YAAP (Yet Another AI Podcast)

Tavily built a Deep Research Agent with production in mind. Something they could actually scale. So they did the unsexy work. They went through millions of agent logs, found where tokens were being wasted, and optimized each section of the system.
The result surprised them: they cut token consumption by more than half (!), then tested quality and discovered they topped the DeepResearch Bench without even trying.
In this YAAP episode, Yuval sits down with Dean from Tavily to break down how they built it, what they did differently from the usual top approaches, and which design choices made better results possible with far fewer tokens.
What you’ll learn:
How to reduce token burn without tanking quality
Why reading millions of logs beats chasing the flashiest tech
The design choices that pushed quality up while tokens dropped hard

You wanted to build an agent.
You ended up debugging GPUs, scaling workers, and chasing OOMs.
In this episode of YAAP, Yuval sits down with Linda from Anyscale to unpack why Ray exists and how it helps AI teams scale without turning every developer into a distributed systems expert.
We trace Ray’s roots in reinforcement learning research, then zoom out to how it’s used today across the AI pipeline: data processing, training, inference, and agents. Along the way, we cover why libraries like vLLM build on Ray, when Ray vs. SaaS makes sense, and why unstructured and multimodal data push traditional big-data tools to their limits.

Banks love GenAI. They just don’t trust it. Yet.
 
In this episode of YAAP, Yuval talks with Renee Lau from AWS, a financial services industry specialist who works hands on with banks, insurers, and hedge funds as they try to move generative AI from pilots into production. Renee shares what she sees across the market, what actually works, and where teams get stuck.
 
They explore the two sides of GenAI adoption in finance. Cost cutting back office automation and revenue driven use cases like hedge fund research. Along the way, they dig into compliance, pricing, human in the loop workflows, and the crawl walk run path to deployment.
 
You will also hear why every bank believes it is a special snowflake, why that instinct is understandable, and how builders can still create solutions that scale across financial services.

Everyone’s building on the same foundation models. So how do you stand out?
For Imagen AI, the answer isn’t bigger models, it’s smarter loops. CEO Yotam Gil joins Yuval to unpack how personalization, workflow integration, and continuous feedback turned Imagen’s photo-editing engine into a true moat.
But that’s only half the story. The other half is speed: how a two-person Commando Squad at Imagen uses “vibe-coding in production” to prototype new ideas in one or two sprints, test them in the wild, and kill what doesn’t stick — without hurting the core product.
It’s a conversation about differentiation when models are commodities, and about building a culture that moves as fast as the tech it’s built on.

Three years ago, it was just a house full of friends geeking out about AI.
Today, it’s where researchers, founders, and engineers collide — and where hackathon demos turn into real startups.
In this episode, Yuval sits down with Henry Yin, Co-founder & CTO of AGI House, to unpack how a pandemic project became the Bay Area’s builder epicenter. From fine-tuning meetups to venture funding, they trace the journey of turning one house into the heart of a movement.

Everyone (and we do mean EVERYONE) needs data, and the web is the largest database humanity has ever built. But tapping into it at scale requires more than technical skills. If your product touches web data, scraping isn't just a backend task, it can be risky and have real consequences.
In this episode, Yuval sits down with Rony Shalit, Chief Compliance and Ethics Officer at Bright Data, to talk about what can go wrong when you treat data collection as “just an implementation detail”. From lawsuits with Meta and X to wild edge cases and vendor breakdowns, they dive into what it takes to collect data responsibly and stay out of trouble.

Your LLM gave a great answer. But who decides what “great” means?
 
In this episode, Yuval talks with Noam Gat about judge language models — reward models, critic models, and how LLMs can be trained to rate, rank, and critique each other. They dive into the difference between scoring and feedback, how to use judge models during inference, and why most evaluation benchmarks don’t tell the full story.
 
Turns out, getting a good answer is easy. Knowing it’s good? That’s the hard part.

Think you know fine-tuning? If your answer is RLHF, you don’t. In this episode, Itay, who leads the Alignment group at AI21, gives a no-fluff crash course on RLVR (Reinforcement Learning with Verifiable Rewards), the method powering today’s smartest coding and reasoning models. He explains why RLVR beats RLHF at its own game, how “hard to solve, easy to verify” tasks unlock exploration without chaos, and the emergent behaviors you only get when models are allowed to screw up. If you want to actually understand RLVR (and use it), start here.
Key topics:
How RLVR outsmarts RLHF in real-world training
The “verified rewards” trick that kills reward hacking
Emergent skills you don’t get with hand-holding: self-verification, backtracking, multi-path reasoning
Why coding models took a giant leap forward
Practical steps to train (and actually benefit from) RLVR models

RAG Is Not Solved – Your Evaluation Just SucksYour RAG pipeline is passing benchmarks, but failing reality. In this episode, Yuval sits down with Niv from AI21 to expose why most RAG evaluation is fundamentally flawed. From overhyped retrieval scores to chunking strategies that collapse under real-world complexity, they break down why your system isn’t as good as you think — and how structured RAG solves problems that traditional pipelines simply can't. Bonus: what do Seinfeld trivia, World Cup stats, and your enterprise SharePoint have in common? (hint: your RAG pipeline chokes on all of them).Key Topics:Why most RAG benchmarks reward the wrong thing (and hide real failures)The chunking trap: how bad segmentation sabotages good retrievalWhen LLMs ace the answer—but your pipeline still failsStructured RAG: pipeline that solves RAG problem over aggregative data (such as financial reports)Evaluation tips, tricks, and traps for AI builders

The Call Is Coming From Inside the Agent (And It Has Your Credentials)You’ve shipped your first agent. It works. It’s useful. It might also be a security liability you don’t even know about. In this episode, Yuval talks to Zenity CTO Michael Bargury about how easy it is to hijack popular agent systems like Copilot and Cursor, what “zero-click” attacks look like in the agent era, and how to monitor, constrain, and secure your AI Agent in production. From sneaky prompt injections to memory-based persistence and infected multi-agent workflows, this is the “oh no” moment every builder needs.Key Topics:Why “ignore previous instructions” still works better than it shouldHow one agent goes rogue… and infects the othersReal-world attacks: social media triggers, CRM leaks, and logic bombsObservability 101 for AI: logs, reasoning traces, and root cause sanityThe new rule: build like it will go rogue—because one day it will