Hacker News Reader

Comment by chrisaycock | Hacker News Reader

I did Startup School in 2018 and was one of the inaugural grant winners.

The program was extremely helpful for getting me through to the launch. My product is a moonshot sold to enterprises, so it took an enormous amount of work upfront to get something into users' hands.

The accountability of a cohort was critical for the lonely days of coding something that is both ambiguous and ambitious. Also, constantly being forced to explain my product really helped me hone my message and sales pitch.

I am still friends with some of the people I met back then. I seriously recommend this program to anyone who is starting out.

View on HN

Seconded. Was also one of the grant winners as a biotech, and went through it again later mainly for the practice in pitching/ability to connect with the local startup ecosystem. I actually liked that in the second run through, there were not that many other biotech startups, it was nice to have a community of diverse companies (both in terms of fields and to an extent founders, since I think the cohort was a lot more diverse than the startup space as a whole), and I actually particularly enjoyed one of the group sessions where I somehow ended up grouped with companies in Chile. I wish there was a "don't group me with similar companies" option since it was almost more interesting and more useful to have a conversation with a Chilean legal tech company and learn about how they approach problems they face than talk to another biotech. It was a really interesting experience, and the deals weren't that bad either.

I've been working on an enterprise SaaS startup and have the same challenges. Explaining a general purpose tool and new capabilities that no one has seen before is really daunting. I'd love to hear more about your experience and how it turned out, especially the sales side.

I was pretty bad at explaining Empirical in the beginning. During alpha testing, I emphasized the technical merits (statically typed Dataframes!) rather than an application domain. Once I figured-out I needed to push time-series analysis, then the user feedback became more focused.

After starting the public beta, I heard the following sentence several dozen times: "This looks amazing! Does it handle streaming?" And thus began the quest I am currently on.

About the only advice I can give is to keep practicing and keep an open mind. The feedback will get less negative the more you hone your pitch.

Big fan of Empirical! I'm excited to continue following the development.

Do you have customers, or potential customers in the pipeline?

The business model I've eyed is to license to service providers, like securities brokers and data vendors in the finance industry. That way, their customers can execute queries in the cloud.

For example, a crypto broker wants their traders to write their own models. A stock-exchange advisor wants their asset managers to compute their own transaction costs. Etc.

Basically, Empirical will be a value-add component for existing customers of these larger providers. Instead of shuffling market data over the Internet, the user will "send the query to the data."

Every firm I've shown Empirical to has had the same word-for-word response that most end users have had: "Does it handle streaming?" I put the sales calls on hold while I try to get this done.

What are the advantages of Empirical vs q/kdb+?

Empirical is statically typed. I can run a backtest overnight knowing it won't crash because of a type error or misspelled column name.

I'm currently in the (long) process of adding streaming computation. Once that's ready, Empirical will be able to handle everything from gargantuan CSV files to real-time data using the same language primitives.

I talked about some of these goals when I launched on HN last year:

https://news.ycombinator.com/item?id=19969390

Reply on news.ycombinator.com