Product Catalog

SC

Sci-Trust

Show HN: Sci-Trust – Compare trust among researchers based on their citations

WC

We chased a weather balloon across Montana and never found it

Since April, I have been working with Sam Flynn (https://drook.dev) to make this balloon payload, UpLink. We did a similar launch last year with Hack Club but this was our first independent launch. UpLink was a 491 gram payload testing the insulation properties of 3D printing filaments, while also transmitting 320x240 images over a radio link -- up from the 18x10 images last year! This is a writeup on our engineering process, mistakes made, and learning experiences. It covers: - Custom electronics designed in KiCad - Firmware design - Results from the data we received on the ground - Image transmission - Launch day logistics, and where things went wrong All hardware, software, firmware, and CAD is available on GitHub: https://github.com/radeeyate/UpLink, licensed + certified as open source hardware: https://certification.oshwa.org/us002826.html If you just want to see the images received, I put up a gallery here: https://uplink.gallery.radi8.dev/ If you have any questions, comments, or concerns, let me know. I'm happy to answer anything!

WY

Why You Lost

Show HN: Why You Lost – a Dota 2 post-match autopsy

OS

Open-source Stripe Connect alternative

Hey, I'm Ben. I built Zoneless because I was paying so much to use Stripe Connect on my own marketplace. The fees were really, really bad, and it was also limiting in terms of the seller countries I could onboard. To give you an idea, I was paying around $9,000 per month in fees just to run payouts. Using Zoneless, that cost goes down to around $6. I've been using it personally for the past few months, and onboarded 5,000+ sellers and done 3,000+ payouts. 74% of new sellers on my marketplace choose Zoneless over Stripe, which is really interesting. I appreciate crypto and stablecoins are a bit of a touchy subject, but for this use case of sending global payouts cheaply, it's perfect. The project is open source with an Apache 2.0 licence, which means there's the benefit of no lock-in and no risk of your account getting flagged or shut down. It also has an almost identical API and dashboard to Stripe. Would love to hear any feedback you may have in the comments.

CI

Check if any of the $656M in unclaimed royalties at The MLC is yours

Hi HN, I built this. Quick background on why it exists: When music is streamed on digital streaming platforms (think Spotify, Apple Music, Pandora), there are two separate royalty streams: one for the recording, paid through your distributor (DistroKid, TuneCore, CDBaby), and one for the underlying work (generally known as publishing). The work side's mechanical royalties are collected by The MLC, a nonprofit that was created by the 2018 Music Modernization Act (MMA). If you haven't registered your songs with The MLC, there are issues with your metadata, or about half a dozen other reasons, that money will never reach you, it just collects in a big pile we call the "black box". That pile is big. The MLC's own dashboard currently shows over $656M is held (themlc.com/blanket-royalties), and the biggest problem is the MMA dictates that The MLC cannot hold that money indefinitely. At some point, by law, The MLC must distribute this big pile of cash, and since they don't know who it's supposed to go to, they pay it out through a process called "market share" (themlc.com/marketshare). Market share means they pay it out, pro-rata, to the artists, songwriters and publishers that are in the system, which in practice means the largest publishers collect most of the leftovers. This is slated to begin in January 2027 and will pay portions of the pool out monthly (themlc.com/unclaimed-accrued-royalties), as of this morning, the next 12 months of market share sums up to $76.61M, starting with $6.41M in January. Full disclosure: I run Doubly, which is an independent publishing administrator, so I have a commercial interest in this space. That being said, we're a team of two people and we don't have the bandwidth to directly assist the hundreds of thousands of artists and songwriters who are going to start losing this money in January 2027. That's why I built this self-service tool, no signup, no email, no paywall, the reports are completely actionable without needing anything from us. The tool: paste a Spotify artist link (or search for a Spotify artist by name). It pulls every release on that profile, checks each recording against The MLC's public bulk data, and tells you per-song whether it's fully claimed, partially claimed, registered but unmatched, or missing entirely. It then provides a link to the exact MLC tool that fixes each case. It also estimates the dollars stuck, as a rough range. Even if you're not an artist yourself, you probably know someone who is a musician, please share it with them. Thanks for your time and I'm happy to answer any questions about the industry as a whole, The MLC, the tech, anything really.

IT

I trained a 125M model to autocomplete piano on-device

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.

FT

Frugal Tokens

I wanted to share a project I’ve been working on called Frugal Tokens. I originally built it because I was curious to see how much all of my sessions cost and how much cache misses affected that spend. I’d noticed people had widely different spend profiles and wanted to better understand what might contribute to that. As I’ve worked on this, the tool has grown to show more usage patterns across all of your sessions. It shows overall usage, estimated working time and overlapping sessions, and where your spend is coming from across models and cache misses. I also have a few session level metrics with percentile breakdowns, along with a list of your sessions and high level info. Clicking into a session opens an explorer where you can see individual model calls and tool inputs and outputs. You can also jump directly to where a cache miss happened. There’s also a rough cost comparison that shows what the recorded session would have cost with another model’s pricing, or for Anthropic, with 5m vs 1h caching. In the future, I’d love to collect more information to see which patterns might make people’s workflows more expensive, e.g. long sessions, high context usage, many turns, etc. The tool requires deno, but is just one command to run once that is installed. The demo provided has some of the data scrubbed, but helps to show what it looks like before running it. Would appreciate any thoughts or ideas https://github.com/dpclark4/frugal-tokens

PF

Privacy focused client side password generator

Show HN: Privacy focused client side password generator

TE

Techdirt

FOIA Documents Show Epstein Files Had Flag List Which Included the Term 'POTUS'

PA

PantheonGPU

Hi HN, I built PantheonGPU because I wanted a better way to answer a simple question: is this GPU actually healthy and performing the way it should? A GPU can show normal temperatures and utilization and still be underperforming, unstable under certain workloads, or have memory, PCIe, or configuration issues. PantheonGPU actively tests the GPU instead of only monitoring telemetry. It currently includes 45+ tests covering compute, tensor workloads, memory, cache, PCIe, thermals, stability, and AI/LLM inference. It supports both NVIDIA CUDA and AMD ROCm. I’m also exploring a larger use case: running Pantheon across GPU fleets to identify individual GPUs that behave differently from the rest of a server or cluster. I’d especially appreciate feedback from people running AI infrastructure, multi-GPU systems, local LLMs, or GPU clouds.

AS

A sample dataset of computer-use tasks on professional software

Show HN: A sample dataset of computer-use tasks on professional software

OX

Oxide

Show HN: Oxide – the back end (un)framework that won't get in your way