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📦 Ran out of storage on your laptop? Here’s a tip on how to use AI to free up disk space on your laptop in minutes 🧹 By using the Q Dev CLI agent, Linda was able to find & remove leftover app files & unused data from 3+ years ago in minutes. No more manual hunting through folders! Here’s how to get started: 1️⃣ Install Q Developer in the CLI specifically 2️⃣ Type "q chat" in your terminal 3️⃣ Ask in natural language for it to help clear up storage 4️⃣ Start iterating based on the options it gives 💡Pro tip: Ask it to scan for leftover files from deleted apps 🔗 Try out Q Dev CLI agent for free via link in bio! Follow @awsdevelopers for more cloud content. ————————— #AI #DevTools #generativeAI #AWS #Tech #coding #CloudComputing
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lindavivah
📦 Ran out of storage on your laptop? Here’s a tip on how to use AI to free up disk space on your laptop in minutes 🧹 By using the Q Dev CLI agent, Linda was able to find & remove leftover app files & unused data from 3+ years ago in minutes. No more manual hunting through folders! Here’s how to get started: 1️⃣ Install Q Developer in the CLI specifically 2️⃣ Type "q chat" in your terminal 3️⃣ Ask in natural language for it to help clear up storage 4️⃣ Start iterating based on the options it gives 💡Pro tip: Ask it to scan for leftover files from deleted apps 🔗 Try out Q Dev CLI agent for free via link in bio! Follow @awsdevelopers for more cloud content. ————————— #AI #DevTools #generativeAI #AWS #Tech #coding #CloudComputing
Smart development just found its home base. 🏠🎯💻 Now in preview, Amazon Q Developer brings powerful #generativeAI capabilities directly to #GitHub. Transform issues into code, modernize Java apps & get instant reviews—all under one roof. #AWS Link in bio. 🔗
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lindavivah
Smart development just found its home base. 🏠🎯💻 Now in preview, Amazon Q Developer brings powerful #generativeAI capabilities directly to #GitHub. Transform issues into code, modernize Java apps & get instant reviews—all under one roof. #AWS Link in bio. 🔗
👀 NEW Claude can now control your computer: Mouse, keyboard, and screen, giving it the ability to use any app without an MCP or connector. It does first check if you have a connector an will prompt you for permission. Read the “Claude Cowork Safety”official doc if you plan to use this. Here I’m using dispatch via Claude cowork to search and export one of the videos I edited locally in CapCut 🤯 (It did prompt me for specific permissions) It can open your apps, navigate your browser, fills in spreadsheets….anything you’d do sitting at your desk. Currently works on Mac for both Claude Cowork & Claude Code [ai engineer, tech news, agentic ai]
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lindavivah
👀 NEW Claude can now control your computer: Mouse, keyboard, and screen, giving it the ability to use any app without an MCP or connector. It does first check if you have a connector an will prompt you for permission. Read the “Claude Cowork Safety”official doc if you plan to use this. Here I’m using dispatch via Claude cowork to search and export one of the videos I edited locally in CapCut 🤯 (It did prompt me for specific permissions) It can open your apps, navigate your browser, fills in spreadsheets….anything you’d do sitting at your desk. Currently works on Mac for both Claude Cowork & Claude Code [ai engineer, tech news, agentic ai]
🎶What are "The Invisible Systems" in Tech? Learn about them via an original song sponsored by @RobertHalf, breaking down the systems that keep your apps running strong. We often celebrate features... but it’s the unseen layers that make or break your product: 🖥 Infrastructure: Cloud-native, autoscaling, remote-ready 📡 Data Systems: Reliable pipelines and real-time access 🔐 Security & Governance: Risk mitigation, role-based access 🚀 Deployment & DevOps: CI/CD, rollout agility 📊 Monitoring & Observability: Detect issues before users do 🗓 Lifecycle Planning: Manage tech debt, sunset tools, upgrade ERP 📘 These are just some of the invisible systems explored in the new Robert Half report, which outlines how to build a tech team to help your company: ✅ Modernize without disruption ✅ Accelerate innovation with AI ✅ Build systems that scale and adapt Invisible systems don’t maintain themselves. Great teams do. 👉 If you’re building a tech team, check out Robert Half’s guide via the link in bio! #RobertHalfPartner #DevOps #Tech #Coding #CloudComputing #InformationTechnology
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10mo ago
lindavivah
🎶What are "The Invisible Systems" in Tech? Learn about them via an original song sponsored by @RobertHalf, breaking down the systems that keep your apps running strong. We often celebrate features... but it’s the unseen layers that make or break your product: 🖥 Infrastructure: Cloud-native, autoscaling, remote-ready 📡 Data Systems: Reliable pipelines and real-time access 🔐 Security & Governance: Risk mitigation, role-based access 🚀 Deployment & DevOps: CI/CD, rollout agility 📊 Monitoring & Observability: Detect issues before users do 🗓 Lifecycle Planning: Manage tech debt, sunset tools, upgrade ERP 📘 These are just some of the invisible systems explored in the new Robert Half report, which outlines how to build a tech team to help your company: ✅ Modernize without disruption ✅ Accelerate innovation with AI ✅ Build systems that scale and adapt Invisible systems don’t maintain themselves. Great teams do. 👉 If you’re building a tech team, check out Robert Half’s guide via the link in bio! #RobertHalfPartner #DevOps #Tech #Coding #CloudComputing #InformationTechnology
Context vs Memory vs Harness engineering explained in 40 seconds by Richmond Alake ⚡️ 💡These 3 disciplines are core to building agents that can actually remember and reason over time Most agents work well within a single session but lose everything the moment it ends. Memory engineering treats long-term memory as first-class infrastructure. Richmond Alake (Director of AI DevEx at Oracle) walked me through all three in NYC🗽 ✈️ I’m PSYCHED to be heading to an offsite with the Oracle developer team very soon to go even deeper on agent harnesses & excited to share learnings with y’all! If you want to learn how to build memory-aware agents, Richmond has a free short DeepLearning.AI course in collaboration with Oracle that he co-taught with his colleague Nacho! 📚 Comment ✨DEVELOPER✨ and I’ll DM you the link to the free DeepLearning agent memory course & the AI Developer GitHub repo by Oracle to help you start building 💻
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lindavivah
Context vs Memory vs Harness engineering explained in 40 seconds by Richmond Alake ⚡️ 💡These 3 disciplines are core to building agents that can actually remember and reason over time Most agents work well within a single session but lose everything the moment it ends. Memory engineering treats long-term memory as first-class infrastructure. Richmond Alake (Director of AI DevEx at Oracle) walked me through all three in NYC🗽 ✈️ I’m PSYCHED to be heading to an offsite with the Oracle developer team very soon to go even deeper on agent harnesses & excited to share learnings with y’all! If you want to learn how to build memory-aware agents, Richmond has a free short DeepLearning.AI course in collaboration with Oracle that he co-taught with his colleague Nacho! 📚 Comment ✨DEVELOPER✨ and I’ll DM you the link to the free DeepLearning agent memory course & the AI Developer GitHub repo by Oracle to help you start building 💻
Comment “MCP” & I’ll send you the links via DM ✨ 📜 The concept of MCP is very new. In late 2024, Anthropic introduced MCP (Model Context Protocol) because LLMs needed a cleaner, safer way to interact with the outside world. MCP is like giving models a shared language to access tools, trigger workflows, and pull data. The entire point of MCP is to make LLM’s more capable in a standardized way. 💡These are some of the resources I found helpful when starting to build MCP servers & clients. I limited it to beginner friendly resources I thought were great but lmk if it would be helpful to share more advanced finds as well. Hope this helps others! Are there others you would add to the list? Lmk in the comments & as always Happy Building! 💻 Relevant tags 🏷️ [ai engineer, genai career, software engineer, software developer, MCP roadmap, how to learn ai, python for ai, lim from scratch, build in public, ai agent builder, deep learning 2025, ai portfolio project, ml roadmap, ai learning journey, agentic ai]
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lindavivah
Comment “MCP” & I’ll send you the links via DM ✨ 📜 The concept of MCP is very new. In late 2024, Anthropic introduced MCP (Model Context Protocol) because LLMs needed a cleaner, safer way to interact with the outside world. MCP is like giving models a shared language to access tools, trigger workflows, and pull data. The entire point of MCP is to make LLM’s more capable in a standardized way. 💡These are some of the resources I found helpful when starting to build MCP servers & clients. I limited it to beginner friendly resources I thought were great but lmk if it would be helpful to share more advanced finds as well. Hope this helps others! Are there others you would add to the list? Lmk in the comments & as always Happy Building! 💻 Relevant tags 🏷️ [ai engineer, genai career, software engineer, software developer, MCP roadmap, how to learn ai, python for ai, lim from scratch, build in public, ai agent builder, deep learning 2025, ai portfolio project, ml roadmap, ai learning journey, agentic ai]
7 core Claude Code features explained in 90 seconds.. let’s go! ⚡ To unlock Claude Code’s full potential you need to know what’s available to you. Here’s the TLDR: 🔷 CLAUDE.md → a markdown file Claude loads as context each session 🔷 Skills → packaged instructions Claude loads when the task matches 🔷 MCP → standard protocol for connecting Claude to external tools and data 🔷 Hooks → commands that run automatically at points in Claude’s lifecycle 🔷 Subagents → isolated Claude instances with their own context and tool access 🔷 Plugins → skills, hooks, and MCP configs bundled into one installable unit 🔷 Agent Teams → parallel Claude sessions with a lead, messaging each other directly
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lindavivah
7 core Claude Code features explained in 90 seconds.. let’s go! ⚡ To unlock Claude Code’s full potential you need to know what’s available to you. Here’s the TLDR: 🔷 CLAUDE.md → a markdown file Claude loads as context each session 🔷 Skills → packaged instructions Claude loads when the task matches 🔷 MCP → standard protocol for connecting Claude to external tools and data 🔷 Hooks → commands that run automatically at points in Claude’s lifecycle 🔷 Subagents → isolated Claude instances with their own context and tool access 🔷 Plugins → skills, hooks, and MCP configs bundled into one installable unit 🔷 Agent Teams → parallel Claude sessions with a lead, messaging each other directly
🤩 Claude Code just launched Channels! Let’s set it up together 🎉 Channels allow you to control your Claude Code sessions through select MCP’s, starting with Telegram and Discord Comment ✨CHANNELS✨ & i’ll send the doc to your DM [AI Engineer, agentic AI, coding]
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lindavivah
🤩 Claude Code just launched Channels! Let’s set it up together 🎉 Channels allow you to control your Claude Code sessions through select MCP’s, starting with Telegram and Discord Comment ✨CHANNELS✨ & i’ll send the doc to your DM [AI Engineer, agentic AI, coding]
Comment “MCP” & I’II send you the links via DM ✨ In late 2024, Anthropic introduced MCP (Model Context Protocol) because LLMs needed a cleaner, safer way to interact with the outside world. In just a year it already has over 97 million SDK downloads and 2 weeks ago Anthropic announced it’s donating MCP to the Linux Foundation. MCP is like giving models a shared language to access tools, trigger workflows, and pull data. The entire point of MCP is to make LLM’s more capable in a standardized way. These are some of the resources I found helpful when starting to build MCP servers & clients. I limited it to beginner friendly resources I thought were great but Imk if it would be helpful to share more advanced finds as well. Hope this helps others! Are there others you would add to the list? Lmk in the comments & as always Happy Building! Relevant tags 🏷️ [ai engineer, genai career, software engineer, software developer, MCP roadmap, how to learn ai, python for ai, lim from scratch, build in public, ai agent builder, deep learning 2025, ai portfolio project, ml roadmap, ai learning journey, agentic ai]
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lindavivah
Comment “MCP” & I’II send you the links via DM ✨ In late 2024, Anthropic introduced MCP (Model Context Protocol) because LLMs needed a cleaner, safer way to interact with the outside world. In just a year it already has over 97 million SDK downloads and 2 weeks ago Anthropic announced it’s donating MCP to the Linux Foundation. MCP is like giving models a shared language to access tools, trigger workflows, and pull data. The entire point of MCP is to make LLM’s more capable in a standardized way. These are some of the resources I found helpful when starting to build MCP servers & clients. I limited it to beginner friendly resources I thought were great but Imk if it would be helpful to share more advanced finds as well. Hope this helps others! Are there others you would add to the list? Lmk in the comments & as always Happy Building! Relevant tags 🏷️ [ai engineer, genai career, software engineer, software developer, MCP roadmap, how to learn ai, python for ai, lim from scratch, build in public, ai agent builder, deep learning 2025, ai portfolio project, ml roadmap, ai learning journey, agentic ai]
Let’s see if I can cover the ML pipeline in 60 seconds ⏰😅 Machine learning isn’t just training a model. A production ML lifecycle typically looks like this: 1️⃣ Define the problem & objective 2️⃣ Collect and (if needed) label data 3️⃣ Split into train / validation / test sets 4️⃣ Data preprocessing & feature engineering 5️⃣ Train the model (forward pass + backpropagation in deep learning) 6️⃣ Evaluate on held-out data to measure generalization 7️⃣ Hyperparameter tuning (learning rate, architecture, etc.) 8️⃣ Final testing before release 9️⃣ Deploy (batch inference or real-time serving behind an API) 🔟 Monitor for data drift, concept drift, latency, cost, and reliability 1️⃣1️⃣ Retrain when performance degrades Training updates weights. Evaluation measures performance. Deployment serves predictions. Monitoring keeps the system healthy. It’s not linear. It’s a loop. And once you move beyond a single experiment, that loop becomes a systems problem. At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle. Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍
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lindavivah
Let’s see if I can cover the ML pipeline in 60 seconds ⏰😅 Machine learning isn’t just training a model. A production ML lifecycle typically looks like this: 1️⃣ Define the problem & objective 2️⃣ Collect and (if needed) label data 3️⃣ Split into train / validation / test sets 4️⃣ Data preprocessing & feature engineering 5️⃣ Train the model (forward pass + backpropagation in deep learning) 6️⃣ Evaluate on held-out data to measure generalization 7️⃣ Hyperparameter tuning (learning rate, architecture, etc.) 8️⃣ Final testing before release 9️⃣ Deploy (batch inference or real-time serving behind an API) 🔟 Monitor for data drift, concept drift, latency, cost, and reliability 1️⃣1️⃣ Retrain when performance degrades Training updates weights. Evaluation measures performance. Deployment serves predictions. Monitoring keeps the system healthy. It’s not linear. It’s a loop. And once you move beyond a single experiment, that loop becomes a systems problem. At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle. Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍
Love the way @gregisenberg phrased it 💡 Comment ✨OPTIMIZE✨ and I’ll send you 5 prompts for your vibe coding tool of choice to help your site get read and cited by AI agents 🌐
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lindavivah
Love the way @gregisenberg phrased it 💡 Comment ✨OPTIMIZE✨ and I’ll send you 5 prompts for your vibe coding tool of choice to help your site get read and cited by AI agents 🌐
Saying goodbye to a place where I’ve grown so much professionally, creatively, personally …. & made friends for life 🧡 After 3+ incredible years, I’ve made the decision to step into a new chapter - with deep deep gratitude for this one. Prior to working at AWS, I was part of the community as a customer & an AWS Community Builder … and I still plan to be building in the cloud 🥰 At AWS, I had the privilege of building hands-on technical content, launching programs that helped developers learn and connect, and working with brilliant teams across the org. But more than any livestream or launch… it’s the people I’ll carry with me. Thank you to the colleagues, collaborators, and community who’ve shaped this chapter. 💙 I’ll be sharing what’s next very soon! You’ll still find me building, learning, and supporting this incredible tech community. Especially now - when tech is evolving faster than ever we all need spaces to grow, navigate, and skill up together. Stick around - the next chapter’s just beginning 😊🫶 #developerdiaries #techcommunity #farewellpost #awscommunity #buildinpublic #cloudcomputing
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lindavivah
Saying goodbye to a place where I’ve grown so much professionally, creatively, personally …. & made friends for life 🧡 After 3+ incredible years, I’ve made the decision to step into a new chapter - with deep deep gratitude for this one. Prior to working at AWS, I was part of the community as a customer & an AWS Community Builder … and I still plan to be building in the cloud 🥰 At AWS, I had the privilege of building hands-on technical content, launching programs that helped developers learn and connect, and working with brilliant teams across the org. But more than any livestream or launch… it’s the people I’ll carry with me. Thank you to the colleagues, collaborators, and community who’ve shaped this chapter. 💙 I’ll be sharing what’s next very soon! You’ll still find me building, learning, and supporting this incredible tech community. Especially now - when tech is evolving faster than ever we all need spaces to grow, navigate, and skill up together. Stick around - the next chapter’s just beginning 😊🫶 #developerdiaries #techcommunity #farewellpost #awscommunity #buildinpublic #cloudcomputing
The beauty about tech is there is no one path. 🌱 One of the biggest lessons throughout my journey has been that what felt like the hardest parts often involved “figuring out my next growth step”. The decision is sometimes harder than the execution. ⚙️ Overtime, I learned those phases of “going in circles” are totally normal & actually a lot of the work within your journey. 🔁 For example, it took me a while to figure out that I would like to go in to SRE from webdev etc. 🔁 the same thing happened when I decided it was time to go to a startup and the AI infra end. Have you experienced something similar? Lmk in comments 🤎 ⬇️ 💡 Remember, YOU are the artist of your own life 🎨 Summary of my Untraditional Journey in to Tech & Cloud & AI: 💻 2013: graduated BA in Philosophy & started working in media 💻 2015: started teaching myself how to code & a few months later attended a coding bootcamp (@flatironschool Fullstack Web Dev immersive) 💻 2016: started working as a javaScript Web Developer in media 💻 2018: Started teaching myself Cloud Computing via certs & projects, specifically AWS 💻 2020: Shifted in to an SRE role to work in Cloud on Infra end & later infrastructure/devops 💻 2022: Started working as a cloud Developer Advocate @ AWS 💻 2023: When chatGPT launched late 2022 overtime that year upskilled and kept experimenting 💻 2024: my role at AWS officially focused on AI developer advocacy & AWS genAI services + kept upskilling in AI/ML, AI engineering 💻2025: Started to work as a Staff AI infra developer advocate at Anyscale (distributed computing). This was also a shift from corporate to startup for me and I wanted to go to the AI infra end. Currently get to work on Ray which is an open source distributing compute engine that is especially suited for AI/ML workloads (Batch Inf, Distributed Training, etc..) Relevant tags: [ AI engineer, career journey, machine learning, cloud computing, programmer, computer science, devops, MLOps, coding, setup inspiration, tech career ]
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lindavivah
The beauty about tech is there is no one path. 🌱 One of the biggest lessons throughout my journey has been that what felt like the hardest parts often involved “figuring out my next growth step”. The decision is sometimes harder than the execution. ⚙️ Overtime, I learned those phases of “going in circles” are totally normal & actually a lot of the work within your journey. 🔁 For example, it took me a while to figure out that I would like to go in to SRE from webdev etc. 🔁 the same thing happened when I decided it was time to go to a startup and the AI infra end. Have you experienced something similar? Lmk in comments 🤎 ⬇️ 💡 Remember, YOU are the artist of your own life 🎨 Summary of my Untraditional Journey in to Tech & Cloud & AI: 💻 2013: graduated BA in Philosophy & started working in media 💻 2015: started teaching myself how to code & a few months later attended a coding bootcamp (@flatironschool Fullstack Web Dev immersive) 💻 2016: started working as a javaScript Web Developer in media 💻 2018: Started teaching myself Cloud Computing via certs & projects, specifically AWS 💻 2020: Shifted in to an SRE role to work in Cloud on Infra end & later infrastructure/devops 💻 2022: Started working as a cloud Developer Advocate @ AWS 💻 2023: When chatGPT launched late 2022 overtime that year upskilled and kept experimenting 💻 2024: my role at AWS officially focused on AI developer advocacy & AWS genAI services + kept upskilling in AI/ML, AI engineering 💻2025: Started to work as a Staff AI infra developer advocate at Anyscale (distributed computing). This was also a shift from corporate to startup for me and I wanted to go to the AI infra end. Currently get to work on Ray which is an open source distributing compute engine that is especially suited for AI/ML workloads (Batch Inf, Distributed Training, etc..) Relevant tags: [ AI engineer, career journey, machine learning, cloud computing, programmer, computer science, devops, MLOps, coding, setup inspiration, tech career ]
AI Engineer vs ML Engineer explained (while my youngest naps in Central Park 😂👶🍃) 🧠 ML engineers primarily focus on model training and performance optimization. That typically includes: • Data preprocessing and feature engineering • Designing and maintaining training pipelines • Selecting architectures and loss functions • Running experiments and tracking metrics • Hyperparameter tuning • Evaluating generalization performance • Scaling distributed training workloads Their center of gravity is improving how a model is trained and how well it performs. 🏗️ AI engineers primarily focus on system design and production deployment of AI capabilities. That typically includes: • Integrating trained or foundation models into applications • Designing RAG pipelines and agent architectures • Orchestrating tools, APIs, and external services • Managing state, retries, and failure handling • Implementing guardrails and evaluation frameworks • Optimizing latency, throughput, and cost • Scaling inference and serving infrastructure Their center of gravity is ensuring the AI system behaves reliably, safely, and efficiently in real-world environments. 🎯 Same end goal: production-ready AI. But they operate at different layers of the stack. 💡If you want a sticky way to remember it: ML engineers build and tune the brain. AI engineers build the nervous system and body around it. One optimizes how intelligence is trained. The other optimizes how intelligence is expressed and delivered. 🏷️ #AIEngineer #MLEngineer #DistributedSystems #LLMs #AgenticAI AIInfrastructure MachineLearning
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lindavivah
AI Engineer vs ML Engineer explained (while my youngest naps in Central Park 😂👶🍃) 🧠 ML engineers primarily focus on model training and performance optimization. That typically includes: • Data preprocessing and feature engineering • Designing and maintaining training pipelines • Selecting architectures and loss functions • Running experiments and tracking metrics • Hyperparameter tuning • Evaluating generalization performance • Scaling distributed training workloads Their center of gravity is improving how a model is trained and how well it performs. 🏗️ AI engineers primarily focus on system design and production deployment of AI capabilities. That typically includes: • Integrating trained or foundation models into applications • Designing RAG pipelines and agent architectures • Orchestrating tools, APIs, and external services • Managing state, retries, and failure handling • Implementing guardrails and evaluation frameworks • Optimizing latency, throughput, and cost • Scaling inference and serving infrastructure Their center of gravity is ensuring the AI system behaves reliably, safely, and efficiently in real-world environments. 🎯 Same end goal: production-ready AI. But they operate at different layers of the stack. 💡If you want a sticky way to remember it: ML engineers build and tune the brain. AI engineers build the nervous system and body around it. One optimizes how intelligence is trained. The other optimizes how intelligence is expressed and delivered. 🏷️ #AIEngineer #MLEngineer #DistributedSystems #LLMs #AgenticAI AIInfrastructure MachineLearning
What should do you do if you’re looking for a job in 2025? @lindavivah (Staff Developer Advocate at @anyscalecompute) shares her insights! ~~~~~~~~~~~~~~~~ 💻 Follow @madeline.m.zhang for coding memes & insights ~~~~~~~~~~~~~~~~ 🏷️ #ai #aws #anyscale #amazon #programming #jobsearch #girlswhocode #softwareengineer #softwaredeveloper #developerlife #explore #tech
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lindavivah
What should do you do if you’re looking for a job in 2025? @lindavivah (Staff Developer Advocate at @anyscalecompute) shares her insights! ~~~~~~~~~~~~~~~~ 💻 Follow @madeline.m.zhang for coding memes & insights ~~~~~~~~~~~~~~~~ 🏷️ #ai #aws #anyscale #amazon #programming #jobsearch #girlswhocode #softwareengineer #softwaredeveloper #developerlife #explore #tech
Comment “CLOUD” & I’ll send you the link via DM ✨ 🕹️There are 42+ cloud architecture diagrams to explore for FREE in a gamified way! A Generative Al path was just added to AWS Card Clash, with 15 new architecture designs. AWS Card Clash has 3 paths to currently choose from (4th coming soon): 📍Cloud Practitoner 📍Solutions Architect 📍Generative AI 📱You can also play it on the go! Let me know if you have questions in the community & as always Happy Building! 💻 Relevant Hashtags: #techskills #cloudcomputing #amazonwebservices #machinelearning #solutionsarchitect #gaming #awscertification
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Comment “CLOUD” & I’ll send you the link via DM ✨ 🕹️There are 42+ cloud architecture diagrams to explore for FREE in a gamified way! A Generative Al path was just added to AWS Card Clash, with 15 new architecture designs. AWS Card Clash has 3 paths to currently choose from (4th coming soon): 📍Cloud Practitoner 📍Solutions Architect 📍Generative AI 📱You can also play it on the go! Let me know if you have questions in the community & as always Happy Building! 💻 Relevant Hashtags: #techskills #cloudcomputing #amazonwebservices #machinelearning #solutionsarchitect #gaming #awscertification
Comment ✨LAB✨ and I’ll DM you the link. Happy Building! 💡These hands-on labs are built specifically around real AI/ML production challenges. If you’re looking to take your AI/ML projects to production, this is a great place to start

🔗 https://bit.ly/ai-prod-labs
 This resource is full of runnable labs that walk you through production scenarios:

🔹Distributed training and fine-tuning across GPUs 🔹Deploying MCP servers to connect models and tools 🔹Running large-scale batch inference on thousands of inputs 🔹Serving models as APIs that handle real traffic 🔹 Building a video-processing pipeline that analyzes and tags content 
& much more!
 You can filter the examples by complexity, workload, framework. Great resource to upskill in general or on specific use-cases. 

Happy building and please don’t hesitate to reach out with questions!
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lindavivah
Comment ✨LAB✨ and I’ll DM you the link. Happy Building! 💡These hands-on labs are built specifically around real AI/ML production challenges. If you’re looking to take your AI/ML projects to production, this is a great place to start

🔗 https://bit.ly/ai-prod-labs
 This resource is full of runnable labs that walk you through production scenarios:

🔹Distributed training and fine-tuning across GPUs 🔹Deploying MCP servers to connect models and tools 🔹Running large-scale batch inference on thousands of inputs 🔹Serving models as APIs that handle real traffic 🔹 Building a video-processing pipeline that analyzes and tags content 
& much more!
 You can filter the examples by complexity, workload, framework. Great resource to upskill in general or on specific use-cases. 

Happy building and please don’t hesitate to reach out with questions!
Join us for 🥗 @sweetgreen as Robert Nishihara explains the difference between Reinforcement Learning (RL) vs Regular Training #techvlog [Machine Learning, Tech Education ]
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lindavivah
Join us for 🥗 @sweetgreen as Robert Nishihara explains the difference between Reinforcement Learning (RL) vs Regular Training #techvlog [Machine Learning, Tech Education ]
A historical take on the “AI celebrity selfie” trend ✨ Between 1843 and 1968, these ideas helped lay the groundwork for modern computing. 🎥 I used @higgsfield.ai (nano banana pro & kling) A bit more historical info: ✨Ada Lovelace (1843) - wrote the first computer algorithm, introducing the idea that machines could follow abstract instructions. This is where software begins. ✨Alan Turing (1936) - invented the Turing Machine, defining how an algorithm can be executed by a machine and what it even means for a machine to compute. ✨Claude Shannon (1948) - created information theory, introducing the bit and making information measurable, compressible, and transmittable. ✨Grace Hopper (1952) - built the first compiler, proving that programming languages could be written for humans instead of machines. ✨Douglas Engelbart (1968) - pioneered interactive computing, introducing the mouse and showing how humans could work with computers in real time. These aren’t the only foundations … but they’re some of the core ideas that transformed computing from theory into something people could actually use. What other inventors would you highlight? Should we make this a series? [celebrity series trend]
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4mo ago
lindavivah
A historical take on the “AI celebrity selfie” trend ✨ Between 1843 and 1968, these ideas helped lay the groundwork for modern computing. 🎥 I used @higgsfield.ai (nano banana pro & kling) A bit more historical info: ✨Ada Lovelace (1843) - wrote the first computer algorithm, introducing the idea that machines could follow abstract instructions. This is where software begins. ✨Alan Turing (1936) - invented the Turing Machine, defining how an algorithm can be executed by a machine and what it even means for a machine to compute. ✨Claude Shannon (1948) - created information theory, introducing the bit and making information measurable, compressible, and transmittable. ✨Grace Hopper (1952) - built the first compiler, proving that programming languages could be written for humans instead of machines. ✨Douglas Engelbart (1968) - pioneered interactive computing, introducing the mouse and showing how humans could work with computers in real time. These aren’t the only foundations … but they’re some of the core ideas that transformed computing from theory into something people could actually use. What other inventors would you highlight? Should we make this a series? [celebrity series trend]
When I’m approaching system design, there are three steps I always keep top of mind to start. Let’s walk through them using @lucidsoftware and its newer AI features that help speed up that early process! 👇 In this video, I specifically used @lucidsoftware ’s new AI chat in Lucidchart built for diagramming that can also help you go from knowledge to real world architecture. 1️⃣ Clarify the requirements Before drawing anything, get clear on what the system must do and which constraints matter most. Requirements should drive the architecture, not the other way around. 2️⃣ Draw the simplest diagram that satisfies the core need Start with the smallest possible diagram that works. Focus on correctness first and avoid adding scale or complexity too early. 3️⃣ Iterate & refine one constraint at a time Refine the diagram intentionally… adding components when they solve a specific problem like throughput, durability, or observability. Check out Lucid with the link in my bio! #LucidPartner #LucidforWorkplaceAcceleration #LucidForProductivity
20.9K
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3mo ago
lindavivah
When I’m approaching system design, there are three steps I always keep top of mind to start. Let’s walk through them using @lucidsoftware and its newer AI features that help speed up that early process! 👇 In this video, I specifically used @lucidsoftware ’s new AI chat in Lucidchart built for diagramming that can also help you go from knowledge to real world architecture. 1️⃣ Clarify the requirements Before drawing anything, get clear on what the system must do and which constraints matter most. Requirements should drive the architecture, not the other way around. 2️⃣ Draw the simplest diagram that satisfies the core need Start with the smallest possible diagram that works. Focus on correctness first and avoid adding scale or complexity too early. 3️⃣ Iterate & refine one constraint at a time Refine the diagram intentionally… adding components when they solve a specific problem like throughput, durability, or observability. Check out Lucid with the link in my bio! #LucidPartner #LucidforWorkplaceAcceleration #LucidForProductivity

Linda Y (@lindavivah) Instagram Stats & Analytics

Linda Y (@lindavivah) has 104K Instagram followers with a 0.66% engagement rate over the past 12 months. Across 83.0 posts, Linda Y received 165K total likes and 25.6M impressions, averaging 1.99K likes per post. This page tracks Linda Y's performance metrics, top content, and engagement trends — updated daily.

Linda Y (@lindavivah) Instagram Analytics FAQ

How many Instagram followers does Linda Y have?+
Linda Y (@lindavivah) has 104K Instagram followers as of May 2026.
What is Linda Y's Instagram engagement rate?+
Linda Y's Instagram engagement rate is 0.66% over the last 12 months, based on 83.0 posts.
How many likes does Linda Y get on Instagram?+
Linda Y received 165K total likes across 83.0 posts in the last 12 months, averaging 1.99K likes per post.
How many Instagram impressions does Linda Y get?+
Linda Y's Instagram content generated 25.6M total impressions over the last 12 months.