Crazierl
Crazierl is an experimental/hobby operating system based around BEAM. I've linked the browser based demo; I don’t recommend using a phone; it does work, slowly, on the phones I tested, but it’s very awkward to use. You can share a link with a hashtag with your friends and click the consent checkbox, and it (should) link up into dist and I’ve also included a chat application you can start with chat:start(). (quit chat with /quit, or use the shell menu with ctrl-g to switch between shells etc). The browser demo relies on the v86 javascript x86 virtual machine. You can also run Crazierl on a real x86 system, but I’ve had mixed luck on modern systems, it uses some esoteric legacy VGA features and support for that isn’t getting better. Crazierl is fairly limited: 32-bit x86, BIOS boot, only two NIC drivers virtio-net and realtek 8168. But it's got enough to become part of an Erlang dist cluster. It also supports SMP, but it’s crashy with high core counts in qemu; there’s almost certainly several concurrency bugs in the kernel. There's also a lot of excess tcp debug spew (sorry). Source code is available (Apache) https://github.com/russor/crazierl/
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I built a free app for New Yorkers to save money on groceries
I built this because I see that grocery savings are achievable in NYC. People usually just go to the store they're used to going to, and it's rarely worth the effort of combing through card cashback, weekly coupons, CPG rebates. Most people leave real money on the table by not stacking them, and even more don't even know that these deals are out there.... so I built a way to automate it. You can use it for free, no login, currently NYC-only with ~690 stores. I built it so that you just search whatever you want (use commas if you want to search multiple items). Or - use the AI tool to help shop for you. If you're curious, it's powered by a trained LLama model. Honest limitations are coverage and freshness. Id love some feedback on where the data looks wrong or is stale. Question for the room - what to prioritize if you're working with messy, multi-source retail/pricing data? Is freshness or coverage the top priority if you cant get a uniform response from every source? curious on what to prioritize here.