Show HN: Turn a video of an app into a functional prototype with Claude Opus https://ift.tt/uCXTrbK

Show HN: Turn a video of an app into a functional prototype with Claude Opus Hey everyone, I’m the maintainer of the popular screenshot-to-code repo on Github (46k+ stars). When Claude Opus was released, I thought to myself what if you could send in a video of yourself using a website or app, would the LLM be able to build it as a functional prototype? To my surprise, it worked quite well. Here are two examples: * In this video, you can see the AI replicating Google with auto-complete suggestions and a search results page (failed at putting the results on a separate page). https://ift.tt/cYyegTH * Here, we show it a multi-step form ( https://ift.tt/5AgYUhW ) and ask Claude to re-create it. It does a really good job! https://ift.tt/2HfF1Yy The technical details: Claude Opus only allows you to send a max of 20 images so 20 frames are extracted from the video, and passed along with a prompt that uses a lot of Claude-specific techniques such as using XML tags and pre-filling an assistant response. In total, 2 passes are performed with the second pass instructing the AI improve on the first attempt. More passes might help as well. While I think the model has Google.com memorized but for many other multi-page/screen apps, it tends to work quite well. You can try it out by downloading the Github repo and setting up a Anthropic API key in backend/.env Be warned that one creation/iteration (with 2 passes) can be quite expensive ($3-6 dollars). https://ift.tt/yCn58zO March 22, 2024 at 12:36AM

Then and Now at Kirkland Division, Muni’s Oldest Motor Bus Yard

Then and Now at Kirkland Division, Muni’s Oldest Motor Bus Yard
By Jeremy Menzies

Tucked away on the northeast edge of San Francisco is our transit system's oldest motor bus yard. Small but mighty, Kirkland Division has been home to some of Muni’s fleet of motor buses for nearly 75 years.  

These two panoramic photos show Kirkland Division during and after construction. Top photo taken July 20, 1950, bottom September 14, 1950. 

Kirkland was built in 1950 amidst freight rail yards and factories. Its namesake comes from a former Southern Pacific Railroad official, William B. Kirkland, who worked in a rail yard on the site during World War II.  Today, the division is nestled among Pier 39 attractions, parking garages and hotels. 

An aerial view from 1972 shows Kirkland in the upper center of the photo. Industrial uses in the area have begun to give way to residential and tourist areas.

The yard was primarily designed as an operations facility. It has shops and equipment only for routine maintenance and light repairs. With a capacity of around 125 buses, it’s nearly half the size of the SFMTA’s largest yard, Woods Division. 

This color photo from 1971 shows a yard full of old and new Muni buses. To the north lies the remnants of a once massive freight rail yard.

Today, some of the system’s longer cross-town routes run out of Kirkland. Operations and maintenance staff keep the 12, 19, 28, 28R, 43, and 21 (weekend only) on the road. 

By the time this 1980 photo was taken, the rail yard north of Kirkland was replaced by a parking garage for Pier 39 attractions.

Kirkland could play a key role in our work to electrify our fleet. The yard is being studied for reconstruction as a potential battery-electric bus facility. Proposals for this project include building an overhead grid system that would allow buses to charge while in the yard. You can learn more on our Kirkland Yard Electrification Project webpage (SFMTA.com/KirklandYard). 

This 2023 view shows a much-changed neighborhood but a relatively unchanged Kirkland Yard.

Kirkland Yard was a crucial part of the Muni system when it was built in 1950. It remains one today as we look toward the future of transportation in San Francisco. 

Published March 22, 2024 at 12:40AM
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Show HN: Memories, FOSS Google Photos alternative built for high performance https://ift.tt/34O7xAW

Show HN: Memories, FOSS Google Photos alternative built for high performance Memories is a FOSS Google Photos alternative that you can self-host (it runs as a Nextcloud plugin). Website: https://ift.tt/tCRedsj GitHub: https://ift.tt/S4skW8f Demo Server: https://ift.tt/GUjIY7A (demo runs in San Francisco on a free-tier cloud vm) Memories has been built ground-up for high performance and is extremely fast when configured correctly. In our testing environment, it can load a timeline view with 100k photos in under 500ms, including query and rendering time! Some features to highlight: * A timeline similar to Google Photos where you can skip to any time in history instantly. * AI-based tagging that runs locally on your server, identifying and tagging people and objects. * Albums and external sharing. * Metadata editing support * A world map of your photos, supported both on mobile and the web * Did I mention it's extremely fast? Would love to hear feedback from the HN community! :) https://ift.tt/tCRedsj March 22, 2024 at 12:55AM

Show HN: DaLMatian – Text2sql that works https://ift.tt/Cn5VEc4

Show HN: DaLMatian – Text2sql that works Hey HN, we've built DaLMatian, a text2sql product that meets the needs of data analysts working with enterprise data. We built this app because as a data analyst at an enterprise I could not find a text2sql product that was (1) actually useful for my day-to-day and (2) easy to set up on my computer. Existing products either fall apart when tested on gnarly enterprise data/queries or require going through a sales/integration process that I wasn't in a position to push for - I just wanted something that I could quickly set up to help make my job easier. Our goal is to make this a reality for any data analyst that feels the same. There are many constraints that make this reality difficult to achieve. The product needs to scale to databases with millions of columns and extract business logic from very complex queries. It also needs to be fast, at least faster than an analyst would take to write the query. On top of all this, an analyst needs to be allowed to use it from a security standpoint. Our app meets all the key requirements of an enterprise data analyst while also being lightweight enough to run locally on a typical laptop. Here's how it works. To get started, you simply need to open a file of past queries in our IDE (try it here: https://ift.tt/x0YS14y ) and add a file with your database schema (instructions here: https://ift.tt/bZ2gm5o ). There is also an option to connect a database to auto pull your schema (no actual data is seen by the LLM). We do not see anything you input since the app is local and the only external connection is with OpenAI. It's just like asking ChatGPT for help with queries, but in a streamlined way. If you'd download our free IDE and try to break it, we'd love to hear what you come up with! https://ift.tt/x0YS14y March 21, 2024 at 10:41PM

Show HN: Personal Knowledge Base Visualization https://ift.tt/3ltrpC5

Show HN: Personal Knowledge Base Visualization My personal knowledge base is hosted on GitHub at https://ift.tt/0VmrORy . It scans the documents I like every day using GitHub Action, Zotero, HackerNews upvote and Github Likes. It's not yet optimized for smartphones. It cost me $5 to host it for a year. https://ift.tt/PSNKbei March 21, 2024 at 03:28AM

Show HN: GritQL, a Rust CLI for rewriting source code https://ift.tt/8rL7V1y

Show HN: GritQL, a Rust CLI for rewriting source code Hi everyone! I’m excited to open source GritQL, a Rust CLI for searching and transforming source code. GritQL comes from my experiences with conducting large scale refactors and migrations. Usually, I would start exploring a codebase with grep. This is easy to start with, but most migrations end up accumulating additional requirements like ensuring the right packages are imported and excluding cases which don’t have a viable migration path. Eventually, to build a complex migration, I usually ended up having to write a full codemod program with a tool like jscodeshift. This comes with its own problems: - Most of the exploratory work has to be abandoned as you figure out how to represent your original regex search as an AST. - Reading/writing a codemod requires mentally translating from AST names back to what source code actually looks like. - Performance is often an afterthought, so iterating on a large codemod can be painfully slow. - Codemod frameworks are language-specific, so if you’re hopping between multiple languages—or trying to migrate a shared API—you have to learn different tools. GritQL is an attempt to develop a powerful middle ground: - Exploratory analysis is easy: just put a code snippet in backticks and use $metavariables for placeholders. - Incrementally add complexity by introducing side conditions with where clauses. - Reuse named patterns to avoid rebuilding queries, and use shared patterns from our standard library for common tasks like ensuring modules are imported. - Iterate on large codebases quickly: we use Rust for maximum performance GritQL has already been used on thousands of repositories for complex migrations[1] but we're excited to collaborate more with the open source community. [1] Ex. https://ift.tt/iVSYHk5 https://ift.tt/utJmezR March 21, 2024 at 12:53AM

Show HN: Automated Software Documentation for GitHub Codebases https://ift.tt/oicvE3f

Show HN: Automated Software Documentation for GitHub Codebases Hey Hackers, My team and I have been working on an automated software documentation and impact analysis platform for the last 3 years. Our long-term goal is to enter safety/mission-critical applications, where improper documentation can lead to disastrous outcomes, e.g., costly reworks/overruns or endangering human lives. But, in an effort to recognize revenue in the near term with our existing functionality, we have found initial traction with use cases focused on reverse engineering legacy systems. Where getting up to speed with an existing system requires a team of engineers to manually review large amounts of code, taking weeks or months to come to grips with. ______________________________________________ Our Self-Service release is a no-frills offering to leverage a subset of our document generation capabilities. Using only the code, SAFA is able to: -Summarize Code Files -Generate an overall project summary -Generate Upstream Documentation, like Features and Functional Requirements -Map relationships between all code and generated documentation with explanations Our approach leverages our own LLM pipeline, which applies a variety of clustering/refinement techniques, embedding models, and LLMs to keep your entire system within context when generating documentation, change summaries, api flow, and more. We do not use customer data to train or refine our models. We currently only support Github integrations for self-service but will implement flat-file support in the near term. When using self-service, you will receive Code Summaries and a Project Overview for free, but we charge for generating documentation and relationships: 20 cents per code file and generated document (100 File Codebase = $35). Currently, self-service has a 1000 code file limit. ________________________________________________ If you want to see the quality of the documents SAFA generates before trying it with your code, feel free to check out our public codebases page ( https://ift.tt/a43fD6s ). We have serious ones like Autoware's AV Control Module, and more fun ones, like Super Mario 64. Otherwise, our app is directly accessible via https://app.safa.ai (apologies, we do require an account to be made). I very much look forward to your feedback and insights. Feel free to email me directly at aarik@safa.ai. https://www.safa.ai March 21, 2024 at 12:24AM