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Curated stories from TechCrunch, The Verge, MIT Tech Review & more — delivered fresh every morning. No noise, just what matters.
TechCrunch · The Verge · MIT Tech Review · +9 sources
Systems built to solve practical problems — for individuals and businesses alike. Useful outcomes, dependable execution, measurable impact.
Core domains — computer vision, image processing, generative image models, and chat intelligence.
System engineering — architecture design, embedded systems integration, and end-to-end AI/data pipelines.
Production operations — security, observability, monitoring, reliability, and scale-readiness.
Growing up inside a family restaurant teaches you things no engineering course will. You see what happens when a system breaks during a lunch rush. You learn that the person taking orders doesn’t care about your architecture diagram — they need things to work, now.
Swami Sagar is where the instinct for practical engineering comes from. Menu systems, pricing logic, inventory flow — none of it was a side project. It was the real thing, with real consequences, long before any of it became a professional discipline.
Live systems built to move real work forward. Data, automation, and AI models wired together for production delivery.
AWS
Azure
Docker
Kubernetes
Grafana
Datadog
Coralogix
OpenClaw
Currently building production software for data-intensive AI systems — robust pipelines, engineered data flows, LLM features that stay dependable at scale. The work spans data engineering, big data processing, and agentic AI patterns where autonomous agents collaborate inside systems, not single isolated prompts.
Production AI systems, data engineering, and the challenges nobody talks about. Building in public — what actually works when you move from benchmark to production.
Interactive charts that let the data speak for itself. Tap in, explore the patterns, ask your own questions. This is what raw numbers look like when they have something to say.
tableauTechnology is a tool for solving real problems — not an academic exercise. Computer vision, image processing, generative models, chat systems, and the architecture needed to run all of it safely in production.
Beyond platforms, the real work is building people. Workshops, mentoring, and practical methods that help teams take ownership of delivery, observability, and systems after go-live.
If your team is building a production AI system (or wants to) — harishkhollam@gmail.com
Always open to meaningful collaborations.