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impressions
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likes
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584
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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
3.80M
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4mo 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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7mo 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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7mo 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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5mo 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!
7 core Claude Code features explained in 90 seconds.. let's go! ⚡ #aiengineer #claudecode #techtok #agenticai #EduTok
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1mo ago
lindavivah
7 core Claude Code features explained in 90 seconds.. let's go! ⚡ #aiengineer #claudecode #techtok #agenticai #EduTok
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 Al DevEx at Oracle) walked me through all 3 in NYC 🗽
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3w ago
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 Al DevEx at Oracle) walked me through all 3 in NYC 🗽
MLOps explained - what’s the difference between DevOps & MLOps #techtok #machinelearning
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3
2mo 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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3mo 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
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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2mo 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
✨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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5mo 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
Honeycomb is hosting a FREE 3 day virtual event all about “observability in the agent era”! 💻 Observability in the agent era isn't just eyes for humans…it's also for your agents, so they can investigate and increasingly fix things themselves. And because LLMs are non-deterministic → you can't reliably reproduce a bug, you can only capture it when it happens. Which means observability isn't a nice to have. When agents are shipping your code, it's your last line of defense. That's why I'm SO excited to partner with Honeycomb on Innovation Week, May 12-14, where they're extending a decade of observability leadership into the most consequential engineering challenge of this era: how to understand, debug, and improve your AI systems running in production #agenticai
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2w ago
lindavivah
Honeycomb is hosting a FREE 3 day virtual event all about “observability in the agent era”! 💻 Observability in the agent era isn't just eyes for humans…it's also for your agents, so they can investigate and increasingly fix things themselves. And because LLMs are non-deterministic → you can't reliably reproduce a bug, you can only capture it when it happens. Which means observability isn't a nice to have. When agents are shipping your code, it's your last line of defense. That's why I'm SO excited to partner with Honeycomb on Innovation Week, May 12-14, where they're extending a decade of observability leadership into the most consequential engineering challenge of this era: how to understand, debug, and improve your AI systems running in production #agenticai
Claude Chat vs Claude Code vs Claude Cowork - when do you use what? I got you 👇 It comes down to two things: 1️⃣ How much access Claude has? 2️⃣ How much YOU want to stay in the loop vs delegate? 🍪 Quick preface though…. no one eats an Oreo the same way these 3 have overlap by design especially Claude Code and Cowork. Best way is to try it out ✨ The coolest thing about AI is personalization & there are constant updates from @Anthropic (there is no right or wrong). It takes time to find a routine and i would first approach it as experimentation with no pressure of choosing 😊 #aiengineer #techtok #EduTok
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2w ago
lindavivah
Claude Chat vs Claude Code vs Claude Cowork - when do you use what? I got you 👇 It comes down to two things: 1️⃣ How much access Claude has? 2️⃣ How much YOU want to stay in the loop vs delegate? 🍪 Quick preface though…. no one eats an Oreo the same way these 3 have overlap by design especially Claude Code and Cowork. Best way is to try it out ✨ The coolest thing about AI is personalization & there are constant updates from @Anthropic (there is no right or wrong). It takes time to find a routine and i would first approach it as experimentation with no pressure of choosing 😊 #aiengineer #techtok #EduTok
There has never been a better time to bring your full self to tech Loved getting to chat with Anaiya Raisinghani ( Senior Technical Evangelist @mongodb ) about the question we're hearing from so many developers right now: 𝙒𝙝𝙖𝙩 𝙨𝙠𝙞𝙡𝙡𝙨 𝙞𝙣𝙘𝙧𝙚𝙖𝙨𝙞𝙣𝙜𝙡𝙮 𝙢𝙖𝙩𝙩𝙚𝙧 𝙖𝙨 𝘼𝙄 𝙖𝙘𝙘𝙚𝙡𝙚𝙧𝙖𝙩𝙚𝙨? The technical barrier to building has dropped and has democratized access. What we're seeing more and more is that the combo matters: technical depth alongside taste, judgment, and storytelling. And honestly even the way we think about building technical systems is starting to reflect that. Whether you come from a technical background and want to upskill on AI, or you're newer to tech and ready to start building, MongoDB's free AI Learning Hub has resources for all levels. Guides, on-demand videos, skill badges, and quick starts.
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1mo ago
lindavivah
There has never been a better time to bring your full self to tech Loved getting to chat with Anaiya Raisinghani ( Senior Technical Evangelist @mongodb ) about the question we're hearing from so many developers right now: 𝙒𝙝𝙖𝙩 𝙨𝙠𝙞𝙡𝙡𝙨 𝙞𝙣𝙘𝙧𝙚𝙖𝙨𝙞𝙣𝙜𝙡𝙮 𝙢𝙖𝙩𝙩𝙚𝙧 𝙖𝙨 𝘼𝙄 𝙖𝙘𝙘𝙚𝙡𝙚𝙧𝙖𝙩𝙚𝙨? The technical barrier to building has dropped and has democratized access. What we're seeing more and more is that the combo matters: technical depth alongside taste, judgment, and storytelling. And honestly even the way we think about building technical systems is starting to reflect that. Whether you come from a technical background and want to upskill on AI, or you're newer to tech and ready to start building, MongoDB's free AI Learning Hub has resources for all levels. Guides, on-demand videos, skill badges, and quick starts.
📖 The story behind Anthropic’s new Claude’s new computer use feature #EduTok #claude #claudecowork #aiengineer #agenticai
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1mo ago
lindavivah
📖 The story behind Anthropic’s new Claude’s new computer use feature #EduTok #claude #claudecowork #aiengineer #agenticai
Claude Code just launched ✨Channels✨ … let’s set it up together 🎉 #EduTok #techtok #aiengineering #coding
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2mo ago
lindavivah
Claude Code just launched ✨Channels✨ … let’s set it up together 🎉 #EduTok #techtok #aiengineering #coding
Ray Summit 2025 is happening Nov 3-5 in SF! Hope to see you there 🎉 #techtok #machinelearning #sanfrancisco
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7mo 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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8mo ago
lindavivah
Let’s talk about deploying custom MCP servers 🚀 #aiengineer #aiengineering #machinelearning #techtok #agenticai @Anyscale
Vibecon is a creative Al conference by @Replit happening in NY June 17th - 18th - Hope to see you there!
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1w ago
lindavivah
Vibecon is a creative Al conference by @Replit happening in NY June 17th - 18th - Hope to see you there!
3 Claude Dektop shortcuts on Mac ✨ #EduTok #techtok
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1mo ago
lindavivah
3 Claude Dektop shortcuts on Mac ✨ #EduTok #techtok
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
3.55K
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3mo 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

lindavivah (@lindavivah) Tiktok Stats & Analytics

lindavivah (@lindavivah) has 79.3K Tiktok followers with a 0.67% engagement rate over the past 12 months. Across 59.0 videos, lindavivah received 28.2K total likes and 4.30M views, averaging 478 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 79.3K TikTok followers as of May 2026.
What is lindavivah's TikTok engagement rate?+
lindavivah's TikTok engagement rate is 0.67% over the last 12 months, based on 59.0 videos.
How many likes does lindavivah get on TikTok?+
lindavivah received 28.2K total likes across 59.0 videos in the last 12 months, averaging 478 likes per video.
How many TikTok views does lindavivah get?+
lindavivah's TikTok content generated 4.30M total views over the last 12 months.