Splitly – AI Expense Tracker

Hacker News

I’ve been working on an AI agent called Splitly, designed to help people manage personal finances directly through chat. Splitly can record spending, bills, and borrowed or lent money. Instead of just typing, users can also upload bill photos or send audio messages, and the agent extracts and logs the details automatically. It then provides summaries, balance sheets, and recommendations such as highlighting overspending, or suggesting limits. Splitly lives inside NexChat, a messaging app we’re building where people can connect not only with other users but also with AI agents. Users can interact with Splitly privately as their personal finance assistant, or they can invite it into a one-to-one chat so that two people can collaborate with the agent in real time. Beyond Splitly, anyone will be able to build and submit agents to NexChat, making it an open ecosystem where agents can become active participants in conversations. The broader goal is to treat AI agents as first-class chat participants, useful not just for individual productivity but also in shared, human-to-human contexts. I’d appreciate feedback from the HN community on both Splitly and the idea of an open agent ecosystem in chat.

Visit product View on Hacker NewsSep 19, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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TrustMRRFits verified-revenue profile · Strong signals: personal, users · Missing: mobile apps, ios, entrepreneurs

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, real time, finances · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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