This is an interesting approach, especially being data oriented I think is really helpful. For example, someone may not be the best at "pitching" traditional investors but may have the numbers to prove it. Which is good to give everybody a shot.
How about just pre-revenue businesses who are working towards numbers but have serious promise? Also, what categories like ecommerce vs. Sass etc.
I do a lot of stuff with GPT-3 now but could use help depending upon what startup segments are available to get investment.
We are planning on having a path for pre-revenue by summer, so definitely get on the waitlist.
We process the waitlist on a first come, first serve basis.
If we think we can help you, we will make you an offer, and give you access to our data-driven next actions / OKRs.
Love the idea and think this could be a huge market. Just wondering if you guys help with theft, damage, or insurance too? What are the best practices for this?
Not to be harsh, but have you actually run a rental store yourself before? Because id management, theft, and damage are the main issues that people face when doing so. If you haven't considered it, then I suggest you start researching immediately, because your customers WILL come back and say their tool was stolen and they need help or will request compensation.
I haven’t, it’s interesting to hear feedback from those who haven’t. Whenever I rent a car, I pay in advance online, and then pay a security deposit/they put a hold on my card in the store. Presumably a feature like that would address your concerns here?
Table stakes but likely won’t be enough. If it’s a stolen card, the owner will charge back and you can’t keep the deposit. If you get too many chargebacks your payment provider will drop you. If you charge someone for damage they did that they don’t want to own up to, they will issue a chargeback. You will likely win the dispute if you can point to ToS violations but it’s a huge pain and ties up money for a few weeks.
In your example, the car rental company is doing something on the backend to determine your trustworthiness before they give you the car—you should consider a similar approach.
Depends on how good Radar is these days. I’d recommend some sort of identity verification service for new accounts. Not a full background check but a few additional hurdles weed out many unsophisticated fraud attempts. Anecdotally, a friend told me that calling new signups and manually verifying them works well. At this stage this would have the bonus for you of learning more about the folks using your system.
This is actually covered by stripe connect - users wanting to accept card payments through Rentify need to go through stripe’s is verification process, which involves uploading copies of your ID. Stripe also provides chargeback protection, which I have enabled, which covers any costs due to fraud
Search is alive and well. I'd recommend reading some of the latest textbooks and research papers on information retrieval. The industry was given new life about 5 years back with knowledge graphs and has been reborn again with recent innovations in machine learning, cloud computing, and data mining technologies.
I'm working on a project now which has indexed billions of pages and answers queries similar to a web search engine like Google:
https://www.AtSign.co/
The only difference is that it's a keyword + location based business contact information engine but operates on the same principles as a real web search engine client.
We're a small team and it would have been unthinkable even a years back to launch something of this scale effectively ... But here we are! Amazing space to be in right now
ugh. compare the results for tech support bridgeport, ct to google. or just look at them without comparing to google. awful! No offense but you aren't even doing the most obvious rule based filtering/ordering on cities/states in your result sets.
hi Greg, we don't offer filtering by cities at the moment, only states/countries, so, you wouldn't have been able to look up, "Bridgeport" specifically, right? A lot of people punch in a city and hit "search" but what they get as a response are matches from "any country" which is the default. Which is why you didn't see the basic filtering you were expecting.
Regardless, I just looked at our results for tech support in CT and I agree we need to work harder on our results, but comparing to Google, they only had tech support jobs (not even business listings)... which makes sense in their product use case.
I can see what you're saying and Google is by far, the industry benchmark, but it's also difficult to compare results sometimes ... it's like Apples and Oranges.
I don't want to get too much into the weeds, but there is a whole subset in Information Retrieval which relates to IR system evaluation, or search engine result evaluation. One simple way of doing it is simply labeling the accuracy of each result via human curator as either a 0 or a 1.
But it can get really complicated, for example, sometimes there just aren't relevant documents in the index in the first place ... so, you can't really blame your ranking factors too much. The opposite can happen too, where a word occurs too frequently in which case you might resort to other kinds of ranking factors (most notably pagerank).
In our case, we're focused on broadening our state/country level coverage right now for keywords (more listings), then we're going to focus on making sure our location accuracy is a lot better (it needs work). Overtime, you should get the results you're expecting more often :)
Imagine not just DALL-E 2 but a single model which be trained on different kinds of media and generate music, images, video and more.
The series talks about:
- essential lessons for AI creatives of the future
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By my understanding, it's the most comprehensive set of videos on this topic.
The series is free to watch entirely on YouTube: GPT-X, DALL-E, and our Multimodal Future https://www.youtube.com/playlist?list=PLza3gaByGSXjUCtIuv2x9...