{"@context":"https://schema.org","@type":"HowTo","@id":"https://bkamp.ai/fr/recipes/6806ffca-374f-4b2a-a150-a49cc86fb812","name":"[WIV_SG] HelperHub - Une application de gestion du foyer basée sur l'IA pour les mamans employant du personnel de maison","description":"Une application de gestion domestique basée sur l'IA pour les mères actives qui emploient du personnel domestique logé à domicile — conçue comme une PWA mobile-first.","url":"https://bkamp.ai/fr/recipes/6806ffca-374f-4b2a-a150-a49cc86fb812","image":"https://bkamp.ai/bkamp_home.png","inLanguage":"fr-FR","datePublished":"2026-04-08T09:37:46.808648Z","dateModified":"2026-04-08T09:37:46.807254Z","author":{"@type":"Person","name":"yoonsarah_4b4ccc","url":"https://bkamp.ai/fr/users/yoonsarah_4b4ccc"},"publisher":{"@type":"Organization","name":"bkamp","url":"https://bkamp.ai"},"educationalLevel":"초급","category":"개발","step":[{"@type":"HowToStep","position":1,"name":"Why and What","text":"

Why I built this: Weekly meal planning is repetitive and tedious, but starting from scratch every week is even more exhausting. I wanted to automate the most time-consuming part of running a household with a helper — so I could set it once and let it run.

Phase 1 - Now

AI Meal Planner

Target: Korean working moms in Singapore & Hong Kong

✓7-day AI meal plan generation

✓Step-by-step recipes (Grade 3 English)

✓Lunchbox & snackbox generation

✓Employer ↔ Helper invite system

✓Supabase auth + RLS

✓Deployed on Vercel

○Grocery list

○Helper view + Approve & send

○Cooking log

Phase 2 - Next

Full Household OS

Target: All working moms with live-in helpers, globally

○Shared household calendar

○Daily task list for helper

○House rules documentation

○Helper proﬁle & repertoire

○Favorites + AI weighting

○Multi-language support

○Baby/toddler separate meal plan

Goal: a complete system that works even when your helper changes — onboard a new helper in minutes, not days.

Service ﬂow

Onboarding→ AI generates meal plan→Employer approves→Helper executes

Helper joins via one-time invite code · Role-based access · Supabase RLS scoped to household

"},{"@type":"HowToStep","position":2,"name":"Troubleshooting - feedback requested","text":"

YouTube recipe matching accuracy

Problem: Claude API guesses VIDEO_IDs — \"Korean egg toast\" matched a \"Korean street toast\" video (completely diﬀerent dish). YouTube Data API has a 10,000 unit/day free quota, which runs out fast. What I tried: Switched to free thumbnail URL pattern (img.youtube.com/vi/VIDEO_ID/...) + added strict dish-

name matching rules in the prompt.

Where I'm stuck: Is there a scalable, cost-free way to reliably match a dish name to a YouTube video?

Supabase RLS recursive policy issue

Problem: The proﬁles table RLS policy referenced itself → inﬁnite recursion. On signup, INSERT into households was also blocked because no proﬁle row existed yet (auth.uid() returned null).

What I tried: Created a SECURITY DEFINER function get_user_household_id() to bypass RLS on the proﬁles

lookup. Built create_employer_household() as an atomic RPC to handle both inserts in one transaction.

Where I'm stuck: Is this the right pattern for multi-table bootstrapping in Supabase, or is there a cleaner

approach?

Parsing user-submitted recipe links (Instagram / YouTube)

Goal: Let users paste a recipe link, and have Claude rewrite it to match the helper's cooking level. Problem: Instagram blocks scraping. YouTube requires transcript API or description parsing — unclear how

reliable this is in production.

Where I'm stuck: What's a realistic, production-ready approach to extracting recipe content from social media

links?

Deployed link : https://helperhub-zeta.vercel.app/

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