This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

If you’ve ever run a home food business—whether baking custom cakes, prepping weekly tiffin boxes, or running a late-night cloud kitchen—you know the Margin Trap:

• Over-prep by 15 portions, and your entire profit margin for the day goes straight into the trash as spoiled ingredients.
• Under-prep by 15 portions, and you turn away hungry customers while leaving guaranteed money on the table.

Most home food entrepreneurs make daily prep decisions based on pure gut feeling. They don't have access to enterprise demand planning software, nor do they have time to manually analyze weather forecasts, mid-month wallet slumps, or temperature spikes.

CraveCast changes that. It is a specialized, open-source AI operational co-pilot built specifically for micro food side-hustles.

Local Inputs
│
▼
┌──────────────────┐
│ TabPFN Engine │ ──► Zero-Shot Portion Demand & Waste Risk %
└──────────────────┘
│
▼
┌──────────────────┐
│ Dish Procurement │ ──► Dynamic Grocery Matrix (Flour, Butter, Meat, Boxes)
└──────────────────┘
│
▼
┌──────────────────┐
│ Gemma Synth │ ──► Pre-Order Broadcast (1-Click WhatsApp Launch)
└──────────────────┘

What CraveCast Actually Does:

1. Predicts Exact Prep Batches: Uses a zero-shot tabular AI model to calculate optimal portion targets based on hyper-local operational signals (weekday vs. weekend, ambient heat, rain forecasts, and payday cycles).
2. Explains the "Why" (TabPFN Decision Matrix): Breaks down the reasoning into clear drivers (e.g., +15 Weekend Surge, -8 Rain Impact, +10 Payday Boost) so the cook understands the logic.
3. Generates a Dish-Specific Grocery Matrix: Type any custom dish—like "Belgian Fudge Chocolate Cake", "Steamed Beef Momos", or "Spicy Szechuan Hotpot"—and CraveCast dynamically calculates the raw material shopping list down to kilograms, liters, and packaging units.
4. Locks in Pre-Orders Before Cooking (Gemma Synthesis): Drafts an optimized pre-order campaign with a direct 1-click WhatsApp launch button so sellers capture upfront orders before buying ingredients.
5. Real-Time Profit Guard: Detects when cost per portion exceeds selling price and warns the user before they lock in a deficit.

Demo

🔗 Live Application: https://crave-cast-seven.vercel.app/

Try It Yourself in 30 Seconds:

1. Open the live link and pick your local currency (PKR ₨, USD $, EUR €, AED 🇦🇪, etc.).
2. In the Dish / Menu Item field, test different items like "Belgian Chocolate Cake" vs. "Steamed Beef Momos".
3. Toggle the weather forecast to Rainy or temperature to 35°C.
4. Click Run CraveCast Agent ✨.
5. Watch the dashboard calculate portion counts, swap ingredient matrices, highlight spoilage risk, and generate a WhatsApp broadcast caption ready to send!

Code

How I Built It

I designed CraveCast around a fast, modern full-stack TypeScript stack styled in a custom Dark Chocolate & Cream aesthetic (#1C100D background with warm terracotta accents):

1. TabPFN (Tabular Demand Engine)

Food demand forecasting isn't an NLP problem—it's a tabular pattern matching problem. Standard LLMs struggle with precise numerical reasoning across multi-variable tables. I used TabPFN as a zero-shot tabular engine inside the backend route (src/app/api/forecast/route.ts). It evaluates environmental features in milliseconds without needing an expensive, heavy training pipeline.

2. Gemma (Pre-Order Broadcast Synthesis)

Once TabPFN outputs the recommended portion count, Gemma formats that numerical state into compelling, action-oriented pre-order copy designed for social broadcasts (Instagram stories & WhatsApp status updates).

3. Sentry Agent Telemetry & Instrumentation

AI pipelines fail silently if you don't trace them properly. I instrumented the backend with Sentry startSpan tracking across every step:

• agent.pipeline (Parent execution wrapper)
• ai.tabpfn (Tabular demand & decision matrix calculation)
• ai.gemma (Social caption synthesis)

Every execution passes back a real-time sentryTraceId to verify pipeline health and monitor response latency directly on the dashboard.

4. Next.js 16 & Tailwind v4

Built using Next.js 16 (App Router + Turbopack) for fast server route evaluation, paired with Tailwind CSS v4 and Lucide React icons for a responsive mobile layout.

Why Does Open Innovation Matter?

When building tools for micro-entrepreneurs, software cost is everything.

A solo home baker making $150 a week cannot afford a $49/month SaaS subscription or pay-per-token API bills that spike unexpectedly. Open-source models like TabPFN and open-weight models like Gemma democratize enterprise-grade intelligence.

Open innovation lets us embed sophisticated prediction logic directly into lightweight web apps that any small business owner can use for free—keeping privacy intact and operating costs near zero.

My Agent Session

I built CraveCast through interactive human-AI pair programming inside Cursor IDE. Using iterative terminal agent runs, we resolved PostCSS configuration setups for Tailwind v4, constructed dynamic ingredient mapping logic for custom dish inputs, and linked Sentry spans across the backend route.

My First User

I put CraveCast to the test with a friend running a home-based bakery side business.

The Before:

On hot mid-month weekdays, she used to bake a standard batch of 50 portions based on habit. On slow days (35°C heat + mid-month wallet slumps), up to 15 portions remained unsold, burning over Rs 8,000 ($30+) in wasted butter, cream, and packaging every single week.

The Test Run:

We plugged her parameters into CraveCast. The TabPFN matrix flagged a high spoilage risk day (35% risk index) and recommended cutting the prep batch down to 40 portions. She clicked Launch WhatsApp, blasted the Gemma-generated pre-order text to her customer broadcast list, and locked in 32 pre-orders before turning on the oven.

Result? Zero wasted inventory for the day and protected profit margins!

Prize Categories

• Primary Track: Hacktoberfest Week 2 — Side Hustle
• TabPFN Track: Best use of TabPFN for zero-shot tabular demand prediction & waste risk evaluation
• Sentry Track: Full multi-span agent pipeline instrumentation and telemetry tracing (agent.pipeline → ai.tabpfn → ai.gemma)