Previous post in the series. Let’s get into it!
The Gist
Things got busy! It’s been two weeks since my last post. Paying customers came much sooner than expected. We now have 4 design partners totaling $18K MRR. Ba dun tss.
That’s a good number for continuous real-world feedback, so that phase is full. Not all customer feedback should be actioned - more on core competencies in later posts.
Highlights
Gathered a round of feedback for desired MVP scope and gave date estimates.
70%+ odds of a mid-5 digit ARR AI/ML security contract from the Pentagon.*
* If signed and permissible, I’ll cover it in anonymized, customer-general terms.
Priorities
Finalized Statement of Work, MSA, EULA, and TOS updates by 10/03.
Pre-hiring: I need to find my last qualified ML engineer for the project by 10/06.
That’s it! Staying razor-focused and only having 1-3 priorities at a time is important.
Process
Established POCs/SOPs for Customer Success (me): emails, Slack channels, etc
Basic sales pipeline and status tracking (also me): just Google Sheets for now.
I had to set up federated SSO (Google Workspace made this a breeze) and will share how to do this for common IT vendors like Linear, Notion, and more in a post soon!
Notes
If you read ML papers, make sure you read this. I’ve been using this approach for years, but it’s good to see it quantified. TL;DR: if you can automatically verify step-by-step problem solving, you can also automatically improve it with RLAIF.
Also: a notable, more accessible, and readily applicable paper on in-context learning.
