Chat was a great prototype. It’s a terrible product.

In this episode, Yuval sits down with CopilotKit co-founder Atai to unpack why most agentic apps stall at “chat + vibes”, and why the real bottleneck in production AI isn’t models or reasoning. It’s UI.

They break down what actually changed in the last year, why agents fundamentally break the request-response paradigm, and how a new generation of protocols is emerging to connect agents to real users. The conversation covers:

If you’ve built an agent that works but users still bounce, this episode explains why, and what the new “glue layer” of AI UIs is starting to look like.

MCP standardized tool calling for agents but breaks down once agents start mutating state. In this episode, Yuval sits with Eran Gat from AI21 to dig into what happens when writing agents run in parallel, why shared environments fall apart, and how workspace isolation becomes a missing execution layer. Using real coding workloads and benchmarks, we walk through the architectural trade-offs behind making concurrent agents actually work.

Leaderboards reward “best average score.”

Real users reward “answer fast, don’t hallucinate, don’t bankrupt me.”

In this special deep dive episode, AI21’s CTO Barak Lenz walks through four gaps between what models can do and what real AI systems deliver: validation, contextualization (pick the right approach per input), latency (parallelize and stop early), and decomposition (making those choices continuously inside long workflows).

Less “best model.” More “best execution.”

Running multiple agents can improve quality. Doing it right is the hard part.

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:

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.