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
- Summarizing maintenance and care instructions
- Shopify App & Google Chrome Extension
- Ability to compare products
Read the full blog post here:
> 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.
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.
2. You are right, the prompts can become quite big which increases the costs.
3. Let's see where it goes once it gets bigger :)
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.
It can produce text that at a glance looks like it could have been written by a human, but a human that is not very competent at the task it set out to do and makes basic mistakes. For example:
- Blade will rust easily unless oiled.
- The locking mechanism is not as secure as it could be, and can be difficult to disengage when the blade is being used for heavy cutting tasks.
- The blade is softer than most stainless steel blades, so the knife will dull quicker than others.
There's also no way for the startup to fix these issues, unless they have the resources to actually improve GPT3 itself.
This sentence seems particularly bad.
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).
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.
> 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 disagree entirely with this assessment.
There are plenty of badly made mechanisms that fail (eg, the knife flexes enough to slip past the lock) but are stiff and difficult to disengage.
As a specific example of a knife with this exact problem the CRKT M16-01 has complaints about it being stiff to disengage and complaints about the lock failing
Is it possible to extend it to run statistical analyses to spot fake reviews without third party tools or at least discrepancies and contradictions among reviews?
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.
Could you please provide alternative shopping links? Amazon.com links don’t resolve for Amazon.ca customers
My guess is that they are opening it up a bit more as the models and the API are becoming more mature. Their slack channel currently has over 20k people in it.