juxtaposicion

5 ms·

1.
by juxtaposicion 8mo ago
Chrisemoody.com
2.
by juxtaposicion 9mo ago
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
3.
by juxtaposicion 1y ago
Got it, thanks! Yeah, so it makes sense that any age-bucketing like this would have a similar effect
4.
by juxtaposicion 1y ago
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 same dynamics. Is that expected? I (naively?) expected
5.
by juxtaposicion 1y ago
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 D the best price per serving here ( https://popgo
6.
by juxtaposicion 1y ago
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 got to find the best & the cheapest, and I have
7.
by juxtaposicion 1y ago
thanks! let me know if y'all have any feedback :)
8.
by juxtaposicion 1y ago
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 -- we use the product with the most ratings as a
9.
by juxtaposicion 1y ago
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 for repeat prompts just because one input cell updated)
10.
by juxtaposicion 1y ago
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) so you see the actually cheapest optio
11.
by juxtaposicion 1y ago
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@<our site>.com
12.
by juxtaposicion 1y ago
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-perishables / daily essentials that are not fres
13.
by juxtaposicion 1y ago
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 here?) to chris @ <our site>.com I'll send you
14.
by juxtaposicion 1y ago
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?
15.
by juxtaposicion 1y ago
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 time, and especially if you are not bran
16.
by juxtaposicion 1y ago
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 listings (“family size”, “mega pack”, etc.) to surface
17.
by juxtaposicion 2y ago
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 like MLPs? (And then of course compared to CNNs and othe
18.
by juxtaposicion 2y ago
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 ClassificationWithLogPro
19.
by juxtaposicion 2y ago
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?
20.
by juxtaposicion 2y ago
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 70B seems to be a Llama-3.1 finetune that encourages the model
21.
by juxtaposicion 2y ago
Oh, thanks! I added my email to my profile. Look forward to replying to your note!
22.
by juxtaposicion 2y ago
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 interested, can you email or DM me using my profile in
23.
by juxtaposicion 3y ago
For a "small" dataset of 50M and 0.5TB in size with 20 results get around 50-100ms.
24.
by juxtaposicion 3y ago
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 primary key -- easily took 700ms on beefy GCP ins
25.
by juxtaposicion 3y ago
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 can think of 50ms of latency is good enough. The best part i
26.
by juxtaposicion 3y ago
We’re also building billion-scale pipeline for indexing embeddings. Like the author, most of our pain has been scaling. If you only had to do millions, this whole pipeline would be a 100 LoC. but billions? Our system is at 20k LoC and growi
27.
by juxtaposicion 3y ago
We use Lance extensively at my startup. This blog post (previously on HN) details nicely why: https://thedataquarry.com/posts/vector-db-4/ but essentially it’s because Lance is a “just a file” in the same way SQLi
28.
by juxtaposicion 3y ago
What’re the techniques that’ll get this to run on a single GPU?
29.
by juxtaposicion 4y ago
It is exciting that you could train a CLIP-style model from scratch with only 4M datapoints. But if you’ve got that data, why not fine tune a pretrained model with your 4M points? It seems likely to outperform the from-scratch method.
30.
by juxtaposicion 4y ago
How does this compare to Quickwit or other Tantivy-powered engines?

More ›