Loading video...

Video Failed to Load

Go Home

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning...

Al_Grigor's profile picture

Alexey Grigorev

28,768 subscribers

13,694 views • 6 months ago •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.
4:43

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.

The Startup Ideas Podcast (SIP) 🧃

14,202 views • 4 months ago

This April AMA is here. In this session, the team breaks down how biodata (like sleep and brain data) can be standardized, valued, and turned into real-world assets (RWA). What you’ll learn: 1. The 4 key milestones for Biodata RWA in 2026 2. Why sleep data is the first step in building a data economy 3. How data becomes structured, scored, and monetizable 4. The role of MAN token in rewarding and settling data usage 5. How privacy is protected with distributed storage & computation 6. Ownership models: sell vs license your data 7. Why institutions (AI, pharma, insurance) need verified biodata In the future, your data won’t just exist, it will work for you.
35:10

This April AMA is here. In this session, the team breaks down how biodata (like sleep and brain data) can be standardized, valued, and turned into real-world assets (RWA). What you’ll learn: 1. The 4 key milestones for Biodata RWA in 2026 2. Why sleep data is the first step in building a data economy 3. How data becomes structured, scored, and monetizable 4. The role of MAN token in rewarding and settling data usage 5. How privacy is protected with distributed storage & computation 6. Ownership models: sell vs license your data 7. Why institutions (AI, pharma, insurance) need verified biodata In the future, your data won’t just exist, it will work for you.

Matrix AI Network

15,296 views • 4 months ago

Anthropic hired this engineer at $250K-$750K a year because he knows how to build harnesses for multi-agent systems In this 15-minute workshop, he shows exactly how to build one from scratch AI → Agents → Harness → Loops → Graphs step 1 → start with the Claude Agent SDK - the harness handles loops, context, and sandboxing step 2 → separate the brain from the hands - reasoning in one place, tools in a sandbox, 60% faster to first token step 3 → run it server-side and log every step - close your laptop and it keeps running, crashes resume from the log step 4 → make failure cheap - retry dead sandboxes and replay lost context instead of starting over step 5 → turn yesterday's logs into new memory and skills - the harness wakes up smarter Anthropic calls this "dreaming" Most people spend weeks building this by hand You don't have to Bookmark and watch it Then read the full harness engineering guide below ↓
12:00

Anthropic hired this engineer at $250K-$750K a year because he knows how to build harnesses for multi-agent systems In this 15-minute workshop, he shows exactly how to build one from scratch AI → Agents → Harness → Loops → Graphs step 1 → start with the Claude Agent SDK - the harness handles loops, context, and sandboxing step 2 → separate the brain from the hands - reasoning in one place, tools in a sandbox, 60% faster to first token step 3 → run it server-side and log every step - close your laptop and it keeps running, crashes resume from the log step 4 → make failure cheap - retry dead sandboxes and replay lost context instead of starting over step 5 → turn yesterday's logs into new memory and skills - the harness wakes up smarter Anthropic calls this "dreaming" Most people spend weeks building this by hand You don't have to Bookmark and watch it Then read the full harness engineering guide below ↓

rari

183,635 views • 18 days ago

Grok Bot + Kimi K3 can be turned into something bigger than an agent: an AI operating system the formula: AI OS = Router + Reasoning + Memory + Tools + Loops + Verification not one giant assistant. six layers that keep work moving without you step 1 -> Grok Bot becomes the operator. you give it the goal, it breaks the goal into jobs, assigns priorities and decides what part of the system should act next. step 2 -> Kimi K3 becomes the reasoning core. hard research, synthesis, long context and planning move here instead of forcing every task through the same model. step 3 -> externalize memory. store goals, decisions, failed attempts, artifacts and current state outside the chat. close the session, come back tomorrow, and the system still knows where it is. step 4 -> connect tools: search, code, files, APIs, docs and data. reasoning decides what should happen. tools actually make it happen. step 5 -> add the loop engine: plan -> execute -> inspect -> update memory -> retry. the loop can wait for new information, rerun a failed task, hand work to another agent or stop when the goal is complete. step 6 -> verify before output. tests, source checks, constraints and explicit completion rules decide whether the system ships the result or sends it back into the loop. that's the difference between an AI assistant and an AI operating system. an assistant waits for your next message. an operating system carries state, routes work and keeps moving. Grok Bot handles orchestration, Kimi K3 handles deeper reasoning, memory keeps the state alive, tools execute, the loop keeps the system running, verification decides when it is actually done. build one reliable loop and you have an agent. connect reasoning, memory, tools and multiple loops around it and you start building infrastructure. the full Grok Bot + Kimi K3 AI OS breakdown is below ↓
0:20

Grok Bot + Kimi K3 can be turned into something bigger than an agent: an AI operating system the formula: AI OS = Router + Reasoning + Memory + Tools + Loops + Verification not one giant assistant. six layers that keep work moving without you step 1 -> Grok Bot becomes the operator. you give it the goal, it breaks the goal into jobs, assigns priorities and decides what part of the system should act next. step 2 -> Kimi K3 becomes the reasoning core. hard research, synthesis, long context and planning move here instead of forcing every task through the same model. step 3 -> externalize memory. store goals, decisions, failed attempts, artifacts and current state outside the chat. close the session, come back tomorrow, and the system still knows where it is. step 4 -> connect tools: search, code, files, APIs, docs and data. reasoning decides what should happen. tools actually make it happen. step 5 -> add the loop engine: plan -> execute -> inspect -> update memory -> retry. the loop can wait for new information, rerun a failed task, hand work to another agent or stop when the goal is complete. step 6 -> verify before output. tests, source checks, constraints and explicit completion rules decide whether the system ships the result or sends it back into the loop. that's the difference between an AI assistant and an AI operating system. an assistant waits for your next message. an operating system carries state, routes work and keeps moving. Grok Bot handles orchestration, Kimi K3 handles deeper reasoning, memory keeps the state alive, tools execute, the loop keeps the system running, verification decides when it is actually done. build one reliable loop and you have an agent. connect reasoning, memory, tools and multiple loops around it and you start building infrastructure. the full Grok Bot + Kimi K3 AI OS breakdown is below ↓

Alex

13,312 views • 11 days ago

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:
1:39

Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by , experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Andrew Ng

124,656 views • 1 year ago

Anthropic engineer: "Harness engineering is a really rare skill right now - and it's hard to know if you're even good at it." how he actually thinks about building the full system: • step 1 → the model. already smarter than we use it. "freeze development - you'd find new capabilities for another 6-12 months" • step 2 → the harness. delete the steering. "we stopped using plan mode - the model thinks correctly" • step 3 → the agents. workflows let Claude build its own harness per task and spin sub-agents. what didn't fit in one context window now runs across many • step 4 → the context. "RAG is an anti-pattern. use grep." memory via files, clean context window, prompt caching. that's the stack most people optimize step 1. he optimizes all four. Watch the full video, then read the guide below on how to build an agentic system.
46:31

Anthropic engineer: "Harness engineering is a really rare skill right now - and it's hard to know if you're even good at it." how he actually thinks about building the full system: • step 1 → the model. already smarter than we use it. "freeze development - you'd find new capabilities for another 6-12 months" • step 2 → the harness. delete the steering. "we stopped using plan mode - the model thinks correctly" • step 3 → the agents. workflows let Claude build its own harness per task and spin sub-agents. what didn't fit in one context window now runs across many • step 4 → the context. "RAG is an anti-pattern. use grep." memory via files, clean context window, prompt caching. that's the stack most people optimize step 1. he optimizes all four. Watch the full video, then read the guide below on how to build an agentic system.

Codez

44,590 views • 2 months ago

Anthropic hired this engineer at $250k-$750k/year because he can build knowledge Graphs for multi-agent systems: in this 15-min workshop he explain exactly how, using loops - from scratch step 1 → start from the Claude Agent SDK - you get the loop, context, and sandbox for free - bring the task, not the plumbing step 2 → split the brain from the hands - reasoning in one place, tools in a sandbox - the model thinks while the container boots: ~60% faster to first token step 3 → run it server-side, log every step - close your laptop and it keeps going - crashes resume from the log, not from zero step 4 → make failure cheap - dead sandbox retries, session log replays the context it dropped - that's what survives production step 5 → batch yesterday's logs into new memory and skills - it wakes up smarter - Anthropic calls this "dreaming" most people wire this by hand for weeks - the point is you don't have to watch & bookmark - then read how to get full graph system below ↓
12:01

Anthropic hired this engineer at $250k-$750k/year because he can build knowledge Graphs for multi-agent systems: in this 15-min workshop he explain exactly how, using loops - from scratch step 1 → start from the Claude Agent SDK - you get the loop, context, and sandbox for free - bring the task, not the plumbing step 2 → split the brain from the hands - reasoning in one place, tools in a sandbox - the model thinks while the container boots: ~60% faster to first token step 3 → run it server-side, log every step - close your laptop and it keeps going - crashes resume from the log, not from zero step 4 → make failure cheap - dead sandbox retries, session log replays the context it dropped - that's what survives production step 5 → batch yesterday's logs into new memory and skills - it wakes up smarter - Anthropic calls this "dreaming" most people wire this by hand for weeks - the point is you don't have to watch & bookmark - then read how to get full graph system below ↓

codila

119,500 views • 1 month ago

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:
2:07

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

Andrew Ng

126,675 views • 1 year ago

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: ​ 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. ​ 2. The connector ecosystem to load data from unstructured data sources is very immature. ​ 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. ​ The goal of a RAG Pipeline is to solve these problems. ​ The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. ​ At a high level, there are four different stages in the architecture of a RAG pipeline: ​ 1. Ingestion: Here is where the pipeline loads the information from the data source. ​ 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. ​ 3. Transform: Where the pipeline chunks the data and generates document embeddings. ​ 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. ​ There are different rabbit holes at each one of these stages. Here are three of them: ​ 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. ​ 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. ​ 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. ​ In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. ​ I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. ​ ​ If you have a few documents lying around, set up a free account and give it a try.
3:33

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: ​ 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. ​ 2. The connector ecosystem to load data from unstructured data sources is very immature. ​ 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. ​ The goal of a RAG Pipeline is to solve these problems. ​ The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. ​ At a high level, there are four different stages in the architecture of a RAG pipeline: ​ 1. Ingestion: Here is where the pipeline loads the information from the data source. ​ 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. ​ 3. Transform: Where the pipeline chunks the data and generates document embeddings. ​ 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. ​ There are different rabbit holes at each one of these stages. Here are three of them: ​ 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. ​ 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. ​ 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. ​ In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. ​ I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. ​ ​ If you have a few documents lying around, set up a free account and give it a try.

Santiago

40,627 views • 1 year ago

The shift from Copilot to Autopilot is happening now. This is the Agentic Frontier, and while 82% of leaders are investing in it, most are not ready. Why? Because they're stuck in Phase 1 (AI Assistants) while the winners are already building for Phase 3 (Human-Led, Agent-Operated) operations. The gap between pilot and production is where most AI initiatives die. It's not a tech problem. It's a culture, governance, and data problem. But it's possible to cross this chasm. Enjoyed attending Microsoft Ignite with the EY team this past week. I learn about their "Client Zero" journey, 400,000+ people were enabled with operational AI tools. How? They didn't just deploy AI—they became AI-first. Here's the 3-step action plan to cross the frontier: 1️⃣ Mindset Shift: From prompting AI → delegating outcomes to AI. 2️⃣ Laser Focus: Target one painful, high-volume, low-creativity process to prove ROI and build momentum. 3️⃣ Build the Foundation: Create an AI-ready data estate and robust governance. You can't build a skyscraper on a swamp. Start small. Scale fast. I learn one think during my San Francisco visit. It is no longer the question of whether autonomous agents will reshape your industry, but when—and whether you'll lead or follow. Learn how >
2:53

The shift from Copilot to Autopilot is happening now. This is the Agentic Frontier, and while 82% of leaders are investing in it, most are not ready. Why? Because they're stuck in Phase 1 (AI Assistants) while the winners are already building for Phase 3 (Human-Led, Agent-Operated) operations. The gap between pilot and production is where most AI initiatives die. It's not a tech problem. It's a culture, governance, and data problem. But it's possible to cross this chasm. Enjoyed attending Microsoft Ignite with the EY team this past week. I learn about their "Client Zero" journey, 400,000+ people were enabled with operational AI tools. How? They didn't just deploy AI—they became AI-first. Here's the 3-step action plan to cross the frontier: 1️⃣ Mindset Shift: From prompting AI → delegating outcomes to AI. 2️⃣ Laser Focus: Target one painful, high-volume, low-creativity process to prove ROI and build momentum. 3️⃣ Build the Foundation: Create an AI-ready data estate and robust governance. You can't build a skyscraper on a swamp. Start small. Scale fast. I learn one think during my San Francisco visit. It is no longer the question of whether autonomous agents will reshape your industry, but when—and whether you'll lead or follow. Learn how >

Sabine VanderLinden

236,535 views • 9 months ago

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:
2:05

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

Andrew Ng

89,792 views • 1 year ago

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓
38:54

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

61,260 views • 2 months ago

Anthropic Engineer: “the biggest mistake in AI right now is people are building graphs and loops without a self-improving eval agent. we made that mistake at anthropic. it cost us two years.” here’s how they built what solved it: step 1 → take 50 real user prompts and run your agent. if it passes 80%+, your eval is too easy. the sweet spot is around 50%. step 2 → every failed run becomes a transcript. feed it to haiku: “what went wrong?” your eval set keeps growing on its own. step 3 → score two things: the right answer and the right path. a right answer with the wrong path will break later. step 4 → when a new model drops, run the same eval again. one model looked 9% better. but 6% of that gain came from hiding a bug. the transcript caught it. the dashboard didn’t. step 5 → plug your evals into ci. no green evals = no deploy. that’s how the system keeps improving itself. 99% of people never build this. but it pays for itself on day one. save this and build it today.
28:54

Anthropic Engineer: “the biggest mistake in AI right now is people are building graphs and loops without a self-improving eval agent. we made that mistake at anthropic. it cost us two years.” here’s how they built what solved it: step 1 → take 50 real user prompts and run your agent. if it passes 80%+, your eval is too easy. the sweet spot is around 50%. step 2 → every failed run becomes a transcript. feed it to haiku: “what went wrong?” your eval set keeps growing on its own. step 3 → score two things: the right answer and the right path. a right answer with the wrong path will break later. step 4 → when a new model drops, run the same eval again. one model looked 9% better. but 6% of that gain came from hiding a bug. the transcript caught it. the dashboard didn’t. step 5 → plug your evals into ci. no green evals = no deploy. that’s how the system keeps improving itself. 99% of people never build this. but it pays for itself on day one. save this and build it today.

MIKE

102,478 views • 1 month ago

AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.
0:29

AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.

Lior Alexander

808,154 views • 3 years ago

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!
2:24

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

85,107 views • 1 year ago

PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:
0:53

PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:

Faheem Ullah

15,270 views • 1 year ago

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 views • 10 months ago

Google's team published a 9-page PDF on Harness Engineering. one formula replaced prompt engineering: Agent = Model + Harness the twist: same Claude Sonnet, same benchmark. change only the harness the playbook, six steps: step 1 -> add guides AGENTS.md, rule files, constraint docs. every line is a past agent failure turned into a permanent fix step 2 -> add sensors linters, tests, validation scripts the agent runs on its own output before a human sees it step 3 -> build the agentic loop plan, execute, verify, fix. bounded retries, budget caps, escalation when stuck step 4 -> externalize memory the model forgets every session. the harness holds state, decisions and artifacts across all of them step 5 -> enforce permissions which tools, how many writes, what needs approval. safety lives in the harness, never in the model step 6 -> wire observability track every tool call, cost and retry. trip wires fire when behavior drifts the result: your agent turns from a demo into infrastructure every failure makes the system permanently better, not just the next conversation that's also the line between an agent you show people and one a client pays you to leave running one harness gets you a reliable loop. connect several and it becomes graph engineering, where one agent's failure turns into another agent's sensor the PDF stops at the harness. the graph is the next layer that breakdown, running on Kimi, is below ↓
0:24

Google's team published a 9-page PDF on Harness Engineering. one formula replaced prompt engineering: Agent = Model + Harness the twist: same Claude Sonnet, same benchmark. change only the harness the playbook, six steps: step 1 -> add guides AGENTS.md, rule files, constraint docs. every line is a past agent failure turned into a permanent fix step 2 -> add sensors linters, tests, validation scripts the agent runs on its own output before a human sees it step 3 -> build the agentic loop plan, execute, verify, fix. bounded retries, budget caps, escalation when stuck step 4 -> externalize memory the model forgets every session. the harness holds state, decisions and artifacts across all of them step 5 -> enforce permissions which tools, how many writes, what needs approval. safety lives in the harness, never in the model step 6 -> wire observability track every tool call, cost and retry. trip wires fire when behavior drifts the result: your agent turns from a demo into infrastructure every failure makes the system permanently better, not just the next conversation that's also the line between an agent you show people and one a client pays you to leave running one harness gets you a reliable loop. connect several and it becomes graph engineering, where one agent's failure turns into another agent's sensor the PDF stops at the harness. the graph is the next layer that breakdown, running on Kimi, is below ↓

Annatar.md

78,338 views • 12 days ago