Sunday, 30 April 2023

Saturday, 29 April 2023

New top story on Hacker News: Tell HN: Cloudflare verification is breaking the internet

New top story on Hacker News: Tell HN: Cloudflare verification is breaking the internet

on 4/29/2023 12:28:00 am 0 Comment
Tell HN: Cloudflare verification is breaking the internet
152 by statquontrarian | 101 comments on Hacker News.
Across many different pages including science journals, ChatGPT, and many others, CloudFlare verification goes into an infinite loop of: 1. "Verify you are a human" 2. Check the box or perform some other type of rain dance 3. "Please stand by, while we are checking your browser..." 4. Repeat step 1 I'm on Fedora Linux 37 using Firefox 110. The workaround is to use Chrome. After experiencing this dozens of times and getting annoyed of needing to use Chrome, I finally went and deleted all my cookies and cache which I had been dreading to do. It did not help. I don't have a CloudFlare account so I wrote up a detailed post on their community forums. I offered a HAR file and was willing to do diagnostics. It received no responses and it was auto-closed. It's unacceptable that CloudFlare is breaking the internet while offering no community support. Edit: I'm in Texas. I'm not using a VPN or Tor, just AT&T Fiber. I don't have ad-blockers. No weird extensions. Nothing special (besides being on Linux). Edit2: Since this got traction, I opened a new community post: https://ift.tt/Vbgioqe To be clear, I'm not against CloudFlare doing DDoS protection, etc., but it can't be breaking the internet while ignoring community posts on it . Edit3: The CloudFlare team has engaged. Thank you HN!

Friday, 28 April 2023

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Wednesday, 26 April 2023

Tuesday, 25 April 2023

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Sunday, 9 April 2023

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Friday, 7 April 2023

New top story on Hacker News: Show HN: Machato — A native macOS client for ChatGPT

New top story on Hacker News: Show HN: Machato — A native macOS client for ChatGPT

on 4/07/2023 03:28:00 am 0 Comment
Show HN: Machato — A native macOS client for ChatGPT
25 by fliife | 21 comments on Hacker News.
Machato is a tiny ChatGPT client that supports all basic features, and more ! I was frustrated with other native implementations that focused on quick access but didn't give easy access to a history of what has been said. Since ChatGPT is a learning tool for me, I'm always ruffling through past conversations. The client supports markdown rendering as well as LaTeX. Feel free to try it out ! For those who see this thread early, you can use the promo code EARLY_BIRD to get a free lifetime license. Let me know if some aspects can be improved or if there are features you'd like to see implemented in native clients. EDIT: Since the early bird discount has expired before this post could reach HN, I'll leave you with 50% off with the code MACHATO50 !

New top story on Hacker News: Show HN: Sym, define just-in-time access workflows in code

New top story on Hacker News: Show HN: Sym, define just-in-time access workflows in code

on 4/07/2023 12:28:00 am 0 Comment
Show HN: Sym, define just-in-time access workflows in code
27 by abuggia | 3 comments on Hacker News.
Hello HN, My cofounder (jon918) and I started Sym three years ago because we were frustrated with how hard it was to manage access to cloud infrastructure. We wanted to build a tool for JIT access that was actually designed for developers. We were wary of tools that tried to accommodate both devs and IT but ended up with usability compromises for both. First, we figured no one wants another web app to log into so we let administrators define access workflows in Terraform and let developers request and gain access via Slack. That seemed to pay off: being code-based was a big plus for our early customers since it let them manage the logic in version control and test in CI/CD. Second, we knew that updating permissions/roles/access was a major source of toil and risk in the world of cloud infrastructure. Have you ever tried to avoid annoying, persistent access requests by setting policies that are a bit more permissive than you’d like? We felt that fully automated just-in-time access + approvals could really help here. But we also knew that a simple approval tool could end up leading to request fatigue - kind of defeating the purpose. So we built an SDK to let you define checks in code (e.g. pagerduty.on_call, okta.is_user_in_group, github.get_repo_collaborators) in order to dynamically route requests or fast-track access when appropriate. This seems to be paying off: users are creating Slack-based approvals in front of different types of risky actions like production access, sensitive queries and triggering Lambdas. We’d love your feedback on our approach so far. Does this make sense to you? Is this a tool you'd use? What would you want to see out of it? To learn more, check out the video that Nick (nmeans (Sym VPEng)) made [1]. You can also check out our docs [2] or set up your own flow [3]. thanks! -adam [1] https://ift.tt/AZ1qsuO [2] https://docs.symops.com [3] https://ift.tt/Li2KfX0

Thursday, 6 April 2023

New top story on Hacker News: Show HN: Quadratic – Open-Source Spreadsheet with Python, AI (WASM and WebGL)

New top story on Hacker News: Show HN: Quadratic – Open-Source Spreadsheet with Python, AI (WASM and WebGL)

on 4/06/2023 01:08:00 am 0 Comment
Show HN: Quadratic – Open-Source Spreadsheet with Python, AI (WASM and WebGL)
29 by davidkircos | 10 comments on Hacker News.
Hi, I am David Kircos. The Founder of Quadratic ( https://QuadraticHQ.com ), an open-source spreadsheet application that supports Python, SQL (coming soon), AI Prompts, and classic Formulas. Unlike other spreadsheets, Quadratic has an infinite canvas (like Figma). As a result, you can pinch and zoom to navigate large data sets, and everything renders smoothly at 60fps. Our vision is to build a place where your team can collaborate on data analysis. You can write Python, AI Prompts, and Formulas in one spreadsheet feeding each other data and updating automatically. Quadratic is built using WebGL and Rust WASM. To render a large grid of cells smoothly, we tile the spreadsheet similar to google maps. If you are interested in the technical details, check us out on GitHub ( https://ift.tt/uSPmhqw ) You can use AI to help you write Python and then run the code directly in Quadratic. Then, we feed the result back to the AI model so it can follow along, help you debug, and modify your existing code. AI can also be used to directly generate data onto the sheet with prompts. It knows the context of what's on the sheet and how the data it's inserting fits in. Try it out. SQL is coming soon... stay tuned!

New top story on Hacker News: Show HN: Want something better than k-means? Try BanditPAM

New top story on Hacker News: Show HN: Want something better than k-means? Try BanditPAM

on 4/06/2023 12:12:00 am 0 Comment
Show HN: Want something better than k-means? Try BanditPAM
8 by motiwari | 0 comments on Hacker News.
Want something better than k-means? I'm happy to announce our SOTA k-medoids algorithm from NeurIPS 2020, BanditPAM, is now publicly available! `pip install banditpam` or `install.packages("banditpam")` and you're good to go! k-means is one of the most widely-used algorithms to cluster data. However, it has several limitations: a) it requires the use of L2 distance for efficient clustering, which also b) restricts the data you're clustering to be vectors, and c) doesn't require the means to be datapoints in the dataset. Unlike in k-means, the k-medoids problem requires cluster centers to be actual datapoints, which permits greater interpretability of your cluster centers. k-medoids also works better with arbitrary distance metrics, so your clustering can be more robust to outliers if you're using metrics like L1. Despite these advantages, most people don't use k-medoids because prior algorithms were too slow. In our NeurIPS 2020 paper, BanditPAM, we sped up the best known algorithm from O(n^2) to O(nlogn) by using techniques from multi-armed bandits. We were inspired by prior research that demonstrated many algorithms can be sped up by sampling the data intelligently, instead of performing exhaustive computations. We've released our implementation, which is pip- and CRAN-installable. It's written in C++ for speed, but callable from Python and R. It also supports parallelization and intelligent caching at no extra complexity to end users. Its interface also matches the sklearn.cluster.KMeans interface, so minimal changes are necessary to existing code. PyPI: https://ift.tt/PsGxUWe CRAN: https://ift.tt/GpDxulk Repo: https://ift.tt/PCQWtcB Paper: https://ift.tt/cp2yuln If you find our work valuable, please consider starring the repo or citing our work. These help us continue development on this project. I'm Mo Tiwari (motiwari.com), a PhD student in Computer Science at Stanford University. A special thanks to my collaborators on this project, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, and Ilan Shomorony, as well as the author of the R package, Balasubramanian Narasimhan. (This is my first time posting on HN; I've read the FAQ before posting, but please let me know if I broke any rules)

Wednesday, 5 April 2023

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