Ruby on Rails Upgrade Services: How Nextware Systems Modernizes Legacy Rails Applications Without Breaking Production
Is Your Ruby on Rails Application Falling Behind?
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Hotwire in Practice: Turbo Frames vs Turbo Streams
When to reach for a Turbo Frame, when to broadcast a Turbo Stream, and how to keep a Hotwire app fast and debuggable.
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Scaling a Rails Monolith Without Microservices
Rails monoliths scale further than the internet admits. Service objects, good indexes, and background jobs get you remarkably far.
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Choosing a Vector Database for AI Search
pgvector, Pinecone, Qdrant, Weaviate — how to pick a vector store based on scale, filtering needs, and operational appetite.
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Building Reliable AI Workflows with LangChain
LangChain helps compose LLM steps, tools, and memory. Where it helps, where it gets in the way, and how to keep chains debuggable.
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Server Components in Next.js 15: A Practical Mental Model
RSC changes where code runs. A working mental model for fetching, the client boundary, and shipping less JavaScript.
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Subdomain and Multi-Tenant Routing in Next.js
Serving different apps or tenants by hostname — middleware rewrites, local development with .localhost, and production DNS.
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TypeScript Patterns That Actually Pay Off
Discriminated unions, branded types, and inference-friendly APIs — the handful of TS patterns worth the extra keystrokes.
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Zod and Runtime Validation at the Edges of Your App
Types vanish at runtime. Validate untrusted input with Zod at the boundary and infer your types from the schema.
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ISR and Caching Strategies in the Next.js App Router
Static, dynamic, and incremental — choosing a rendering strategy per route, and using revalidate and tags effectively.
Read moreAI Coding Assistants: Getting Real Value from Pair Programming with LLMs
AI assistants speed up the right tasks and slow down the wrong ones. How senior engineers actually use them well.
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MCP Explained: The Model Context Protocol for Tool-Using AI
MCP standardises how AI apps connect to tools and data sources. Why a common protocol matters and how servers and clients fit together.
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Background Jobs in Rails 8 with Solid Queue
Solid Queue brings durable, database-backed jobs to Rails by default. Setup, recurring tasks, and when you still want Sidekiq.
Read moreServer Components in Next.js 15: A Practical Mental Model
RSC changes where code runs. A working mental model for fetching, the client boundary, and shipping less JavaScript.
Read more
Deploying with Docker and Kamal on a Plain VPS
Containerize once and deploy anywhere you can SSH. Kamal brings zero-downtime deploys and TLS to a cheap server.
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Rails 8 Is Here: The No-PaaS Default Stack Explained
Rails 8 ships with Kamal 2, Solid Queue, Solid Cache and Propshaft, making a database-only, deploy-anywhere stack the default. Here is what changes.
Read moreZod and Runtime Validation at the Edges of Your App
Types vanish at runtime. Validate untrusted input with Zod at the boundary and infer your types from the schema.
Read moreMCP Explained: The Model Context Protocol for Tool-Using AI
MCP standardises how AI apps connect to tools and data sources. Why a common protocol matters and how servers and clients fit together.
Read moreAI Coding Assistants: Getting Real Value from Pair Programming with LLMs
AI assistants speed up the right tasks and slow down the wrong ones. How senior engineers actually use them well.
Read moreHotwire in Practice: Turbo Frames vs Turbo Streams
When to reach for a Turbo Frame, when to broadcast a Turbo Stream, and how to keep a Hotwire app fast and debuggable.
Read moreTypeScript Patterns That Actually Pay Off
Discriminated unions, branded types, and inference-friendly APIs — the handful of TS patterns worth the extra keystrokes.
Read more
RAG Done Right: Retrieval-Augmented Generation in Production
Naive RAG is easy; good RAG is mostly retrieval engineering. Chunking, hybrid search, reranking, and evaluation that actually moves quality.
Read moreISR and Caching Strategies in the Next.js App Router
Static, dynamic, and incremental — choosing a rendering strategy per route, and using revalidate and tags effectively.
Read moreBuilding Reliable AI Workflows with LangChain
LangChain helps compose LLM steps, tools, and memory. Where it helps, where it gets in the way, and how to keep chains debuggable.
Read more