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Wispr Flow

Wispr Flow + Cursor: Dictation Built for Developers

प्लैटफ़ॉर्म
YouTube
अवधि
1:29
बोली जाने वाली भाषा
अमेरिकी अंग्रेज़ी
व्यूज़
1.2 हज़ार
औसत कट
14.74s
स्क्रीन पर चेहरा
94%
गहरे फ़्रेम
0%
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Wispr FlowWispr Flow + Cursor: Dictation Built for Developers
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Wispr FlowWispr Flow + Cursor: Dictation Built for Developers
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Wispr Flow Wispr Flow + Cursor: Dictation Built for Developers at 1:01corner inset
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Wispr Flow Wispr Flow + Cursor: Dictation Built for Developers at 1:19corner inset
Wispr FlowWispr Flow + Cursor: Dictation Built for Developers
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Wispr Flow Wispr Flow + Cursor: Dictation Built for Developers at 1:25
Wispr FlowWispr Flow + Cursor: Dictation Built for Developers
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Wispr Flow + Cursor: Dictation Built for Developers की ट्रांसक्रिप्ट

फ़िल्म के अपने ऑडियो से एक स्पीच मॉडल द्वारा लिखा गया, ब्रांड द्वारा नहीं। उस पल पर जाने के लिए किसी भी पंक्ति पर क्लिक करें।

  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
22 पंक्तियाँ · 398 शब्द

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