Writing
Notes on AI, agents, web technologies, and building with LLMs
Technical articles on AI integration, agents, agent harnesses, MCP, web technologies, and emerging tools. Working notes from a Google Developer Expert with 25+ years of building and teaching.
241 articles · Page 5 of 25
Filter by tag ▾
- All posts
- ADK
- AGENTS.md
- AI
- AI Agents
- AI Strategy
- API
- AX
- AXI
- Agent Experience
- Agent Skills
- Agentic AI
- Agents
- Angular
- AngularJS
- Architecture
- Automation
- Bash
- Browser APIs
- C2PA
- CLI
- CSharp
- Cloudinary
- Community
- Conference
- Content Credentials
- Core Web Vitals
- DevFest
- DevRel
- Developer Tools
- Docker
- Embeddings
- Enablement
- Engineering
- FDE
- Firebase
- Frontend
- Function Calling
- Gemini
- Ghost
- Google ADK
- Google chrome
- GraphQL
- Harness Engineering
- Image Formats
- Image Optimisation
- Jamstack
- JavaScript
- LLM
- Large Language Models
- Learn To Code
- Local-first
- Loop Engineering
- MCP
- MCP Apps
- MEAN
- Machine Learning
- Memory
- MongoDB
- MySQL
- NLP
- Next.js
- NoSQL
- Node.js
- Open Source
- PHP
- Performance
- Polymer
- Product Strategy
- Production AI
- Progressive Web App
- Progressive Web Apps
- Provenance
- RAG
- React
- Recruitment
- Review
- RxJS
- SQLite
- Security
- Serverless
- Service Worker
- Sponsored
- SynthID
- Taipei
- Thought Leadership
- Tool Use
- Tooling
- Transformers.js
- TypeScript
- VMware
- Veo
- Vercel AI SDK
- Vue.js
- Web
- Web Assembly
- Web Development
- Web Performance
- WebMCP
- this || that
Consuming Streamed LLM Responses on the Frontend: A Deep Dive into SSE and Fetch
Learn how to build a responsive, real-time user experience by consuming streamed Large Language Model responses on your frontend. This article provides a comprehensive guide to using both Server-Sent Events (SSE) and the Fetch API with Readable Streams, complete with code examples and a detailed comparison.
Filling in the Blanks: Teaching AI to Inpaint
A hands-on guide for exploring how to train a simple AI model using TensorFlow.js to inpaint missing parts of images - without needing large datasets or prior machine learning experience.
Agentic AI: Multi-Agent Systems and Task Handoff
The final article in the Agentic AI series explores multi-agent systems: how specialised agents collaborate through structured handoffs to complete complex user goals.
Understanding the Orchestrator-Worker Pattern
The orchestrator-worker pattern brings scalable structure to agentic AI workflows by cleanly separating high-level planning from specialised task execution. Through a practical trip planning example, this article demonstrates how LLMs can dynamically coordinate expert agents, grounded in schema-driven logic and real-world data.
Building with Reflection: A Practical Agentic AI Workflow
This article explores how to implement a reflection loop-an agentic AI pattern where a model generates, critiques, and iteratively improves its output - using image captioning as a practical example.
Parallelisation as an Agentic Workflow
Unlock faster, more diverse reasoning by running multiple LLM prompts in parallel and aggregating their responses into a single, cohesive output.
How Transformers and LLMs Actually Work - A Developer's Guide with Code
A hands-on walkthrough for web developers to demystify large language models by actually building a mini Transformer from scratch.
Routing: Building Step-by-Step AI Reasoning
Explore how to intelligently route AI queries using schema-guided function calling and contextual categorisation.
Prompt Chaining: Building Step-by-Step AI Reasoning
Learn how prompt chaining enables AI to tackle complex tasks through step-by-step reasoning, boosting both accuracy and interpretability.
LCP and low-entropy images
In this article we take a look at how to see if an image has low-entropy for LCP calculation.