the device's RAM (Random Access Memory). The kernel is the core of the Android operating system. It manages the device's resources, such as the CPU, memory, and input/output operations. Init Process: Once the kernel is loaded, the init process (short for initialization) is started. This process is responsible for initializing the Android system and launching essential system services and daemons. Android Runtime (ART) Initialization. This is a critical step as ART is the runtime environment where Android applications come to life. It's responsible for a significant task and compiles the application code into machine code, ensuring efficient execution on the device's hardware. This makes ART a crucial part of the booting process. Starting System Services: After the initialization of ART, various system services are started. These services include the Android System Server, which manages core system components like the package manager, activity manager, and window manager. Launcher and User Interface: Once the system services are up and running, the launcher (the user interface that displays your home screen, app icons, and widgets) is started. This allows you to interact with your device and access your installed applications. Loading Applications: Any pre-installed applications and services configured to start automatically are loaded. These may include system apps like the dialer, messaging app.

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What happens under the hood when you turn on your Android mobile phone When you power on your mobile phone, a meticulous self-test is conducted on all the hardware components. This comprehensive check ensures that every part of your device is functioning flawlessly. Once this rigorous hardware test is successfully completed, the Boot ROM, stored on the CPU, initiates the loading process. The bootloader loads Android OS, validating certain aspects of the initial loading. The kernel loads device drivers and background processes and manages the system resources. The init process loads and mounts directories and files and starts various processes. The Android runtime is initialized and is mainly used by the Android operating system. As the runtime, ART executes the Dalvik executable (DEX) format and DEX bytecode specification. After these system services are started, the activity manager kicks in to bring the user the initial screen to start interacting. Power-On Self-Test (POST): When you press the power button, the phone undergoes a quick self-check process known as POST. This test ensures that essential hardware components like the processor, memory, and battery function correctly. Bootloader: After the POST is successfully completed, the bootloader, a small yet crucial program stored in the phone's read-only memory (ROM), takes charge. It not only initializes the hardware but also orchestrates the booting process, including selecting the operating system to load. Loading the Kernel: The bootloader loads the Android kernel into the device's RAM (Random Access Memory). The kernel is the core of the Android operating system. It manages the device's resources, such as the CPU, memory, and input/output operations. Init Process: Once the kernel is loaded, the init process (short for initialization) is started. This process is responsible for initializing the Android system and launching essential system services and daemons. Android Runtime (ART) Initialization. This is a critical step as ART is the runtime environment where Android applications come to life. It's responsible for a significant task and compiles the application code into machine code, ensuring efficient execution on the device's hardware. This makes ART a crucial part of the booting process. Starting System Services: After the initialization of ART, various system services are started. These services include the Android System Server, which manages core system components like the package manager, activity manager, and window manager. Launcher and User Interface: Once the system services are up and running, the launcher (the user interface that displays your home screen, app icons, and widgets) is started. This allows you to interact with your device and access your installed applications. Loading Applications: Any pre-installed applications and services configured to start automatically are loaded. These may include system apps like the dialer, messaging app.

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System Design interview question asked at Atlassian : "Real-Time Emoji Broadcast for 10 Million concurrent users?" Have you wondered how Emoji's backend works at Disney+ Hotstar for millions of concurrent users? I have dissected this problem in my latest article in my newsletter - "Y School Of Tech" designed for engineers curious to learn the why behind every Tech concept. I have written a systematic approach to solving this real-world System Design problem. The link is in the comment. I am writing tech designs for real-world System Design problems. Consider subscribing to keep yourself updated here - https://lnkd.in/gt2Xuv-n #InterviewQuestion #YSchoolOfTech #Staycurious #SystemDesign #Architecture

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𝐓𝐇𝐑𝐄𝐀𝐃 𝐒𝐀𝐅𝐄𝐓𝐘 - 𝙆𝙚𝙚𝙥𝙞𝙣𝙜 𝙩𝙝𝙚 𝙆𝙞𝙩𝙘𝙝𝙚𝙣 𝘾𝙡𝙚𝙖𝙣 𝙒𝙝𝙞𝙡𝙚 𝙈𝙪𝙡𝙩𝙞𝙥𝙡𝙚 𝘾𝙤𝙤𝙠𝙨 𝙒𝙤𝙧𝙠 𝙏𝙤𝙜𝙚𝙩𝙝𝙚𝙧 👩‍🍳👨‍🍳 🧠 𝐉𝐚𝐯𝐚 𝐁𝐮𝐟𝐟𝐞𝐭 : Day 6 of #30DaysOfJava (Extending analogy of Post 3 : https://lnkd.in/g3uBywUN) Picture a busy kitchen where multiple cooks are working on different dishes. Now, imagine they’re all using the same cutting board, and if they’re not careful, they could end up spilling ingredients everywhere, mixing up their dishes, or even cutting each other’s fingers! This is why we need thread safety in @Java. In Java, thread safety ensures that multiple threads can work with shared resources (like a cutting board) without causing errors or conflicts. Properly managing these shared resources, whether it’s by using locks or other mechanisms, keeps everything organized and prevents mistakes—just like in a well-run kitchen where each cook respects the shared tools and space. 📌 Key Takeaway: Thread safety in Java is like making sure multiple cooks can work together in a shared kitchen without making a mess or hurting each other! #java #Oracle

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𝐃𝐄𝐀𝐃𝐋𝐎𝐂𝐊 - 𝙒𝙝𝙚𝙣 𝙏𝙬𝙤 𝘾𝙤𝙤𝙠𝙨 𝘼𝙧𝙚 𝙎𝙩𝙪𝙘𝙠 𝙒𝙖𝙞𝙩𝙞𝙣𝙜 𝙛𝙤𝙧 𝙀𝙖𝙘𝙝 𝙊𝙩𝙝𝙚𝙧 🍳🍲 🧠 𝐉𝐚𝐯𝐚 𝐁𝐮𝐟𝐟𝐞𝐭 : Day 4 of #30DaysOfJava (Extending analogy of Post 3 : https://lnkd.in/gE9mb6dJ) Imagine two cooks in a kitchen. One has a frying pan and needs the spoon to finish their dish, while the other has the spoon but needs the pan. Each cook is waiting for the other to release the tool they need, but neither will budge. As a result, nothing gets done, and both dishes remain uncooked! This is what deadlock in Java looks like. In Java, deadlock occurs when two or more threads are stuck waiting for each other to release resources. Each thread holds a resource and refuses to give it up until it gets another resource from the other thread. Since neither can proceed, the program grinds to a halt—much like our cooks staring at each other in frustration, unable to finish their dishes. 📌 Key Takeaway: Deadlock in Java is like two cooks waiting for each other to give up a tool they need. Both end up stuck, and nothing gets done! Thoughts on the analogy ? If you are looking for referrals and other tech job related stuff, check out Ramit Kundu 's discord community : https://lnkd.in/g9Mupq_3 #java #Oracle #jobs #referral

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