Recent Blog Posts
Insights on AI Agents, Modern Web, and the Future of Engineering.
Linux Foundation Launches the Tokenomics Foundation to Define the Economics and ROI of AI Value
The Linux Foundation launches the Tokenomics Foundation with 30 founding members to establish open, vendor-neutral standards, benchmarks and best practices for measuring the true cost, value and ROI of AI spend.
Web Scraping for RAG: Clean Data Extraction at Scale
Junk in, junk out. Learn to extract LLM-ready Markdown from JS-heavy sites with Playwright, strip boilerplate, and chunk the results for a RAG pipeline.
Building an AI Pair Programmer: From IDE Plugin to CLI
An AI pair programmer is an agent with file tools and a loop. Learn the architecture, then build a minimal CLI pair programmer in Python with Gemini function calling.
Time-Series Forecasting with Foundation Models
Chronos, TimesFM, and Lag-Llama forecast series they have never seen. Learn how time series became token sequences, and when to use a foundation model over ARIMA.
Knowledge Graphs for AI: GraphRAG Explained
Naive RAG misses the answer that connects many documents. GraphRAG builds a knowledge graph from text and queries it. Learn the indexing pipeline, community detection, and query modes.
Semantic Caching to Reduce Latency and Spend
Users ask the same question a hundred different ways. Semantic caching serves cached answers for similar queries — cutting LLM spend by up to 86% and latency from seconds to milliseconds.
LangGraph: Stateful, Graph-Based Agent Orchestration
LangGraph turns an agent into an explicit graph of nodes with durable state. Learn StateGraph, checkpointers, thread_id, and crash-resilient execution.
Tools, Function Calling, and the Agent Execution Loop
The agent loop is a model deciding which tools to call, observing results, and iterating. Learn function declarations, tool_choice modes, and parallel calling.
Vector Search at Scale: pgvector vs. Qdrant vs. Pinecone
Three vector stores, one decision. Compare pgvector, Qdrant, and Pinecone on latency, filtering, scale ceiling, and cost to pick the right one for your RAG stack.
Prompt Caching Strategies to Cut LLM API Costs
Cached input tokens are ~90% cheaper than fresh ones. Learn implicit vs explicit caching, TTL design, and how to structure prompts so your cache hits.
LLM Application Architecture Patterns in 2026
The architecture of an LLM application is no longer one prompt to one model. Learn the RAG, agentic, and caching patterns that production teams actually ship in 2026.
Beyond Gemini 3: Preparing for the Age of Fully Autonomous Digital Entities
Gemini 3 was the inflection, not the destination. Prepare your architecture, governance, and mindset for fully autonomous digital entities that run on their own.