Muhammad Hasan K.

Day 51/100 of 100-day DSA Q.1 https://lnkd.in/gwR9ext7 Q.2 https://lnkd.in/gerrh9kw #DataStructures #Programming #Coding #Tech #ComputerScience #SoftwareEngineering #LeetCode #CodeOptimization #TechCommunity #ProblemSolving #Cpp #ProgrammingChallenges #TechSkills #100DaysOfCode #100DaysOfDSA #DSAChallenge #DailyCoding #CodeEveryday #CodingChallenge #Backtracking #Stacks #Queues #SlidingWindow #TwoPointers

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Amazon MemoryDB: A fast and durable memory-first cloud database with Redis protocol support https://lnkd.in/eJUwwuR6

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You can now create a million S3 buckets per account, but the buckets beyond two thousand will now be charged at a rate of $0.02 per bucket. That’s… I have no words. If you were relying on quotas to prevent bucket sprawl, well you’ll have to change that. https://lnkd.in/ge7_FacW #aws #s3

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Is Protobuf the future default for everything? Timothy Chen & I chatted with Buf CTO Akshay Shah about the future of Protobuf, their new Kafka-compatible streaming layer, and everything he learned scaling Uber that led him to build Buf. This episode covered a pain point I've repeatedly experienced while working at Snyk & see at many other companies: managing cross-team migrations and building a productive internal API ecosystem. Here's a great example: You have two services (A and B), and A depends on B. How do you modify an API or Event in A without requiring B to do a complicated lock-step migration? These are the challenges in any at-scale organization where a project that should take 30-60 days turns into months as you try to navigate alignment and delivery. They're mundane papercuts that turn into hundreds of hours of lost productivity. I love what Buf are doing to help make this a problem of the past.

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Ok, so I get a lot of people asking how to setup custom, secure, safe, “ChatGPT Like” tools with access to proprietary information. In the procurement world this could be Spending and Transaction Policies, Delegations of authority, spend thresholds or any other or a myriad SOP’s. It can even be highly detailed category specific knowledge that you you want to surface to specific individuals. Now you can with #AmazonQ. Whether it’s straight for q&a or more advanced clause extraxtion, it’s all available right now. It’s easy to setup, super safe and secure and empowers procurement teams like nothing else! If you want to know how to get the most out of Generative AI tools in procurement (whether it’s a RAG like solution like this or something else) ping me and I will be glad to share my experiences! The world is changing procurement folk… let’s make sure we change with it! #aws, #amazon, #llm, #chatgpt, #innovation, #procurement, #generativeAI

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when using structured outputs with llms, mirascope makes it easy to retry on validation error and insert the error into the subsequent call this feature ensures end users maintain full control over the retry logic while keeping it simple ti implement docs: https://lnkd.in/gUFY2x-P

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Cloud-native development enables running applications on more powerful & sustainable hardware platforms. GitHub Actions hosted runners open the possibility for developers to refactor their applications to take advantage of the price & performance benefits of Arm architecture: https://lnkd.in/d7CeNUS4

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Gemini 1.5 Flash: The Tool-Use Champ Budget-friendly, yet powerful: Gemini 1.5 flash function calling is both cost-effective and highly capable. Outperforms GPT-4o and Claude 3.5 Sonnet in tool-use: Especially shines in function calling and tool-use scenarios. Why it stands out: Seamless function calling makes workflows faster and easier. Consistently beats more expensive models in speed and accuracy for tool-use tasks.

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Redis is a powerful tool, but what happens when you need to scale it without clustering? Check out our recent blog post on "Scaling Redis without Clustering". We share insights on how to manage performance and speed without compromising on Redis's capabilities. Find it here: https://hubs.la/Q02x3ZNG0

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Cache eviction strategies are crucial in optimizing memory usage and maximizing system performance by efficiently managing cached data. 𝗕𝗲𝗹𝗼𝘄 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗖𝗮𝗰𝗵𝗲 𝗘𝘃𝗶𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 : ✔️ First In First Out (FIFO): The cache removes the first accessed block without considering its frequency or previous access count. ✔️ Last In First Out (LIFO): The cache removes the most recently accessed block without considering its frequency or previous access count. ✔️ Least Recently Used (LRU): Removes the least recently used ✔️ Most Recently Used (MRU): Removes the most recently used items. ✔️ Least Frequently Used (LFU): Removes least frequently used items by counting. ✔️ Window TinyLFU (W-TinyLFU): Prioritizes cache entries based on recentness and frequency of access to retain the most relevant data. ✔️ Time To Live (TTL): Cache entries expire based on set dates, ignoring access frequency or recency. #cache #distributedsystems #systemdesign #redis

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Cache eviction strategies are crucial in optimizing memory usage and maximizing system performance by efficiently managing cached data. 𝗕𝗲𝗹𝗼𝘄 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗖𝗮𝗰𝗵𝗲 𝗘𝘃𝗶𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 : ✔️ First In First Out (FIFO): The cache removes the first accessed block without considering its frequency or previous access count. ✔️ Last In First Out (LIFO): The cache removes the most recently accessed block without considering its frequency or previous access count. ✔️ Least Recently Used (LRU): Removes the least recently used ✔️ Most Recently Used (MRU): Removes the most recently used items. ✔️ Least Frequently Used (LFU): Removes least frequently used items by counting. ✔️ Window TinyLFU (W-TinyLFU): Prioritizes cache entries based on recentness and frequency of access to retain the most relevant data. ✔️ Time To Live (TTL): Cache entries expire based on set dates, ignoring access frequency or recency. #cache #distributedsystems #systemdesign #redis

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Large-scale distributed business systems can produce events at an enormous rate. A fundamental factor for business success is being able to learn from and react to these events. Allen (Xiaozhong) Wang explains in this blog post (https://lnkd.in/ddgfzrqQ) and this presentation (https://lnkd.in/dFThaaS3) how DoorDash created a real-time event processing system able to process a trillion events per day. The building blocks were Kafka, Schema Registry, Flink, and Kubernetes. The solution had to work with multiple heterogeneous data sources and destinations. It should deliver a great developer experience for easy adoption: data consumers should be able to access real-time events easily. The following technical requirements were also critical: - low latency and reliable data ingestion, - data quality: end-to-end schema enforcement and schema evolution. Allen Wang states that a few principles are common patterns in successful real-time data architectures: - decoupled stages, - leveraged by an effective stream processing framework, - abstractions that reduce cognitive load, - fine-grained failure isolation and scalability. Allen Wang dives into the main challenges and solutions in Doordash's journey to create Iguazu (real-time event processing system) and Riviera (SQL-based DSL framework to enable easy making of real-time ML features). #RealTimeData #EventProcessing #TechInnovation #Flink #Kafka

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Coffee & Systems (Study Group - Bengaluru) Week#5 (HPBN Speedread - Part 2 [HTTP]) December 22, 2024, 4:00 PM → 5:30 PM (Sunday) - The Market RMZ Ecoworld ------ Building on our discussion from last week, we'll explore the basic building blocks of HTTP. Since this is a comprehensive topic, we're splitting it across two weeks, with this session focusing on HTTP 1.x. Next week, we'll cover HTTP/2 and QUIC. - Brief History of HTTP - https://lnkd.in/gzPSvwwu - Primer on Web Performance - https://lnkd.in/gQnxzQkd - HTTP/1.x - https://hpbn.co/http1x/

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Earlier this week, we announced an update to how Amazon Q Developer Software Agent works - https://lnkd.in/gVmTUQhA. Our efforts since making Q Developer Generally Available in May '24 have propelled the agent for software development to new heights. The SWE-bench scores have increased significantly, rising from 25.6% to 38.8% on the verified dataset and from 13.82% to 19.75% on the full dataset. This progress is a result of our customer-centric approach, incorporating developer feedback, and rapidly iterating to address pain points. What sets our agent apart is its architecture, which not only leverages the best LLMs but also equips the agent with the ability to explore multiple paths continuously to find optimal solutions for problems, similar to how developers work. There is a FREE version of Amazon Q Developer available (https://lnkd.in/gJdU3pB5) for you to try and experiment with. Install or update to the latest version of the Q Developer IDE extension, then type "/dev" in the chat window to put the Q Developer Agent to work.

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LLM without access to your latest data is like a genius who’s been living under a rock for years. RAG is the solution: Learn how Airbnb is getting best out of LLMs by augmenting it with their data using RAG, embeddings and Vector Datastore. In the video Moutupsi Paul Xiaotang Wang and I diving deep into Airbnb comprehensive Embedding Platform that can support multiple use cases such as image search. You will Learn why OpenSearch and LLMs are better together and how to get most value with OpenSearch. Kudos and Thanks to the awesome people at Airbnb and OpenSearch Project to make this possible. Henry Saputra Tomas F. Yangbo Zhu Soumyadip Banerjee Mukul Karnik Carl Meadows Jon Handler Pavani Garimella Baddepudi Vamshi Vijay Nakkirtha Bob Taylor Parth Shah Ritz H. Mikhail Smelik Madhuri Peri JC Lima Lauren Larscheid #AI #LLM #OpenSearch #Airbnb #GenAI

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Coffee & Systems (Study Group - Bengaluru) Week#5 (HPBN Speedread - Part 3 [HTTP Cont.]) January 04, 2024, 4:00 PM → 5:30 PM (Satruday) - The Market RMZ Ecoworld ------ Continuing from last week we explore HTTP/2 and HTTP3/QUIC. They promise to make our applications faster, simpler, and more robust. HTTP/2 was specifically created to reduce the load latency of web pages by addressing some of the well-known performance limitations of HTTP/1.1. At the same time, QUIC/HTTP3 presents a new transport mechanism altogether to replace TCP. Companies are still rolling out a lot of these optimisations. This represents the cutting edge of advancements in networking protocols. Happy reading and see you all this Saturday :) - HTTP/2 - Chapter from HPBN --> https://hpbn.co/http2/ - QUIC: next-generation multiplexed transport over UDP --> https://lnkd.in/gc4M8ZFg - Explaining QUIC: the protocol that is both very similar to and very different from TCP By Peter Door --> https://lnkd.in/gnu2QBnV - HTTP/3: the past, the present, and the future --> https://lnkd.in/g6MSCpaS

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