FeeDings! RSS, BenchCalc & Influence
UK-based software company building apps
What it does
UK software studio behind three apps: FeeDings RSS reader for news, podcasts & YouTube — BenchCalc, the lab calculator for scientists — and Influence, AI-powered choose-your-own-adventure stories.
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- EAExtract an RSS feed from almost anything2023 · rssfeedasap.com · ▲151
Howdy! RSSfeedASAP scratches my own itch. I run a regional podcasting directory which gets dozens of messy submission for podcasts. Often they don't even include an xml file and me being a good samaritan I sometimes do the manual work and find it myself. I got tired of that manual work and decided to build a microapp. RSSfeedASAP is this app and I decided to release it in case someone else finds any use in it.
- RBRSS Brain2022 · rssbrain.com · ▲139
I wrote an RSS reader called RSS Brain recently. The motivate is the current RSS readers either don't sort articles by priority, which makes it hard to read posts from HackerNews and Reddit, or sort it with some "smart" algorithm which I don't trust. I also like the Google News feature that can show related story from different source, but the sources are not configurable and the algorithm is not transparent either. So with RSS Brain, I implemented these features: * Recommend related articles from the feeds of your choice. It's backed by ElasticSearch and the algorithm is described on RSS…
- RURssnix – Unix-style filesystem-based RSS/Atom/JSON Feed fetcher/reader2022 · github.com · ▲101
- IVI've Implemented a RSS Reader2024 · play.google.com · ▲73
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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 · 27d ago · cactuscompute.com
Launched alongside, March 2026
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Switch from ChatGPT to Claude with import memory feature
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