Contributed Content
Why “Tokenmaxxing” Was Always the Wrong Way for Developers to Measure AI Productivity
Drew Thompson | | ai, AI Development, AI security, application security, developer productivity, software developmentThe term "tokenmaxxing" left the developer lexicon just as quickly as it arrived, and like most viral technology concepts, it means different things depending on who's using it. In practice, the term ...
Beyond Log Search: What We Learned Building a RAG-Based Incident Diagnosis System
Abhimanyu Garg | | AI for SRE, AI hallucinations, AI incident diagnosis, AIOps, devops, devops automation, FAISS, incident management, incident remediation, incident response, knowledge management, knowledge retrieval, LLM grounding, LLM incident response, observability, postmortems, production AI, RAG AIOps, re-ranking, retrieval-augmented generation, root cause analysis, Runbooks, semantic search, SREA RAG-based AIOps framework can cut incident diagnosis time by grounding LLM reasoning in real runbooks, tickets and postmortems, improving root-cause accuracy while giving SREs source-backed answers they can trust ...
Scalable Jenkins Management: Empowering Enterprises With Centralized Control for 150+ Instances
Thrivikram Eskala | | ansible, ArgoCD, AWS, azure, CI/CD Automation, developer platform, devops automation, ELK stack, gitops, grafana, Helm, Jenkins, Jenkins administration, Jenkins backups, Jenkins control plane, Jenkins fleet, Jenkins management, Jenkins observability, Jenkins rollback, Jenkins upgrades, kubernetes, multi-cloud CI/CD, platform engineering, Prometheus, self-service platformA centralized Jenkins control plane can reduce fleet sprawl, automate upgrades and backups, improve observability, and give application teams safe self-service across large multi-cloud CI/CD estates ...
Preparing Infrastructure for the Next Phase of Agentic AI
Gretchen Stewart | | agent orchestration, agentic AI, AI agents, AI data architecture, AI infrastructure, AI modernization, AI observability, AI resource management, AI workflows, AI workload management, autonomous agents, cloud infrastructure, data movement, federal agencies, government AI, government IT, human-in-the-loop, hybrid infrastructure, infrastructure modernization, multi-agent systems, on-premises AI, public sector AIAgentic AI is changing government infrastructure requirements, pushing agencies to rethink workflows, observability, data movement and resource prioritization before investing in new hardware ...
Production-Grade AI Eval Systems. What I Learned Putting LLMs on Call
Anjaiah Methuku | | AI evals, AI guardrails, AI observability, AI quality monitoring, AI reliability engineering, AI SRE, AI testing, answer relevance, CI evals, context precision, context recall, faithfulness, hallucination detection, human-in-the-loop, LLM evaluation, LLM judges, LLM reliability, model monitoring, production AI, production-grade AI, RAG evaluation, RAG metrics, regression testing, retrieval-augmented generationProduction-grade AI reliability requires more than uptime and latency. A layered eval system helps teams detect hallucinations, RAG failures and quality regressions before customers do ...
Waterfall 2.0: Controlling LLM-Driven Software Development with Stage-Gated Discipline
Georgy Starikov | | ADRs, Agile vs Waterfall, AI coding, AI development lifecycle, AI Software Engineering, AI-assisted engineering, architecture decision records, combi-shaper, engineering workflows, generative AI development, knowledge corpus, LLM engineering, LLM software development, MCP, Model Context Protocol, software architecture, software testing, system design, Waterfall 2.0Waterfall 2.0 reimagines classic stage-gated software development for the LLM era, combining fast AI generation with structured validation, cheap backtracking and a shared knowledge corpus ...
Why API Test Generation Is a Judgment Problem, Not a Code Generation Problem
Abhishek Saikia | | adaptive test coverage, AI API testing, AI quality engineering, AI test generation, API test generation, API testing, Automated testing, cross-field testing, DevOps Testing, fine-tuning, LLM testing, QA automation, RAG, software testing, test assertions, test coverageWhen we started using large language models for API test generation at KushoAI, the results were impressive on the surface. Tests appeared in seconds. Coverage breadth went up. The team was excited ...
Why Cryptographic Inventory Is the First Step Toward Quantum Readiness
Michael Carter | | CBOM, CI/CD security, crypto-agility, cryptographic dependencies, cryptographic discovery, cryptographic inventory, cryptography bill of materials, devops security, devsecops, Kubernetes security, ML-KEM, NIST PQC migration, post-quantum cryptography, PQC, quantum readiness, Software Supply Chain SecurityPost-quantum readiness starts with visibility. DevOps teams need a continuous cryptographic inventory to map algorithms, keys, certificates, libraries, infrastructure and third-party dependencies before PQC migration begins ...
Elevating Technical Documentation: Using Predictive AI to Standardize Visual Knowledge Bases
Nazim Ghori | | AI documentation, AI visual validation, architecture diagrams, CI/CD documentation, computer vision, developer documentation, developer experience, devops automation, DevOps documentation, documentation automation, documentation drift, documentation quality, documentation quality gates, Git workflows, knowledge bases, OCR, predictive AI, screenshot validation, technical documentation, visual validationEvery release changes documentation alongside the code. Developers depend on that documentation during implementation, maintenance and troubleshooting. A diagram becomes misleading once services are renamed or integrations change without a corresponding documentation ...
How to Avoid Repeating the “Automate Everything” Mistake Due to AI FOMO
Alexey Golubev | | AI engineering, AI FOMO, AI Governance, AI implementation, AI in DevOps, AI maturity model, AI productivity, AI ROI, AI SDLC maturity, AI strategy, AI tools, AI-Driven DevOps, artificial intelligence, Cursor, DevOps AI adoption, devops automation, GitHub Copilot, Kubernetes AI, software development lifecycleThe DevOps community has already experienced technological extremes. You might recall the time when automation was thought of as a panacea for all possible engineering issues, and the slogan “Automate Everything” was ...
Why Self-Healing Tests Need a Deployment Gate
Suneet Malhotra | | artificial intelligence, CI/CD, devops, Self-Healing Automation, software testing, test automationWhen an end-to-end test fails after a front-end change, the repair often looks routine. A class name changed. A button moved. A selector that used to be unique now matches two elements ...
Are LLMs Equally Good (or Bad) at Building Secure Software?
Pieter Danhieux | | agentic AI, AI agents, AI code security, AI coding assistants, AI coding costs, AI coding models, AI Development, AI model benchmarking, AI-generated code, application security, Developer Security, framework security, LLM security, OWASP Top 10, SAST, secure code generation, secure coding, software development, software security, token costsAn SCW study finds a large variance in how frontier and budget LLMs perform across different frameworks, and explains the striking cost variance ...
