{"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/6ab0ac71010fc0f7a3e740f8?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"You cannot tell which parts of your software should stop calling an LLM. My Jev guide has a prompt that scans your projects and names them.","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's really happening when a model can read a complicated input but only choose among answers you supply?</p><p>Nate explains why Jev's general-purpose classification could change where intelligence appears in software. He walks through support routing, tax documents, research prioritization, agent orchestration, and spreadsheets that respond to meaning.</p><p><br></p><ul><li>Why complicated inputs and simple outputs define a useful class of problems.</li><li>How classifiers, generative models, and ordinary code fit together.</li><li>What lower classification costs make possible for teams and individual builders.</li><li>Where testing still matters, and how to try Jev with a coding agent.</li></ul><p><br></p><p>For builders and operators, the opportunity is to revisit decisions that were previously too expensive to automate—and test what happens when those decisions become cheap enough to use throughout a workflow.</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"}