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What is autonomous validation? The future of CI/CD in the AI era

Oct 17, 2025 By Blake Buisson In CircleCI
Over the past decade, CI/CD has redefined how modern software is built and shipped. CircleCI has been a leader in that transformation, working alongside the world’s best engineering teams to build a reliable foundation for continuous delivery at scale. Today, those foundations are under new pressure as AI reshapes every aspect of the delivery cycle. Developers are producing more change with less certainty about what those changes touch.
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Implementing image recognition with React and continuous deployment

Oct 14, 2025 By Victor Mayowa In CircleCI
Integrating artificial intelligence (AI) into web applications can significantly enhance user experience. AI offers features like image recognition to process and analyze user-uploaded images. Combining this with a robust continuous integration and continuous deployment (CI/CD) pipeline using CircleCI ensures seamless updates and reliable delivery. In this article, you will learn how to build a React app that uses TensorFlow.js for client-side image recognition and set up automated testing with CircleCI.
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Building LLM agents to validate LangGraph tool use and structured API responses

Oct 14, 2025 By Muhammad Arham In CircleCI
Transitioning LLM agents from intriguing prototypes to reliable, production-grade solutions introduces a unique and significant challenge: the inherent stochasticity of LLMs. Unlike conventional software, where inputs predictably yield precise outputs, an LLM’s response can exhibit variability even when presented with identical prompts. To ensure the dependability of your LLM agent, you will need a rigorous validation strategy.
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Navigating AI transformation ft. Meg Adams, Senior Director of Engineering at The New York Times

Oct 10, 2025 By CircleCI In CircleCI
In this episode of The Confident Commit, Rob Zuber sits down with Meg Adams, Senior Director of Engineering at The New York Times, for a deep dive into leading engineering teams through the AI revolution while staying true to organizational mission. Meg shares how the Times approaches AI adoption with a "measured but focused" strategy, emphasizing experimentation and opinion-formation over mandates, and why she believes AI serves as a force multiplier for what already exists in your organization and workflows.
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The new AI-driven SDLC

Oct 10, 2025 By Jacob Schmitt In CircleCI
For decades, the software development life cycle (SDLC) has been the framework teams use to understand how software moves from idea to production. It breaks complex work into familiar phases: planning, design, development, testing, deployment, and maintenance. This structure gave organizations a shared way to coordinate teams, track progress, and build with confidence.
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Automating Expo app build delivery to QA with CircleCI and EAS webhooks

Oct 8, 2025 By Kevin Kimani In CircleCI
Manually sharing mobile app builds with Quality Assurance (QA) engineers can be a tedious and error-prone process. Developers often find themselves exporting.apk or.ipa files, uploading them to Google Drive or Dropbox, and then pinging the QA team on Slack to announce the upload, all while juggling deadline and code reviews. This manual process not only slows down feedback cycles but also leaves room for human error, miscommunication, or outdated builds being tested.
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Building and deploying a Python MCP server with FastMCP and CircleCI

Oct 7, 2025 By Armstrong Asenavi In CircleCI
Extending Large Language Models (LLMs) with custom tools has become increasingly valuable in today’s AI landscape. Model Context Protocol (MCP) servers provide a standardized way to connect external tools and resources to LLMs. This can enhance their capabilities beyond basic text generation. While thousands of pre-built MCP servers exist, creating your own allows you to address specific workflows. You can implement use cases that off-the-shelf solutions cannot handle.
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Automated RAG pipeline evaluation and benchmarking with RAGAS

Oct 6, 2025 By Muhammad Arham In CircleCI
Retrieval-Augmented Generation (RAG) pipelines have become an integral part of how Large Language Models (LLMs) access information beyond their training cutoff. These pipelines enable LLMs to deliver current, accurate, and grounded responses. By fetching relevant external documents, RAG mitigates common LLM challenges like factual inaccuracies and hallucinations. However, this methodology introduces a new complexity: evaluating RAG pipeline performance is particularly challenging.
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7 ways AI agents are transforming software delivery

Oct 2, 2025 By Jacob Schmitt In CircleCI
For most teams, the slowest part of delivery isn’t writing code, it’s everything that happens after: automated tests, manual reviews, bug fixes, final approvals, and the long wait for deployment. The longer these phases run, the more expensive and painful late fixes become. As AI makes it easier to generate code at scale, those bottlenecks only get bigger.
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Code coverage standards for a Next.js project using CircleCI and Coveralls

Oct 1, 2025 By Daniel Efe In CircleCI
An essential part of software development, testing helps catch bugs and errors early, improves software quality, and ultimately prevents costly issues from being deployed to production. The effectiveness of software testing will remain uncertain until it can be measured and that is where code coverage comes in. Code coverage is a metric that tells developers what portion of their codebase is executed when specific tests are run.
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