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SplitMyExpenses – Modern bill splitting app w/AI + Bank/CC connections

Hacker News

SplitMyExpenses – Modern bill splitting app w/AI + Bank/CC connections

Hey HN I'm Brad, the creator of SplitMyExpenses (SME)! I've always dreaded splitting expenses with friends after meals, so I'd usually do it as a batch process after a few weeks. I always dreamed of a tool that fit that workflow seamlessly and couldn't find it, so I did the thing most software tinkerers do, build your own tool! It has evolved substantially over the past few years, from a side project that almost never made it out the graveyard, to a polished web app with powerful API integrations and even AI! Splitting expenses sounded simple up front, but a few challenges worth calling out: - Import expenses via file uploads (.csv, .excel, .txt) 500 different styles and patterns/behaviors (column names, $ positive negative, date styles, etc.) across different financial institutions <-- a massive project within itself! - Money split logic: I wanted to ensure you get all your money back and dividing payments "evenly" turns out to be uneven! I had to pull in a frontend and backend library to do calculations on this (IE: $5 divided by 3 people is: $1.67, $1.67, $1.66) - Charging on payment services: Venmo, CashApp, PayPal, and Zelle all have their own URL/deeplink formats to link into and some don't even allow you to! A big of digging / reverse engineering here to figure this out, but mashing it all together so you don't have to saves a ton of time! - Split logic: Dependent state where all bill splits need to be calculated "together". If you have $10 bill, split between 4 people it will be $2.50 each. Sweet! But if you add one more person, that will change the previously saved splits per person, so you have to save the splits all together. ^ I originally built this as a save per split, but this did not scale well at all and didn't make sense A few cool callouts: - Clean up your expense names automatically: AI summarization / extraction of useful text describing your expenses as you do a file import - Receipt itemization: Using Amazon's fantastic receipt APIs, can extract line items and subtotal + tax + tip, so no more typing that all out - Charts for your effective spending: see how much you are spending AFTER you split your expenses Tech stack: Laravel (PHP), Vue.js, and Inertia.js I'm super pumped to get it out the door last week during my Product Hunt launch, excited to share it with the technical folks on HN who can appreciate some of the challenges here :) Let me know if you have any feedback! Any and all comments are welcome

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: using, apis · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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