Platform engineering helps DevOps teams scale with golden paths, DevEx metrics, automation, and AI guardrails that reduce friction and improve delivery.
AI generates code faster than tests can cover. Coverage stays green while gaps grow. Treat AI code as untested by default and scale testing to match generation speed.
Unbounded retries and autoscaling can turn minor latency into cascading outages. API reliability must be bounded and load-aware to prevent retry storms.
CV data issues keep recurring. I built cv-quality — a toolkit to audit datasets, catch annotation errors, find mislabeled samples, and streamline labeling.
GPUStack is an open-source tool that turns a bunch of scattered GPU machines into one managed cluster for deploying AI models behind an OpenAI-compatible API.
SAP cloud TCO is driven more by landscape sprawl than by EC2 costs; optimize environments and use Terraform, S3, and EFS lifecycle policies to reduce costs.
Retesting isn’t a checkbox — it’s discipline: reproduce, verify fixes, test edges, run regression, validate in staging, document, automate, and never skip it.
A practical approach to enhancing DAG failure detection using AI to improve pipeline reliability and enable proactive intervention in large-scale data environments.
Learn how agentic testing reshapes QA by adding governance, traceability, and accountability to AI-driven workflows, ensuring speed doesn’t compromise quality.
Have you ever needed to generate OpenAPI documentation directly from your code and, more importantly, do it in a way that fits cleanly into a CI pipeline?
Docker packages applications to ensure consistent and portable deployments. Kubernetes manages them with scaling, reliability, and automation in production.
Multi-cloud sounds strategic, but usually happens by accident. Networking, IAM, and observability all break at boundaries. Only attempt it if you have no choice.