Taichi Lang
14 by whereistimbo | 1 comments on Hacker News.
Thursday, March 9, 2023
New top story on Hacker News: Show HN: Livecycle – Get visual feedback, in context, on every pull request
Show HN: Livecycle – Get visual feedback, in context, on every pull request
37 by assafk | 10 comments on Hacker News.
Hi HN! This is Assaf, Matan, and Yshay from https://livecycle.io/ Livecycle enables dev teams to collaborate and comment in context, on top of any preview environment. Using Livecycle, developers get clear feedback earlier in the release cycle leading to higher-quality products, a faster release cadence, and fewer context switches and misunderstandings. Livecycle builds and pushes a dev-like environment for every branch in your repo (or, if you prefer, you can bring your own environments). Any containerized application will work, and support for multiple containers via docker-compose is coming soon. You get a unique, shareable link for every branch, which automatically updates for every commit pushed to that branch. Each link contains not only your deployed environment but also includes: - A dashboard to view and manage all of your environments and users - Collaboration features - create screenshots, record audio/video clips, suggest CSS/content changes, and leave comments with rich text and internal threads - Integration with Jira/Linear (view tickets associated with a PR or create new tickets from comments users left on the environment) - Integration with GitHub/GitLab - view your build status in the PR/MR (with a link to the environment), comments left on Livecycle will be synced to PR/MR comments so that devs can easily gather feedback from both devs and other stakeholders in one place - Even more stuff: Slack integration, integrated network, and console logs, etc… We’re thrilled to see a wide variety of teams already benefitting from Livecycle - large companies, startups, freelance developers, dev shops, and more. And we invite you to check out how Livecycle can bring value to you and your team. And please let us know if you have any comments or questions :-)
37 by assafk | 10 comments on Hacker News.
Hi HN! This is Assaf, Matan, and Yshay from https://livecycle.io/ Livecycle enables dev teams to collaborate and comment in context, on top of any preview environment. Using Livecycle, developers get clear feedback earlier in the release cycle leading to higher-quality products, a faster release cadence, and fewer context switches and misunderstandings. Livecycle builds and pushes a dev-like environment for every branch in your repo (or, if you prefer, you can bring your own environments). Any containerized application will work, and support for multiple containers via docker-compose is coming soon. You get a unique, shareable link for every branch, which automatically updates for every commit pushed to that branch. Each link contains not only your deployed environment but also includes: - A dashboard to view and manage all of your environments and users - Collaboration features - create screenshots, record audio/video clips, suggest CSS/content changes, and leave comments with rich text and internal threads - Integration with Jira/Linear (view tickets associated with a PR or create new tickets from comments users left on the environment) - Integration with GitHub/GitLab - view your build status in the PR/MR (with a link to the environment), comments left on Livecycle will be synced to PR/MR comments so that devs can easily gather feedback from both devs and other stakeholders in one place - Even more stuff: Slack integration, integrated network, and console logs, etc… We’re thrilled to see a wide variety of teams already benefitting from Livecycle - large companies, startups, freelance developers, dev shops, and more. And we invite you to check out how Livecycle can bring value to you and your team. And please let us know if you have any comments or questions :-)
Wednesday, March 8, 2023
New top story on Hacker News: Show HN: BBC “In Our Time”, categorised by Dewey Decimal, heavy lifting by GPT
Show HN: BBC “In Our Time”, categorised by Dewey Decimal, heavy lifting by GPT
72 by genmon | 26 comments on Hacker News.
I'm a big fan of the BBC podcast In Our Time -- and (like most people) I've been playing with the OpenAI APIs. In Our Time has almost 1,000 episodes on everything from Cleopatra to the evolution of teeth to plasma physics, all still available, so it's my starting point to learn about most topics. But it's not well organised. So here are the episodes sorted by library code. It's fun to explore. Web scraping is usually pretty tedious, but I found that I could send the minimised HTML to GPT-3 and get (almost) perfect JSON back: the prompt includes the Typescript definition. At the same time I asked for a Dewey classification... and it worked. So I replaced a few days of fiddly work with 3 cents per inference and an overnight data run. My takeaway is that I'll be using LLMs as function call way more in the future. This isn't "generative" AI, more "programmatic" AI perhaps? So I'm interested in what temperature=0 LLM usage looks like (you want it to be pretty deterministic), at scale, and what a language that treats that as a first-class concept might look like.
72 by genmon | 26 comments on Hacker News.
I'm a big fan of the BBC podcast In Our Time -- and (like most people) I've been playing with the OpenAI APIs. In Our Time has almost 1,000 episodes on everything from Cleopatra to the evolution of teeth to plasma physics, all still available, so it's my starting point to learn about most topics. But it's not well organised. So here are the episodes sorted by library code. It's fun to explore. Web scraping is usually pretty tedious, but I found that I could send the minimised HTML to GPT-3 and get (almost) perfect JSON back: the prompt includes the Typescript definition. At the same time I asked for a Dewey classification... and it worked. So I replaced a few days of fiddly work with 3 cents per inference and an overnight data run. My takeaway is that I'll be using LLMs as function call way more in the future. This isn't "generative" AI, more "programmatic" AI perhaps? So I'm interested in what temperature=0 LLM usage looks like (you want it to be pretty deterministic), at scale, and what a language that treats that as a first-class concept might look like.
