KasiMargin—From Sales to Growth

Kholofelo Robyn Rababalela — Sun, 18 Oct 2026 21:40:54 +0000

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

What I Built

Three haircuts can make for a busy day. But a busy day does not automatically tell a barber how much money is available to spend tomorrow.

Imagine two customers pay R50 each in cash, a third pays R50 by card, and the barber spends R20 on supplies.

That is R150 in recorded sales, but it isn't necessarily R150 in available cash. The card payment might not have settled. Rent may still be due. Other business expenses might be missing.

That distinction inspired KasiMargin, a local-first financial-clarity prototype for South African owner-operated microbusinesses.

The idea grows out of a broader question I've been exploring through KasiLink and Kopano Labs: how can small businesses turn the information they already have into better-informed decisions, without needing complicated accounting software?

KasiMargin currently offers two connected experiences:

Record today: Enter paid cash or card sales, record cash spending, view daily totals and correct mistakes without erasing the original record.
Check my money: Review counted cash or cleared balances, outstanding obligations, committed spending and money that needs to remain protected.

The application distinguishes estimated operating margin from conditional cash headroom. When important evidence is missing, the result remains UNKNOWN/HOLD, rather than quietly treating an unknown amount as zero.

Our latest interface improvement also makes the decision explanation visible, connects the two workflows through simple navigation, and displays previously recorded daily totals as a read-only reference. Recorded sales are never automatically promoted into independently verified cash.

Who might pay for this? An owner-operated barber shop, spaza shop, car wash or food business that finds daily financial clarity useful enough to return to. That is our proposed customer, not a proven commercial result. We have not yet completed a real operator trial or demonstrated willingness to pay.

Demo

Watch the 97-second synthetic KasiMargin demonstration

Direct video file

The recording demonstrates actual local browser interaction with fictional barber records: entering sales, recording spending, correcting an amount and inspecting the separate financial-check workflow.

Correcting one R50 cash haircut to R60 changes recorded sales from R150 to R160 while retaining the earlier entry's history.

The video has captions and no audio. It was recorded before the latest interface improvement and demonstrates the earlier deterministic explanation experience. It is not a recording of new live Gemma inference.

Code

Explore KasiMargin on GitHub

The project was created during the October 12–18 challenge window.

The submitted source baseline is main@85d3cc9, with a successful GitHub Actions run.

The latest operator-experience improvements were introduced through PR #8, followed by documentation alignment in PR #9.

The project code is MIT-licensed. Gemma model weights remain subject to their own terms. Development involved AI-assisted coding, and this is a solo project submission.

How I Built It

KasiMargin combines:

Python and Flask for the local service and deterministic financial calculations.
SQLite for revisioned, operator-reported transaction history.
TypeScript for the browser experience.
Google Gemma 4 E2B, accessed through local Ollama, as an optional constrained explanation selector.

An important design decision is that Gemma does not perform the financial arithmetic.

The financial engine computes values using exact integer cents, handles incomplete evidence and produces required warnings. Gemma receives a permitted catalogue of facts, warnings and possible actions, then selects identifiers within that catalogue.

Its selections undergo validation before a trusted renderer produces the final explanation. The model cannot invent new amounts, change ledger entries or authorise purchases. If inference fails, the system uses a clearly labelled deterministic fallback.

What did Gemma actually contribute?

We completed a recorded local evaluation across seven synthetic financial scenarios, with three model calls per scenario.

The results were:

21/21 model selections admitted under the output contract.
18/21 selections identical to the deterministic baseline.
3/21 selections different, all involving an uncollected-credit scenario.

In that scenario, Gemma selected preserve_headroom but omitted another permitted action, compare_restock_quote. Required financial amounts and warnings remained unchanged.

These results demonstrate bounded local inference, not proven improvements in financial understanding.

The recorded warm median inference time was approximately five seconds, while the cold median was approximately 89 seconds. This exposes a real hardware and responsiveness trade-off.

Read the complete Gemma technical evaluation.

Why Does Open Innovation Matter?

A financial-clarity application for a small business should be inspectable, reproducible and careful about the information it is trusted with.

Running an open-weight model locally gives us a way to experiment with explanations without sending the inference payload to a hosted model API. It also lets us compare model behavior against deterministic output, identify failures and question whether the AI genuinely adds value.

There are trade-offs. Local model weights require storage and memory, cold inference can be slow, and an open model does not automatically produce better financial guidance.

For KasiMargin, open innovation matters because it allows us to investigate personalized intelligence while keeping financial calculations, provenance and human decision authority separate.

What I Learned and What Comes Next

The first interface was too technical. It displayed financial terminology and implementation details before answering what the business operator actually needed to understand.

The later design moved toward everyday tasks, visible financial explanations and a more direct connection between recording activity and reviewing money.

But I want to take the product further.

South African microbusinesses operate across different languages, sectors and working conditions. My longer-term direction is to investigate visual-first, accessible and context-aware financial intelligence, with optional language and voice support where those capabilities genuinely help.

That direction is not implemented functionality today.

The next important experiment is a consented operator walkthrough. Can a barber record a sale, correct a mistake and explain the difference between recorded sales and available money without coaching? Does the experience fit their working routine? Does Gemma's explanation improve understanding compared with the deterministic version?

Those are questions for real users, not assumptions we can settle with synthetic tests.

KasiMargin does not currently verify bank balances or card settlement, scan receipts, provide unrestricted financial advice or demonstrate measurable economic impact.

The purpose of this first version is to build a trustworthy foundation for financial understanding, then allow the people who use it to help shape what comes next.

Prize Categories

Best Use of Gemma

KasiMargin includes a locally evaluated Gemma 4 E2B integration with constrained output selection, explicit validation and deterministic fallback. Its incremental benefit to operator comprehension remains unmeasured.

Paws & Potjie — Comfort Food for the Whole Pack 🐾🫕

Kholofelo Robyn Rababalela — Mon, 17 Aug 2026 07:00:11 +0000

This is a submission for the DEV Weekend Challenge: Dog Days Edition.

I built Paws & Potjie, a South African comfort-food experience designed around dogs and their humans sharing the same social ritual — while keeping their food, interaction logic, and needs deliberately separate.🇿🇦🎢💯

Paws & Potjie — A South African Comfort Kitchen for the Whole Pack 🐾🫕

Kholofelo Robyn Rababalela — Mon, 17 Aug 2026 06:50:59 +0000

This is a submission for Frontend Challenge - Comfort Food Edition, Perfect Landing.

What I Built

I built Paws & Potjie, a fictional South African comfort kitchen for dogs and their humans.

The experience is built around the warmth of a potjie and the feeling of a long Sunday meal. Instead of making a static restaurant page, I wanted the landing page to feel alive.

The main interaction is the Comfort Compass: choose the human's mood and the dog's current energy, and the site deterministically pairs a human comfort dish with a separate dog-side bowl.

The frontend uses:

React 19
TypeScript 7
Vite 8
Three.js + React Three Fiber/Drei
Framer Motion
Adaptive PWA behaviour
Offline support
Reduced-motion, Save-Data and WebGL-aware fallbacks

The 3D potjie hero only loads on capable devices. Lower-resource devices receive a lightweight CSS version instead, so immersion does not come at the expense of usability.

Demo

Live browser demo:

https://stackblitz.com/github/RobynAwesome/paws-and-potjie?startScript=stackblitz

Source code:

https://github.com/RobynAwesome/paws-and-potjie

Journey

I wanted the project to feel distinctly South African rather than like another generic restaurant template.

That led to a visual language built around potjie culture, warm maize yellow, veld green, tomato red, cream surfaces, shared meals and dogs being part of the table rather than an afterthought.

The most important technical decision was making the immersive layer adaptive.

The runtime classifies the experience as lite, balanced, or full. A capable device gets the interactive Three.js potjie scene, while constrained devices, Save-Data users, reduced-motion users, or browsers without WebGL keep the lightweight experience.

I'm especially proud that the 3D is not just decoration. Its rendering budget, motion, DPR and fallback behaviour are governed by the user's device and preferences.

The project was built with AI-assisted engineering under my direction, while the concept, product decisions, constraints and final acceptance remained human-directed.

Built in Cape Town, South Africa 🇿🇦

DEV Community: Kholofelo Robyn Rababalela

KasiMargin—From Sales to Growth

What I Built

Demo

Code

How I Built It

What did Gemma actually contribute?

Why Does Open Innovation Matter?

What I Learned and What Comes Next

Prize Categories

Paws & Potjie — Comfort Food for the Whole Pack 🐾🫕

Paws & Potjie — A South African Comfort Kitchen for the Whole Pack 🐾🫕

What I Built

Demo

Journey