Show HN: Torii – a framework agnostic authentication library for Rust https://ift.tt/b0PxB15

Show HN: Torii – a framework agnostic authentication library for Rust https://ift.tt/4pKZVt2 March 1, 2025 at 04:46AM

Show HN: Find out if you qualify for an O-1 visa https://ift.tt/salSghL

Show HN: Find out if you qualify for an O-1 visa https://o1pathways.com/ March 1, 2025 at 03:49AM

Show HN: Betting game puzzle (Hamming neighbor sum in linear time) https://ift.tt/jOY0By7

Show HN: Betting game puzzle (Hamming neighbor sum in linear time) In Spain, there's a betting game called La Quiniela: https://ift.tt/jraveKc Players predict the outcome of 14 football matches (home win, draw, away win). You win money if you get at least 10 correct, and the prize amount depends on the number of winners. Since all bets are public, the number of winners and the corresponding payouts can be estimated for each of the 3^14 possible outcomes. We can also estimate their probabilities using bookmaker odds, allowing us to compute the expected value for each prediction. As a side project, I wanted to analyze this, but ran into a computational bottleneck: to evaluate a prediction, I had to sum the values of all its Hamming neighbors up to distance 4. That’s nearly 20,000 neighbors per prediction (1+28+364+2912+16016=19321): S_naive = sum from k=0 to r of [(d! / ((d-k)! * k!)) * (q-1)^k] (d=14, q=3, r=4) This took days to run in my first implementation. Optimizing and doing it with matrices brought it down to 20 minutes—still too slow (im running it in GAS with 6 minutes limit). For a while, I used a heuristic: start from a random prediction, check its 28 nearest neighbors, move to the highest-value one, and repeat until no improvement is possible within distance 3. It worked surprisingly well. But I kept thinking about how to solve the problem properly. Eventually, I realized that partial sums could be accumulated efficiently by exploiting overlaps: if two predictions A and B share neighbors, their shared neighbors can be computed once and reused. This is achieved through a basic transformation that I implemented using reshape, roll, and flatten (it is probably not the most efficient implementation but it is the clearest), which realigns the matrix by applying an offset in dimension i. This transformation has two key properties that enable reducing the number of summations from 19,321 to just 101: - T(T(space, d1), d2) = T(T(space, d2), d1) - T(space1, d) + T(space2, d) = T(space1+space2, d) Number of sums would be the result of this expression: S_PSA = 1 + (d - (r-1)/2) * r * (q-1) I've generalized the algorithm for any number of dimensions, elements per dimension, and summation radius. The implementation is in pure NumPy. I have uploaded the code to colab, github and an explanation in my blog. Apparently, this falls under Hamming neighbor summation, but I haven't found similar approaches elsewhere (maybe I'm searching poorly). If you know or you've worked on something similar, I'd love to hear your thoughts! colab: https://ift.tt/mfUvczN... github: https://ift.tt/ax5XhwC blog: https://ift.tt/xkrJKih... March 1, 2025 at 02:03AM

Show HN: News-briefing-generator – Local LLM-powered news digest https://ift.tt/G4g3oNV

Show HN: News-briefing-generator – Local LLM-powered news digest Hey HN, I created a tool to generate personalized news briefings from RSS/Atom feeds using local LLMs through Ollama. It currently works in two modes: fully automatic or with an interactive review where you can select which "main topics of the day" to include in your briefing. The result is a HTML document with summaries for each topic. https://ift.tt/pabim2V February 28, 2025 at 10:45PM

Show HN: Superglue – open source API connector that writes its own code https://ift.tt/QW5v4CP

Show HN: Superglue – open source API connector that writes its own code Hi HN, we’re Stefan and Adina, and we’re building superglue ( https://superglue.cloud ). superglue allows you to connect to any API/data source and get the data you want in the format you need. It’s an open-source proxy server which sits between you and your target APIs. Thus, you can easily deploy it into your own infra. If you’re spending a lot of time writing code connecting to weird APIs, fumbling with custom fields in foreign language ERPs, mapping JSONs, extracting data from compressed CSVs sitting on FTP servers, and making sure your integrations don’t break when something unexpected comes through, superglue might be for you. Here's how it works: You define your desired data schema and provide basic instructions about an API endpoint (like "get all issues from Jira"). superglue then does the following: - Automatically generates the API configuration by analyzing API docs. - Handles pagination, authentication, and error retries. - Transforms response data into the exact schema you want using JSONata expressions. - Validates that all data coming through follows that schema, and fixes transformations when they break. We built this after noticing how much of our team's time was spent building and maintaining data integration code. Our approach is a bit different to other solutions out there because we (1) use LLMs to generate mapping code, so you can basically build your own universal API with the exact fields that you need, and (2) validate that what you get is what you’re supposed to get, with the ability to “self-heal” if anything goes wrong. You can run superglue yourself ( https://ift.tt/juqd9r7 - license is GPL), or you can use our hosted version ( https://ift.tt/alcYXPg ) and our TS SDK (npm i @superglue/client). Here’s a quick demo: https://www.youtube.com/watch?v=A1gv6P-fas4 You can also try out Jira and Shopify demos on our website ( https://superglue.cloud ) Excited to share superglue with everyone here—it's early so you'll probably find bugs, but we'd love to get your thoughts and see if others find this approach useful! https://ift.tt/juqd9r7 February 27, 2025 at 10:50PM

Show HN: LLM plays Pokémon (open sourced) https://ift.tt/y92WcPS

Show HN: LLM plays Pokémon (open sourced) I built a bot that plays Pokémon FireRed. It can explore, battle, and respond to game events. Farthest I made it was Viridian Forest. I paused development a couple months ago, but given the launch of ClaudePlaysPokemon, decided to open source! https://ift.tt/vhSOKJw February 27, 2025 at 01:01AM

Show HN: Real estate market insights map https://ift.tt/uqOUI0a

Show HN: Real estate market insights map https://ift.tt/KrCT3So February 26, 2025 at 11:28PM