Try Merlin: https://www.getmerlin.in/

X: https://x.com/MerlinAIByFoyer

Youtube: https://www.youtube.com/@merlin_ai/videos

South Bay Gen AI: https://genaimeetup.com/

Mark's Youtube Channel: https://www.youtube.com/@markkuczmarski896

https://rtrvr.ai/

Join hosts Shashank from Google AI Labs and Mark (currently on hiatus from Amazon) as they interview Arjun and Pavani, the founders of Retriever AI. This episode explores how Retriever's innovative browser extension brings agentic LLM capabilities directly to your desktop browser, offering unique advantages over cloud-based alternatives. Learn how Retriever can automate repetitive tasks, extract data across multiple websites, and interact with your personal accounts while maintaining security and privacy. The founders share their journey from big tech to startup life, demonstrate real-world use cases, and reveal their exciting vision for a federated network of browser agents that could revolutionize how we interact with the web.

https://www.genspark.ai/ https://genaimeetup.com/ Follow the podcast: https://podcast.genaimeetup.com/

Join hosts Shishank and Mark as they dive deep into the world of generative AI agents with Lenzoy Lin, Engineering Lead at Gens Park. Discover how this rapidly growing startup is revolutionizing productivity through their suite of AI agents - from their groundbreaking phone call agent to deep research tools and slide creation capabilities. Learn how Gens Park has grown from $10M to $22M in ARR in just one month, their approach to building reliable AI systems, and get a glimpse into the future of human-AI collaboration. Whether you're a tech enthusiast, entrepreneur, or AI professional, this episode offers valuable insights into one of 2025's most promising AI startups.

In this episode:

Lenzoy Lin's journey from Google to leading Gens Park's engineering team How Gens Park's mixture of agents approach solves complex tasks Behind the scenes of their phone call agent, deep research tools, and slide creation capabilities The technical challenges of building reliable AI agents at scale Gens Park's position in the competitive AI landscape and future roadmap

https://www.realwear.com/

Timon Binder: https://www.linkedin.com/in/timon-binder/

Join us for a special episode as we sit down with Timon Binder, CTO of Realware, a leading AR hardware company transforming the enterprise landscape. Timon shares his journey from co-founding a startup in Switzerland to leading innovation in the US, revealing how Realware’s AR headsets are revolutionizing industries like manufacturing, logistics, and healthcare.

We dive deep into:

• Realware’s approach to solving real-world problems with AR
• The challenges and opportunities in global hardware manufacturing
• How AI and voice assistants are reshaping user interaction
• The future of consumer AR and the transition from B2B to B2C
• Startup lessons, work culture differences, and advice for aspiring founders

Whether you’re a tech enthusiast, entrepreneur, or curious about the future of augmented reality, this episode is packed with insights, practical advice, and candid stories from the frontlines of innovation.

Call to Action: If you’re interested in AR, app development, or want to connect with Realware, check out the links in the episode description for open roles and collaboration opportunities!

Langflow: https://www.langflow.org/

https://www.producthunt.com/products/langflow

Langflow Desktop: https://www.langflow.org/desktop

In this insightful interview, Rodrigo from Langflow discusses the evolution and future of their low-code agent building platform. Starting with his background in machine learning and data science, Rodrigo explains how Langflow began as a vision to connect specialized AI models years before ChatGPT existed.

The conversation covers Langflow's journey from open-source project to being acquired by DataStacks while maintaining its commitment to open-source principles. Rodrigo announces the exciting launch of Langflow Desktop, designed to democratize AI development by eliminating technical barriers through an intuitive drag-and-drop interface.

Rodrigo details how Langflow serves both technical and non-technical users, supporting three main application types: LLM pipelines, RAG systems, and multi-agent applications. The interview highlights Langflow's integration with the new MCP protocol for more structured and efficient tool usage by AI agents.

Looking to the future, Rodrigo envisions advanced agent orchestration systems where AI agents can assign tasks to each other, with humans serving as collaborators in the process. This episode offers valuable insights for anyone interested in the rapidly evolving landscape of AI agent development and deployment.

In this special episode, hosts Mark and Shashank take a break from their usual news coverage to explore the rapidly evolving world of AI agents. They define what agents are, examine their current capabilities, and make bold predictions about how these technologies will transform our lives over the next 1, 5, and 10 years.

The hosts discuss how AI agents are already revolutionizing software development, research, and reasoning tasks, while exploring the imminent impact on knowledge work, self-driving vehicles, and scientific breakthroughs. Looking further ahead, they predict the widespread adoption of humanoid robots, the "YouTubeification" of product creation, and fundamental shifts in employment patterns.

Whether you're new to AI or a seasoned professional, this episode offers fascinating insights into how exponential growth in AI capabilities will reshape our society, economy, and daily lives in ways we're only beginning to imagine.

In this episode, hosts Mark and Shashank dive into recent developments in generative AI technology. They begin with NVIDIA's latest GTC announcements, including partnerships with GM for self-driving technology and advancements in robotics with Google DeepMind and Disney. The hosts debate the merits of camera-only versus LiDAR-based autonomous driving systems, referencing Mark Rober's viral comparison video. They also discuss NVIDIA's upcoming Ruben chip, which promises a 10-15x performance increase over the current Blackwell architecture. The conversation shifts to a correction about the DeepSeek model's computational requirements before culminating in a thought-provoking discussion about the challenges of creating generalized robotics systems and how simulation environments might accelerate development. Throughout the episode, the hosts share insights on what these technological advancements might mean for the future of AI and robotics.

This conversation delves into the latest developments in AI, particularly focusing on Google's Gemma models and their capabilities. The discussion covers the differences between various types of language models, the significance of multimodal inputs, and the training techniques employed in AI models. The hosts also explore the implications of open-source versus proprietary models, the hardware requirements for running these models, and the limitations of benchmarks in evaluating AI performance. Additionally, they touch on the future of robotics and the cultural differences in AI adoption, particularly between Japan and the United States. takeaways

Open source models are pushing the boundaries of AI. Gemma models are capable of multimodal inputs. Different types of LLMs serve different purposes. Benchmarks can be misleading and should be approached with caution. Training techniques like RLHF are crucial for model performance. The hardware requirements for AI models vary significantly. Cultural differences affect the adoption of robotics and AI. Robots are increasingly filling labor gaps in societies with declining populations. AI benchmarks should be tailored to specific use cases. The future of robotics and AI feels imminent and exciting.

Chapters 00:00 Introduction to the Week's AI Developments 00:50 Exploring Google's Gemma Models 03:21 Understanding Different Types of LLMs 05:32 Gemma's Multimodal and Multilingual Capabilities 08:45 Training Techniques Behind Gemma 15:48 Open Source Models and Their Impact 20:34 Benchmarking AI Models 28:30 Gaming Benchmarks in AI 34:10 The Ethics of Benchmarking in AI 44:56 Language Learning and AI Models 49:12 The Importance of Benchmarks 52:35 Vibe Checks and User Preferences 01:01:09 Top AI Models and Their Performance 01:13:35 Robotics and the Future of AI 01:27:20 Cultural Perspectives on Automation