Billpal
Billpal is an AI bookkeeping assistant that collects invoices and receipts, extracts clean data, and prepares exports for your accountant.
Billpal launched on May 8, 2026 with 3 votes, #3,449 of 20,538 launches that month and more than 50% of that year's launches.
What it does
Billpal is an AI bookkeeping assistant that collects invoices from your email and automatically matches them to your bank transactions so you never have to do accounting again.
Billpal is an AI bookkeeping assistant that collects invoices from your email and automatically matches them to your bank transactions. Billpal takes the most annoying part of bookkeeping off your plate. Because you’ve got a business to run, not spreadsheets to fill. Billpal grabs your invoices and receipts, extracts clean data, and keeps everything ready to export. Simply click export and ship it off to your tax guy. Connect your inbox and send receipts over WhatsApp so invoices and receipts land in one clean workspace. Billpal automatically downloads invoice PDFs from your inbox so you never have to dig through emails again. Got a paper receipt? Just snap a photo and send it over to…from billpal.io
Does a similar job
all alternatives →
Agent Mode by Receiptor AIJun 2026 · receiptor.ai · ▲515Bookkeeping assistant that runs receipt workflows end-to-end
BillBuddy – Automated Invoice Processing2017 · billbuddy.io · ▲39Made with Softr, the easiest way to turn your data into internal tools and client portals. No coding required.




Source: Product Hunt launch ↗
Launched alongside, May 2026
the whole month →

Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai



Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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