{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/69ab3b7c7036d739021982df/6ab8328dcf7654f56a036c7c?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":" How to Scale AI Developer Productivity Across a Team","description":"<p>For deeper playbooks and analysis: <a href=\"https://natesnewsletter.substack.com/\" rel=\"noopener noreferrer\" target=\"_blank\">https://natesnewsletter.substack.com/</a></p><p><br></p><p>What helps a team turn faster AI coding into useful work that actually ships? Nate examines the setup behind Lauren Tan’s self-reported pull-request volume and six principles teams can apply to their own agents.</p><p><br></p><ul><li>Share useful agent work across the team.</li><li>Preserve history outside a temporary chat.</li><li>Keep humans accountable and give agents reliable checks.</li><li>Leave clear handoffs for the next session.</li><li>Remove process that no longer helps.</li></ul><p><br></p><p>The goal is to improve the systems around the agents so more people can ship valuable work.</p><p><br></p><p>Subscribe for daily AI strategy and news.</p><p>Hosted on Acast. See acast.com/privacy for more information.</p>","author_name":"Nate B. Jones"}