The best Hacker News stories from Show from the past day
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Show HN: I made a silly personal landing page
Also, yes I am looking for a new role. And yes, I should have been spending my time looking for suitable roles instead of building this.
Show HN: Numbat – A programming language with physical dimensions as types
Show HN: Numbat – A programming language with physical dimensions as types
Show HN: Convert any screenshot into clean HTML code using GPT Vision (OSS tool)
Hey everyone,<p>I built a simple React/Python app that takes screenshots of websites and converts them to clean HTML/Tailwind code.<p>It uses GPT-4 Vision to generate the code, and DALL-E 3 to create placeholder images.<p>To run it, all you need is an OpenAI key with GPT vision access.<p>I’m quite pleased with how well it works most of the time. Sometimes, the image generations can be hilariously off. See here for a replica of Taylor Swift’s Instagram page: <a href="https://streamable.com/70gow1" rel="nofollow noreferrer">https://streamable.com/70gow1</a> I initially had a hard time getting it to work on full page screenshots. GPT4 would code up the first couple of sections and then, get lazy and output placeholder comments for the rest of the page. With some prompt engineering, full page screenshots work a whole lot better now. It’s great for landing pages.<p>Lots of ideas of where to go from here! Let me know if you have feedback and you find this useful :)
Show HN: Convert any screenshot into clean HTML code using GPT Vision (OSS tool)
Hey everyone,<p>I built a simple React/Python app that takes screenshots of websites and converts them to clean HTML/Tailwind code.<p>It uses GPT-4 Vision to generate the code, and DALL-E 3 to create placeholder images.<p>To run it, all you need is an OpenAI key with GPT vision access.<p>I’m quite pleased with how well it works most of the time. Sometimes, the image generations can be hilariously off. See here for a replica of Taylor Swift’s Instagram page: <a href="https://streamable.com/70gow1" rel="nofollow noreferrer">https://streamable.com/70gow1</a> I initially had a hard time getting it to work on full page screenshots. GPT4 would code up the first couple of sections and then, get lazy and output placeholder comments for the rest of the page. With some prompt engineering, full page screenshots work a whole lot better now. It’s great for landing pages.<p>Lots of ideas of where to go from here! Let me know if you have feedback and you find this useful :)
Show HN: Convert any screenshot into clean HTML code using GPT Vision (OSS tool)
Hey everyone,<p>I built a simple React/Python app that takes screenshots of websites and converts them to clean HTML/Tailwind code.<p>It uses GPT-4 Vision to generate the code, and DALL-E 3 to create placeholder images.<p>To run it, all you need is an OpenAI key with GPT vision access.<p>I’m quite pleased with how well it works most of the time. Sometimes, the image generations can be hilariously off. See here for a replica of Taylor Swift’s Instagram page: <a href="https://streamable.com/70gow1" rel="nofollow noreferrer">https://streamable.com/70gow1</a> I initially had a hard time getting it to work on full page screenshots. GPT4 would code up the first couple of sections and then, get lazy and output placeholder comments for the rest of the page. With some prompt engineering, full page screenshots work a whole lot better now. It’s great for landing pages.<p>Lots of ideas of where to go from here! Let me know if you have feedback and you find this useful :)
Show HN: Convert any screenshot into clean HTML code using GPT Vision (OSS tool)
Hey everyone,<p>I built a simple React/Python app that takes screenshots of websites and converts them to clean HTML/Tailwind code.<p>It uses GPT-4 Vision to generate the code, and DALL-E 3 to create placeholder images.<p>To run it, all you need is an OpenAI key with GPT vision access.<p>I’m quite pleased with how well it works most of the time. Sometimes, the image generations can be hilariously off. See here for a replica of Taylor Swift’s Instagram page: <a href="https://streamable.com/70gow1" rel="nofollow noreferrer">https://streamable.com/70gow1</a> I initially had a hard time getting it to work on full page screenshots. GPT4 would code up the first couple of sections and then, get lazy and output placeholder comments for the rest of the page. With some prompt engineering, full page screenshots work a whole lot better now. It’s great for landing pages.<p>Lots of ideas of where to go from here! Let me know if you have feedback and you find this useful :)
Show HN: GPT-4-Vision UX audit for your landing page (relaunch)
Last week, I got seriously fascinated by the new GPT-4 Vision API introduced on OpenAI DevDay, so I decided to build a UX Audit tool with this API.<p>It's pretty simple to use - just go to <a href="https://flawless.is" rel="nofollow noreferrer">https://flawless.is</a> and enter your homepage or landing page URL.<p>I'm using the urlbox API to get a screenshot of your page, then I utilize GPT-4V to analyze the screenshot, find potential usability and conversion issues, suggest fixes, and highlight the relevant areas in the screenshot (although the position is sometimes inaccurate).<p>Last week, I launched it on Hacker News (<a href="https://news.ycombinator.com/item?id=38207892">https://news.ycombinator.com/item?id=38207892</a>), but I wasn't ready for the 20,000 visitors that came next... Unfortunately, GPT-4V has a rate limit of 100 events daily, so almost no one could use the service - EPIC FAIL.<p>To manage demand, I now priced the service at $1.99 (still less than a cup of coffee), which should keep the demand at bay, and also cover the costs of GPT-4V, the screenshot API, and hosting.<p>As a proof of concept, there are some demo audits on the homepage you can click and check out.<p>Please let me know your thoughts and suggestions on how to improve it!
Show HN: Add auth to Next.js and deploy in 60 seconds – no manual config
Show HN: Extend your platform via custom code with Deno Subhosting
Hey all, Andy from the Deno team here. We're excited to share with you Deno Subhosting, an easy way to extend your platforms functionality by securely running untrusted JavaScript written by your users.<p>When we first launched Deno Deploy in 2021, we were surprised at the volume of requests from companies about getting access to the APIs needed to run Deno Deploy. Many companies wanted to give their users the ability to write custom logic in their app, but setting this up in the cloud presents security concerns and a ton of infra work/maintenance.<p>We realized that there was an opportunity for Subhosting to solve a larger problem, which is allowing companies to easily and securely run custom code written by their users, without the hassle of maintaining said infrastructure.<p>Though we do have a few subhosting customers (Netlify being one of them), this launch makes our Subhosting product self-service, so any development team interested in extending their platform via their users' custom code can do so by signing up and reading our docs. We have an updated pricing model for Subhosting as well, including a generous free tier fit for kicking the tires and building a proof-of-concept.<p>We'd love to get your feed back. Have you ever talked to your co-workers about allowing external devs to "have at it" with your platform? What would it look like to unlock the final 10% for your top customers? How have you approached this problem in the past?<p>Thanks for reading and the Deno team will be responding to comments!
Show HN: Llama Running on a Microcontroller
Show HN: Multi-Object Tracking in Python
Hello! I've created a small library for tracking, along with a tutorial. I plan to continue developing it.<p>Tracking is an important topic, closely related to object detection. However, I've noticed that it doesn't receive as much attention compared to machine learning approaches. Or, the focus is on filters like the Kalman filter. This tutorial begins with single object tracking and progressively complicates the tasks, introducing various models and a hypothesis tree to solve them.
Show HN: Multi-Object Tracking in Python
Hello! I've created a small library for tracking, along with a tutorial. I plan to continue developing it.<p>Tracking is an important topic, closely related to object detection. However, I've noticed that it doesn't receive as much attention compared to machine learning approaches. Or, the focus is on filters like the Kalman filter. This tutorial begins with single object tracking and progressively complicates the tasks, introducing various models and a hypothesis tree to solve them.
Show HN: MonkeyPatch – Cheap, fast and predictable LLM functions in Python
Hi HN, Jack here! I'm one of the creators of MonkeyPatch, an easy tool that helps you build LLM-powered functions and apps that get cheaper and faster the more you use them.<p>For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in <5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior.<p>After about 200-300 calls, these functions will begin to get cheaper and faster. We've seen 8-10x reduction in cost and latency in some use-cases! This happens via progressive knowledge distillation - MonkeyPatch incrementally fine-tunes smaller, cheaper models in the background, tests them against the constraints defined by the developer, and retains the smallest model that meets accuracy requirements, which typically has significantly lower costs and latency.<p>As an LLM researcher, I kept getting asked by startups and friends to build specific LLM features that they could embed into their applications. I realized that most developers have to either 1) use existing low-level LLM clients (GPT4/Claude), which can be unreliable, untyped, and pricey, or 2) pore through LangChain documentation for days to build something.<p>We built MonkeyPatch to make it easy for developers to inject LLM-powered functions into their code and create tests to ensure they behave as intended. Our goal is to help developers easily build apps and functions without worrying about reliability, cost, and latency, while following best software engineering practices.<p>We're only available in Python currently but actively working on a Typescript version. The repo has all the instructions you need to get up and running in a few minutes.<p>The world of LLMs is changing by the day and so we're not 100% sure how MonkeyPatch will evolve. For now, I'm just excited to share what we've been working on with the HN community. Would love to know what you guys think!<p>Open-source repo: <a href="https://github.com/monkeypatch/monkeypatch.py">https://github.com/monkeypatch/monkeypatch.py</a><p>Sample use-cases: <a href="https://github.com/monkeypatch/monkeypatch.py/tree/master/examples">https://github.com/monkeypatch/monkeypatch.py/tree/master/ex...</a><p>Benchmarks: <a href="https://github.com/monkeypatch/monkeypatch.py#scaling-and-finetuning">https://github.com/monkeypatch/monkeypatch.py#scaling-and-fi...</a>
Show HN: MonkeyPatch – Cheap, fast and predictable LLM functions in Python
Hi HN, Jack here! I'm one of the creators of MonkeyPatch, an easy tool that helps you build LLM-powered functions and apps that get cheaper and faster the more you use them.<p>For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in <5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior.<p>After about 200-300 calls, these functions will begin to get cheaper and faster. We've seen 8-10x reduction in cost and latency in some use-cases! This happens via progressive knowledge distillation - MonkeyPatch incrementally fine-tunes smaller, cheaper models in the background, tests them against the constraints defined by the developer, and retains the smallest model that meets accuracy requirements, which typically has significantly lower costs and latency.<p>As an LLM researcher, I kept getting asked by startups and friends to build specific LLM features that they could embed into their applications. I realized that most developers have to either 1) use existing low-level LLM clients (GPT4/Claude), which can be unreliable, untyped, and pricey, or 2) pore through LangChain documentation for days to build something.<p>We built MonkeyPatch to make it easy for developers to inject LLM-powered functions into their code and create tests to ensure they behave as intended. Our goal is to help developers easily build apps and functions without worrying about reliability, cost, and latency, while following best software engineering practices.<p>We're only available in Python currently but actively working on a Typescript version. The repo has all the instructions you need to get up and running in a few minutes.<p>The world of LLMs is changing by the day and so we're not 100% sure how MonkeyPatch will evolve. For now, I'm just excited to share what we've been working on with the HN community. Would love to know what you guys think!<p>Open-source repo: <a href="https://github.com/monkeypatch/monkeypatch.py">https://github.com/monkeypatch/monkeypatch.py</a><p>Sample use-cases: <a href="https://github.com/monkeypatch/monkeypatch.py/tree/master/examples">https://github.com/monkeypatch/monkeypatch.py/tree/master/ex...</a><p>Benchmarks: <a href="https://github.com/monkeypatch/monkeypatch.py#scaling-and-finetuning">https://github.com/monkeypatch/monkeypatch.py#scaling-and-fi...</a>
Show HN: Watermelon – copilot for code review
Show HN: Watermelon – copilot for code review
Bay Bridge: the cheapest H100 training clusters
Show HN: SvelteKit SaaS Boilerplate to help launch your product fast
Hi HN!<p>I am a indie hacker and love building apps with SvelteKit, so I built a boilerplate with the tech stack I always use.<p>It has almost everything needed to launch a SaaS/tool/AI app, like auth, db + orm, email, payments and styling.<p>You can view everything that's included on the website and the docs (<a href="https://docs.launchleopard.com" rel="nofollow noreferrer">https://docs.launchleopard.com</a>)<p>Would love to hear what other features or tech you'd want to see in a boilerplate like this!
Soccer video analysis from your match videos
I created a tool to generate awesome soccer video analysis from match videos.<p>I'm no pro player, just play with my friends weekly, record our matches, and use this tool to check out our performance.<p>My friends really enjoy it and have suggested adding features like measuring player speed, tracking players positions, and more.