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80.9K
impressions
4.49M
likes
36.3K
comments
620
posts
81
engagement
0.822%
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$99.7K
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55.5K

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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
3.82M
1.80K
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8mo 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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11mo 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]
Principal Al Architect & Engineer @codewithbrij walks through the 8 layers from LLM to Agentic Al 🧠 LLM → predicts the next word. Smart, but frozen. 📚 + RAG → reads your docs before answering. 🔧 + Tool Calling → now it can DO things. Call APIs, run code, query DBs. 💾 + Memory → remembers yesterday. Learns what works. 🤖 = AI Agent → LLM + RAG + Tools + Memory. 🤝 + Agentic AI → agents delegating to agents. Planning. Self-correcting. 🧩 + Skills & Hooks → modular know-how + lifecycle triggers. 🛡️ + Governance & Observability → traces, guardrails, audit. This is what makes it production. #techtok #EduTok #agenticai
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4mo ago
lindavivah
Principal Al Architect & Engineer @codewithbrij walks through the 8 layers from LLM to Agentic Al 🧠 LLM → predicts the next word. Smart, but frozen. 📚 + RAG → reads your docs before answering. 🔧 + Tool Calling → now it can DO things. Call APIs, run code, query DBs. 💾 + Memory → remembers yesterday. Learns what works. 🤖 = AI Agent → LLM + RAG + Tools + Memory. 🤝 + Agentic AI → agents delegating to agents. Planning. Self-correcting. 🧩 + Skills & Hooks → modular know-how + lifecycle triggers. 🛡️ + Governance & Observability → traces, guardrails, audit. This is what makes it production. #techtok #EduTok #agenticai
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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9mo 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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5mo 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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5mo 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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7mo 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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8mo 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
There is a difference when building skills for agents vs building skills for humans. It comes down to a horizontal vs vertical approach. Let’s walk with a Google engineer, Pier Paolo, as he breaks it down in 60 seconds: 🔹 Vertical skills ➡️ one skill built for one specific product, one job 🔹 Horizontal skills ➡️ a skill that travels from agent to agent, bridging different services, products, even ecosystems #techtok #agenticai #EduTok
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2mo ago
lindavivah
There is a difference when building skills for agents vs building skills for humans. It comes down to a horizontal vs vertical approach. Let’s walk with a Google engineer, Pier Paolo, as he breaks it down in 60 seconds: 🔹 Vertical skills ➡️ one skill built for one specific product, one job 🔹 Horizontal skills ➡️ a skill that travels from agent to agent, bridging different services, products, even ecosystems #techtok #agenticai #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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6mo 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
Traditional RAG vs Graph RAG explained in 60 seconds by Nacho Martínez ⚡ (Data Scientist Advocate @Oracle ) 💡 Both pull external knowledge into an LLM. The difference is whether what comes back is isolated, or actually connected. Traditional RAG chunks your docs, vectorizes each chunk, similarity search returns the top matches. Each piece stands alone. Graph RAG organizes knowledge as a graph with edges between related items. Retrieval can follow those edges, so you get a match plus what it connects to. Nacho Martínez walks through it at the Oracle office 🏢…. Also pretty sure the architecture is shaped like databases haha #techtok #EduTok
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4mo ago
lindavivah
Traditional RAG vs Graph RAG explained in 60 seconds by Nacho Martínez ⚡ (Data Scientist Advocate @Oracle ) 💡 Both pull external knowledge into an LLM. The difference is whether what comes back is isolated, or actually connected. Traditional RAG chunks your docs, vectorizes each chunk, similarity search returns the top matches. Each piece stands alone. Graph RAG organizes knowledge as a graph with edges between related items. Retrieval can follow those edges, so you get a match plus what it connects to. Nacho Martínez walks through it at the Oracle office 🏢…. Also pretty sure the architecture is shaped like databases haha #techtok #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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10mo 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
Andrew Ng and the @DeepLearning.AI job postings + expert interviews and just shared the skills that matter most in AI engineering right now for developers. The 4 skills: 🔹 Building & deploying AI applications: AI systems have unpredictable outputs, so the work is learning the building blocks (RAG, evals, agentic workflows, context engineering) and using them to measure, steer, and govern how the system behaves 🔹 Software engineering fundamentals: so much of software engineering is actually trade-offs (cost, speed, scalability, reliability, security). It's making decisions, and if you don't know the trade-offs, it's hard to make those decisions 🔹 Using coding agents: building a mental model of what agents can and can't do, managing context, balancing planning vs execution, and keeping current as the tools evolve 🔹 Shaping the build: participating in the spec decisions themselves, which takes product sense and an understanding of business context and customer goals. Part of the skill is knowing when to spin up an MVP and when to build carefully
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1mo ago
lindavivah
Andrew Ng and the @DeepLearning.AI job postings + expert interviews and just shared the skills that matter most in AI engineering right now for developers. The 4 skills: 🔹 Building & deploying AI applications: AI systems have unpredictable outputs, so the work is learning the building blocks (RAG, evals, agentic workflows, context engineering) and using them to measure, steer, and govern how the system behaves 🔹 Software engineering fundamentals: so much of software engineering is actually trade-offs (cost, speed, scalability, reliability, security). It's making decisions, and if you don't know the trade-offs, it's hard to make those decisions 🔹 Using coding agents: building a mental model of what agents can and can't do, managing context, balancing planning vs execution, and keeping current as the tools evolve 🔹 Shaping the build: participating in the spec decisions themselves, which takes product sense and an understanding of business context and customer goals. Part of the skill is knowing when to spin up an MVP and when to build carefully
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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5mo 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
How to customize an LLM for your app (song edition 🎶) A good way to mind map it is thing of it as 3 buckets: 1️⃣Change what the model sees examples: prompting, RAG, memory augmentation 2️⃣Change the system around it examples: tools, guardrails, agentic AI 3️⃣Change the model itself examples: fine-tuning, reinforcement learning In production you're usually combining all three. Microsoft Foundry has the tools to build across all of them. Which bucket are you building in right now? @Microsoft Developer #MicrosoftPartner #AIEngineering #SoftwareDeveloper #Techtok
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4mo ago
lindavivah
How to customize an LLM for your app (song edition 🎶) A good way to mind map it is thing of it as 3 buckets: 1️⃣Change what the model sees examples: prompting, RAG, memory augmentation 2️⃣Change the system around it examples: tools, guardrails, agentic AI 3️⃣Change the model itself examples: fine-tuning, reinforcement learning In production you're usually combining all three. Microsoft Foundry has the tools to build across all of them. Which bucket are you building in right now? @Microsoft Developer #MicrosoftPartner #AIEngineering #SoftwareDeveloper #Techtok
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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5mo 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
⚡ What are Declarative Pipelines in less than 90 seconds? Let's walk with Michael Armbrust, Distinguished Engineer at Databricks who led the design & development of Declarative Pipelines + created Spark SQL, Structured Streaming, Delta Lake, & much more as he breaks it down! 💡 TLDR: instead of telling the system HOW to do every step, you just tell it WHAT you want to exist. Spark figures out the how. The technology itself is open source in Apache Spark, and Databricks offers a managed version on top called Lakeflow Spark Declarative Pipelines. #techtok #EduTok #DatabricksPartner #databricks
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3mo ago
lindavivah
⚡ What are Declarative Pipelines in less than 90 seconds? Let's walk with Michael Armbrust, Distinguished Engineer at Databricks who led the design & development of Declarative Pipelines + created Spark SQL, Structured Streaming, Delta Lake, & much more as he breaks it down! 💡 TLDR: instead of telling the system HOW to do every step, you just tell it WHAT you want to exist. Spark figures out the how. The technology itself is open source in Apache Spark, and Databricks offers a managed version on top called Lakeflow Spark Declarative Pipelines. #techtok #EduTok #DatabricksPartner #databricks
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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6mo 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.
Claude Code just launched ✨Channels✨ … let’s set it up together 🎉 #EduTok #techtok #aiengineering #coding
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6mo ago
lindavivah
Claude Code just launched ✨Channels✨ … let’s set it up together 🎉 #EduTok #techtok #aiengineering #coding
You can now add multiple Gmail accounts to @ChatGPT ChatGPT > plugins > gmail > connect another account + you can nickname them
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1mo ago
lindavivah
You can now add multiple Gmail accounts to @ChatGPT ChatGPT > plugins > gmail > connect another account + you can nickname them

lindavivah (@lindavivah) TikTok Stats & Analytics

lindavivah (@lindavivah) has 80.9K TikTok followers with a 0.82% engagement rate over the past 12 months. Across 81.0 videos, lindavivah received 36.3K total likes and 4.49M views, averaging 448 likes per video. This page tracks lindavivah's performance metrics, top content, and engagement trends — updated daily. To benchmark @lindavivah against other accounts, use Socialpruf's TikTok competitor analysis tool.

lindavivah (@lindavivah) TikTok Analytics FAQ

How many TikTok followers does lindavivah have?+
lindavivah (@lindavivah) has 80.9K TikTok followers as of October 2026.
What is lindavivah's TikTok engagement rate?+
lindavivah's TikTok engagement rate is 0.82% over the last 12 months, based on 81.0 videos.
How many likes does lindavivah get on TikTok?+
lindavivah received 36.3K total likes across 81.0 videos in the last 12 months, averaging 448 likes per video.
How many TikTok views does lindavivah get?+
lindavivah's TikTok content generated 4.49M total views over the last 12 months.
Can I compare lindavivah with other TikTok accounts?+
Yes. Add @lindavivah to a competitor set with Socialpruf's TikTok competitor analysis tool and benchmark it side by side with any other public accounts on views, engagement rate and follower growth, with history back to each account's first post.