Show HN: Schedual https://ift.tt/lhaw4GD

Show HN: Schedual No nonsense tasks. https://schedual.app/ March 2, 2025 at 01:10AM

Show HN: Open-source tool that send UI feedback with context https://ift.tt/cHnwMvd

Show HN: Open-source tool that send UI feedback with context https://ift.tt/Sv5aOm8 March 2, 2025 at 01:11AM

Show HN: I built an app to convert ChatGPT Deep Research to PDFs with footnotes https://ift.tt/0tbh1JH

Show HN: I built an app to convert ChatGPT Deep Research to PDFs with footnotes Whilst ChatGPT Deep Research is very useful for generating in-depth reports, it's time consuming to copy, reformat the text (thousands of words) and clean referenced hyperlinks for use in a professional context. Out of frustration, I built deep research docs to help save time by automating the reformatting, cleaning links, footnote references, and conversion to shareable PDF format. Hopefully this helps you save time to focus on meaningful work. Let me know your feedback. https://ift.tt/WD7l143 March 1, 2025 at 06:22PM

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