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Your AI Agent Knows What. It Doesn't Know Why.

There's a reason we don't find our keys by scanning every room like a security camera. We replay the tape. We remember the groceries, the front door, the distraction. We reconstruct the *why* to find the *where*. Our brains are commit logs, not snapshots. Most agentic AI systems today work more like the camera — a static frame of the world at a given moment. They store state. They retrieve context. They produce an answer.

Presenting The Bugfender MCP: Use Your AI Agent to Find and Fix Bugs

You asked for it. We built it. Our new MCP server means you can debug directly inside your AI coding tool using real app data from Bugfender. You can use it to: It works with Cursor, Claude Code, Codex and Gemini CLI. This article will show you how to install the Bugfender MCP server, which tools your agent can access, and how the companion skills help you fix bugs faster.

From EHR to Telemedicine: Types of Healthcare Software Transforming the Industry

The emergence of digital transformation technologies led to a nationwide change, causing a profound impact on various industries throughout the world. Among the conventional sectors affected by it, the healthcare industry emerged prominently. Interestingly, it not only disrupted but also provided a significant impetus to the healthcare sector, thereby positively influencing the different types of healthcare software and the medical software industry.

Why Node.js Developers Need Production Context Inside the IDE

Modern Node.js development no longer happens across isolated tools. As developers, we no longer just write code. We constantly move between terminals, logs, dashboards, cloud platforms, tracing suites, CI pipelines, browser tools, and production environments to understand what our applications are doing. For years, that fragmented workflow became normal. But modern IDEs are changing that. Today, AI assistants live directly inside VS Code.

SwiftData Tutorial: Swift Data Storage for iOS Apps

Since its debut in June 2023, SwiftData has fundamentally changed how Apple developers approach persistence. Devs the world over love it for its versatility, its declarative ease and its powerful querying system. But if you’re new, SwiftData can take some getting used to. Failures can feel less transparent and relationships can play out differently to how you might expect. So in this tutorial we’ll show you how SwiftData works and how to.

Building an API Gateway with Koa and AppSignal

In an API-driven setup, a gateway often sits between clients and backend services: it can validate input, aggregate upstream responses, and give you one place to observe traffic. Koa is a strong fit for that role. Its core stays small, async/await is first-class, and middleware composes in a predictable stack. In this article, you will build a compact API gateway with Koa that: You will also wire up AppSignal for the Node.js stack.

Oracle MCP Server: Connect Oracle Database to AI Agents Safely

Last updated: May 2026 An Oracle MCP server is a service that exposes Oracle Database data as tools an AI agent can call through the Model Context Protocol (MCP). Rather than handing an LLM direct credentials to a database holding ERP, financial, or healthcare records, you put an MCP server between the agent and Oracle.

Snowflake MCP Server: Conversational Analytics with AI Agents

Last updated: May 2026 A Snowflake MCP server is a service that exposes Snowflake warehouses as tools an AI agent can call through the Model Context Protocol (MCP). It sits between AI clients like Claude or ChatGPT and your Snowflake data, translating discoverable tool calls into governed SQL — with row access policies, dynamic data masking, query budgets, and audit logging applied automatically.

7 Challenges Delivering Secure Aerospace Software in the Age of AI (with Solutions)

The challenge of any aerospace company is to deliver new capabilities without compromising safety, reliability, or precision. At our current juncture, legacy technology runs into conflict with modern tool stacks. Artificial intelligence (AI) creates fissures in compliance and auditability, and innovation and productivity gains come at a cost of greater complexity. Despite these seismic shifts, the central question remains the same.