Agentic AI & Agent Harnesses AI ArchitectureHot
Multi-agent orchestration, tool calling patterns, deterministic evaluation loops, and stateful human-in-the-loop workflows.
Key Technical Focus:
- Function/tool calling reliability and error recovery strategies.
- Agent memory persistence across long conversations.
- Evaluation benchmarks for autonomous tool-using agents.
Potential Content Ideas:
- “Building Production-Ready AI Agents with Google AI Studio & Tool Calling”
- “Evaluating Agentic Workflows: Unit Testing Your LLM Prompts & Tools”
- “Human-in-the-Loop Pattern: Gated Approvals in Serverless Agent Flows”
Gemini 1.5 & Multimodal Context Processing LLM EngineeringHot
Leveraging 1M-2M token context windows, native video/audio understanding, structured JSON schema outputs, and prompt compression.
Key Technical Focus:
- Processing hours of audio/video natively without pre-transcription.
- Context Caching to drastically reduce latency and cost for repeated context.
- Strict JSON schema enforcement for downstream system integration.
Potential Content Ideas:
- “Analyzing 1-Hour Long Videos Natively with Gemini 1.5 Pro”
- “Context Caching Masterclass: Save 80% on Token Costs”
- “Structured Outputs in Production: Ensuring 100% Valid JSON Responses”
Firebase Genkit & Full-Stack AI Development Developer FrameworksEmerging
Open-source framework for building, testing, tracing, and deploying AI-powered applications in TypeScript & Node.js.
Key Technical Focus:
- Local Developer UI for rapid prompt engineering and flow inspection.
- Built-in telemetry, distributed tracing, and OpenTelemetry integration.
- Seamless integration with Firebase Auth, Firestore, and Cloud Functions.
Potential Content Ideas:
- “Getting Started with Firebase Genkit: From Zero to Deployed AI Flow”
- “Tracing and Monitoring Production LLM Workloads with Genkit”
- “Building a Retrieval-Augmented Generation (RAG) App in 15 Minutes”
Cloud Run for Serverless AI Microservices Cloud InfrastructureEvergreen
Deploying containerized AI workloads, serverless GPU inference, streaming response handlers, and zero-scale cost efficiency.
Key Technical Focus:
- NVIDIA GPU support on Cloud Run for local LLM & diffusion model inference.
- Server-Sent Events (SSE) & WebSockets for real-time streaming LLM responses.
- Direct integration with Vertex AI endpoints and Google Secret Manager.
Potential Content Ideas:
- “Deploying Custom Model Endpoints & Sidecars on Cloud Run”
- “Serverless GPUs: Running Open Source LLMs on Cloud Run”
- “Handling Real-Time Streaming Responses with Next.js & Cloud Run”
AI Token Economics & FinOps Operations & StrategyHot
Strategies for optimizing LLM API costs, model routing (Flash vs. Pro), token budgeting, and cost allocation.
Key Technical Focus:
- Model routing strategies: cascading queries from low-cost to high-capability models.
- Calculated ROI of Context Caching vs. Vector RAG for large documents.
- Setting rate limits, quotas, and cost guardrails in production.
Potential Content Ideas:
- “Model Routing: When to Use Gemini 1.5 Flash vs. 1.5 Pro”
- “AI FinOps 101: Preventing Runaway LLM API Bills in Cloud Run”
- “Token Budgeting for Multi-User SaaS Applications”
RAG vs. Fine-Tuning Decision Framework AI ArchitectureEvergreen
Evaluating when to use Retrieval-Augmented Generation (RAG), long-context prompting, or parameter fine-tuning.
Key Technical Focus:
- Grounding responses with Google Search Grounding vs. custom Vector Search.
- Fine-tuning Gemini models on Vertex AI for customized voice & domain knowledge.
- Trade-offs: freshness, latency, accuracy, cost, and maintenance overhead.
Potential Content Ideas:
- “RAG vs. Fine-Tuning vs. Long Context: Decision Matrix for 2025”
- “Grounding Gemini with Google Search in 5 Lines of Code”
- “Building Hybrid Search with Vertex AI Vector Search & Firestore”
WebMCP & In-Browser Agent Interfaces Web & AI StandardsHot
Standardizing client-side tool execution via navigator.modelContext, turning web applications into Model Context Protocol (MCP) servers for browser-native AI agents.
Key Technical Focus:
- Exposing web app functions natively to browser AI extensions without UI scraping.
- Security models, permission prompting, and user consent loops for client-side tool execution.
- Polyfilling WebMCP across modern browsers with @mcp-b/global and local relay servers.
Potential Content Ideas:
- “Making Your Next.js App Agent-Ready with WebMCP in 10 Minutes”
- “WebMCP vs API Endpoints: Designing Client-Side Tools for Browser AI”
- “Testing WebMCP Tool Registrations with the Chrome Model Context Inspector”
Model Context Protocol (MCP) Enterprise Architecture AI InfrastructureHot
Connecting LLM agents and enterprise data platforms securely using standardized Model Context Protocol (MCP) servers and cloud gateways.
Key Technical Focus:
- Building custom MCP servers in TypeScript and Go for internal microservices.
- Google Cloud MCP Toolbox for Databases: Bridging Gemini CLI & Claude Desktop with Cloud SQL & BigQuery.
- Authentication, rate limiting, and observability patterns for production MCP servers.
Potential Content Ideas:
- “Architecting Enterprise AI Agents with Google Cloud & MCP Toolbox”
- “Building an Internal MCP Server with Node.js and Cloud Run”
- “MCP Security Best Practices: Securing Tool Calling in Production”
Reasoning Models & Visible Chain-of-Thought LLM EngineeringHot
Leveraging transparent step-by-step reasoning models (e.g., Gemini 2.0 Flash Thinking) for complex problem solving, debugging, and verifiable AI decisions.
Key Technical Focus:
- Extracting and inspecting internal reasoning steps before generating final outputs.
- Prompt design strategies tailored for reasoning LLMs in math, coding, and logical evaluation.
- Balancing latency vs. accuracy: choosing between instant Flash models and reasoning variants.
Potential Content Ideas:
- “Inspecting LLM Thoughts: Building Trust with Gemini 2.0 Flash Thinking”
- “Reasoning LLMs in Code Generation: Debugging Complex Algorithms Automatically”
- “Chain-of-Thought vs. Few-Shot Prompting: Benchmarking Accuracy on Edge Cases”
AI Agent Evals & Automated Continuous Benchmarking DevOps & EvalsEmerging
Implementing deterministic and LLM-as-a-judge evaluation frameworks for agentic tool calling, prompt regressions, and multi-turn workflows.
Key Technical Focus:
- Building CI/CD evaluation pipelines to test LLM prompt changes automatically.
- Measuring tool invocation accuracy, arguments parsing, and loop recovery.
- Synthesizing dataset ground truth for multi-step agent trajectory verification.
Potential Content Ideas:
- “Unit Testing AI Agents: Building CI/CD Eval Pipelines with GitHub Actions”
- “LLM-as-a-Judge vs. Deterministic Assertions for Tool Calling”
- “Measuring Agent Drift: How to Catch Regressions Before Production Deployments”
Event-Driven Serverless AI Pipelines Cloud InfrastructureEvergreen
Combining Cloud Run, Eventarc, Pub/Sub, and serverless background tasks for resilient, long-running agentic workflows and streaming responses.
Key Technical Focus:
- Decoupling LLM request processing with pub/sub event channels for async execution.
- Handling extended agent tasks with serverless execution limits and checkpointing.
- Streaming live progress updates back to frontend clients via WebSockets & Server-Sent Events (SSE).
Potential Content Ideas:
- “Building Async AI Background Workers with Cloud Run & Pub/Sub”
- “Handling 5-Minute Agent Tasks Serverlessly on Google Cloud”
- “Real-Time SSE Agent Dashboards with Next.js and Serverless Workflows”