Pennyflo
Contract-to-invoice in seconds & get paid faster
What it does
Automating the contract-to-invoice workflow and chase the invoices to get-paid faster using AI agents.
Does a similar job
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- IBI built a tool that turns every issue in the project to a tiny-invoice2024 · sectar.co · ▲10
Does this make sense? So, here’s the context: I ran a product design agency for about 3 years and grew it to 6 figures. For those 3 years, we never got the invoice cleared on time. We would chat with the client regularly, 2-3 times a week on average, get some work approved to move on. But, when it came to the invoice, it never got paid on time. So, I thought, how about we turn each task into an invoice that gets auto paid on approval from the client. Like an escrow, but on a much granular-task level. This ensures work is paid on time regularly, and there’s no need to raise invoices, follow…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 28d ago · cactuscompute.com
Launched alongside, April 2025
the whole month →- IB
Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
Dev tools · 2025 · runpyxl.com
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Life & fun · 2025 · filiph.github.io
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https://the-pocket.github.io/Tutorial-Codebase-Knowledge/
AI · 2025 · github.com
