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A Gen Academy Playlist

Global AI Builder Series

Build practical AI systems with expert-led Live Labs from the Global AI Builder Series. Explore agents, RAG, memory, evaluation, deployment, security, and AI product building with Aishwarya Srinivasan, Arvind Narayanamurthy, and guest practitioners.

  • Aishwarya Srinivasan
  • Arvind Narayanamurthy
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0 upcoming·30 past

Past sessions

30 sessions
Build with Jev: System-1 Models in Action cover

Build with Jev: System-1 Models in Action

  • Aishwarya Srinivasan
  • Arvind Narayanamurthy
View lesson
Optimizing Open Models for Production cover

Optimizing Open Models for Production

Sujee Maniyam

Developer Relations @ Nebius · Nebius

View lesson
Build with rabbit OS3: The OS You Talk To cover

Build with rabbit OS3: The OS You Talk To

Jesse Lyu & Aidan Shain

Founder & CEO; Software Developer · rabbit

View lesson
Loop & Harness Engineering Masterclass cover

Loop & Harness Engineering Masterclass

  • Aishwarya Srinivasan
  • Arvind Narayanamurthy
View lesson
AI Tokenomics Explained cover

AI Tokenomics Explained

Haritha Thilakarathne

Senior Developer Relations Manager · NVIDIA

View lesson
Secure AI Agents: Identity, Secrets & Access in Production cover

Secure AI Agents: Identity, Secrets & Access in Production

Mariane Bekker

Director of Developer Relations · 1Password

View lesson
Inside LinkedIn: Building AI Products at Global Scale cover

Inside LinkedIn: Building AI Products at Global Scale

Tim Jurka

VP of Consumer Products · LinkedIn

View lesson
Figma MCP vs. Paper MCP: What Evals Actually Tell You cover

Figma MCP vs. Paper MCP: What Evals Actually Tell You

Jessica Wang

Developer Advocate · Braintrust

View lesson
Deploying Sovereign AI Agents in the Enterprise cover

Deploying Sovereign AI Agents in the Enterprise

Siva Surendira

Founder and CEO · Lyzr AI

View lesson
Fireside Chat with Chris Taylor, VP & Fellow at Google cover

Fireside Chat with Chris Taylor, VP & Fellow at Google

Christopher Taylor

VP and Engineering Fellow · Google

View lesson
How to Deploy AI Agents to Production (Live RAG Demo) cover

How to Deploy AI Agents to Production (Live RAG Demo)

Rami Krispin

Data Science and Engineering Leader; Docker Captain · Docker community / LinkedIn Learning

View lesson
The Signal Loop: What to BUILD when you can build anything cover

The Signal Loop: What to BUILD when you can build anything

Lena Hall

Senior Director, Developer and AI Strategy · Akamai

View lesson
LLM Fine-Tuning Decision Framework cover

LLM Fine-Tuning Decision Framework

Sinan Ozdemir

Head of AI Developer Education · Fireworks AI

View lesson
Fireside Chat with Avinav Pashine (Product Lead @ Meta) cover

Fireside Chat with Avinav Pashine (Product Lead @ Meta)

Avinav Pashine

Product Lead, AI Wearables · Meta

View lesson
The Skills Companies Look For When Hiring FDEs cover

The Skills Companies Look For When Hiring FDEs

Phani Kolanuvada

Senior Director of Forward Deployed Engineering · Salesforce

View lesson
Secure Autonomous Agents with NVIDIA NeMo Claw cover

Secure Autonomous Agents with NVIDIA NeMo Claw

Jay Rodge

Senior Developer Advocate for LLMs · NVIDIA

View lesson
Agentic Coding at Scale: Verification Loops That Hold cover

Agentic Coding at Scale: Verification Loops That Hold

Daisy Hollman

Member of Technical Staff · Anthropic

View lesson
Can AI Actually Self-Improve? cover

Can AI Actually Self-Improve?

Nikhil Pareek

Founder and CEO · Future AGI

View lesson
Fireside Chat with Ben Relles (CEO- Make Believe) cover

Fireside Chat with Ben Relles (CEO- Make Believe)

Ben Relles

Founder and CEO · Make Believe

View lesson
Codex Masterclass cover

Codex Masterclass

Jason Liu

Developer Experience @ OpenAI · OpenAI

View lesson
Memory for Agents: Personalization Beyond the Context Window cover

Memory for Agents: Personalization Beyond the Context Window

Rudraj Mehta

Product @ Mem0 · Mem0

View lesson
Fireside Chat with Abhi Muchhal (Product @ OpenAI) cover

Fireside Chat with Abhi Muchhal (Product @ OpenAI)

Abhi Muchhal

Product @ OpenAI · OpenAI

View lesson
AEO and the Era of Agent-Assisted Discovery cover

AEO and the Era of Agent-Assisted Discovery

Vivek Pandya

Director @ Adobe · Adobe

View lesson
A Taxonomy of Production Agents: What Teams Are Building Now cover

A Taxonomy of Production Agents: What Teams Are Building Now

Sam Bhagwat

Co-founder and CEO @ Mastra · Mastra

View lesson
How a 2-Person AI Team Can Outlearn a 2,000-Person Company cover

How a 2-Person AI Team Can Outlearn a 2,000-Person Company

Praneet Dutta

Co-founder and CEO of Pomo · Pomo

View lesson
Shipping Reliable AI Agents: Evals to Control Plane cover

Shipping Reliable AI Agents: Evals to Control Plane

Nick Nolan

Solutions Engineering Manager, Fiddler AI · Fiddler AI

View lesson
From Base Model to Agent: Inside NVIDIA’s PostTraining Stack cover

From Base Model to Agent: Inside NVIDIA’s PostTraining Stack

Chris Alexiuk

Product Research Engineer and Developer Advocate @ NVIDIA · NVIDIA

View lesson
Fireside Chat with Alan Hsia (SVP- Nebius) cover

Fireside Chat with Alan Hsia (SVP- Nebius)

Alan Hsia

Senior Vice President - Marketing @ Nebius · Nebius

View lesson
The RAG Playbook: Retrieval Techniques for Production AI cover

The RAG Playbook: Retrieval Techniques for Production AI

Arjun Patel

Senior Developer Advocate @Pinecone · Pinecone

View lesson
Building Context-Rich Agents with LlamaIndex Workflows cover

Building Context-Rich Agents with LlamaIndex Workflows

Logan Markewich

Head of Open Source @ LlamaIndex · LlamaIndex

View lesson
Build with Jev: System-1 Models in Action cover

Build with Jev: System-1 Models in Action

Agentic workflows need decisions at every step: which tool to use, what to prioritize, and when to ask for help. Jev lets you turn those questions into structured outputs your code can use. In this session, you'll build a small request-routing workflow and learn how to handle uncertainty before it triggers the next action.

  • Aishwarya Srinivasan
  • Arvind Narayanamurthy
View lesson
Optimizing Open Models for Production cover

Optimizing Open Models for Production

Open models are getting dramatically larger and more capable, which makes serving them efficiently a systems problem of its own. This session explores modern inference techniques used to make state-of-the-art open models practical in production, covering caching, kernels, quantization, speculative decoding, and disaggregated inference, with lessons from the recent Kimi K3 launch and its production-scale vLLM integration.

Sujee ManiyamNebius

View lesson
Build with rabbit OS3: The OS You Talk To cover

Build with rabbit OS3: The OS You Talk To

Jesse Lyu and Aidan Shain introduce rabbit OS3 and demonstrate its approach to an agentic operating system. Explore conversational tasks, connected devices, model switching, and the controls that guide actions across the system.

Jesse Lyu & Aidan Shainrabbit

View lesson
Loop & Harness Engineering Masterclass cover

Loop & Harness Engineering Masterclass

Loop and harness engineering is not another AI buzzword. In this masterclass, we move beyond the hype and focus on what it actually takes to build agents that work through tasks reliably. You’ll see how thoughtful loop design and the system around it can turn a promising demo into something capable of handling real work.

  • Aishwarya Srinivasan
  • Arvind Narayanamurthy
View lesson
AI Tokenomics Explained cover

AI Tokenomics Explained

Haritha Thilakarathne explains the economics of AI tokens, connecting demand and supply with inference efficiency and monetization. Explore how these factors influence capacity, operating costs, and sustainable scaling.

Haritha ThilakarathneNVIDIA

View lesson
Secure AI Agents: Identity, Secrets & Access in Production cover

Secure AI Agents: Identity, Secrets & Access in Production

Mariane Bekker explores how to protect production AI agents as they connect to business systems. Learn to manage agent identities, restrict access, safeguard credentials, and secure tool connections.

Mariane Bekker1Password

View lesson
Inside LinkedIn: Building AI Products at Global Scale cover

Inside LinkedIn: Building AI Products at Global Scale

Tim Jurka shares a leadership perspective on developing AI-powered consumer experiences at LinkedIn, where product decisions must work across a large professional network and multiple engineering disciplines.

Tim JurkaLinkedIn

View lesson
Figma MCP vs. Paper MCP: What Evals Actually Tell You cover

Figma MCP vs. Paper MCP: What Evals Actually Tell You

Jessica Wang compares Figma and Paper MCP workflows to show how explicit tasks, representative datasets and scoring criteria turn AI-tool selection into a repeatable evaluation.

Jessica WangBraintrust

View lesson
Deploying Sovereign AI Agents in the Enterprise cover

Deploying Sovereign AI Agents in the Enterprise

Siva Surendira examines deployment choices for organizations that need control over AI data and infrastructure, including isolated environments and governance across existing agent systems.

Siva SurendiraLyzr AI

View lesson
Fireside Chat with Chris Taylor, VP & Fellow at Google cover

Fireside Chat with Chris Taylor, VP & Fellow at Google

Christopher Taylor discusses enterprise adoption of AI, the importance of context and data infrastructure, and how graph-based approaches and agent workloads change the engineering tradeoffs.

Christopher TaylorGoogle

View lesson
How to Deploy AI Agents to Production (Live RAG Demo) cover

How to Deploy AI Agents to Production (Live RAG Demo)

Rami Krispin demonstrates container-based deployment for an AI retrieval application, connecting reproducible development environments with the operational requirements of running AI services in production.

Rami KrispinDocker community / LinkedIn Learning

View lesson
The Signal Loop: What to BUILD when you can build anything cover

The Signal Loop: What to BUILD when you can build anything

Lena Hall discusses choosing valuable problems and communicating product value when AI makes similar-looking products easy to produce. The session connects positioning, customer trust and feedback-driven adoption.

Lena HallAkamai

View lesson
LLM Fine-Tuning Decision Framework cover

LLM Fine-Tuning Decision Framework

Sinan Ozdemir outlines how to decide whether model fine-tuning is justified by measurable quality, latency and cost needs, compared with improvements elsewhere in an AI system.

Sinan OzdemirFireworks AI

View lesson
Fireside Chat with Avinav Pashine (Product Lead @ Meta) cover

Fireside Chat with Avinav Pashine (Product Lead @ Meta)

Avinav Pashine explores how wearable devices give AI access to physical context, and the product tradeoffs involved in making multimodal systems useful, proactive and trustworthy.

Avinav PashineMeta

View lesson
The Skills Companies Look For When Hiring FDEs cover

The Skills Companies Look For When Hiring FDEs

Phani Kolanuvada explains how customer-facing AI engineers turn ambiguous enterprise needs into deployed systems, and how candidates can demonstrate the technical judgment and delivery skills employers seek.

Phani KolanuvadaSalesforce

View lesson
Secure Autonomous Agents with NVIDIA NeMo Claw cover

Secure Autonomous Agents with NVIDIA NeMo Claw

Jay Rodge introduces NVIDIA components for controlling autonomous agents, covering isolated execution, monitoring, framework interoperability and model options for practical experimentation.

Jay RodgeNVIDIA

View lesson
Agentic Coding at Scale: Verification Loops That Hold cover

Agentic Coding at Scale: Verification Loops That Hold

Daisy Hollman examines why coding agents struggle in large teams and how automated checks, shared context and reusable team conventions can reduce the human review burden.

Daisy HollmanAnthropic

View lesson
Can AI Actually Self-Improve? cover

Can AI Actually Self-Improve?

Nikhil Pareek presents a practical improvement cycle for AI agents: simulate situations, evaluate failures, identify causes, propose changes and validate the results with appropriate human oversight.

Nikhil PareekFuture AGI

View lesson
Fireside Chat with Ben Relles (CEO- Make Believe) cover

Fireside Chat with Ben Relles (CEO- Make Believe)

Ben Relles discusses interactive video and digital humans through Make Believe's Reid AI project, exploring product reliability, creative possibilities and decisions in a rapidly changing model ecosystem.

Ben RellesMake Believe

View lesson
Codex Masterclass cover

Codex Masterclass

Codex can do far more than generate code—but only when it has the right context, tools, and feedback loops. This session shows how to delegate bigger tasks, let Codex validate its own work, and review results efficiently, so teams can move faster without lowering their standards.

Jason LiuOpenAI

View lesson
Memory for Agents: Personalization Beyond the Context Window cover

Memory for Agents: Personalization Beyond the Context Window

Long-lived agents need continuity, but poor memory creates stale context, privacy risk, and user mistrust. Builders need patterns for what to remember, how to retrieve it, and when to forget so agents become more useful over time without becoming unpredictable or intrusive.

Rudraj MehtaMem0

View lesson
Fireside Chat with Abhi Muchhal (Product @ OpenAI) cover

Fireside Chat with Abhi Muchhal (Product @ OpenAI)

AI is shifting from reactive prompting to persistent, connected workflows. Codex shows what this looks like in practice: shared context, interoperable tools, proactive automation, and enterprise collaboration. This conversation explores how agentic workspaces can change how teams build, create, and operate.

Abhi MuchhalOpenAI

View lesson
AEO and the Era of Agent-Assisted Discovery cover

AEO and the Era of Agent-Assisted Discovery

Discovery is no longer a list of blue links. Consumers now start with an assistant that answers, compares, and increasingly acts on their behalf. AI-referred traffic to retail, travel, and financial services sites is compounding fast, and those visitors convert differently. Brands that learn how to earn visibility inside model-generated answers now will own demand before agentic traffic scales.

Vivek PandyaAdobe

View lesson
A Taxonomy of Production Agents: What Teams Are Building Now cover

A Taxonomy of Production Agents: What Teams Are Building Now

Teams are no longer debating whether agents matter; they are deciding which kind to build, where value shows up, and what breaks in production. Sam's taxonomy gives builders a practical map across customer-facing, internal, and developer-platform agents, plus the cost, accuracy, rollout, and platform lessons that separate prototypes from shipped systems.

Sam BhagwatMastra

View lesson
How a 2-Person AI Team Can Outlearn a 2,000-Person Company cover

How a 2-Person AI Team Can Outlearn a 2,000-Person Company

Powerful AI models are rapidly becoming accessible to everyone. The real advantage will come from how quickly your team learns. This session explores how agents, living business context and compounding memory can help small teams make better decisions, move faster and build an intelligence advantage that grows over time.

Praneet DuttaPomo

View lesson
Shipping Reliable AI Agents: Evals to Control Plane cover

Shipping Reliable AI Agents: Evals to Control Plane

Agents are shipping decisions into production faster than enterprises can govern them. Dashboards only tell you what went wrong after it happened. The next phase of adoption will be defined by who operationalizes trust and an AI control plane like Fiddler's is how enterprises govern AI activity as it happens, not after.

Nick NolanFiddler AI

View lesson
From Base Model to Agent: Inside NVIDIA’s PostTraining Stack cover

From Base Model to Agent: Inside NVIDIA’s PostTraining Stack

Modern AI capability is increasingly shaped after pre-training. This session breaks down the systems behind NVIDIA Nemotron, from SFT and RLVR to verifiers, reasoning control and infrastructure, so builders can understand how today’s reasoning and agentic models are actually trained, evaluated and improved.

Chris AlexiukNVIDIA

View lesson
Fireside Chat with Alan Hsia (SVP- Nebius) cover

Fireside Chat with Alan Hsia (SVP- Nebius)

AI is moving faster than traditional product and GTM playbooks can keep up with. As open models reshape economics and model capabilities commoditize, startups must rethink where durable differentiation comes from. This conversation explores how product, marketing, developer relations, distribution, and brand work together to build enduring AI companies.

Alan HsiaNebius

View lesson
The RAG Playbook: Retrieval Techniques for Production AI cover

The RAG Playbook: Retrieval Techniques for Production AI

RAG helps AI applications find the right information before generating an answer. But the quality of that answer depends entirely on how the data is stored, searched, and retrieved. Arjun will show how to use Pinecone to choose the right retrieval method, structure data effectively, and improve RAG accuracy across different types of user queries.

Arjun PatelPinecone

View lesson
Building Context-Rich Agents with LlamaIndex Workflows cover

Building Context-Rich Agents with LlamaIndex Workflows

Most useful agents depend on proprietary context, not just model intelligence. Builders need patterns for connecting data, tools, and workflows while preserving observability and quality, so agents answer with the right context and can be improved after deployment.

Logan MarkewichLlamaIndex

View lesson
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