AI Discovery Hub

From Notes to Agents: Using NotebookLM with Google AI Studio (and Python) NotebookLM and Google AI Studio solve different parts of an applied-AI workflow. NotebookLM helps a person inspect and synthesize a bounded collection of sources. AI Studio helps a developer prototype prompts and model behavior that can later be called from software. The useful connection is not a hidden NotebookLM API. It is a reviewed handoff: a person uses NotebookLM to understand sources, exports a structured evidence artifact, and a Python service validates that artifact before asking a model to perform a narrow task. Know the boundary NotebookLM is designed for interactive research over sources. It can help identify themes, compare documents, and point back to evidence. Google AI Studio is a development environment for Gemini models and API experiments. Do not automate browser actions to scrape NotebookLM output or treat a conversational summary as verified data. If a production pipeline needs automat...

Building a Roblox-Style Game with GenAI — From Idea to Online Generative AI can shorten the distance between an idea and a playable Roblox prototype, but it cannot replace game design, testing, or secure server code. The best use of AI is to create small artifacts you can inspect: a mechanic specification, one Luau function, a test matrix, or visual references. This walkthrough builds the core of a simple checkpoint game while showing where generated code must be constrained. Define the loop before prompting Write the game loop in one sentence: The player crosses an obstacle course, activates checkpoints, earns coins at the finish, and spends coins on cosmetic effects. Now define measurable rules: Rule Initial value Checkpoints 8 Expected run time 3–5 minutes Finish reward 25 coins Reward cooldown Once per completed run Paid advantage None These constraints give an AI assistant something concrete to review. “Make a fun game” does not. Build the ...

Building Customized GenAI Product Development Services in Python Generic chat interfaces are easy to demo and surprisingly hard to turn into reliable products. A useful GenAI service needs a narrow job, measurable success criteria, controlled access to data, and predictable behavior when the model is uncertain. This tutorial designs a small product-recommendation service in Python. The interesting part is not the API call. It is the boundary around the model: structured inputs, candidate retrieval, constrained output, validation, and evaluation. Start with a product contract Suppose a user describes a need and expects three suitable products. Before choosing a model, define the contract: Input Output Non-negotiable rule Free-text need, budget, region Up to three product IDs with reasons Never invent a product Optional preferences Confidence and missing information Respect budget and availability Catalog snapshot Machine-readable JSON Explain the evidence use...

AgentSpace: The Next Evolution of Human-AI Collaboration 2026 update: Google Cloud’s enterprise agent offering has evolved beyond the original Agentspace name. Google now presents the broader product family as Gemini Enterprise , including the Gemini Enterprise app and Gemini Enterprise Agent Platform. The architectural questions from Agentspace remain important: how should people discover agents, connect them to company data, approve actions, and govern the resulting work? This article uses “Agentspace” when discussing the earlier concept and “Gemini Enterprise” for the current product direction. From answers to governed work Enterprise search retrieves information. An assistant summarizes or drafts. An agent can also take a multi-step action: investigate an incident, prepare a customer response, or update a business system. That added capability changes the design problem. A successful platform needs more than a strong model: Identity for users, agents, and tools Permi...

Building Smarter AI Search with Structured Query Understanding Search quality often fails before retrieval begins. A user asks for “a quiet laptop for travel under $1,200,” but a keyword engine treats every word as equally important. A semantic retriever may understand the theme while still missing the hard price constraint. Structured query understanding solves this by converting natural language into a typed search plan. The system separates intent, filters, concepts, and ambiguity before it touches the index. The query plan For the example above, a useful representation is: { "intent": "product_search", "must_filters": { "category": "laptop", "price_usd_lte": 1200 }, "semantic_concepts": ["quiet operation", "travel friendly"], "sort": ["relevance", "weight_asc"], "needs_clarification": false } The important distinction is betwee...