Cursor

What is Mem-Port and Why Portable Agentic Memory Matters Mem-Port is an open-source MCP (Model Context Protocol) memory server that gives AI coding agents persistent, portable long-term memory using an embedded SurrealDB database. Unlike traditional memory solutions that lock agent context to a single machine or tool, Mem-Port lets you carry your AI agent’s entire memory — facts, episodes, skills, and entity relationships — between machines, copilots, and teams as a single portable JSON bundle. Think of it as a thumb drive for your AI’s brain. ...

What Are AI Coding Tool System Prompts and Why Do They Matter? System prompts are the hidden instruction sets that define how AI coding tools behave, think, and interact with developers. Every major AI coding assistant — from Claude Code to Cursor, OpenAI Codex to Windsurf — operates on a carefully crafted system prompt that encodes its personality, tool usage rules, safety constraints, and communication style. In 2026, these prompts have become a matter of public fascination: the largest GitHub collection of AI tool system prompts has amassed over 141,981 stars, and The Washington Post covered system prompt extraction as a mainstream security story in May 2026. Understanding these prompts reveals not just how AI tools work, but how their creators want them to think. ...

Your AI Coding Assistant Has a Hidden Bias — and It’s Reshaping the Software Ecosystem I’ve been running AI coding agents daily for over a year now — Claude Code, Cursor, GitHub Copilot — and I’ve noticed something unsettling. When I ask them to generate a React component, the output is near-perfect on the first try. When I ask for the same thing in SvelteKit or Solid, I get hallucinated APIs, wrong imports, and code that looks like someone read a blog post from 2023 and guessed the rest. ...

If your AI coding agent spends half its tool calls grepping files, reading source to find function definitions, and tracing call chains, you already know the pain. The question is which tool to install. CodeGraph and Graphify are the two most popular solutions, but they solve different problems, and picking the wrong one wastes time. Here is the short version: use CodeGraph when your bottleneck is AI agents burning tokens on source-code discovery during edits. Use Graphify when you need a shareable project memory graph spanning code, docs, schemas, PDFs, and diagrams, especially for a team. ...

By mid-2026, CodeGraph has become one of the fastest-growing developer tools on GitHub — 57,000+ stars, MIT license, and active development since January 2026. If you use Claude Code or Cursor on a codebase larger than a few thousand lines, you’ve probably seen the claims: 70% fewer tool calls, 59% lower token consumption, 49% faster response time. Those numbers come from benchmark repos, not every project, but the underlying idea is sound. ...

If you’ve been using AI coding agents for more than a few months, you’ve hit the same wall I have: every new project needs the same setup — linting rules, test conventions, deployment scripts, API patterns — and you end up repeating yourself in prompts or pasting the same instructions into every new session. Agent skills are the fix, and in 2026 every major platform has shipped their own version. But they’re not all the same, and picking the wrong one for your team costs real time. ...

If you use Claude Code, Cursor, or Codex on a codebase larger than a few thousand lines, you’ve felt the pain: every new session starts from zero. The agent greps for a function signature, reads the file, greps for callers, reads more files, and burns through tokens just to reconstruct context you already understood last session. AiDex is an MCP server that tries to fix this with a persistent Tree-sitter code index, semantic search, and cross-session memory. I spent a week testing it against four direct competitors. Here is what I found. ...

Why This Guide Exists Sentry MCP hit 751 stars on GitHub in July 2026, and for good reason — it’s the most polished error-monitoring MCP server I’ve seen. It lets Claude Code, Cursor, and Codex CLI query Sentry issues, triage errors, and even run AI-powered search across your projects. But after the agentjacking disclosure in June 2026, I’ve had a lot of teams ask me: “Is Sentry MCP safe to use?” ...

What Is Agentjacking? In June 2026, researchers at Tenet Security disclosed a new attack class they called agentjacking — and it’s the most practical AI agent supply chain attack I’ve seen in production. The premise is deceptively simple: an attacker injects a malicious error event into your Sentry project, and when your AI coding agent (Claude Code, Cursor, or OpenAI Codex CLI) reads that event via the Sentry MCP server, it executes the attacker’s embedded payload with your system privileges. ...

On June 25, 2026, the Mozilla 0DIN team demonstrated an attack that should change how every team deploys AI coding agents. They published a normal-looking Python repository on GitHub. A developer cloned it and pointed Claude Code at it. The agent read the README, installed the requirements, hit a routine initialization error, and — trying to be helpful — ran the suggested fix. That fix queried a DNS TXT record, decoded the value, and executed it as a shell command, opening a reverse shell on the developer’s machine. ...