The latest articles on DEV Community by Tracko Everestwalk (@tracko__). https://dev.to/tracko__ https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4092214%2F3ce93c29-9a68-4378-8729-289ac17c0809.png https://dev.to/tracko__ en Tracko Everestwalk Mon, 31 Aug 2026 11:32:07 +0000 https://dev.to/tracko__/we-solved-meeting-transcription-did-we-solve-the-meeting-problem-23gf https://dev.to/tracko__/we-solved-meeting-transcription-did-we-solve-the-meeting-problem-23gf <p>I had a meeting recently where the conversation itself was useful, but what happened afterward wasn't.</p> <p>We had a recording. We had notes. We had messages exchanged afterward.</p> <p>Yet there was still uncertainty around what had actually been decided, who owned which task and what needed to happen next.</p> <p>This made me think about the way we approach meeting technology.</p> <p>Transcribing a conversation tells us what was said. It doesn't necessarily tell us what matters.</p> <p>A business meeting contains structured information hidden inside natural conversation:</p> <p>decisions<br> commitments<br> action items<br> customer requirements<br> objections<br> urgency<br> buying signals</p> <p>The interesting technical challenge, to me, is turning that unstructured conversation into something actionable.</p> <p>I'd love to hear from developers working with LLMs or meeting intelligence: what information do you think is hardest to reliably extract from real conversations?</p> ai llm productivity