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The latest News and Information on Continuous Integration and Development, and related technologies.

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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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grafana

A serverless approach to CI/CD observability with GitLab and Grafana

Oct 10, 2025 By Daniel Fitzgerald In Grafana
In today’s fast-paced development environment, it’s critical that you understand what’s happening in your CI/CD pipeline. And yet, many teams struggle with fragmented tooling that makes it difficult to get a holistic view of their dev lifecycle. For example, if you’re using GitLab for CI/CD and Grafana for observability, you’ve probably faced this challenge: how do you bring your GitLab events into your existing observability and alerting infrastructure?
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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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circleci

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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circleci

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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jfrog

Enhancing JFrog Internal Operations with Near Zero Downtime Migration

Oct 8, 2025 By Karam Jaber In JFrog
Data migrations have long been a significant source of anxiety for businesses and IT teams alike. The thought of moving critical databases often conjures images of prolonged downtime, service interruptions, and the ever-present risk of data loss. Indeed, statistics show that “90% of businesses experience unexpected downtime during database migrations, leading to significant revenue loss and customer dissatisfaction”.
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Real Estate App Development for Ops & Product Teams: From MVP to Scale

Real Estate App Development for Ops & Product Teams: From MVP to Scale

Oct 8, 2025 By OpsMatters In OpsMatters
In the competitive world of real estate technology, developing an app that can scale from a Minimum Viable Product (MVP) to a fully-fledged solution is crucial. For operations and product teams, this journey involves strategic planning and execution to ensure the app meets evolving market demands and user expectations.
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speedscale

Testing AI Code in CI/CD Made Simple for Developers

Oct 7, 2025 By Srujana Maddula In Speedscale
Generative AI can produce code faster than humans, and developers feel more productive with it integrated into their IDEs. That productivity is only real if CI/CD tests are solid and automated. When not appropriately tested, you may encounter a production issue that you haven’t seen before. According to the State of Software Delivery 2025 report, 67% of developers spend more time debugging and resolving security vulnerabilities in code generated by AI.
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circleci

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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circleci

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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