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Wispr Flow

Wispr Flow + Cursor: Dictation Built for Developers

플랫폼
YouTube
길이
1:29
사용 언어
영어(미국)
조회수
1.2천
컷 간격 중앙값
14.74s
얼굴 노출
94%
어두운 프레임
0%
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Wispr Flow + Cursor: Dictation Built for Developers 자막 전문

브랜드가 작성한 것이 아니라, 영상의 오디오를 음성 모델이 받아쓴 것입니다. 아무 줄이나 클릭하면 그 순간으로 이동합니다.

  1. 0:00We recently had... wait, I have Whisperflow! We had a feature that was writing to Redis and it
  2. 0:06was using one key per user ID, but we didn't put a TTL or expiry on the keys. And so the memory
  3. 0:11footprint in Redis just kept growing and growing until our Redis instance ran out of memory and
  4. 0:14crashed. So I already fixed a problem at a call site, but what I want you to do now is to go
  5. 0:17through all of the places in our backend where we write to Redis and classify them as having a TTL
  6. 0:22or expiry directly on the call, having a TTL or expiry somewhere adjacent that we still think is
  7. 0:26safe or having neither of those things and being another risk for a memory leak.
  8. 0:31Okay, that was quick. But look at the size of this prompt. This is something that I would never
  9. 0:34bother to type all the way out. But with Whisperflow, I can just talk to it the way I would
  10. 0:37explain it to my teammate or to my boss and get all of that context directly into cursor. It also
  11. 0:41caught all of my technical terms like Redis and TTL and it turned my list into a structured list
  12. 0:45so that the agent understands that I want structured output. So let's see what it comes back with.
  13. 0:52Okay, we've got the results. Looks like I got all of the structured output I wanted.
  14. 0:54Let's ask a follow-up question. What should we do to make that one safer?
  15. 1:00Look, it caught this file name, which is a real link in cursor.
  16. 1:04It works great in cursor, but unlike cursor's built-in voice mode, I can take it with me
  17. 1:06everywhere I work. Let's track what we're doing in linear. Make sure we're not at risk of a memory
  18. 1:09leak. I also use this to flow through PR review on GitHub. Even if I were at my desk in my office
  19. 1:13and there were tons of people around, I could whisper. Can we call this slug instead?
  20. 1:16It understands me perfectly. Anywhere I'm doing my work, whether that's in cursor or in other
  21. 1:19applications at my desk in my office or in my house or on my computer or even on my phone
  22. 1:23whisper understands me perfectly and i never have to repeat myself try it for free at whisperflow.ai
22줄 · 398단어