Come and explore with me
ezpm2gui v1.4.0 — A Complete Tailwind UI Revamp for Your PM2 Dashboard
Context Engineering Strategies: Designing Prompts for Cache Efficiency, RAG Pipelines, and Production Scale
Semantic Caching with Redis 8.6: Vector Similarity Matching for LLM Cost Optimization in Production
Context Caching with Gemini 3.1 Pro and Flash-Lite: Implicit vs Explicit Caching, Storage Costs, and Python Production Implementation
Prompt Caching with GPT-5.4: Automatic Caching, Tool Search, and C# Production Implementation
Prompt Caching with Claude Sonnet 4.6: cache_control Breakpoints, TTL Strategies, and Node.js Production Implementation
Enterprise IT Under Siege in 2026: 22-Second Breaches, Zero Trust Imperatives, and the Industrialized Threat Machine
Prompt Caching and Context Engineering in Production: What It Is and Why It Matters in 2026
Group Code: The VS Code Extension Built for Vibe Coders Who Move Fast and Build Things
Group Code v1.8.0 — Hover Cards, 193 Tests, and Smarter @group Navigation for VS Code 
ezpm2gui v1.4.0 is here with a full Tailwind CSS UI overhaul, dark/light mode across every page, two new reusable components, and backend refactoring for cleaner request handling.
Context engineering is the discipline of designing what goes into your LLM context window, in what order, and how to structure it for maximum cache efficiency, retrieval quality, and cost control. This part covers static-first architecture, cache-aware RAG design, prompt versioning, and token budget management.
Semantic caching operates above the model layer, using vector embeddings to match similar queries to previously computed responses. With Redis 8.6, you can achieve 80 percent or higher cache hit rates without calling the LLM at all. This part covers the full architecture, similarity thresholds, cache invalidation, and production implementations in both Node.js and Python.
Google Gemini 3.1 Pro and Flash-Lite offer both implicit and explicit context caching, with the most generous default TTL of any major provider at one hour. This part covers how both modes work, how to account for storage costs, and a complete Python production implementation for Vertex AI and the Gemini API.
GPT-5.4 makes prompt caching automatic with no configuration required. This part covers how OpenAI’s caching works under the hood, how to structure prompts for maximum hit rates, how the new Tool Search feature reduces agent token costs, and a full production C# implementation with cost tracking.
Claude Sonnet 4.6 gives developers explicit control over prompt caching through cache_control breakpoints. This part covers how to structure your prompts, configure TTL, use multi-breakpoint strategies, and implement a production-ready caching layer in Node.js.
The M-Trends 2026 report and WEF Global Cybersecurity Outlook 2026 reveal an enterprise IT threat landscape that has fully industrialized. Attackers hand off compromised access in 22 seconds, ransomware operators exploit zero-days before patches exist, and geopolitical tensions are rewriting how organizations architect and fund their defenses. Here is what every IT leader needs to know right now.
Prompt caching is one of the most impactful yet underused techniques in enterprise AI today. This first part of the series explains what it is, how it works under the hood, and why it should be a default part of your production AI architecture in 2026.
Vibe coders build fast and ship faster — but that speed creates messy codebases. Group Code is the VS Code extension that keeps up with you, organizing your code by what it does rather than where it lives.
Group Code v1.8.0 is out — bringing rich hover cards for @group annotations, a 193-test suite, and a full CI/CD pipeline to your VS Code workflow.
