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4.48M
likes
35.0K
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673
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0.796%
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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
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5mo 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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8mo 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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3w 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
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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9mo 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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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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2mo 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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2mo 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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4mo 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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4mo 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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3mo 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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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
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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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
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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1mo 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
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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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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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
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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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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lindavivah
📖 The story behind Anthropic’s new Claude’s new computer use feature #EduTok #claude #claudecowork #aiengineer #agenticai
Databricks just launched the industry's first Context Engineer Associate cert! 💻 It assesses your ability to design, assemble, and govern the information AI agent systems receive at inference time: retrieval, memory, tool integration, context-window management, and governance. Check out the 🔗in bio for the cert and virtual registration for the Data + AI Summit! #EduTok #techtok
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lindavivah
Databricks just launched the industry's first Context Engineer Associate cert! 💻 It assesses your ability to design, assemble, and govern the information AI agent systems receive at inference time: retrieval, memory, tool integration, context-window management, and governance. Check out the 🔗in bio for the cert and virtual registration for the Data + AI Summit! #EduTok #techtok
Claude Code just launched ✨Channels✨ … let’s set it up together 🎉 #EduTok #techtok #aiengineering #coding
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lindavivah
Claude Code just launched ✨Channels✨ … let’s set it up together 🎉 #EduTok #techtok #aiengineering #coding
Tech talk & walk… the dog. 🐕 Walk with Simba Khadder, Chups 🐾, & me as he explains Redis Iris in 30 seconds! Redis Iris is Redis's new unified, real-time context engine for AI agents. Featuring Simba Khadder, Head of Context Engine at Redis & founder of Featureform (acquired by Redis). It's made up of a few components: 🔹Context Retriever: turns your business data into structured tools agents call directly, so they never touch your database and can only use what you've explicitly defined. 🔹Agent Memory: gives agents short-term session memory plus long-term memory across interactions, so context compounds instead of resetting every session. 🔹LangCache: keeps repeated and semantically similar LLM work fast by serving cached responses from Redis inside the agent's latency budget.
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lindavivah
Tech talk & walk… the dog. 🐕 Walk with Simba Khadder, Chups 🐾, & me as he explains Redis Iris in 30 seconds! Redis Iris is Redis's new unified, real-time context engine for AI agents. Featuring Simba Khadder, Head of Context Engine at Redis & founder of Featureform (acquired by Redis). It's made up of a few components: 🔹Context Retriever: turns your business data into structured tools agents call directly, so they never touch your database and can only use what you've explicitly defined. 🔹Agent Memory: gives agents short-term session memory plus long-term memory across interactions, so context compounds instead of resetting every session. 🔹LangCache: keeps repeated and semantically similar LLM work fast by serving cached responses from Redis inside the agent's latency budget.

lindavivah (@lindavivah) Tiktok Stats & Analytics

lindavivah (@lindavivah) has 80.7K Tiktok followers with a 0.80% engagement rate over the past 12 months. Across 70.0 videos, lindavivah received 35.0K total likes and 4.48M 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 80.7K TikTok followers as of July 2026.
What is lindavivah's TikTok engagement rate?+
lindavivah's TikTok engagement rate is 0.80% over the last 12 months, based on 70.0 videos.
How many likes does lindavivah get on TikTok?+
lindavivah received 35.0K total likes across 70.0 videos in the last 12 months, averaging 500 likes per video.
How many TikTok views does lindavivah get?+
lindavivah's TikTok content generated 4.48M total views over the last 12 months.