Wednesday, May 29, 2024

Tuesday, May 28, 2024

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

Show HN: Meal planning – without the mental load https://ift.tt/3M7YuAa

Show HN: Meal planning – without the mental load TLDR; I applied the concept of "don't make me think" to the task of selecting meals, and assembling the shopping list for your grocery run. The basic idea for what I wanted is very simple: Rather than making a shopping list, I wanted to create a re-usable 'meal', with a list of ingredients I'd need to add to my shopping list to make that meal. Then, after selecting meals for the week I'd have a quick 'check' step, where I'm prompted to check the cupboard for each ingredient, before it's added to the shopping list (ie: I'll need ground beef to make tacos, but I already HAVE ground beef in the freezer). I originally built this out just for myself, but the result has been such a helpful and stress-free experience that I thought others might appreciate it as well. I think this tool could be well-suited for younger folks that are new to the labour of meal-planning. College students, newlyweds, and families with young children. You can try it out without needing to register or provide any personal information, and I'd love your feedback! https://supperstock.ca/ May 28, 2024 at 12:34AM

Monday, May 27, 2024

Show HN: I've Created the First Artificial Memory (and It's Open-Source) https://ift.tt/4UCoIdR

Show HN: I've Created the First Artificial Memory (and It's Open-Source) https://ift.tt/71bIG2h May 27, 2024 at 05:54AM

Show HN: FlashText with Rust for Python https://ift.tt/yE4Jc9a

Show HN: FlashText with Rust for Python LeNLP is a toolbox dedicated to NLP, made with Rust, dedicated to Python https://ift.tt/APZ6f7Q May 27, 2024 at 03:09AM

Show HN: Anti-Cluely – Detect virtual devices and cheating tools on exam systems https://ift.tt/onuTQWR

Show HN: Anti-Cluely – Detect virtual devices and cheating tools on exam systems Anti-Cluely is a lightweight tool designed to detect common...