Finding and researching good products can be very time-consuming and frustrating. Every time I want to buy a product, I waste hours reading reviews and researching the quality, durability and maintainability of it.
Wouldn't it be great to have a service that does all this for me?
That's why I built the AI-Reviewer.
The AI-Reviewer summarizes product reviews from all over the web into simple bullet-point lists.
#How does it work?
1. Scraping reviews from trusted sources on the web
2. Running it through a fake detection
3. Doing a sentiment analysis
4. The AI-Reviewer generates a brief and concise summary of all the reviews by using GPT-3
#What sources do I use?
I asked users where they look for product reviews and focused on the most trusted sources.
Besides the reviews of buyforlife, the sources are:
Reddit, Wirecutter, Amazon, GearLab and some other.
#How do I prevent fake reviews?
This is a question that always comes up. There is no
satisfactory solution to this problem, but I'm trying my best. A few things I'm doing:
- Running Amazon reviews through fakespot.com
- Diversifying sources and cross checking them
- Adding weight to reviews from trustworthy sources like Reddit and buyforlife.
I plan to continuously increase the number of products with an AI-Review.
In addition, I can think of a few more use-cases:
- Summarizing warranty terms and conditions of brands into simple bullet-point lists
Not really related to the GPT-3 thing, which seems cool, but a point of confusion and / or criticism:
> if warranty = lifetime, then price/expected lifetime
The #4 item showing for me is a tape measure, which costs $20 and has an estimated monthly cost of $.28. That means you are estimating this tape measure with a lifetime warranty will only last 6 years.
The #3 item showing for me is an iron skillet, which costs $180 and has an estimated monthly cost of $.25. That means you are estimating this skillet with a lifetime warranty will last 60 years.
Later on down the list you have another cast iron skillet, which cost $20 and has an estimated monthly cost of $.29. This means you are estimating this skillet will last less than 6 years. I happen to own this skillet. It's made of solid iron. I promise you no one who is taking care of this thing at all is going to see less than 10 years of use out of it. I plan to hang on to mine for decades.
What's the basis for this estimation? Pretty much every one I've seen seems completely random and mostly unjustified. E.g. if my tape measure with a lifetime warranty breaks after 5 years I'm definitely taking it in for a free replacement, so what's the deal here? (My high quality tape measures have never broken that quickly anyway.)
All that said I think the implementation of "badges" was really neat and what I can see of the GPT reviews so far look pretty good (although I'm a bit worried that scraping certain review sites may lead to a garbage in, garbage out problem). I'll be checking out your site again in the future.
in 10 years from now we will all agree the cast iron skillet enthusiasts were some of the most annoying. make sure you guys put it in the dishwasher to make it last awhile. avoid getting oil or salt on it, that rusts the iron. and definitely don’t scrub it, you’ll get iron filings in your food.
1. For summarizing, in my experience GPT-3 still has some ways to go. It gets it right a lot of the times, but when it misses, it misses bad.
2. Assuming that after scraping we feed all the scrapped data as a prompt from which GPT-3 generates bullet points, that will be a very big prompt. Since prompts are also counted as tokens, it might end up costing 10cents minimum to generate one summary.
3. I think the core USP from the process the OP has detailed is in steps 1,2 and 3. Step 4 is a good hook to get people to try this out, but have to test it properly and check the costs also.
Looked at a few products I'm familiar with, and it appears you're doing pretty damn well at summarizing the high and low points for them. Nice work, bookmarked. It's pretty rare to find an affiliate model type site that actually adds value.
Edit: You'll want to watch out for people scraping your work and hawking it as their own though. I did something kinda similar by taking poor OEM images and cleaning them up, adding some rotation/depth, improving product descriptions, specifications by hand, etc. Scraped and copied by competitors pretty soon thereafter. Then a whack-a-mole DMCA game of getting rid of the ones that didn't bother to even change the copy.
Thanks for the feedback :)
Sad to hear about the copycats, anything you tried to prevent that besides DMCA? I guess it's unavoidable in the long term, but by then the competitive advantage is hopefully big enough.
It tends to work in practice anyway. Competitors that stoop that low aren't likely to actually contest it. I understand there's a legal liability there, but again, if you research who is doing it, the risk can be very low. And if you focus just on google SERPS, they often don't notice their pages got yanked from the index.
Looks cool, but I do think there are ethical concerns with "scraping" reviews other people write and then not giving them credit. It reminds me of a talk I watched where a guy was saying how AI is essentially just taking work from others and taking the profits. For example, no one cares about the thousands of translators that lost their jobs due to AI translation, but those services were built on their work.
> A locking mechanism on pocket knives holds the knife open. "Not as secure as it can be" implies that the knife closes in on itself (possibly snagging your fingers) under heavy use.
> But... the next sentence says "hard to disengage during heavy use", which is the exact opposite (the knife will stay open after heavy use, and its difficult to close).
I own Opinels, and these points are actually not mutually exclusive. The Opinel locking mechanism serves to keep the blade closed as well as keeping it open, and it is sometimes not as secure as it could be, causing the knife to open when you don't want it to (e.g. in your pocket), but it can also be difficult to disengage when the knife is open (still love them though!).
> These two seem to contradict each other, but that's my knowledge of knife-steels talking. Softer steels are less prone to rusting but dull more quickly.
I don't think this is necessarily GPTs fault—it seems to be a pretty divisive issue. If you Google now "stainless steel vs high carbon knifes", in the top few results you'll find articles claiming that stainless holds an edge longer and others claiming that high carbon holds an edge longer. I always thought that stainless held an edge longer while high carbon is easier to sharpen (and rusts easier, of course), but maybe I've been wrong on that.
I'd say the bigger challenge is building something that is defensible. It's not that hard to design a GPT-3 prompt that summarizes reviews or writes ad copy, so you quickly find yourself with a bunch of competition and not much to differentiate on besides price.
Is anyone selling (or open sourcing) a solution like this? I would love to feed our company's reviews into a program that could generate a bulleted list like this to summarize what our customers are saying.
Thanks for the feedback. Improving the fake review detection by using statistical analysis is definitely on my list once this gets past the MVP state. If anyone has inputs on how to implement this, you are welcome to contact me :)
Dont know about you, but I dont give a flying F about random peoples reviews regurgitated by black box machine learning. When I buy something I either dont care about quality at all, or I look for an opinion of someone I actually trust.
Letting some company feed me recommendations is no better than believing in claims straight from product commercials. You are giving up your agency to a party treating you as a product.