... quickly. Words flicker. Silence becomes text. The model completes a cutoff with something plausible. The final half-second of audio is especially dangerous because the model has just enough signal to guess, but not enough signal to know. So Juno treats live transcription as two separate objects: committed text - Stable enough to show as real speech. tail text -Useful as preview, but still provisional. The interesting part is the boundary between them. Juno

... logic behind these new suggestions, and then run a focussed outbound/ABM campaign on those list. Comment from Sudipta Biswas(@sudipta_biswas4): @elias_motionfy  So, let's say you have two existing customers from student debt financing sector: Juno and Lending Tree. This is how you can configure Jesse to find more similar companies. Comment from Rahul Bhavsar(@rahulbhavsar): I agree with this. Most teams spend too much time chasing new ICP theories and not enough time studying the customers ... ... like. The piece that's hard isn't finding lookalikes. It's defining the "alike" axis correctly. Two brands with identical Shopify revenue, traffic, and ad spend can be completely different customers if their creative cycle is 1 week vs

... Raise your hand if you've Googled a symptom at 2am and convinced yourself it was something terrible That's exactly why we built the AI symptom checker in Juno. What's the wildest thing you've ever self-diagnosed? Comment from Daniel Sakel(@daniel_sakel): I self-diagnosed myself with 4 different rare diseases in one night. FOUR. My doctor laughed. I did not. This app is genuinely saving my sanity and my search history Comment from Chuck Schappert ... ... chuck_schappert): Googled a weird mole and spent 3 hours reading WebMD at 1am. Told my mom I loved her the next morning just in case . It was a skin tag. I needed Juno