Comparisons
Cursor vs Claude Code vs Copilot 2026: The Only Comparison You Need
A practical 2026 comparison of GitHub Copilot, Cursor, and Claude Code based on real production use, with a focus on context, agentic workflows, and pricing.
Nicola·
AllAI Models(2)Antigravity(2)Best Practices(3)Comparisons(14)Context Engineering(17)Copilot(4)Cost & Optimization(9)Cursor(6)Features & Workflow(14)Language Specific(6)Token & Cost(17)Windsurf(3)
Token & Cost
How to Reduce Claude Code Token Usage by 58% (Without Manual Context Management)
Use a dependency-graph MCP server (vexp) to feed Claude Code only structurally relevant context and cut token costs by ~58%—no prompt changes required.
Nicola·
Context Engineering
What Is Context Engineering? Why It Matters More Than Prompt Engineering
Prompt engineering shapes intent; context engineering shapes what the model actually knows. For serious AI coding, context wins by a mile.
Nicola·
Cost & Optimization
How to Cut AI Coding Assistant Costs by 58%: A Benchmarked Approach
A controlled benchmark on a real FastAPI app shows dependency-graph context cuts Claude Code costs by 58%, speeds tasks up 22%, and shrinks output tokens 63%.
Nicola·
Context Engineering
Session Memory for AI Coding Agents: Why Your Agent Forgets
Why current AI coding agents forget your codebase between sessions, and how vexp’s session memory, staleness tracking, and cross-agent graph fix it.
Nicola·
Context Engineering
What Is a Dependency Graph for AI Coding? (And Why It Cuts Tokens by 63%)
AI coding agents waste tokens by loading hundreds of irrelevant files. Dependency graphs fix this with precise, structural context selection.
Nicola·
Context Engineering
Context Engineering for AI Coding Agents: The Complete Guide
Why context, not prompts, is the real bottleneck for AI coding agents—and how dependency graphs, session memory, and token budgets fix it.
Nicola·