Gaurav Maheshwari - Oodle AI

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Gaurav Maheshwari

"A body of clay, a mind full of play, a life of a moment - that's me" - Harivansh Rai Bachchan

How we taught our AI to draw

How we taught our AI to draw

We tried making our AI write JSON to render UI components. It worked, until OpenUI showed us the better interface was declarative text.

Three Clouds, One AI Assistant: How Wisdom AI Unified Observability on Oodle

Three Clouds, One AI Assistant: How Wisdom AI Unified Observability on Oodle

🌟Wisdom AI runs customer deployments across AWS, GCP, and Azure. Their observability was scattered: AWS CloudWatch for one account, GCP Cloud Logging for another, Azure Monitor for a third. Engineers resorted to CLI scripts just to search logs. Self-hosted Grafana kept crashing. Creating dashboards/alerts were procrastinated due to

Meet Oodle: Unified and AI-Native Observability

Meet Oodle: Unified and AI-Native Observability

You can watch Oodle's Unified AI-assisted debugging experience on youtube. Production is down! You open your laptop and start the familiar dance: An API timeout? Check metrics for the spike, switch to logs for the error, open traces for the slow dependency — three tools, three contexts, one

Dashboards are Dead!

Dashboards are Dead!

TL;DR The static, pre-built dashboard is dead. The always-on wallboard you build by hand, then maintain through every reorg and schema change, no longer keeps up. What isn't dead is the view itself: it stops being something you make in advance and becomes something your

Scaling Prometheus: How to Go From a Single Node to Enterprise-Grade Observability

Scaling Prometheus: How to Go From a Single Node to Enterprise-Grade Observability

TL;DR * The problem: Prometheus is easy to run on one node, but its memory grows with every active time series, so a single server eventually hits a wall. * The scaling paths for running Prometheus at scale, in order of effort: reduce cardinality first, then split load with functional sharding,

Go Profiling in Production

TL;DR * Go's built-in profiler, pprof, exposes CPU, heap, goroutine, block, and mutex profiles through net/http/pprof; run it on a dedicated localhost-only port. * go tool pprof -http=:8080 "http://localhost:6060/debug/pprof/profile?seconds=30" collects a 30-second CPU profile