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78.5K
impressions
3.94M
likes
24.5K
comments
581
posts
49
engagement
0.636%
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$85.6K
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Taking you along with me to build an audio-reactive focus tool with Claude’s new Opus 4.5 model 🎧💻 Try it out for yourself via the 🔗 in bio! #claudepartner
3.49M
1.55K
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2mo ago
lindavivah
Taking you along with me to build an audio-reactive focus tool with Claude’s new Opus 4.5 model 🎧💻 Try it out for yourself via the 🔗 in bio! #claudepartner
Walk with the founder of @Anyscale Robert Nishihara & I in NYC with 10% charge as I get to pick his brain on the 5 key differences between LLM inference VS Regular inference Let's see how much we can get through before our mic dies!😅 @robertnishihara [Machine Learning, Al Engineering, Artificial Intelligence, Tech Education, Python, Al infrastructure]
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5mo ago
lindavivah
Walk with the founder of @Anyscale Robert Nishihara & I in NYC with 10% charge as I get to pick his brain on the 5 key differences between LLM inference VS Regular inference Let's see how much we can get through before our mic dies!😅 @robertnishihara [Machine Learning, Al Engineering, Artificial Intelligence, Tech Education, Python, Al infrastructure]
Thinking Machines Lab just launched their first product, Tinker Tinker is a distributed training API for fine-tuning language models 🔹Gives researchers & developers low-level control over algorithms and data while abstracting away the complexity of distributed training & deployment 🔹 Lets you fine-tune open-weight models like Llama and Qwen, including large MoE variants like Qwen3-235B-A22B The @Anyscale team had early access to experiment with the API and wrote up a simple end to end example using Tinker and Ray It's a neat look at how Tinker + Ray fit together to make fine-tuning open-weight models easier to experiment with. #ainews #machinelearning #techtok #artificialintelligence
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6mo ago
lindavivah
Thinking Machines Lab just launched their first product, Tinker Tinker is a distributed training API for fine-tuning language models 🔹Gives researchers & developers low-level control over algorithms and data while abstracting away the complexity of distributed training & deployment 🔹 Lets you fine-tune open-weight models like Llama and Qwen, including large MoE variants like Qwen3-235B-A22B The @Anyscale team had early access to experiment with the API and wrote up a simple end to end example using Tinker and Ray It's a neat look at how Tinker + Ray fit together to make fine-tuning open-weight models easier to experiment with. #ainews #machinelearning #techtok #artificialintelligence
One of my favorite use cases for Claude Code @Claude in the CLI is helping me clear disk space and organize local files. If you are running out of storage on your laptop, check out how you can use Claude Code in the CLI with Opus 4.5 to clear your disk space in minutes! #ClaudePartner Check out the 🔗 in bio to try it out for yourself!
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3mo ago
lindavivah
One of my favorite use cases for Claude Code @Claude in the CLI is helping me clear disk space and organize local files. If you are running out of storage on your laptop, check out how you can use Claude Code in the CLI with Opus 4.5 to clear your disk space in minutes! #ClaudePartner Check out the 🔗 in bio to try it out for yourself!
POV: watching my 7 year old “vibe coding”in the CLI building a memory match game with historical figures 🕹️ #coding #programming #aiagents #agenticai #ai #softwaredeveloper #techtok
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12mo ago
lindavivah
POV: watching my 7 year old “vibe coding”in the CLI building a memory match game with historical figures 🕹️ #coding #programming #aiagents #agenticai #ai #softwaredeveloper #techtok
MLOps explained - what’s the difference between DevOps & MLOps #techtok #machinelearning
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1mo ago
lindavivah
MLOps explained - what’s the difference between DevOps & MLOps #techtok #machinelearning
Let’s see how much we can fit in 60 seconds⏰😅… High-level overview of the ML pipeline. 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. #edutok
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2mo ago
lindavivah
Let’s see how much we can fit in 60 seconds⏰😅… High-level overview of the ML pipeline. 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. #edutok
Applies to all the AWS foundational & associate level certifications: ☁️Cloud Practitioner ☁️ AI Practitioner ☁️ Solutions Architect - associate ☁️ Developer - associate ☁️ Data Engineer - associate ☁️ SysOps Administrator - associate ☁️ Machine Learning Engineer - associate #awscertified #cloudcomputing #softwaredeveloper #awscertification #EduTok
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12mo ago
lindavivah
Applies to all the AWS foundational & associate level certifications: ☁️Cloud Practitioner ☁️ AI Practitioner ☁️ Solutions Architect - associate ☁️ Developer - associate ☁️ Data Engineer - associate ☁️ SysOps Administrator - associate ☁️ Machine Learning Engineer - associate #awscertified #cloudcomputing #softwaredeveloper #awscertification #EduTok
✨Anthropic just announced it’s donating MCP to the Linux Foundation, under a brand-new Agentic AI Foundation called AAIF, co-founded by Anthropic, OpenAI, and Block. #aiengineer #technews #agenticai
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4mo ago
lindavivah
✨Anthropic just announced it’s donating MCP to the Linux Foundation, under a brand-new Agentic AI Foundation called AAIF, co-founded by Anthropic, OpenAI, and Block. #aiengineer #technews #agenticai
80%+ of the world’s data is unstructured 🌎🤯….. here’s why that matters for AI more than people realize #techtok #aiengineer #techtrends #machinelearning #EduTok
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3w ago
lindavivah
80%+ of the world’s data is unstructured 🌎🤯….. here’s why that matters for AI more than people realize #techtok #aiengineer #techtrends #machinelearning #EduTok
📖 The story behind Anthropic’s new Claude’s new computer use feature #EduTok #claude #claudecowork #aiengineer #agenticai
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2w ago
lindavivah
📖 The story behind Anthropic’s new Claude’s new computer use feature #EduTok #claude #claudecowork #aiengineer #agenticai
Ray Summit 2025 is happening Nov 3-5 in SF! Hope to see you there 🎉 #techtok #machinelearning #sanfrancisco
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6mo ago
lindavivah
Ray Summit 2025 is happening Nov 3-5 in SF! Hope to see you there 🎉 #techtok #machinelearning #sanfrancisco
Let’s talk about deploying custom MCP servers 🚀 #aiengineer #aiengineering #machinelearning #techtok #agenticai @Anyscale
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7mo ago
lindavivah
Let’s talk about deploying custom MCP servers 🚀 #aiengineer #aiengineering #machinelearning #techtok #agenticai @Anyscale
Claude Code just launched ✨Channels✨ … let’s set it up together 🎉 #EduTok #techtok #aiengineering #coding
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3w ago
lindavivah
Claude Code just launched ✨Channels✨ … let’s set it up together 🎉 #EduTok #techtok #aiengineering #coding
One of the most underrated skills I learned in tech? 📌 Being Resourceful What’s your? #techtok #compsci #womenintech #techcareer
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11mo ago
lindavivah
One of the most underrated skills I learned in tech? 📌 Being Resourceful What’s your? #techtok #compsci #womenintech #techcareer
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! 💻
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8mo ago
lindavivah
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! 💻
#ad Come with me to the @amazon Devices 2025 launch event! 🎉 There were so many awesome announcements! Here is the TLDR: 🔔 Ring → New 4K + 2K cameras, Search Party to help find lost dogs, and smarter features like Alexa+ Greetings + Familiar Faces 📷 Blink → Outdoor + Mini 2K+ cameras, plus Blink Arc for a 180° panoramic stitched view 📖 Kindle → Kindle Scribe Colorsoft with color writing, AI-powered notebook search, and a thinner, paper-like design 📺 Fire TV → Faster Fire TV Stick 4K Select + new TVs, all with Alexa+ for conversational search + smarter recs 🔊 Echo → Four new Echo devices, redesigned audio + displays, and Alexa Home Theater to link up to 5 speakers ✨all with AI & Alexa+ creating a connected, intelligent, more personalized experience #amazondevices2025
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6mo ago
lindavivah
#ad Come with me to the @amazon Devices 2025 launch event! 🎉 There were so many awesome announcements! Here is the TLDR: 🔔 Ring → New 4K + 2K cameras, Search Party to help find lost dogs, and smarter features like Alexa+ Greetings + Familiar Faces 📷 Blink → Outdoor + Mini 2K+ cameras, plus Blink Arc for a 180° panoramic stitched view 📖 Kindle → Kindle Scribe Colorsoft with color writing, AI-powered notebook search, and a thinner, paper-like design 📺 Fire TV → Faster Fire TV Stick 4K Select + new TVs, all with Alexa+ for conversational search + smarter recs 🔊 Echo → Four new Echo devices, redesigned audio + displays, and Alexa Home Theater to link up to 5 speakers ✨all with AI & Alexa+ creating a connected, intelligent, more personalized experience #amazondevices2025
What’s the difference between 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. #techtok #machinelearning #LLMS #techcareer
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1mo ago
lindavivah
What’s the difference between 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. #techtok #machinelearning #LLMS #techcareer
💡These hands-on labs are built specifically around real Al/ML production challenges. If you're looking to take your Al/ML projects to production, this is a great place to start #machinelearning #aiengineer #techtok
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4mo ago
lindavivah
💡These hands-on labs are built specifically around real Al/ML production challenges. If you're looking to take your Al/ML projects to production, this is a great place to start #machinelearning #aiengineer #techtok
Cursor just launched its own frontier model called Composer- let’s try it out! Also Cursor 2.0 is packed with fantastic features. Excited to play around with it more! #aiengineer #machinelearning
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5mo ago
lindavivah
Cursor just launched its own frontier model called Composer- let’s try it out! Also Cursor 2.0 is packed with fantastic features. Excited to play around with it more! #aiengineer #machinelearning

lindavivah (@lindavivah) Tiktok Stats & Analytics

lindavivah (@lindavivah) has 78.5K Tiktok followers with a 0.64% engagement rate over the past 12 months. Across 49.0 videos, lindavivah received 24.5K total likes and 3.94M views, averaging 500 likes per video. This page tracks lindavivah's performance metrics, top content, and engagement trends — updated daily.

lindavivah (@lindavivah) Tiktok Analytics FAQ

How many TikTok followers does lindavivah have?+
lindavivah (@lindavivah) has 78.5K TikTok followers as of April 2026.
What is lindavivah's TikTok engagement rate?+
lindavivah's TikTok engagement rate is 0.64% over the last 12 months, based on 49.0 videos.
How many likes does lindavivah get on TikTok?+
lindavivah received 24.5K total likes across 49.0 videos in the last 12 months, averaging 500 likes per video.
How many TikTok views does lindavivah get?+
lindavivah's TikTok content generated 3.94M total views over the last 12 months.