Everseen

✅ Actively involved in all stages of ML project lifecycle.

𝐄𝐯𝐞𝐫𝐬𝐞𝐞𝐧 is an AI company that empowers end-to-end visibility across the entire supply chain for the biggest retail clients in the world, aiming to  reduce shrink and increase perpetual inventory accuracy using it’s VisualAI platform.

☑ Worked on a theft-prevention system that was showcased at 𝐍𝐞𝐰𝐘𝐨𝐫𝐤 𝐍𝐑𝐅2023, and at numerous demo-sessions that 𝐚𝐭𝐭𝐫𝐚𝐜𝐭𝐞𝐝 3 𝐧𝐞𝐰 𝐜𝐥𝐢𝐞𝐧𝐭𝐬 into wanting the product for their stores. 

☑ 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 project/tooling 𝐫𝐚𝐦𝐩-𝐮𝐩 phase for new members within the team, successfully reducing the period from 1 𝐦𝐨𝐧𝐭𝐡 𝐭𝐨 𝐥𝐞𝐬𝐬 𝐭𝐡𝐚𝐧 1 𝐰𝐞𝐞𝐤.

☑ Handled 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 engineering and 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 to 5 𝐩𝐢𝐥𝐨𝐭 𝐬𝐭𝐨𝐫𝐞𝐬.

☑ Improved the 𝐢𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐬𝐩𝐞𝐞𝐝 within the team by 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐌𝐋𝐎𝐩𝐬 practices such as Structured Model Experimenting, Resources Monitoring, Model Registry, CI/CD and Jira Integrations.

☑ 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐞𝐝 and established a 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 which enabled us to 𝐫𝐞𝐚𝐜𝐡 > 70% 𝐜𝐨𝐯𝐞𝐫𝐚𝐠𝐞 both on code,tools documentation and custom models tailored for various clients.

☑ Worked on a live camera feed processing pipeline that ran 3 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐮𝐥𝐞𝐬 while achieving 24-25 FPS on a 4K video stream with high QPS rate.

☑ Implemented a 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐟𝐨𝐫 𝐨𝐮𝐫 𝐞𝐝𝐠𝐞 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬 that saved time with hardware requirements and optimisations using Prometheus, Grafana, Kibana and ES.

☑ Actively 𝐩𝐚𝐫𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐞 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 of the current project, providing support to QA, Development and Deployment processes.

☑ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 the multi-gpu 𝐦𝐨𝐝𝐞𝐥 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 and integration with high-throughput Model Serving technologies like Nvidia Triton Server.

Senior Machine Learning Engineer

Neural Bits

✅ Writing the Neural Bits Newsletter to help AI/ML Engineers upskill in their career.
☑ Making AI accessible to a larger general audience by explaining concepts in simple terms.
☑ Sharing insights and practical advice on AI/ML Systems.

Author

Decoding ML

Sharing insights and actionable advice towards building production-ready ML systems.

Co-Founder

HandsOnLLMs

✅ Contributed to a free hands-on course on end-to-end LLMops.

Worked alongside 𝐏𝐚𝐮 𝐋𝐚𝐛𝐚𝐫𝐭𝐚 𝐁𝐚𝐣𝐨 𝐚𝐧𝐝 𝐏𝐚𝐮𝐥 𝐈𝐮𝐬𝐳𝐭𝐢𝐧 into developing this end-to-end course to help students 𝐛𝐮𝐢𝐥𝐝 𝐚 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐋𝐋𝐌 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 while following the 𝐛𝐞𝐬𝐭 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 both in software development, model deployment and MLOps integrations.

☑ Implemented the EDA stage on LLM fine-tuning dataset.
☑ Implemented the LangChain logic and modules that connected the QDrant vector database to inference pipeline and model registry.
☑ Worked on the training/fine-tuning pipeline and prompt monitoring using CometML.
☑ Provided support and approved Pull Requests.
☑ Implemented and documented best practices on Prompt Templates and LLM workflow.

Check the course here: http://bit.ly/3Rxjyvp

Co Author

IVFuture

✅ Worked on on a suite of tools including Generative AI, LLM’s and Vision Models.

𝐈𝐕𝐅𝐮𝐭𝐮𝐫𝐞’𝐬 mission is to transform common businesses, communities and people’s needs into intelligent and innovative software solutions, to simplify life through scalable technology.

☑ Implemented a 𝐝𝐞𝐦𝐨 for virtual try-on fashion tool called 𝐓𝐀𝐈𝐒, which was presented to investors and attracted interest from various fashion providers across Romania.

☑ Implemented a CV solution, that was able to accurately showcase over 25 𝐛𝐨𝐝𝐲 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭𝐬 𝐢𝐧 3𝐃 using 2D cameras, with an 𝐞𝐫𝐫𝐨𝐫-𝐫𝐚𝐭𝐞 𝐨𝐟 5-10% while using cheap hardware, <700$ 𝐩𝐞𝐫 𝐬𝐞𝐭𝐮𝐩, considering industry available setups 𝐞𝐱𝐜𝐞𝐞𝐝 10.000$.

☑ Developed and integrated an 𝐋𝐋𝐌 13𝐁 𝐜𝐡𝐚𝐭 𝐚𝐠𝐞𝐧𝐭, fine-tuned on specific descriptions that was used by the team to automatically 𝐥𝐚𝐛𝐞𝐥 𝐡𝐮𝐧𝐝𝐫𝐞𝐝𝐬 𝐨𝐟 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬.

☑ Fine-tuned 𝐒𝐭𝐚𝐛𝐥𝐞 𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧1.5 and tailored it to specific product generation use-case.

☑ Worked on a solution for 𝐓𝐓𝐒 𝐚𝐧𝐝 𝐒𝐓𝐓 and exposed it as an API, which was able to accurately 𝐜𝐥𝐨𝐧𝐞 𝐯𝐨𝐢𝐜𝐞𝐬 𝐟𝐫𝐨𝐦 𝐚 𝐬𝐡𝐨𝐫𝐭 5𝐬𝐞𝐜 𝐚𝐮𝐝𝐢𝐨 𝐜𝐥𝐢𝐩 and synthesize text-to-speech and speech-to-text.

☑ Worked on a 𝐑𝐀𝐆 𝐚𝐠𝐞𝐧𝐭, that was able to ingest document context and provide assistant-like answers on user queries.

Machine Learning Consultant - Generative AI

Odeen Software

✅ Worked on Flowstate, a Computer Vision solution developed for 𝐔𝐑𝐁𝐍𝐒𝐔𝐑𝐅 𝐌𝐞𝐥𝐛𝐨𝐮𝐫𝐧𝐞, 𝐀𝐮𝐬𝐭𝐫𝐚𝐥𝐢𝐚, that generates 𝐬𝐨𝐜𝐢𝐚𝐥-𝐦𝐞𝐝𝐢𝐚 𝐫𝐞𝐞𝐥𝐬 𝐚𝐧𝐝 𝐬𝐡𝐨𝐫𝐭𝐬 for park attendees.

𝐎𝐝𝐞𝐞𝐧 is a company that provides IT services, staff augmentation and SASS solutions on various technology stacks, including Machine Learning, Web Development and more.

☑ Implemented a 𝐦𝐮𝐥𝐭𝐢-𝐜𝐚𝐦𝐞𝐫𝐚 𝐭𝐫𝐚𝐜𝐤𝐢𝐧𝐠 feature using SOTA Centroid ReID to track and re-identify person detections in multiple camera views.

☑ Integrated 𝐅𝐀𝐈𝐒𝐒 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 to store track embeddings and perform ReID searching for similar tracks in a batch fashion.

☑ Used 𝐃𝐞𝐞𝐩𝐅𝐚𝐜𝐞 to detect and group track detections with face detections, and human pose prediction to filter accurate poses to consider when generating reels.

☑ Worked on the video streaming workflow, offline batch tracking and edge integration with Viso.AI.

Machine Learning Consultant

Upwork

💎 Top Rated • 💯 Job Success Rate • ⭐⭐⭐⭐⭐ Reviews

✅ Developed plug-and-play ML/CV modules.

☑ Client had over 50𝐤 𝐫𝐞𝐜𝐞𝐢𝐩𝐭𝐬 from various stores, and needed to automate the process of analyzing these receipts. I’ve implemented a 𝐌𝐋 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐬𝐚𝐯𝐢𝐧𝐠 𝐮𝐩 𝐭𝐨 80% of the time used to detect, decode and organise these receipts by their QR code metadata.

☑ Client wanted a 𝐄𝐝𝐮𝐓𝐞𝐜𝐡 𝐰𝐞𝐛 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 that allows parents to create quizz tests for their children based on an uploaded document (.pdf, .docx). I’ve implemented a full-stack web application with included ML components which loaded a document, applied ETL, generated quizz test and construct the UI view.

☑ Client wanted a CV plugin that would detect various icons from a phone screen, classify them and automatically create app testing workflows. Using template matching, pyramid-detection and super-resolution GAN’s, I’ve managed to 𝐨𝐩𝐭𝐢𝐦𝐢𝐬𝐞 𝐚𝐧𝐝 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐜𝐥𝐨𝐬𝐞 𝐭𝐨 100% on a few use-cases, and around 30-40% 𝐨𝐧 𝐭𝐡𝐞 𝐫𝐞𝐬𝐭 of the use-cases required.

☑ 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐞𝐝 on various Computer Vision topics and project ideas.

Top Rated Machine Learning Engineer

✅ Developed and deployed CV/ML modules into existing API's.

☑ Client wanted a CV solution that 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐬𝐩𝐨𝐫𝐭𝐬 𝐠𝐚𝐦𝐞𝐬 𝐚𝐧𝐝 𝐬𝐞𝐧𝐝𝐬 𝐧𝐨𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐡𝐞𝐧𝐞𝐯𝐞𝐫 𝐭𝐡𝐞 𝐬𝐜𝐨𝐫𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐬. whenever the score changes. I’ve applied smart-template matching, targeted motion detection and OCR to extract the team names, scores and send a push message whenever the score changed.

☑ Client wanted a CV solution that scans documents for hand signatures, identifies faulty ones and groups them in a specific structure. Solution was based on deep learning and OCR post-processing, deployed as a docker ML service which helped the client to 𝐬𝐩𝐞𝐞𝐝-𝐮𝐩 𝐭𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐛𝐲 𝐚 3𝐱 𝐟𝐚𝐜𝐭𝐨𝐫, relying far less on Humans-in-the-Loop activity.

☑ Client wanted a project concept report on how to apply custom texture from an image onto a 3D model. I’ve researched and planned a workflow that included Object Detection, Instance Segmentation and UV mapping.

Machine Learning Engineer

Elektrobit

✅ Developed multiple Computer Vision solutions and deployed them on edge devices.

𝐄𝐥𝐞𝐤𝐭𝐫𝐨𝐛𝐢𝐭 is key supplier of embedded software solutions for the automotive industry.

☑ Worked on a 𝐣𝐨𝐢𝐧𝐭-𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐰𝐢𝐭𝐡 𝐁𝐌𝐖 𝐌𝐮𝐧𝐢𝐜𝐡 𝐟𝐨𝐫 𝐄𝐏𝐈 (European Processor Initiative 2020) and deployed 𝐚 𝐜𝐚𝐫 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐩𝐚𝐭𝐡-𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 on an Nvidia Jetson AGX board, placed in a real 𝐁𝐌𝐖 𝐱5 𝐭𝐞𝐬𝐭 𝐜𝐚𝐫, which was able to detect and map the environment around the test vehicle using video feed from 2xIntelCameras and 1xLidarSensor.

☑ 𝐒𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥𝐥𝐲 𝐩𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐚 𝐬𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐩𝐚𝐩𝐞𝐫 titled GFPNet, under the supervision of the Head of AI @Elektrobit Romania, at the 𝐈𝐄𝐄𝐄 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐋𝐞𝐭𝐭𝐞𝐫𝐬 2020, within the Machine Learning and Robotics field that studied the shape reconstruction of occluded 3D objects.

☑ Generated synthetic data using 𝐂𝐚𝐫𝐥𝐚𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐨𝐫 (𝐔𝐄4) to train deep neural networks for specific autonomous driving scenarios, improving the accuracy of certain prediction.

☑ Worked on development and optimisation of a vehicle direction and position classifier neural network, integrated directly on the Renesas 𝐑-𝐂𝐚𝐫-𝐇3𝐄 𝐜𝐡𝐢𝐩 (𝐒𝐨𝐂), further deployed to one of the clients test car.

☑ Worked on a 𝐦𝐨𝐝𝐞𝐥 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 for edge devices, that allowed a model to be ported in Python/C++ with no further intervention, 𝐬𝐩𝐞𝐞𝐝𝐢𝐧𝐠 𝐮𝐩 𝐭𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐛𝐲 𝐨𝐯𝐞𝐫 50%.

✅ Researched computer vision topics, mainly targeted towards autonomous driving scenarios.

☑ Researched 𝐇𝐲𝐝𝐫𝐚𝐍𝐞𝐭𝐬 for 𝐚𝐮𝐭𝐨𝐦𝐨𝐭𝐢𝐯𝐞 𝐯𝐢𝐬𝐢𝐨𝐧 𝐭𝐚𝐬𝐤𝐬, using ResNet (50, 100) encoder backbones and multiple prediction heads (classification, 3D bounding-box, object detection and orientation), the research 𝐫𝐞𝐩𝐨𝐫𝐭 𝐛𝐞𝐢𝐧𝐠 𝐟𝐮𝐫𝐭𝐡𝐞𝐫 𝐮𝐬𝐞𝐝 to develop, train and deploy a model on a small 𝐟𝐨𝐮𝐫-𝐰𝐡𝐞𝐞𝐥𝐞𝐝 𝐫𝐨𝐛𝐨𝐭.

☑ Researched 𝐏𝐨𝐢𝐧𝐭𝐂𝐥𝐨𝐮𝐝 𝐬𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐟𝐫𝐨𝐦 𝐃𝐞𝐩𝐭𝐡 using a X-Box KinectSensor, feature that was further tested in a stack of models 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐨𝐧 𝐚 𝐟𝐥𝐲𝐢𝐧𝐠 𝐝𝐫𝐨𝐧𝐞 that followed QR markers on the floor.

☑ Worked on a model-development dashboard that 𝐚𝐥𝐥𝐨𝐰𝐞𝐝 𝐮𝐬𝐞𝐫𝐬 𝐭𝐨 𝐮𝐩𝐥𝐨𝐚𝐝 𝐝𝐚𝐭𝐚, 𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥, 𝐬𝐜𝐡𝐞𝐝𝐮𝐥𝐞 𝐚 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐣𝐨𝐛, inspect metrics and download artifacts on specific targets (.pt, .onnx, .trt, .gzip).

Junior Machine Learning Engineer

Universitatea Transilvania din Brașov

✅ Teaching 𝐢𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 programming concepts and intro to 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠.

☑ Java OOP, Python, Git
☑ Data Structures and Algorithms, Graphs
☑ Artificial Neural Networks
☑ Supervised and unsupervised learning
☑ House Price Prediction
☑ Activation Functions
☑ Hand Digits Recognition using Computer Vision and Deep Learning

Teaching Assistant

Siemens

✅ Platform to manage/schedule an industrial process workflow.

𝐒𝐢𝐞𝐦𝐞𝐧𝐬 𝐂𝐨𝐫𝐩𝐨𝐫𝐚𝐭𝐞 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 is a strategic partner to Siemens operative units. It provides important services along the entire value chain – from research and development to production and quality assurance, as well as optimized business processes.

☑ 𝐏𝐥𝐚𝐧𝐧𝐞𝐝 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 for an app that would allow users to inspect and manage stock for a potato-chips factory.

☑ Implemented the 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐥𝐚𝐲𝐞𝐫 for admin, observer, user with different functionalities for each category.

☑ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥-𝐬𝐭𝐚𝐜𝐤 𝐥𝐨𝐠𝐢𝐜, communications between components and frontend dashboard.

☑ 𝐖𝐨𝐧 3’rd place amongst over 10 projects presented.

Student Internship

Hard work always pays off, be consistent!

I'm a Senior Machine Learning Engineer with a keen interest in developing AI/ML solutions for real world problems and helping others get started on their Machine Learning Journey.

Let's connect @:
📘 https://medium.com/@alexandrurazvant
✉️ alexandrurazvant@gmail.com
🌐 https://www.neuraleaps.com

Alex Razvant

Want to learn about Agents, MCP, Multimodal AI, and LLMOps?

Miguel Otero and I have released an open-source course where we build an Multimodal Agent that can understand videos, step by step.

The project idea is simple: 𝐘𝐨𝐮 𝐮𝐩𝐥𝐨𝐚𝐝 𝐚 𝐯𝐢𝐝𝐞𝐨, 𝐚𝐧𝐝 𝐭𝐡𝐞𝐧 𝐜𝐡𝐚𝐭 𝐰𝐢𝐭𝐡 𝐢𝐭..

However, there are many complex challenges, underlying this simple idea.

Some of them are:
- How to build and debug MCP Servers in Python
- Understanding Multimodal Ingestion (video, audio, text, images)
- Designing efficient FastAPI APIs, Background Tasks
- Tool Calling / Router Agents from scratch, in Python
- Understanding Vision Language Models (VQA), Audio Transcription Models (STT)
- How to connect APIs (FastAPI) with UIs (React JS)

Throughout the course,  we explain each module with an general audience pace in mind.

We aimed for it to be easy to follow, easy to understand.

However, we still kept it technical enough.

You don't have to register or be an AI Expert to take this course. It's free.

The course lessons split:
1/ Course Overview
🔗 https://lnkd.in/dm73KT_i

2/ Understand Multimodal Pipelines
🔗  https://lnkd.in/dmn_RBfm

3/ Advanced FastAPI and Pydantic (extra)
🔗  https://lnkd.in/drpKtPFD

4/ Understand the MCP Protocol (extra)
🔗  https://lnkd.in/dJzNp8mH

5/ Build an MCP Server for Multimodal Ingestion
🔗  https://lnkd.in/d33Rsm4e

6/ Building a Tool Calling Agent from Scratch
🔗  https://lnkd.in/dDz6-zZU

7/ Building the full-stack (API, Agent, UI) Application
🔗 https://lnkd.in/dJezq4ry 

8/ Kubrick: The Multimodal Agent Round-up
🔗 https://lnkd.in/drZWiBdT 

Find the GitHub Repository here (give us a ⭐ ):
🔗 https://lnkd.in/dudc7BZS


Tech Stack:
- FastAPI, React JS, FastMCP, MCP Inspector, Docker Compose
- LLMOps with Opik (by @Comet)
- Multimodal Processing with @Pixeltable
- LLMs, VLMs and STT (Speech To Text) AI Models.

What you'll learn as a result:
- Build an MCP Server
- Build an API in Python with FastAPI
- Add LLM/Agent observability
- Process video/audio multimodal data.
- Connect multiple components into an AI System

Have fun learning & building!
✌️

Senior AI Engineer | Author @ NeuralBits | Helping engineers understand, build, and master AI.

CNNs remember where. ViTs remember what  

In a majority of Computer Vision models, the feature extractors (i.e, backbone networks) were built either as CNN encoders, ResNets, or Pyramid Networks.

Regardless of how large the Backbone is, CNNs are not that good at learning full-image context, due to how Receptive Fields work.

```
In deep learning, a receptive field is the specific area of the input data that influences the calculation of a single output feature or neuron in a CNN layer. 
```

1/ CNNs have inductive biases baked into their design
2/ Local receptive fields, hard to scale to High-Res images.
3/ Hierarchical feature extraction
4/ This can be improved, but at the cost of the model's depth

In the top section of the attached image, the `blue-square trace represents a receptive field from High-Level features onto Low-Level features`.

The red ❌  signs represent the feature regions that are not covered by the Receptive Field.

Thus, with CNNs, a larger image context is left out.

---

ViTs, on the other hand, see an image as a sequence of patches and mix them globally through self-attention. 

When feeding an image to a transformer, we first split it into patches of NxN pixels.
Each patch receives a positional encoding vector.
The resulting embedding vector is passed through the Transformer layers.

That makes Transformers:
1/ Permutation-invariant, positional embeddings are required.
2/ Focus on learning global + local level features 
3/ Adversarial Robustness (due to global-context, can spot tinkered features)
3/ Attention Mechanism over patches is computed in Many-to-Many order.
4/ Better for tasks that require image-wide context.

To summarize:
CNNs are limited in capturing global context due to their Receptive Fields.
ViTs compute relationships with and between each patch in the image.

CNNs are better for locality-focused tasks, e.g, Object Detection.
ViTs are better for context-focused tasks, e.,g VQA.

♻️ Reshare and let others learn this too.
Follow me to learn more about AI Engineering and production-ready AI Systems!

"What should I know about NVIDIA AI Stack?"

Here's my answer, tailored for those starting out in AI regarding NVIDIA's ecosystem.

𝗧𝗵𝗲 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺
NVIDIA doesn't just build the GPUs that power a large chunk of AI Infrastructure.

They've built a full end-to-end ecosystem.
> 𝙃𝙖𝙧𝙙𝙬𝙖𝙧𝙚: All the architectures and iterations (Turing, Ada, Hopper, Blackwell, etc.)
> 𝙎𝙤𝙛𝙩𝙬𝙖𝙧𝙚: CUDA, cuDNN, TensorRT, Triton, NeMo, Dynamo, and more.

This stack powers everything from training LLMs to deploying models at the edge, across various fields (Robotics, Vision, Language, Speech, Agentic and Embodied AI).

But for a beginner, these are too many, each with its place in the end-to-end AI workflow.
It's easy to get lost.

So, here's my advice, split into 3 categories:
1/ General Important Details
2/ Engineering Advice
3/ Software Stack

𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗲𝘁𝗮𝗶𝗹𝘀
First, I recommend that you learn at a very high level the GPU components.

This will get you started in understanding the hardware black box and differentiating between various GPU/DSA architectures.

Learn about:
1. GPU Cores
What are they? What's the difference between a CUDA Core and a Tensor Core?

2. Memory Hierarchy
Differentiate between VRAM, SRAM, and Registers. This will open doors to understanding FlashAttention, which is a key optimization in LLMs.

3. Precision & Data Types
The majority of LLMs are trained in FP32 or Mixed (FP32/FP16). For Inference, FP16/BF16,  FP4. This will also help you understand the quantization data types.

𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗔𝗱𝘃𝗶𝗰𝗲
These will help you understand how to work more efficiently with NVIDIA's hardware and software stack.

1. Learn to read a GPU Cheatsheet (details about a GPU's performance)
2. Learn to use the Framework Compatibility Matrix (mandatory for SWEs, AIEs)
3. Learn to use NVIDIA's pre-built containers (built in robust, isolated environments)

𝗧𝗵𝗲 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗦𝘁𝗮𝗰𝗸
We have a lot on this layer, but for starters, just learn what each of these does:

1. 𝘊𝘰𝘳𝘦 > CUDA & cuDNN, the backbone for deep learning, both training and inference.
2. 𝘋𝘢𝘵𝘢 𝘗𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 > DeepStream for real-time video pipelines, cuDF for Dataframes on GPU.
3. 𝘐𝘯𝘧𝘦𝘳𝘦𝘯𝘤𝘦 > TensorRT/LLM for optimizing models, Triton, and Dynamo for deployment.
4. 𝘎𝘦𝘯𝘈𝘐 > Nemotron Models, NeMo Framework to create, customize, and deploy GenAI models.
5. 𝘌𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦 > NIM for batteries-included GenAI deployments. Riva for Speech.
6. 𝘌𝘥𝘨𝘦 𝘈𝘐 > Jetson Xavier, DGX Spark, Orin platforms.
7. 𝘚𝘤𝘢𝘭𝘪𝘯𝘨 > K8S GPU Operator and KAI Scheduler for GPU resource management at scale.

---
There are a few more, but this is a solid start.

In the end, if you spend a few hours reading through these, you'll get a good overview of what's out there.

♻️ Share this post, and research it later.

Helping Techies Build Personal Brands for Career & Business Growth | AI-Assisted LinkedIn Strategist to CTOs, Directors, and Tech Founders & Coaches | 100K+ Insta | Mentored 1K+ Techies | Engineering Manager @ Jio

With over 9 years of industry experience, I help tech professionals achieve professional growth by crafting influential personal brands and leveraging platforms like LinkedIn.

As an Engineering Manager and Career Coach, I’ve mentored over 1000 professionals, helping them enhance their profiles, expand their networks, and implement proven growth strategies. My clients have successfully transitioned to better roles, expanded their influence, and built standout online presences.

What I Offer:
👉5-Step Career Branding Strategy: Tailored to job seekers, network builders, and those aiming to strengthen their LinkedIn presence.
👉Insights from 8+ Years in Tech: Experience across startups and giants like Byju’s and Jio, decoding how organizations operate in the digital era.

Recognized by: Byju’s, The Voice of Women, and Indian Express for my impact in career branding and coaching.

My Approach:
Speak like a leader. Work as an entrepreneur. Sell like Apple.

If you’re navigating a job transition, looking to boost your career, or want to become a recognized brand in tech, I’m here to help. Let’s connect and collaborate.

💡 Reach Me:
👉DM me on LinkedIn or Topmate.
👉Find me on other social media with @careerwithhina
👉For collaborations, email me at careerwithhina@gmail.com.

Let’s build a thriving community together. I’m just a message away!

Hina Arora

AI vs ML vs DL vs GenAI vs LLM vs AI Agent - What’s the Difference?

Credit: Prem N.

In the evolving AI landscape, it’s essential to distinguish between foundational technologies and their specialised branches. This guide provides a clear breakdown:

𝟏. 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈):
A broad field focused on designing systems that replicate human intelligence — including reasoning, learning, and decision-making.

𝟐. 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐌𝐋):
A subset of AI where systems learn from data patterns and improve over time, enabling predictions and automation without explicit programming.

𝟑. 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐃𝐋):
A specialized area within ML that leverages layered neural networks to process complex data, such as images or natural language.

𝟒. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 (𝐆𝐞𝐧𝐀𝐈):
An AI approach that creates original outputs - text, code, visuals - by learning from existing data structures and patterns.

𝟓. 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬):
Deep learning models trained on large text corporate, enabling machines to understand and generate human-like language at scale.

𝟔. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬:
Autonomous systems powered by LLMs or other models, capable of perceiving, planning, and executing tasks based on defined goals.

Follow Hina for more such content

AI-Assisted Personal Branding for Tech Leaders | 9yrs+ in Tech | Helping Tech Leaders & Coaches Build Personal Brand for Professional Growth | Partnered with 20+ CTOs, Directors & Founders | Done-for-You Service

I wake up at 6 AM.
I don’t touch my phone.
I drink warm water and vegetable juice on an empty stomach.
After freshening up, I do yoga for 50 minutes.
Shower.

Schedule my LinkedIn post.
Spend an hour connecting with people on LinkedIn.
Then leave for office.

By 9 AM, I’m in meetings.
I connect with my teammates,
Plan tasks for them.
Work, work, and work.
Lunch.
More work.

At 5 PM, I take a break — coffee in hand, mind a little lighter.
Wrap up by 6 PM and head home.

Healthy dinner with family at 7:30 PM.
Meditate for 20 minutes before bed.
Write down my thoughts for 10 minutes.

In bed by 10:00 PM.
Smiling.

That sounds like a perfect day.
And it happens… maybe once a qurter.
And those days, I live the version of me I aspire to be.

But...
Most days?

I wake up late.
Skip yoga.
Rush through shower and brushing my teeth.
Have breakfast in the car.

Schedule posts on weekends (and honestly, I love that about me).
Connect with people while traveling.
Work, work, and more work.
Skip lunch.
You guessed it, more work.

Evening cold coffee? Mandatory.
Leave for home and tell myself, “I’ll try harder tomorrow.”

And you know what?

I still go to bed smiling.

Because even on the “off” days, I showed up. 
I tried.

Every day doesn’t have to be perfect.
You don’t have to be your best self every day to be successful.

Progress isn’t linear.
Success is a marathon.

Give yourself grace.
You’re doing better than you think.

AI-Assisted Personal Branding for Tech Leaders | 9yrs+ in Tech | Helping Tech Leaders & Coaches Build Personal Brand for Professional Growth | Partnered with 21+ CTOs, Directors & Founders | Done-for-You Service

Picture a digital world where software works together like a team of tiny superheroes, each with its own superpower, to create amazing applications that run smoothly and efficiently. 

Welcome to the world of microservices, where small is mighty!

Microservices are small, independent pieces of software that work together to build an application. They communicate using clear rules, and each piece can be developed, deployed, and scaled on its own.

Some commonly used design patterns that can be beneficial when designing and implementing microservices:

✅ Gateway Pattern
✅ Service Registry Pattern
✅ Circuit Breaker Pattern
✅ SAGA Pattern
✅ CQRS (Command Query Responsibility Segregation) Pattern
✅ Bulkhead Pattern
✅ Sidecar Pattern
✅ API Composition Pattern
✅ Event-Driven Architecture Pattern
✅ Database per Service Pattern
✅ Retry Pattern
✅ Configuration Externalization Pattern
✅ Strangler Fig Pattern
✅ Leader Election Pattern

--------------------------------------------------------------
I help technical individuals create their career brands on LinkedIn.
👉https://lnkd.in/gAK7PqUd

Follow Hina Arora to learn more

Microservice Design Patterns

❌ Don't only apply for the job but also ensure to establish a network with the recruiter for the future.

Interview Mail Tips-

You can achieve this by sending thoughtful emails.

✅ 𝗔𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗳𝗼𝗿 𝗷𝗼𝗯 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Application for [Job Title] - [Your Name]

Dear [Hiring Manager's Name],

I hope this message finds you well. I am writing to express my interest in the [Job Title] position at [Company Name] that I recently came across. I believe my skills and experience align well with the requirements of the role.

With a background in [Relevant Skills/Experience], I am excited about the opportunity to contribute to [Company Name]'s [specific project/department/goal], and I am confident in my ability to make a positive impact. I have attached my resume for your consideration.

I would appreciate the chance to discuss how my background and expertise could benefit your team. Please let me know if there is a convenient time for a call or a meeting.

Thank you for considering my application. I look forward to the opportunity to speak with you.

Best regards,
[Your Name]

✅ 𝗙𝗼𝗹𝗹𝗼𝘄-𝗨𝗽 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Follow-Up on My Interview

Hi [Hiring Manager's Name],

I hope you're doing well. I wanted to follow up on the interview we had for the [Job Title] position at [Company Name]. I'm really excited about the opportunity and would love to hear about the next steps in the process.

Looking forward to your response.

Best regards,
[Your Name]

✅ 𝗥𝗲𝗷𝗲𝗰𝘁𝗶𝗼𝗻 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Appreciation and Future Consideration

Hi [Hiring Manager's Name],

I hope this message finds you well. I wanted to express my gratitude for considering me for the [Job Title] position. Although I didn't make it to the next round, I'm thankful for the chance to learn about [Company Name]. I look forward to potentially crossing paths again in the future.

Thank you once again.

Best regards,
[Your Name]

✅ 𝗔𝗰𝗰𝗲𝗽𝘁𝗮𝗻𝗰𝗲 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Accepting the [Job Title] Position

Hello [Hiring Manager's Name],

I hope you're doing well. I wanted to formally accept the offer for the [Job Title] position at [Company Name]. I'm really excited about joining the team and contributing to [Company Name]'s success. Please let me know the next steps and any additional information you need from my end.

Thank you and looking forward to starting on [Start Date].

Best regards,
[Your Name]

✅ 𝗦𝗮𝗹𝗮𝗿𝘆 𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Salary Discussion for [Job Title] Position

Hello [Hiring Manager's Name],

I hope this message finds you well. I'm excited about the offer for the [Job Title] role at [Company Name]. I would like to discuss the compensation package to ensure that it aligns with my skills and experience. Could we set up a time to talk about this further?

Thank you and looking forward to your response.

Best regards,
[Your Name]




#emailtemplates

𝐓𝐨𝐨 𝐦𝐚𝐧𝐲 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬. 𝐂𝐚𝐧'𝐭 𝐝𝐞𝐜𝐢𝐝𝐞 𝐰𝐡𝐢𝐜𝐡 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧? 
Here are the 6 categories, understand these and tool selection becomes obvious:

𝟏. 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐩𝐮𝐫𝐩𝐨𝐬𝐞 𝐋𝐋𝐌 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐫𝐬
Models like Claude, OpenAI, Gemini, Llama, Mistral, Grok, DeepSeek, Cohere, and AI21Labs power everything from training to fine tuning to deployment.

𝟐. 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐚𝐧𝐝 𝐦𝐮𝐥𝐭𝐢 𝐚𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬
Tools like AutoGen, CrewAI, Superagent, CamelAI, MGX, Manus, AgentVerse, LangGraph, and AutoGPT help orchestrate agents, memory, planning, and complex workflows.

𝟑. 𝐋𝐋𝐌 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬
Platforms like LangChain, LlamaIndex, Haystack, OpenLLM, Kernel, DSPy, and Guardrails AI let you build end to end LLM applications with chains, memory, and tools.

𝟒. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐨𝐨𝐥𝐬
PromptLayer, Promptable, Langfuse, ChainForge, PromptPerfect, and PromptTools help you design, test, monitor, and optimize prompts at scale.

𝟓. 𝐕𝐞𝐜𝐭𝐨𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬
Systems like FAISS, Chroma, Pinecone, Weaviate, Redis, Qdrant, Milvus, and Relevance AI store embeddings for RAG pipelines and semantic search.

𝟔. 𝐍𝐨 𝐜𝐨𝐝𝐞 𝐚𝐧𝐝 𝐥𝐨𝐰 𝐜𝐨𝐝𝐞 𝐀𝐈 𝐛𝐮𝐢𝐥𝐝𝐞𝐫𝐬
FlowiseAI, Langflow, n8n, Replit, Cursor, Lovable, and Chatbase make it possible to build full LLM workflows without heavy coding.

Learning these six categories is the fastest way to understand how modern AI products are actually built.

♻️ Repost this to help your network get started
➕ Follow Hina for more

Which category should I break down into a deep dive next?

#LLM #AItools #GenAI #AIAgents

AI has asked a founder to layoff 30% of his workforce...

Founder asked AI : “How can we make the company more profitable?”
You can’t imagine what it answered…

A startup was not able to make their company profitable.

So, a founder asked an AI how to make their company profitable….

The model thought for a few seconds.
Then replied…

“Lay off 30% of employees”
“Automate repetitive tasks. Reduce office costs.”

It made perfect business sense.
But human sense? No…

When the team read the answer, the room went silent.

No one said it out loud, but everyone was thinking the same thing…

Is this what the future of decision-making looks like?

And a person who agree with this decision has been laid off…. (Not funny, but karma)

AI did what it was trained to do.
Analyze data.
Find efficiency.
Cut waste.

But it couldn’t see the people behind the payroll.
The single parent paying school fees.
The developer who stays late helping juniors.
The designer who brings fresh energy to every meeting.

It couldn’t understand loyalty, culture, or the quiet trust that holds teams together.

Profitability isn’t wrong to chase.
Every company needs it to survive.
But when it comes at the cost of the very people who built that company… 
that’s when logic stops being leadership.

AI gives answers.
Leaders create direction.

And maybe, the real question we should start asking isn’t…

“How can we be more profitable?”
but
“How can we grow without losing what makes us human?”


What are your thoughts on this ?

𝐘𝐨𝐮𝐫 𝐣𝐨𝐛 𝐭𝐢𝐭𝐥𝐞 𝐦𝐢𝐠𝐡𝐭 𝐧𝐨𝐭 𝐞𝐱𝐢𝐬𝐭 𝐢𝐧 𝟐 𝐲𝐞𝐚𝐫𝐬. 
Here are the 12 AI skills that will:

𝟏. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
Design precise and structured prompts for models like ChatGPT, Claude, and Gemini to boost accuracy and efficiency across every AI task.

𝟐. 𝐀𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧
Automate repetitive tasks using tools like Zapier, Make, Bardee, and n8n without writing heavy code.

𝟑. 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬
Build and deploy multi agent systems for research, reasoning, and workflow automation using CrewAI, LangGraph, and AutoGen.

𝟒. 𝐑𝐀𝐆
Improve LLM response quality by connecting models to private or enterprise data using Vectora, LangChain, or LlamaIndex.

𝟓. 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈
Use models like Grok, Gemini, Claude Sonnet, and GPT O3 to work across text, audio, images, and mixed modality tasks.

𝟔. 𝐅𝐢𝐧𝐞 𝐭𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐈 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭𝐬
Create specialized AI assistants or fine tuned models using OpenAI GPT Builder and Hugging Face.

𝟕. 𝐕𝐨𝐢𝐜𝐞 𝐀𝐈 𝐚𝐧𝐝 𝐚𝐯𝐚𝐭𝐚𝐫𝐬
Generate realistic avatars and voiceovers using HeyGen, Synthesia, ElevenLabs, or Vapi to enhance content.

𝟖. 𝐀𝐈 𝐭𝐨𝐨𝐥 𝐬𝐭𝐚𝐜𝐤𝐢𝐧𝐠
Integrate tools like Notion, Zapier, and ClickUp to automate entire workflows and boost productivity.

𝟗. 𝐀𝐈 𝐯𝐢𝐝𝐞𝐨 𝐜𝐨𝐧𝐭𝐞𝐧𝐭 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧
Turn articles into videos using Runway, VEED, or OpusClip with built in editing and voice capabilities.

𝟏𝟎. 𝐒𝐚𝐚𝐒 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭
Build revenue generating AI products using APIs and platforms like Bubble, Cursor, and Softr.

𝟏𝟏. 𝐋𝐋𝐌 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭
Measure and optimize model performance, cost, and hallucinations using Helicone, Trulens, and PromptLayer.

𝟏𝟐. 𝐒𝐭𝐚𝐲𝐢𝐧𝐠 𝐮𝐩𝐝𝐚𝐭𝐞𝐝
Keep yourself sharp through newsletters, podcasts, and updates from platforms like Mindstream and OpenAI News.

These 12 skills will shape the AI professionals and builders of 2026.

♻️ Repost this to help your network get started
➕ Follow Hina for more

Which skill do you want a learning roadmap for next?

#AISkills #GenAI #AIEngineering #FutureSkills

Recruiters only spend 6 to 9 seconds reading each resume. 
Let's make the best one.

I have come up with some of the best resume tips for you. 

🚀 Resume name format
🚀 Resume colour
🚀 One-page myth
🚀 Technical skills
🚀 Priority first
🚀 Work Impact
🚀 Outside contribution
🚀 Innovation & creativity
🚀 Good header

Check the PDF to learn more about it.


LinkedIn
LinkedIn Guide to Creating
---------------------------------------
Follow Hina Arora for more such information.


Hashtags:

#resume #resumewriting #resumetips #hinaarora #cv #cvwriting

Resume

Ever Wondered How Netflix Works So Well? Here's the Secret! 

Netflix brings your favorite shows and movies right to your screen, but how does it do that?

Here’s a look at the cool tech behind it:

1. Microservices Architecture
Netflix uses something called "microservices," which means instead of one big computer doing everything, there are lots of small computers working together.
This helps Netflix run smoothly and fix problems quickly.

2. AWS Cloud
Netflix uses a service called AWS (Amazon Web Services) to store all its data and run everything online. It’s like Netflix's superpower that helps it work from anywhere in the world.

3. Open Connect
Netflix has its own special system, called Open Connect, to send shows and movies quickly to everyone, no matter where they are.

4. Programming Languages
Netflix uses different computer languages like Java, Python, and Node.js to build both the backend (stuff happening behind the scenes) and the front end (stuff you see on the screen).

5. Big Data & AI
Netflix uses tools like Apache Kafka and Apache Spark to understand what you like to watch and recommend new shows and movies just for you!

6. Security
Netflix makes sure your personal information is safe by using strong security systems, kind of like a digital lock for your data.

7. Chaos Engineering
Netflix even uses a fun-sounding thing called Chaos Monkey! It randomly tests Netflix to make sure it can keep working even if something goes wrong.

Netflix’s tech helps it stay fast, smart, and fun. It's like a magic trick behind the scenes that makes watching easy for us!

------------------------------------------------
👉 👉 Follow Hina Arora for more such content.

Don't miss this opportunity. 

A 5-Day AI Agents Intensive - a free course designed to help you build and deploy the next frontier of AI. (November 10 - November 15)

𝐖𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐠𝐞𝐭 𝐭𝐡𝐢𝐬 𝐭𝐢𝐦𝐞 𝐚𝐫𝐨𝐮𝐧𝐝:
➡️ Daily lessons from Google's engineers covering AI agents
➡️ Hands-on code labs you work through at your own speed
➡️ Live Q&A sessions with people who build this stuff 
➡️A capstone project to build something real (with prizes)

𝐖𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐥𝐞𝐚𝐫𝐧:
➡️ Day 1: What agents actually are and how they're different.
➡️ Day 2: How agents use tools to actually take action 
➡️ Day 3: Building memory so agents can handle complex, multi-turn tasks 
➡️ Day 4: Making sure your agents work reliably (logging, testing, metrics) 
➡️ Day 5: Taking agents from your laptop to production

𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫?
AI agents go beyond tools; they don’t just chat, they act. 
They complete tasks, use tools, remember context, and work autonomously. 

For anyone in dev, product, ops, or strategy, knowing how to build and deploy them is a real edge.

The course is free, with no prerequisites except curiosity and a willingness to try a bit of code.

Here's that registration link you've been waiting for...
https://lnkd.in/gZwZnTyf


#google #lifeatgoogle #ai #training #education #courses

𝐈𝐟 𝐲𝐨𝐮𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐥𝐨𝐨𝐤 𝐥𝐢𝐧𝐞𝐚𝐫... 𝐫𝐞𝐚𝐝 𝐭𝐡𝐢𝐬 𝐭𝐢𝐥𝐥 𝐞𝐧𝐝.

I wasn’t a Full-Stack Developer.
I was a 𝐉𝐚𝐜𝐤 𝐨𝐟 𝐚𝐥𝐥 𝐭𝐫𝐚𝐝𝐞𝐬, 𝐦𝐚𝐬𝐭𝐞𝐫 𝐨𝐟 𝐧𝐨𝐧𝐞.

And for years, I thought that was a flaw.

I started with 𝐂, 𝐂++,
explored 𝐀𝐧𝐝𝐫𝐨𝐢𝐝,
moved to 𝐏𝐇𝐏,
worked on 𝐟𝐫𝐨𝐧𝐭-𝐞𝐧𝐝,
even touched 𝐑𝐎𝐑,
and later found comfort in 𝐏𝐲𝐭𝐡𝐨𝐧.

It looked 𝐦𝐞𝐬𝐬𝐲.
But every switch was shaping me...
to 𝐥𝐞𝐚𝐫𝐧 𝐟𝐚𝐬𝐭𝐞𝐫,
𝐚𝐝𝐚𝐩𝐭 𝐪𝐮𝐢𝐜𝐤𝐞𝐫,
and connect dots others couldn’t see.

I went on to lead teams as an 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐌𝐚𝐧𝐚𝐠𝐞𝐫,
And today, I’m the 𝐅𝐨𝐮𝐧𝐝𝐞𝐫 𝐨𝐟 𝐚 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐁𝐫𝐚𝐧𝐝𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐜𝐲 𝐟𝐨𝐫 𝐓𝐞𝐜𝐡 𝐋𝐞𝐚𝐝𝐞𝐫𝐬.

From debugging backend code
to helping engineers, founders, and architects build their presence...
It all connects.

Being a 𝐣𝐚𝐜𝐤-𝐨𝐟-𝐚𝐥𝐥-𝐭𝐫𝐚𝐝𝐞𝐬 didn’t make me less focused.
It made me more 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 today.

If your career feels non-linear...
You’re not lost.
You’re just collecting dots to connect later.

“𝐈𝐟 𝐲𝐨𝐮 𝐰𝐞𝐫𝐞 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐨𝐨𝐝, 𝐲𝐨𝐮 𝐰𝐨𝐮𝐥𝐝𝐧’𝐭 𝐛𝐞 𝐥𝐚𝐢𝐝 𝐨𝐟𝐟.”

I have actually heard this line.
And honestly, it is one of the most damaging misconceptions in the job market.

A friend of mine, a highly respected CTO, was laid off earlier this year.
Not because he lacked vision.
Not because he was not delivering.
But because his company restructured and shut down an entire business unit overnight.

𝐆𝐮𝐞𝐬𝐬 𝐰𝐡𝐚𝐭? 𝐖𝐢𝐭𝐡𝐢𝐧 𝐰𝐞𝐞𝐤𝐬 𝐨𝐟 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐡𝐢𝐬 𝐣𝐨𝐛 𝐬𝐞𝐚𝐫𝐜𝐡, 𝐡𝐞 𝐤𝐞𝐩𝐭 𝐡𝐞𝐚𝐫𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐬𝐚𝐦𝐞 𝐛𝐢𝐚𝐬, 
“𝐈𝐟 𝐲𝐨𝐮 𝐰𝐞𝐫𝐞 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐨𝐨𝐝, 𝐰𝐡𝐲 𝐰𝐨𝐮𝐥𝐝 𝐭𝐡𝐞𝐲 𝐥𝐞𝐭 𝐲𝐨𝐮 𝐠𝐨?”

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐭𝐫𝐮𝐭𝐡 𝐈 𝐡𝐚𝐯𝐞 𝐬𝐞𝐞𝐧 𝐨𝐯𝐞𝐫 𝐚𝐧𝐝 𝐨𝐯𝐞𝐫 𝐚𝐠𝐚𝐢𝐧:
✔️ Layoffs are about business decisions, not talent.
✔️ Leadership skills do not vanish because a company restructures.
✔️ Some of the most resilient, driven professionals are the ones who have bounced back after layoffs.

If you are a hiring manager holding this mindset, you are not filtering out “𝐰𝐞𝐚𝐤” candidates.
You are filtering out leaders who could transform your team.

And if you are a tech leader who is been laid off, 
It does not define you. It repositions you.

𝐔𝐬𝐞 𝐭𝐡𝐢𝐬 𝐩𝐡𝐚𝐬𝐞 𝐭𝐨:
📌 Show up online and share your expertise.
📌 Build visibility around your leadership track record.
📌 Reframe your story, so people see your potential, not just your past.

Because in today’s market, skills and leadership get you in the door.
But personal branding makes people trust your story.

DM me ‘Brand’ if you want to reframe your career story after a layoff.

I wrote my first production code 9 years ago.
And like many of you, it didn’t go well.

I still remember the feeling.
Pushing the changes.
Refreshing the screen.
And watching everything break in real time.

No one prepares you for that moment.
You learn fast that production is a different world
and confidence doesn’t come from writing code
it comes from fixing the mess you created.

Over the years, I realised something important.
Good engineers aren’t the ones who never make mistakes.
They’re the ones who stay calm when things go wrong
own their decisions
and get better with every failure.

If your first production push didn’t go well
welcome to the club.
If your last one didn’t go well
you’re still learning.

And that’s the whole point of this journey.

Everyone is talking about AI skills right now.
But no one tells you the uncomfortable truth.

The real “AI skill” of 2025/206 isn’t prompt writing.
It isn’t fine-tuning.
It isn’t building agents.

It’s learning how to think with AI.

Because the people winning today aren’t the ones who know the most tools.
They’re the ones who know how to turn AI into an extension of their mind.

Here’s what I’m seeing everywhere:

People who treat AI like Google → stuck.
People who treat AI like a partner → unstoppable.

AI isn’t here to replace your skill.
It’s here to amplify it.

If you know how to break problems down
ask better questions
structure context
and guide the model’s reasoning —
you’ll be ahead of 90% of the industry.

The tools will change every 3 months.
Your ability to think with AI will stay.

So if you’re wondering what to learn next:
Learn how to work with AI the way leaders work with people —
with clarity, direction, and intention.

That’s the real skill that will define the next decade.

Say NO to these 20 corporate situations for a better work life balance.


Learning to say No isn’t about being uncooperative—it’s about protecting your time, energy, and career growth.

Here’s what you should stop saying Yes to:

❌ No to projects that don’t align with your goals.
❌ No to unrealistic deadlines.
❌ No to working late nights.
❌ No to working on weekends.
❌ No to unnecessary meetings.
❌ No to last-minute urgent tasks that aren’t your responsibility.
❌ No to taking blame for others’ mistakes.
❌ No to extra work without recognition or compensation.
❌ No to toxic work culture.
❌ No to office politics and gossip.
❌ No to overcommitment just to please others.
❌ No to being constantly available on calls/messages after work.
❌ No to being micromanaged.
❌ No to staying silent when something feels wrong.
❌ No to working without clear expectations.
❌ No to leadership roles if you don’t want them.
❌ No to sacrificing personal growth for job security.
❌ No to undervaluing yourself.
❌ No to compromising work-life balance.
❌ No to staying in a job that no longer serves you.

Your corporate journey is yours to control—set boundaries, stand firm, and thrive!


What’s the hardest No you’ve had to say at work?

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The Battle

Alex Razvant

Hina Arora

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The Battle

Alex Razvant

Hina Arora

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Alex Razvant's LinkedIn Performance

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