Viewing profile — juxtaposicion

Chrisemoody.com

Nice work. I’ve also tinkered on unit pricing! I worked on Popgot.com, which is similar but for the US and tracks non-perishable staples

Got it, thanks! Yeah, so it makes sense that any age-bucketing like this would have a similar effect

I'm not sure I understand. Your model shows that different group buckets (eg 20-24yo vs 25-29yo) peak at different years (in your figure, 2022 vs 2024) despite being driven by the …

Yeah, agree most daily purchases are humdrum and shouldn’t command all of my attention. Incidentally, my last project is about buying by unit price. Shameless plug, but for vitmain…

The LLMs are in fact quite expensive! We run dozen of LLM calls across thousands of products. That's thousands to tens of thousands of calls per search query . The idea is we've go…

thanks! let me know if y'all have any feedback :)

I had to look at that carefully, but I think that "save you $57.65 on 33 fl oz" is both technically and meaningfully correct. It compares our best choice to the most popular choice…

That’s pretty interesting. I’ve using Airtable’s “field agents” for a similar use case, but would love to use this instead. Does it automatically cache values? (Don’t want to pay f…

I’m building Popgot ( https://popgot.com ): compare unit prices (per oz/sheet/lb) across Costco, Walmart, Target, and Amazon. We normalize fuzzy sizes (“family,” “mega,” multipacks…

My pleasure! Happy you could use it as much as I do. Anyway we can chat in person? I'd love to make more stuff for you. chris@ .com

Yeah, I agree. It is a pain to search product by product instead of sticking to one store. Also popgot.com can only do what's online & shipped to you -- so really just the non-peri…

Ah, hell yeah! My buddy on this project has been itching to add sweetmarias.com ... he just needed this as an excuse. So yeah, we'll add it. If you shoot me an email (or post it he…

I'm so glad you like it! We have historical price tracking in the database, but haven't exposed it as a product yet. What do you have in mind / what would you use it for?

Glad you guys mentioned Costco -- I happen to have written a blog post on exactly that: https://popgot.com/blog/retailer-comparison Surprisingly, Costco does not win most of the ti…

I’m working on Popgot ( https://popgot.com ), a tool that tracks unit prices (cost per ounce, sheet, pound) across Costco, Walmart, Target, and Amazon. It normalizes confusing list…

It’s interesting to see how differentiable logic/binary circuits can be made cheap at inference time. But what about the theoretical expressiveness of logic circuits vs baselines l…

Right the hope was to go further. E.g. if the input is: ``` class Classification(BaseModel): color: Literal['red', 'blue', 'green'] ``` then the output type would be: ``` class Cla…

This looks great; very useful for (example) ranking outputs by confidence so you can do human reviews of the not-confident ones. Any chance we can get Pydantic support?

Like other comments, I was also initially surprised. But I think the gains are both real and easy to understand where the improvements are coming from. Under the hood Reflection 70…

Oh, thanks! I added my email to my profile. Look forward to replying to your note!

We're building that spreadsheet as a product. I'd love to show you. I'd message you a private link to a prototype but you have no contact info on your profile. If you are intereste…

For a "small" dataset of 50M and 0.5TB in size with 20 results get around 50-100ms.

I'd love to know the answer here too! I've ran a few tests on pg and retrieving 100 random indices from a billion-scale table -- without vectors, just a vanilla table with an int64…

Disk retrieval is definitely slower. In-memory retrieval typically can be ~1ms or less, whereas disk retrieval on a fast network drive is 50-100ms. But frankly, for any use case I …