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 the same groups to rise, fall and have peaks at the same times.
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://popgot.com/vitamin-d3)
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 spared no expense in doing so. (Plus we have GCP credits.)
Eventually products will overlap between search queries, so we can serve fast and low latency results that have been pre-processed by LLMs. That will be near zero cost. And of course LLM prices will continue to drop quickly.
We monetize via affiliate fees -- you buy something off that list, and we get 1-4% back at no cost to you.
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 proxy for that. Nizoral 2-in-1 has a crazy 100k reviews, but it is in fact almost 20x more expensive per fluid ounce! And it is the most popular product@
If you hover the text it explains the logic (you can see that in this screenshot https://imgur.com/a/hO7fiWR). But to replay the logic here:
Equate 2 in 1 Dandruff Shampoo 28.2oz is 21¢/fl oz (for 33 fl oz it costs
$6.99) is the Popgot choice.
But the most popular (e.g. most reviewed) product is "Nizoral 2-in-1 Anti-Dandruff Shampoo" and that costs a whopping $1.96/fl oz (33 fl oz it costs $64.63)
So yes, the most popular anti-dandruff shampoo (which I used to use, until I saw this shampoo list https://popgot.com/shampoo?attributes=scalp_concern%3Adandru...) is literally 20x more expensive, so you can do a lot better by picking alternatives at the top of that list.
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)
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 option for staples.
New: a deep research mode that, on demand, crawls thousands of product pages and uses visual LLMs to read label photos (ingredients, counts, square footage) when the text is messy. First run takes ~60–90s, then it’s cached.
A good torture test: 20×25×1 MERV 13 home air filters—listings mix single/4/6/12-packs and vague claims (“3-month,” “allergen defense”), which wreck per-unit comparisons. I’d love feedback on misses (coupons/Subscribe & Save/region), categories to add, and to collaborate with a grocery-list app, budgeting tool, or anyone in the frugal/deals space. [email protected]
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 fresh groceries. But even when limited to consumables I save ~$100/mo by basically buying by unit price.
Uploading a receipt to see how much you can save... that's a good idea. I think I can find your email via your personal site. Can I email you when we have a prototype ready?
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 a link when it's done. Should take a day or two.
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 brand loyal. Costco has famously low-margins, but it turns out that when you sort by price-per-unit they're ok, but not great.
@mynameisash I'm curious what you learned... maybe I can help more people learn that using Popgot data.
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 the actual cheapest option for daily essentials.
On top of that, it uses a lightweight AI model to read product descriptions and filter based on things like ingredients (e.g., flagging peanut butter with BPA by checking every photograph of the plastic or avoiding palm oil by reading the nutrition facts) or brand lists (e.g., only showing WSAVA-compliant dog foods). Still reviewing results manually to catch bad extractions.
Started this to replace a spreadsheet I was keeping for bulk purchases. Slowly adding more automation like alerting on price drops or restocking when under a threshold.
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 other kernels.) Are logic circuits roughly equivalent in terms of memory and compute being used? For my use case, I don’t care about making inference cheaper (eg the benefit logical circuits brings). But I do care about the recursion in space and time (the benefit from CAs). Would your experiments work if you still had a CA, but used dumb MLPs?
Don't take this too literally; I'm not convinced that this is the right way to do it. But it would provide structure and scores without dealing with a mess of complex JSON.
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 to add <think>, <reflection> and <output> tokens and corresponding phases. This is an evolution of Chain-of-Thought's "think step by step" -- but instead of being a prompting technique, this fine-tune bakes examples of these phases more directly into the model. So the model starts with an initial draft and 'reflects' on it before issuing a final output.
The extra effort spent on tokens, which effectively let the model 'think more' appears to let it defeat prompts which other strong models (4o, 3.5 Sonnet) appear to fumble. So for example, when asked "which is greater 9.11 or 9.9" the Reflection 70b model initially gets the wrong answer, then <reflects> on it, then spits the right output.
Personally, the comparison to Claude and 4o doesn't quite seem apples-to-apples. If you were to have 4o/Claude take multiple rounds to review and reflect on their initial drafts, would we see similar gains? I suspect they would improve massively as well.