Continuous integration and continuous delivery serve different purposes in the development pipeline — optimizing each independently leads to better outcomes.
Create a ReactJS component to match webcam faces to ID photos using AI, all client-side. Great for fast prototyping of secure apps like banking or event check-ins.
Build serverless WebSockets with Cloudflare Workers for routing and Durable Objects for state. This creates a scalable, low-latency real-time app at the edge.
A deep dive into the importance of data quality and strategies for improvement. We also analyze some real-world examples demonstrating the importance of data quality.
Federated learning enables collaborative AI by training models where data lives, sharing only updates — not raw data — to ensure privacy, compliance, and trust.
This article provides an overview of vibe coding and LLM-first development, exploring their impact on workflows, productivity, and software engineering.
Learn how one-week sprints with vibe coding boost Agile success by enabling faster delivery, reducing AI errors, and improving collaboration across teams.
In this guide, learn how to simplify data tasks with AI in Databricks SQL — summarize, translate, analyze sentiment, and mask PII with one-liner queries.
In this article, I share the key stages of building a secure startup — from IDPs and network planning to SIEM, SOAR, and post-live security best practices.
Multi-agent KYC architectures use specialized AI agents to automate document verification, risk assessment, and compliance decisions with full audit trails.
Build a real-time data mesh using Apache Iceberg for scalable, versioned table storage and Apache Flink for continuous stream processing across domains.
It's all about AI transformation déjà vu: This article provides a look into why today’s failures look uncannily like yesterday’s “Agile transformations.”
The focus of this article is to talk about the fast pace of change in models and the consequent updates we need to make to our LLM-powered solution design.
Sharing my experience of working in multiple design system teams, and it will not be a technical post, but more about the goals, pains, and successes of it.
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