#devops
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Auditing AI-Built Products: The 6 Pillars of Production Readiness
AI tools can generate functional code, but they lack the architectural foresight to ensure security, scalability, and reliability. Before shipping, you must manually audit your project across six critical domains to avoid catastrophic failure.
Building Deterministic Infrastructure for Non-Deterministic AI Agents
To move AI agents from demos to production, engineers must shift focus from prompt engineering to building a robust 'agent control plane' that enforces determinism, safety, and resource governance over stochastic model outputs.
The Three Pillars of Modern Cloud Infrastructure
Cloud providers are evolving from simple app hosting to comprehensive AI platforms, offering new primitives for agentic workflows, AI gateways, and secure sandboxing.
Maximilian SchwarzmullerModernizing Legacy Systems with Agentic Coding
Agentic coding uses AI to map complex dependencies and automate discovery in legacy systems, allowing developers to focus on high-level architecture and validation rather than manual code archaeology.
IBM TechnologyKubernetes vs. OpenShift: Platform Engineering Trade-offs
Kubernetes provides the raw container orchestration engine, while OpenShift offers an opinionated, integrated platform that bundles CI/CD, security, and management tools to reduce operational overhead.
IBM TechnologyThe Critical Necessity of Automated Certificate Lifecycle Management
Digital certificates are the foundation of machine identity and trust, but manual management is failing as industry standards force shorter lifespans. Automation is no longer optional to prevent catastrophic system outages.
IBM TechnologyBuilding an End-to-End Ansible Automation Lab
Learn to build a complete, local Ansible automation environment using Google Colab to master playbooks, roles, dynamic inventories, custom modules, and security with Vault.
Moving From Raw Logs to Observability Narratives
Logging is not the same as visibility. To debug production failures effectively, you must move beyond isolated log lines and implement request-based tracing that tells a coherent story of every execution.
The Expand-Contract Pattern for Zero-Downtime Django Migrations
Avoid production outages during complex schema changes by decoupling database updates from code deployments using the multi-step 'expand-contract' pattern.
Overcoming Enterprise Friction in Agentic AI Projects
Enterprise agentic projects fail not due to code, but due to rigid, human-speed governance. Success requires shifting to hypothesis-driven delivery, VC-style portfolio funding, and building a 'living memory' moat.
AI EngineerMoving AI Agents from Development to Production
Production-grade AI agents require moving beyond code generation to automated observability, real-time telemetry integration, and human-in-the-loop remediation to bridge the gap between SRE and development workflows.
Google Cloud TechTurning Python Scripts into Reliable Production Systems
Moving from a one-off script to a production system requires shifting focus from simple execution to reliability, observability, and operational discipline.
Building Modular ML Pipelines with Azure ML Components
Azure ML pipelines improve training efficiency and MLOps readiness by breaking complex workflows into reusable, independently managed components defined via Python or YAML.
GitOps and ArgoCD: Principles and Architecture
GitOps uses Git as the single source of truth for infrastructure, employing pull-based agents like ArgoCD to continuously reconcile the live state of a Kubernetes cluster with the desired state defined in code.
Debugging Silent Production Failures in Python
Production failures often stem from environmental drift and invisible assumptions rather than logic errors. To prevent silent failures, prioritize explicit configuration and defensive data validation.
Free Tool Fixes AI Coders' 12-Month AWS Lag
AI coding tools like Claude Opus confidently suggest outdated AWS solutions, missing services launched 12 months ago; a free plug-in tool updates them instantly for accurate answers on the same model and prompt.
Shadow AI Outruns Enterprise Policies in 2026
40-65% of employees use unapproved AI tools for productivity, exposing sensitive data; bans fail, so shift to tiered approvals and real-time DLP to channel usage into governed paths.
Custom Elevated Sandbox Enables Safe Codex on Windows
OpenAI built a custom Windows sandbox for Codex using dedicated users, restricted tokens, firewall rules, and multi-binary setup to limit writes to workspace, block outbound network by default, and grant user-like reads without constant approvals.
CI/CD Breaks for Agents: Use Continuous Compute Loops
Traditional CI/CD chokes on thousands of agent PRs with cache thrash and merge bottlenecks; replace with intent-driven agent loops featuring inline validation, premerge reconciliation, and stateful continuous compute for sub-minute iterations.
MRC: Resilient Networking for 100K+ GPU AI Training
OpenAI's MRC protocol uses multi-plane topologies and packet spraying across hundreds of paths with SRv6 source routing to eliminate congestion, route around failures in microseconds, and connect 131k GPUs with just two switch tiers, enabling non-stop frontier model training.
OpenAI's Codex Controls: Sandbox, Rules, Telemetry
OpenAI deploys Codex coding agents with sandboxing for bounded execution, auto-approvals for low-risk actions, network/command restrictions, and OpenTelemetry logs to enable safe, auditable developer workflows without broad access.
AWS KMS Envelope Encryption Secures Data at Scale
Encrypt data efficiently with AWS KMS envelope pattern: Use master keys to generate ephemeral AES-256 DEKs for fast local encryption/decryption, storing only encrypted DEKs alongside ciphertext for auditable, revocable access.
MRC: OpenAI's Protocol for Resilient AI Training Networks
OpenAI's MRC extends RoCE with multipath spraying, microsecond failure recovery via SRv6, and multi-plane designs to deliver predictable performance in 131k-GPU clusters, using 2/3 fewer optics and 3/5 fewer switches than traditional setups.
MRC Enables 100k+ GPU Clusters with Resilient Multipath Networking
OpenAI's MRC protocol spreads packets across hundreds of paths for microsecond failure recovery, connecting 100,000+ GPUs via just 2 switch tiers—cutting power, cost, and downtime in AI training supercomputers.
Ditch preferred_username for Azure AD Guest Auth
Using preferred_username as identity anchor worked for employees but failed silently for all B2B guests, causing 403 errors post-launch. Anchor on oid instead for reliable identification.
SIE: Dynamic Inference for Small Models on Shared GPUs
Open-source SIE engine from Superlinked enables hot-swapping small embedding models (e.g., Stella, ColBERT) on one GPU via LRU eviction, cutting costs and solving context rot in agents by preprocessing data.
AI EngineerSecure AI Agents via MCP Toolbox Custom Tools
MCP Toolbox prevents confused deputy attacks by letting developers pre-write constrained SQL tools with bound parameters, separating agent flexibility from app-controlled security for runtime agents.
Replace Cron with Temporal for Reliable Data Jobs
Cron fails on retries, overlaps, and writes due to zero observability. Temporal workflows add retries (3s initial, 2x backoff, 8 max attempts), atomic writes, unique output files per run ID, SKIP overlap policy, and full execution history via UI—surviving crashes with state in Temporal.
Self-Host Vane + Ollama for Private AI Web Research
Install Vane in Docker on Windows 11 with local Ollama and Qwen3.5:9b to run citation-backed searches privately, bypassing cloud services like OpenAI.
Proactive Synthetic Monitoring Catches DevOps Failures Early
Simulate user actions like logins, searches, and API calls to detect regressions, availability issues, and performance degradation before production traffic, integrating tests into CI/CD for consistent validation.
IBM TechnologyShowing 30 of 90