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How Tech Is Making Car Transport Even Easier

Transporting a car used to be a complex and time-consuming process, filled with phone calls, paperwork, and logistical headaches. But thanks to advancements in technology, the car transport industry has been transformed into a more streamlined, transparent, and user-friendly experience. From instant quotes to GPS tracking, digital innovation is making it easier than ever to move your vehicle from one place to another, whether you're relocating across the country or simply sending a car to a buyer in another state.

Time to Value-Getting to ROI Faster with AI-Powered Data Pipelines

When security data volumes double every two to three years but budgets stay mostly flat, achieving a fast return on investment is the only way most security organizations can get new technologies approved. Teams can’t afford to wait months to see results—they need solutions that pay off starting on the first day of the proof-of-value period. AI-powered data pipelines make that possible.

AI's Unrealized Potential: Honeycomb and DORA on Smarter, More Reliable Development with LLMs

Charity Majors, CTO and Co-founder at Honeycomb, and Phillip Carter, Principal Product Manager at Honeycomb, recently hosted a webinar with DORA's Nathen Harvey on AI's unrealized potential. As part of this, we created a 3-minute highlight reel of the webinar that you can watch.

Why Smarter Distribution Networks Are Winning the Race for Customer Loyalty

In today's competitive market, customer loyalty is no longer secured by product quality alone. It is increasingly determined by how well businesses deliver on expectations-literally. That's why more and more companies are turning totop distribution management software like SimplyDepo to streamline operations and meet rising customer demands from the very first mile to the last.

How CircleCI implemented llms.txt for better AI discoverability

At CircleCI, we’re committed to making our platform work seamlessly with the AI-powered tools that developers increasingly rely on. Our journey into AI integration is focused on creating a robust Model Context Protocol (MCP) server that allows AI assistants to access and understand CircleCI data in real-time. This enables developers to debug build failures, analyze test results, find and fix flaky tests, and improve pipelines using natural language within their favorite AI tools.

Why AI Won't Replace Engineers: Solving Problems for Eternity

Is AI engineering a career killer or a game-changer? Dive into a thought-provoking discussion on how AI might redefine the role of software engineers. Discover why problem-solving engineers will always be in demand, even as the tools and methods evolve. If you enjoy tackling challenges, your career might just be future-proof.

Explore CircleCI projects from your IDE with AI assistance

CircleCI gives you deep visibility into your builds, workflows, and tests, but jumping between browser tabs, copying project URLs, or re-authenticating across tools can slow things down. What if your IDE could just show you the projects you’re working on and let you act on them directly? This post shows how to use the list_followed_projects tool in the CircleCI MCP server to browse and interact with your CircleCI projects by chatting with an AI assistant inside your IDE.

The Role of Open Source in Shaping AI Innovation

Can open source survive in an AI-driven world? As AI seems to take over, there's a burning question about the future of open source contributions. Discover the unexpected role open source plays in training AI, and ponder the long-term implications. Can we keep feeding the beast, or will AI eventually stand alone?

Building an end-to-end Retrieval- Augmented Generation (RAG) workflow

One of the most critical gaps in traditional Large Language Models (LLMs) is that they rely on static knowledge already contained within them. Basically, they might be very good at understanding and responding to prompts, but they often fall short in providing current or highly specific information. This is where RAG comes in; RAG addresses these critical gaps in traditional LLMs by incorporating current and new information that serves as a reliable source of truth for these models.