Weekly Check-in: 1/12

A sci-fi landscape with mountains, Stable Diffusion 1.5

Hello everyone!

I’m taking on a new problem, and here I’ll explain how I’ve been thinking about it.

Where I've been

The past several months, I've explored 4 different projects in some depth. I don't think any of them is my next project.

• Daybird - note-centric productivity platform: consumer productivity is very crowded, and beyond building a product for my own needs, I don't have a lot of passion for the work it'll take to make this successful.
• Polyglot - multi-language integration test runner: building an open-source testing tool is an interesting problem, but it'll be challenging to build a company around that, and I haven't encountered a lot of energy around doing integration testing better.
• DataSiv - low-code tools for business analysts: the tool is quite general purpose, so the work of building the company is primarily a GTM challenge
• InsightLoop - quick journaling and reflection app: we're exploring the ways AI can assist with journaling. Interesting stuff, but the path to building a company is long here.

What's next

I’m working on a data validation tool called Concorda to help data and engineering teams work better together to fix data quality issues.

Why I’m working on it:

• this unites several of my interests - data, collaboration, and testing
• i’ve experienced this problem myself at multiple companies
• i’ve talked to several data scientists who’ve expressed some form of “most of my job is investigating data issues”

However, I’m doing a few things differently. First, I’m spending a lot more time up front on customer discovery. My previous projects were motivated by my own pain, so I wanted to explore them through code. I learned a lot about the problem space, but did not end up with a clear target persona and go-to-market strategy. The problem of data quality is one that I only understand a piece of, and so this means I'm going deep into how various types of companies ingest their data.

And another big change is around how I’m thinking about co-founders. I’ve talked to a number of people who are solo founders looking for a co-founder. I think it’s extremely difficult to find a good match through that process - not impossible, but I wonder if the odds of finding a good co-founder through the match process are any better than the odds of making a successful company solo. I'm building for the long-run, which means I want to continue to spend quality time with my family and maintain my energy level even if the company takes years to get off the ground, and that's not a mindset that seems to resonate with the typical founder.

As I’ve been iterating on my ideas “in market”, I’ve learned a lot, and have really focused on the actual process of learning as the main goal. That seems obvious, but at some point in the journey, learning turns into building and improving and growing and scaling, and I needed to remember to learn again a I start from the beginning.

Thanks to everyone I’ve had a conversation with in the past few months. I love you all.

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Goals

I appreciated having Q4 goals. I reached my Daybird goal (via Insightloop) of having 10 testers. I’ll write more about InsightLoop in a future journal I think, but in short, we’re exploring how AI (e.g. GPT-3) can help the journaling and reflection process.

I got to 80% of my cycling goal - (226 miles of 270), but the end of December was a real mess in terms of no childcare and lots of family obligations. In reflection, having the cycling goal really made me exercise more, but it was also odd to I passed up opportunities for other forms of exercise.

My Q1 goals are as follows:

Concorda:

• Talk to 50 prospective customers (eng / data science)
• Get 3 design partners / pilot customers

Personal:

• Work out 20 times per month (~5x a week)