2026-06 on Funky Si's Blog

Portfolio: Live Azure Workloads, AI Search, and Dev Tools

funkysi1701@gmail.com (funkysi1701) — Mon, 01 Jun 2026 20:00:00 +0000

Featured Projects

Three live Azure workloads I own end-to-end — CI/CD static delivery, AI-backed search on real content, and full-stack Blazor with actual users.

🔧 Episode Atlas

Track TV progress at scale with per-user state and cloud-backed data.

What it does: Per-user episode lists, “last watched” markers, and curated metadata links — so long-running franchises stay manageable.

Tech used: .NET Blazor, Azure Static Web Apps, Azure Functions, Cosmos DB, GitHub auth.

Why it’s impressive: Demonstrates scalable data modelling and cloud-first architecture for user-driven applications.

Deployed to AzureImplements authentication / APIs / caching

Outcome: Turned a personal “where was I in 900+ episodes?” problem into a maintainable multi-tenant pattern for identity, persistence, and UX for ongoing shows.

Links: episodeatlas.com

🔧 Semantic Search

AI-powered natural language search for discovering content beyond keywords.

What it does: Accepts conversational queries, retrieves meaningfully related posts, and surfaces them alongside the site’s JSON index search.

Tech used: .NET, Azure, Cosmos DB, OpenAI.

Why it’s impressive: Showcases practical LLM integration in .NET beyond basic chat interfaces.

Deployed to AzureDesigned with clean architecture

Outcome: Explored how vector-style retrieval and operational concerns (cost, latency, content freshness) fit next to a static Hugo site without compromising the authoring workflow.

Links: search.funkysi1701.com · linked from Search

🔧 Blog Platform

Technical blog on Azure with CI/CD—fast static pages, search, and comments without babysitting an app server.

What it does: Publishes posts, generates RSS and a JSON search index, integrates Giscus for discussions, and deploys on every merge.

Tech used: Hugo, GitHub Actions, Azure Static Web Apps, Giscus.

Why it’s impressive: Highlights modern static-first architecture with automated deployment pipelines.

Deployed to AzureCI/CD via GitHub Actions

Outcome: Demonstrates a production-shaped static pipeline — preview builds, repeatable deploys, and content-driven SEO — suitable as a reference architecture for docs and marketing sites.

Links: GitHub repository

💼 Cloud & Production Systems

The featured trio — Episode Atlas, this blog (Hugo on Azure), and Semantic Search (search.funkysi1701.com ) — is fully described in Featured Projects above and not repeated here. Below is the rest of the catalogue by category.

🔧 Thorne Pentecostal Church

Dependable church presence online—clear for newcomers, simple for volunteers to keep current.

What it does — Presents service times, beliefs, and visitor information with a simple structure so members and newcomers get answers in one place.

Tech used — HTML/CSS, Azure Static Web Apps.

Why it’s impressive — Demonstrates proportionate engineering: production-grade static hosting and clear information architecture for stakeholders who need reliability, not complexity.

Engineering signals: Deployed to Azure · CI/CD via GitHub Actions

Outcome: Delivered a first web presence focused on clarity and sustainability so non-developers can keep content current with minimal process overhead.

Links: Thorne Pentecostal Church

🛠 Tools & Utilities

🔧 Mandelbrot Generator

Interactive Mandelbrot exploration in the browser—smooth zoom, pure WASM, zero server-side rendering cost.

What it does — Renders the Mandelbrot set in the client with zoom for desktop and mobile, demonstrating CPU-bound UI work in WASM.

Tech used — .NET Blazor WebAssembly, Azure Static Web Apps.

Why it’s impressive — Demonstrates client-side compute discipline in Blazor WASM: rich, interactive UI with predictable cost because heavy work never hits the server.

Engineering signals: Deployed to Azure · Designed with clean architecture

Outcome: Built the tool to stress-test interactive Blazor WASM performance and touch UX patterns — a small codebase that still informs decisions about heavier client-side workloads.

Links: Mandelbrot Generator

💡 Ongoing exploration

Areas I keep hands-on with outside these shipped projects:

.NET Aspire — Cloud-native composition and local dev parity
Kubernetes — Orchestration and service boundaries
Elasticsearch — Search and analytics stacks
Azure Functions — Event-driven and serverless patterns
DevOps automation — CI/CD, IaC, and release hygiene

🤝 Collaborate With Me

Interested in collaborating on a project or discussing an idea?

Contact Me — Get in touch
GitHub — Code and experiments
LinkedIn — Connect professionally

📚 More resources

Tools & Resources — Recommended development tools
About Me — Background and experience
Blog Posts — Technical writing

Episode Atlas: Track Your Star Trek Rewatch Journey

funkysi1701@gmail.com (funkysi1701) — Mon, 01 Jun 2026 12:00:00 +0000

If you have ever tried to rewatch Star Trek properly—not just dip into a favourite episode, but work through hundreds of instalments across multiple series—you will know the feeling. You pause Deep Space Nine for a few weeks, start something else, and suddenly you have no idea which episode you were on. Spreadsheets help for a while, then they do not. Notes on your phone get lost. Memory is unreliable when you jump between TNG, DS9, and whatever new show has just dropped.

That is the problem Episode Atlas exists to solve: search 900+ episodes and films, mark what you have watched, and always know what to watch next—without treating a rewatch like a project-management exercise.

Why I built it

I write about Star Trek on this blog from time to time—episode reviews, top-50 lists, thoughts on new series. The honest origin is simpler than that, though: I wanted a better way to track my own rewatches.

Generic TV trackers are fine if you watch a bit of everything. They are less helpful when you care about one enormous franchise with inconsistent stardates, films mixed in with series, and the urge to jump to “all the Borg episodes” rather than the next sequential instalment. Episode Atlas is deliberately narrow (if you can describe a database of over 900 episodes and films narrow!): all of Star Trek, in one place, with features shaped by how fans actually rewatch.

You can use Episode Atlas without creating an account. Search the catalogue, open episodes, browse collections, and spin the random picker—no signup required.

When you want your progress to stick, sign in and start your captain’s log. Mark episodes watched, and the site remembers where you left off across every series—even if you detour from DS9 to TNG and back again.

Your service record

Once you are logging watches, the homepage gives you a service record at a glance: how many of the 900+ episodes you have seen, your percentage through the franchise, and your current watch rank (from the early ranks up through Lieutenant, Commander, and beyond). The site shows how many more episodes you need for the next rank, so a long rewatch feels like progress rather than a vague “I think I am halfway through Voyager.”

You can also earn commendations and build an active duty streak—small nudges to keep visiting when you are in the middle of a serious rewatch. It is the same idea as a fitness streak, but for Star Trek.

Find any episode in seconds

Search is the front door. Most visits start with “I know roughly what I want” or “which episode was that again?” Filter by series and season to narrow the list; keywords catch titles and descriptions you only half remember. Every series and the films are in one catalogue.

Episode pages: context and next steps

Open any episode and you get what you need for a rewatch session: metadata, previous and next navigation within the series, season progress, and actions to mark the episode watched or jump to the suggested next episode. Your captain’s log ties it together—what you logged today, what you have already seen, and where to pick up when you return.

The flow the site describes is simple: search → open → mark watched → move on. Three gestures, no spreadsheet.

Collections when you want a mood, not a season

Collections answer a different question from search: “It is late; show me something good in this mood.” Each collection points at Borg stories, time-travel episodes, holodeck highlights, and similar lists—fan knowledge encoded as browseable sets, so you skip filler without digging through every season.

Random episode when you cannot decide

The random episode picker is for the evenings when you and the sofa have agreed on Star Trek but not on which Star Trek. One click, one suggestion—useful when commitment is the hard part.

How it compares to other sites

Episode Atlas is not trying to replace Memory Alpha for lore or IMDb for cast lists. It is built for progress: what you have watched, what rank you have earned, and what to play next. If you have ever bounced between a wiki, a ratings site, and a generic tracker and still not known where you were in DS9, this is the gap it fills.

Try it

episodeatlas.com — free to browse, sign in when you want your captain’s log saved. Search, mark your first episode, and see your rank climb.

How do you track long rewatches—spreadsheet, app, or pure memory? If you try Episode Atlas, I would genuinely like to know what works for you and what does not: features you love, gaps that annoy you, or anything that would make a rewatch easier. Leave a comment below, or use the feedback option on episodeatlas.com —both reach me, and both help shape what I build next.