DavidFerris

The initial ABC dataset is from public Onshape files -- clearly some people had a reason to design a dog model parametrically!

There's a pretty big bias for mechanical engineering components in the dataset- very few organic forms. It's one of the limitations we call out in the dataset card.

There are a few though! Try "dog" or "cookie cutter" for example.

This isn't meant to be a commercially useful search engine- just a demonstration. You'll only be able to search for terms that the VLM could directly discern.

From the blog post: Our search demo proves that it works quite well. As anticipated, text search works well, returning sensible results for even irregular or poorly formed queries. It’s worth mentioning that this is very different from 3D part libraries like Thingiverse or GrabCAD. Search in those repositories requires users to tag or annotate parts with a description, the text of which is used in search. Our system takes only an unnamed part as input, requiring no additional labelling.

We rendered the one million part ABC dataset from Deep Geometry, and open-sourced the data. We also built a fun demo with the following pipeline: CAD > render > caption > embed.

Open-sourced dataset: https://huggingface.co/datasets/daveferbear/3d-model-images-...

Blog writeup: https://www.finalrev.com/blog/embedding-one-million-3d-model...

Super cool! btw I love the name "Greptile" :)

Inspired by Ian Hickon's reflection on his 18 years at Google, I reflect on how Google got into their current slump- and what they can do to bounce back.

Interesting idea! One of the problems with any primary research (surveys included) is the delay in collecting responses, which can take hours to weeks depending on sample, IR, incentives, etc. This would solve that!

It's not surprising that LLMs can predict the answers to survey questions, but really good primary research generates surprising insights that are outside of existing distributions. Have you found that businesses trust your results? I have found that most businesses don't trust survey research much at all, and this seems like it might be even less reliable.

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Context: I co-founded & sold survey software company (YC W20).

Very cool! I constantly struggle trying to do things in spreadsheets that are easy in Python. But I/O makes it annoying to write one-off scripts for a 30 second op. This would solve that pain point!

I would love a Google Sheets integration, since that's where I already live with most of my CSV/Sheets data + it would seamlessly fit into my workflow. If this was a Chrome extension I would have installed it today.

As is, I don't see myself using another spreadsheet app.

I wrote about a bad thing that happened to me recently and what the web3 people can learn from it.

"However, the algorithm is only valuable for detecting scams accurately after they have been executed."

Forgive me, but isn't it only useful to detect scams before they happen?

I've been using Superpowered for the past 2 weeks and can honestly say the experience is great. I don't live in the browser and the Mac calendar app is pretty awful, so this has been a definitely workflow improvement for me. I probably click on it 10-20x per day.

That said, when my trial expired today I elected to not upgrade in no small part to the insane memory usage.

A survey platform designed around voice and video.

https://phonic.ai

It turns out that a LOT of podcasts are recorded asynchronously since it's easier to schedule.

"I don't know if camera based computer vision by itself will ever be enough for full self driving."

Rebuttal: If you had a 3D camera sitting in the driver's seat of a car and hooked up the control to a remote controller, I believe that car could be drive-able. A sufficiently advanced perception/control system could theoretically use the same 3D camera feed and replace the driver. Not saying it's easy, just that all the necessary info to drive a car safely can be contained in camera feeds.