Launch HN: Phind 3 (YC S22) – Every answer is a mini-app
by rushingcreek | 2025-12-03 17:47:15+00:00
Hi HN,

We are launching Phind 3 (https://www.phind.com), an AI answer engine that instantly builds a complete mini-app to answer and visualize your questions in an interactive way. A Phind mini-app appears as a beautiful, interactive webpage — with images, charts, diagrams, maps, and other widgets. Phind 3 doesn’t just present information more beautifully; interacting with these widgets dynamically updates the content on the page and enables new functionality that wasn’t possible before.

For example, asking Phind for “options for a one-bedroom apartment in the Lower East Side” (https://www.phind.com/search/find-me-options-for-a-72e019ce-...) gives an interactive apartment-finding experience with customizable filters and a map view. And asking for a “recipe for bone-in chicken thighs” gives you a customizable recipe where changing the seasoning, cooking method, and other parameters will update the recipe content itself in real-time (https://www.phind.com/search/make-me-an-recipe-for-7c30ea6c-...).

Unlike Phind 2 and ChatGPT apps, which use pre-built brittle widgets that can’t truly adapt to your task, Phind 3 is able to create tools and widgets for itself in real-time. We learned this lesson the hard way with our previous launch – the pre-built widgets made the answers much prettier, but they didn’t fundamentally enable new functionality. For example, asking for “Give me round-trip flight options from JFK to SEA on Delta from December 1st-5th in both miles and cash” (https://www.phind.com/search/give-me-round-trip-flight-c0ebe...) is not something that neither Phind 2 nor ChatGPT apps can handle, because its Expedia widget can only display cash fares and not those with points. We realized that Phind needs to be able to create and consume its own tools, with schema it designs, all in real time. Phind 3’s ability to design and create fully custom widgets in real-time means that it can answer these questions while these other tools can’t. Phind 3 now generates raw React code and is able to create any tool to harness its underlying AI answer, search, and code execution capabilities.

Building on our history of helping developers solve complex technical questions, Phind 3 is able to answer and visualize developers’ questions like never before. For example, asking to “visualize quicksort” (https://www.phind.com/search/make-me-a-beautiful-visualizati...) gives an interactive step-by-step walkthrough of how the algorithm works.

Phind 3 can help visualize and bring your ideas to life in seconds — you can ask it to “make me a 3D Minecraft simulation” (https://www.phind.com/search/make-me-a-3d-minecraft-fde7033f...) or “make me a 3D roller coaster simulation” (https://www.phind.com/search/make-me-a-3d-roller-472647fc-e4...).

Our goal with Phind 3 is to usher in the era of on-demand software. You shouldn’t have to compromise by either settling for text-based AI conversations or using pre-built webpages that weren’t customized for you. With Phind 3, we create a “personal internet” for you with the visualization and interactivity of the internet combined with the customization possible with AI. We think that this current “chat” era of AI is akin to the era of text-only interfaces in computers. The Mac ushering in the GUI in 1984 didn’t just make computer outputs prettier — it ushered in a whole new era of interactivity and possibilities. We aim to do that now with AI.

On a technical level, we are particularly excited about:

- Phind 3’s ability to create its own tools with its own custom schema and then consume them

- Significant improvements in agentic searching and a new deep research mode to surface hard-to-access information

- All-new custom Phind models that blend speed and quality. The new Phind Fast model is based on GLM-4.5-Air while the new Phind Large model is based on GLM 4.6. Both models are state-of-the-art when it comes to reliable code generation, producing over 70% fewer errors than GPT-5.1-Codex (high) on our internal mini-app generation benchmark. Furthermore, we trained custom Eagle3 heads for both Phind Fast and Phind Large for fast inference. Phind Fast runs at up to 300 tokens per second, and Phind Large runs at up to 200 tokens per second, making them the fastest Phind models ever.

While we have done Show HNs before for previous Phind versions, we’ve never actually done a proper Launch HN for Phind. As always, we can’t wait to hear your feedback! We are also hiring, so please don’t hesitate to reach out.

– Michael