GigPitch: I lost 15 logo contests in a row, so I built a local AI that writes my bids

hongpengtan0-max — Thu, 15 Oct 2026 08:06:49 +0000

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

What I Built

Since October 4, I've been trying to earn my first money as a freelance logo designer on 猪八戒 (Zhubajie), China's largest gig marketplace. I entered 15 logo design contests (比稿) and won zero of them. Total earnings: ¥0.

Looking back, the designs weren't the main problem. The problem was everything around them: reading vague briefs, guessing a price, and writing the same bid again and again in Chinese and English, often at 2 a.m. Every losing contest cost me an hour of unpaid admin work.

GigPitch is the tool I wish I'd had from day one. You paste a gig brief from 猪八戒, Upwork, Fiverr, or anywhere else, and about 20 seconds later you get:

Brief analysis: brand, industry, style keywords, whether VI or packaging is needed, urgency, and budget
A quote suggestion: calculated by transparent rules, with every line of the reasoning shown, and a warning when the client's budget is below it
Bilingual bid text in 中文 and English, citing my own most relevant portfolio cases
A deliverables checklist (formats and versions), so scope is clear before the client says yes
Three logo concept prompts to kick off the actual design

Who would pay for it? Solo designers and small studios who bid on many small jobs. Saving 30 to 45 minutes per bid is worth far more than a small monthly fee, and because the tool runs locally, it costs almost nothing to operate.

Demo

Code

hongpengtan0-max / gigpitch

Local open-source AI bid assistant for freelance designers (Mastra + Gemma)

GigPitch 🎯

A local bid assistant for freelance designers. Paste a gig brief from 猪八戒, Upwork, Fiverr, or anywhere else. GigPitch returns:

Brief analysis: brand, industry, style keywords, whether VI is needed, urgency, budget
Quote suggestion: computed by transparent pricing rules (not invented by the model), with the reasoning shown
Bilingual bid text: one Chinese and one English draft, citing your own matched portfolio cases
Deliverables checklist: file formats and versions
Logo prompt ideas: three concepts for an image generator

Everything runs on your own machine: Gemma 3 (open weights) served by Ollama, orchestrated by a Mastra workflow. No API keys, no per-token bills, and client briefs never leave the machine.

Built for the DEV × Hacktoberfest 2026 Open-Source AI Challenge, Week 2 ("Side Hustle").

Architecture

brief ─▶ [Mastra step: analyze]  Gemma 3 → JSON (tolerant parser + rule guards)
      ─▶ [Mastra step:

…

View on GitHub

MIT licensed. Run it with ollama pull gemma3:1b && npm install && npm start.

How I Built It

Model: Gemma 3 (open weights), run locally with Ollama. I used gemma3:1b because my box only had about 3 GB of free RAM, and it runs CPU-only at roughly 44 tokens per second. With more memory, switching to gemma3:4b is a single environment variable.
Orchestration: Mastra. GigPitch is a Mastra createWorkflow with three createSteps around a Mastra Agent backed by Gemma (via ollama-ai-provider-v2):
1. analyze: Gemma extracts structured JSON from the brief.
2. quote: a deterministic pricing function plus retrieval from my portfolio file (cases.md). I deliberately kept money out of the model's hands.
3. write: four Gemma calls in parallel (Chinese bid, English bid, checklist, prompts).
Making a 1B model reliable. Small models return almost-valid JSON (I got a stray curly quote ” that broke JSON.parse) and sometimes miss obvious facts. The fixes:
- a tolerant, field-by-field JSON recovery parser
- rule-based guards for VI, urgency, budget (including $ budgets converted to ¥), and brand names in 「」 or quotes
- prompt rules plus post-filters: no self-praise like "经验丰富" or "experienced", no Chinese characters in the English bid, prices in the right currency, one - [ ] checklist format, no [Client Name] placeholders, and no claims that drafts already exist
UI: one HTML file and a small Express endpoint. npm test runs the whole workflow end to end.

Why Does Open Innovation Matter?

Cost decides whether the business works. My actual revenue so far is ¥0. A side hustle that loses money on API calls before it lands its first client is not a side hustle. With open weights on my own CPU, each extra bid costs nothing.
Client data stays private. Briefs include unreleased brand names and business plans. Nothing leaves the machine, which I can tell clients truthfully.
No lock-in, works across borders. I'm moving between China and the US, and not every closed API works or bills smoothly in both places. Ollama + Gemma + Mastra works the same anywhere and keeps working even if a vendor changes its pricing.
Small models are good enough when the system is designed well. With deterministic pricing and rule guards, a 1B open model handles a real niche task, and I can fine-tune it later on my own winning bids.

Prize Categories

Best Use of Mastra: the whole pipeline is a Mastra workflow (createWorkflow / createStep) around a Mastra Agent on an open model.
Best Use of Gemma: Gemma 3 runs locally through Ollama and handles every language task.

Built with help from an AI coding assistant; I directed the design and tested the outputs.

DEV Community: hongpengtan0-max