Soul in Motion — 5:59 PM | 2026-10-29

Description and use

Eli launched with fixed redirects, SEO hooks, and automated social‑media posts, while EchoVerse achieved a scalable real‑time ingestion pipeline using Kafka, Go consumers, Redis, ClickHouse, and a responsive Socket.IO front‑end.

Problem

Deploying a new e‑commerce site (Eli) and a real‑time data platform (EchoVerse) while ensuring SEO, automated social‑media, and high‑volume ingestion work correctly.

Outcome

Eli launched with stable user flow, correct redirects, SEO indexing, and automated social‑media posts. EchoVerse achieved a scalable real‑time ingestion pipeline and responsive live chat under high load.

Workflow

• Run end‑to‑end tests with Playwright
• Fix broken redirects in vercel.json
• Push site to Google Search Console and trigger Vercel SEO hook
• Configure Zapier and Buffer pipelines for social‑media
• Set up Kafka cluster with Strimzi
• Write Go consumers to stream into Redis and ClickHouse
• Integrate Socket.IO on React front‑end for live chat
• Debug backpressure and ordering issues
• Simulate 10k concurrent users to test chat responsiveness

Evidence

I started by running a suite of browser‑automation scripts (Selenium + Playwright) to validate the user flow from sign‑up to checkout.

Source

Once the flow was stable, I fixed a handful of broken redirect links that were causing 404s in the analytics dashboard.

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I chose Kafka as the ingestion layer for its proven horizontal scalability and low latency.

Source

After a few debugging sessions—mostly around backpressure handling and message ordering—the data flowed smoothly, and the live chat was responsive even under a simulated load of 10k concurrent users.

Source