I keep losing trip research across ChatGPT, Claude, and Cursor. So I built Wayfare: a travel bucket list with savings tracking, plus MCP so connected chatbots can use the same trip context.
A customer reported that their Kafka Connect MQ Sink connector looked completely healthy — no errors on the connector CR, no failed tasks — but no messages were reaching MQ. This had been going on for hours.
My wife was losing track of feeds, so I built a one-tap nursing timer that grew into a pumping log, and shipped it for free on GitHub Pages with no database and no app store.
Most habit apps want an account, a subscription, and a notification every hour. I built a minimalist challenge tracker that stores everything locally, exports to PDF, and ships free on GitHub Pages.
Every Kafka record with a Long value you produce sends 8 bytes over the wire, even when the value is 42. Bijou64 encodes integers in 1–3 bytes with drop-in serializers, cutting network transfer and replication traffic on integer-heavy topics by up to 50% or more.
The Celery Question # For years, when someone asked “How do I handle background tasks in Django?”, the answer was almost always “Use Celery”. And for good reason. Celery is mature, battle-tested, and has solved async task processing for countless Django applications. It has extensive documentation, a large ecosystem of plugins, and most Django developers have at least some experience with it.
The Challenge of Reliable Background Operations # Building web applications with Flask is straightforward until you need to handle operations that can fail. Database operations timeout, external APIs become unavailable, and network connections drop. These failures are inevitable in production systems, yet handling them gracefully often requires significant engineering effort.
The Problem: Connecting AI Assistants to Your APIs # As AI assistants like Claude and VS Code Copilot become more powerful, developers are discovering a critical bottleneck: these AI tools can’t directly interact with our existing REST APIs.
The Problem: Getting Real-time Data into Kafka # Have you ever tried to stream Wikipedia changes or other real-time Server-Sent Events (SSE) updates into Kafka? If you have, you probably wrote custom Kafka producer and consumer code to handle these events, managed reconnections, and built your own error handling.
The Problem: When APIs Meet AI Assistants # A few weeks ago, I was experimenting with Model Context Protocol (MCP) for a Kafka integration project. The experience was eye-opening - being able to interact with complex systems through natural language felt like a glimpse into the future of development workflows.
What if I told you that you could build, host, and maintain data APIs completely free using just GitHub? Here’s how I built a production-ready data platform without spending a penny on hosting.