Chrisemoody.com
297 karma · joined November 12, 2013
chrisemoody@gmail.com
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)
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.
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.
Not sure why you didn't see toilet paper, but it is right here: https://popgot.com/toilet-paper
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. chris@popgot.com
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?
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.
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?
@mynameisash I'm curious what you learned... maybe I can help more people learn that using Popgot data.
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.
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?
```
class Classification(BaseModel):
color: Literal['red', 'blue', 'green']
then the output type would be:
```
class ClassificationWithLogProbs(BaseModel):
color: Dict[Literal['red', 'blue', 'green'], float]
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.
Any chance we can get Pydantic support?
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.
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 instances. And that was without a vector index.
Entirely possibly my take was too cursory, would love to know what latencies you're getting bryan0!
The biggest surprise to me here is using Weavite at the scale of billions — my understanding was that this would require tremendous memory requirements (of order a TB in RAM) which are prohibitively expensive (10-50k/m for that much memory).
Instead, we’ve been using Lance, which stores its vector index on disk instead of in memory.