Show HN: I made an Apple Vision Pro review roundup with AI using Jumprun(jumprun.ai)
jumprun.ai
Show HN: I made an Apple Vision Pro review roundup with AI using Jumprun
https://jumprun.ai/share/canvas/01HNXB2K3GM7KPRP45Y2CVVJSC
We recently launched Jumprun as a private beta, and made this public canvas showing a roundup of Apple Vision Pro reviews. Let me know if you're interested in trying out Jumprun to build your own canvas and I'll get an invite to you. We're keeping it as a closed beta for the moment to control costs.
2 comments
Just a little more context: Jumprun let's you connect different data sources (like web searches/pages, APIs, X, Youtube videos, Notion etc) and use LLMs to analyze and visualize the data.
We support rich components like tables, timeseries, charts and maps. We're working on automations at the moment so that you can provide natural language conditions that trigger actions (like sending you an email or changing updating a page in Notion).
Our long-term vision is that canvases become more interactive and interconnected, so that you end up building mini applications without it feeling like you're using a low-code app builder.
We support rich components like tables, timeseries, charts and maps. We're working on automations at the moment so that you can provide natural language conditions that trigger actions (like sending you an email or changing updating a page in Notion).
Our long-term vision is that canvases become more interactive and interconnected, so that you end up building mini applications without it feeling like you're using a low-code app builder.
How hard would it be to do a comparative analysis, say of Vision Pro, Oculus, and Quest? Does that capability exist? Or is it more of a summarization/vision board thing?
The UI looks great, btw.
The UI looks great, btw.
Very easy! You just need to ground it with the right data sources. Eg. you can add a web search datasource for "oculus reviews", and then add a table component to your canvas, instructing it to provide a comparative analysis.
The source material doesn't need to provide a comparative analysis itself: as long as it finds data detailing Vision Pro specs (which it already found) and Oculus specs it'll be able to do the analysis.
Canvases also have memory and auto-refresh, so once it finds the specs it can remember them across refreshes/updates, but also incorporate any new information it finds.
The source material doesn't need to provide a comparative analysis itself: as long as it finds data detailing Vision Pro specs (which it already found) and Oculus specs it'll be able to do the analysis.
Canvases also have memory and auto-refresh, so once it finds the specs it can remember them across refreshes/updates, but also incorporate any new information it finds.
Here you go, AVP vs Quest 3: https://jumprun.ai/share/canvas/01HNXG4VK47Y0J1CC403Z6JSM0
This is almost spooky... it produced the results of my typical google research process. Thanks for this, I'm impressed.