Well you're talking about two related but different pieces of data: 1.
Well you're talking about two related but different pieces of data: 1. Demand/Market for a solution to a given problem 2. How well you've achieved product-market-fit with your particular solution. Determining #1 is fairly straightforward: A. Google searches for a given query (unmet need) B. Chatter about that problem or other solutions (buzz) C. Competitors selling products in that vertical (precedent) D. Competitors bidding on keywords related to that vertical Determining #2 is harder / more nuanced; and to a certain extent is a fruitless endeavor until you've hit significant sample sizes. The first 10 people you try to sell might say no, that doesn't mean the product is wrong for the market; that could just be random chance. The only sure-fire way to test product-market fit is to get your product in front of a statistically significant number of users, and see if they bite. I use the term "bite" because I don't mean "convert". Optimizing your conversion process (getting users to actually sign up / purchase, etc) takes time and that comes later. In the beginning all you need to test is if they care about your product at all (are engaged when presented the idea). That's where the field of engagement analytics comes into play, and that's a big subject...but hopefully I've pointed you in a useful direction.
Posted by MalcolmDiggs on Aug 16, 2014, 12:06 AM
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- Not sure what you suggest to do.
- Why is this not yet published on the official website and why is there no CVE?
- The biggest problem with having an LLM do this, from what I've observed of at least frontier models, is that the way they are trained...
- The biggest problem with MOND is that it was hand-crafted with additional parameters simply to fit the data.
- (My comment is about detached houses).
- The biggest problem with this is that I can't run my company's own internal benchmarks against this API, because they will be trained int...
Raise real-world problems, find people facing the same issue, and discuss solutions.9,000 issues0 comments
hackernews (Other)
Not sure what you suggest to do.
NewWhy is this not yet published on the official website and why is there no CVE?
NewThe biggest problem with having an LLM do this, from what I've observed of at least frontier models, is that the way they are trained...
The biggest problem with MOND is that it was hand-crafted with additional parameters simply to fit the data.
(My comment is about detached houses).
The biggest problem with this is that I can't run my company's own internal benchmarks against this API, because they will be trained int...
> steer Claude away from over-engineering parts of the system in response to hypothetical concerns.
Why is there no comparison with FLUX 3 Image?
Well you're talking about two related but different pieces of data: 1.
Well you're talking about two related but different pieces of data: 1. Demand/Market for a solution to a given problem 2. How well you've achieved product-market-fit with your particular solution. Determining #1 is fairly straightforward: A. Google searches for a given query (unmet need) B. Chatter about that problem or other solutions (buzz) C. Competitors selling products in that vertical (precedent) D. Competitors bidding on keywords related to that vertical Determining #2 is harder / more nuanced; and to a certain extent is a fruitless endeavor until you've hit significant sample sizes. The first 10 people you try to sell might say no, that doesn't mean the product is wrong for the market; that could just be random chance. The only sure-fire way to test product-market fit is to get your product in front of a statistically significant number of users, and see if they bite. I use the term "bite" because I don't mean "convert". Optimizing your conversion process (getting users to actually sign up / purchase, etc) takes time and that comes later. In the beginning all you need to test is if they care about your product at all (are engaged when presented the idea). That's where the field of engagement analytics comes into play, and that's a big subject...but hopefully I've pointed you in a useful direction.
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