
Cloud ☁️ AI 🧠 DevOps - CS grad @UNTsocial 🎓
Texas, USA
Joined April 2024
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DK retweeted
Whoa that’s a full rainbow, all the way, double rainbow ohmygod, it’s a double rainbow all the way
DK retweeted
Learn how to supercharge #generativeAI workflows with NVIDIA DGX Cloud on #AWS & #AmazonBedrock Custom Model Import
👉 go.aws/44YoyQw
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Sundar Pichai@sundarpichai16 Jul 2025
United States
United StatesConnected via United States Android AppAccount-level information, not a live location or per-post device.
New advanced AI features coming to Search:
Gemini 2.5 Pro and Deep Search are rolling out in AI Mode to help with more complex questions and research - available now to Google AI Pro/Ultra subscribers. Plus, a new agentic capability directly in Search: AI-powered calling to local businesses, rolling out to all users in the US.
DK retweeted
Today is the start of a new era of natively multimodal AI innovation.
Today, we’re introducing the first Llama 4 models: Llama 4 Scout and Llama 4 Maverick — our most advanced models yet and the best in their class for multimodality.
Llama 4 Scout
• 17B-active-parameter model with 16 experts.
• Industry-leading context window of 10M tokens.
• Outperforms Gemma 3, Gemini 2.0 Flash-Lite and Mistral 3.1 across a broad range of widely accepted benchmarks.
Llama 4 Maverick
• 17B-active-parameter model with 128 experts.
• Best-in-class image grounding with the ability to align user prompts with relevant visual concepts and anchor model responses to regions in the image.
• Outperforms GPT-4o and Gemini 2.0 Flash across a broad range of widely accepted benchmarks.
• Achieves comparable results to DeepSeek v3 on reasoning and coding — at half the active parameters.
• Unparalleled performance-to-cost ratio with a chat version scoring ELO of 1417 on LMArena.
These models are our best yet thanks to distillation from Llama 4 Behemoth, our most powerful model yet. Llama 4 Behemoth is still in training and is currently seeing results that outperform GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM-focused benchmarks. We’re excited to share more details about it even while it’s still in flight.
Read more about the first Llama 4 models, including training and benchmarks ➡️ go.fb.me/gmjohs
Download Llama 4 ➡️ go.fb.me/bwwhe9
DK retweeted
Llama 4 Herd is here!
It brings a lot of goodies, like MoE architecture and native multimodality, enabling developers to build personalized multimodal experiences.
With Day 0 support in vLLM, you can deploy Llama 4 with @vllm_project now!
Let's dig into it.
(a thread)
DK retweeted
Thrilled to bring Meta’s Llama 4 Scout and Maverick to Foundry today, as we continue to make Azure the platform of choice for the world’s most advanced AI models. azure.microsoft.com/en-us/bl…
DK retweeted
𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝘃𝘀 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿 𝘃𝘀 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗣𝗿𝗼𝘅𝘆
When designing a system, we often need clarification about the roles of a Load Balancer and an API Gateway. Most of our resources focus on their implementation rather than real-life use cases.
🔹 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 sits between a client and a group of backend services. It performs the function of a reverse proxy by accepting all application programming interface (API) calls, aggregating the different services needed to fulfill them, and returning the right outcome. 𝗨𝘀𝗲𝗿 𝗮𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗿𝗮𝘁𝗲 𝗹𝗶𝗺𝗶𝘁𝘀, 𝗮𝗻𝗱 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 are typical duties that API gateways take care of on behalf of a system of API services. Also, the API gateway can handle faults (circuit breaker) and log and monitor.
🔹 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿 is a service that distributes incoming traffic across many servers or resources. Usually, we have two or more web servers on the backend, and it 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝘀 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘁𝗵𝗲𝗺. Its primary purpose is to use resources optimally. A more equal task allocation and increased capacity can enhance the system's responsiveness and reliability. There are three high-level load balancers: hardware-based, cloud-based, and software-based.
🔹 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗣𝗿𝗼𝘅𝘆 server resides in front of backend servers and transfers client requests to these servers. Reverse proxies are typically used to increase security, speed, and dependability. A reverse proxy receives a request from a client, forwards it to another server, and then returns it to the client, giving the impression that the first proxy server handled the request. These proxies ensure that users don't access the origin server directly, giving the web server anonymity. They are usually used for Load balancing, where we need to deal with incoming traffic flow so that we may distribute that traffic between multiple backend servers or use them for caching.
So, the main thing that differs these two is that an 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝗶𝘀 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝗿𝗼𝘂𝘁𝗶𝗻𝗴 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 to the appropriate service and it handles requests for APIs, while a 𝗟𝗼𝗮𝗱 𝗯𝗮𝗹𝗮𝗻𝗰𝗲𝗿 𝗶𝘀 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗻𝗴 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 𝗲𝘃𝗲𝗻𝗹𝘆 between a group of servers and handles requests that are sent to a single IP address, which works at protocol or socket level (TCP, HTTP).
Some 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 of API Gateways are Amazon API Gateway, Ocelot (.NET-based), Tyk, or Apache APISIX, while Load Balancers are Azure Load Balancer, HAProxy, or Seesaw.
An example of reverse proxy services is 𝗔𝗽𝗮𝗰𝗵𝗲 𝗣𝗿𝗼𝘅𝘆, 𝗡𝗴𝗶𝗻𝘅 𝗼𝗿 𝗜𝗜𝗦 with additional modules (Url Rewrite).
#technology #softwareengineering #programming
DK retweeted
RIP Google and LinkedIn!
Deepsearch in Grok 3 is the future of job hunting.
Here’s a step~by~step guide 👇
(Bookmark for later)
Kubernetes Security (where to focus)🔥🔥
1. RBAC
2. Network Policies
3. Secrets Manager
4. PSD & PSA
5. Image Security & Runtime Protection
DK retweeted
Challenges of Using Different IaC Tools and Role of Terraform
👉Using different Infrastructure as Code (IaC) tools for different cloud providers presents several challenges.
➡️Here's a breakdown of the issues:
👉Vendor Lock-In and Script Incompatibility:
1.When an organization uses tools specific to a cloud provider, such as AWS CloudFormation Templates (CFT) for AWS or Azure Resource Manager for Azure, the scripts created for one cloud platform are not compatible with others.
2.This means that if the organization decides to switch cloud providers, all of the existing automation scripts must be rewritten for the new platform. This can be very time-consuming and require a lot of effort.
👉Increased Complexity in Hybrid Cloud Environments:
Many companies operate in a hybrid cloud model, utilizing multiple cloud platforms such as AWS and Azure. In this model, if different tools are used for each cloud, a DevOps engineer has to learn multiple tools to manage the infrastructure across these different cloud providers.
👉Need to Learn Multiple Tools:
The variety of tools for different platforms means that DevOps engineers need to learn multiple tools, such as AWS CFT, Azure Resource Manager, and Heat templates for Openstack, depending on the platforms the organization uses. This increases the learning curve and the complexity of managing infrastructure.
👉Difficult Migrations:
The need to rewrite scripts when migrating from one cloud provider to another is a major problem. The scripts written for one cloud provider can not be used on another, requiring significant effort to rewrite them.
➡️Terraform addresses these issues by acting as a common interface that translates instructions into API calls for each specific cloud provider.
➡️This tool can manage infrastructure across different cloud providers using a single language and set of scripts.
🚀 Choosing the Right AWS Database: RDS vs. DynamoDB vs. ElastiCache! 🚀
💡 Struggling to decide which database is best for your workload?
✅ Need structured SQL data with transactions? → RDS
✅ Looking for high-speed, serverless NoSQL? → DynamoDB
✅ Want ultra-fast caching to boost performance? → ElastiCache
⚡ Each has its strengths - pick the right one for your needs! Let’s break it down. ⬇️
DK retweeted
this is how i learn gpu programming and CUDA to make it easier for me:
- the most important part is to really understand how the GPU is structured and the memory hierarchy within it i.e. global memory, constant memory, caches, shared memory, registers, and so on.
- secondly, stop thinking sequentially. i mean think about how the execution of your operation will look like in parallel. think about the data flow and the operations involved.
- CUDA relies on grid of blocks, and blocks of threads. understand the distinction between when to map your data to the threads, and when to map the threads to your data.
- before jumping to writing any kernels, take a pen and a paper and just start visualizing the threads (and blocks) of the kernel and what you want them to do.
- for CUDA, make sure you understand pointers and how you can play around with the indices to fetch/write data.
- really pay attention while reading the book below, and also watch the corresponding video. read the code of others to try and understand what's happening there.
- practice, practice, practice.
Amazon provides three primary options for running containers: ECS (Elastic Container Service), EKS (Elastic Kubernetes Service), and Fargate.
Let's understand them one by one 👇👇


























