OweMyGod：微調 8B 模型解讀雅加達欠款藉口，準確率達 92%（關鍵字規則：60%）

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

In Indonesia there's a saying: yang ngutang lebih galak. The person who borrowed the money gets more offended than the person who lent it.

A friend of mine—let's call him Reza—is generous enough to spot people cash, but he absolutely hates the cognitive load of chasing it. The last time he manually followed up on a debt, the guy got defensive, and Reza spent more mental energy agonizing over his next reply than the money was actually worth.

So I built OweMyGod, a sloth-paced debt reminder for friends who'll pay you back… eventually.

• The home screen looks like a chat list: who owes you, how much, and who is due today (bold, like an unread chat).
• When a friend replies, you log what they said. "Abis gajian ya" (after payday) and the app waits until the 25th. "Besok gabisa, jumat aja" (can't tomorrow, Friday then) and it waits for Friday.
• On the day, it drafts a reminder in the right tone (gentle, then playful, then firm) that refers back to their own excuse: "udah gajian kan? 😄". You tap Open in WhatsApp and press send.
• One push notification at 09:00, only when someone is due or promised to pay today.
• It never sends two reminders within 3 days, and after three firm ones with no answer it tells you to talk in person and stops. Slow and steady keeps the friendship.

The lazy payoff: Reza put in the three debts he's carrying right now. His verdict: "bagus, biar kita bisa diingetin, ada notifikasinya" ("nice, now we get reminded, there's a notification"). The first thing he noticed was the money: he typed 1jt, logged a 50k partial payment, and it showed Rp950k left, written the way people here actually write amounts. The second was the replies: he copies what his friend said on WhatsApp into the app, and the next reminder is tailored to it. What amazed him most was "abis gajian": the app knew that "after payday" means near the end of the month and waited until payday (the 25th by default, adjustable in Settings) instead of nagging the next day. And for once there's a record of who paid how much, and what they promised when.

Demo

https://owemygod.onrender.com → Try the demo gives you a private sandbox with four fictional friends in every state. Turn on notifications in Settings and press Send a test notification to feel the 09:00 nudge.

Code

OweMyGod

Chasing friend debts slowly, which is the only way to stay friends.

A small web app for the friend who always lends and never gets paid back. It keeps a list of who owes you, reads their replies ("after payday ya", "besok gabisa, jumat aja"), drafts a gentle-to-firm WhatsApp reminder at the right moment, and nudges you once at 09:00 when someone is due.

Built for the DEV × Hacktoberfest 2026 Open-Source AI Challenge, week 3: Slow and Steady.

Live: owemygod.onrender.com (tap Try the demo for a sandbox with fictional friends)

Who it's for

People in Jakarta who lend money to friends and hate chasing it. In Indonesia there is even a saying for it yang ngutang lebih galak, the borrower gets more offended than the lender. OweMyGod does the remembering and the awkward wording; you only press send.

• A chat-list home: who owes you, how…

How I Built It

The model translates, code counts

The tempting design is "LLM, here's the excuse, what date should I remind them?" Language models are bad at calendars. So the model never outputs a date. It outputs a kind of time:

{"kind": "promise", "when": {"type": "payday"}}

There are seven kinds: tomorrow-ish, a weekday, next week, a day of the month, payday, start or end of the month. Plain Python turns that into a date in your time zone (payday = the 25th, or whatever you set), and a small schedule engine decides the next reminder. The model reads Jakarta slang; code does arithmetic.

Fine-tuning on Tinker, and what RL taught me

The reader is Qwen3-8B with a LoRA adapter trained on Tinker, on 2,000 synthetic replies in English–Indonesian code-mix ("literally", "which is", "deh", "bsk", typos, emoji). Each reply is generated from its label, so labels are never guessed by another model.

Round 1 humbled me. My keyword-rules baseline scored 91% on my own synthetic test, because I wrote both. So I asked a friend to write 50 replies the way they'd really text. On those, rules got 50% and my first fine-tune 80%, but it still reminded too early 14% of the time. Almost every miss was the same: "tgl 28 ya pas payday" → the model said "payday" (the 25th) instead of the date the friend actually named.

Round 2 fixed the generator for what that dev set exposed (an explicit date beats payday, pertengahan bulan is the 15th, tar malam is today) and retrained. Then we wrote 25 brand-new replies and ran them once.

RL found nothing to fix, and I'm keeping that in the story. I'd planned reinforcement learning with a lopsided reward (exact +1, late −1, too early −5), because a too-early reminder is the one that costs a friendship. It ran twice, and both times the fine-tuned model answered every training reply identically and correctly, so there was no difference to learn from (trained_on=0). The honest lesson: once the data covered the hard patterns, supervised fine-tuning was enough. Total Tinker spend: about $1.52.

Results (25 hand-written replies, never seen in training, evaluated once)

Both remaining misses are one day early, and both are genuinely tricky: "tgl 25 pas payday ya! eh tgl 25 minggu kan ya? senin deh" (it kept the 25th, not the corrected Monday) and "akhir bulan ya bro tgl 28/29" (it picked the 28th). That's why the app always shows how it read a reply and lets you fix the date in one tap.

The app

FastAPI, server-rendered HTML and a tiny bit of JavaScript, SQLite on a Render disk, and Web Push (one standard for Android Chrome and iPhone home-screen apps on iOS 16.4+). The design is a buku kasbon, the debt notebook every Indonesian corner shop keeps: the history is a stack of paper slips, replies are in handwriting, and paid debts get a LUNAS stamp.

If the model is down, keyword rules read replies and templates write reminders, and the screen says so.

Why Open Matters

• I own the weights. The adapter is mine. If Tinker's endpoint changes, or I want it on my own laptop, I can move it. For an app that holds friends' money troubles, I don't want to depend on someone else's model staying the same.
• A small, specialised model is enough. Reading "abis gajian ya" doesn't need a frontier model. An 8B model trained for this one job beats keyword rules by 32 points on real replies, and costs a fraction of a cent per reply.
• Privacy by design, honestly stated. Names, phone numbers and amounts never go to any model; they stay in the app's database. The writer works with {name} and {amount} placeholders. The text of a logged reply is sent to Tinker's inference endpoint to be read; that's the trade-off, and with open weights I can remove it later by hosting the model myself.
• I could measure and fix it myself. With open weights and Tinker's training loop I found a specific failure ("payday" beating the date they named), fixed the data and retrained in an afternoon, for about a dollar.

Prize Categories

• Best Use of Tinker: LoRA SFT of Qwen3-8B in two rounds driven by a hand-written dev set, an RL loop with a custom asymmetric reward (and an honest account of why it had nothing to learn), and the shipped adapter served through Tinker's OpenAI-compatible endpoint; rules vs. base vs. v1 vs. v2 on a clean hand-written test.
• Best Use of Render: a Blueprint (render.yaml) with a Starter web service, a persistent disk for SQLite, an in-process daily scheduler that sends the 09:00 pushes, secrets kept out of the repo, and a /healthz that reports whether the model is reachable.

Team: solo. I built this with Claude Code. That tool is not open source, and I don't count it as the open-source AI in this project: the model that reads the replies is Qwen3-8B, fine-tuned on Tinker.