Show HN: A spreadsheet that follows Ask HN and summarizes the answers https://ift.tt/CqGKxST

Show HN: A spreadsheet that follows Ask HN and summarizes the answers https://ift.tt/7GBaSXe May 29, 2024 at 11:49PM

Show HN: ThreadPay: WeTransfer for money – a medium for money transfers https://ift.tt/MEBoiLh

Show HN: ThreadPay: WeTransfer for money – a medium for money transfers Often cross-border bank transfers default to 3-5 business days, on the other side P2P fintechs need signup, KYC, and other steps. I built an app that eliminates the need to open an account or go through long verifications, allowing users to send money immediately. How? Via Visa and Mastercard card-to-card real-time transfers, money can be sent straight to a card using the card number only, without the requirement for IBAN, Swift Code, or any extra routing data. *Link to MVP*: [ https://app.threadpay.io](https://app.threadpay.io/) *Link to Memo with demo recordings*: https://ift.tt/nCieqE5 *Link to Landing page*: [ https://threadpay.io](https://threadpay.io/) https://ift.tt/s6MqYTI May 29, 2024 at 05:28AM

Show HN: Open-source Tableau alternative but as React component plus DuckDB-WASM https://ift.tt/af1Y0g9

Show HN: Open-source Tableau alternative but as React component plus DuckDB-WASM https://ift.tt/VQYOhRc May 28, 2024 at 11:59PM

Show HN: Awesome CI/CD Attacks https://ift.tt/bnTeFWh

Show HN: Awesome CI/CD Attacks https://ift.tt/24HSNJ8 May 28, 2024 at 10:41PM

Show HN: A New Kind of Chat Room https://ift.tt/Si3JuqK

Show HN: A New Kind of Chat Room I’ve developed an application that reimagines chat rooms by integrating them with a world map. Each user can claim a rectangular piece of land on the map, referred to as a "banner." Users within close proximity are grouped into a chat room by the app. The banner’s size can expand or shrink based on the density of users in the area and the number of coins in the user’s account. Key Features: Real-World Interaction Model: Users are pseudo-anonymous, akin to real-world interactions. Coin balance determines user size and visibility ("stature"), while endorsements and other activities form a unique endorsement chart, serving as a digital representation. This allows for interaction without revealing one’s entire social or professional network. Community Clusters: Users can form open communities based on real-world locations. New users can discover and join these clusters via heatmaps, without needing specific URLs or hashtags. Initial Coins: New users start with 1000 coins. These coins can be used to endorse other users' content, earning stakes in the endorsed banner. When others endorse that banner, the original endorser gains more coins. This endorsement economy is experimental, and could eventually involve crypto tokens. Useful Points: Banner Interactions: Clicking on a banner opens it fully. A button with a chart icon on the top right flips the banner to display the activity chart. Compatibility: The application currently works best on Google Chrome. Heatmap View: Zooming out on the map reveals a heatmap of banners. Side Panel Tabs: First Tab: Displays a feed of content from the visible map area. Second Tab: Contains the cluster chat. Current User Base: At the moment, only my friends and family are using the application, so banners are predominantly found in the Chicago area. For more details, refer to the white paper provided. I’m excited to share this application and look forward to your feedback! https://beescribe.com/ May 28, 2024 at 09:37AM

Show HN: Get paid to do your own ML research https://ift.tt/P9EVGSL

Show HN: Get paid to do your own ML research I'm launching an experimental research grant that I call Cat's grant (I'll find a better name later). tldr: - you get paid to do your own research and report to me - you keep IP/ownership rights - 10 months duration - choose a grant size of $10k, $50k or $100k total (paid in monthly chunks) - Apply by sending an email to not_a_cat@fastmail.com How it works: You specify the grant size when applying: 10k, 50k and 100k. This total amount will be distributed over a period of 10 months. I will review each application within 1 week. The deadline to apply is June 9. The start date is flexible and can be the start of June, July, August or September. The total budget I will allocate to this is around 100-200k I haven't yet made a proper contract reviewed by lawyers. If interest is strong enough, I will do it and try hard to keep the spirit of what is said here. The contract will be under Swiss law. Rationale: I get to meet cool people, promote and follow cool research. You get to do the research you want with little red tape (I'll be the sole decision maker for applications. Paperwork will be done with the help of other people). Application process: You choose the grant size you apply for. You can apply to multiple ones at the same time. Then, you may be accepted for one grant option or rejected for all. The process has 3 stages: - Email application - First screening call - Second call with more in-depth questions If you complete the 3 steps, you are accepted in the grant program. Reporting requirements: You are expected to produce the following: - A weekly email report that can be as short as a single sentence (meant as a pulse that you are still here) - A monthly research update that has to be public, in the format you want (github file, blog post…) - Do a monthly call with me, discussing the monthly research update Payment schedule: One payment of 10% of the total amount will be made at the end of each month while the grant is active. The grant may be canceled if the reporting and effort is insufficient. The bar for this will be reasonably low. Effort and time spent will be considered good enough for keeping the Grant active. Research directions: You are free to decide what topic to research. Reading and studying during the research is considered normal. I will try to be helpful and suggest research directions and ideas. I will only consider applications in the domains of Machine Learning, Deep Learning and AI broadly speaking. With a preference for topics related to the following: - LLMs and Transformer architectures - Mechanistic interpretability - World models - Self play / synthetic data - Probabilistic programming Copyright and IP rights: The research remains your intellectual property. You can use it and commercialise it as if you produced it independently of the grant. Time commitment: You are expected to spend at least 50% of your working time on research related to the grant. You may have other commitments at the same time, as long as you can free up enough time. Selection criteria: - How excited I am about the research you want to do - Whether I believe you can make good progress on it - Intrinsic motivation and strong determination How to apply: Send an email to not_a_cat at fastmail com with the following: - Subject: "Application for Cat's Grant [10k, 50k, 100k]". Only keep the grant size(s) that you actually apply for, eg [50k] or [10k, 50k]. - Info about yourself, please include links to github/linkedin and/or resume - Recent projects/research you've done if any - Outline of the research you want to do as part of the grant. It's ok if you only have a vague idea, but better if you have something specific. It can be new or existing research. - Anything else you think is relevant. Evidence of strong capability is a plus, even if unrelated to ML research. Happy to answer questions or comments. May 27, 2024 at 08:24PM

Show HN: Blue Noise – Interactive Explanation of Void and Cluster Algorithm https://ift.tt/YUndVRq

Show HN: Blue Noise – Interactive Explanation of Void and Cluster Algorithm After reading about the generation of blue noise here on HN a few times my goal was to implement my own variant of the the Cluster and Void algorithm in the most straight-foward way possible, while also visualizing each step. (JavaScript is required in order to step through the algorithm) Most other Blue Noise generator implementations are optimized for speed. Many explanations of the Cluster and Void algorithm I found online were overly complicated or focusing on details that do no help the initial understanding. My implementation is optimized for readability and understanding. I find it very inspiring see an algorithm broken down to its most essential steps. For one in order to better understand the algorithm itself but also for transfering its key concepts to other tasks, for example when designing my own algorithms. Eg in my rather high level python/numpy implementation one can easily understand that the two phases of the algorithm (phase 2 and phase 3) have no data dependency between each other and can therefor be parallelized. Additionally the numpy implementation demonstrates how the application of high level concepts like rank-polymorphism and convolution allow to express a sophisticated algorithm in only a few lines of code. Hope you like it. https://ift.tt/z2D0RfT May 28, 2024 at 03:37AM