Three weeks ago I introduced Open Delivery Spec (ODS), my side project for governing AI-assisted code in CI. Since then the CLI has shipped 5 releases and grown two new pillars: deterministic merge-confidence signals that answer “is this AI PR safe to merge?”, and an auto-generated, standards-based evidence document on every PR. As always, this post sticks to what actually ships â no hype.
Recently, my attitude towards GitHub Copilot has undergone a significant change: from “praising” to “bashing.” After comparing it with Claude Code, I’ve found that Copilot has clear deficiencies in cross-repository support and “senior engineer” awareness, to the point where I’m no longer willing to discuss problems with it.
Over the past few months, I’ve been working on an open-source organization called Open Delivery Spec (ODS) in my spare time. The initiative stemmed from the increasing volume of AI-generated code and the lack of reliable governance mechanisms within teams. This article records the thought process, progress, and boundaries based on the project’s real-world status, without any exaggeration.
The Conventional Branch specification receives its first major update. Version 1.1.0 adds support for AI Coding Agent branch prefixes (ai/, copilot/, cursor/, claude/, codex/), and provides a machine-readable spec.json and agent registry, making the specification useful not only for humans but also for tools.
Previously, jenkinsfilelint required connecting to an available Jenkins server for syntax validation. Now, v1.5.0 introduces a new standalone mode. With just Docker, you can launch a minimal Jenkins environment locally to validate Jenkinsfile syntax. This article details the implementation idea and usage of this mode.
AI tools like Claude Code by default embed signatures in commits, something many people don’t even notice. Commit Check v2.11.0 introduces AI Attribution Governance, allowing a single line of configuration to reject commits with AI signatures at the CI level. This article also discusses the boundaries of this feature and other pain points Commit Check has solved in the past six months.
I wrote a small tool to validate Jenkinsfiles, initially just wanting it to appear in Jenkins’ official list of development tools. However, a Jenkins maintainer suggested: why not just transfer the project directly into the jenkinsci organization? This article records the complete process from PR to transfer to an official blog post, including an interesting pitfall encountered along the way.
This article breaks down an architectural approach for enterprise-grade Agentic Applications from eight dimensions: Agent boundary design, Tool Engineering, observability, evaluation framework, Memory layering, Human-in-the-Loop, cost control, and security.
The Conventional Branch project website has officially migrated from conventional-branch.github.io to conventionalbranch.org. From last year’s user suggestion to this year’s final implementation, let’s talk about the hesitations, pitfalls, and reflections behind the domain migration.
C/C++ tooling in the pre-commit ecosystem has long been limited. cpp-linter-hooks is currently the only pre-commit hook that supports both clang-format and clang-tidy, with built-in compilation database auto-detection, version pinning, and auto-fix capabilities.
A user opened an issue hoping Conventional Branch could provide an official Agent Skill, which I thought was a very reasonable request. I implemented it the same day, and now it can be downloaded and used with a single npx skills add command. Coincidentally, the project also surpassed 100 Stars this week, so I’ll talk about that too.
Since taking over mkdocs-ng maintenance, four versions have been released, over a dozen bugs fixed, and the mergedeep dependency removed. The upcoming v1.8.0 will be a feature release, planning to drop support for older Python versions, remove CDN dependencies from built-in themes, and provide a stable Python API.
Many people have asked how to build a GitHub Copilot-like AI Agent inside Jira. This post shares my hands-on experience building one â covering account setup, Jira Automation triggers, webhook integration, and a Skills-based capability system. The core philosophy: leverage existing infrastructure to get it running fast and cheap.
Codex auto-renewed this month. I hadn’t really planned on continuing my subscription, but this renewal prompted me to re-evaluate the actual experience with Codex, DeepSeek V4 series, and Copilot. For me, the core difference among AI coding tools is no longer just model capability, but rather which one can help me write code more stably, affordably, and consistently.
Over the past ten days, I’ve made over a hundred commits to RepoKeeper, evolving it from a proof-of-concept to v1.2.0. It now boasts six modules, two backends, supports DeepSeek/OpenAI/Claude, and can even develop itselfâthis post will explain what it has evolved into.
After reading Mario Zechner’s “I’ve sold out” and then reviewing AGENTS.md and CONTRIBUTING.md in the pi repository, I found that this project differs from common open-source collaboration methods in many ways. New contributors’ issues and PRs are closed by default, no reviews on weekends, and don’t submit PRs if you don’t understand the code. It seems tough, but behind it is a serious attempt to address a problem: how open-source projects can avoid being bogged down by low-quality contributions in the AI era.
Codex’s $22 monthly subscription fee and usage limits prompted me to seek a cheaper, stable, and always-ready backup solution. After an unsuccessful attempt with OpenCode, I turned to the Pi + DeepSeek combination. The result was a bit unexpected: writing an entire blog post cost only 0.24 RMB.
The Explain Error Plugin has recently received several important updates: AI Auto-Fix for automatically creating fix PRs, usage statistics and quota management, and added support for four new AI providers: DeepSeek, Qwen, Azure OpenAI, and Custom Okta.
In today’s era of rapid AI technological advancement, many companies are chasing the AI wave. But do we truly understand the distinction between “Automation” and “AI Agent”? This article will explore, from a practical application perspective, in which scenarios deterministic automation should be used, and in which scenarios AI Agents should be introduced. Through comparative analysis, we hope to help readers make more informed technical choices in this “all-in-AI” era.
A Python supply chain attack has occurred, where a single pip install can steal all your credentials. This post introduces how to use pipguard to intercept malicious packages before installation.
Hadolint’s pre-commit integration has long lacked an automatic installation solution, a problem that has plagued the community for three years. This article explains how, by packaging the Hadolint binary into a Python Wheel and publishing it to PyPI, pre-commit can integrate Hadolint with a single command, just like any Python tool, completely eliminating the burden of manual downloads and environment configuration.
In the previous article, I wrote about receiving a donation from Canonical, and a reader asked: How did you get selected?
At first, I didn’t fully understand it either. Later, after carefully studying thanks.dev’s operational mechanism, I finally figured out the underlying logic of the whole thingâand what kind of projects are more likely to be funded by downstream vendors.
Today, I unexpectedly discovered that Canonical has been donating to me via thanks.dev for four consecutive months to support the development of badgepy. Although the amount is small, this feedback means a lot to me. It made me realize that sometimes, if we just keep doing the right thing without worrying too much about rewards, the results might come back to us in a warm and unexpected way, from a place we never anticipated.
Today’s job market values practical skills and the ability to create value more than ever before, rather than just academic qualifications. This post shares the story of a high school dropout who successfully joined OpenAI through self-study and actively creating opportunities, and why directly showcasing your “nuclear weapon” achievements might be the most effective strategy for job searching.
I recently undertook a major UI redesign for the open-source project GitStats, adding dark/light mode toggle, responsive adaptation, and a modernized visual design, making the reports look like a real product.
OpenClaw is a great idea, but it won’t be a great solution. High costs, security risks, big tech competition, and a lack of user educationâthese four obstacles combined make it difficult to sustain a long-term prosperous ecosystem.
The latest ‘2025 State of DevOps Report’ reveals that DevOps skills have become an essential requirement for career survival. 40% of enterprises list them as “must-have,” with another 43% indicating they will soon be mandatory. Employers value practical experience more than training certifications. Despite the high popularity of AIOps and DevSecOps, their actual implementation is still in its early stages.
Recently, I encountered a related but more advanced conceptâAgentic DevOps. After spending time reading Microsoft Azure’s introductions, GitHub’s latest documentation, and some open-source practices, I compiled these notes. The purpose is to document my learning process and provide reference for colleagues. The following content is based on publicly available information and my understanding, without any exaggerated predictions.
The Explain Error Plugin introduces two significant updates: support for custom context information and folder-level AI provider configurations. These two features make the plugin more flexible and robust for use in enterprise environments.
Recently, I received some user feedback, and I immediately enhanced the Explain Error Plugin, adding two very practical features: support for specifying the language of the explanation content output and support for obtaining AI return values in Pipeline.
Today, with the widespread adoption of microservices, hybrid clouds, and containerized deployments, IT systems have become exceptionally complex. When thousands of alert messages flood in, traditional operations models struggle. AIOps (Artificial Intelligence for IT Operations), an AI-driven transformation, is emerging as the “lifeline” for IT operations management. This article combines key insights from IBM, ServiceNow, GitHub, and Red Hat to provide a comprehensive overview of AIOps.
This article provides a detailed explanation of GitHub’s AI-related concepts and their hierarchical relationships through fact-based explanations and analogies, helping readers clarify the meaning and function of terms like Models, Agents, Spaces, and Spark.
In the world of DevOps, with so many tools, many people get confused about their responsibilities. Using a car industry analogy, this article helps you understand the positioning and collaboration of Packer, Terraform, Ansible, Docker, and Kubernetes all at once.
py-eol is a lightweight tool that helps you quickly determine if a Python version is expired or approaching EOL. It’s suitable for local use and can also be integrated into CI/CD for more reliable version management in projects.
As an open-source project maintainer, I deeply understand the pain of “installing dependencies”. To save users a couple of command lines, I spent time packaging gnuplot into an out-of-the-box Python packageâgnuplot-wheel. This article shares the birth story and practical value of this little wheel.
In software development, Brooks’s Law states that “adding manpower to a late software project makes it later.” This post uses the analogies of roasted chicken and pregnant women to deeply explore the reasons behind this phenomenon and offers practical solutions.
After a month of intermittent development and testing, I have finally completed this major update. This is the biggest update Commit Check has received since its inception.
This article introduces the new feature of Jenkins Explain Error Plugin, which is the support for Ollama local models, helping users more efficiently analyze and resolve build errors.
Discovered my blog has more English than Chinese readers? So I used GitHub Actions + Gemini API to automatically translate articles into English, saving time and effort.
Some say open source is useless, yielding neither profit nor time savings. But through four years of dedication, I’ve discovered three unexpected rewards: increased visibility for my work, connections with exceptional people and projects, and the accumulation of long-term value. These rewards are applicable to every developer.
How to assess and improve your team’s DevOps maturity. Official release of the DevOps Maturity open source project, with assessment tools and practical guides.
This article records the process of revamping my blog after eight years, from migrating from Hexo to Hugo, to the functional and design improvements of the new blog.
This article introduces a new feature of the Jenkins Explain Error Plugin that supports for Google Gemini model for error analysis. It provides configuration methods and an example video.
Introducing my first Jenkins plugin: Explain Error Plugin. It automatically analyzes build failure log information and generates readable error explanations, helping developers locate and solve problems faster.
How to temporarily fix it via the Script Console, and how to make it permanent by modifying Jenkins startup parameters. This method is suitable for internal Jenkins environments and has been tested on modern Jenkins installations.
Leverage Bitbucket’s Commit Message Templates feature to automatically write PR descriptions into Git commit messages, ensuring crucial information isn’t lost with tool changes.
In Jenkins, the disableConcurrentBuilds option is used to manage concurrent builds. This article explains how to conditionally set the abortPrevious value based on the branch being built, allowing for more flexible build management.
In DevOps, CI/CD pipelines require continuous maintenance and refactoring. This article explores why CI/CD is not a one-time construction project, but a system that requires long-term investment and continuous evolution.
asdf-clang-tools is an asdf plugin for installing and managing Clang Tools, such as clang-format, clang-tidy, clang-query, and clang-apply-replacements.
In enterprises, documentation tools like Wiki and Confluence, if lacking unified management and review mechanisms, can lead to information chaos and knowledge sedimentation failure. This article explores how to avoid this situation and draws on the successful experiences of open-source communities.
uv is a Python package management tool developed by the Astral team. It replaces the functionality of pip, venv, and pip-tools, offering faster dependency resolution and a more modern project management approach.
cpp-linter-action is a GitHub Action that provides C/C++ code formatting and static analysis capabilities. It uses clang-format and clang-tidy, supporting various configurations and custom rules. Since its creation in 2021, the project has been used by several well-known organizations and open-source projects.
Many programmers, especially those in China, are frustrated by access issues with GitHub. This article shares my personal experiences and reflections on network problems.
In open-source projects or team collaborations, Sphinx + ReadTheDocs provides an easy-to-maintain, automatically deployable documentation system. This article documents the configuration process and considerations.
Markdown and reStructuredText (RST) are two commonly used markup languages. This article compares their advantages and disadvantages and shares usage suggestions in different scenarios.
Following Broadcom’s acquisition of VMware, many enterprise users are seeking alternatives. Nutanix, as a hyper-converged infrastructure (HCI) solution, offers lower costs and a simpler management interface, making it a good option.
After two months of continuous improvement, gitstats now supports JSON output, code refactoring, argparse replacing getopt, and full compatibility with Windows and macOS. Welcome to use and Star support!
This article explains how to use Jenkins Docker Cloud for building and deploying applications, including setting up a Docker host and creating custom Docker images.
This post details the revival of the GitStats project, including its migration to Python 3, the creation of a modern CI/CD pipeline, publication to PyPI and Docker, and future improvement plans.
This article introduces the differences between pip and pipx, helping developers choose the right tool to manage Python packages and command-line tools.
This article discusses the optimizations made during the Jenkins upgrade, including using Docker Compose for deployment, refactoring the Jenkins Shared Library, introducing Windows Docker Containers, and more to enhance the efficiency and security of the CI/CD process.
This article introduces the Conventional Branch specification, which provides a structured naming convention for Git branches to enhance readability and collaboration.
This article introduces the installation, usage, and verification methods of the GaraSign code signing tool, helping developers achieve secure code signing.
This article introduces the concept and importance of code signing, along with a comparison of two common code signing tools, emphasizing its role in software supply chain security.
This article introduces the Reusable Workflows feature of GitHub Actions, helping developers and teams manage and reuse CI/CD processes more efficiently.
This article introduces the new feature of cpp-linter-action Pull Request Review, allowing developers to directly submit code modification suggestions on GitHub, improving code quality and collaboration efficiency.
Introducing the catchError step in Jenkins pipeline to handle specific errors without failing the entire build, allowing for more flexible error management.
This article introduces how to implement software supply chain security using SLSA and Witness for both GitHub and non-GitHub projects, enhancing the security of software development and deployment processes.
This article introduces the concept and working mechanism of Witness, and how to use Witness to generate and verify the provenance of software artifacts, emphasizing its importance in improving software supply chain security.
This article documents the problems encountered when upgrading from IBM XLC 10.1 to XLC 17.1 (IBM Open XL C/C++ for AIX 17.1.0) and how to fix the errors.
This article describes how to resolve SSL certificate validation issues when uploading artifacts from a Jenkins agent to Artifactory, including generating a security certificate file and importing it into Java’s cacerts.
This article explains how to resolve an SSL certificate verification issue on AIX when uploading artifacts to Artifactory via Jenkins, including updating Javaâs cacerts file.
This article documents the problems and solutions encountered when creating a NuGet Organization, especially regarding the use of corporate email addresses.
This article introduces the concept, advantages, use cases, and how to use Docker Buildx Bake to accelerate the building and management of multi-platform images.
This article introduces the definition of SBOM, its relationship and differences with SLSA and Black Duck, best practices, and available generation tools, helping readers better understand and apply SBOM.
This article introduces how to use the Commit Check tool to verify whether Git commit messages, branch names, committer usernames, and committer email addresses conform to specifications.
This article introduces the concept, purpose, and levels of the SLSA framework, and how to apply SLSA in the software supply chain to improve security. It helps readers understand the importance of SLSA in software development and deployment.
This article introduces some best practices for Jenkins pipelines, aiming to help developers and operations personnel optimize Jenkins’ performance and maintainability.
This article explains how to implement [skip ci] functionality in Jenkins multi-branch pipelines, allowing you to skip builds based on commit messages.
This article explains how to resolve the “Temporary failure in name resolution” issue in WSL by configuring DNS settings and ensuring persistent changes.
This article explains how to restrict access to a critical Linux machine by modifying the /etc/security/access.conf file, allowing only specific users to log in.
This article introduces how to develop on Windows using WSL, VS Code, and Docker Desktop. It provides detailed installation and configuration steps, as well as the advantages and experience of using these tools.
This article introduces the key components and standards in the container ecosystem, such as Docker, containerd, CRI, CRI-O, OCI, and runc, explaining their relationships and how they work together.
This article describes how to separate a forked repository from its parent repository using GitHub Support, avoiding data loss from deletion and reconstruction, and helping developers better manage forked repositories.
This article explains how to resolve the “The Pip Inspector tree parse failed to produce output” error in Black Duck Detect, including the root cause and solution.
This article compares Vagrant and Docker, analyzing their respective use cases and advantages to help readers choose the right tool for managing virtual machines or containers.
This article introduces the concept and history of Vagrant, and how to use Vagrant to create and manage virtual machines, emphasizing the advantages of Vagrant over traditional virtual machines.
This article introduces the basic concepts, advantages, and ranking of the Go programming language. It also guides beginners on choosing between learning Python or Go, providing practical learning suggestions and resources.
This article shares some best practices to follow when writing Dockerfiles and using Docker, including suggestions on multi-stage builds, image optimization, and security.
This article presents the key findings and trends from the 2021-22 World Quality Report (WQR), highlighting the impact of the COVID-19 pandemic on software quality and testing, and the crucial role of QA in Agile and DevOps.
This article introduces the basic concepts of Polaris, the supported programming languages, how to use the SaaS platform, and how to configure and run Polaris for static code analysis. It also provides example YAML configuration files and how to view the analysis results.
