Topic
18 episodes
-
Introducing TabFM: A zero Shot foundation model for tabular data
Justy and Cody examine TabFM, Google Research’s zero-shot foundation model for tabular classification and regression. They unpack its hybrid row-column attention design, synthetic-data training, TabArena evidence, the trade-off between out-of-the-box convenience and tuned ensembles, and whether BigQuery integration could make this genuinely useful in everyday data workflows.
-
Introducing OpenRL: A self Hosted post training API for fine tuning LLMs | Google Open Source Blog
Justy and Cody discuss Google’s OpenRL, a self-hosted post-training API that tries to separate RL research loops from the Kubernetes and GPU infrastructure underneath them.
-
The Millions of Songs Mashed Into AI Generated Music
The article discusses the use of millions of songs to train AI music generators, raising concerns about copyright infringement and the impact on artists.
-
Announcing the Agentic Resource Discovery specification Google Developers Blog
Justy and Cody chat about Google’s new Agentic Resource Discovery (ARD) spec, dissect its core claim, examine the technical trade‑offs, and wonder who should actually care about a universal catalog for AI tools.
-
A VM for Every Container Apple Ships
Apple's container project reaches 1.0 — a Swift-native tool for running OCI containers on macOS with a per-container VM architecture that fundamentally differs from Docker Desktop's shared VM model. The hosts debate whether hardware-level isolation per workload is genuinely useful or overengineered for local dev.
-
Google's new open source Gemma 4 12B analyzes audio, video — and runs entirely locally on a typical 16GB enterprise laptop
Justy and Cody debate whether Google's new Gemma 4 12B—an 11.95B-parameter model that runs locally on 16GB laptops with encoder-free multimodal processing—is a genuine breakthrough for edge AI or just a cleverly marketed niche tool. They clash on the practical trade-offs: Cody questions the real-world performance and fine-tuning complexity, while Justy highlights the enterprise use cases where offline, private inference is non-negotiable. They land on it being a specialized win for specific scenarios, not a universal replacement.
-
Hallucinations Undermine Trust; Metacognition is a Way Forward
Justy and Cody dig into a paper arguing that the real trust problem with language models is not merely being wrong, but being wrong with unwarranted confidence. They unpack the paper’s shift from answer-versus-abstain to ‘faithful uncertainty,’ where a model’s wording should reflect its actual internal uncertainty. Cody breaks down the discrimination-versus-calibration distinction and why that matters for both chatbots and tool-using agents. Justy pushes on what this means in production, where hedging can either build trust or feel slippery if it is not tied to real behavior.
-
Google AI breakthrough means chatbots use six times less memory during conversations without compromising performance
Google's TurboQuant compresses AI working memory (the KV cache) by up to 6x in real time using two novel techniques — PolarQuant and QJL — without degrading model performance. Justy and Cody dig into what this actually means for inference costs, who benefits first, and why the 'DeepSeek moment' framing is both apt and a little overblown.
-
Building with Gemini Embedding 2: Agentic multimodal RAG and beyond Google Developers Blog
Exploring Next, episode 357. Gemini Embedding 2 just made multimodal retrieval a lot more practical: text, images, video, audio, and PDFs can all land in one embedding space, which changes search, RAG, and agent workflows.
-
Developer’s Guide to AI Agent Protocols Google Developers Blog
Izzo and Boone explore Google's new Agent Development Kit and the emerging protocols solving AI agent integration hell - MCP for data connections, A2A for agent-to-agent communication, and UCP for commerce workflows. They build a restaurant supply chain agent live, showing how these protocols eliminate custom integration code.
-
Google finds that AI agents learn to cooperate when trained against unpredictable opponents
Google's Paradigms of Intelligence team discovered that AI agents naturally develop cooperative behaviors when trained against diverse, unpredictable opponents rather than being programmed with hardcoded coordination rules. This breakthrough offers a scalable alternative to traditional multi-agent frameworks by using standard reinforcement learning techniques to produce adaptive social behaviors through in-context learning.
-
Reddit The heart of the internet
In today's episode, we dive deep into an exciting achievement in the world of game development using AI. One developer crafted a pixel-art open-world shooter in just 24 hours using Gemini 3.0 Pro for both coding and art. We explore what this means for developers, the implications of using AI in creative workflows, and the future of game design. Join us as we unpack the significance of this innovative approach and its potential impact on the gaming industry.
-
Choosing an LLM in 2026: The Practical Comparison Table (Specs, Cost, Latency, Compatibility)
In this episode, we dive into the nuances of selecting the right large language model (LLM) in 2026. With insights on context, cost, latency, and compatibility, we discuss how these factors shape effective prompt engineering and the importance of making informed model choices. Our conversation also explores real-world implications and provides practical examples for businesses looking to leverage LLMs.
-
Introducing Agent Development Kit for TypeScript: Build AI Agents with the Power of a Code First Approach Google Developers Blog
The Agent Development Kit (ADK) for TypeScript allows developers to create powerful AI agents using a code-first approach, enhancing flexibility and control in AI development. This creates a seamless integration for JavaScript/TypeScript developers, enabling them to leverage existing skills and tools for more complex, autonomous systems.
-
OpenAI, Anthropic, Google Agree to Develop Agent Standards Together
In an unprecedented collaboration, major players like OpenAI, Anthropic, and Google are agreeing to set technical standards for AI agents that could revolutionize how we automate white-collar work. This dialogue explores the significance of these standards and their potential real-world applications.
-
Google launches managed MCP servers that let AI agents simply plug into its tools | TechCrunch
Google's launch of managed MCP servers aims to simplify how AI agents interact with various tools and data, reducing the complexity developers face while integrating these systems. This innovation could lead to more effective AI solutions for businesses and other sectors, as it streamlines connections to Google's robust services.
-
Google and Anthropic Approach LLMs
This episode delves into the contrasting approaches to large language models (LLMs) by Google and Anthropic. We explore their engineering-focused culture versus a philosophical approach to AI, the implications for users, and how these developments impact the tech landscape.
-
Chat in NotebookLM: A powerful, goal focused AI research partner
NotebookLM has received significant upgrades, enhancing its chat capabilities with a larger context window, improved memory, and personalized goal settings, making it an even more powerful AI research partner.