Hi all, I'm JJ Nguyen, founder of Makelog, a changelog tool for modern software teams. We just launched a demo of an up-and-coming feature: the ability to autogenerate changelog entries with GPT-3 based on Jira, Linear, and Shortcut issues.
We've been in the release communication space for the last couple years and realized that one of the main challenges to getting stakeholders to read release notes is translating technical content into laymen’s terms while also making the content contextful and engaging.
We thought GPT-3 could be useful in addressing this problem that plagues many software teams and decided to build a mini demo site of how it might work.
What do you think about this application of GPT-3 for release communication? What are some other ways to scale human-readable release notes, particularly for teams that ship very frequently?
Makelog is building a modern release communication platform for fast-moving companies. We’re on a mission to help technical and nontechnical teams row in the same direction, enabling them to deliver customer value faster and more effectively.
To learn more about who we are, our engineering culture, and whether this is the right place for you, read our Key Values profile: https://www.keyvalues.com/makelog
Hi, I’m JJ, founder and CEO at Makelog. We're helping product teams automate their release communications process to empower their users and internal customer-facing teams.
Why Makelog: If a feature ships and nobody knows about it, did it really ship? We're set on helping companies truly unlock the value of their product updates by making it easy to auto-compile, curate, and distribute the right update for the right user at the right time.
Team: We're a small team of three that share a passion for the product and a desire to make it so much easier for companies to share what they ship. We're backed by a tier 1 firm (soon to be announced!), as well as the founders of Segment, Pilot, Honeycomb, and Radar
Hi, I’m JJ, founder and CEO at Makelog. We're helping product teams automate their release communications process to empower their users and internal customer-facing teams.
Why Makelog: If a feature ships and nobody knows about it, did it really ship? We're set on helping companies truly unlock the value of their product updates by making it easy to auto-compile, curate, and distribute the right update for the right user at the right time.
Team: We're a small team of two that share a passion for the product and a desire to make it so much easier for companies to share what they ship. We soft launched Makelog in April and count companies like Monte Carlo, Tango, and Rematter among our users.
We've been in the release communication space for the last couple years and realized that one of the main challenges to getting stakeholders to read release notes is translating technical content into laymen’s terms while also making the content contextful and engaging.
We thought GPT-3 could be useful in addressing this problem that plagues many software teams and decided to build a mini demo site of how it might work.
What do you think about this application of GPT-3 for release communication? What are some other ways to scale human-readable release notes, particularly for teams that ship very frequently?
Thanks!