Show HN: GodUI – Open-source React components with a shared motion system https://ift.tt/pxqiGwQ

Show HN: GodUI – Open-source React components with a shared motion system https://godui.design July 9, 2026 at 11:23PM

Show HN: Reverse-engineering web apps into agent tools https://ift.tt/rMsiupm

Show HN: Reverse-engineering web apps into agent tools Hey HN! We built a browser-based agent that runs inside an authenticated web app, watches how the app calls its own APIs, and automatically turns those into agent tools. You can think of it as an auto-generated MCP server that self-updates as the host app changes. The result is a skilled AI assistant that actually integrates deeply with any product (not just chat and RAG) with minimal effort. Check out these short demos below that show the agent in software you're probably familiar with: - Jira: https://ift.tt/TyCdF7I - Spotify: https://ift.tt/XQ8gkE0 - Hacker News (lol): https://ift.tt/AvFxMlN - Full Demo: https://ift.tt/ZFOCe41 As you can see in the examples, you can do way more (and faster) than what you normally would be able to via point and click. And we never even touched the source code of these products! Why do this? In an ideal world, every application has an MCP server or an easily-digestible API available for AI agents to feed from. In practice, we found that even very modern software tends to have a spider web of confusing APIs and services that AI agents simply cannot use out of the box. Security also becomes a huge issue as applications have different (often homebrewed) standards for how endpoints are secured (JWTs/cookies/mix of both). Finally, having an actual browser agent go in and use the application on behalf of the user (i.e. computer-use), is simply too brittle, slow, and burns a lot of tokens. We took our existing browser agent that’s already trained to use and learn authenticated applications, and added an extra step that automatically turns the app’s authenticated APIs into "recipes". A recipe is a mix of the following: - API endpoint + method - Authentication method (and how to retrieve refresh auth tokens/cookies) - Response schema - Input schema (for POST/PUT) - Human readable description of what the tool does Putting it all together, these become reusable tools for LLMs, all without writing or maintaining any code. Even if the APIs change our agent figures this out and replaces the recipe for the tool with the updated version. Adding tools to an AI agent becomes super simple this way: - Our agent trains on the app and builds the recipes - The app owner enables discovered tools from our dashboard - The agent can now take actions on the user’s behalf directly inside the application. For instance, saying something like "invite my teammate to my workspace" would securely call the existing API endpoint for inviting users without proxying or relaying through a third party. Of course, there's a ton of edge cases you run into when you try to do this - every application is intrinsically different despite how many "standards" exist. Fun fact: graphql was by far the worst API to work with in standardizing the recipes. Looking forward to your feedback/comments! July 9, 2026 at 10:45PM

Show HN: Hover over your UI element to get its exact location in code https://ift.tt/Ued5KNM

Show HN: Hover over your UI element to get its exact location in code https://loerei.github.io/HoverSource/ July 9, 2026 at 12:44AM

Show HN: Maps for e-scooters (hills and battery routing) https://ift.tt/IxWrhQB

Show HN: Maps for e-scooters (hills and battery routing) I built BattMap, AMA! Living in San Francisco is hilly. Google Maps is great for bike maps, but I wanted to know if my scooter could make it up the hills, how much battery it would burn, and if I could make it back home without recharging it. https://battmap.com July 8, 2026 at 11:06PM

Show HN: Signal for LLM – The Modulator Architecture (Theory Complete) https://ift.tt/yG0Vmhl

Show HN: Signal for LLM – The Modulator Architecture (Theory Complete) https://divinecanon.github.io/signalengine-EN/ July 7, 2026 at 11:11PM

Show HN: A website that shows every pro triathlete's swim, bike, and run gear https://ift.tt/U60MCjc

Show HN: A website that shows every pro triathlete's swim, bike, and run gear https://racekit.pro July 6, 2026 at 11:18PM

Show HN: Pulpie – Models for Cleaning the Web https://ift.tt/iJD0c4V

Show HN: Pulpie – Models for Cleaning the Web Hey HN, I'm Shreyash, founder of Feyn. We built Pulpie, a family of Pareto optimal models for cleaning the web. Pulpie strips boilerplate (ads, footers, sidebars) from raw HTML and returns just the main content as HTML or Markdown. We match SOTA extraction quality while being 20x cheaper. Cleaning 1 billion webpages costs $7,900 with Pulpie versus $159,000 with Dripper, the current leading extractor. The gains come from architecture. Today's leading extractors are decoders that generate output one token at a time. Each step reads the full model from memory to produce a single token. Conversely, Pulpie models are encoders. They run one forward pass over the full input HTML and label each block as boilerplate or content. As a result, Pulpie is compute-bound while decoders are memory-bound. Cheaper GPUs have relatively more compute than memory bandwidth. This makes Pulpie easy to run optimally. Here's Pulpie and Dripper cleaning the same pages side by side: https://www.youtube.com/watch?v=ibd-tIiQECo . You can try a side-by-side comparison yourself: https://ift.tt/yzcDG2w Our motivation for Pulpie came from building a deep research harness. Every search API returns noisy content that contains ads, nav elements, and sidebars. In one instance, an ad for "Gemini on Pixel" slipped into our search results, got passed into LLM context, and ended up in the final answer served to the user. Pretty embarrassing moment for us but it helped us realize how bad data kills model intelligence. We built Pulpie to get clean data for cheap. All models are open source on Hugging Face. You can read about our training process and how to use Pulpie here: https://ift.tt/oD3VSZa... Happy to answer any questions! https://ift.tt/ZJST2Rq July 6, 2026 at 11:04PM

Show HN: Handoff – a verified context bridge between Claude Code sessions https://ift.tt/DOdQBIG

Show HN: Handoff – a verified context bridge between Claude Code sessions https://ift.tt/gvjKn3A July 6, 2026 at 12:18AM

Show HN: EdgeRunner – run GGUF models with Swift and Metal https://ift.tt/hV5tuaw

Show HN: EdgeRunner – run GGUF models with Swift and Metal https://ift.tt/uw8NM6v July 6, 2026 at 12:21AM

Show HN: Gemma 3 inference in pure C++ with Metal acceleration https://ift.tt/yUlhuzV

Show HN: Gemma 3 inference in pure C++ with Metal acceleration https://ift.tt/moh9BIz July 4, 2026 at 10:54PM

Show HN: Groot – Kubernetes incident evidence in one .tar.gz https://ift.tt/ln9rjNb

Show HN: Groot – Kubernetes incident evidence in one .tar.gz https://ift.tt/4OyaLKE July 4, 2026 at 07:31PM

Show HN: Dockside – I turned unused space around the macOS Dock into a workspace https://ift.tt/GUCEmhY

Show HN: Dockside – I turned unused space around the macOS Dock into a workspace https://ift.tt/YUzr7Ou July 4, 2026 at 01:05AM

Show HN: Mcpsnoop – Wireshark for MCP (transparent proxy and live TUI) https://ift.tt/Yz7Ddt5

Show HN: Mcpsnoop – Wireshark for MCP (transparent proxy and live TUI) https://ift.tt/VIqWCAd July 3, 2026 at 11:53PM