Category: SDLC
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AI Coding Competencies: What Inspires Awe — and 5 Ways They Spark Dread
The rise of AI in coding has sparked awe and concern among developers. While AI tools like Claude and Codex enhance coding efficiency, experts fear issues such as trust in AI-generated code, security risks, and potential loss of coding discipline. Ultimately, developers must ensure rigorous reviews to mitigate these risks.
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Are Engineers Prepared for the Emerging Agentic AI Software Development World?
Agentic AI software development is revolutionizing low-code and no-code platform capabilities. Reports indicate a significant rise in AI-assisted coding among developers. As AI agents become more autonomous, traditional coding may decline, posing questions about the future roles of software engineers and enhancing business capabilities.
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10+ Awesome LLM and Generative AI Capabilities for DevOps and IT Ops
DevOps, SREs, IT Ops can use generative AI and LLMs to improve incident management, root cause analysis, and automations.
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Beware Technical Debt: How Bad Code Smells Like Rotten Cheese
What is tech debt? The code smells, like rotten cheese. Lessons for agile and DevOps Digital Trailblazers on prioritizing technical debt
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What is Your Survival Plan for Managing Technical Debt?
Developing a plan to manage technical debt; a video by Digital Trailblazer author Isaac Sacolick with ten of his top DevOps blog posts on tech debt.
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How to Avoid Bogging Down Your IT Org With Technical debt
Avoid technical debt by adopting agile continuous planning, defining devops principles, and specifying non-negotiables
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How AIOps Help SREs Measure Error Budgets and Fulfill SLOs
SREs using AIOps have an advantage working with agile DevOps teams when managing to an SLO and measuring error budgets
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How More Teams can be Successful with Effective Microservices
Microservices and containers enables modularity and reuse especially when applied to strategic business services
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How to Fix Bugs, Raise Team Happiness, and Improve Developer Productivity
Adding QA to DevOps and investing error monitoring to fix bugs faster, raise happiness, and improve software developer productivity
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How a Java Low-Code Development Platform Drives Useful Innovation
Java developers can use low-code to develop rich workflow, mobile, and API based applications
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5 Principles Full Stack Developers and Solutions Architects Must Understand About Machine Learning
Principles on connecting apps to machine learning models
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Beyond microservices; Software architecture driven by machine learning
How machine learning will define next gen software architecture