Forem: Zoe Lin

Hack Desk — Minimal Coding Setup for Global Hack Week

Zoe Lin — Tue, 14 Apr 2026 10:33:13 +0000

My Global Hack Week setup

My laptop, mouse, and a clean desk setup for building, debugging, and shipping projects.

LogoForge — AI SVG Logo Generator for Brand Concepts

Zoe Lin — Mon, 13 Apr 2026 08:48:05 +0000

What I Built

I built LogoForge, a small web app that generates SVG logo concepts from a brand name, industry, style, and keywords using the Gemini API.

The app supports single and 3-variation generation, validates the model output as structured JSON, retries once if validation fails, and lets users preview the SVG, download it, and copy the JSON output.

I also refactored the project into a frontend and backend proxy setup so the API key is not exposed in the browser. That made the app feel much closer to something I would actually deploy instead of just keeping as a local prototype.

Demo

In the demo, I show the input flow, SVG generation results, color palette and brand story output, and the difference between single and 3-variation generation.

My Experience

This project taught me that prompting works much better when I treat it like writing a product spec instead of giving a loose instruction. The clearer I was about the UI, output format, and SVG constraints, the better the generated app became.

The biggest lesson was reliability. A raw model response can look correct but still fail in subtle ways, so adding validation and a retry step made the app much more stable.

I also learned a lot from the deployment side. I started with a simpler client-side setup, then moved the Gemini call behind a backend proxy and deployed the frontend and backend separately. That part felt much more like real engineering work than just prompt experimentation.

During testing, I also occasionally ran into temporary Gemini API availability issues under high demand. The overall app flow and deployment setup still worked as expected, but it reminded me that real-world AI apps need both prompt design and engineering safeguards.

What I Learned

Clear constraints produce better results
Structured output still needs validation
SVG is a great way to create visual output without image generation
Small engineering steps like retries and safer secret handling make a big difference
AI tools can speed up prototyping, but reliability still depends on developer judgment

Dark Dish Lab — Cursed Recipe Generator for Strange Culinary Ideas

Zoe Lin — Sun, 05 Apr 2026 18:21:11 +0000

What I Built

Dark Dish Lab is a tiny, delightfully useless web app that generates cursed food or drink recipes.

You pick:

Hated ingredients
Flavor chaos (salty / sweet / spicy / sour)

Then it generates a short “recipe” with a horror score, a few steps, and a warning.

It solves no real-world problem. It only creates regret.

Demo

YouTube demo

Code

GitHub repo

How I Built It

Frontend: React (Vite)
- Ingredient + flavor selection UI
- Calls backend API and renders the generated result
Backend: Spring Boot (Java 17)
- POST /api/generate endpoint
- Generates a short recipe text and returns JSON
Optional AI: Google Gemini API
- If AI is enabled and a key is provided, it asks Gemini for a very short recipe format
- If AI is disabled or fails, it falls back to a non-AI generator
Notes
- Only Unicode emojis are used (no emoji image assets)
- API keys are kept in local env files and not committed

How I Leveraged Google AI

Gemini (via the Gemini API) is the text generator behind Dark Dish Lab’s “cursed recipe” output.

Instead of using AI as a generic chatbot, I use it as a formatting engine:

Input: selected ingredients + flavor axes (salty/sweet/spicy/sour)
Output: a short, structured recipe (name, 3 steps, 1 warning)

To keep the experience stable and demo-friendly, I added a few guardrails:

Server-side API key only (kept in backend/.env.local, not committed)
Strict prompt constraints (no emojis, short length, fixed format)
Fallback generator when Gemini is disabled/unavailable
Output trimming to avoid unexpectedly long responses

Prize Category

Best Use of Google AI — I integrated the Gemini API to generate short, structured cursed recipes from the user’s selected ingredients and flavor axes.
Community Favorite angle — Try to generate the most cursed combo you can. Comment your ingredient + flavor picks and the horror score you got. Bonus points if your friends refuse to read it.

Ops Hub — Notion MCP Dashboard for Assets, Subscriptions & Daily Digest

Zoe Lin — Thu, 26 Mar 2026 18:34:30 +0000

What I Built

Ops Hub is a Notion-first dashboard for asset and subscription operations (built with Notion MCP + Python).

It helps centralize scattered operational info (devices, warranties, renewals) into one workspace and turns it into a daily workflow:

Assets Ops: track equipment (3C / appliances), maintenance schedule, warranty dates, and manuals.
Subscription Ops: track renewals, pricing, and generate cancellation drafts.
Daily Digest: one command generates a Daily Digest — YYYY-MM-DD page in Notion with:
- KPI summary (counts + upcoming renewal cost)
- actionable lists with deep links to the exact items

All outputs stay inside Notion, so it’s easy to review and act (human-in-the-loop friendly).

Video Demo

Show us the code

GitHub Repo

Run locally (Poetry):

cp .env.example .env
poetry install
poetry run python -c "from homeops_mcp.server import create_daily_digest; print(create_daily_digest(14))"

How I Used Notion MCP

Notion is the source of truth (databases, views, templates, and the dashboard). The MCP server turns Notion context into an automated ops workflow:

Reads Notion databases to find:
- assets due for maintenance soon
- warranties expiring soon
- subscriptions renewing soon (within N days)
Writes back into Notion:
- status fields like Next Action and Lifecycle Stage
- helpful notes like Manual Notes (maintenance guidance) and Cancel Instructions (cancellation draft)
Generates a Daily Digest page in Notion with KPIs + clickable action lists, so I can review everything in one place.

This makes the workflow feel like “superpowers”: I stay in Notion, run one command, and get an updated dashboard plus a daily report ready to act on.

Data Model (Notion Databases)

Assets

Maintenance: Last Maintenance + Maintenance Interval (days) → Next Maintenance (formula) → Next Maintenance Date (calendar)
Ops fields: Next Action, Lifecycle Stage
Docs: Manual Notes, Manual URL, Warranty End

Subscriptions

Renewal tracking: Renewal Date, Billing Cycle, Status
Ops fields: Next Action
Output: Cancel Instructions (draft)

Daily Digest (Auto Report)

A single run creates a Daily Digest — YYYY-MM-DD page in Notion with:

KPI Summary (maintenance / renewals / renewal cost / warranty)
Clickable action lists linking to the exact items

Generated by create_daily_digest(14) and saved as a child page under the dashboard.

Tech Stack

Python (MCP server)
Notion MCP + Notion API
Poetry (dependency & virtualenv)